REVIEW OF LITERATUR…..

PHD WORK!!!!1

I am looking for someone to write a REVIEW OF LITERATUR (Research Literature review ) which is related to petroleum or we can say oil reservoir. The topic is about the “tarmat” on oil reservoir which i need someone to review many SPE papers, jurnals, and other research. Also, i have to many materials that i can send to you to help you to write my (Literature review). Also, i need you to write the refrence for each paper in APA style. They are 19 SPE and jurnal papers that i want you to review which i can email them to you

Vol. 18 No. 3 CHINESE JOURNAL OF GEOCHEMISTRY 1999

G e o c h e m i c a l Characteristics and Origin o f Tar M a t s

f r o m the Yaha Field in T a r i m Basin, China

Z H A N G M I N ( ] ~ ~ ) A N D Z H A N G J U N ( ~ {.~)
( Geochemistry Research Center o f Jianghan Petroleum University, Jingzhou 434102, China )

Abstract: Tar mats were firstly discovered and determined accurately in terrestrial oil and gas
reservoirs associated with Lower Tertiary sandstone reservoirs in the Yaha field of the Tarim
Basin, China, by thin-layer chromatography-flame ionization detector ( T L C – F I D ) and Rock-
Eval analysis. The relative content of asphaltene in gross composition of tar mat extracts ac-
counts for more than 3 0 % , that in the corresponding oil leg less than 20 %. In the geochemical
description profile of oil and gas reservoirs, drastic changes in asphaltene contents between tar
mats and oil legs could be discovered. This is an important marker to determine tar mats. Dis-
tribution characteristics of saturated and aromatic hydrocarbons from reservoir core extracts and
crude oils in the Yaha oil and gas reservoirs in the Tarim Basin are described systematically in
this paper, and the results show there are similarities among n-alkane distribution characteris-
tics, biomarker distribution characteristics and their combined characteristics of saturated hy-
drocarbons, and the geochemical characteristics of aromatic hydrocarbons for tar mats, oil leg,
asphaltic sand and crude oil. These characteristics suggest the hydrocarbons in these samples
were originated from the common source rocks. However, the geochemical characteristics of tar
mats revealed that the mechanism of formation of tar mats is the precipitation of asphaltene
from crude oils in petroleum reservoirs caused by increased dissolved gas in oil legs (gas injec-
tion).

Key words: tar mat; asphaltene; geochemical characteristic; oil and gas reservoir; Tarim
Basin

I n t r o d u c t i o n

T a r m a t s r e p r e s e n t a r e s e r v o i r z o n e c o n t a i n i n g p e t r o l e u m h i g h l y e n r i c h e d in a s p h a l t e n e r e l –

a t i v e t o r e l a t e d oil l e g p e t r o l e u m ( W i l h e l m s e t a l . , 1 9 9 4 a ) , a n d i t is o f t e n s i t u a t e d in g e o l o g i c a l

d i s c o n t i n u i t i e s , g a s – c o n d e n s a t e r e s e r v o i r s a n d l i g h t oil r e s e r v o i r s . T a r m a t s a r e i m p o r t a n t o r –

g a n i c b a r r i e r s for oil a n d g a s r e s e r v o i r s . I n t h e p a s t e i g h t y e a r s , p e t r o l e u m g e o c h e m i s t s h a v e

p u t t h e f o c u s of r e s e a r c h o n oil p r o d u c t i o n a n d r e s e r v o i r – r e l a t e d p r o b l e m s ( E n g l a n d , 1990;
L a r t e r e t a l . , 1 9 9 5 ) . E s p e c i a l l y , t h e d e t e r m i n a t i o n o f t a r m a t s h a s b e c o m e p o s s i b l e s i n c e t h e

t h i n – l a y e r c h r o m a t o g r a p h y – f l a m e i o n i z a t i o n d e t e c t i o n ( T L C – F I D , I a t r o s c a n ) t e c h n i q u e is a p –

p l i e d t o o r g a n i c g e o c h e m i c a l d e s c r i p t i o n o f p e t r o l e u m r e s e r v o i r s ( K a r l s e n e t a l . , 1 9 8 9 , 1 9 9 1 ) .

T h e g e o c h e m i c a l c h a r a c t e r i s t i c s a n d f o r m a t i o n m e c h a n i s m s o f t a r m a t s w h i c h w e r e d e r i v e d f r o m

m a r i n e s o u r c e r o c k s a n d t h e i r f o r m a t i o n m e c h a n i s m s h a v e b e e n d o c u m e n t e d ( D a h l e t a l . ,

1 9 8 6 ; W i l h e l m s e t a l . , 1 9 9 4 ) . H o w e v e r , t h e o c c u r r e n c e a n d g e o c h e m i c a l s i g n i f i c a n c e o f t a r

m a t s d e r i v e d f r o m t e r r e s t r i a l s o u r c e r o c k s h a v e n o t y e t b e e n i n v e s t i g a t e d s y s t e m a t i c a l l y ( Z h a n g

M i n , 1 9 9 6 a ) .

ISSN 1000-9426
* This project was supported by the Youth Foundation of China National Petroleum Corporation.

No. 3 CHINESE JOURNAL OF GEOCHEMISTRY 251

The objective of this study is to determine the geochemical characteristics and origin of tar
mats in terrestrial petroleum reservoirs using the thin-layer ehromatography-flame ionization de-
tection ( T L C – F I D , Iastroscan) rapid screening techniques in combination with routine organic

geochemical methods.

Geological Setting

T h e Yaha field, discovered in 1992, is a larger oil and gas accumulation at the northern
margin of the Tarim Basin, the largest sedimentary basin of China. Hydrocarbons derived from
Triassic-Jurassic terrestrial source rocks in the Kuche depression were trapped in the Tertiary
sandstone reservoirs (Zhang Min et al. 1997), and an individual well has been proven to yield
44 – 225 m 3 condensate oil and 120000 – 370000 m 3 gas per day. The oil and gas reservoirs are
composed of front sheet sandstones of alluvial fan facies and braided channel sandstones. The
tar mats studied (well YH2) are recognized in the Lower Tertiary Suweiyi sandstones which
are compositionally sublithic arkose and subarkosic litharenite with a detrital mode of O60-70
F10-25R10-30. Feldspars are mostly present in unweathering form. Many of the sandstones are

different in grain size. Generally, the Lower Tertiary Suweiyi reservoir sandstones are poorly
sorted and medium to coarse in grain size, with higher porosity, ranging from 3 % to 15 % and
higher horizontal permeability, 30 x 10-3 ttm 2 to 100 x 10-3 ttm 2″

Experimental

Samples

Reservoir rock and crude oil samples were collected from well YH2 in the Yaha field of the
Tarim Basin. Reservoir cores were preserved in order to avoid any variation in hydrocarbon
composition. T h e cores were taken from two main reservoir units: the Suweiyi formation

(E2-3s) and the Cretaceous reservoir formation.

Separation

Crude oils were subjected to column chromatography on silica gel with elution by n-hexane
to isolate the saturated hydrocarbon fraction and with elution of benzene/n-hexane to isolate the
aromatic fraction. Extraction of the crushed reservoir rock samples with dichlormethane :
methanol 9 3 : 7 vol%, n-hexane, cyclohexane and toluene were used as mobile phases for the

Chromarods.

Instrumental

T h e saturated and aromatic hydrocarbon fractions were analyzed using a Shimadzu GC-14A
with dual F I D / F P D and Finnigan TSQ-45 gas chromatography-mass spectrometry with the Su-
per-INCOS data system. The GC column was an elastic silica capillary column, coated with SE-
54 (30m • 0. 259 mm i. d ) . The temperature was kept at 100″(2 for 2 min, programmed from
100 to 300*(2 at 4*(2/min and isothermally at 300*(2 for 20 min. T h e mass spectrometer operat-
ed at an electron energy of 70eV, with the injector temperature being 300*(; and helium being
used as carrier gas. Samples were analyzed using the selective ion monitoring method. Quan-
tifications were based on the peak heights for GC analyses, and the peak areas for G C / M S anal-

yses.

252 C H I N E S E J O U R N A L OF G E O C H E M I S T R Y Vol. 18

T h e gross compositions of

reservoir rock e x t r a c t s and crude

oils w e r e analyzed on an I a t r o s c a n
T H – 1 0 , M K I V ( I a t r o n L a b s I N C ,

T o k y o ) , equipped w i t h a flame

ionization d e t e c t o r ( F I D ) and inter-
faced w i t h an electronic i n t e g r a t o r ,

for rod scanning and quantification.
T y p e C h r o m a r o d – S 111 and C h r o –

m a r o d T y p e A silica rods w e r e
used. T h e reservoir rocks w e r e ana-

lyzed on a R o c k – E v a l HI analyzer.

R e s u l t s and D i s c u s s i o n

T h e gross composition o f p e t r o l e u m

c o l u m n :

T w o t a r m a t s m e a s u r i n g 0 . 9
and 1. 5 m in thickness ( d e p t h

ranges: 5 1 0 5 . 2 0 – 5 1 0 6 . 1 0 m and

5 1 2 5 . 0 0 – 5 1 2 6 . 5 0 m , respective-
ly) w e r e found in well Y H 2 of the

Y a h a field in the T a r i m Basin ( F i g .

1 ) . T h e t a r m a t s located in oil and gas
reservoirs, w h i c h can yield 225 m 3 con-

densate oil and 308728 m 3 gas p e r day.

T h e p r e s e n t o i l – w a t e r contact is a p p r o x i –

m a t e l y 50 m d o w n d i p p i n g t h e t a r m a t s .
T h e t a r m a t s can be recognized using

T L C – F I D analysis. T h e asphaltene con-
t e n t of t a r m a t s is higher t h a n t h a t of oil

legs, so t h e t a r m a t s are s h a r p l y s e p a r a t e d

f r o m oil legs in the geochemical descrip-
tion profile of p e t r o l e u m reservoirs. T h e
absolute c o n t e n t of total h y d r o c a r b o n s

( s a t u r a t e d and a r o m a t i c h y d r o c a r b o n s ,

m g / g r o c k ) and asphaltene of the t a r m a t s
in the Y a h a field are m o s t l y a b u n d a n t

( F i g . 2, T a b l e 1 ) . T h e total h y d r o c a r b o n

a m o u n t s r a n g e f r o m 0 . 4 4 m g / g r o c k to
2 . 2 9 m g / g rock, a v e r a g i n g 0 . 7 3 – 0 . 9 4

m g / g rock, asphaltene 0 . 1 5 – 1 . 2 2 m g / g
rock, a v e r a g i n g 0 . 3 5 – 0 . 4 5 m g / g rock.
I n contrast, t h e total h y d r o c a r b o n c o n t e n t

in t h e oil legs r a n g e s generally f r o m 0 . 3 5

Fig. 1. Rock properties and organic geochemical signatures of the
tar mats (Yaha field, Tarim Basin).

Fig. 2. Plot of gross composition and extract yield versus
depth (Yaha field, Tarim Basin).

N o . 3 C H I N E S E J O U R N A L OF G E O C H E M I S T R Y 253

SAT(%)
100 0

9 0 ~ 0 ~ Oil leg

8 ~ 0 ~ 13 Tea” mat
7 0 / V ~r \ 3 0 +Oil

5 0 ~ 5 0

30/VV /VV ,TO

0 ~ 100
1 0 0 90 80 70 60 50 40 30 20 10 0

ARO(%) POL(%)

Fig. 3. Triangular diagram of the gross composition of core
extracts (as determined by Iatroscan) from well YH2, Yaha
field, Tarim Basin.

m g / g rock to 0. 60 m g / g rock,

averaging 0 . 0 7 – 0 . 0 9 m g / g rock, b u t

the asphaltene content ranges f r o m 0 . 0 4
m g / g rock to 0 . 1 2 m g / g rock w i t h an

a v e r a g e of 0 . 0 7 – 0 . 0 9 m g / g rock. I n

addition, t h e total h y d r o c a r b o n c o n t e n t

in the barriers r a n g e s f r o m 0 . 3 0 m g / g
r o c k to 0 . 4 3 m g / g rock, w i t h a n aver-

age of 0 . 4 0 m g / g rock, b u t the asphal-

tene content is close to t h a t of t h e oil
legs. H o w e v e r , the highest s a t u r a t e d

h y d r o c a r b o n content is produced up to
0. 35 m g / g rock, followed b y lower

c o n t e n t s of t a r m a t s and the lowest con-

t e n t s of barriers. I n contrast, the high-
est a r o m a t i c h y d r o c a r b o n c o n t e n t s

( 0 . 1 1 m g / g r o c k ) are noticed in t a r
m a t s , a b o u t 0 . 0 5 m g / g r o c k in oil legs

and lower t h a n 0 . 0 3 m g / g rock in barri-

ers.
I n t a r m a t s , the relative c o n t e n t s of asphaltene are higher t h a n 30 % , w i t h an average of

4 2 . 2 % – 4 4 . 5 % ( T a b l e 1 ) . S a t u r a t e d h y d r o c a r b o n content is lower t h a n 30 % , a r o m a t i c h y –

drocarbon content is a b o u t 10 % and resin content is lower t h a n 20 % . I n contrast, in oil leg ex-
t r a c t s the relative c o n t e n t of asphaltene is lower t h a n 20 % , b u t s a t u r a t e d h y d r o c a r b o n c o n t e n t
is h i g h e r ( o v e r 45 % ) .

Table 1. Gross composition data of reservoir core solvent extracts from

well YH2 in the Yaha field, Tarim Basin

Absolute yield (mg/g rock) Relative composition ( % )
Sample type

SAT ARO RES ASP SAT ARO RES ASP

Oilleg(6) 0 . 2 1 – 0 . 3 5 0 . 0 3 – 0 . 0 7 0 . 0 8 – 0 . 1 1 0 . 0 6 – 0 . 1 2 4 8 . 4 – 6 1 . 3 9 . 3 – 1 4 . 4 1 5 . 1 – 2 2 . 4 1 1 . 7 – 2 4 . 2
(0.27) (0.05) (0.10) (0.09) (52.5) (10.4) (19.0) (18.1)

Tarmatl (5) 0 . 1 3 – 0 . 4 5 0 . 0 4 – 0 . 3 4 0 . 1 3 – 0 . 2 8 0 . 1 6 – 1 . 2 2 1 9 . 6 – 3 4 . 4 8 . 7 – 1 4 . 7 1 1 . 9 – 2 5 . 6 3 6 . 5 – 5 3 . 6
(0.23) (0.11) (0.15) (0.45) (27.7) (10.8) (19.3) (42.2)

Lean zone (2) 0.13-0.18 0.03-0.03 0.08-0.13 0.06-0.08 43.0-43.7 7 . 4 – 8 . 5 26.2-29.6 20.0-21.6
(0.16) (0.03) (0.11) (0,07) (43,4) (8.0) (27.8) (20.8)

Oilleg (17) 0.15-0.31 0.03-0.10 0.08-0.27 0.04-0.14 35.6-56.2 7.4-23.5 22.5-41.1 6.1-23.5
(0.21) (0.06) (0.19) (0.07) (46.1) (12.4) (26.4) (15.1)

Tar mat2 (3) 0.09-0.21 0.04-0.13 0.13-0.15 0.15-0.66 17.6-39.5 8.1-11.6 12.9-23.7 27.8-57.9
(0.17) (0.07) (0.14) (0.35) (25.1) (9.6) (20.8) (44.5)

Oil 84.1 11.3 3.0 1.6
Note: The numbers in the parentheses a r e t h e numbers of samples.

T h e r e is a significant difference b e t w e e n asphaltene and s a t u r a t e d h y d r o c a r b o n contents in
b o t h t a r m a t s and oil legs, b u t the relative c o n t e n t s of a r o m a t i c h y d r o c a r b o n and resin in t a r

m a t s and oil legs are of no difference.
Fig. 3 s h o w s the r o u g h compositional variations of reservoir core e x t r a c t s in t a r m a t s and

oil legs. T h e t a r m a t e x t r a c t s s h o w high contents of polar compounds ( a s p h a l t e n e and r e s i n )

2 5 4 CHINESE JOURNAL OF GEOCHEMISTRY Vol. 18

Depth
(m)

ASP
(mg/grock)

0.00 0.~8
5102-

5107- (”

5113″

5118.

5124. ! ~….

i I I
ASP PG $4 TPI
(%) (rag HC/mg rock)(mg CO2/g rock)

0.0 35.0 ]0.0 ?i0 10.0 7? 0.000.50

Fig. 4 . Identification of the tar mats using Rock-Eval analysis
in well Y H 2 , Yaha field, Tarim Basin.

( 6 0 % ) , but those of saturated and
aromatic hydrocarbons are relatively
l o w . Saturated hydrocarbon c o n t e n t of
crude oils is higher than 80 % ; aromat-
ic hydrocarbon content, about 1 0 % ;
and asphaltene and resin contents, o n l y
4 % . However, asphaltene and resin
contents in oil legs range from 30 % to
40 % ; saturated hydrocarbon contents,
from 50 % to 60 % ; and aromatic h y –
drocarbon contents, 10 %. It should be
noticed that the reservoir core extracts
from tar mats and oil legs are very sim-
ilar in gross composition, falling in a
small range as in Fig. 3. So the results
imply that the extracts from tar mats
and oil legs have genetic relationship.

Rock-Eval parameters have great
differences from oil and gas reservoirs
(well Y H 2 ) in both the tar mats and

Yaha-field tar mat saturated hydrocarbon C,-C

Produced oil Oil leg

a Pr/Ph=2.82 b Pr/Ph=l.52 nCi7 Pr/nCtT=O.18 | Pr/nC]7—O.19
I I I l l Ph/nCis=0.07 ~ ~ ] Ph/nCts=0.09

//rnC2,

_: i[ L_ 3

Tar mat

Asphaltic sand

i c Pr/Ph=l.50 net7 d pr/Ph=l.54

P~nC.=010 / I l l P~nC,,~012
I 510S.5m I / ~ / / / |

5137.0m

nC2s nC2s

i RCI34 31

Fig. 5. Characteristic saturated hydrocarbon fraction gas chromatograms of D S T oil, tar
mat and oil leg extracts from well Y H 2 , Y a h a field, Tarim B a s i n .

No. 3 CHINESE JOURNAL OF GEOCHEMISTRY 255

t h e oil legs ( F i g . 4 ) . T a r m a t s have higher 81 and $2 yields. P G ( P G = $1 + $2, m g H C / g rock)
c o n t e n t s of t a r m a t s are h i g h e r t h a n 3 m g H C / g rock. I n addition, t h e r e is a close relationship

b e t w e e n $4 content ( m g C O 2 / g r o c k ) and asphaltene c o n t e n t . T h e $4 c o n t e n t of oil legs is

a b o u t 1 m g C O 2 / g rock, and t h e $4 content of t a r m a t s is h i g h e r t h a n 4 m g C O z / g rock, w i t h

t h e highest value up to 13 m g C O 2 / g rock. H o w e v e r , the production indices [ S 1 / ( S 1 + $ 2 ) ]

have significant differences b e t w e e n the oil legs and the t a r m a t s . T h e indices of t a r m a t s are

higher, relative to those of oil legs. T h i s p r o b a b l y reflects a high p r o p o r t i o n of non-volatile

c o m p o u n d s in t a r m a t s as c o m p a r e d w i t h oil legs.

Yaha-field tar mat GC-MS m/z 191

” 53~20 ‘

Produced oil

a Ts/Tm=l.15
s/(S+R)=0.58

1 1 afl(afl+fla)=0″87

Ts Tm fla S R

Tar mat

c Tsfrm=l.32
S/(S+R)=0.59
up(ap+pa)=0.88

5105.5m

I I I I I

60.00 66.40 73.20

Retention time(rain)

Oil leg
b Ts/Tm =1.19

S/(S+R)=0.57
afl Carl +fla)=0.87

53.20

Asphaltic sand

d Ts/Tm=1.45
S/(S+R)=0.58
afl ( afl + fla)=0.87

5137.0m

;0.00 66.40 73.20
Retention time(rain)

Fig. 6. Characteristic mass chromatograms ( m / z 191) of saturated hydrocarbon fractions
in DST oil, tar mat and oil leg extracts from well YH2, Yaha field, Tarim Basin.

T h e s a t u r a t e d h y d r o c a r b o n gas c h r o m a t o g r a m s of t h e t a r m a t and oil leg e x t r a c t s s h o w a

similar n-alkane distribution w i t h carbon n u m b e r s r a n g i n g f r o m C15 to C35. N o n e of these h y –
d r o c a r b o n e x t r a c t s s h o w s a n y evidence of biodegradation ( F i g . 5 ) . P r i s t a n e / p h y t a n e ratios
r a n g e f r o m 1 . 5 2 to 1 . 5 0 for the t a r m a t a n d oil leg e x t r a c t s , and those of the produced oil are
2 . 8 2 . Similar t r e n d s can be seen in pristane/n-C17 and p h y t a n e / n – C l 8 ratios, which range f r o m

0 . 2 0 to 0 . 1 0 for oil legs and 0 . 1 9 to 0 . 1 0 for t a r m a t s .

Biomarker compounds o f saturated hydrocarbons

N o n d r i m a n e , d r i m a n e and h o m o d r i m a n e series c o m p o u n d s in the t a r m a t e x t r a c t s are
a b u n d a n t , t h e i r distribution p a t t e r n s are consistent w i t h those of oil leg e x t r a c t s , and h o m o d r i –
m a n e c o m p o u n d s s h o w a highest p e a k . L o w e r c a r b o n – n u m b e r c o m p o u n d s in tricyclic t e r p a n e s
in the t a r m a t e x t r a c t s are higher, and C2t c o m p o u n d s are highest, characterized b y the p r e –
dominance of C19-C23 t e r p a n e s . T h e p a t t e r n s have a good m a t c h w i t h those of tricyclic t e r p a n e s
of terrestrial crude oils in this basin ( Z h a n g M i n et a l . , 1 9 9 6 b ) . C27 to C35 hopane series corn-

256 CHINESE JOURNAL OF GEOCHEMISTRY Vol. 18

Yaha-field tar mat G-C-MS m/z 217

Produced oil
20S/(20S+20R)=0″48 / 1
flfl (flfl + aa)=.O.3 7 ,,.

! i I t I

Oil leg
b 20S/(20S+20R)=.0.44 / /

tiff(tiff+eta)=0.43 /
5104.2m /

/
/

.,h,
!

Tar m a t

e 20S/(20S+20R)=0.42
tiff (tiff + tra)=0.42

43120 ‘ 46140 ‘ 50:00
Retention time(rain)

A~phaltie sand / /
20S/(20S+20R)=0.41 /
tiff (tiff +aa)=0.39 / .:.7% /

l i I i I.
43.20 46.40 50 00

Retention time(rain)

Fig. 7. Characteristic mass ehromatograms (m/z 217) of saturated hydrocarbon fractions
in DST oil, tar mat and oil leg extracts from well YH2, Yaha field, Tarim Basin.

pounds and gammacerane are i m p o r t a n t biomarkers of pentacyclic triterpanes in the t ar m a t ex-
tract, oil leg e x t r a c t and produced oil, w i t h similar distribution characteristics ( F i g . 6 ) . T h e
T s / T m ratios in the tar mat and oil leg extract s are in t h e range 1 . 1 5 to 1 . 4 5 , w i t h t h e 2 2 S /
( 2 2 S + 2 2 R ) ratio of C22 isomerization in 1 7 ~ ( H ) , 2113( H ) C32-hopane in the m_nge 0 . 5 7 to 0 . 5 9 .
T h e results indicated these extracts might be similar in m a t u r i t y . Similar distribution of C27 to

C29 regular steranes and diasteranes can be seen in t ar mat and oil leg extracts and produced oil,

and the 2 0 S / ( 2 0 S + 2 0 R ) ratios in 5a, 14a, 17a-ethylcholestanes are more t h a n 0 . 4 0 ( r a n g i n g
0 . 4 1 from 0 . 4 8 ) ( F i g . 7 ) . T h e results suggest these hydrocarbons were derived mainly f r o m
the same source rock and are similar in m a t u r i t y .

D i s t r i b u t i o n o f p o l y c y c l i c a r o m a t i c h y d r o c a r b o n s ( P H A )

Polycyclic aromatic hydrocarbons such as naphthalenes, phenanthranes, biphenyals,
dibenzofuranes, dibenzothiophenes, fluorenes, benzofluorenes, naphthobenzothiophenes, fluo-
ranthenes, pyrenes, chrysenes, perylenes and t h ei r derivatives of a wide molecular w e i g h t range
in the aromatic fractions of reservoir rocks and crude oils have been identified on t h e basis of
G C / M S and MS. Tricyclic and tetracyclic compounds in t h e aromatic fractions of t ar mats are
i m p o r t a n t compounds, and the relative abundance of t h e p h e n a n t h r a n e and chrysene series is

N o . 3 C H I N E S E J O U R N A L O F G E O C H E M I S T R Y 2 5 7

m o r e t h a n 50 % o f t h e a b u n d a n c e o f P H A . T h e b i p h e n y a l series c o m p o u n d s are h i g h in a b u n –

d a n c e . I n g e n e r a l , t h e c o m p o s i t i o n a l c h a r a c t e r i s t i c s o f P H A in t h e t a r m a t e x t r a c t s h a v e a g o o d

m a t c h w i t h t h o s e o f P H A in oil leg e x t r a c t s a n d p r o d u c e d oil.

C o n c l u s i o n s

I n t e r r e s t r i a l oil a n d g a s reservoirs, t h e relative c o n t e n t s o f a s p h a l t e n e in t h e t a r m a t e x –

t r a c t s r a n g e f r o m 30 % t o 60 % , b u t t h o s e of t h e c o r r e s p o n d i n g oil legs are less t h a n 2 0 % .

B a s e d o n t h e geological a n d g e o c h e m i c a l c h a r a c t e r i s t i c s , t h e a u t h o r h a s p r o p o s e d a m o d e l o f t a r

m a t s in t h e Y a h a field as f o l l o w s : t h e f o r m i n g m e c h a n i s m of t a r m a t s is p r e c i p i t a t i o n o f a s p h a l –

t e n e f r o m c r u d e oils in p e t r o l e u m r e s e r v o i r s c a u s e d b y t h e i n c r e a s e d dissolved g a s c o n t e n t o f a n

oil leg ( g a s i n j e c t i o n ) . T a r m a t s are o f i m p o r t a n t g e o c h e m i c a l significance in b o t h p e t r o l e u m

r e s e r v o i r e x p l o r a t i o n a n d e x p l o i t a t i o n . T a r m a t is a n o r g a n i c b a r r i e r in oil a n d g a s r e s e r v o i r s ,

a n d it is a n o n – p r o d u c t i o n r e s e r v o i r zone. S o t h e g e o c h e m i c a l m o d e l o f t a r m a t s in oil a n d g a s

fields is e s t a b l i s h e d t o p r o v i d e useful i n f o r m a t i o n o n b o t h p r o d u t a b l e r e s e r v e s a n d l o c a t i o n s o f

i n j e c t i n g w a t e r wells. G e o c h e m i c a l c h a r a c t e r i s t i c s o f t h e t a r m a t s c a n help u s t o d e t e c t t h e di-

r e c t i o n a n d t i m e o f oil a n d g a s filling i n t o t h e reservoirs. Based o n t h e g e o c h e m i c a l c h a r a c t e r i s –

tics a n d f o r m a t i o n m e c h a n i s m o f t a r m a t s , t h e f o r m i n g m e c h a n i s m a n d e v o l u t i o n h i s t o r y o f oil

a n d g a s reservoirs can be revealed.

R e f e r e n c e s

Dahl, B. and G.C. Speers, 1986, The geochemical characterization of a tar mat in the Oseberg field Norwegian
sector, North Sea: Organic Geochemistry, v. 10, p. 547 – 558.

England, W . A . , 1990, The organic geochemistry of petroleum reservoirs: Organic Geochemistry, v. 16, p. 415
– 426.

Karlsen, D. and S.R. Larter, 1989, A rapid correlation method for petroleum population mapping within indi-
vidual petroleum reservoirs-application to petroleum reservoir description, in J. Collins, eds., Correlation in
hydrocarbon exploration: Norwegian Petroleum Society, p. 7 7 – 85.

Karlsen, D. and S.R. Larter, 1991, Analysis of petroleum fraction by TLC-FID: applications to petroleum reser-
voir description: Organic Geochemistry, v. 17, p. 603 – 617.

Latter, S . R . and A.C. Aplin, 1995, Reservoir geochemistry: methods, applications and opportunities, in J. M.
Cubitt and W. A. E~fgland , eds., The geochemistry of reservoirs= v. 86, p. 5 – 32.

Wilhelms, A. and S.R. Latter, 1994a, Origin of tar mats in petroleum reservoirs, Part I: introdution and case
studies: Marine Petroleum Geology, v. l l , p . 4 1 8 – 4 4 1 .

Wilhelms, A. and S . R . Latter, 1994b, Origin of tar mats in petroleum reservoirs, Part I I : formation mecha-
nisms for tar mats: Marine Petroleum Geology, v. 11, p.442 – 4 5 6 .

Zhang Min, 1996a, Discovery and its geological significances of the tar mats in terrestrial gas-condensate reser-
voirs: Chinese Science Bulletin: v. 41, p. 1 9 6 7 – 1969.

Zhang Min, Lin Renzi, and Mei Bowen, 1997, Reservoir geochemistry—approach to the Kuche petroleum sys-
tem of the Tarim Basin, China: Chongqing, Chongqing University Press, 25p.

Zhang Min and Zhu Yangming, 1996b, Geochemical characteristics of crude oils of the Kuehe petroleum system:
Geological Review, v. 42, p. 229 – 235.

This paper was prepared for presentation at the International Petroleum Technology
Conference held in Dubai, U.A.E., 4–6 December 2007.

This paper was selected for presentation by an IPTC Programme Committee following review
of information contained in an abstract submitted by the author(s). Contents of the paper, as
presented, have not been reviewed by the International Petroleum Technology Conference
and are subject to correction by the author(s). The material, as presented, does not necessarily
reflect any position of the International Petroleum Technology Conference, its officers, or
members. Papers presented at IPTC are subject to publication review by Sponsor Society
Committees of IPTC. Electronic reproduction, distribution, or storage of any part of this paper
for commercial purposes without the written consent of the International Petroleum Technology
Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not
more than 300 words; illustrations may not be copied. The abstract must contain conspicuous
acknowledgment of where and by whom the paper was presented. Write Librarian, IPTC, P.O.

ox 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

Abstract
Both core description and the log detection have evidenced the
presence of bitumen inside the Bul Hanine Field (figure 1),
which can be particularly abundant in some wells. This tar mat
severely impacts reservoir production behaviour because it
acts as a permeability reducer and a barrier to flow. Properly
understanding its distribution and its propagation throughout
the reservoir is then essential for the prediction of reservoir
performance under various development plans, for instance
when water flooding the field.

The objectives of this study were to:
• Characterize the tar mat and understand its formation

mechanism.
• Evaluate its occurrence in wells: type, thickness and

distribution, in the various rock types.
• Propagate this distribution in a 3D reservoir model for

the entire field.
Fulfilling these objectives has allowed more accurate

volumetric estimations, taking the tar mat into account in the
dynamic reservoir modelling as well as in planning further
development of the field.

Tar mat occurrence was investigated across more than
5400 ft of cores from 26 wells, 90 well logs and a large
number of cuttings samples. Two tar mats were identified in
the reservoir. The upper tarmat was formed in the crestal area
at early stage of the oil charging (early phase segregation?).
The second major one was formed at deeper depth.

The tar mat in the Jurassic reservoirs is composed of
asphaltenes. Tar mat formation is explained as follows:

• A charging of oil, expelled from the Source Rock,
followed by

• Gravity segregation of Asphaltene Precursor Entities
(APE) within the oil column on top of permeability
barriers and paleo-OWC,

• The precipitation of asphaltenes triggered by a
secondary light oil charge.

The methods applied in this study include geochemical
characterisation of the bitumen of the Bul Hanine Field, a
quantification of the tar content in cores using simple
techniques (optical observation, Rock-Eval, Iatroscan, image
analysis), and extending this quantification through wireline
data in non-cored wells and then, subsequently across the
field. In the reservoir model, through the relationship between
reservoir quality (rock-type) and bitumen content, the
distribution of tar mats can be inferred and traced across the
entire field.

Introduction

Tar mat occurs in the Bul Hanine Field, particularly in
Jurassic reservoir 1. Bitumen occurrence can be a problem due
to its effect on oil in place calculation (since bitumen is not
movable, it should then be removed from the volumetric
calculation) and its impact on reservoir quality. Tar mat
impacts on the development plans of an oil field when it
behaves as a permeability barrier. Injecting water under the
Tar mat might result in inadequate pressure support because of
poor communication across the Tar mat 2.

For these reasons, it is important to know where Tarmat
occurs in the field (both laterally and vertically), and what
controlled its distribution. This information, supplemented by
a good knowledge of compartmentalization of the field, could
then be used to plan the location and design of peripheral field
injectors and ensure optimum sweep efficiencies.

Objectives of This Paper

A series of investigations was carried out with the aim of
data collection in order to:

• Identify the presence of solid bitumen in rock samples,
• Suggest assumption on its origin to help in predicting

its occurrence in the field,
• Give a quantitative estimation,
• Describe and model its distribution in the reservoir,
The detailed study of the tarmat in Bul Hanine Field was

carried out using standard techniques used in Organic
Geochemistry.

Definition of Tar Mat
The word Tar mat is used in this paper as a generic term for
tarmat and bitumen.

The definition of Tar mat, as used in Organic
Geochemistry, is given by Wilhelms et al. 3,4 and Bhullar et
al5:

IPTC 11812

Characterisation, Origin and Repartition of Tar Mat in the Bul Hanine Field in Qatar
N.M. Jedaan, A. Al Abdulmalik, Qatar Petroleum; D. Dessort, Total; V.L.N. de Groen, Beicip-Franlab; C.J. Fraisse, Total;
E. Pluchery, Beicip-Franlab.

2 IPTC 11812

“Reservoir zone containing petroleum strongly enriched in
asphaltenes relative to the related oil leg petroleum. Tar mats usually
have a sharp boundary with the oil leg”

“Tar mats can best be described as compositionally sharply
defined zones of petroleum columns often close to geological
discontinuities including, but not limited to, oil-water contacts, which
are enriched in asphaltenes relative to the oil leg up to
concentrations of around 20-60wt.% of the C15+ fraction of
petroleum. Although ‘tar mat’ is the historical term used for these
petroleum reservoir zones, a more general term is viscous oil zone
and the terms polar enriched and heavy oil tar zone have also been
used. Tar mats generally underlie higher gravity petroleums and
show distinct compositional contacts with the overlying oil column”.

Some authors differentiate Tar mat and Bitumen on an API
gravity and/or viscosity basis: Tar mat API degree would be
less than 18 and its viscosity higher than 1000 Cp whereas
bitumen API degree would be less than 8.5 or 10 and its
viscosity above 10000 Cp. However API degree and viscosity
measurements cannot be performed in the porous network;
they can be performed only on produced fluids. Therefore this
definition for Tar mat and bitumen is not applicable to the
present study.

Field data

The main producing reservoir of the Bul Hanine Field is
the Late Jurassic, which lies at an average depth of 2,332
meters. The thickness varies from 75 meters to 160 meters and
contains more than 90% of the field reserves.

At bottom of a Tar mat occurs near the Free Water Level
and at other instances inside the oil column and represents a
restriction to the permeability from core data. The Tar mat
varies in thickness and restriction from one location to the
other and shows a dip in a northerly direction. It probably acts
as a local restriction to vertical permeability, but laterally is
discontinuous in nature. It is most well developed in the south
where the reservoir quality is best and becomes more patchy
and thinner towards the North.

The fluids in Bul Hanine Field contain generally hydrogen
sulphide and carbon dioxide in various quantities.

Samples and Analytical Program
The analytical program of the figure 2 was applied on the
samples:

• Optical observation and image analysis of thin sections
using Jmicrovision® software (figure 3),

• Rock Eval of reservoir rock (quality and quantity of
bitumen),

• Quantity and gross composition of organic extract
using Iatroscan,

• Detailed analysis of selected extracts using Gas
Chromatography / Mass Spectrometry (GCMS).

One oil sample was analysed for the occurrence of the
Thiadiamondoids series. This series of Sulfur-bearing
compounds is specific of Thermochemical Sulphate Reduction
(TSR) which produces acid gas in carbonate reservoirs 6-8.

Optical Study of Thin Sections
Reflectant Bitumen or Asphaltenes fills an important part of
the porous network (figure 4). Measurement of the reflectance
by white reflected light gives a maturity between 0.76% and

0.83% Ro eq., after correction using the Jacob’s formula. This
maturity corresponds to the first half of the “oil window”.

The observation shows that a part of the bitumen is not
soluble in organic solvents, even after a prolongated
extraction. From the optical study it can be concluded that the
Tarmat:

• Is not kerogen,
• Is not the result of biodegradation of oil because

bitumen deposit formed by the biodegradation are
usually completely soluble in organic solvents,

• Doesn’t undergo thermal alteration.

Composition of Oil and Bitumen
The gross and detailed compositions of the extracted bitumen
and oil sample (figure 5) are key parameters to determine the
origin of Tar mat.

There is an excess of distillate C15- in the oil (42% of the
whole oil) compared to type II-S oils of the same maturity in
the Gulf (typically ~25% of distillate at 0.75 % Ro eq., data
from unpublished internal studies, Total). Similarly there is a
large excess of asphaltenes in the reservoir extracts (> 50%)
compared to the oil (2%). Finally asphaltenes in bitumen are
hydrogen-rich, showing that they were not formed by thermal
alteration of oil in reservoir (pure pyrobitumen are usually
hydrogen-lean).

Oil & Bitumen alteration
From GCMS data:

• Oil is not biodegraded or paleobiodegraded,
• Oil is not altered by gas or water washing,
• Bitumen extracted from the reservoir does not show

any proof of biodegradation,
• Molecular and isotope data on oil samples do not show

evidence of secondary cracking in reservoir.
Nevertheless oil was altered by TSR as showed by the

occurrence of the Thiadiamondoids series in the oil sample.
Thiadiamondoids series is specific of TSR which produces
acid gas (H2S and CO2) in carbonate reservoirs

6-8.

Source & maturity of Oil and Bitumen
Molecular fingerprinting of heavy biomarkers in the produced
oil and organic extracts are similar. They are typical of the
type II-S Source Rock.

The maturity of oil samples and bitumen extracts are close
to 0.75% Ro eq. (first half of the “oil window”).

Comparison of Rock-Eval VI and Iatroscan data
Rock-Eval & Iatroscan analyses were performed on the same
crushed sample. These techniques gave similar results in term
of bitumen quantity (figure 6). Nevertheless it can be noted
that the extraction yield measured by Iatroscan is not perfectly
correlated to the Total Organic Carbon (TOC) given by the
Rock-Eval. The samples having high TOC values show the
lowest extraction yield. We suggest that high TOC values are
associated to the occurrence of insoluble bitumen.

After extraction, insoluble Bitumen or Asphaltenes fills an
important part of the porous network (figure 7). The quantity
of non-extractible bitumen in porosity varies according to a
very wide range (from 0% to 100% of the total bitumen).

IPTC 11812 3

Comparison of Rock-Eval / Iatroscan methods and image
analysis (JMicrovision®)
The TOC (% weight) and the image processing data (%Area)
are corrected by taking into account the density of the
reservoir rock and the bitumen.

Optical methods give different results than Rock-Eval
TOC and Iatroscan, because:

• The Rock-Eval and Iatroscan methods measure the
residual oil in porosity. This is not the case with optical
methods,

• The Rock-Eval and Iatroscan methods use ~10 grams
of crushed rock, giving an averaged quantity of organic
matter. These methods are well designed to measure
the global bitumen content in rock samples. On the
opposite the measurement of the % bitumen by image
processing is performed on a size-limited, 2D sample
surface: this method is best designed to measure the
bitumen content at the pore scale.

Consequently the results obtained by the Rock-Eval or
Iatroscan methods cannot give the same results, even on the
same sample (figure 8).

Tar mat repartition in porosity according to the rock type
60 core samples from 4 wells were taken in the Tar mat layers
for Rock-Eval & Iatroscan analyses. Thin sections were
prepared on the same samples and image analysis was
performed using JMicrovision ® software.

TOC, quantity and composition of organic extract and the
relative quantity of Bitumen obtained by the image processing
were compared. These results were tentatively correlated to
the rock type characterized for each sample (figure 9).

The main conclusion is: there is no obvious correlation of
the bitumen content with the rock type whatever the method
used to quantify the bitumen even bitumen is more frequently
localized in grainstones.

Vertical Distribution of Tarmat
The vertical distribution of the bitumen was studied in detail
according to the analytical results.

Selected geochemical logs are showed (figure 10).
Arbitrarily, the zones with bitumen were defined when the
bitumen occupies more than 40% of porosity. In this figure the
following observations can be made:

• The bitumen deposits corresponds to an excess of
asphaltenes in the extracts, confirming that the tar mat
are mainly precipitation of asphaltenes from the oil,

• The bitumen deposits are localised in layers of high
porosity / permeability surmounting zones of lower
porosity / permeability. This observation shows the
importance of local contrasts of petrophysical
properties for the bitumen formation,

• The distribution of the insoluble bitumen does not
correlate with the total quantity of bitumen in general.
That could mean that the insolubilisation and the
asphaltene precipitation are independent phenomena.

Origin of Tarmat & scenario of formation
The analytical results show that biodegradation, “in reservoir”
maturation, evaporation of light ends from oil, water washing

did not trigger the precipitation of asphaltenes. These results
let us to propose the following mechanism of Tarmat
formation (figure 11):

• Oil was expelled from the Source Rock and filled the
reservoir,

• Gravity segregation of Asphaltene Precursor Entities
(APE) within the oil column

• Precipitation of asphaltenes near to the paleo-OWC or
above permeability barriers was triggered by a
secondary light oil charge filling the reservoir or
change in P&T conditions.

• TSR (or vulcanization?) altered the fluid & bitumen,
the consequence could be the insolubilisation of the
polar compounds and the production of acid gases H2S
and CO2.

Cross Ether Reticulation was proposed by Walters et al.9 to
explain the occurrence of non soluble bitumen in petroleum
fields such as Tengiz (Kazakhstan). Cross Ether Reticulation
can be caracterised by a high Oxygen Index. However it
cannot explain the occurrence of insoluble bitumen in Bul
Hanine Field because the Oxygen Index of this material is
very low.

TSR and/or a phenomenon similar to the vulcanization
(Cross Linking of Polars by Sulfur and production of H2S)
could explain the formation of variable quantity of insoluble
bitumen in reservoirs filled with sulphur-rich oils. The
vulcanization of asphaltenes is a chemical reaction rather
badly known.

Bitumen distribution at the field scale
It is important to model the bitumen distribution because:

• Since bitumen is not movable during production, it
should then be removed from the volumetric
calculation,

• Since bitumen affects permeability, it should be
included in the properties of the reservoir model to be
taken into account during the dynamic flow modeling.

Logs of bitumen occurrence detected along the wells were
loaded into Petrel and then upscaled in the cells of the model.
Three different interpretations of bitumen detection were
calculated and loaded in Petrel for the volumetric purpose
(pessimistic, medium case and optimistic bitumen detection)
corresponding to cases that are further used for 1P-3P
volumetric calculation. As an example the figure 12 shows the
three different upscaled bitumen logs in a selected well.

The bitumen where modeled using the SIS methodology,
using well data, vertical proportion curves and variograms.
The same variogram parameters were used to simulate
bitumen for the median or 2P case and for the 1P and 3P cases.

The figure 13 shows the results of bitumen distribution in
a layer and on a cross section. Cumulated height maps where
calculated based on the bitumen distribution (figure 14). The
model shows that the bitumen distribution is very different
across the different intervals.

Conclusion

Tarmat can behave like a permeability barrier. They should
be removed from the volumetric calculation.

4 IPTC 11812

The analytical results let us to propose the following
mechanism of Tar mat formation in Bul Hanine Field.

• Oil was expelled from the Source Rock and filled the
reservoir,

• Asphaltene Precursor Entities (APE) were gravity-
segregated within the oil column,

• Precipitation of asphaltenes near to the paleo-OWC or
above permeability barriers was triggered by a
secondary light oil charge filling the reservoir or
change in P&T conditions.

• TSR (or vulcanization) altered the fluid & bitumen,
triggering the insolubilisation of the polar compounds
and the production of acid gases H2S and CO2.

The Tar mat is localised in layers of high porosity /
permeability surmounting zones of lower porosity /
permeability (paleo OWC can behave as permeability barrier
for asphaltenes). This observation shows the importance of
local contrasts of petrophysical properties for the bitumen
formation and accumulation.

Cumulated height maps where calculated based on the
bitumen distribution. The model shows that the bitumen
distribution is very different across the different intervals.

Acknowledgements
The authors would like to thank Qatar Petroleum, Total and
Beicip-Franlab managements for granting permission to
publish this paper. Special thanks are also extended to
colleagues for their support.

References
1. Munn, D. and Jubralla, A.F.: “Reservoir Geological Modeling of

the Arab D Reservoir in the Bul Hanine Field, Offshore Qatar:
Approach and Results”. SPE paper n°15699, 1987.

2. Al-Ajmi, H., Brayshaw, A.C, Barwise, A.G and Gaur, R.S.: “The
Minagish Field Tar Mat, Kuwait: Its Formation, Distribution and
Impact on Water Flood”. GeoArabia, Vol. 6, No. 1, 2001.

3. Wilhelms, A and Larter, S.R.: “Origin of tar mats in petroleum
reservoirs. Part I: introduction and case studies”. Marine and
Petroleum Geology, Volume 11, Issue 4, August 1994, Pages
418-441.

4. Wilhelms, A and Larter, S.R.: “Origin of tar mats in petroleum
reservoirs. Part II: formation mechanisms for tar mats”. Marine
and Petroleum Geology, Volume 11, Issue 4, August 1994, Pages
442-456.

5. Bhullar, A.G., Karlsen, D.A., Lacharpagne, J.-C. and Holm, K.:
“Reservoir screening using Iatroscan TLC-FID and identification
of palaeo-oil zones, oil–water contacts, tar-mats and residual oil
saturations in the Frøy and Rind petroleum accumulations”.
Journal of Petroleum Science and Engineering 23 p 41–63, 1999.

6. Charrié-Duhaut, A., Lemoine, S., Adam, P, Connan, J. and
Albrecht, P: “Abiotic oxidation of petroleum bitumen under
natural conditions”. Organic Geochemistry, Volume 31, Issue
10, October 2000, Pages 977-1003.

7. Dessort, D. Montel, F. and Caillet, G.: “Organic geochemistry of
oils and condensates associated to sour gas in Gulf”. GEO2004,
Bahrein, march 7-10, 2004.

8. Dessort, D. Caillet, G., Lescanne, M., Insalaco, E. and Montel,
M.: “Geochemical characterization and interpretation of Khuff
Reservoir Fluids, North Dome”. GEO2006, Bahrein, March 27-
29, 2006.

9. Walters, C.C., Kelemen, S.R., Kwiatek, P.J., Pottorf, R.J.,
Mankiewicz, P.J., Curry, D.J. and Putney, K.: “Reactive polar
precipitation via ether cross-linkage: A new mechanism for solid
bitumen formation”. Organic Geochemistry 37, pp 408-427
(2006).

IPTC 11812 5

Figure 1: Location map of the Bul Hanine Field.

Figure 2: Analytical program.

6 IPTC 11812

Figure 3: Bitumen quantification by colour thresholding and extraction (JMicrovision Software).

Figure 4: Optical study and reflectance measurement of bitumen

IPTC 11812 7

Figure 5: gross and detailed compositions of the extracted bitumen and oil sample

Figure 6: Relationship between Rock-Eval TOC and Extractible Organic Matter (EOM%).

8 IPTC 11812

Figure 7: Microscope observation of bitumen after solvent extraction

Figure 8: Example of “patchy” bitumen (A) and dispersed bitumen (B) (in red) obtained by image analysis.

IPTC 11812 9

Figure 9: Attempt to correlate the bitumen occurrence and the rock type.

10 IPTC 11812

Figure 10: Geochemical Logs showing the bitumen layers and the petrophysical properties of the reservoir samples.
B = Bitumen; TOC = Total Organic Carbon; EOM = Extractible Organic Matter

IPTC 11812 11

Figure 11: Proposed mechanism for the Tar Mat formation in Bul Hanine field.

Figure 12: different upscaled bitumen logs in a selected well Figure 13: Bitumen distribution and cross section.

12 IPTC 11812

Figure 14: Cumulated Bitumen height.

ITPC 13451

Innovative Integration of Seismic and Well Data to Characterize Tar Mat
in Carbonate Reservoirs
T.M. Matarid, C.T. Lehmann, K.I. Ibrahim, D.O. Cobb, Abu Dhabi Marine Operating Company; A. Smith,
CGGVeritas

This paper was prepared for presentation at the International Petroleum Technology Conference held in Doha, Qatar, 7–9 December 2009.

This paper was selected for presentation by an IPTC Programme Committee following review of information contained in an abstract submitted by the author(s). Contents of the
paper, as presented, have not been reviewed by the International Petroleum Technology Conference and are subject to correction by the author(s). The material, as presented, does
not necessarily reflect any position of the International Petroleum Technology Conference, its officers, or members. Papers presented at IPTC are subject to publication review by
Sponsor Society Committees of IPTC. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the
International Petroleum Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied.
The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, IPTC, P.O. Box 833836, Richardson, TX 75083-3836,
U.S.A., fax +1-972-952-9435.

Abstract

This paper presents an integrated approach using the 3D seismic and well data to enhance our understanding of the
lateral and/or vertical distribution of the Tar Mat.
The study was carried out utilizing a recent stat-of-the-art, high resolution and high quality 3D ocean-bottom seismic
dataset (OBC) acquired offshore Abu Dhabi and several wells with an excellent suite of logs, thousands of feets of core
data and geochemical studies.
A Model Based Acoustic Impedance Inversion was conducted following the 3D seismic reservoir mapping. A
comprehensive porosity prediction analysis and validation were conducted for each well. The observation of the abrupt
destruction of porosity in the well data associated with Tar Mat presence in the core led to the idea of computing the
porosity derivative cube from the seismically predicted porosity cube. This significant and dramatic change in porosity
associated with the Tar presence suggested that this porosity destruction might be visible in the seismically predicted
porosity cube.
The derivative of the porosity volume after post-stack Impedance inversion was generated to visualize the rate of
changes in porosities. The high negative porosity derivative in a highly porous section may represent the top of a Tar
mat. The high positive porosity derivative values also can be used to indicate Tar free developed porosity. Good match
was found between the generated porosity derivative volume and the top tar from wells.
Cross-plots between the seismic acoustic impedance and porosity for all wells (including Tar wells) suggest difficulty
to distinguish between Tar and lithology change for porosities less than 12.5%.
The lateral Tar distribution was found to be predictable utilizing this approach, through blind test well validation. The
seismic Tar mat prediction on the porosity volume has provided new and important interpretation of the top of the Tar
in the inter-well region and for the static model. Different Tar prediction schemes from seismic have been evaluated for
further refinement. Differentiating tight rocks from the porosity plugged with tar remains ambiguous in the lower
reservoir tight rocks. Therefore, a detailed sampling and geochemical analysis of the tar is being performed on the core
to determine its base.

Study area

The subject structure is undeveloped and located about 120 km offshore North-West of Abu Dhabi (Figure 1). The
undeveloped structure is a North-South elongated anticline, approximately 15 Km by 7 Km in size. The structure relief
with 1200ft is considered one of the largest in the area. The structure developed as a result of periodic deep-seated salt
plug associated with basement faulting. The field was first recognized in 1955 following the interpretation of the
earliest seismic survey acquired in ADMA-OPCO concession in 1954. The first exploratory well-1 was drilled 1969
followed by 8 additional wells, between 1970 and 2007, with the objective to appraise the structure and evaluated the
reserves.

2 IPTC 13451

Background and motivation

Following the discovery of well-1, an intensive coring program for the Arab reservoir was implemented in the
subsequent wells # 2, 3, 5, 6& 7 (see Figure 2 for core with tar from Well-2, 3 and 9). Top Tar mat had been identified
from the core description and from the Open Hole log interpretation. The reserve estimates post drilling Well-9 were
significantly different because the estimated Tar had been encountered much deeper as per production test, core and
logs and interpretation. These results encouraged further study to understand the Tar distribution. The occurrence and
distribution of Tar have been identified as one of the main subsurface uncertainties impacting oil in place
characterization, the structure development plans and its production. The Tar mat plugs porosity in the reservoir section
and acts as a vertical permeability barrier potentially separating the aquifer from the oil filled reservoir. Therefore,
understanding the lateral distribution of tar is important in well placement for the subsurface development plan.

In this field the carbonate, reservoir named Arab group, and lies immediately below 200’ thick cap rock of anhydrite
and can be divided into upper and lower reservoirs. The upper Arab is thin and heterogeneous reservoirs 15’-25’,
consisting of carbonate dolomite and interbedded anhydrite, and lies immediately below thick cap rock. The main
hydrocarbon container Lower Arab and named Arab”D” is a thick 450’ parasequence coarsening carbonate reservoir
and lies immediately below the upper Arab. The cap rock “Hith” and the upper Arab are together responsible for the
data quality deterioration for the underlying seismic. The thick anhydrite and the thin intercalation of dolomite and
anhydrite are very likely causing energy attenuation, multiple diffraction and wavefield deformation in the upper Arab
and the underlying Arab”D” seismic image.

ADMA-OPCO acquired a stat-of-the-art Ocean Bottom Seismic (OBC) with offset and azimuth diversity on Q1 2007.
Well driven seismic processing and true relative amplitude with zero phase data were achieved. Distinct amplitude
anomaly observed from the first seismic cross-section and associated only under the structure closure. The amplitude
anomaly in the N-S seismic line is interpreted to reflect the reservoir properties after seismic modeling (Figure 3).

Wells located at the structure apex have almost no Tar mat, while structure flank wells shows variable Tar mat (Figure
4). Tar mat occurs in this field, primarily in the Arab C & D. The Arab ”D” is the main reservoir container of that
structure and the subject work considered only the D reservoir. Regarding core description side, the lower dense
Arab”D” was difficult to be distinguished from the Tar mat. Generally the Tar mat occurs in Arab”D” reservoir and tar
top been picked in most of the cored wells (Figure 4).
The Arab D is a coarsening upward succession with a gradual increase in porosity toward the top of the reservoir
section. Arab ”D” is divided into three main sub-zones (upper-D, middle-D and lower-D. The Tar mat interpretation
results from the core and Open Hole log indicated difficulty to identify the base Tar-mat since as it coincide with the
lower-D dense zone or dense Diyab.

The good seismic data quality with its inversion led to reasonable interwell region porosity computation with
reasonable level of confidence. The observation on the computed well porosity of abrupt porosity destruction at the Tar
surface encourage us consider the computed porosity volume for tar prediction.

Tar mat interpretation from Core and Open-Hole log

Several wells have penetrated tar at different depths in the main reservoir Arab section (Arab”D”) (Figure 5). Tar mat
interpretation and zonation from log data was a challenging because Neutron and resistivity logs having similar
response with heavy oil. In addition, poor vertical resolution and dated logs made for identification difficulties.
The following resistivity log characteristics were used as indication of Tar mat presence. When both the deep and
shallow resistivity logs tends to read high, this suggest that there is no indication for Tar, indicating there is no invasion.
That criterion has been used with the Rxo indicating much reduced movable hydrocarbon in zone where Rt shows good
hydrocarbon saturation (Figure 4).
The core grain density versus log grain density data were used to identify Tar mat zone in the cored wells. The criteria
of 2.7 gm/cc grain density reading versus 2.71 gm/cc in limestone were used to identify Tar mat zone.
The Tar mat well pick interpretation from logs using the above critetion was found to be consistent with core sample
interpretation.

Seismic Modeling

Extensive seismic modeling and spectral analysis were performed in parallel with the 3D processing. The objective was
to generate well driven seismic processing and to understand the seismic signature from the existing well data.

IPTC 13451 3

Several conclusions are drawn from the seismic modeling: Firstly, the acoustic impedance contrast at the target Arab
group is dominated by density contrasts (Figure 6) due directly to porosity. In short, seismic amplitudes can be used to
map porosity in this target. Secondly, temporal resolution measurements suggest that we need to measure frequencies
up to 100Hz to be able to resolve the upper individual Arab reservoirs (see also Arab A, Arab B, Arab C in figure 5).
Thirdly, there is variation of amplitude with offset at the Arab reflectors which will facilitate pre-stack and or angle
stacks interpretation.
Synthetic seismic were generated at all existing wells with inconsistent number of cycles for the Arab reservoir at only
one well which might reflects heterogeneous reservoir or poor vertical resolution logs .The seismic reflections of Arab
reservoir spectral analysis show a maximum frequency of 50-60Hz with dominant frequency of 35-40Hz. Because of
the modeling and spectral analysis results, the focus on Tar mat prediction from seismic only considered the lower thick
Arab”D” reservoir.
Based on core and log interpretation, the Arab reservoirs are heterogeneous carbonate in this structure with fair to poor
reservoir properties, with porosity ranging from 2-22%. The Well-3 synthetics show a peak in front of the top tar and
tie with the same peak character in the seismic data. However, it was difficult to laterally follow that peak within the
cube. The calculated maximum positive amplitude map (Figure 7) for Arab “D” suggests difficulty to map that peak.
The maximum positive amplitude map (Figure 7) for Arab”D” did show some correlation with the later (Figure 12)
computed porosity derivative map.

Seismic Inversion

A Model Based Acoustic Impedance Inversion (MBI) was conducted following the 3D seismic reservoir mapping. The
model based Impedance 3D volume was generated after inversion analysis and validated with blind wells.
The seismic cube data were inverted into Acoustic Impedance (AI) cube utilizing color inversion (CI) techniques as
well. Focus will be directed to MBI as its results would match the existing seismic amplitude cube accurately through
an iterative process and showed better correlation to porosity. The computer CPU time, incidentally, was several times
greater than the CI because of the iterative process of the MBI technique.

The available 9 wells in SARB structure have sonic and density logs with reasonable quality. A complete recent suite
of logs including DSI were acquired with drilling the latest appraisal well SR-9, but due to operational problem 9470-
9810ft interval were not recorded for Top Hith to lower Thammama. The missing interval is un-predictable from the
other wells.

Individual sonic log correction for individual wells was conducted with deterministic and statistical wavelet extraction.
The extracted wavelets were zero phase, but with different amplitude spectrum. The best well tie and wavelet
extraction results were found at well-1. Because we will deconvolve the seismic amplitude data with a single wavelet,
the decision was to select the statistical wavelet at the structure apex well, well-1, with the best tie. The residual error
(seismic amplitude data-derived synthetic) and the AI prediction error were significantly lowered.

Some selected well logs (AI) and two surfaces (Thammama II and Hith) were used to establish the initial Geological
model (Initial AI Model). Different well combinations were tested and others were left blind to compute the inversion
error.

Inversion analysis at individual wells was carried out to compare the original AI well log versus the inverted results
from the initial model. Inversion constraints window were used to control and steer prediction computation within well
log values and towards seismic results.

The computed MBI AI and the Edge detection seismic attribute cubes were found to be very useful when superimposed
on the Amplitude cube for the second final phase of interpretation. The computed AI data found useful to map the top
reservoir (Arab”A0”) after inverting the seismic reflection amplitude to layers similar to geology (Figure 8).

Porosity Prediction

Step wise regression was carried out to choose the best seismic multi-attribute list that best model the porosity. A
probabilistic neural network was used to combine the selected attributes in a non-linear manner. The training results
were validated at all wells. The ability to confidently model porosity and predict it from seismic was achieved. A
comprehensive porosity prediction analysis and validation was conducted for each well. The cross-plots between the
seismic acoustic impedance and porosity for all wells (including Tar wells) suggests difficulty to distinguish between
Tar and lithology change for porosities less than 12% (Figure 13).

4 IPTC 13451

The computed Inversion cubes were followed by computing Porosity for inter-well-region using the well porosity as a
control point and the computed seismic Inversion and amplitude attributes.
Several seismic amplitude attribute calculations for the Arab”D”-Diyab layer were conducted to understand the lateral
distribution of the High amplitude reflections which is related to reservoir porosity as per earlier seismic modeling and
the AI versus well porosity (Figure 7)

The seismic amplitude attribute maps (Figure 7) over the Arab”D”-Diyab reservoir interval show an abrupt change
might be linked with the stratigraphic architecture. There is a possibility of amalgamated Arab”D” grainstones with
improved porosity and that is shown with a positive seismic anomaly.
The abrupt change in the amplitude (Figure 7) might be linked as well to the structure growth during deposition with
improved porosity on structure apex. The deteriorated amplitude anomaly between well-3 &5 can be referred to data
quality due to faulting and the tar which has left only 30’ porous reservoir (see Figure 5). In addition poor reservoir
porosity to the South can be related to the interpreted poor reservoir facies in the South.

Prediction analysis had been conducted for individual wells to compare the target well porosity logs with the seismic
amplitude trace and the other inversion cubes. Training the data is required to learn the relationship between the log
porosity (PHIE) and seismic attributes through the multi attributes analysis and neural network. It should always be true
that adding more attributes will predict the data better. This does not always mean that adding attributes will predict the
data more reliably. Eventually, adding more attributes will simply predict the details or “noise” in the log or in the
attribute themselves. Adding more attributes is similar to fitting a higher order polynomial to a set of points. The
average error plot for all wells with the number of seismic attributes to properly model and validate porosity was found
between 7 to 8 seismic attributes. The number of seismic attribute (10 vs. 7 attributes) was tested with variable operator
length (1 point to 9 points operator) and computed the prediction error. The computed average error percentage curve
shows no better log prediction using more than 7 seismic attributes.

Once we have the list of attributes that give a high correlation coefficient and a small error, neural network training can
be performed on that list to find the “hidden” relationship/network for predicting porosities. The input for the porosity
prediction is the log porosity, original post-stack seismic and impedance volumes.
The red curve is the validation error and this can help us to decide when we have added too many attributes. Each point
in the validation error has been calculated by “hiding” each of the wells and predicting its values using the operator
calculated from the other wells.
For examples the last red point correspond to 10 attributes has been calculated away and the 10 attribute has been
arranged according to the table. The first well has been removed from the calculation. The weights for the eight ten
attributes have been calculated using only wells 2 to 9. The derived operator is then used to predict the value at well 1.
since we already know the exact value, the RMS error for well 1 has been stored. Then we hide well two and repeatthe
computation, and so on.

Cross plotting the actual log porosity versus the predicted porosity shows a cluster around the perfect correlation line
with 0 intercept and 1 slop with 66% overall correlation.
The average prediction error found was 2.6- 3.7% with and average of 3.3%.

The extracted East West cross section from the computed reservoir porosity volume shows improved porosity for the
main reservoir Arab”D” over structure apex relative to structure flanks (Figure 9). The average porosity map over the
main reservoir Arab”D” shows a 15% overall with possible improving average porosity in the North to 17-18% (Figure
10). The computed porosity map can be related in the North with the interpreted high porosity high permeability capped
Arab”D” with Stromatoporoids which were described in well-9 core. The computed average porosity map for the
Arab”D” can be divided into three sectors with different level of confidence using the traffic signal color(Figure 10).
The porosity prediction in the red sector might be impacted by the Tar mat presence in SR3 (only 30’ of calculated
porous Arab”D” at the top) or due to relative data quality as a results of faulting.

Porosity Derivative

The lateral distribution of Tar is a significant subsurface uncertainty for both the oil-in-place characterization and for
implementing the full field development for SARB. Based on well data the tar plugs porosity in the reservoir section
and acts as a vertical permeability barrier (Tar Mat) potentially separating the aquifer from the oil filled reservoir.
Therefore, understanding the lateral distribution of tar is important in well placement for the subsurface development
plan.

IPTC 13451 5

Several wells have penetrated the Tar at different depths in the main reservoir section (Arab D). The Arab D is a
coarsening upward succession with a gradual increase in porosity toward the top of the reservoir section (Figure 4).
The thick Tar mat column observation at the structural peripheral wells (SR2, SR4, SR5 and SR3) might be indicative
as it has been generated with the paleo-OWC.

The Cross plot of the logs P-Impedance versus computed porosity for the tar wells over the reservoir level shows
possibility to separate Tar mat if it is located in the top highly porous zone above 12.5%porosity and below 45000
ft/s*g/cc Figure 13.
Superimposing the Porosity-Impedance cross-plots of the tar wells (highlighted in Black) with the non tar wells
indicates difficulty to separate the Tar mat in the lower Arab”D” dense zone.

An abrupt change of porosity especially in the upper part of the reservoir section is presumed to be due to presence of
Tar in this section. This is confirmed from the detailed core description. Based on the above observation the Porosity
Gradient Cube was calculated in order ideally help in identifying the blocked porosity by tar mat. The porosity
derivative cube was utilized to interpret a pseudo top tar mat surface in the Arab”D” reservoir.

The derivative of the porosity volume after post-stack Impedance inversion was generated to visualize the rate of
changes in porosities. The high negative porosity derivative in the high porous section may represent the top of a tar
mat. The high positive porosity derivative values also can be used to indicate Tar free developed porosity. Good match
was found between the generated porosity derivative volume and the top Tar mat from core and logs (Figure 11). The
North South derivative cross-section show the truncation of the red reflector (Tar mat) and the interpreted to Arab”D”
(Figure 11).

The Red reflector was considered for 3D interpretation, depth conversion and grided with 100m X 100m as a pseudo
Tar mat surface for the input to the static model. However, the Arab”D” mapped horizon was used to compute the
Maximum Negative Porosity Derivative map to understand the Tar mat lateral distribution (Figure 12). The derivative
map shows possible Tar mat layer to the Northern structure half and patchy in the Southern structure have. There is
some sort of correlation between the derivative map (Figure 10) and the maximum positive amplitude map (Figure 6).
The maximum positive amplitude map might represents the identified peak (Figure 5) for top tar in well-3 which was
difficult to map. Figure 13 shows the tar intersection with top Arab “D” reservoir work progress based on guesstimate
(Red outline), drilling results and paleo-owc (Orange) and from seismic porosity (Green).

Conclusion

The seismic Tar mat prediction on the porosity volume has provided new and important interpretation of the top of the
Tar in the inter-well region and for the static model. Different Tar prediction schemes from seismic will be further
evaluated and refined. Differentiating Tar in tight rocks and to recognize the remaining porosity plugged with tar
remains ambiguous in the lower reservoir tight rocks.
The lateral Tar distribution was found to be predictable utilizing post stack 3D seismic acoustic impedance inversion
followed by porosity prediction and its derivative volume.
The seismic Tar mat prediction on the porosity volume has provided new and important interpretation of the top of the
Tar in the inter-well region and for the static model. Different Tar prediction schemes from seismic have been
evaluated for further refinement. Differentiating in tight rocks and to recognize the remaining porosity plugged with tar
remains ambiguous in the lower reservoir tight rocks. Therefore, a detailed sampling and geochemical analysis of the
tar is being performed on the core to determine the base of the tar.

Acknowledgments

The authors thank the management of the Abu Dhabi Marine Oil Company for the constructive comments and
permission to publish this paper. We thank peer reviewers for their comments and helpful suggestion. The subject new
field reservoir model has been built by team, the author also thank the team members.

6 IPTC 13451

Figure 1 : The study location map and structure map.

Figure 2: Tar filling porous reservoir Well-2 & 3. Tar filling lower dense reservoir Well-9.

IPTC 13451 7

Figure 3: N-S seismic line with simplified Tar thickness in pink

Figure 4 : Tar versus non Tar wells (Tar Wells found to be structure flank wells)

8 IPTC 13451

Figure 5 : Arab”D” cartoon with vertical Tar mat distribution from cores and logs

Figure 6: Well-3 with abrupt Φ destruction Due to Tar mat

IPTC 13451 9

Figure 7: Arab”D”-Diyab Maximum Negative (LHS) and Maximum positive (RHS) Amplitude maps

Figure 8: AI section with cartoon for Arab reservoir sequences

10 IPTC 13451

Figure 9: Φ log with tar (Top), computed Φ section (Bottom)

Figure 10: Arab”D” Average Φ map

IPTC 13451 11

Figure 11 : N-S porosity derivative Cross section

Figure 12: Arab D based Maximum Negative Porosity map superimposed With the oil column above Tar surface

12 IPTC 13451

Figure 13: AI versus Φ for Tar wells and all wells.

SPE
Society of Petroleum Engineer’S

SPE 2100

Characterization of Tar From a Carbonate Reservoir in Saudi
Arabia: Part I-Chemical Aspect
A.S. Harouaka* and H.K. Asar, * KFUPM/RI; AA AI-Artaj and A.H. AI-Husaini, KFUPM;
and W.A. Notal, KFUPM/RI
‘SPE Members

This paper was prepared for presentation at the SPE International Symposium on Oilfield Chemistry held in Anaheim, California, February 20-22, 1991.

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper,
as presented, have not been reviewed by the Soci~ty of Petroleum Engineers and are sUbject to correction by the author(s). The material, as presented, does not necessarily reflect
any position of the Society of Petroleum En~meer~, ItS officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society
of Petroleum Engineers. Permission to copy IS restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgment
of where and by whom the paper is presented. Write Publications Manager, SPE, P.O. Box 833836, Richardson, TX 75083-3836 U.S.A. Telex, 730

9 SPEDAL.

ABSTRACT

Tar. mats are extra heavy oil zones sandwiched
between aquifers and adjoining oil columns. They
isolate either partially or completely an oil
reservoir from its aquifer. This results in a rapid
pressure drop, a premature high gas-oil ratio and a
low primary oil recovery; all of which point t

some form of pressure maintenance early in a
field’s life.

Eventhough tar mats represent considerable
hydrocarbon reserves as they are rather common in
the Middle East and Africa, it is their impact on oil
recovery from adjoining oil columns which is of
interest for the time being. Tar must be
characterized to evaluate its mobility and ways of
establishing contact between an oil column and its
aquifer as well as to design an optimum water
injection scheme.

The present paper discusses a detailed chemical
characterization of tar from a carbonate reservoir
in Saudi Arabia. Thermal stability variation was
evaluated by thermal gravimetry’ (TG), and
differential thermal analysis (DTA). Elemental
analysis of preserved and non preserved samples
were carried out with a Carlo Erba 1106 elemental
analyser. The sulfur content was also determined
by two different ASTM methods. The presence
of Nickel, Vanadium and Iron, the major metals
usually found in hydrocarbons, was investigated
by X-ray fluorescence (XRF) spectroscopy. The

tar major hydrocarbon group components were
separated and quantified by high performance liquid
chromatography (HPLC).

Experimental results showed variation, in tar
properties, with depth and area within the same
field. The carbon to hydrogen ratio increased
systematically with a decrease in API gravity. The
sulfur contents obtained with the Carlo Erba
elemental analyser were in good agreement with
those obtained by the general bomb method (ASTM
D-129-64). The content of Hexane insolubles was
relatively high at about 38 % by weight. The polar
compounds ranged between 5 and 9 % by weight.

INTRODUCTION
Experimental work has been conducted at The

Research Institute of KFUPM to characterize tar
from the tar mat of a carbonate reservoir in Saudi
Arabia. This reservoir will be referred to as Field
A throughout the remainder of this paper.

As indicated earlier, tar occurence is generally
between an oil column and its aquifer. This
situation creates several problems from a
production standpoint, to say the least, keeping in
mind that a tar zone or mat can be as thick as the oil
column above it.

Tar mats are rather common in the Middle East
and their importance as a hydrocarbon resource has
been discussed elsewhere (1). However, very little is
known about tar properties and their variation with
depth and area.

2 CHARACIERIZATION OF TAR FROM A CARBONATE RESERVOIR IN SAUDI ARABIA – PART I:
CHEMICAL ASPECT

SPE21004

Tar characerization is an important part of an
ongoing investigation to evaluate tar mobility and
methods of improving injectivity in tar mats. As
such, this work has been directed towards a
characterization mainly from a reservoir
engineering standpoint.

Experimental results showed that several tar
properties do vary with depth and area within the
same field.

This paper presents results of a laboratory
evaluation of several tar chemical properties. The
samples analyzed were either extracted from non
preserved cores or retrieved as bottomhole fluid
samples. Both extracted and bottomhole samples
were from the tar mat of Field A. Part I of this
paper presents the chemical aspect of this
characterization while part 2 is devoted to the
physical aspect.

SAMPLING

It is well known that representative tar samples
are extremely difficult to obtain. There are
basically two methods for retrieving a fluid sample
from a tar mat:

I) by extraction from cores or as a

2) bottomhole fluid sample.

Experimental work was carried out on several
extracted and bottomhole samples from the tar mat
present in Field A.

EXTRACTED TAR SAMPLES

The extraction process was conducted in two
steps: Dean Stark extractors were used to determine
the water content of core plugs while tar extraction
was conducted in Soxhlet extractors with Toluene as
a solvent. Few samples were extracted with Carbon
disulfide and the tar-solvent separation was carried
out at room temperature.

A good material balance along with a simulated
distillation of the solvent used were the main
criteria for evaluating the outcome of an extraction
process. Another criteria has been the comparison
of thermogravimetric analysis (TGA) and simulated
distillation for a tar sample extracted with toluene
to similar tests conducted on a twin sample

extracted with a low boiling point solvent such as
Carbon disulfide.

Unfortunately, the cores available for this
investigation were not preserved. Extracted tar
samples were considerably altered by aging and
exposure. Under these conditions measured
chemical properties cannot be considered
representative of the reservoir conditions in Field
A. However, analyses of these samples were useful
for comparative purposes especially, for assessing
tar properties variation with depth and area.

BOTTOMHOLE SAMPLES.

Bottomhole samples were repeat formation tester
(RFT) samples. Two RFT tars, from two different
wells in Field A, were made available for this
investigation. One of these two samples was found
severly contaminated with mercury. The cores
available for extraction and the RFT fluid samples
were from different wells.

RFT samples are preserved, they were retrieved,
under pressure, from the test chamber of an RFT
sampler. These samples are believed to be
representative of the tar mat in Field A.

EXPERIMENTAL PROCEDURE AND
CONDITIONS

The following is a description of the procedures
and conditions under which the chemical
characterization of tar samples from the tar mat
in field A was achieved. Table 1 shows a brief
description of some of the analyzed samples.
Among the samples listed in Table I, FI is believed
to be the most representative of the tar present
around the tar/water contact in well WFI. Samples
F5A, F6A and F7A along with F5B, F6B and F7B
were extracted from a nonpreserved core. The
latter has been left to the open atmosphere for few
months before extractions took place.

ELEMENTAL ANALYSIS

Amounts of Carbon, Hydrogen, Nitrogen,
Oxygen and Sulfur present in a tar sample were
obtained with a Carlo Erba 1106 elemental
analyser. Two additional methods (ASTM D-1551-
68 and ASTM D-129-64) were used to determine

SPE21004 A. S. HAROUAKA, H. K.ASAR, A. A. AL-ARFAJ, A. R. AL-HUSAINI, W. A. NOFAL

the sulfur content of the tar samples under
consideration.

Measurements were carried out in duplicate for
reproducibility purposes. The level of accuracy of
the elemental analysis has been evaluated as
follows: Seven parts from the same tar, taken at
random, were analysed for Carbon, Hydrogen and
Nitrogen content.

HYDROCARBON GROUP
DETERMINATION

High performance liquid chromatography
(HPLC) is probably the best technique available for
separation and quantification of hydrocarbon
groups from tar and heavy crude samples.

Several preserved and nonpreserved tar samples
from the tar mat in Field A were analyzed with a
High Performance Liquid Chromatograph (HPLC).
The column applied was a specialised 3.9 mm *

cm energy analysis (NH2) liquid chromatographic
column designed specifically for hydrocarbon
separation.

The calibration procedure has been proposed
by the manufacturer. Briefly, this calibration was
achieved by collecting the fraction (aromatics and
polars) peaks, evaporating the solvent and weighing
the fractions. The response factor or the fraction
concentration (weight %) divided by the fraction
peak area was calculated for each group. Sample
F6A was utilized to conduct the calibration.

With three fractions known by weight (Hexane
insolubles, Aromatics and Polar compounds), the
Saturates were calculated by the difference.
Response of the differential in refractive index
between Hexane and sample components was
extremely smalL A determination of the Saturates
fraction based on the refractometer response would
lead to large errors. Also, determination by
difference gave a better reproducibility.

X-RAY FLUORESCENCE

X-ray fluorescence (XRF) analyses of nine tar
bearing rock samples were conducted to determine
the presence, if any, of metals such as Nickel,
Vanadium and Iron. In energy dispersive X-ray
fluorescence spectroscopy, only those elements

having an atomic number of 11 or more can be
detected. Also, the sensitivity to low atomic number
elements is not good because Mylar foil was utilised
and the specimen chamber of the X-ray
fluorescence spectrometer was flushed with helium.
The smallest aperture collimator was used. The
main components of tar must be carbon, hydrogen
and eventually oxygen, none of which can be
detected by X-ray fluorescence.

Clays and silts with a two microns diameter or
less can mix with tar during the extraction process.
An extracted tar sample taken at random was
dissolved in Hexane (two gram of tar in 80 ml of
Hexane). After filtration through a 0.45 micron
Millipore filter, the dry part was analysed by XRF
along with tar bearing rock samples.

THERMAL ANALYSIS

The thermal analysis included thermal
gravimetry (TG) and differential thermal analysis
(DTA). The simultaneous thermal analyser used in
this investigation performs TG and DTA
simultaneously. The temperature of the sample is
measured with thermocouples of Platinum and
Platinum plus 10 % Rhodium.

The parameters recorded were temperature ,
change in weight, derivative of change in weight
and the difference in temperature between the tar
sample and a reference.

The instrument was calibrated using Ammonium
Nitrate. Helium was employed to generate a
nonoxidizing atmosphere while Oxygen provided an
oxidizing atmosphere.

RESULTS AND DISCUSSIONS

The chemical characterization of several tar
samples from the tar mat in Field A included sulfur
and overall elemental analysis, hydrocarbon group
separation and quantification, TGA and DTA.
Whenever feasible data from the literature were
added for comparative purposes.

A comparison of the properties of samples F5A,
F6A and F7 A gives a good indication of these
properties variation with depth. Similar indications
are evident from a comparison of samples F5B,
F6B and F7B properties. Areal properties variation

4 CHARACfERIZATION OF TAR FROM A CARBONATE RESERVOIR IN SAUDi AKAHIA – PAKT 1:
CHEMICAL ASPECT

may be illustrated by comparing the properties of
sample F2 and either F7A or F7B.

ELEMENTAL ANALYSIS

The results of the elemental analysis are
summarized in Table 2. Amounts of Carbon,
Hydrogen, Nitrogen, Oxygen and Sulfur present in
a tar sample were determined. The results are
shown in Table 2 unless the concentration was at or
below the detection limit of the instrument.

It can be seen from the data summarised in Table
2 that the total concentrations of the analyzed
elements (CHNOS) exceed

.8 % indicating that
other elements soluble in Toluene (and Carbon
disulfide for samples F5A, F6A and F7 A) not
included for analysis were, as expected, only
present in minute quantities. The C/H ratio
increased as the API gravity decreased.

Tar samples extracted with Carbon disulfide may
show a sulfur content above normal probably due to
solvent entrapment within the tar. Carbon disulfide
has a low boiling point and the tar-solvent
separation is carried out at room temperature. This
procedure may and usually does inflate the sulfur
content of the sample extracted under these
conditons.

The level of accuracy of this elemental analysis
has been evaluated, under prevailing experiemntal
conditions. The results of this evaluation are shown
in Table 3. Reproducibility with respect to carbon
and hydrogen was very good. Nitrogen content was
at or below the detection limit of the instrument.

SULFUR ANALYSIS

The sulfur content obtained with the Carlo Erba
1106 elemental analyser was compared to the results
of the Quartz tube method (ASTM D-1551-68) and
the general bomb method (ASTM D-129-64). The
results are shown in Table 4. The data obtained with
the quartz tube method are somewhat different.
This method is recommended for the determination
of sulfur in concentrations ranging from 0.1 to 5 %
by weight. It is reported to give inaccurate results
when applied to samples containing phosphorous,
nitrogen and metallo-organic compounnds. Based
on the fact that the results of the general bomb

method agree with those of the Carlo Erba analyser,
the data obtained using these two methods are
believed to be more reliable.

X·RAY FLUORESCENCE ANALYSES

Table 5 shows the results of sample F6A XRF
analysis. Detected elements were normalised to

100

%. For instance, the sulfur content of sample F6A is
about 4.3 % by weight, as indicated in Table 4.

Nickel and Vanadium contents of several tar
bearing rock samples, from the same depth as
samples F5A, F6A and F7 A, were below the
detection limit of the instrument.

HYDROCARBON GROUP
DETERMINATION

The Hexane insoluble content of the tar samples
analyzed ranged between 13 and 44 % by weight,
while the content of aromatics and polar compounds
ranged between 38 to 66 % and 4 to 17 % by
weight respectively. Saturates were determined by
the difference and their content ranged between 2 to
30 % by weight. Saturates determination is
considered the least accurate mainly because of the
way it has been conducted, that is by difference.
The detailed hydrocarbon groups determination
results are displayed in Table 6. Other results from
samples taken outside Field A were also included in
Table 6 for comparative purposes.

Within the scope of the samples analysed there is
a noticeable difference in fractions distribution
when comparing samples from different wells.
However, the differences between fractions
distribution of tar samples taken from the same well
but at various depths are considered to be
inconclusive or even within experimental errors.

Elemental analyses of the tar samples presented
in Table 2 showed a good agreement between the
variation in N,O,S content of tar samples and the
variation in the content of HPLC polar compounds.
For instance, the sulfur content of samples
F5A, F6A and F7A ranged between 4.2 and 4.7 %
by weight. This gradual increase is relatively close
when compared to the HPLC data for the same
samples where the polar compounds ranged between
4.77 and 6.00 % by weight. A similar comparison

SPE2IOO4 A. S. HAROUAKA, H. K.ASAR, A. A. AL-ARFAJ, A. R. AL-HUSAINI, W. A. NOFAL

1) The presence of a tar mat disrupts natural
water drive as the reservoir is partially to
completely isolated from the aquifer beneath.
The knowledge of tar properties becomes
essential for an efficient management of the oil
column above the tar zone.

Tar properties can vary with depth and area
within the same field. This variation becomes
more pronounced in the neighborhood of the
tar/water contact.

SUMMARY AND CONCLUSIONS

ACKNOWLEDGEMENT

The authors wish to acknowledge the support of
Saudi Aramco for this work under KFUPMjRI
project No. 21061.

The contribution -to this work by H. Alpustun, B.
Mtawea, A. Fuseni and A. lob is also gratefully
acknowledged.

The aging factor must be taken into
consideration for a proper interpretation of the
generated chemical paramet~rs. Since it is v~ry
difficult and costly to obtam a representative
tar sample, non preserved samples may be
considered provided the aging effect is well
defined.

Field A tar has a high content of Hexane
insolubles, above 36 % by weight. The content
of Aromatics and Polars ranged between 38 to
66 and 4 to 7 % by weight respectively.

The HPLC technique is a fast and adequate way
for the separation and quantification of
hydrocarbon groups from tars and heavy
crudes. However, major errors may result
from weighing and the peak collection process.

6) The elemental analysis showed a consistent
trend, as the API gravity decreased the C/H
ratio increased. Three methods were used to
determine the sulfur content of various tar
samples from Field A. The results obtain~d
with the Carlo Erba elemental analyser were In
good agreement with those obtained by the
general bomb method or ASTM D-I29-64.

THERMAL ANALYSIS

between samples FI and -F2 shows that the (N,O,S)
content of samples FI and F2 varied between 5.00
and 5.90 % by weight. This relative variation
matched the HPLC results where the content in
polar compounds for sample FI was 5.62 % by
weight and that of sample F2 was higher at 8.6 %
by weight.

TGA tests for samples Fl and F2 along with a
refinery residue were conducted at a heating rate of

18 of/min in a Helium atmosphere. The data are
shown in Figure 1. For the refinery residue and
sample F2 (extracted tar), significant weight losses
were not observed until the temperature exceeded

300 OF. However, for sample FI (dead RFT tar), a
gradual weight loss started at the beginning o~ ~he
test, showing the presence of low volatilIty
components. Clearly, aging and exposure have
stripped sample F2 from most of its light
components. At a higher temperature range, the
thermal weight loss characteristics of samples

and F2 were quite similar a indicated in Figure 1.

Sample F4 is a separator oil which was distilled
until it showed the same viscosity as sample FI
under reservoir conditions of pressure and
temperature. The thermograms for samples Fl and
F4 are given in Figure 2.

Thermograms for samples F5A, F6A and F7A
indicated that sample F7A, which was taken near the
tar/water contact, has more heavier components
than samples F5A and F6A. The differential
thermal analyses of the latter samples produced a
marked difference between the three samples when
a nonoxidative atmosphere is compared to an
oxidative one. Figures 3,4 and 5 show the DTA for
samples F5A, F6A and F7 A respectively. The
endothermic peaks are shown downwards and the
exothermic peaks upward.

TGA and DTA results clearly indicate that tar
lighter compnents content decreased with depth.
The same holds for the impact of an oxidative (air)
atmosphere which diminished with depth.

6 CHARACTERIZATION OF TAR FROM A CARBONATE RESERVOIR IN SAUDI ARABIA – PART I:
CHEMICAL ASPECT

SPE 21004

REFERENCES

1) Harouaka A.S. and Asar H.K.:”Tar Mats
Evaluation-A Resource and a Nuisance,”First
Saudi Symposium on Energy Utilization and
Conservation, March 4-7, 1990, Jeddah Saudi
Arabia.

2) Chirinos, M.L., Gonzalez, J. and Layrisse I.:
“Rheological Properties of Crude Oil From the
Orinoco Oil Belt and Their Mixtures With
Diluents,”Rev. Tec. Intevep July, 1983.

3) Starr, J., Prats, J.M. and Messulam,
S.A.:”Chemical Properties and Reservoir
Characteristics of Bitumen and Heavy Oil
From Canada nad Venezuela, “First
International Conference on the Futur of Heavy
Crude and Tar Sands Sponsored by UNITAR,
AOSTRA and U.S. DOE, June 4-12, 1

4) Hasan, M.U., Ali, S.M. and Bukhari,
A.:”Structural Characterization of Saudi
Arabian Heavy Crude Oil by NMR
Spectroscopy,”Fuel, May, 1983.

TABLE 1

* This sample has been distilled until its viscosity matched that of sample FI under
reservoir conditions.

Fluid Samples Description

Sample ID

F4*

F5A

F6A

FlA

F5B

F6B

FlB

Depth Interval

Bottom of tar mat

Top of tar mat

Middle of tar mat

Bottom of tar mat
Top of tar mat
Middle of tar mat
Bottom of tar mat
Bottom of tar mat

Comments

RFT sample from well WF1.

Toluene extracted- core from well WF2.

Separator oil from well WF 1.

Distilled separator oil from well WF1.

Distilled separator oil from well WF3.

CSz extracted – core from well WF4.

CSz extracted – core from well WF4.
CSz extracted – core from well WF4.

Same as F5A but toluene extracted.

Same as F6A but toluene extracted.

Same as FlA but toluene extracted.

RFT sample from well WF3 contaminated
with mercury.

SPE 2 1004

TABLE 2

Elemental Analysis

Tar/Oil Sample (.API)
Element (wt %)

C H CIH N a S Total

Saskatchewan Heavyffi (14.2) 83.0 11.0 7.55 0.5 <0.1 3.0 -97.

Venezuela Heavl (11.0) 0.7 5.2

Athabasca Heavy3 (8-10) 83.1 10.6 7.84 0.4 1.1 4.8 100.0

Fl
m

(10.7) 79.6 10.2 7.80 0.5 <0.1 4.4 -94.8

F2m (10.2) 83.0 10.5 7.91 0.5 1.2 4.2 99.4

Saudi Heavy
4

(7.7) 84.1 10.7 7.86 0.2 5.0 -100.0

F3 (-28) 85.0 12.1 7.07 0.1 2.8 100.0

F4 (-15) 85.1 11.4 7.46 0.2 3.6 100.3

F5A ( ) 84.8 10.9 7.78 4.2 -99.9

F6A ( ) 84.5 10.7 7.90 4.3 -99.5

FlA ( ) 84.0 10.3 8.16 4.6 -98.9

m: Values measured at KFUPM/RI.

2,3,4: Values from the literature, the supercripts correspond to entries in
the reference list.

TABLE 3

Statistical Analysis of the Elemental Measurements

TABLE 4

Sulfur Analysis (% by weight)
Run #

2
3
4
5
6

Mean

Std. dev.

ReI. std. dev.

Precision at

% confidence level

0.2

0.33

0.2

%C %H

84.48 11.29

11.36
Tar/Oil Sample· Carlo Erba Bomb Quanz-Tube84.82

Analyzerl MethodZ Method3

85.06 11.34

84.66 11.37 Saskatchewan Heavy 3.0 3.50 2.75

84.77 11.38 California Heavy 6.2 6.10 5.95

84.80 11.39 FI 4.4 4.21 3.84

84.72 11.37
F2 4.2 4.80 3.31

84.76 11.36
F5A 4.2 4.2

0.176 0.0336 F6A 4.3 4.1

0.21 0.3
FlA 4.6 4.7

0.41% 0.60%
I: Model 1106
2: ASTM 0-129-64
3: ASTM D-1551-68

•

TABLE 5

X-ray Fluorescence Analysis of Sample F6B

Component Conc (%)

S 84.266

Ca 6.725

V 1.771

Fe 2.422

Ni 0.567

Cu 1.759

Zn 2.490

TABLE 6

Hydrocarbon Group Type Characterization

Hydrocarbon groups (wt %)

Hexane
Sample Saturates Aromatics Polars Insolubles

F5A 17.20 40.65 5.29 36.86
F6A 17.24 42.00 5.29 34.76 •
F7A 13.46 43.51 4.77 38.26
F6B 14.26 38.00 4.64 43.

F3 29.23 47.31 9.58 13.88
F4 2.43 65.47 16.40 15.

FI 15.29 39.79 5.62 39.30
F5 6.76 43.60 7.70 41.94
F2 4.93 43.17 8.60 43.30
F8 13.37 26.74 3.87 56.02··
California Heavy 4.32 58.00 12.00 25.68
Saskatchewan Heavy 7.46 62.00 18.00 12.54

Sample F6A was used to calculate the aromatic and polar response factors .

•• Sample F8 was contaminated with mercury.

The most precise value is the aromatics content since they elute without backflushing.

The least precise is the saturates value, it was obtained by difference. However, it
showed good reproductivity.

100
90

g
60e..

E 50..
c…..
.r;

40en
“iii
~

Chevron Tor

**** Fl

1050 1200 1350 1500
O+—–‘f—-+—+—+—-1—+–+—I—–+—f

o 150 300 450 600 750 SOO
Temperature (Deg F)

Figure 1
Thermogravimetric Analysis of Three Tar Samples

SPE 21004
100
90
80

70
g
Ol 60

.IE:
c
‘S

50E
‘”0::

-oJ 40-§,
‘;
:t

30
20
10

O-l—+–~–+—I—-1—I–+-‘:””””‘-+–~–l

o 150 300 450 600 750 900 1050 1200 1350 1500
Temperature (Oeg F)

~~~~o~ravimetrie Analysis in Air Medium for Samples F1 and F4

u
.~

:5
o
‘0
C

Air Medium

Helium Medium

.. _ .

200,T'””————————-……,u

~
“………”-……. ::g

, , x
O+-‘.,—r—–,r—:’f/’-‘–..–.””:-….–r—-,r—._-.–,-__.,-_w”-‘-l

-..:: _—-_ ~ .;,,’ ;
“‘. .. …..:——.. :

. ~~—- ……,.. ,:.t’

\\ /
~ :
! :
.’ ..’ ..’ ..’ .
: ~ ;
‘V

100

…….
!!
o
>
e -100
u
~
~-200

~
~

“2 -300
‘”
~

:!- -400
D
~ -500
f

~ -600a

-700

200 400 600 800 1000 1200 1400 1600 1800 2000 2200
Temperature (Oe9 F)

-800-1—————————-‘
o

Figure 3
Differential Thermal Analysis for F5A Tar Sample

100

2oo-r—————————TIu-,
.~

~ ~

//”” ….// \ ~
:B O+”='””-…,.—r—+…—-..–“7.,..—r—r—“:T””-,’…,.—r—‘w~_j
“0 r:…… .. ..’ \ I U
> ‘. ‘————,’ ~ .~
~ -100 . -“”:::::~::-:>”,. i ~
‘-‘ -200 ~.~ ‘\: ~
f 0_. \ :

~ -300 ‘-~ r\ :
GJ ~ : \ :

: ~:: Ai, “0&= It 1;
f ‘u .,:·u ., I
~ -600 Hc11um Mcd:um ~ :; V

-700

800 1000 1200 1400 1600 1800 2000 2200
Temperature (Deg F)

600400200
-800..J…————————–‘

Figure 4
Differential Thermal Analysis for F6A Tor Sample

‘- ………. ..
— :”.’..,———”

…….\ . f[

.~, {‘f\o., ,;’:.::'”\J.,

\\ Ii
\ Ii
\ ::
~f,.

Air Medium
100

-600

-700

15
~ -500
~

~
o

200.,.—————————-u,……,

1
~

..c:
“.,-………, … -…….. 15

./ , x
2 O+…:..”…….,.—-,r–o’..<-/...----..--·.,,·,..-,r---...----.---,..-,............-...----r--'W-'--l "0 -------------.... >
e -100
u
~
— -200
~
:;)

1! -300
~

E
~ -400

800 1000 1200 1400 1600 1800 2000 2200
Temperature (Deg F)

600400200
-800.J—————————-.J

Figure 5
Differential Thermal Analysis for F7A Tor Sample

Image001
Image002
Image003
Image004
Image005
Image006
Image007
Image008
Image009
Image010

Society of Petroleum ErVneers

SPE 25609

Geochemical Study of Tar in the Uthmaniyah Reservoir
M.H. Tobey, * H.1. Halpern, G.A. Cole, J.D. Lynn, * J.M. AI-Dubaisi, and P.C. Sese, Saudi Aramco

* SPE Members

This paper was prepared for presentation at the SPE Middle East Oil Technical Conference & Exhibition held in Bahrain, 3-6 April 1993.

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper,
as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necesSarily reflect
any position Of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society
of Petroleum Engineers. Permission to copy is restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgment
of where and by whom the paper is presented. Write Librarian, SPE, P.O. Box 633836, Richardson, TX 75083-3836, U.S.A., Telex, 163245 SPEUT.

ABSTRACT

Tar is believed to be the main factor impeding production in
certain regions of the Uthmaniyah area, Ghawar field. The
effect of extraction with several solvents on the permeability and
porosity of core plugs from tar zones in the Arab-D Formation
was determined in order to understand to what extent tars
contribute to obstructing the rock pores. Thin section
examinations of the extracted rock were conducted to discern
where the tar was distributed microscopically and how that
distribution corresponded with the permeability and porosity
data. The data show that in general, while organic matter
continued to be removed by increasingly polar solvents, the
effective permeability, which is controlled by the macropore
system, showed little improvement. While the major pore
network and macroporosity can generally be improved in the
initial extraction, the marginally accessible porosity is still
largely occluded by tar. Elemental and pyrolytic analyses of
core samples before and after extraction indicate that the tar is
neither itself homogeneous, nor uniformly distributed through an
individual well, or from well to well. Some components of the
tars are not soluble to any of the organic solvent systems
utilized, and evidence that some of the tar may result from
thermochemical sulfate reduction (TSR) is presented.

INTRODUCTION

The Uthmaniyah area is located in the southern portion of the
Ghawar field in Eastern Saudi Arabia (see Figure 1). Most of
the wells in the field produce from the Arab-D Formation, an
Upper Jurassic limestone sealed by anhydrite. In the eastern

131

portion of the reservoir, a tar mat up to 500 ft. thick is hindering
both oil production and water injection. Earlier studies had
examined the tar at Ghawar and concluded that the major
mechanism of tar formation was gas de-asphaItening of the
reservoired Uthmaniyah oil [1]. The gas was postulated to have
been generated more recently in geological time from the same
source rock as the oil. Migration of the gas up-dip from the east
to Uthmaniyah would explain why the tar is observed only in the
eastern portion of the reservoir. Other potential origins of tar
such as thermochemical sulfate reduction (TSR) were just being
postulated at the time of the Riley et aI. report [1].

Another early study found that extraction with benzene removed
essentially all the tar from Uthmaniyah Well-C core samples [2].
A more recent study concluded that tars extracted from off-shore
Abu Safah field, were more effectively extracted with toluene
than benzene [3]. The studies presented here, however, show
that the tar consists of different components, often with different
solubilities. The fact that different tars can be identified and
characterized has implications for the origins of the tar that have
not yet been addressed fully.

The goals of this report are to: (1) determine to what extent the
tar is affecting reservoir permeability and porosity, (2)
understand the chemical and physical properties of the tars, and
(3) gain an insight into the origin of the tar. The first objective
was accomplished by testing the permeability and porosity of
core plugs from Uthmaniyah Well-A and Uthmaniyah Well-B at
each step in a series of solvent extractions. The plugs were first
extracted with naphtha, then toluene, then twice with either
methylene cWoride or tricWoroethane. Thin section microscopy

2 GEOCHEMICAL STUDY OF TARIN THE UTHMANIYAH RESERVOIR SPE 25609

of the extracted cores was used to evaluate the efficiency of the
extractions and how residual tar was distributed throughout the
microporosity. The second objective was accomplished through
total organic carbon and sulfur and pyrolytic analyses of
extracted and unextracted core samples. Additionally, organic
petrographic assessment of core samples before extraction
permitted the identification of discreet components of the tar.
This work involved samples from Well-C in addition to samples
from Well-A and Well-B. Finally, a mechanism consistent with
the chemical properties of the insoluble tar is proposed.

PROCEDURES

Core plugs from Well-A and Well-B were extracted by reflux
for at least 72 hours in a Soxhlet extractor separately with
naphtha, toluene, and either methylene chloride or
trichloroethane. After each extraction, the plugs were dried and
tested for permeability and porosity. A period of several weeks
followed the toluene extraction, so these plugs were retested for
permeability and porosity prior to extraction with chlorinated
solvents. The extraction with chlorinated solvents was repeated
for each sample and colored extract continued to be obtained
even after the second extraction. Following the final extraction,
the plugs were cut in half lengthwise and rock from the center
of the plugs and from the exterior of the plugs was drilled. This
rock powder was analyzed for total organic carbon and was also
pyrolyzed with a Rock-Eval II pyrolytic analyzer. Thin sections
of the extracted rock were examined with a petrographic
microscope.

Some core chips from Well-A and Well-B were acidized with
concentrated HCI to dissolve the carbonate rock matrix, and
recover the insoluble organic matter. The residue recovered after
acidization was washed with de-ionized water and dried. It was
then treated with MAC solvent (15% methanol, 15% acetone,
and 70% chloroform) and the insoluble material was further
treated with HF, water washed and dried. After a final MAC
wash, elemental analysis was conducted on the remaining HF
treated material.

Finally, core samples from Well-C, a well studied by Sobocinski
in 1976 [2], were examined petrographically before and after
thorough extraction with benzene and then 90% benzene/lO%
methanol.

RESULTS AND DISCUSSION

Porosity and Permeability

The porosity and permeability data for the Well-A and Well-B
cores is presented in Figures 2-5. Figure 2 shows the effects of
the methylene chloride exft-actions on the core from Well-A.
Changes in porosity and permeability as a function of the

extraction stage are illustrated. The pre-test data (i.e., the
porosity and permeability data obtained for the core plugs before
extraction with chlorinated solvents) is reasonably close to the
data obtained after toluene extraction several weeks beforehand.
All the samples show small increases in porosity after each of
the methylene chloride extractions. The average porosity of all
three samples increased 2.6% (15.9% relative to the pre-test
porosity) after the second extraction. For the same samples,
permeability shows only a minor change. Given the numerous
extraction steps involved, and the degree of sample handling, the
changes in measured permeability are not considered significant.

Figure 3 shows the data for the trichloroethane extraction from
core plugs from the same well. These plugs were taken from
positions adjacent to the core plugs used for the methylene
chloride extractions. As before, the samples showed increases
in porosity after both of the extractions, with the total increase
after the two extractions averaging 2.7% (16.4% relative to the
pre-test porosity) for the three samples. The changes in air
permeability are again trivial.

Figure 4 presents the porosity and permeability data for three
core plugs from Well-B subjected to methylene chloride
extraction. In this case, the average increase in porosity for the
three samples was 2.8% (24.6% relative to the pre-test porosity)
while air permeability, as with the Well-A samples, was
negligibly changed.

Core plugs taken from positions adjacent to those used for
methylene chloride extraction were extracted with
trichloroethane, and their permeability and porosity
characteristics are shown in Figure 5. Two of the samples
showed inconsistencies over the time of the toluene extraction
and the pre-test measurement. Repeated testing at pre-test
verified that the pre-test values were correct. Again, the
porosity increase for the three samples averaged 3.1 % (21.3 %
relative to the pre-test porosity) while the air permeabilities were
basically unchanged.

Review of Figures 2-5 shows that for most of the samples,
extraction with more polar solvents tended to remove organic
material and increase porosity. However, air permeability did
not increase as this organic matter was removed. This would
imply that the heavier tar — the material not removed by naphtha
— may be confined to pore walls and the more inaccessible
portions of the pore spaces. It could also be that while the
macropore network is initially cleaned by naphtha, the smaller
pore throats remain plugged. Thin section examination of the
core plugs was conducted to understand the phenomenon of
increased porosity and unchanged permeability after extraction.

Thin Section Examination of The Extracted Cores

132

8PE 25609 M.H. TOBEY ET AL. 3

Thin section examination of the extracted cores showed that the
samples fall into different lithological groups according to well:
the Well-A samples are limestones while the samples from Well-
B are dolomites. All the samples exhibited the same general
behavior when extracted with the various solvents. The major
difference attributed to the lithologic nature of the samples may
be the fact that the increase in porosity after the extraction with
chlorinated solvents averaged about 5 % higher, relative to the
pre-test porosity, for the dolomitic samples. This may be due to
the relatively simpler pore structure formed by the dolomitization
of the original limestone matrix at the Well-B well. The pore
complex seen in the Well-A samples was quite intricate, with
numerous dead-end pores, many of which were filled with the
tar material (after naphtha and toluene extraction). Well-B
samples showed the simple polymetric pores often associated
with recrystallization of a limestone. The pores were much
cleaner, although there was still a minor amount of residual
organic material lining parts of the pore despite two extractions
with trichloroethane.

The most important insight into the permeability properties of
the cores following extraction was provided by the microscopic
examination after naphtha and toluene extraction. The
controlling influence on permeability in a porous medium will be
the largest pores. For nearly all the samples, the central, large
scale pores were completely clear of organic material. The
secondary pores were generally blocked by the “tar” material,
and were not available to contribute to ~e overall flow of fluids.
Even those samples which were extracted with trichloroethane
and methylene chloride showed a large amount of residual
material in the pore structure.

Additional extraction will continue to gradually increase the
porosity by accessing more and more of the organic material
lining large pores and the smaller pore structures, but overall
permeability will be only marginally affected by the removal of
the organic material since the pore structure controlling the flow
has already been cleared. The fact that tar material remained in
the pore system after the extractions with chlorinated solvents
supports a case for different types of tar being present in the
reservoir.

In some samples, most of the tar appeared to be confined to the
pore walls and for others it was mainly at the more inaccessible
portions of the pore space. While the pores were being cleaned
of tar material, many of the smaller pore throats remained
partially or completely plugged. This too would explain the
noted increase in porosity while not providing any substantial
gain in permeability.

Total Organic Carbon Content and Pyrolytic Analyses

The total organic carbon content measured at the interior and

133

exterior of the core plugs extracted with methylene chloride and
trichloroethane is presented in Table 1 and illustrated graphically
in Figure 6. As can be seen from the Table and Figure,
organics are not being preferentially extracted from the exterior
of the core plugs. For most samples, there is little difference in
the organic carbon content at the exterior as opposed to the
interior. The amount of unextracted tar distributed through the
extracted plugs would appear to be dependent on both the pore
network of the rock and the insolubility of some of the tars. As
shown by the photomicrographs, most tar is extracted from the
larger pores. Areas of the core where organic matter remains
can be attributed either to restricted access to the solvent and/or
to the insolubility of some of the tar even when access is
provided. External areas of the core should provide the greatest
access to the solvent. The fact that a substantial amount of
organic matter in those external areas remains unextracted for
some of the samples is evidence that some of the tar is largely
insoluble and therefore chemically distinct from the material
which is extractable.

Characterization of the unextracted organic matter by Rock-Eval
II pyrolysis provides some indication of the chemical nature of
this organic matter. Table 2 lists the 81, 82, and hydrogen index
(HI) for the core plugs. Rock-Eval II pyrolysis .is commonly
used to evaluate the source potential of hydrocarbon source
rocks. The 8. pyrolytic yield is indicative of the distillable, or
oil-like, hydrocarbons in a source unit. In this study, the S.
values are all essentially negligible, as would be expected for
rocks subjected to extraction, and these do not provide any
useful information concerning the nature of the tar. The S2
pyrolytic yields are indicative of the non-distillable, heavier,
hydrocarbons remaining in the rock. after the lighter components
have been thermally desorbed. The 82 yield is obtained by
thermally cracking (in an inert atmosphere) the heavy organic
material into lighter components. The heavy Qlaterial may
consist of asphaltenes, kerogen, or pyrobitumen. When related
to the total organic carbon content (TOC), the hydrogen index
can be calculated (HI= l00*S/TOC). The hydrogen index
provides information about the hydrogen content of the organic
matter. For petroleum source rock screening, gas-
prone/condensate sources have HIs between 100 – 400. Oil
prone rocks have HIs >400. For this study, the HI can be used
to compare the unextractable organic matter from the two wells.

Figure 7 plots ~ vs. TOC for external and internal samples
taken from the two wells after extraction with the chlorinated
solvents. The slope of this line represents the hydrogen index
for that group of samples. As can be seen, there is no
distinction between the internal vs. external samples within one
well, but there is a difference in the nature of the tars between
the wells. Whereas the unextracted organic matter in the Well-A
carbonates has a hydrogen index of approximately 163, the
unextracted organic matter in the Well-B dolomites has a

4 GEOCHEMICAL STUDY OF TAR IN THE UTHMANIYAH RESERVOIR SPE 25609

hydrogen index of approximately 67. The R2 variance for the
HI lines are 0.97 and 0.82 for Well-A and Well-B, respectively.
The different trends are apparent. The unextracted matter in
Well-B, in general, is not the same as that in Well-A. The
greater hydrogen deficiency of the Well-B material could be due
to the degree of aromatization, cross-linking, or incorporation of
heteroatoms. It must also be considered that, as mentioned, the
simpler pore system of the dolomite permitted more thorough
extraction, and thus the residual organic matter in the dolomites
may be more hydrogen deficient as a result of the greater
extraction efficiency in these samples — i.e., the Well-A samples
may contain some extractable organic matter which the solvent
could not access while nearly all of the extractable matter in the
dolomite was removed leaving only the especially insoluble and
more hydrogen deficient tar. However, because the quantity of
organic matter in the dolomites is generally greater than in the
carbonate samples, the extraction efficiency does not appear to
be an important factor in the differences seen in the residual
organic matter when the samples are thoroughly extracted with
strong solvents.

Samples of unextracted core from Well-A and Well-B were
acidized and the organic residue isolated. The same treatment
was given a core sample from Well-A. Of the material
recovered, 69 % was insoluble in MAC solvent for the Well-B
sample while 32 % was insoluble in MAC for the Well-A
sample. The insoluble material from Well-B was further treated
with HF, washed and extracted with MAC again. Elemental
analysis was conducted on the residual insoluble material. The
data is presented in Table 2. This sample was from an interval
18 ft. deeper than that of any of the Well-B samples used for the
multiple extraction experiments. The contrast in solubilities of
the organic matter from each of the wells is compelling evidence
that different types of tars are distributed through the reservoir.
The sulfur content of the insoluble organic matter is an important
parameter in pyrobitumens which result from TSR, as is
discussed later below.

Organic Petrography of Well-C Cores

Core samples from Well-C, a well studied earlier by Sobocinski
[2], were selected for organic petrographic study using reflected
light methods. The study sought to evaluate the bitumens in the
core samples before and after extraction, with the objective of
identifying insoluble (probably high reflecting) solid bitumens
and soluble (low reflecting) bitumens and bitumen staining. The
extraction solvents used were benzene and 90% benzene/l0%
methanol which further work showed to have nearly identical
extraction efficiencies as MAC.

A survey of unextracted samples covering the depth interval
7194-7323 ft. showed that the basal portion of this interval
(7318-7323 ft.) contained significant amounts of solid, high

134

reflecting bitumens similar to grahamite or the very early stages
of epi-impsonite [4]. These bitumens were assumed to be mostly
insoluble because little to no dissolution was observed using
immersion oils. Most soluble bitumens will partially dissolve in
immersion oils. These solid bitumens were found as interstitial
pore fillings and as the fillings of large vugs in limestones.
Moderate to heavy bitumen staining was also observed in this
interval. The upper portion of the interval consisted of lesser
amounts of solid, high reflecting bitumens. Most of the
bitumens were low reflecting and partially dissolved in the
immersion oil. This upper section was also very heavily
bitumen stained.

A sample selected from the upper portion of the interval at 7204
ft. contained < 25 % high reflecting bitumen prior to and after extraction. The heavy bitumen stain and low reflecting bitumens, however, were removed through extraction. A sample taken from the basal portion of the interval at 7318.9 ft. showed similar results. Interestingly, differences were found in the bitumen reflectance of this grahamite/epi-impsonite. Pre- extraction bitumen reflectance values were 0.68 % for the sample at 7204 ft. and 0.60% for the sample at 7318.9 ft. Post extraction results for the sample bitumens were 0.75 % and 0.76%, respectively. The differences in reflectance before and after extraction suggest that the pre-extraction bitumen exhibited absorbed staining of the solid bitumen, which would decrease the pre-extraction reflectance value. The post-extraction reflectance value is probably more indicative of the true reflectance.

Thus, solid bitumens insoluble to organic solvents can easily be
identified in the reservoir rock. Their distribution is not uniform
throughout the well but their reflectance values are
approximately 0.75%.

Thermochemical Sulfate Reduction· (TSR)

The large proportion of insoluble organic matter isolated from
the acidized rock, coupled with its high sulfur content, indicate
the possibility that some of the tars are actually pyrobitumens
which were formed by TSR. TSR is thought to be a reaction
which is initiated by high temperature (100 – 120° C, though
there is some evidence that TSR can occur at lower
temperatures). Required elements for TSR are a source of
sulfate, water, and oil (or gas). Dissolved anhydrite provides a
ready source ofsulfate. Because water is necessary, TSR occurs
at the oil/water interface. One of the major products of TSR is
pyrobitumen — an essentially inert solid organic material which
is heavily cross-linked with sulfide. Also produced is hydrogen
sulfide gas which may be present as such or as metallic sulfides
[5]. Because the sulfides are derived from sulfate (anhydrite),
which has a different isotopic signature than organic sulfur found
in petroleum, the pyrobitumen sulfur isotopic data can be used
to determine whether TSR has occurred. Other signs of TSR

SPE 25609 M.H. TOBEY ET AL. 5

include the presence of primary and secondary anhydrite with
secondary anhydrite often below the oil/water contact, hydrogen
sulfide gas in the oil and/or the formation water, and the
replacement of anhydrite with calcite [6]. TSR has been linked
to hydrothermal dolomitization in northeastern British Columbia.
Products formed included pyrobitumen, dolomite, methane,
hydrogen sulfide, and water [5].

Because its sulfur content is not as high as might be expected for
TSR pyrobitumen, the insoluble organic matter from Well-B may
be indicative of incipient TSR. Isotopic data are still being
collected in an investigation of TSR at the Uthmaniyah reservoir
as the circumstantial evidence is not conclusive. Nevertheless,
the tar mat occupies the eastern edge of the field near the
oil/water contact. There is a plentiful source of sulfate in the
form of anhydrite and Uthmaniyah oils contain dissolved
hydrogen sulfide. Finally, the tar mat is composed, at least in
part, of high sulfur, insoluble organic matter.

CONCLUSIONS

The macroporosity of core plugs was found to improve with
extraction with more powerful solvents, but the permeability
showed little change after the initial extraction with naphtha.
Microscopic examination showed that residual tar remained even
after extraction with chlorinated solvents, but the macropores
which control the permeability were essentially clean after
naphtha and toluene extractions. Bearing in mind that a
relatively small interval of samples was examined, it does not
appear that all the tar can be mitigated by either hot water
flooding or solvent flooding, particularly if the tar distribution is
extensive. Understanding the origin of the tar and how soluble
and insoluble tars are distributed through the reservoir may
allow well locations to be selected which avoid the impermeable
tar zones.

The tars were found to consist of soluble (hydrogen rich) and
insoluble (hydrogen deficient) components. The insoluble tars
differ in their hydrogen indices for Well-A and Well-B,
suggesting that the tar mat is composed of several classes of tar
which may result from a variety of mechanisms. Some of the
insoluble tar resembles bitumens with reflectance values of
approximately 0.75%. Analyses of some insoluble tar showed
at least one sample with a sulfur content of approximately 7.5 %.

The inert nature of some of the tar, coupled with its sulfur
content, suggest that it may result from thermochemical sulfate
reduction. Other, more hydrogen-rich, residual organic matter,
however would not be due to TSR, but may instead be the result
of gas de-asphaltening (soluble tars) or may be kerogen
(insoluble).

ACKNOWLEDGEMENTS

The authors thank the Southern Area Reservoir Management
Department of Saudi Aramco for their on-going support of this
investigation. The assistance of Mr. Mohammad I. Khan for his
contributions during the permeability and porosity studies are
also gratefully acknowledged. We thank Mr. E.L. Colling for
his critical review of this manuscript and his valuable
suggestions and comments. Finally, we thank the Saudi Arabian
Ministry of Petroleum and Mineral Resources and Saudi Aramco
for permission to publish this paper.

REFERENCES

1. Riley, C.M., Rodgers, M.A., and Young, W.A.: “Physical-
Chemical Characteristics of Tar at Uthmaniyah Area, Ghawar
Field, Saudi Arabia — Explanatory and Predictive Model for
Such Occurrences,” Report EPR.47ES.77 (1977), Exxon
Production Research Company, Houston, Texas.

2. Sobocinski, D.P.: “An Evaluation of the Characteristics of
Uthmaniyah Tar and the Potential for Stimulating Water
Injectors Containing the Tar,” Report EPR.112PS.76 (1976),
Exxon Production Research Company, Houston, Texas.

3. Dabbagh, A.E.: “A Study of Tar Properties and Methods of
Improving Injectivity in Tar Mats,” Project Number PN 21061
(1989), The Research Institute, King Fahd University of
Petroleum and Minerals, Dhahran, Saudi Arabia.

4. Robert, P.: Organic Metamorphism and Geothermal History:
Microscopic Study of Organic Matter and Thermal Evolution of
Sedimentary Basins, Elf-Aquitaine and D. Reidel Publishing Co.,
Dordrecht, Holland (1988) 311.

5. Teare, M.R. and Reimer, J.D.: “Thermochemical Sulfate
Reduction and Hydrothermal Dolomitization (TSR-HTD): A
Diagenetic Process That Created and Modified Middle Devonian
Reservoirs in Northeastern British Columbia,” presented at the
1992 AAPG mee!ing in Calgary, Canada, June 19-25.

6. Heydari, E. and Moore, C.H.: “Burial Diagenesis and
Thermochemical Sulfate Reduction, Smackover Formation,
Southeastern Mississippi Salt Basin,” Geology Vol. 17 (1989)
1080-1084.

135

6 GEOCHEMICAL STUDY OF TAR IN THE UTHMANIYAH RESERVOIR SPE 25609

TABLE 1:

TABLE 2:

Total organic carbon content and pyrolysis data for Well-A and Well-B core plugs after final methylene chloride
(MC) or trichloroethane (TCE) extraction. Samples were taken at the interior (INT) and exterior (EXT) of the core
plugs.

FINAl TOC S1 S2 H

CORE SOLVENT (wt. ‘MI) (mg HC/g rock) (mg HC/g rock)

WELL PLUG EXTRACTION INT. EXT. INT. EXT. INT. EXT. INT.

WELL-A 24 MC 2.4

WELL-A 52 MC 1.8

WELL-A 116 MC 0.4

WELL-A 23 TCE 1.6

WELL-A 51 TCE 1.0

WELL-A 115 TCE 0.7

WELL-B 182 MC 1.3

WELL-B 188 MC 1.8

WELL-B 201 MC 0.3

WELL-B 181 TCE 0.7

WELL-B 187 TCE 4.8

WELL-B 200 TCE 0.2

Elemental data for insoluble organic matter isolated from Well-B core sample at 6951 feet.

Carbon (wt. %) 81.2

Hydrogen (wt. %) 6.4

Sulfur (wt. %) 7.4

Oxygen (wt. %) 1.7

Nitrogen (wt. %) 1.4

Iron (wt. %) < 0.01

Loss on Ignition at 500 C (wt. %) 97.5

Loss on Ignition at 800 C (wt. %) 99.0

Loss on Ignition at 900 C (wt. %) 99.0

136

SPE 25609 M.H. TOBEY ET AL. 7

FIGURE 1: The Ghawar field showing the Uthmaniyah area, Saudi Arabia.

137

8 GEOCHEMICAL STUDY OF TAR IN ‘THE UTHMANIYAH RESERVOIR SPE25609

251″””T””——,,——,——–r—-.,…., tOGO rr——-,——,—–,.——rl

….

tao I-+—-+—–t—–t—–+-l

~.._.._.._.. ….._.._.._..-….._.._.._..-

‘I
S
~ to

:2
15
IL

…………..
……

…………………….. ~.._.._.._..-
20 1-t———1I-…-,.._-:c.::-:.-:=;.=.-……–~’——–+—-+-l

iII i
~ ··f·· .
~151+–~!!-i–+ —+–_–:;;;;;t”””~~===~
i i ~
~ —-+–.
t: i
~ 10 H–j-i—ti”‘””‘””~_……..~=….1…_==;,—t-i
~! SAMPLE 11. 11-10 MD

i SAMPLE 24111-100 MD
i _..__ SAMPLE 12 >100 MD

i
IIICLWTCll.I.- PM·lUT III!CU-‘

EXTRACTION STEP

o.t L…L.. -.l.. …..L. …..,I. –LJ

–MICL3-2TOLUeNI PRI·T1!8T MICL3-1EXTRACTION STEP
5L…L…–_—JL– -‘– —-‘- .L..J

NAPHTHA

Figure 2: Permeability and porosity changes In Well-A plugs after naphtha,
toluene, and methylene chloride extractions.

25r;:c::====:::::Jc:=====::I:==;:—-,—1l 10000rr—–,—–,—–,——-r-I

10 H—–+—–+—–+—–+-l

………….
. .

10001-+—-+—-+—-+—-+-1

1
~! 100 1-+—-+—-+—-+—–+-1
:::E
ffi ,…- –
a.
II:
:c

lAM nill-to MD

lAM 24 til-tao MD
lAM II >tao MD

_………_..
24

221-+—-t——+–~’—-___f—-+-I

./.~,..

! 21 _ •• _ •• _ •• – •
..I •• – •• _ •• _ ~…….–

~ 20 f-+—–j——+——–f—–H

~ 181-+—-t——+———.i=….~~..-.=d_l
i
“.fa …
i 171-+—-t——+—-,.:.–/-+4—-+-I
~ 1.1-+—___f–.:-:>..,……..:.:·:-··+—+—I——-+-l

… /…………
1.~…. _…-+——+-+—-+——+-1

_/
14 H—–t——+—-___f—-+-I

PRE·TEIT TRICHLORO-1 TRICHLORO-ITOWENE
t …….—–‘——-‘——-‘—–~
NAPHTHATRICHLOROo2TRICHLOROot”R!oTEITTOLUINI

t3!:::::::!::====:’:=::===::::::!:=======’====::!:::’
NAPHTHA

EXTRACTION STEP EXTRACTION STEP

Figure 3: Permeability and porosity changes In Well-A plugs after naphtha,
toluene, and trichloroethane extractions.

138

SPE25609 M. H. TOBEY ET AL. 9

1I…-r—–r—–“””‘T””–…-..,..-r——-r-1 1DDD rr——,.—-r—-…——r-l
.0· … ,.. ,

IAMPLa 11111010 MD
IAMPLa 14 10-100 MD
SAMPLa IZ >100 MD

.. ,.. ,
100 H—–t—-t——+—–+-l

‘I ….._.._.._..__.._.._../

J”H—+–j–+—t—–f—H
I
a:
:ii

………………. ‘
.’

10 H—–Jt—-“””‘——=—-+—-H

I.. /
~/
i _.._..-.._I _..,,-

10 ……….._..-.._..-. ~.._.._.._..- ~.

1′-‘- .l….- -J- .l….- –l……J
0.1 …… –L. I..- -‘– …l….I

NAPHTHA TOLUENE PRE·TEIT MECL3-1 MECL3-1
NAPHTHA TOWENE PRE·TEST MECL3-1 MECL3-Z

EXTRACTION STEP
EXTRACTION STEP

Figure 4: Permeability and porosity changes In Well·B plugs after naphtha,
toluene, and methylene chloride extractions.

3Or;c==========:::;–T—“Il 1- r-;——r—-,——–r—–ro

1I1r—T—T—‘–:j::======:ti

~ /~
e• 1/ /” …_-_.-
!111-+——-=~–____:::::;~·’—·…._··-….-··-_t—-++
i” ‘-” —-..-.._1_ ••-··……-··-~.._..-….. . .0° ••
i ··’…. …….. ,2 10 I-+—–…:.:.J.”””-‘”.’-‘-‘••=..=………..:..:… .:..:….’-‘.1.1-••_••_.—-1—-++

* SAMPLa 11.0-10 MD
SAMPLE 14 10-100 MD
SAMPLE II >100 MD

……………………………………

I 100 I-I——+—–+—-+——+-l

I “.//…. –‘-j-“-“-“-“”‘–“-‘
; 10 H—–+/—–J<---+-----+------t-t

+—-f

TRICHLDIlOo1 TRICHLDRDoIPRE·TEITTOWENENAPHTHATRICHLOROo1 TRICHLOROoZPR!·T!8TTOW!N!

I’-‘—–‘——-‘——–‘—-….L..J

NAPHTHA
EXTRACTION STEP
EXTRACTION STEP

Figure 5: Permeability and porosity changes in Well·B plugs after naphtha,
toluene, and trichloroethane extractions.

139

10 GEOCHEMICAL STUDY OF TAR IN THE UTHMANIYAHRESERVOIR SPE 25609

Preferential Extraction
From Plug Exterior

‘\

5.0—————–?fl. 4.5
• 4.0..

. ~ 3.5-o 3.0
O 2.5 Preferential Extraction
~ 2.0 From Plug Interior

as 1.5 .. • WELL·A Me
C 0 WELL·A TCE
~ 1.0
CI) …. WELL·S Me
… 0.5 .. WELL.S TCE

.5 0.0 ~~””””””””T’I””I”‘I”””””””””””””’~;:;:;;:;::;:;:;;:;:;;:~
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

4.5 5.0

External Toe (wt. 0/0)

. FIGURE 6: Total organic carbon cross-plot showing that solvent extraction of the core plugs did not preferentially remove
organic matter from the exterior or interior of the plugs. TOC differences are more likely due to the heterogenous
distribution of the organic matter through the rock.

1.0 1.5 2.0 2.5 3.0 3.5

4.0

Total Organic Carbon (wt. %)

4.0

~ 3.5
Co)

0
3.0-a– 2.5(J

:J:

a 2.0
E-
~

1.5

.!
1.0>

N
tn 0.5

0.0
0.0 0.5

….
..

Slope = HI of 67
r2 = 0.82

• WELL-A INTERIOR
o WELL-A EXTERIOR

.. WELL-B INTERIOR

.. WELL-B EXTERIOR

4.5 5.0

FIGURE 7: ~ vs. TOC plot which permits group hydrogen indices to be cal~ulated. Data was obtained for the extracted core
plugs listed in Table 1. Note that the Well-A unextracted organic matter has a HI of 163 while the Well-B
unextracted organic matter has a HI of 67.

140

Image001
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Image004
Image005
Image006
Image008
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Image010

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Exhibition and Conference, 13-16 October 2002.

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presented, have not been reviewed by the Society of Petroleum Engineers and are subject to
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Abstract

A detailed physical characterization of tar from a
carbonate reservoir in Saudi Arabia was made to
evaluate its mobility and ways of establishing contact
between the lighter oil and its aquifer. Density and
viscosity measurements were carried out on several tar
samples, under simulated reservoir conditions of
pressure and temperature. Other physical parameters
such as simulated distillation, pour point and penetration
index were also experimentally determined.

Tar physical properties were found to vary with
depth and area within the same field. The obtained
experimental results showed a gradual increase in
density and viscosity from the tar/oil contact towards the
tar/water contact. This increase was much more
pronounced in the neighborhood of the tar/water contact.
Density and viscosity of tar diluted with toluene were in
excellent agreement with those of pure tar.

The density of non preserved tar varied between

0.956 g/cc at 200°F and 1.008 g/cc at 76°F while that of
preserved tar varied between 0.944 g/cc at 200°F and 0.
991 g/cc at 76°F. The tar samples analyzed appear to
behave as Newtonian fluids.

Introduction

The present paper discusses detailed physical
characterization of several extracted and RFT bottom
hole tar samples obtained from a carbonate reservoir in
Saudi Arabia. The chemical aspect has already been
presented elsewhere [1]. Tar is defined as extra heavy oil
with a gravity ranging between 29 and 9°API albeit the
distinction between heavy oil and tar is rather shady.

Tar mat is present in abundance in the Middle

East, Africa and elsewhere. In recent publications
Kaufman et al. [2] mentionned the presence of a tar zone
at the water/oil contact in Burgan field. This has been
known for a long time. However, to ascertain lateral and
vertical delineation of the tar zones appear to be still
unresolved. Thick tar zones identified through visual
observation and Latroscan analyses of weathered core
samples were reported in the Raudhatain field [3].

A tar mat is generally a thick column laying

between an aquifer underneath and a much lighter oil
reservoir above. This peculiar location poses a host of
challenging problems for an efficient management of the
lighter oil and ultimately for a proper management of the
tar zone itself [4].

In this study, density of tar was measured with a

digital Anton Parr densiometer having a maximum
temperature range of 300 °F and a pressure limit of 6000
psi. Based on these measurements, specific gravity and
API gravity were determined.

A rolling ball viscometer was used to measure

tars viscosity at elevated pressures and various
temperatures. The effect of visbreaking or permanent
viscosity reduction due to thermal alteration has also
been examined.

SPE 78538

Characterization of Tar From a Carbonate Reservoir in Saudi Arabia: Physical
Aspects

Harouaka A. S, B. Mtawaa and W. A. Nofal, SPE, KFUPM/RI

2 [Harouaka A. S, B. Mtawaa and W. A. Nofal ] SPE 78538

The pour point, which measures the lowest
temperature at which tar is observed to flow when
cooled and examined under prescribed conditions, was
determined following ASTM standard procedure: ASTM
D97-66.

Tar does not change from its semisolid state to

the liquid state at any definite temperature, but it
gradually becomes softer as the temperature rises. For
this reason, the determination of the softening point has
been carried out following the standard procedure
described in IP 58/83 [5]. The penetration index and
flash point have also been determined by standard
procedures.

Distillation of tar has been conducted using the

procedure described in ASTM D402-73. The sample is
distilled at a controlled rate to a temperature of 680 °F,
and the volumes of distillate obtained at the specified
temperatures are measured. The residue and the
distillates can then be tested as required.

Experimental procedures and background

Modifications of existing oil characterization
methods were necessary to facilitate experimental
determination of the physical properties of tar. One
typical modification is to dilute tar with a suitable
organic solvent prior to testing. Measurements are then
made using different dilution ratios. The Physical
properties of pure tar are determined by extrapolation to
zero dilution ratio.

Chirinos M. L. et al. [6] studied the effect of
temperature (ambiant to about 194°F) and percentage of
added diluent on the density and viscosity of nearly 100
crude oils obtained from the Venezuelan Orinico Oil
Belt. An Atomix mixer, a Fisher specific gravity
hydrometer and a Haak Rotovisco RV3 viscometer were
used to perform this study. These crudes and their
mixtures with diluents behave as Newtonian but not as
plastic fluids. Expressions to estimate the density and
kinematic viscosity of these oils and their mixtures with
diluents at any temperature were also presented.

Tobey et al. [7] reported that when core plugs
obtained from tar zones near the oil-water contact in the
Arab-D formation in Saudi Arabia were extracted with a
series of solvents (naphtha, toluene, and methylene
chloride or trichloroethane) for at least 72 hours; their
porosity improved and their permeability showed only a

small change after the initial cleaning with naphtha. It
was hypothesized that macro pores were easily accessed
by the solvents and hence cleaned right after the naphtha
and toluene extractions. The micro pores and the smaller
pore-throats remained completely or partially plugged by
tar that differ from one location to another within the
same reservoir.

Puttagunta V. R. et al. [8] developed a
generalized viscosity correlation to predict, with good
accuracy, the viscosity of any Alberta bitumen’s or
heavy oils, which are known to vary widely from deposit
to deposit, over a wide range of pressure and
temperature, usually encountered in in-situ recovery
methods. This correlation requires only one viscosity
measurement at 86°F and atmospheric pressure.

Erno B. P. et al. [9] measured the viscosity of oil
samples extracted at 194°F using a Beckman 28/M
heated centrifuge from preserved cores obtained from
various depths and different wells in the Lower
Cretaceous Clear water “B” heavy oil reservoir in the
Caribou lake area. They observed that the viscosity
increases consistently with depth and correlates well
with structural elevation. Simulated distillation data
showed that the viscosity variations are due to
compositional differences. Samples from other heavy oil
reservoirs such as McMurray, the Wabiskaw and the
Waseca sand at Pikes Peak in west central Saskatchewan
were found to exhibit similar trends.

Svrcek and Mehrotra [10] measured the
viscosity, density and solubility of CO2, CH4 and N2 in
bitumen samples obtained by toluene extraction from the
Athabasca tar-sands. These measurements were carried-
out over a temperature range of 77 to 212°F and a
pressure up to 1450 psi. They observed that CO2 has the
highest solubility in Bitumen and reduces its viscosity
drastically. On the other hand N2 has quite low solubility
in the bitumen and less effect on its viscosity. They also
reported that dead oil viscosity decreases drastically with
the increase of temperature and is less affected by the
pressure while that of live oil decreases with pressure
and temperature.

Following is a brief description of the
experimental procedures and conditions under which the
physical characterization of tar samples retrieved from
the tar mat zone, in a carbonate reservoir from Saudi
Arabia, was determined.

SPE 78538 [Characterization of Tar From a Carbon ate reservoir in Saudi Arabia: Physical Aspects ] 3

Density

Density measurements were carried-out using a
digital Anton Paar densiometer (DMA-60). The
measuring principle involves the period of oscillation of
a vibrating U-shaped sample tube filled with the sample
or through which the sample flows continuously. It was
equipped with a Ruska mercury injection pump to
charge the tar sample into the sample tube and pressurize
it to the desired pressure, and a Heto constant
temperature oil bath to maintain the sample tube at the
desired temperature.

Before being used, the densiometer was
calibrated using vacuum and a standard oil sample with
a density close to that of the tar samples to be analyzed.
The density is calculated once the sample is introduced
into the sample tube and its period of oscillation is
stabilized at the desired pressure and temperature.

1. Extracted tar

To measure the density of extracted tar, it must
be injected into the densiometer’s sample tube as
explained earlier. However, at atmospheric pressure and
different temperatures, it does not have the necessary
mobility to be injected. This is why it has been diluted in
toluene. The density of extracted tar-toluene
homogeneous mixtures was then measured at different
toluene concentrations (weight %). The density of the
extracted tar is simply taken as the value extrapolated to
zero toluene concentration.

2. Dead RFT tar

A tar sample taken out of the RFT bottom hole
sample transfer cell and saved in a container was left
open to atmosphere for few days until the associated gas
was liberated. Its density was then measured following
the same procedure used for the extracted tar.

3. Live RFT tar

The cylinder containing the live RFT tar was
connected to the inlet port of the densiometer sample
tube while another cylinder was connected to its outlet
port to let the tar sample flow continuously from the
source cell to the second cylinder via the sample tube.
The sample cell was then heated to the desired
temperature using a temperature regulator and display
along with heating tape.

The sample was subsequently pressurized to the
desired pressure using a Ruska mercury injection pump.
A second pump was used to maintain flow through the
densiometer sample tube. The density of the undisturbed
RFT tar sample was then determined at different
pressures and temperatures.

Viscosity

Viscosity measurements of extracted and dead
RFT tar were carried out at ambient pressure and
different temperatures using a highly sensitive torque
measuring system made of a plate-cone type Contraves
Low Shear-30 viscometer and a Haak-M regulated
constant temperature oil bath.

Tar samples (0.50 cc) were heated to the desired

temperature and poured into the measuring cup of the
viscometer. Similarly, the viscosity of tar/toluene
homogeneous mixtures were measured at various
toluene concentrations (volume %). Results were
extrapolated to zero toluene concentration to find the
pure tar viscosity.

Viscosity measurements of extracted and live
RFT tar were also obtained at various high pressures and
temperatures using an ROP rolling-ball viscometer
DT14001, along with a Haak-M constant-temperature oil
bath and a Ruska mercury injection pump. An important
feature of this viscometer is the utilization of a magnetic
field for detecting the rolling ball. Accurate ball location
can be detected even when the ball is completely coated
with the high viscosity tar.

The viscometer was calibrated using a standard

oil sample with a viscosity of 27 cp at 210°F and 477 cp
at 100°F. Viscosity values were calculated using the
following relation:

µ = A *(?b- ?t) * t + B

Where:

µ is the tar viscosity (cp),
A is the slope of the calibration curve at the desired
angle,
?b and ?t are the densities of the steel ball and tar
respectively,
t is the measured time (sec.) and
B is the intercept of the calibration curve at the same
angle.

Simulated distillation

There are several distillation methods covering
the boiling range distribution of petroleum products. The
extended ASTM D2887-73 (standard test methods for
boiling range distribution of petroleum fractions by gas
chromatography) was followed for the distillation of
extracted and RFT tars. This method is mainly
applicable to petroleum products and fractions with a
final boiling point of 1000 °F or less but extended to
cover a higher boiling point range.

The apparatus used included a Hewlet-Packard

level 4,5880A Gas Chromatograph, a flame ionization
detector (FID), a liquid nitrogen cooling system, and a
cartridge tape unit. The column used was a 1/8 x 20
inch stainless steel, 10% UC-W982 on 80/100 mesh
chromosorb P-AW.

The results obtained are shown in Table 1

below. The initial boiling point (IBP) of extracted tar
(552 ºF) was almost twice that of RFT tar (228ºF).
However, at the end, their yields (around 1000ºF) are
almost equal 10-11% of the total sample. This indicates
that their compositions are mainly the same.

The difference at low temperature may be due to

the fact that the extracted tar has lost its lighter
components keeping in mind the cores were exposed to
atmosphere for a long period of time.

Table 1. Distillation data of extracted and RFT tars.

Extracted tar RFT tar
% OFF Temperature °F

IBP* 552 228
1 590 297
2 639 377
3 675 444
4 702 518
5 723 588
6 752 662
7 790 736
8 828 811
9 871 892
10 916 999
11 975

*Initial boiling point: The point at which a cumulative area count equal
to 0.5% of the total area under the chromatogram is achieved.

Other Physical Parameters

1. Penetration index

The penetration index is the consistency of a

bituminous material expressed as the distance in tenths
of a millimeter a standard needle vertically penetrates a
sample of the material under known conditions of
loading, time, and temperature. Penetration tests were
carried out for the extracted and RFT tar samples at
77oF according to ASTM D5-83 using the penetration
apparatus (Penetrometro-697) and its accessories
(sample containers, timer, a water bath, needles, and
weights). It was found that penetration index of the
extracted and RFT tar samples were 67 and 196 units,
respectively as shown in Table 2 below.

Table 2. Penetration index of extracted and RFT tar.

Tar sample Penetration*
Extracted Tar 67

RFT tar 196

*One penetration unit = 0.1 mm, Time = 5 sec, needle weight with
spindle = 50 gms.

2. Flash point

The flash point is defined as the lowest

temperature, corrected to a barometric pressure of 760
mmHg, at which application of a test flame causes the
vapor of the sample to ignite under specified testing
conditions. A Gallenkamp-Autoflash apparatus, a form
of the Pensky-Martens closed tester, was utilized to
determine the flash point of the extracted and RFT tar
samples according to ASTM D93-80.

3. Pour point

The pour point is the lowest temperature,
expressed as a multiple of 5°F at which the oil/tar is
observed to flow when cooled and examined under
prescribed conditions. The pour point tests were
conducted according to ASTM D 97 using a test
apparatus assembled in house (Brookfield Ex 200
thermostatic bath, a test jar, and two certified
thermometers). The obtained pour points were 163 oF
for the extracted tar and 89 oF for the RFT tar.

4 [Harouaka A. S, B. Mtawaa and W. A. Nofal ] SPE 78538

Results and discussions

This section summarizes the main objective of
this study, the physical properties determination of
several tar samples including extracted and RFT
bottomhole samples.

Extracted Tars: Various extracted tar/toluene
mixtures (20, 40, 60, 80 and 100% by weight) were
prepared manually. Once homogenization was
completed, densities were measured at atmospheric
pressure and temperatures ranging from ambient to
210oF to include the reservoir temperature, estimated at
190 oF. At each temperature, the density of the mixtures
behaved as expected, giving a linear variation with
solvent concentration as shown in Figure 1. Each
straight line was extrapolated to zero toluene
concentration to determine the density of extracted tar at
that particular temperature.

It has been determined that within the range of
temperatures considered in these experiments, the
densities of tar/toluene mixtures follow an equation of
the form:

ρm = Wd *(ρd – ρt) + ρt

Where,
ρm is the density of the mixture in g/cc,

ρd is the density of the diluent (toluene) in g/cc,
ρt is the density of tar in g/cc and
Wd is the weight fraction of diluent.

Experiments to evaluate the effect of pressure on
the density of extracted tar were performed at three
different temperatures (220, 230, and 240oF). They were
generated maintaining a constant temperature while
decreasing the pressure from 3500 to 1000 psig. The
results obtained are shown in Figure 2.

As in the case of extracted tar, densities of RFT
tar/toluene mixtures were measured at atmospheric
pressure and various temperatures (102, 120, 160, 180,
190 and 200o F). Here also there is a linear relationship
between densities and solvent concentrations as
indicated by the straight lines shown in Figure 3.
Extrapolation to zero toluene concentration yields the
density of RFT tar at the desired temperature.

When these densities are plotted against

temperature along with the densities of extracted tar as
shown in Figure 4 one can see, rather cle arly, that at
temperatures less than 180o F the density of the RFT tar
is lower than that of the extracted tar. However, beyond
180o F the two curves coincide. This is expected since at
higher temperatures RFT tar loses its lighter components
and has the tendency to become similar in composition
to the extracted tar.

Experiments to determine the effect of pressure
on the density of RFT tar were also performed at three
temperatures (160, 190, and 220o F). The data were
taken while decreasing the pressure from 3500 to 1000
psig and maintaining the temperature constant. The
density increases linearly, at constant temperature, with
increasing pressure within the specified pressure range
as shown in Figure 5.

Viscosity Measurements

Extracted Tars: The viscosity of extracted tar
was measured at different temperatures ranging from
160 to 270 oF and atmospheric pressure. The results
obtained are shown in Figure 6. The log-log plot of shear
rate vs. shear stress depicted in Figure 7 shows a straight
line with a slope of one indicating the extracted tar
behaves as a Newtonian fluid with some plastic behavior
as the yield (intercept) is nonzero.

As in the case of extracted tar the viscosity of

RFT tar was also measured at atmospheric pressure and
the same temperatures as the extracted tar. The results
obtained are shown in Figure 8. A comparison between
Figures 6 and 8 indicates clearly that the RFT tar has a
lower viscosity than the extracted tar.

The RFT tar is also believed to behave like a

Newtonian fluid as indicated in the log-log plot of shear
rate vs. shear stress (Figure 9). The slope shown in
Figure 9 is slightly less than one indicating a
pseudoplastic behavior. The yield on the other hand is
essentially zero. One should keep in mind that the
extracted tar is implicitly filtered during the extraction
process as most solid particles are separated from the
recovered tar. The RFT tar, on the other hand, has not
been filtered. Based on this analysis, it is believed that
the RFT tar behaves like a Newtonian fluid under
reservoir conditions.

The standard way to plot viscosity vs.

temperature is on ASTM viscosity vs. temperature charts
for liquid petroleum products, which for Newtonian

SPE 78538 [Characterization of Tar From a Carbon ate reservoir in Saudi Arabia: Physical Aspects ] 5

fluids generally yield a straight-line relationship between
kinematic viscosity and temperature. The straight line is
based on a mathematical expression that allows the
calculation of viscosity, in centistokes (cSt) at any
temperature T in degree Rankin, as follows:

Log log (? + 0.7) = A – B log T.

Where, ? is the kinematic viscosity in centistokes,
A is the intercept and
B the slope.

Various tar toluene mixtures, ranging from nine
to 32 % by weight of toluene, were prepared manually
for both extracted and RFT samples. Once
homogenization was achieved, viscosity measurements
of tar/toluene samples were performed at 180 and 190oF
and atmospheric pressure. The results are shown in
Figures 10 and 11 respectively. At each temperature the
viscosity of the tar/toluene mixtures exhibited a linear
variation with solvent concentration as shown in Figures
10 and 11. Each straight line was then extrapolated to
zero toluene concentration to find the viscosity of
extracted and RFT tar samples at that particular
temperature.

At any temperature the viscosity of tar/toluene
mixtures appear to follow an equation of the form:
Y = A-BC.

Where,

Y = log log (? + 0.7),
A is the straight-line intercept,
B is the slope of the straight line and
C is the solvent concentration in volume %.

The straight-line equation, at 180oF, for
extracted tar/toluene mixture viscosity is:

Y = 0.70651-0.015519*C.

The straight-line equations for RFT tar/toluene
mixture viscosity at 180 and 190o F are respectively:

Y = 0.62468 – 0.01474 * C.
Y = 0.59805 – 0.015742 * C.

Figure 12 shows a plot of kinematic viscosity vs.
solvent concentration for both extracted and RFT
tar/toluene mixtures at 180 and 190o F, and atmospheric
pressure. One may observe from Figure 12 that an

extrapolation to zero solvent concentration gives
kinematic viscosities comparable to those for pure tar at
a given temperature for both extracted and RFT tars.

The viscosity of RFT tar was also measured at
different temperatures (160 up to 230oF) and pressures
ranging from 1000 to 3500 psig. The results are shown
in Figures 13. It can be seen from Figure 13 that the
viscosity of the RFT tar decreases with increasing
temperature and decreasing pressure, unlike that of the
dead RFT, which decreases significantly with
temperature and remains more or less unchanged with
pressure.

CONCLUSIONS

The following conclusions may be drawn from

the detailed physical characterization of several
extracted and live RFT bottom hole tar samples obtained
from a carbonate reservoir in Saudi Arabia:

1. The physical properties of the analyzed tar
samples were found to vary with depth and area
within the same field.

2. A gradual increase in density and viscosity from
the tar/oil contact towards the tar/water contact
was observed. This increase was much more
pronounced in the neighborhood of the tar/water
contact.

3. At each temperature, the density of tar/toluene
mixtures behaved as expected, giving a linear
variation with solvent concentration.

4. Density and viscosity of tar diluted with toluene
were in excellent agreement with those of pure
tar.

5. The viscosity of the RFT tar sample decreases
with increasing temperature and decreasing
pressure.

6. The viscosity of dead RFT decreases
significantly with temperature and remains more
or less unchanged with pressure.

7. The extracted tar is believed to behave as a
Newtonian fluid with some plastic behavior

8. The RFT tar is also believed to behave as a
Newtonian fluid with a pseudoplastic behavior.

ACKNOWLEDGEMENTS

The authors which to acknowledge the support
of the Research Institute of King Fahd University of

6 [Harouaka A. S, B. Mtawaa and W. A. Nofal ] SPE 78538

Petroleum and Minerals (KFUPM/RI). They also thank
KFUPM/RI for their permission to publish this paper.
The authors are grateful to A. A. Habelreeh for his
valuable contribution to the final manuscript preparation.

REFERENCES

1. Harouaka A.S., H.K. Asar, A.A. Al-Arfaj, A.H. Al-

Husaini and W.A. Nofal (1991). “Characterization
of tar from a carbonate reservoir in Saudi Arabia:
Part 1-Chemical aspect “Paper SPE 21004
presented at the SPE International symposium on
oilfield chemistry held in Anaheim, California,
Febr. 20-22.

2. Kafman R.L., H. Dashti, C.S. Kabir, J.M. Pederson,
M.S. Moon, R. Quttainah and H. Al-Wael (1997).
“Characterizing the Greater Burgan Field: Use of
Geochemistry and Oil Fingerprinting” Paper SPE
27803 presented at the 10th SPE Middle East Oil
Technical Conference and Exhibition, Bahrain,
March 15-18.

3. Nemcsok S., N.H. Morisson, A. Carruthers and S.H.
Abdullah (1998). “Sedimentary Interpretation of a
Multilayered clastic Oil Reservoir: Impact on
Development Plans For the Zubair Reservoir,
Raudhatain field” Paper SPE 48972 presented at the
Annual Technical Conference and Exhibition, held
in New Orleans, LA, Sept. 27-30.

4. Harouaka A.S. and H.K. Asar (1990). “Tar mats
evaluation- a resource and a nuisance” First Saudi
symposium on energy utilization and conservation
held in Jeddah, Saudi Arabia, March 4-7.

5. Haines W.E. (1976). “Extension of the US Bureau
of Mines- API Scheme For the Characterization of
Heavy Oils” Erdoel Kohle -Ergas-Petrochem V. 29,
No 9, August.

6. Chirinos, M.L., J. Gonzalez and I. Layrisse (1983).
“Rheological Properties of Crude Oils from the
Orinoco Oil Belt and their Mixtures with Diluents”
Rev. Tech. Interp, July 1983, 103-115.

7. Tobey M. H., H.I. Halpern, G.A. Cole, J.D. Lynn,
J.M. Al-Dubaisi and P.C. Sese (1993). “
Geochemical Study of Tar in the Uthmaniyah
Reservoir” Paper SPE 25609 presented at the SPE
Middle East Oil Technical Conference and
Exhibition, Bahrain, April 3-6.

8. Puttagunta V. R., B. Singh, E. Cooper and B.
Thunder (1988). “A Generalized Viscosity
Correlation for Alberta Heavy Oils and Bitumens”
Preprint Paper 158 presented at the fourth
UNITAR/UNDP conference on heavy crude and tar
sands, Edmonton, Alberta, Canada August 7-12.

9. Erno B. P., J.R. Chriest and R.C. Wilson (1991). “
Depth-Related Oil Viscosity Variation in Canadian
Heavy Oil Reservoirs” JCPT, Vo. 30, No. 3, May-
June, 36-41.

10. Svrcek W.Y. and A.K. Mehrotra (1982). “Gas
Solubility, Viscosity and Density Measurements for
Athabasca Bitumen” JCPT, July-August, 31-38.

SPE 78538 [Characterization of Tar From a Carbon ate reservoir in Saudi Arabia: Physical Aspects ] 7

0.75

0 .8

0.85

0 .9

0.95

1.05

0 20 40 60 80 10

SOLVENT Wt% TOLUENE

D
E

N
S

T
Y

200 F 190 F 180 F
160 F 120 F 102 F
76 F

Figure 1. Density vs. toluene concentration for extracted tar.

Fig 2. Density vs. pressure for extracted tar.

0.75

0.85

0.90

0.95

1.00

0 20 40 60 80

100

SOLVENT Wt % TOLUENE

D
E
N
S

IT
Y

, g

200 F 190 F
180 F 160 F
120 F 102 F

Figure 3. Density vs. toluene concentration for RFT tar.

0.90
0.95
1.00
1.05

60 80 100 120 140 160 180 200 220

TEMPERATURE, F

D
E
N
S
IT
Y

, g
m

EXT RFT

Figure 4. Density vs. temperature for extracted and RFT tar.

0.80

0.82

0.84

0.86

0.88

0.90

0.92

0.94

1000 1500 2000 2500 3000 3

500

PRESSURE, psig

D
E

N
SI

T
Y
, g
m

160 F 190F 220F

Figure 5. Density vs. pressure for RFT tar.

200

400

600

800

1000

1200

1400

100 150 200 250

300

TEMPERATURE, F

V
IS

C
O

S
IT

Y
, 1

00
0

c
p 14.7 psia

Figure 6. Viscosity vs. temperature for extracted tar.

0.9300
0.9350
0.9400
0.9450
0.9500
0.9550
0.9600
0.9650
0.9700
0.9750

0 1000 2000 3000 4000

PRESSURE, psig
D
E
N
S
IT
Y
,g

@ 240 F @ 230 F
@ 220 F @ 190 F

8 [Harouaka A. S, B. Mtawaa and W. A. Nofal ] SPE 78538

10
100
1000

10000

100000

0.01 0 .10 1 .00

10 .00

SHEAR RATE, 1/s

S
H

E
A

R
S

T
R

E
S

S
,

si
a

Figure 7. Shear Rate vs. shear stress for extracted tar at
200 F.

0
10

20
30

40
50

60
70

100 140 180 220 260 300

TEMPERATURE, F
V
IS
C
O
S
IT

Y
,

10
00

c
p

Figure 8. Viscosity vs. temperature for RFT tar at 14.7

psia.

1 .00

10 .00

100 .00

1000 .00

0 .01 0 .10 1.00
SHEAR RATE, 1 /s

S
H
E
A
R
S
T
R
E
S
S
, p
si
a

Figure 9. Shear Rate vs. Shear Stress for RFT tar at 200

Figure 10. Kinematic Viscosity vs. solvent

concentration for RFT tar at 180 F.

Figure 11. Kinematic viscosity vs. solvent concentration
for RFT tar at 190 F.

Figure 12. Kinematic viscosity vs. solvent concentration
for Extracted tar at different temperatures.

SPE 78538 [Characterization of Tar From a Carbon ate reservoir in Saudi Arabia: Physical Aspects ] 9

0
100
200
300
400
500
600

1000 1500 2000 2500 3000 3500

PRESSURE, psig
V
IS
C
O
S
IT

Y
, c

160 F 175 F 190 F 220 F

Figure 13. Viscosity vs. pressure for RFT tar at different

temperatures.

10 [Harouaka A. S, B. Mtawaa and W. A. Nofal ] SPE 78538

This paper was prepared for presentation at the 2006 Abu Dhabi International Petroleum
Exhibition and Conference held in Abu Dhabi, U.A.E., 5–8 November 2006.

This paper was selected for presentation by an SPE Program Committee following review of
information contained in an abstract submitted by the author(s). Contents of the paper, as
presented, have not been reviewed by the Society of Petroleum Engineers and are subject to
correction by the author(s). The material, as presented, does not necessarily reflect any
position of the Society of Petroleum Engineers, its officers, or members. Papers presented at
SPE meetings are subject to publication review by Editorial Committees of the Society of
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Abstract
The Upper

Zubair

Formation is a giant reservoir consisting of
350 ft of excellent quality sandstone. This main producing
reservoir has a 100 thick tar mat historically encountered near
oil water contact. Recently, a well completed in the crestal part
of structure above tar mat zone and in good quality sand did
not contribute to oil production and presence of tar was
suspected. A re-look at identification, origin and distribution
of the tar mat has become critical for formulating future
development strategy of the reservoir.

An appraisal well was drilled on the crest with a
comprehensive data acquisition program including coring,
fluid sampling and logging. Geochemical analyses of core
plugs at every 4 ft were carried out along with fluid invasion
studies. Observation from core material was compared with
NMR logs to formulate criteria for identification of tar zones
to supplement information from open hole and cased hole
logs.

NMR transverse T2 relaxation histogram was observed to be
affected by fluid properties and poresize distribution. There is
a consistent shortened T2 distribution along tar mats resulting
in missing porosity compared to neutron-density derived
porosity. Geochemical analyses of core material indicates high
asphaltene zones having undegraded paraffin and are
isotopically lighter with immature biomarkers and higher
concentration of aromatics. The observations indicate early
origin of tarmats due to biodegradation of immature oil at a
lower reservoir temperature. Later, undegraded oil with
undegraded paraffin profile has migrated in to the reservoir
after biodegradation process has ceased.

Biodegraded early origin of tarmat can not explain areal
distribution over the field. Local compartmentalization of tar

mat from light oil leaking along faults leaving behind heavier
fractions during geological history is the possible origin for tar
occurring above tar window. Early high Asphaltene content
from immature charge, Asphaltene precipitation due to later
light oil migrating to trap and gravity segregation explains the
occurrence of tarmat in tar window near oil water contact.
Early charges/asphaltenes were bio-degraded and incorporated
in the tar zone.

Current study helps in identifying and mapping the tar and
heavy oil zones in the reservoir. Distribution of tar mat of
diverse origin needs to be understood well in advance to have
realistic estimation of movable hydrocarbon.

Introduction
Zubair reservoir is one of the prolific producing horizons in
North Kuwait. It is overlain by three other producing
reservoirs: Lower Burgan, Upper Burgan and Mauddud. The
Upper Zubair sand horizon holds most of the inplace oil of the
Zubair reservoir in estuarine channels. It is on continuous
production since 1960.

Presence of tar was initially detected from produced oil in
some of the wells completed in Upper Zubair sand and visual
inspection of core materials. Geochemical studies were
subsequently undertaken to demarcate occurrence of tar in all
the Zubair reservoirs. Tar zone in Upper Zubair Sand was
considered to be structurally controlled and occurred in a
specific depth window around oil-water contact.

Later, presence of a thick zone of tar above the mapped
window was detected from a crestal well completed in thick
estuarine channels. Another well was drilled in the vicinity
and full set of logging and coring followed by geochemical
studies were carried out to understand the characteristics of
this tar zone.

Tar zones are identified with certainty from geochemical study
of core material as zones having high asphaltene content and
high extract yield. In absence of core, indirect methods: such
as PNL, PLT, openhole logs of later drilled wells and
production history were used to demarcate the tar zones. The
massive sands of Z44CH and Z46CH are the main locales of
tar. Smaller channels of other layers are devoid of tar above
the historical tarmat.

SPE 101303

Identification, Origin, and Distribution of Tarmats in Upper Zubair Sand Reservoir,
Raudhatain Field, North

Kuwait

Shaikh Abdul Azim, Salah Al-Anzi, and Yahya Hassan, Kuwait Oil Co.; Stephen James, BP; and D. Mandal and Hamad
Al-Ajmi, Kuwait Oil Co.

2 SPE 101303

Distribution of tarmat from the analysis seems more complex
than can be explained from pure structure or stratigraphy.
Occurrence of historical tarmat has better structural control as
it is restricted to a depth interval. Similar tarmat has been seen
in Minagish field1. The thick tar zone above the historical
tarmat appears to have more geographical control and a
combination of biodegradation of early immature oil and
vertical migration has played important role on its origin.

Geological Setting
The Zubair Formation represents major clastic sedimentation
during Barremian-Aptian time in North Kuwait. The
sedimentation occurred as a low frequency clastic wedge
ranging from lowstand, transgressive to high stand systems
tract. Basinal mud rocks overlying a ramp carbonate setting of
Ratawi Formation forms the base of Zubair Formation. Base
of Zubair is a highstand systems tract abruptly overlying
basinal Ratawi shales. Drowning of Zubair sedimentation
process was initiated during early Aptian resulting in
carbonate succession of Shuaiba Formation. The Zubair
sedimentation occurred on low angle platform of

North

Kuwait. Raudhatain and Sabiriyah fields are the two major
anticlines (Figure 1) holding hydrocarbons in clastics and
carbonates of Cretaceous succession. The Zubair Formation
consists of 1350 ft to 1400 ft of clastic section and is a
commercial producer in Raudhatain field. The Formation
becomes deeper water towards east with a north-south paleo
shoreline. Current North Kuwait structures were inverted in
the Cretaceous and had been the sites of the Jurassic trough2.

Structure
The reservoir structure at Zubair level is an asymmetric
anticline with four-way dip closure in Raudhatain field. 2D
seismic mapped a radial fault pattern over the field. More
complex fault pattern is seen from high resolution 3D seismic
(Figure 2). Main orientations of faults are NW-SE in the
northern part of the field and SW-NE in the southern part.
Subordinate E-W trending faults frequently act as major
barriers as observed in southern part. Most of the faults can be
traced from Mishrief to Ratawi Formations. Minor faults are
not mappable from seismic due to weak Zubair reflectors in
addition to contamination of signals with multiples from
shallower horizons. All faults are normal faults with throw up
to 120 ft.

Gross thickness of the Zubair Formation does not show much
variation over the field. It is vertically divided into six zones
separated by significant marine flooding surfaces. The
composite reservoirs and non-reservoirs are designated as
Upper Zubair Shale (UZSH), Upper Zubair Sand (UZSD),
Middle Zubair Shale (MZSH), Middle Zubair Sand (MZSD),
Lower Zubair Shale (LZSH) and Lower Zubair Sand (LZSD)
(Figure 3). The Shale reservoir units have more shale than
sand. Another zone (Z52) was subsequently added as it had
sparate fluid contact and presuure. The reservoirs have
independent fluid contacts (Figure 4). Within this framework,
the complex reservoir sedimentology and structure have given
rise to the formation of some 11 independent oil reservoirs3, 4.

Stratigraphy and sedimentology
A tidally influenced deltaic system with high frequency
change in sea level prevailed during the deposition of Zubair
Formation5. The interval has been divided in to six major
depositional packages (Z10-Z60) which are correlatable over
the field due to areally extensive markers (Figure. 5). These
depositional packages have been described in Appendix-A.
The packages have been further subdivided into layers: the
marine and shoreface units are easy to correlate while
considerable uncertainty remains in estuarine units due to
frequenting channel cutting and switching. Main producing
intervals within the package are Z10, Z20, Z40 and Z60.

There are four reservoir lithofacies within Zubair Formation:
Cross-stratified Sandstone (Sx), Ripple/Laminated Sandstone
(Sr/l), Bioturbated Sandstone (Sbl), and Carbonaceous
Sandstone (Sc). These were deposited as estuarine channel
fills within a tidal-dominated deltaic environment and as
marine shoreface sand bodies6. The non-reservoir lithotypes
consist of muddy sandstones, mudrocks, coals and, within the
UZSH there are scattered minor limestone stringers, all with
negligible porosity and permeability.

The reservoir lithofacies have been genetically linked to
Estuarine Channel fill Sandstones, Mouth Bar Sandstones,
Delta Top Sandstones, Proximal Shoreface Sandstones and
Distal Shoreface Sandstones. More detailed description on the
genetic units is given in Appendix-B.

The Upper Zubair Sand Reservoir
It consists of reservoir layers Z36 through Z51 and the most
arenaceous unit of Zubair reservoir. Extensive marine
influenced estuarine channels are developed in the interval.
Some of the channels are densely stacked with cross cutting
geometry. Some of the incisions cut 60 ft in to underlying
section. There is excellent vertical connectivity within
reservoir leading to a single fluid contact. Extensive areal
continuity of the reservoir is due to lateral coalescing of
channels. Good reservoir continuity coupled with high
porosity (>20 5) and permeability (up to 1.5 Darcies) enables
the reservoir to be under active aquifer support with less than
600 psi pressure depletion over a production period of 46
years.

Tarmats in Zubair Reservoirs
Occurrence of tar in Zubair has been identified from
production data, log, core and geochemical data. Initially,
hydrocarbon staining in cores was used as guide for
identifying tar zones. Hydrocarbon staining is expressed in
three forms within the permeable sandstones: light, medium
and dark. Iatroscan analysis reveals that the both the light and
medium stained samples yield asphaltene percentages
predominantly below 50% (98% and 92% of the light and
medium stained samples, respectively). This sharply contrasts
with the darkly stained samples, which overwhelmingly
exhibit asphaltene percentages greater than 50% (89% of the
dark stained samples), principally >80%. These darkly stained
intervals therefore represent tar mats. The wells having
Iatroscan analysis in cores of the Zubair interval to dearcate tar
zones are shown in Figure 6.

SPE 101303 3

Tar mats
Tar mats are seen in three principal intervals (Figure 7). These
lie within the following zones (in descending stratigraphical
order):

Upper Zubair Sand (Z40): a thick (101ft), well-defined tar mat
cuts across the stratigraphy within the main reservoir interval.
In the crestal well RA-A, the top is best defined, where it
occurs in core within the middle of a sandbody in Z44 at –
9345ft SS. The top can be projected across into the structurally
deeper wells RA-B and RA-D, where it is constrained, but lies
within a core gap in the stratigraphically higher Z46. The base
is best defined in RA-B where it lies at -9446ft SS within Z36.
Projected across to RA-D the base lies within a mudrock prone
interval within Z44, below this unit the sandstones are
unstained. In RA-A, a thin (<10ft) darkly-stained interval lies above a mudrock unit that defines the base of Z46. This may represent the development of a minor mat (related to the presence of the underlying mudrock unit), but, iatroscan analyses do not show the presence of significant asphaltenes at this level. Middle Zubair Sand (Z24): a thinner mat is present in the relatively thick channel sandbody which extends across the central and southern parts of the field. The top is best defined in core in RA-B at -9895ft SS where it occurs within the sandbody. The sandbody in RA-A lies entirely above the mat. In RA-C the top of the mat occurs in the uppermost part of the sandbody within a carbonaceous-rich sandstone package. In core, the main darkly stained interval extends down to -9932ft SS; below this level the core shows an apparently lighter stain with only patchily developed darker staining, and the iatroscan data shows variable asphaltene levels down to the base of the core at -9936ft SS. Lower Zubair Sand (Z10): within this, highly heterogeneous high asphaltene levels are present within many of the sandbodies in the cored intervals in RA-A and RA-C. None appear to be correlatable, but there is only core data for these two wells. In addition, there are thin (<5ft) apparently uncorrelatable high asphaltene levels within the more heterogeneous sections in Z22, Z24, Z26, Z28 and Z32. Tar above Upper Zubair Tarmat After identifying tar zone in Upper Zubair Sand interval of - 9345 ft to -9646 ft, wells were being completed in layers above this window. One of the crestal wells, RA-E (Figure 8) was completed in the thicker channel intervals ranging from - 9080 ft to -9291 ft. Production logging in May 2001 indicated zones in the interval -9175 to -9291 were not contributing to flow. These intervals show excellent log-derived porosity and permeability with very little residual water saturation. The sand bodies were individually tested and confirmed no flow to surface with recovery of heavy oil on reverse circulation. The well did not have any core and a single pressure reading to make any meaningful interpretation. An offset well, RA-F) was drilled with comprehensive data acquisition plan to understand the nature and distribution this heavy oil zone. The

heavy oil-interval was cored; tightly spaced pressure points
and six fluid samples were taken. Complete suite of open hole
logs including NMR were recorded. Plugs were cut at every 4
ft at well site to carryout geochemical and mud filtrate
invasion analysis.

SARA Analysis
Soxhlet extraction was carried out on 83 core samples of well
RA-F to remove hydrocarbons and tar. The extracts were
quantified and analyzed. Forty-eight of the extracted samples
contained in excess of 2.0% extractable organic material by
weight, with most of the samples being higher than 6.0%. The
extracts of each of the forty-eight contained >50% asphaltenes
with most above 85%. The relatively large extractable organic
content and high asphaltene content verifies that these core
samples contain substantial quantities of tar. Four additional
samples contained extract with >50% asphaltenes. However,
these four samples contained less than 0.35% extractable
organics and, therefore, do not contain large quantities of tar.
Two samples displayed large proportions of polars in the
extract; however, these samples contained less than 0.1%
extractable organic material and are not significant. The
remaining 29 samples contained from 0.11 to 1.23 extractable
organic materials, with most being greater than 0.4%. These
samples contain significant quantities of saturates (33-52 %)
and aromatic (25-31%) hydrocarbons and do not represent tar-
rich samples.

Figure 9 shows the percent extract and saturated, aromatic,
resin (polar) and asphaltene distribution vs. depth. The tar-
rich interval in the core is clearly defined by the high
extractable hydrocarbon content and asphalt-rich extract. This
section is from 9765-9975 ft with two small tar-free intervals
at about 9931ft and 9963ft.

Carbon Isotope Analysis
Stable carbon isotope analysis was performed on 82 of the
core extracts A plot of the carbon isotopic composition vs.
asphaltene content (Figure 10) reveals that the asphalt-rich
samples are about 0.5 ‰ less enriched (lighter or more
negative) than the samples with lower asphalt contents. As the
asphalt fraction is usually more enriched that the other
fractions, this is an unusual result.

Detailed Composition of Extracts
Detailed analysis was carried out on fifteen samples
representing each type of sample (asphalt-rich, resin-rich, etc.)
and each interval. Whole oil gas chromatograms show a
typical distribution of normal paraffins and low pristane/n-C17
ratios (0.21-0.32). The paraffin distributions and pristane/n-
C17 ratios are consistent with undegraded oils. The whole oil
chromatograms contain a large peak between C23 and C24
and a group of peaks at about C26. These components, which
do not appear in the saturated or aromatic chromatograms, are
from the resin (polar) fraction (Figure 11). The components
may represent contamination from drilling fluid or other
sources. The saturated and aromatic fractions of the fifteen
samples were analyzed by gas chromatography. The saturated
fractions display a typical distribution of normal paraffins and
low pristane/n-C17 ratios that are consistent with undegraded

4 SPE 101303

oil. The aromatics display a significant increase in apparent
concentration in the tar-rich intervals as indicated by an
increase in peak amplitude in the tar-rich intervals.

Biomarker GCMS analysis of the saturated fractions revealed
a high degree of similarity among the selected samples. The
samples display low Ts/Tm ratios and significant quantities of
homomoretanes (Figure 12), which are consistent with lower
maturity or early generation oil.

Identification of Tarmats
In addition to geochemical analysis of cores, tars have been
identified from other indirect methods:

Resistivity logs
Presence of immobile tar prevents any mudfiltrate invasion in
to the formation around the well bore. Thus, formation
resistivities from different depths of investigation do not show
any change and all the curves lay on each other, while there is
clear separation of curves in light hydrocarbon bearing
intervals. Tar zones in RA-E show very little resistivity
separations in the tar zone from 9405 ft to 9600 ft (Figure 13).

Micro-resistivity log having depth of investigation few
millimeters reads resistivity of invaded zone close the
borehole. It usually reads lower value than the deeper
resistivity tools in oil zones drilled with a mud of lower
salinity filtrate than formation water. Due to lack of invasion
in tar zone, the micro-resistivity log would have same or
higher resistivity than deeper resistivity logs. The water
saturation of invaded and uninvaded zones would overlie over
the tar zone. The criterion is useful to identify tar in many
wells but is ambiguous in zones with low porosity with
residual oil saturation which has similar response. Resistivity
profiles may be used to detect tar zones with ease where tar is
the main component but the method fails where porous
intervals are partially saturated with tar, oil and water. The tar
window in Upper Zubair Sand have microresistivity reading
similar to shallow resistivity but lower than deep resistivity
and has considerably higher water saturation in it.

Self Potential (SP)
The SP sonde measures the potential developed due to salinity
contrast in permeable beds. Given ample salinity difference
between mud filtrate and formation water, SP is a measure of
permeability. When formation water is more saline, as in the
case of North Kuwait reservoirs, more negative SP is observed
in more permeable reservoirs. Presence of tar reduces
permeability of a reservoir consequently affecting SP
character. SP gives higher readings in tar zones of RA-F (-100
mv Z44CH and Z46CH) compared to light oil zones with
similar petrophysical characteristics (-145 mv in Z48CH:
Figure 13). SP has been qualitatively used to demarcate tar
zones in current study. Limitation of usage of SP is in low
permeable zones where it would show similar character of
tarmat as immobile light oil.

Nuclear Magnetic Resonance (NMR)
NMR technology is found to be a useful supplement to means
available for tar detection. It has been used in the Zubair well

RA-F to characterize fluid in zones completely filled with tar
(above OWC) and in zones partially filled with tar (below
OWC). The main output of NMR logging is Transverse
relaxation distribution (T2) is sensitive to several geological
and petrophysical parameters and thus provides understanding
on pore texture along with saturating fluid.
Surface relaxation, Bulk fluid relaxation and molecular
diffusion mechanisms control fluid relaxation in rock pore
space. The total Decay Rate is given by:

1/T2=1/T2s

1/T2b+1/T2d
Where 1/T2 is total transverse relaxation time, 1/T2s is surface
relaxation, 1/T2b is the bulk fluid relaxation and 1/T2d is the
diffusion relaxation. Useful information can be obtained on tar
and heavy oil from understanding the bulk fluid relaxation.
Tar bulk relaxation corresponds to very short T2s and at
ambient temperature, typical tar falls outside measurement
range of NMR and doesn’t contribute to porosity signal.
As observed in RA-F, the T2 distribution has a lower value in
tar zone than light oil zone (Figure 14). Also, the porosity
from conventional Neutron-Density measurement is compared
with NMR porosity and the deficit can be used as the indicator
of tar (Figure 15). NMR is promising in identifying tarzones
and would be used in future wells. The technology is proved to
be elsewhere in Middle Eastern Carbonates9.

Residual Oil Saturation (Sor)
Tarmats have been mapped from high residual oil
saturation/unswept oil zones in wells drilled during later part
of field life (after 1980) when substantial water movement has
occurred (Figure 16). Log derived oil saturation is unusualy
high in tar zone of RA-E and RA-F above the historical tarmat
window.

Production Logging (PLT)
PLTs are routinely carried out for surveillance activity. Lack
of contribution or low contribution from channel sands have
been used to infer presence of heavy oil. PLT interpretation is
ambiguous in poorer quality rocks. Layer correlation was
found to be useful supplement. Detection of heavier oil and
tarmat is difficult to be distinguished from PLT alone.

Origin of Tarmats
Origin of tar in Upper Zubair Sand appears to be a
combination of processes over geologic time.

Early hydrocarbon filling and Bio-degradation
Tar mats are commonly associated with oils derived from low
siliciclastic source rocks. The initial charge from a carbonate
source rock has high asphaltene content, partly as a result of
its organic matter composition, and partly because of the low
temperatures at which such source rocks expel most of their
petroleum potential. At low maturity (onset of petroleum
expulsion), oils typically have high asphaltene contents.

The burial history of Zubair reservoir and generation and
expulsion history of the source (Figure 17) indicate that
expulsion and migration of hydrocarbons into the reservoir
began about 70MYBP. At that time, the reservoir temperature
was about 40-50°C, a temperature favorable for
biodegradation of the trapped oil. About 35MYBP, the

SPE 101303 5

reservoir temperature of the reservoir reached about 70°C, a
temperature above which biodegradation of oil becomes
insignificant. Thus, hydrocarbons entering the trap after about
35MYBP would be undegraded. The unusual isotopic data,
where the high-asphalt tars are isotopically lighter than the
low asphalt extracts is consistent with the accumulated asphalt
being from earlier generation from the source rock, when
isotopically lighter products are generated.

Multi-phase hydrocarbon filling
Generation and migration of hydrocarbons have continued
over geologic time. As the source rock gets more deeply
buried in a basin, there is ever increasing thermal stress of and
as result the composition of fluid expelled from source rocks
becomes increasingly lighter with higher GORs. Asphaltene
solubility in oil reaches a minimum as lighter oil or gas is
mixed into an existing oil column and it starts to precipitate.
Such asphaltenes in tar mat have formed at a later stages of
oil/gas filling, As the migration pathway into a reservoir is
commonly along the higher permeability zones, the tar occurs
in zones of better quality rock with the best permeability and
higher porosity as seen in thicker channel sands of Upper
Zubair. High-asphalt content in conjunction with an
undegraded paraffin profile, as observed in geochemical data,
is characteristic of heavy oils or tars where undegraded oil has
entered the reservoir at a later date.

Gravitational segregation
Gravitational segregation of fluids takes place in reservoirs
when there is a variation of components which differ in
gravity, such as asphaltenes in a lighter oil. In such instances,
lighter (low asphaltene) oil accumulates near the top of the oil
column, whereas denser (high asphaltene) material settles at
the base of an accumulation, at the oil-water contact. Once the
asphaltene content of oil becomes higher than its solubility,
asphaltene precipitation occurs, and asphaltene drops out of
solution forming a tar layer. Frequent occurrences of tar mats
above permeability baffles in Uper Zubair Sand, often within a
relatively thick oil column, indicate that the tarmats have most
likely formed by the gravitational settling of asphaltenes.

Hydrocarbon migration along leaking faults
The equivalent thick tar observed in wells RA-E and RAF-F
were mapped from PNL and dynamic data. Distribution of the
tar is restricted around few more wells near the crest of the
structure. Occurrence of such thick tar in form a plug filling
right to the top of major channels can not be explained from
gravity segregation. Continuous leakage of hydrocarbon along
faults leading to drop in pressure which accelerates asphaltene
drop out could be the possible reason. Occurrence of a thick
heavy oil zone in a single well of Lower Burgan reservoir is
another example where leakage along a fault could be invoked
for its origin.

Distribution of Tarmats
Geochemical, logging and production data have been used to
map the distribution of tarmat in the field for Upper Zubair
Sand Reservoir. As discussed above, two distinct type tar
zones have been mapped: depth related tar zone and thick tar
zone above it.

Depth related tar zone is observed to be restricted to depth
window -9345 to -9446 ft, SS. Bottom of tar mat is observed
as deep as -9505 ft if the channel is vertically continuous over
the tar window. The occurrence is also restricted to high
permeable layers of Upper Zubair Sand. Low porosity thin
sands lying in the tar window do not contain tarmat. The depth
control on presence of tarmat is imposed by precipitation of
asphaltene at the base of oil column. This tar zone show higher
water saturation than the overlying hydrocarbon zone mainly
because most part of the tar zone lies below the mapped oil
water contact (at -9375 ft, SS). Average water saturation in tar
zone is 55-65% in most of the wells. Only 13 wells have water
saturation in the range of 30 to 50% and they lie closer to
crest. In recently drilled wells, the water saturation in the
water swept zones above tarmat (which were filled with light
oil earlier) is in the range of 65 to 75%. The observation
would suggest that the depth related tarmat had a lot of mobile
hydrocarbon in it. Further support to the theory is from the fact
that the reservoir is under active water drive: getting full
pressure support from the aquifer. In some of the wells, there
is a 10-20ft thick zone immediately below oil water contact
showing low residual oil saturation and this oil has remain
unmoved over time as seen from time lapsed PNL logs. The
depth related tar zones continue to show high oil saturation
with time.

The tar zone observed above depth related tar zones of RA-E
and RA-F has been mapped in the surrounding wells mainly
from PNL and dynamic information. A recent well drilled
600m west of RA-F show a water-swept interval at the level of
tar occurrence in the latter well. The oil water contact is 120 ft
above that seen in RA-A. Well lying in between these two
show water fingering within the zone of immobile oil. Some
of the nearby wells do not show any change on fluid content in
time lapsed PNL logs till date while others show water
encroachment. This criteria of immovable oil combined with
open hole log signatures was used to map this tar zone (Figure
18). This tar zone typically shows very low water saturation
with mostly immobile oil. As observed in geochemical data of
RA-F, there are thin zones of mobile oil trapped within the
thick tar channels.

Production logs have been planned to be recorded for
confirmation of tar plugs. Testing results would also be helpful
in further delineation of tarzone.

Conclusions
The Upper Zubair Sand reservoir has a 100 ft thick tar mat
near the oil water contact restricted to high permeable clean
sand zones. The tar mat has asphaltene content in excess of
80%.

In most of the wells the zone contained movable hydrocarbon:
high water saturation is observed in swept tar zones and in tar
zones occurring below oil water contact.

Early asphaltene from immature charge, bio degradation at
low trap temperature, asphaltene precipitation from
subsequent more mature gaseous charges and gravity

6 SPE 101303

segregation are the possible reasons for formation of the
tarmat.

Occurrence tar zone above historical tar is confirmed from
geochemical studies. However, this tar is restricted to few
crestal wells. Leakage of hydrocarbon along faults is expected
to be the reason for formation this tar.

Tar zones have been characterized and mapped over the field.
Lateral extent of tar above historical tarmat needs to be
confirmed with more dynamic data.

NMR is promising in identifying tar zones and would be used
in future wells.

Acknowledgements
We are grateful to management of Kuwait Oil Company and
Ministry of Oil, state of Kuwait for permission to publish this
work. We appreciate the contribution of many KOC and BP
colleagues during the progress of the modeling work.

References

1. Al-Ajmi, Hamad, Gaur R.S., Brayshaw A.C., “The Minagish

Field Tarmat: Formation, Distribution and Impact on
Waterflood”, presented at GEO1998 International Conference
and Exhibition, Bahrain, 1998.

2. Yousif, S and Nouman G.: “Jurassic Geology of Kuwait,” Geo
Arabia, Vol. 2, No. 1, 1997

3. Nemcsok, S., Morrison, N. H., Carruthers, A., Abdullah, Sh.,
“Sedimentary Interpretation of a Multilayered Clastic Oil
Reservoir: Impact on Development plans for the Zubair
Reservoir, Raudhatain Field,” Paper SPE 48972 presented at the
1998 SPE Annual Technical Conference and Exhibition, New
Orleans, Sept. 23-26.

4. Al-Dashti H, Nemcsok S., Morrison N., Al-Matar B., “The

Development Process; Zubair Reservoir, Raudhatain Field,”
Paper SPE 53171 Presented at the 1999 SPE Middle East Oil
Show & Conference, Baharain, Feb. 20-23.

5. Adamson, K., Coy, G, Cross N, Imelda, G.J., Whear, E.,
“Revised Reservoir Geology of the Zubair, Burgan and
Mauddud Formations of the Raudhatain and Sabiriyah fields,
North Kuwait”, November 2000

6. Davis Roger, Payne Dorothy and Taylor Katy.: “Reservoir
Geology of the Zubair Formation in Raudhatain and Sabiriyah
fields, North Kuwait”, Unpublished KOC-BP Report, Dec.
1997.

7. Brennan, P., 1990, Raudhatain Field – Kuwait. Arabian Basin.
In Atlas of Oil and Gas Fields, Structural Traps.

8. Burger, Jon, Elrod, L., Gupta, D., “Geochemical Analysis of Tar

mat in Raudhatain Field, Upper Zubair Formation, North
Kuwait”, Unpublished KOC Report, March, 2004.

9. Najia, W etal, “Nuclear Magnetic Resonance (NMR), a Valuable

tool for Tar Detection in a carbonate Formation of Abudhabi,
UAE, Paper SPE78485, presented at 2002 SPE Abudhabi
International Petroleum Conference and Exhibition

Appendix-A Reservoir Zonation scheme in Zubair

Z10. A thin basal package composed of stacked fluvially-
dominated mouthbars interpreted as resulting within a phase of
falling base level. This is largely made up of thin generally
high-quality sandbodies (8-20 ft thick) which can be traced
with confidence from well-to-well on a km-scale. Vertical
amalgamation of sandbodies is seen in core and lateral
amalgamation is suggested by correlations. The interbedded
mudrock packages (typically <10 ft) are likely to form local scale (<1-2km) vertical and potentially lateral transmissibility barriers or baffles. The top of Z10 is marked by a field wide flooding surface which forms an intrafield seal. Z20. It comprises of a number of high frequency regressive- transgressive parasequence sets that occur during a background (low frequency) relative sea-level cycle bounded by flooding surfaces. Z20 is complex package which contains three main episodes of channel incision. Channel incisions occur during higher frequency sea level falls as the background sea-level conditions pass from transgressive to regressive. Incisions are the maximum during zones Z24 and Z26 where sequence boundaries form relatively deep incised valleys (up to 80 ft). These estuarine channel-fills can be mapped at different levels across the field. The top of Z20 is marked by a major flooding surface which forms a key intrafield seal. This package is defined as lying between the Z22FS and Z32FS, and is divisible into four principal layers (Z22-Z28, in ascending stratigraphical order). Z30. This zone comprises a large-scale, coarsening-upward package (typically 200’ thick) which is largely mudrock-prone but it does contain good-quality reservoir sands. On the western flank of the field, the overlying channelised interval Z40 is considered to incise into and replace the upper part of the interval. Z30 comprises of several high frequency relative sea-level fluctuations that occur during a low frequency HST. The higher frequency relative sea-level fluctuations result in the progradational and retrogradational stacking of parasequence sets, as well as forming Z36 flooding surface. The zone is dominated by marine mudrocks and subordinate sandstones. Z40. Forms the main reservoir interval of the Zubair Formation. It comprises vertically-stacked and laterally- coalesced estuarine channel-fills. Towards the top of the section individual channel packages become discrete and are bound by flooding surfaces. As one moves up the section there is a progressive reduction in the number and thickness of channel-fills and an increase in the volume of mudrocks. Mapping indicates that Z40 is more deeply incised in to Z30 on the western flanks of the field. Z50. Comprises a predominantly mudrock-prone section which forms a fieldwide seal. The section passes upwards into a composite shoreface sandstone package towards the top. Z60. Comprises a mixed marine mudrock/carbonate package which contains a discrete episode of estuarine channel incision and fill.

SPE 101303 7

Appendix-B Genetic depositional units for Reservoir
Facies

Estuarine Channel-Fill Sandstones
Estuarine channel-fills represent the most important reservoir
facies in the Zubair Formation. The are sharply-based, fining
upward units with vertical thickness of 10 to 70 ft. Cross
bedded sandstones dominate the unit with clean, fine grained,
well sorted sands; rippled and carbonaceous sandstones locally
dominate. Coals, rooted horizons and abundant carbonaceous
material occur frequently. Major channels can be traced on
Km scale. The limits of the channel systems are easily
mapped across the field in areas of dense well control.
Generally they show an east to west trend. The distribution of
the channel-fill trends within the field are expected to continue
beyond honoring the characteristics seen in areas of dense well
control.

Mouth Bar Sandstones
They comprise relatively sharply-based (non-erosional)
sandbodies (8-20 ft thick) which form the upper parts of
larger-scale (<30 ft) coarsening upward units, coarsening up rapidly from mudrocks and wavy laminated heterolithics which may show synaeresis cracks. The sandbodies comprise flat to low-angle cross-stratified, fine-grained sandstones. Locally mud-draped cross-sets are present. Bioturbation is frequently observed. The presence of thin coals (<.5ft) with associated rooted horizons suggests the thicker units are composite. These deposits are interpreted as tidally-influenced mouthbars, the relatively sharp transition and dominance of low angle stratification reflecting relatively unconfined channel mouth deposition. Three flow units (Z10, Z06, and Z04) in the Lower Zubair are interpreted to have been deposited as mouth bar sandstones. The resulting gross isopachs of the flow units are mapped to be relatively isopachous. Reservoir quality was determined to be decreasing rapidly from the west to the east. Delta Top Sandstones These are sharply-based, fining-upward (<10 ft) packages of fine-grained sandstones, displaying cross-stratification or lamination. The upper parts are commonly rooted and pass upwards in to coals (typically <2 ft thick). Carbonaceous material and amber grains are common. These packages comprise highly heterogeneous deposits made up of small scale sandbodies and interbedded mudrocks and coals. Only the Z26 flow unit is interpreted to be mainly composed of delta top sandstones. The depositional model predicts thinning and poorer reservoir quality should occur to the east. However, this unit is heavily incised by the overlying Z26CH channel system that made it very difficult to correlate and map effectively.

Proximal Shoreface Sandstones and Distal Shoreface
Sandstones
These are sharply-based, fine-grained and bio-turbated
sandstones from 10-30 ft thick. The depositional model for the
flow units comprised of predominantly proximal and distal
shoreface sandstones indicates the resulting gross isopachs of
these units should be relatively consistent. This consistent
behavior is shown in all of the proximal and distal shoreface
flow units that are not incised by an overlying channel system.
Strong sand development trends paralleling depositional strike
exist in these environments.

Marine mudrocks
These comprise dark grey to black, typically non-calcareous
mudstones to muddy siltstones (Mb/l) which may contain thin
(<1 ft) argillaceous bioturbated sandstones. Bioturbation is characterised by a mm-scale textural mottling. A relict flat lamination is locally apparent. Thin bioclast-rich lags are present and poorly preserved ammonites have been recovered. Marine mudrocks form fieldwide to subfield scale vertical transmissibility barriers. The thicker packages form intra- Zubair seals. Limestones Bioclast-rich wackestones and packstones, locally rich in Orbitolids are present in the lower and uppermost part of the Zubair Formation (Z10 and in Z64). They represent a shut- down in the clastic system forming condensed horizons possibly associated with flooding events.

8 SPE 101303

Figure 1 Location map of North Kuwait and Geological
setting of Zubair Formation.

Figure 2 Structure on top Zubair and fault pattern mapped
from 3D seismic. Main orientations of faults are NW-SE in the
northern part of the field and SW-NE in the southern part.
Subordinate E-W trending faults frequently act as major
barriers as observed in southern part.

IRA
IRA

SAUD
ARABI

WAFR

BAHRA

Ruge

Mutrib

ABDAL

Kuwait

MEDIN

0 K 4

MINAGIS

GREATE
BURGA

UM
GUDAI

SABIRIYA

RAUDHATAI

RUMAIL

Kr Mar

North

West

South
KuwaiDHARI

ABDULIYA

KHASHMA

Neutra
Zon

bn 1
Heavy

) (
..

) () (

RATQ
Quarternary Holocene Surface

Pleistocene Dibdibba
Tertiary Pliocene Lower Fars

Miocene Ghar
Oligocene Dammam

Eocene Rus
Paleocene Radhuma

Cretaceous Maastrichian Tayarat
Quarna

Campanian Harta
Sadi

Santon Khasib
Coniac

Turonian
Cenomanian Mishrif

Upper Rumaila
Ahmadi

Lower Albian Wara
Mauddud
Burgan

Aptian Shuaiba

Zubair

Barremian
Hauterivian
Valangian

Ratawi sh & ls
Beriassian Minagish

Makhul
Jurassic Tithonian Hith

Gothnia
Kimmerian Nahma

Upper Oxfordian
Callovian
Bajocian Sargelu

Bathonian
Middle Aalen Dharuma

Toarcian Marrat
Pliensbachium

Sinemurium
Lower Hettangium

Triassic Rhaetian Minjur
Norian
Carnian
Ladinian Jilh
Anisian Sudair

Scythian

Lower

Hiatus

Hiatus
Hiatus
Zubair

Quarternary Holocene Surface
Pleistocene Dibdibba

Tertiary Pliocene Lower Fars
Miocene Ghar

Oligocene Dammam
Eocene Rus

Paleocene Radhuma
Cretaceous Maastrichian Tayarat

Quarna
Campanian Harta

Sadi
Santon Khasib
Coniac

Turonian
Cenomanian Mishrif
Upper Rumaila
Ahmadi
Lower Albian Wara
Mauddud
Burgan

Aptian Shuaiba
Zubair

Barremian
Hauterivian
Valangian
Ratawi sh & ls
Beriassian Minagish
Makhul
Jurassic Tithonian Hith
Gothnia
Kimmerian Nahma
Upper Oxfordian
Callovian
Bajocian Sargelu
Bathonian
Middle Aalen Dharuma
Toarcian Marrat
Pliensbachium
Sinemurium
Lower Hettangium
Triassic Rhaetian Minjur
Norian
Carnian
Ladinian Jilh
Anisian Sudair
Scythian
Lower
Hiatus
Hiatus
Hiatus
Zubair

SPE 101303 9

Figure 3 Type log of Zubair Formation showing main subdivisions Upper, Middle and Lower (Sand and Shale).

Figure 4 Cross section showing main subdivisions and the fluid distribution in Zubair Formation. The reservoirs within Zubair have
separate oil water contacts. Upper Zubair Sand is the main producing reservoir with OOWC at -9375 ft TVDSS.

1,400 Feet

UPPER

MIDDLE

LOWER

“ SAND ”

“ SHALE ”

“ SHALE ”
“ SAND ”
“ SHALE ”
“ SAND ”
1,400 Feet
UPPER
MIDDLE
LOWER
“ SAND ”
“ SHALE ”
GR +
“ SHALE ”
“ SAND ”
“ SHALE ”
“ SAND ”

10 SPE 101303

Figure 5 New Zonation scheme based on sequence
staratigraphy: Major subdivisions are correlated with old
scheme.

Figure.6 Location map of Zubair: Iatroscan measurements
were carried out in wells RA-A, B, C and D showing presence
of well-defined tar mats in Upper Zubair and Middle Zubair
Sands. Geo-chemical analysis in Well RA-F was used to
understand heavy oil/tar observed during production logging
and testng of the well RA-E.

Raudhatain Field

OLD NEW General
CLASSIFICATION ZONATION Lithology

Group Subgroup Layer Zone Subzone
SHUAIBA CARBONATES

UPPER
SHALE Z60 Z64
UPPER Offshore
SHALE 1 Z62
SAND Shoreface

Z56

Z50
UPPER Marine
SHALE Mudrock Z54

UPPER
Z52

ZUBAIR 2
Z48

Z40

UPPER 3 Z46
SAND Stacked

4 Estuarine Z44
Channel

5 Fills Z42

MIDDLE 6 Z30 Z36
SHALE Shoreface –

Offshore Z32
MIDDLE 7

ZUBAIR
MIDDLE Z28

SAND 8 Z20 Z26
Delta Top

9 Shoreface – Estuarine Fill
Offshore Z24

LOWER 10 Cycles
SHALE

LOWER Z22

ZUBAIR LOWER 11 Z10
SAND Fluvial Z10

Mouthbars
RATAWI SHALE

Significant Erosional Contact

Max. Flood

Max. Flood
Max. Flood
Raudhatain Field
OLD NEW General
CLASSIFICATION ZONATION Lithology
Group Subgroup Layer Zone Subzone
SHUAIBA CARBONATES
UPPER
SHALE Z60 Z64
UPPER Offshore
SHALE 1 Z62
SAND Shoreface
Z56
Z50
UPPER Marine
SHALE Mudrock Z54
UPPER
Z52
ZUBAIR 2
Z48
Z40
UPPER 3 Z46
SAND Stacked
4 Estuarine Z44
Channel

5 Fills

Raudhatain Field
OLD NEW General
CLASSIFICATION ZONATION Lithology
Group Subgroup Layer Zone Subzone
SHUAIBA CARBONATES
UPPER
SHALE Z60 Z64
UPPER Offshore
SHALE 1 Z62
SAND Shoreface
Z56
Z50
UPPER Marine
SHALE Mudrock Z54
UPPER
Z52
ZUBAIR 2
Z48
Z40
UPPER 3 Z46
SAND Stacked
4 Estuarine Z44
Channel
5 Fills Z42
MIDDLE 6 Z30 Z36
SHALE Shoreface –
Offshore Z32
MIDDLE 7
ZUBAIR
MIDDLE Z28
SAND 8 Z20 Z26
Delta Top
9 Shoreface – Estuarine Fill
Offshore Z24
LOWER 10 Cycles
SHALE
LOWER Z22
ZUBAIR LOWER 11 Z10
SAND Fluvial Z10
Mouthbars
RATAWI SHALE
Significant Erosional Contact
Max. Flood
Max. Flood
Max. Flood

SPE 101303 11

Figure 7 Tarmat in Zubair from Iatroscan Analysis of four
wells. Dark staining in core correlates with high Ashphaltene
content. Two major tarmats are apparent: Upper Zubair Sand
(101 ft) and Middle Zubair Sand (37 ft). Tars are randomly
distributed in Lower Zubair Sand.

Figure 8 Production logging in this crestal well RA-E
indicated no contribution from zone marked by immobile
zone. Subsequent zonal testing re-confirmed presence of
heavy oil/tar.

Figure 9 SARA Stacked and Extract vs. Depth for the well
RA-F. The tar zone (shown in black) is identified from high
extract yield and asphaltene but low saturates, aromatics and
polars. The study confirmed extention tarmat seen at well RA-
E.

Figure 10 Asphaltene content vs. Carbon Number Isotope
value. The asphalt-rich samples are about 0.5 ‰ less enriched
(lighter or more negative) than the samples with lower asphalt
contents.

ORIGINAL TAR

IMMOBILE ZONE

GR + RE
S

+
ORIGINAL TAR

IMMOBILE ZONEIMMOBILE ZONE

GR + RE
S
+

12 SPE 101303

Figure 11 Polar fraction Chromatography showing possible contamination Peaks.

Figure 12 Biomarker distribution showing presence of Homomoretanes. The samples have low Ts/Tm ratios and significant quantities
of homomoretanes: consistent with lower maturity or early generation oil.

SPE 101303 13

Figure 13 Resistivity and SP logs give indication of tar: Clear
separation among resistivities at -9100 ft sand is light oil.
Lesser separation at 9300 ft and below 9250 sand indicates
Tar. SP value at light oil is -145 mv compared to 100 mv at tar
interval.

Figure 14 NMR T2 distribution Light oil and Tar zones
indicating loss of porosity in Tar zones.

Light Oil

Tar

Tar
Light Oil
Tar
Tar

Z
4

8
C

4
8

Z
4

6
C

9150

9200

9250

9300

9350

9400

9750

9800

9850

Light Oil
Tar
Z
4
8
C

H
Z

4
8
Z
4
6
C
H
9150
9200
9250
9300
9350
9400
9750
9800
9850
Light Oil
Tar

14 SPE 101303

Figure 15 Comparison of porosity from NMR with
conventional Neutron-Density measurement NMR indicates
deficit in Tar zone and can be used as the indicator of tar.

Figure 16 Tar zone has high residual oil saturation and doesn’t
move in time lapse PNL: Such high Sor zones can be used as
the indicator of tar.

Tar
Tar

Mobile Oil

Mobile Oil
Tar
Tar
Mobile Oil
Mobile Oil

TarTar

SPE 101303 15

Figure 17 Burial History and Trap Temperature by Barwise, LGC Labs, 1998

16 SPE 101303

A. Map View

Figure 18 Distribution of Tar mat in Tar window (-9345 to -9446 ft) Upper and Tar plug above Tar mat as observed in RA-E and RA-
F: Map View (Above) and Cross Sectional View (Below).

Outer limit of Main Tarmat in
Tar Wndow

Limit of Tarplug
Above Tar window

Outer limit of Main Tarmat in
Tar Wndow
Limit of Tarplug
Above Tar window

WELL: RA-F

OWC

Tar zone in RA-F
Wells producing oil from
these intervals and no
indication of Tar

Oil leaking along faults

Possible Tar Zone

Light oil

Tar Window

WELL: RA-F
OWC
Tar zone in RA-F
Wells producing oil from
these intervals and no
indication of Tar
Oil leaking along faults
Possible Tar Zone
Light oil
Tar Window

SPE 113550

Real Time Well Placement above a Tar Mat, Leveraging Formation Pressure
While Drilling and Pyrolytic Oil-Productivity Index Technologies
Khalid M. Al-Salem, Said S. Al-Malki, Rabea A. Ahyed, Peter J. Jones, Peter M. Neumann/Saudi Aramco

This paper was prepared for presentation at the 2008 SPE Europec/EAGE Annual Conference and Exhibition held in Rome, Italy, 9–12 June 2008.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been
reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its
officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to
reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

Recent development of a large oil field in the Eastern
Province of Saudi Arabia achieved real time well
placement above a tar mat by utilizing Formation Pressure
While Drilling (FPWD) and Saudi Aramco’s Pyrolytic
Oil-Productivity Index (POPI). Placement of 6 ⅛ in.
horizontal power water injector wells in close proximity
above an impermeable, undulating, tar layer posed a
unique geosteering challenge. Additionally, a heavy oil
layer of varying thickness exists above the tar. The
uncertainty was to determine at what vertical depth fluid
mobility stopped and heavy oil and/or tar began in the
high porosity/permeability reservoir.

The heavy oil/tar layer is undetectable with conventional
real-time logging while drilling (LWD) measurements.
Furthermore, new technology devices such as the 6 ⅛ in.
NMR While Drilling tool were not available when the
field was developed. A technique of combining FPWD
with POPI provided a solution to identify and steer away
from the immobile fluids, minimizing the risk of
completing an injection well in an interval with low or no
injectivity.

POPI collectively refers to pyrolytic methods developed
by Saudi Aramco to identify and quantify tar from
residual hydrocarbon staining on drill cuttings. Pyrolytic
Oil-Productivity Index provides a direct assessment of the
residual hydrocarbons present on rock samples and allows
an accurate determination of the volume of tar over a
wide range of concentrations. The business impact is to
assist with geosteering horizontal injector wells in real
time. Formation pressure while drilling and POPI results
were integrated to confirm favorable fluid mobility and
well paths were placed to take advantage of this
knowledge. Initial tests showed high injectivity in all
wells, which indicates that they were placed in the desired
zone. This paper includes selected case studies

demonstrating both entry into and steering away from
impermeable tar layers.

Introduction

The purpose of the reservoir management process is to
appraise, develop and produce hydrocarbon reservoirs.
The appraisal process sometimes identifies development
challenges that asset teams would prefer to ignore. One of
these challenges is tar and associated heavy oil above tar.

The existence of a tar mat in the reservoir complicates the
placement of horizontal injectors. Without tar, injectors
are placed at the original oil-water contact (OOWC) in a
straightforward manner. With tar, the challenge is to place
the horizontal wellpath in a mobile fluid layer to ensure
meeting business plan injection rate targets. The business
impact of placing horizontal injectors optimally above tar
is significant.

Members of the multidisciplinary asset team challenged
themselves to identify the lowest mobile fluid layer at
each location and simultaneously place the injector path
in that layer. This required the application of real time
technologies. The team used two technologies (FPWD
and POPI) with the vision that results from one
technology had to confirm results from the other. This
informational redundancy was utilized throughout the
drilling of the injectors and this process is part of the
discussion below. Positive results after injection startup
confirm the wisdom of the approach. The NMR While
Drilling tool is now a competitor to FPWD. The 6 ⅛ in.
NMR tool became available after the development
campaign.

2 SPE 113550

Figure 1. A 3-D image of the oil field showing a tar mat (black)
around the field, a heavy oil layer (yellow) above the tar and
moveable oil to the crest (green and red)1.

Development Plan

Developing four reservoirs in a large oil field with an
underlying tar mat topped with a low mobility heavy oil
(Figure 1) raises the challenge of placing water injectors
in mobile fluids zone above the tar-oil contact to ensure
adequate pressure support. The subject field was put on
production in the early 1960s. Gravity water injectors
were utilized for pressure support since 1965. Between
1981 and 1983, 18 gravity and power water injectors were
drilled as part of an integrated injection development
plan. These wells were typically tested using RFT, open
hole logs and injectivity tests with flow meters to
determine the tar-oil contact in these prospective injection
wells (Figure 2). The study revealed that poor injection
rates were observed in wells with too thin a perforated
interval above the tar-oil contact. This poor injectivity
would not give sufficient pressure support and negatively
impact ultimate recovery. The study also enabled a proper
mapping of the tar mat in the field. Although, the field
was mothballed in 1983 due to low oil demand and the
injection plan was suspended. The field remained
mothballed and kept as shut-in potential until the early
1990s when it was produced for two years and then it was
mothballed again.

Based on the findings of the 1983 study, a new field
development plan was put in place in 2006. The new plan
utilizes horizontal power water injectors for pressure
support. One of the key elements of the 1983 study is that
the tar mat will work as a barrier preventing the oil from
being lost into the aquifer. This finding was reexamined
by running streamline simulation. The optimum injection
height above the tar for placing the horizontal wells was
selected using both the 1983 study and streamline study
results, coupled with historical data. A height above tar

that would ensure injectivity without sacrificing ultimate
recovery was sought.

Figure 2. Injection profile VS vertical depth. The rates decrease as
the tool gets closer to the tar zone and eventually goes to zero.

Geosteering

All new horizontal injectors were planned to follow a
horizontal path at the specified height above the tar oil
contact. All wells where geosteered to ensure the proper
execution of the well plan. In addition to the standard
LWD logs, the team utilized FPWD and POPI to geosteer
the injectors away from tar and low mobility heavy oil.

Figure 3. The injectors’ paths were planned above the tar mat to
avoid injecting in the tar mat or the heavy oil layer1.

FPWD

Tool Description.

The FPWD tool design is similar to many probe style
wireline tools, where a donut shaped rubber pad forms a
seal around a metal snorkel (Fig. 4). When this probe is
pressed against the wellbore, the snorkel penetrates the
mud cake and contacts the formation to perform the
drawdown and record the pressure measurement. Multiple
pressure gauges are used in the tool; a Quartz Gauge and
Strain Gauge with accompanying electronics. Software,
stored in the tool’s memory controls the test sequence
which is preprogrammed, but can be modified using a
sequence of commands2.

2X 3X 4X 5X 6X 7X 8X 9X 10

X
X
X
X
X

X 2X 3X 4X 5X 6X 7X 8X 9X 10X
X

X
X
X
X
X

SPE 113550 3

Figure 4. Schematic of the Probe, Equalizer Valve, and Drawdown
Manifold1.

FPWD Application

Formation pressure while drilling has the primary
function to measure formation pressure at any point in the
wellbore and additionally provide an associated fluid
mobility value based on the drawdown pressure and
buildup profile3. The drawdown and buildup profiles of
each individual test are indicative of the mobility or
permeability of the zone under test. Pressure transient
mobility values are calculated for each test and can be
provided in real-time with the final buildup pressure
values.

Formation pressure sampling devices, both FPWD and
wireline formation testers are fundamentally flawless
when it comes to identifying “tight” and very low
permeability zones. This characteristic allows one to take
advantage of the normally undesirable aspect of formation
pressure reading devices, namely long slow pressure
buildup times in low permeability zones, no pressure
build up in tight zones, or a lost seal due to the lack of
mud-cake presence. For the purpose of identifying tar and
low mobility oil, a fixed robust drawdown rate of 2 cc/sec
and volume of 10 cc for both the initial and repeat
readings makes the tight and low permeability readings
stand out because the final pressure reading, before the
“timed” retract, will be significantly lower than actual
reservoir pressure (Fig. 5).

Figure 5. Example of a robust drawdown combined with long build
up time in high porosity reservoir indicate the presence of heavy oil
and require the need for a well path change to increase injectivity
potential1.

POPI

Pyrolytic characterization methods developed by Saudi
Aramco provide a direct assessment of the residual
hydrocarbons present in core or cuttings samples. These
methods include: assessment of the API gravity of the
fluid, the POPI4, the Apparent Water Saturation Method5,
the Compositional Modeling Method6, and the Volume of
Organic Matter Method7. Pyrolytic Oil-Productivity Index
instrumentation and methods can accurately quantify tar
volumes to within ½ percent rock volume over a wide
range of concentrations, and are now routinely applied to
drilled cuttings in real-time to assist in geosteering
horizontal development wells. A particular focus for POPI
techniques has been predicting the effect of tar on
reservoir injectivity for horizontal water injector wells
that are drilled along the flanks of many oil fields8.

Case Studies

The combination of FPWD and POPI to geosteer the
wells above the tar/heavy oil proved to be effective. Two
case studies will be discussed to illustrate the successful
application of the technologies.

Well A

Well A was one of the early horizontal injectors drilled in
the field. Data from FPWD and POPI showed that the tar
level was very well identified in the area and the well path
was placed in a zone with mobile fluids. In an attempt to
confirm that FPWD and POPI were working properly, a
decision was made to drop angle in the last 500 ft of the
well path while taking an FPWD reading every 10 ft TVD
and frequently sampling for POPI analysis.

FPWD showed that the mobility was plummeting with
depth while the LWD showed the same rock quality with

4 SPE 113550

porosity around 20% (Fig. 6). Ultimately the tool started
to lose seal, which is an indication of very low mobility.

Figure 6. Well A LWD log showing FPWD readings with tight/lost
seal readings at the end.

Pyrolytic Oil-Productivity Index was showing a tar
amount of 1%-2% of rock volume from Target Entry (TE)
up to 300 ft from Total Depth (TD), where the tar volume
started to increase rapidly. At TD, the tar volume reached
5% of the rock volume and the zone was reported as a
non-injectable zone (Fig. 7).

The initial short-term injectivity test, which was
conducted after the well’s completion, showed a high
injection rate that exceeded the planned rate for the well.
These results proved that the tar was well defined in the
area and the combination of FPWD and POPI was a good
practice to identify tar and low mobility heavy oil zones.

Figure 7. Well A POPI analysis showing the increase of tar volume
at the end of the well path.

Well B

The FPWD tool showed that Well B was placed in a zone
with low mobility fluids (heavy oil) from TE (Fig. 8).
The fluid was still mobile and injection was possible, but
meeting the target rate for the well was doubtful if
continued in the same zone. Pyrolytic Oil-Productivity
Index, on the other hand, showed a tar level that ranged
from 2%-4% of rock volume in the target zone (Fig. 9),
which confirmed the results of FPWD that the zone had
low mobility fluids.

To avoid the possibility of a low injection rate, the well
path was steered updip away from the low mobility zone.
The well path was leveled in the zone when the FPWD
showed good mobility tests and POPI indicated a tar level
that is less than 2% of rock volume (Figs. 8 and 9). The
initial short-term injectivity test showed that the well had
a high injection rate and meeting the target rate was
achievable.

X
X
X
X
X
X
X
X

SPE 113550 5

Figure 8. Well B LWD log showing FPWD readings with low
mobility readings at the beginning of well path.

Figure 9. Well B POPI analysis showing high tar volume with low
API at the beginning of the well path.

Conclusion

The combination of FPWD and POPI is an excellent
practice that works well in identifying tar and zones with
low mobility fluids. This combination was utilized to
successfully geosteer horizontal injectors above a tar/
heavy oil zone in a large Saudi Arabian field. The initial
field results confirmed the success of this practice, where
it showed high injectivity in all injectors.

References

1. Neumann, P.M., Salem, K.M., Tobert, G.P., et
al.: “Formation Pressure While Drilling Utilized
for Geosteering,” SPE paper 110940, SPE Saudi
Arabia Technical Symposium, May 7-8, 2007.

2. Seifert, D.J., Dossari, S.M., Burinda, B.J. and

Kellett, S.: “Application of Formation Testing
While Drilling in the Middle East,” Paper
presented at the 14th SPE Middle East Oil &
Gas Show and Conference held in Bahrain
International Exhibition Center, Bahrain, March
12-15, 2005.

3. Proett, M.A., Walker, M., Welshans, D. and

Gray, C.: “Formation Testing While Drilling, a
New Era in Formation Testing,” SPE paper
84087, SPE Annual Technical Conference and
Exhibition held in Denver, Colorado, USA,
October 5-8, 2003.

4. Jones, P.J. and Tobey, M.H.: “Pyrolytic Oil-

Productivity Index Method for Characterizing
Reservoir Rock,” 1999, U.S. Patent Number
5,866,814.

5. Jones, P.J., Al-Shafei, E.N., Halpern, H.I., Al-

Dubaisi, J.M., Ballay, R.E. and Funk, J.J.:
“Pyrolytic Oil-Productivity Index Method for
Predicting Reservoir Rock and Oil
Characteristics,” 2004, U.S. Patent 6,823,298.

6. Jones, P.J. and Halpern, H.I.: “Compositional

Modeling and Pyrolysis Data Analysis
Methods,” 2003, Patent Pending, U.S. Patent and
Trademark Office.

7. Jones, P.J. and Halpern, H.I.: “Method for

Determining Volume of Organic Matter in
Reservoir Rocks,” 2007, Patent Pending, U.S.
Patent and Trademark Office.

8. Jones, P.J., Halpern, H.I., Dahan, M., et al.:

“Implementation of Geochemical Technology
for “Real-Time” Tar Assessment and
Geosteering: Saudi Arabia,” Offshore

X
X
X
X
X
X
X
X
X
X
X
X

6 SPE 113550

Mediterranean Conference and Exhibition in
Ravenna, Italy, March 28-30, 2007.

Acknowledgements

The authors would like to thank the other members of the
project team for their contribution to the success of the
project. Special thanks go to Nasser Al-Khaldi, Isidore
Bellaci and Gordon Tobert for their help in publishing
this paper.

SPE 117735

Building a Deterministic 3D Model of Tar Mat deposits in a Carbonate
Reservoir in a Geologically Consistent Manner: A Case Study from Offshore
Abu Dhabi
Philippe J. Ruelland, Christoph T. Lehmann, Khalil I. Al Hosany, David O. Cobb, ADMA-OPCO

This paper was prepared for presentation at the 2008 Abu Dhabi International Petroleum Exhibition and Conference held in Abu Dhabi, UAE, 3–6 November 2008.

Abstract
The presence of a Tar Mat in carbonate reservoirs in the Gulf region is common. Tar Mats
occur on different scales from thin seams along stylolites and porous intervals to thick
successions within the reservoirs. In the latter case, it raises important issues regarding field
development options and well placement optimization. The Tar Mat in Field A, offshore Abu
Dhabi, was detected in the first exploration well in 1969. In the 1970s and 1980s, 7 additional
wells were drilled; the most recent well was drilled in 2007. Tar Mat sections were identified in
all the appraisal wells.
The top of the Tar Mat can be clearly seen in the cored reservoir sections where it plugs the
larger pore space. The bottom of the Tar Mat is not as simple to determine visually. Detection
of Tar Mat is less reliable in cuttings and logs especially in tight zones and in the sections with
lower reservoir quality. Thin section observations on the Tar Mat interval in the various wells
appear to show that it is not present in heavily calcite cemented intervals related to paleo-water
legs.
This hypothesis on the Tar Mat generation is based on Gulf analogues, in which the Tar Mat
reflects a fossilized paleo-oil-water contact. In Field A, the top and bottom Tar Mat surfaces are
not flat and their deformation reflects the growth of the structure after Tar Mat generation
ended. The bottom Tar Mat surface is considered as the latest paleo-oil-water contact
preserved in the field. These observations help in building a 3D model of the Tar Mat away from
well control and, therefore, decrease the uncertainty in predicting the distribution of Tar
Mat in the field. Better predicting the distribution of Tar Mat will have a significant
impact in the successful development of this field.

INTRODUCTION

Field A (Fig. 1) is a four-way dip structure located offshore Abu Dhabi. Field A was
discovered by well A-1 in 1969. 8 Appraisal wells have since been drilled, mainly in the 1970s
and 1980s; the last well A-9 was drilled in 2007. The full-field development of Field A is planned
in the near future.

2 SPE 117735

The Four-way dip structure formed, like many similar structures offshore Abu Dhabi, over a
salt-related anti-form. The salt is regionally known to be Late Precambrian or Early Cambrian in
age and was re-activated over different tectonic phases.

The Oil-bearing reservoirs are the Arab A (A2) to D formations, and the

Tar Mat is an essential feature in this field. This was recognized very early in the discovery
and appraisal wells.

Tar Mat has always been considered as a crucial heterogeneity to take into account prior to
developing the field. The water injection scheme would be significantly impaled by the location
of the Tar Mat. However no satisfactory model for the Tar Mat geometry had been proposed so
far. A simple surface interpolation of well tops between wells was the input to early static model
definition; no geological input was applied to understand the distribution of Tar Mat away from
the wells.

KNOWLEDGE ON TAR MAT DEPOSIT IN GULF AREA FIELDS

Tar is a common occurrence in the Gulf area Oil Fields. It is most often present as thin
seams either in the matrix or filling in stylolites and fractures. In a few fields, Tar Mat occurs as
thick units that act as very strong horizontal barriers. Knowing the Tar Mat thickness and
extension is important in order to estimate the oil in place as well as to locate water injectors
correctly. The risk of placing water injectiors below the Tar Mat would have a critical impact on
field pressure maintenance.

Thick Tar Mats have already been described in fields in Kuweit (Minagish Field) by Al-Ajmi
et al, 2001, in Abu Dhabi (Fields named S and Z) by Carpentier et al, 1998 and 2006, and in
Qatar (Bul Hanine Field) by Jedaan et al, 2006.

The geometry of the Tar Mat filled interval varies, but in all cases seems either related to the
position of a paleo-WOC or to the presence of extended low permeability layers in the reservoir.
In the case of paleo-WOC related Tar Mats, the reservoir volume in which the asphaltenes
deposited can take the geometry of a large and thick ring (ex: Minagish Field, Kuweit)

Where the Tar Mat is related to the presence of a low permeability layer (ex: Fields S and Z,
described by Carpentier et al), its geometry is related to the structural deformation of the layer
after the Tar Mat was deposited.

The preferred precipitation of the asphaltenes occurs in the higher permeability layers just
above a WOC or a barrier, when secondary light oil meets a heavier oil already in-place. This
model was described in 2001 for the Minagish Field, and is also proposed in the study of the
Bul Hanine Field (2007).

The geochemical model for the Tar Mat generation established for the Bul Hanine Field
seems to be a good analogue to what could have occurred in Field A. The model (Fig. 2) can be
summarized as follows:

• Oil (primary) was expelled from the Source Rocks and filled the reservoir.

SPE 117735 3

• Asphaltene Precursors were Gravity segregated in the oil column.

• A secondary light oil charge triggered the precipitation of the asphaltenes just above
the paleo-WOC or permeability barriers.

It should be noted that changes in P & T conditions could also result in asphaltene
precipitation. Biodegradation of the oils is not a likely process as this phenomenon usually
occurs when the reservoir temperature is below 70°C (reservoir temperature in Field A is
116°C).

FIELD A DATA OBSERVATIONS

Tar Mat presence is clearly observable in Field A cores. On the logs (Fig. 3) Tar Mat is
characterized by a very high Rt value (high LLD or saturated ILD). However, as Tar Mat –filled
fractures may not give a resistivity response as sharp as that of a Tar Mat-rich layer. There are
some uncertainties as to where its Top and Bottom really are in non-cored wells, especially on
the well depths of the Bottom Tar Mat in wells A-1, A-4 and A-8 which were not cored.

The Tar Mat in Field A occurs mainly in the Arab D reservoir although there is some minor
evidence for Tar Mat in the Arab C reservoir from the A-6 and A-2 wells. Interestingly no Tar
Mat is observed in the Arab D in well A-6.

Thickness of the Tar Mat varies from one well to the other (Fig. 4).Top and Bottom subsea
well depths of the Tar mat also vary from one well to another, giving evidence that the Tar Mat
interval in Field A is not a flat (or sub-horizontal) ring-shaped volume below the oil such as that
in the Minagish Field, Kuweit.

The location of the Tar Mat in the wells is neither related to nor conformable to the
stratigraphic horizons (Fig. 5).

At the crest of the structure, the Tar Mat tends to reach the bottom of the Arab D reservoir,
whereas on the flanks it does not entirely fill the Arab D reservoir.

The evidence from the varying depths and thicknesses of the Tar Mat suggest that the
distribution of Tar Mat occurs rather as a “body” that has an irregular shape. The difficulty lies in
understanding why it is not flat and how it will be possible to model the envelope of this body.
These are the reasons why strong geological and geochemical rationales are needed in the
process.

GEOCHEMICAL AND GEOLOGICAL REASONNINGS

The Geochemical model described in Bul Hanine Field (Qatar) is an analogue to what
happened in Field A (Fig. 2).

The reservoir is initially filled by oil. The reservoir undergoes a secondary in-fill by lighter oil
(or by gas) that modifies the geochemistry of the primary oil and causes the asphaltenes
molecules to precipitate and deposit either at the WOC or above any barrier inside the

4 SPE 117735

reservoir. As time progresses, the asphaltene deposits also act as barriers and cause
asphaltene to deposit just above them.

The geological reasoning must take into account the geochemical model and the Field
observations. The geochemical model implies that the WOC plays a crucial role. In Field A,
however, neither the Top nor the Bottom Tar Mat surfaces are flat. The fact that the Tar Mat
records a paleo-WOC is a key hypothesis.In Field A, the surfaces are not sub-horizontal. This
observation suggests that the original sub-horizontal Tar Mat has been deformed at a certain
stage in time. The structure continued to grow while the Tar Mat was being deposited and after
the end of asphaltene precipitation in the field.The combination of structural growth and
asphaltene precipitation also explains the variations in thickness of the Tar Mat as observed in
the different wells.

This reasoning also implies that the Bottom surface of the Tar Mat is the very last horizontal
surface (WOC) to be fossilized by the asphaltene precipitation process.

This geological rationale based on the geochemical model and field observations represents
a good foundation for the building of the Tar Mat Top and Bottom envelopes. The construction
is based on the structural deformation of an originally flat surface. But, as observed in some
fields (Bul Hanine in Qatar and Fields S and Z in Abu Dhabi), asphaltene will also deposit above
barriers (or low Permeability layers) inside the reservoir. Any Tar Mat-filled originally high
permeability layer will now fall in the category of a local barrier.

GEOMETRICAL OBSERVATIONS AND 2D MODEL

In order to validate the idea of Tar Mat deposition at a paleo-WOC from a geometrical point
of view, it was necessary to investigate the relative localization of the Top and Bottom Tar Mat
at the wells with regards to the Top of Arab D reservoir (Fig. 6).

Cross-plots 1 and 2 illustrate this first investigation: Top Arab well depths correlate better
with the Bottom Tar Mat well depths than with the Top Tar Mat well depths. The regularity of the
Bottom Tar Mat possibly illustrates that it is the last fossilized paleo-WOC. Thicker asphaltene
deposits occurring above local permeability barriers may explain that the Top Tar Mat surface is
locally irregular.

In Fig. 7, the Top Arab D well depths are plotted against the isochore between the Top Arab
D and Bottom Tar Mat (An isochore thickness is the vertical depth difference between two
surfaces, unlike the isopach thickness which is the True thickness when considering the
structural dip of the surfaces). Fig. 7 very clearly shows that a linear relationship exists on the
flanks of the structure between the Top Arab D well depths and the Isochore thickness between
Top Arab D and Bottom Tar Mat. The crestal wells behave differently: the isochore gets thinner
as the Tar Mat bottom is constrained by the basal horizon of the Arab D reservoir. The reason
why the crestal area does not fit the regression is due to the Arab D unit being filled with Tar
Mat down to its base, as observed in wells A-5 and A7 (Fig. 4).

Well A-6, as mentioned earlier, has not encountered the Tar Mat in the Arab D reservoir.
The well was used to verify the consistency of the regression law, which predicts a thinning to
zero of the Tar Mat thickess on the flanks of the structure. In Fig. 8, well A-6 is added and fits
very well with the expected intersection of the bottom tar Mat and the Top Arab D.

SPE 117735 5

All this evidence seems to confirm the deformed paleo-WOC hypothesis.

Tar Mat in the crestal wells fills the Arab D reservoir down to the bottom (except perhaps for
well A-1, which was not cored), therefore these thicknesses cannot be used to model the Tar
Mat body. However this observation gives an indication that the Tarmat developed after the
onset of structuration on Field A. The structuration was re-initiated after the primary oil was in-
place and during secondary oil migration, which triggered asphaltene precipitation.

The bottom Tar Mat is the last fossilized paleo-WOC, once the asphaltene stops
precipitating (ex: end of the secondary light hydrocarbon charge, or complete plugging in the
Bottom-most part of the Tarmat). In the structural context of Field A, the Tar Mat “body” started
off as a flat surface and then thickened preferentially in the central part of the Field where the
deformation was the greatest. The Tar Mat in-fill was initially planar and progressively took on a
deformed non planar 3D geometry.

PETROGRAPHIC EVIDENCE OF DIFFERENCES IN CEMENTATION ABOVE AND BELOW THE TAR MAT

In field A, the cabonates, especially the grainstone-dominated facies, went through extensive
diagenesis spanning from syndepositional processes to early diagenetic processes, to burial
processes. Samples from the grainstone-dominated facies in most of the wells show early
diagenetic rim cements around grains followed by equant calcite cements. Large pore space
might be filled by blocky calcite cement which formed during burial and can be seen in some of
the thin sections.

Hydrocarbon migration inhibits extensive burial diagenesis (cf. example from well A7 in Fig. 9).
In the oil leg, the grainstones show syn-depostionnal to early diagenetic fabrics and to a lesser
extent burial diagenetic fabric. The pore throat distribution of the grainstones is bi-modal;
porosity ranges from 12 to 25% and permeability from 10 to 150mD, depending on the intensity
of cementation.

In the aquifer, carbonates show a more pronounced late stage calcite cementation in the form
of blocky cement (Fig. 9). This is the case in Well 6 (Northern tip of the Field A structure) where
Tar Mat is not developed in the Arab D. This observation supports the previous assumption
(brought forward from a geometrical point of view) that during Tar Mat generation in the Arab D
reservoir, the reservoir section in the well 6 area was located in the water leg. In the grainstone
facies similar to the ones observed in well 7, characterized by a similar bi-modal pore throat
distribution, reservoir quality is suppressed by more extensive burial diagenesis; blocky spar
cements are more pronounced, reservoir quality is reduced. In the more heavily cemented
grainstones, only observed in Well A6, porosity ranges from 7 to 14% and permeability is less
than 4mD.

These observations suggest that hydrocarbon migration and Tar Mat formation suppressed
extensive burial diagenesis, while carbonates within the waterleg or below a paleo-WOC were
subjected to a more extensive and longer burial diagenesis reducing porosity and permeability.
No Tar Mat developed in Well A6 because at the time of Tar Mat generation the interval was
below the paleo-WOC.

6 SPE 117735

3D DETERMINISTIC MODEL OF THE TAR MAT ENVELOPPE

The proposed deterministic 3D model of the Tar Mat envelope in the Arab D formation is
based on the observations described earlier. A bottom Tar mat surface has been constructed
from the Top Arab D surface on the basis of the regression line shown in Fig. 8.

Fig. 10 illustrates the construction steps that enabled to build the Bottom Tar Mat surface.
The Bottom Tar Mat geometrical surface intersects the Bottom Arab D surface; this does not
compromise the method, it simply is a consequence of the observations in the crestal wells. The
Bottom Tar Mat surface from the regression was subsequently cut where it lay beneath the
Bottom Arab D reservoir.

To construct the Top Tarmat surface, it was necessary to relate it to the extrapolated Bottom
Tar Mat surface. The depths, at which the Bottom Tar Mat would have been found in the
crestal wells, had the Arab D reservoir been thick enough, were computed. Fig. 11 shows the
cross-plot of these depths versus the Top Tar Mat well depths. There is a very clear linear
regression between them. The variables being independent, the regression between Top and
Bottom Tar Mat depths is valid. Fig. 12 illustrates the construction of the Top Tar Mat surface.

Once the two surfaces were constructed then the Bottom Tar Mat surface was merged with
the Bottom Arab D (Fig. 10b) where the former is deeper than the latter, and the surfaces were
adjusted at and around the well according to well observations.

The methodology was successfully applied in 3D. The resulting Tar Mat envelope,
constructed deterministically, has been explicitly integrated in the cellular model (Fig. 13); a Net
to Gross (NTG) parameter equal to ZERO has then been attributed to the Tar Mat-bearing cells.

A palynspastic reconstruction (Fig. 14) was also attempted in order to illustrate the growth of
the structure, the oil in-fill and the gradual Tar Mat deposit. The real timing of the growth of the
Field A structure is unknown today.

CONCLUSION

In Field A, the Tar Mat is significant both because of its thickness and its extension. It
impacts greatly on the development scheme of the field.

This study is the first attempt to bring geochemical, geological and geometrical reasoning
together to generate a consistent 3D model of the envelope of the expected Tar Mat-filled
reservoir volume. The geometrical approach is based on the data available to date (Tops and
Bottoms observed in cores).

The approach is based on observed geometrical relationships between the Top Arab D and
the Bottom Tar Mat wells depths.

Observations on the differences in cement types above and beneath the Tar Mat are
consistent with the predicted localisation of the Bottom Tar Mat surface; cementation was
continuing below the Tar Mat while it was less prominent in the Oil leg.

SPE 117735 7

The Top and Bottom Surfaces of the Tar Mat body represent the envelope of the zone
where a Tar Mat is expected. Inside the envelope, the asphaltene precipitation took place
initially in higher permeability layers and then continued in lower permeability layers or zones;
there may also remain patches where Tar Mat did not deposit.

The proposed approach for modeling the Tar Mat has lead to the construction of a
deterministic 3D Tar Mat body. The method is a quick-look approach based on geometry only;
nevertheless it does follow a strong geologically-consistent reasoning.

The depths of Top and Bottom Tar Mat are not always very obvious to pick visually, as the
dark grey to black colour of the Tar Mat may not be discrimiminant enough (especially in dark
grey limestones). Image resistivity logs do spot the thickest intervals but not necessarily the
“disseminated” Tar Mat. Thin sections and specific geochemical analyses are the best available
techniques today to observe and characterize the Tar Mat.

As the Bottom Surface of the Tar Mat is deformed, Oil is expected to have filled the space
between the Tar Mat and the water. The evidence of Oil staining on the cores in well A-6,
located structurally low on the northern flank of the structure, tends to confirm this hypothesis.
The 3D model of the Tar Mat envelope has enabled to estimate the potential volume of oil
underneath it.

This approach for constructing a 3D model of a Tar Mat envelope seems applicable to any
field, regarding that the geochemical and geological hypothesis are solid. The method can also
be used in order to test any paleo-WOC hypothesis.

This study has improved greatly the understanding of the generation of Tar Mat in Field A. It
has enabled to give a picture of the geometry of the expected Tar Mat filled envelope within the
reservoir, and therefore has brought some key elements in the subsequent reservoir simulation
and Field development scheme.

8 SPE 117735

References

Jedaan N.M., Al Abdulmalik A., Dessort D., de Groen V.L.N., Fraisse C.J., Pluchery E., 2007, Characterisation, Origin and Repartition of

Tar Mat in the Bul Hanine Field in Qatar, ITPC11812

Al-Ajmi H, Brayshaw A.C, Barwise A.G, Gaur R.S, 2001, The Minagish Field Tar Mat, Kuwait: Its Formation, Distribution and Impact on

Water Flood, Gulf Arabia Vol.6, No.1, Gulf PetroLink, Bahrain

Carpentier B., Arab H., Pluchery E.,Chautru J.-M., 2006, Tar Mats and Residual Oil Distribution in a Giant oil Field Offshore Abu Dhabi,

2006, Journal of Petroleum Science and Engineering 58 (2007), p472-490

Carpentier B., Huc A.-Y, Marquis F., Distribution and Origin of a Tar Mat in the S. Field (Abu Dhabi, UAE), SPE-49472

SPE 117735 9

FIGURES

Figure 1: Field A Map at Top Arab D reservoir

Figure 2: Proposed mechanism for the Ta Mat formation in Bul Hanine Field (D. Dessort in Jedaan N.M et al., 2001)

TSR: Thermochemical Sulfate Reduction

10 SPE 117735

Figure 3: Well A-9, example of Tar Mat signature on logs (RT, FMI and on cores)

Figure 4: Tar Mat thickness variations in the cored wells (thicknesses in ft)

SPE 117735 11

Figure 5: Tar Mat deposit is not related to stratigraphy

12 SPE 117735

Figure 6: Cross-plots showing the relationship between Top Arab D and Top and Bottom Tar Mat at the wells

6a) Cross-plot 1: Top Arab D vs Top Tar Mat

6b) Cross-plot 2: Top Arab D vs Bottom Tar Mat

SPE 117735 13

Figure 7: Cross-plot showing the relationship of Top Arab D with the isochore calculated between Top Arab D and Bottom Tar Mat at
the wells

Figure 8: In well A-6, no Tar Mat is observed in the Arab D reservoir. The extrapolation of the regression line defined for the flank
wells tends to a zero-thickness of the Tar Mat down-flank. It is consistent with the observation in well A-6.

14 SPE 117735

Figure 9: Petrographic evidence for differences in cementation above and below the Tar Mat.

Figure 10: Bottom Tar Mat surface construction steps.

10a) The Bottom Tar Mat surface is built from the Top Arab D surface. This surface intersects the Bottom Arab D horizon.

SPE 117735 15

10b) The Bottom Tar Mat surface is merged with Bottom Arab D where the former is underneath the latter

10c) The surface is adjusted to locally the well markers

16 SPE 117735

Figure 11: Cross-plot of Top Tar Mat well depths and “calculated” Bottom Tar Mat well depths. The “calculated” depths are those at
which the Bottom Tar Mat would be expected had the Arab D reservoir been thicker.

Figure 12: Top Tar Mat surface construction steps

12a) The Top Tar Mat surface is built from the calculated (un-merged) Bottom Tar Mat surface

SPE 117735 17

12b) The surface is adjusted locally to the well markers

Figure 13: Representation of the Tar Mat-filled reservoir volume in the 3D Grid, as a Net to Gross parameter equal to zero.

18 SPE 117735

Figure 14: Palynspastic reconstruction of the evolution of the structure and the Tar Mat infill of the reservoir. The exact timing of the
events is unknown today. Dotted arrows represent the secondary lighter oil infill.

14a) Time T0: Initial Tar Mat generation, just above the WOC. The Tar Mat interval is horizontal.

14b) Time T1: The structure has grown; the Tar Mat filled reservoir volume thickens towards the center of Field A. In the crestal area,
the Arab D unit is already filled down.

SPE 117735 19

14c) Time T2: The structural growth continues together with the asphaltene precipitation, local over-thicknesses are expected to form
just above the initial Tar Mat Top.

14d) Time T3: Present-day; there is space between the likely current WOC and the Bottom Tar Mat surface for oil to be present.

SPE 141783

Utilizing NMR and Formation Pressure Testing While Drilling to Place Water
Injectors Optimally in a Field in Saudi Arabia
Dhafer Al-Shehri, and Mohammed Kanfar, SPE, Saudi Aramco; Yusuf Al-Ansari, and Syed Abu Faizal, SPE, Baker
Hughes

This paper was prepared for presentation at the SPE Middle East Oil and Gas Show and Conference held in Manama, Bahrain, 25–28 September 2011.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed
by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or
members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is
restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

The presence of tar or heavy oil that does not flow using conventional production technologies brings numerous challenges during
field developments. Tar, acting as a permeability barrier, would often break flow or pressure communication from the aquifer to
the oil zone. This results in inadequate pressure support, which is necessary for sustaining production levels and maximizing oil
recovery. One of the key issues in developing a field with known tar mat accumulation is to optimally place injectors away from
the tar. The problem becomes more complicated when the exact location of a tar mat is uncertain either laterally or vertically. Tar
mats usually are neither flat nor uniform in thickness across a field. These uncertainties pose a challenge in planning wells
especially water injectors.

Detection of tar is critical for reservoir characterization, reserves calculation and well placement. Direct and indirect techniques are
employed to detect tar including core analysis, well testing, wireline logging and Pyrolitic Oil Productivity Index (POPI). These
measurements are good indicators of tar; however, the challenge is to identify the tar while drilling the well. Early detection
requires the deployment of logging while drilling (LWD) technologies for real-time interpretation of data.

In order to accurately identify tar in reservoir sections in real-time, integrating conventional LWD measurements with new
technologies such as the slim hole Nuclear Magnetic Resonance (NMR) and the formation pressure measurements while drilling
(FPWD) is necessary. This will allow for timely adjustment to the well path and prevent costly remedial actions

This paper discusses successful real-time application of slim-hole NMR and FPWD technologies to detect tar and optimally place
water injectors. This is demonstrated with two case studies involving extended reach power water injectors.

Introduction

Many reservoirs in the Middle East are characterized by a layer of heavy immobile oil, also referred to as tar mats, creating
additional challenges regarding pressure support and recovery strategies. Tar mats are present in Middle East reservoirs, including
Iraq, Kuwait Qatar, Oman, Abu Dhabi and Saudi Arabia.1 Tar mat in these reservoirs is found as highly viscous immobile
accumulations between an underlying aquifer and a lighter oil phase above. One of the main issues of such reservoirs is that tar
acts like a barrier between the oil zone and the aquifer impeding the natural bottom water drive and rendering water injection into
the aquifer for pressure maintenance ineffective. The complexity of reservoir management in these types of reservoirs is increased
with the uncertainty of lateral and vertical extent of tar mats throughout the field.

One of the most effective strategies in developing these fields is placing the horizontal injectors as deep as possible just above tar.
The uncertainties related to the tar make it extremely important to detect it early. Required adjustments to well plans can be made

2 SPE 141783

accordingly. Integrated workflows including conventional logs, NMR, FPWD and other tar indicators have been effectively
employed to drill long horizontal wells in carbonate reservoirs characterized with tar accumulations.

Tar Identifying Technologies

Nuclear Magnetic Resonance While Drilling.

Figures 1 and 2 are schematics of the NMR LWD tool and the sensor sub arrangement respectively. The sensor sub consists of
two arrays of permanent magnets that generate a static magnetic field while the coil antenna generates the radio-frequency. The
permanent magnets in the tool polarize the formation near to the wellbore forcing the hydrogen nuclei in the pore fluid to align
according to the direction of the magnetic field. The formation is then subjected to a sequence of radio-frequency pulses
perpendicular to the magnetic field. This causes hydrogen nuclei to reorient frequently and produce a characteristic decaying
signal. Evaluation of the amplitude and decay rate of the signal yields information about the porosity, fluid content and rock.

Figure 1. 4 3/4″ NMR LWD tool assembly for
5 7/8”, 6 1/8” hole size applications.

Figure 2. NMR LWD sensor arrangements.

Nuclear Magnetic Resonance (NMR) directly measures fluid-filled porosity and allows differentiation between movable and
bound fluids. The main advantage of NMR porosity over porosity from other logging tools is that the NMR porosity is
independent of the type of lithology. The NMR measurement does not need radioactive sources, which is the most obvious
safety aspect over nuclear methods. From a petrophysical point of view the NMR measurement delivers much more
information about the formation than porosity only. This includes:

• Partial porosities: Clay Bound Water (CBW), Bulk Volume Irreducible ( BVI), Bulk Volume Movable (BVM)
• T2 relaxation time distribution / pore size distribution
• Permeability index
• Hydrocarbon typing
• Hydrocarbon saturation

SPE 141783 3

NMR plays an important role in identifying tar or low permeable zones. In tar, the NMR total porosity can show a deficit
compared to total porosity from conventional logs such as density and neutron (Figure 3). This is due to the fact that the decay
times of the portion of NMR signal measuring solid hydrocarbon phase are too fast to be detected by the logging tool. Another
good tar indicator, known as excess bound fluid, is the difference in bound fluid porosity from NMR log and the bulk volume
of water from conventional logs. In case of tar the excess bound fluid volume is a positive value. Detailed explanations of this
concept can be found in the paper by Akkurt, et al.2 Furthermore, reservoir fluid viscosity can also be determined using NMR
measurements based on the fact that higher viscosity fluids show up at shorter T2 times.3,4

No tar indicated: NMR
porosity reads equal to

total porosity from
neutron-density: no
excess bound fluid

Tar indicator: T2 is shortened

Tar indicator: NMR porosity
deficit (NMR reads less than

total porosity)

Tar indicator:Excess bound
fluid reads high

Figure 3. Tar indication and characterization from NMR by porosity deficit, excess bound fluid, and T2 distribution shift.

Formation Pressure While Drilling.

Formation pressure while drilling (FPWD) was introduced to measure accurate formation pore pressure in real-time. FPWD
has found many applications over the years including estimating near wellbore mobility, reservoir connectivity and equivalent
circulating density (ECD) management.

FPWD operates by a brief stoppage in drilling while the tool pushes a pad sealing element against the wellbore wall and
performs a series of pressure draw down (DD) and buildup (BU) tests to measure formation pressure. The measurements are
performed relatively quickly to minimize the chance of differential sticking, especially in long horizontal wells.

FPWD has been recently used successfully in detecting heavy/immobile fluids in combination with LWD NMR. In clean
carbonates, zones containing immobile high viscous fluids are generally characterized by lost seals, supercharged pressures,
and very low mobilities in the range of 0.1-0.2 md/cp.5 Real-time detection of zones with immobile fluids and low permeability
allows their avoidance and makes geosteering long horizontal wells into sweet spots possible.

4 SPE 141783

Pyrolytic Oil Productivity Index.

Pyrolytic Oil Productivity Index (POPI) was developed by Saudi Aramco to provide a quantitative assessment of reservoir
quality, productivity, water saturation and tar identification from residual hydrocarbon staining on drill cuttings. Unlike other
logging tools, POPI provides a direct assessment of residual hydrocarbons on rock samples, which can be used to assess
connectivity with the active fluid system in the reservoir.
POPI includes various pyrolysis methods such as assessment of API gravity, the Apparent Saturation Method, and the Volume
Organic Matter method. POPI can accurately quantify tar volume over a wide range of concentrations to support real-time
application and geosteering horizontal wells .6

Integrating Tar Identifying Technologies

Two case studies from a Saudi Arabian field are discussed to demonstrate the successful integration of tar identifying technologies.
Both cases are examples of long horizontal water injectors drilled in oil bearing clean carbonate formations. The knowledge of tar
zone depth gained from the first well was very well utilized to drill the second well. In both cases, the different tar identifying
technologies were effectively used to properly geosteer the wells and achieve the objectives.

Case 1.

Well-X is a water injector that was successfully placed real-time away from tar in a clean carbonate reservoir that is both
heterogeneous and characterized by an undulating tar mat. Based on the best tar-depth estimation from nearby offset wells, the
original plan of Well-X was to enter the reservoir as deep as possible in the low viscosity oil zone but avoid the tar just below
it. Figure 4 a and b shows a cartoon of the planned as well as the actual well paths.

Tar / Immobile Oil

Aquifer

Mobile Oil

Tar / Immobile Oil
Aquifer
Mobile Oil

Plan Actual

Figure 4a. Planned well path for Well-X.

Figure 4b. Actual well path after adjusting real-time for tar.

The challenge was to drill a long horizontal section just above the tar without entering into it. The light oil-tar contact was very
uncertain. The drilling BHA was equipped with tar indicating technologies such as the LWD NMR, FPWD and a surface POPI
unit to real-time analyze the drill cuttings. T2 distribution, NMR derived porosities (free fluids and immovable fluid volumes),
formation pressures and mobilities were transmitted real-time along with the conventional logs like gamma ray, density and
neutron porosity to identify tar and make quick decisions to change well plan if necessary. Along with the conventional curves,
viscosity from NMR and excess bound fluid were also calculated and displayed.

As planned, the well entered into the low viscosity zone (zone A) of the reservoir (Figure 6), but with couple of hundred feet
into the reservoir, the formation pressure tests were performed and showed pressures indicating supercharged (Track 3) and
very low mobilities values (Track 6). The tails of NMR T2 distribution (Track 2) showed a shift towards the left (towards faster
relaxation times). These were the first indications of tar, but the shape and position of the T2 distributions are not a unique tar
indicator, and could be affected by other factors (pore size reduction, wettability alteration, etc.) as well.

SPE 141783 5

Good oil:
• Excellent agreement between NMR
and conventional total porosity

• No excess bound fluid
• No supercharged pressure
• High mobility

Tar zone:
• Deficit in NMR porosity
• Presence of excess bound fluid
• Shortened T2

• Supercharged pressure
• Low mobility

Back into good oil:
• Excellent agreement between NMR
and conventional total porosity

• No excess bound fluid
• No supercharged pressure
• High mobility

X1500

X0500

X1000

X2000

X2500

X3000

Reference
(ft)

Figure 5. Well-X LWD logs from NMR, FPWD and triple combo tools.

Track 4 of Figure 5 compares the total porosity derived from neutron-density logs (blue curve) with free and bound fluid
porosities from NMR shaded in yellow and blue respectively. There is very good agreement between total porosity and NMR
porosity in zone A and C. However in zone B, once the T2 distribution shifts towards faster relaxation times, the NMR shows a
porosity deficit compared to total porosity, and the computed viscosity (Track 6) indicates high viscous tar. Another clear tar
indication comes from the excess bound fluid, which is shown in Track 5 as a black filled curve.

These indications proved to be very useful in making decisions in real-time. The well plan was revised to navigate the well out
of the tar zone. Once the depth of the tar zone was identified, the rest of the well section was maintained above it, which is
clearly indicated by the data from zone C in Figure 5. The benefits of these technologies were clearly visible by a short-term
injectivity test after the well was completed which showed a poor injectivity in the tar identified zone (zone B), but a very good
injection rate in the low viscosity oil zone (zone C).

Case 2.

Based on the knowledge gained from Well-X about the tar depth, another water injector well, Well-Y, was planned to be
drilled and placed above the tar in the low viscosity oil zone. This time it was decided to drop the angle of the well towards the
end in an attempt to confirm the presence of tar just below. Figure 6 describes the proposed well plan for Well-Y.

6 SPE 141783

Tar / Immobile Oil
Aquifer
Mobile Oil

Figure 6. Proposed well path for Well-Y.

The same BHA, employing the tar detection technology, was used in Well-X was utilized in this case as well. As per plan, the
well entered into the low viscosity zone (zone A) of the reservoir (Figure 7) which is indicated by high mobilities from FPWD
(Track 6), of the late peaks in the NMR T2 distribution (Track 2) and the total porosity from neutron-density overlying the
NMR-derived porosity (Track 4).

Good oil:
• Excellent agreement between NMR and
conventional total porosity especially in the
first half of the well

• Negligible excess bound fluid
• No supercharged pressure
• High mobility

Tar zone:
• Deficit in NMR porosity
• Presence of excess bound fluid
• Shortened T2
A
B
X1500
X0500
X1000
X2000
X2500
X3000
Reference
(ft)

Figure 7. Well-Y LWD logs from NMR, FPWD and triple combo tools.

SPE 141783 7

The well angle was held for a few thousand feet to stay at the same TVD and just before 300 feet close to the total depth, the
well was deviated downwards. As the well entered into deeper zones, the NMR T2 distribution shifted towards the left, deficit
in NMR porosity was indicated, and excess bound fluid showed an increase. Thus, all indicators confirmed the presence of tar.

Conclusions

The case studies prove the effectiveness of integrating NMR, FPWD and POPI technologies in real-time for identifying tar zones
in clean carbonates reservoirs.

• In the first case study the tar zone depth was successfully established with the help of tar identifying technologies in a
long horizontal injector. It was confirmed by a short term injectivity test which showed extremely low injectivity in the
identified tar zone.

• The second case study shows the effective use of the tar depth knowledge from the previous well for drilling a new long
horizontal injector deep into the low viscosity zone without entering the tar. The depth of the oil-tar contact was
confirmed by tar identifying technologies when drilling deeper into the anticipated tar zone.

References

1. Al-Kaabi, A., Menouar H., Al-Marhoun, M. A., Al-Hashim, H. S.: “Bottom Water Drive in Tarmat Reservoirs,” SPE Reservoir
Engineering, May 1988.

2. Akkurt, R., Seifert, D. J., Al-Harbi A., Al-Beaiji, T. M., Kruspe, T., Thern, H., Kroken, A.: “Real Time Detection of Tar in Carbonates
Using LWD Triple Combo, NMR and Formation Tester in Highly Deviated Wells,” Paper presented at the SPWLA 49th Annual
Logging Symposium, Edinburgh, Scotland, May 25-28, 2008.

3. Akkurt, R., Seifert, D. J., Eyvazzadeh R., Al-Beaiji, T.: “From Molecular Weight and NMR Relaxation to Viscosity: An Innovative
Approach for Heavy Oil Viscosity Estimation for Real-Time Applications,” Petrophysics, Vol. 51, No. 2, April 2010

4. Chen, J., Chen, S.: “A Mixing Rule of Self Diffusivities in Methane Hydrocarbon Mixtures and the Determination of GOR and Oil
Viscosities from NMR Log Data,” SPE Reservoir Evaluation & Engineering, April 2010.

5. Seifert, D. J., Neuman, P. M., Dossary, S. M., Chew, K., Hahne, U., Bacciarelli, M., Pragt, J.: “Characterization of Arab Formation
Carbonates Utilizing Real-time Formation Pressure and Mobility Data,” SPE paper 109902, SPE Annual Technical Conference and
Exhibition, Anaheim, California, U.S.A, 11-14 November 2007

6. Al-Salem, K. M., Al-Maliki, S. S., Ahyed, R. A., Jones, P.J., Neumann, P. M.: “Real Time Well Placement above a Tarmat,
Leveraging Formation Pressure While Drilling and Pyrolitic Oil Productivity Index Technologies,” SPE paper 113550, Presented at
SPE Europe/EAGE Annual Conference and Exhibition held in Rome, Italy, 9-12 2008.

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SPE 146649

Impact of Asphaltene Nanoscience on Understanding Oilfield Reservoirs
Oliver C. Mullins,1 A. Ballard Andrews,1 Andrew E. Pomerantz,1 Chengli Dong,1 Julian Y. Zuo,1 Thomas Pfeiffer,1
Ahmad S. Latifzai,1 Hani Elshahawi,2 Loïc Barré,3 Steve Larter4
1. Schlumberger Oilfield Services, 2. Shell Exploration and Production Company, Inc,
3. IFP Energies Nouvelles , 4. PRG, University of Calgary & Gushor Inc.

This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in Denver, Colorado, USA, 30 October–2 November 2011.

Abstract
Understanding asphaltene gradients and dynamics of
fluids in reservoirs had been greatly hindered by the lack
of knowledge of asphaltene nanoscience. Gravitational
segregation effects on oil composition, so important in
reservoir fluids, are unresolvable without knowledge of
(asphaltene) particle size in crude oils. Recently, the
“modified Yen model” also known as the Yen-Mullins
model, has been proposed describing the dominant forms
of asphaltenes in crude oils: molecules, nanoaggregates
and clusters. This asphaltene nanoscience approach
enables development of the first predictive equation of
state for asphaltene compositional gradients in reservoirs,
the Flory-Huggins-Zuo (FHZ) EoS. This new asphaltene
EoS is readily exploited with “downhole fluid analysis”
(DFA) on wireline formation testers thereby elucidating
important fluid and reservoir complexities.

Field studies confirm the applicability of this scientific
formalism and DFA technology for evaluating reservoir
compartmentalization and especially connectivity issues
providing orders of magnitude improvement over
tradional static pressure surveys. Moreover, the
mechanism of tar mat formation, a long standing puzzle,
is largely resolved by our new asphaltene nanoscience
model as shown in field studies. In addition, oil columns
possessing large disequilibrium gradients of asphaltenes
are shown to be amenable to the new FHZ EoS in a
straightforward manner. We also examine recent
developments in asphaltene science. For example,
important interfacial properties of asphaltenes have been
resolved recently providing a simple framework to
address surface science. At long last, the solid asphaltenes
(as with hydrocarbon gases and liquids) are treated with a
proper chemical construct and theoretical formalism. New
asphaltene science coupled with new DFA technology
will yield increasingly powerful benefits in the future.

Introduction

It is widely acknowledged that reservoir engineering is
inextricably linked to the use of cubic equations of state to
model compositional gradients and phase behavior. Cubic
equations of state are modifications to the van der Waals
equation which itself is derived from the ideal gas law.
Cubic equations of state are derived to treat gas-liquid
equilibria and are not a formalism to treat solids. Hence,
they are grossly inadequate to handle molecularly or
colloidally suspended solids.

Crude oils contain not only gases and liquids but also
solids, the asphaltenes. It is not proper to treat the solid
asphaltenes with equations derived from the idea gas law.
For example, cubic equations of state require knowledge
of the critical point, the point at which the liquid and gas
properties are identical while asphaltenes have no liquid
phase, no gas phase and no critical point. Specifically,
there had been no first principles method to model
asphaltene gradients in reservoirs. Indeed, this led to a
general misunderstanding of black oils. Condensates have
relatively high GOR compared to black oils [1] and high
GOR fluids generally exhibit large compositional
gradients.[1-3] Cubic equations of state yield
homogeneous compositions for black oils due to their
characteristic low GOR.[1-3] Consequently, there has
been the erroneous assumption that black oils are
homogeneous because cubic equations of state give this
result. Nevertheless, numerous geochemical studies
indicate chemical compositional variations do exist
laterally and vertically in many black oil reservoirs.[4]

As mentioned above, cubic equations of state cannot
model asphaltene gradients in any first principle
approach. Moreover, black oils are best described by their
asphaltene content, not their (low) GOR. Since viscosity
depends exponentially on asphaltene content,[5] it

2 SPE 146649

behooves the operator to model asphaltene gradients. We
are now able to employ a first-principles model of
asphaltene gradients for the first time. The difficulty in
petroleum science had been that nobody knew the size of
the asphaltene (molecular and colloidal) particles in crude
oil. For gravity, size counts. Without known size, the
gravity term is unknowable, precluding modeling
asphaltenes.
Moreover, the use of cubic equations of state for mixtures
works well only for nearly ideal systems such as the
hydrocarbons. Hydrocarbons are very weakly interacting,
for example keeping them in the liquid phase, not solid
phase, at room temperature even for large carbon number.
However, for acid gas components such as CO2 and H2S
and even for H2O, the intermolecular interactions are
stronger than for hydrocarbons. For example, H2O is a
liquid while much heavier hydrocarbons are gases at room
temperature. For treating mixtures with acid gas species
and H2O, the cubic EoS methods are much less effective.
Extending these same cubic EoS methods to the solid
asphaltenes, with their large intermolecular interaction is
ill-advised. A novel first-principles approach is needed.

Asphaltene Nanoscience

After considerable effort by many workers [6], the
molecular and colloidal structure of asphaltenes in crude
oil and in laboratory solvents has been worked out. Figure
1 shows a representation of asphaltene molecular
architecture and two explicit colloidal species. This model
was introduced as the modified Yen model [7,8] and has
also been called the Yen-Mullins model.[9,10] Of course,
asphaltenes contain many different molecules;
nonetheless, this paradigm serves our purposes well as we
shall see.

Figure 1. Asphaltene nanoscience; the modified Yen
model also known as the Yen-Mullins model.[7,8]
For low asphaltene concentrations, such as in
condensates, asphaltenes are dispersed as a true
molecular solution. At somewhat higher
concentrations such as in black oils, asphaltenes are
dispersed as nanoaggregates. At yet higher
concentrations such as in heavy oils, asphaltenes
are dispersed as clusters. Diameters in nanometers
are listed in the figure.

Asphaltene Molecular Structure. There had been an
incorrect consensus that asphaltenes consist of large
polymeric molecules with many isolated polycyclic

aromatic hydrocarbon ring (PAH) components. (Fig. 1,
left, shows a molecule with one central PAH.) The first
molecular diffusion measurements of asphaltenes utilizing
time resolved fluorescence depolarization (TRFD),
yielded two important findings: 1) asphaltene molecules
undergo very fast diffusion, they are monomeric in size,
not polymeric, 2) small PAH ring systems diffuse ten
times faster than large asphaltene PAHs, they are not
cross-linked; thus one PAH per molecule.[11]

Recent results provide powerful confirmation of both
these key results. Two-step laser desorption, laser
ionization mass spectrometry (L2MS) has been applied to
asphaltenes confirming their low molecular weight.[12]
An infrared laser is used to desorb asphaltenes into a
vacuum, a UV laser is then used to ionize the asphaltenes,
and time of flight is used to measure molecular weight.
By increasing the UV laser pulse energy, the resulting
ions can be fragmented.[9] Figure 2 shows the result of
fragmenting asphaltenes and various model compounds in
a unimolecular process.[9]

Asphaltenes

Monomer
Model
Compounds

Polymer
Model
Compounds

Asphaltenes

Figure 2. Two-step laser desorption, laser ionization
mass spectrometry of asphaltenes and model
compounds. At higher UV laser pulse energy,
polymers fragment; asphaltenes and monomers do
not fragment confirming the monomer (one PAH per
molecule) molecular architecture of asphaltenes (cf.
Fig. 1).[9]

Figure 2 shows that polymer model compounds fragment
at higher laser power while monomer model compounds
and asphaltenes are more stable and resist fragmentation.

SPE 146649 3

Asphaltenes survive for geologic time, stability against
fragmentation is expected. Asphaltene molecules are
confirmed to be predominantly monomers, that is, one
PAH per molecule (often with heteroatoms) as depicted in
Fig. 1.

Asphaltene Nanoaggregates. Fluorescence
measurements determine that asphaltene molecules start
to associate at low concentrations (~50 mg/liter in
toluene).[13] Many different methods have been used to
measure the asphaltene critical nanoaggregate
concentration (CNAC) including high-Q ultrasonics [14],
NMR H-Index [15], NMR diffusion [15], AC-
Conductivity [16], DC-Conductivity [17,18], and
centrifugation [19]. All of these measurements are in
agreement that nanoaggregate growth terminates at ~100
mg/liter in toluene.

The size of nanoaggregates and indeed their internal
structure have been delineated in studies where both small
angle neutron scattering (SANS) and small angle x-ray
scattering (SAXS) are directly compared. As is well
known to petrophysicists, neutrons scatter preferentially
off hydrogen nuclei which are concentrated in the alkane
component of asphaltenes while x-rays scatter off
electrons which are concentrated in the carbon rich
aromatic (or PAH) component of asphaltenes.[20]

Figure 3. Direct comparison of x-ray scattering
(SAXS) and neutron scattering (SANS) absolute
cross sections. The divergence in the x-ray versus
neutron scattering that occurs at a wave vector q of
~0.07Å-1 (~2.8 nm diameter) is consistent with the
nanoaggregate structure and size shown in Fig. 1.

The aromatic rings are stacked in the interior and
the alkanes are oriented outwards.

Figure 3 is consistent with the picture that electron rich
aromatics are concentrated in the nanoaggregate interior
while the alkyl substituents are concentrated in the
nanoaggregate periphery. The monomer molecular
structure naturally dictates this nanoaggregate
structure.[14] The PAHs in the molecular interior are
attractive while the alkane substituents act to sterically
repel other asphaltene molecules. After several asphaltene
molecules aggregate, the nanoaggregate periphery is
largely covered with repulsive alkane substituents
precluding further aggregate growth.[14] More recent
analyses of x-ray and neutron scattering data confirms
that there is only a single stack of PAHs in the asphaltene
nanoaggregate.[21] Again this is consistent with the
nanoaggregate in Fig. 1.

There are three different types of molecular sizes
involved, radius of gyration, relevant for SANS and
SAXS measurements, hydrodynamic radius, relevant for
diffusion and Stokes drag, and the physical size, relevant
for gravitation and centrifugation.[22] Of course, exact
agreement among these different sizes is not expected.

Asphaltene Clusters. Asphaltene nanoaggregates are the
smallest colloidal particle of asphaltenes (molecules are
not colloidal), but they are not the only colloidal
asphaltene particle. Many systems exhibit colloidal
structure, such as micelles of soap in water. However, not
all colloidal systems exhibit multiple colloidal particle
sizes.

A clear demonstration of the formation of asphaltene
clusters in toluene was obtained by measurement of the
flocculation kinetics of asphaltene flocs upon addition of
n-heptane to asphaltene-toluene solutions.[23] Below a
concentration of ~3 g/liter, the kinetics of floc formation
are diffusion limited aggregation (DLA); whereas above
this concentration the kinetics are reaction limited
aggregation (RLA). This is consistent with
nanoaggregates that are ‘stickier’ whereas the fractal
clusters [21] are less adherent. A morphological change of
the cluster surface is evidently required for floc
formation; this is consistent with RLA.[24]

2 SPE 146649

Figure 4. Critical cluster concentration of
asphaltenes in toluene. When clusters form from
nanoaggregates, the conductivity per unit mass
decreases due to an increase in Stokes drag of the
charge carriers. Comparison of the slopes provides
an estimate for cluster size to consist of ~8
nanoaggregates.[18]

The size of clusters was recently measured using DC-
conductivity of asphaltene solutions. Less than 10-4 mole
fraction of asphaltene is charged in toluene; charge
carriers act as tracers to monitor asphaltene dynamics.
The critical clustering concentration is ~2.5 g/liter for
asphaltene in toluene.[18] The nanoaggregate is tightly
bound and its size is limited by the asphaltene molecular
architecture with the attractive PAH on the molecular
interior and the sterically repulsive alkane chains on the
molecular exterior. The clusters form only at much higher
concentration because the attractive forces of one
nanoaggregate to another are so much weaker. The fixed
size of the clusters found in the lab and the field is due in
part to the limited range of asphaltene concentrations that
have been probed. At much higher asphaltene
concentrations, the heavy oil will not flow easily.

The latest experiments in asphaltene science demonstrate
repeated consistency with the asphaltene nanoscience
model proposed in Figure 1. This model provides a
foundation that has far reaching implications in many
areas associated with the production of crude oil. The
success of such a simple model relies on its correct
physics!

Asphaltene Interfacial Activity. Asphaltene molecules
are of moderate size and, as such, can exhibit a high
degree of molecular orientation at the interface. For very
small molecules, entropy would presumably disfavor
alignment at room temperature, while for very large
molecules, entanglement would preclude high degrees of
alignment.

The first direct measurements of asphaltene molecular
alignment in Langmuir-Blodgett (L-B) films of
asphaltenes have been performed by the optical method
sum frequency generation (SFG).[25] An IR and visible

laser are focused on the same point on an interface.
Nonlinear mixing at the interface can result in the creation
of sum-frequency (UV) photons. The IR laser wavelength
is scanned across molecular vibrational resonances. By
controlling the polarization of the two lasers and detected
UV beam, molecular orientation is probed. A film of
asphaltene on water is created by evaporation a toluene
solution of asphaltenes on water. The film is then
transferred to a solid substrate for measurement – called a
Langmuir-Blodgett film.

Stretch
CC aromatic

Bend; ‐CH3, ‐CH2‐ aliphatic

D |

D ||

SSP UG8 Asphaltene

SPS UG8 Asphaltene

SFG on
L-B Film

S
H

Asphaltene

Molecule

Wavenumbers (cm‐1)

Sig
n

Figure 5. Asphaltene molecular orientation as
determined by sum frequency generation (SFG),[25]
the nonlinear mixing of an IR and visible photon to
form a UV photon. By polarization methods, it is
shown that the aromatic ring systems of asphaltenes
lie in the interfacial plane on the water surface, while
the alkyl chains lie perpendicular to this plane.

The wettability properties of crude oil have been
addressed largely through phenomenological methods.
With knowledge of asphaltene molecular architecture
contained within the nanoscience model of Fig. 1, it is
now possible to better understand detailed properties of
asphaltene films from first principles. Linking first
principles to surface wetting characterization enables
development of methods to alter these properties.

Oilfield Applications of Asphaltene Nanoscience

Downhole Fluid Analysis (DFA). The use of asphaltene
nanoscience for reservoir characterization is predicated on
the ability to perform measurements of fluid properties
such as asphaltene content and GOR at many points
within the reservoir. Measurement of fluid properties at
only a few points in the reservoir precludes robust
understanding of the primary governing physics
controlling fluid distributions in reservoirs. Indeed, the
antiquated concept of getting “the oil” using a wireline
sampling tool inhibited proper reservoir characterization;
this despite numerous examples of fluid
complexities.[3,4]

Downhole fluid analysis (DFA) is an essential technology
for characterizing fluid gradients and for understanding

SPE 146649 5

corresponding implications for reservoir properties. DFA
is now routinely performed on wireline formation
sampling tools.

Figure 6. Wireline formation sampling tool configured
with DFA optical analyzers. DFA helps acquisition of
representative samples. DFA also reveals reservoir
fluid complexities during the wireline job.
Consequently, the wireline measurement program
can be altered to match the complexity of the oil
column, improving efficiency.

To exploit new developments in asphaltene science as
well as traditional fluid analyses for reservoir evaluation,
it is essential to have adequate data particularly regarding
fluid gradients and fluid discontinuities. DFA is the only
way to achieve this objective; Fig. 6 shows a typical tool
configuration for DFA. DFA reveals fluid complexities
during the wireline job, thus, the complexity and cost of
the wireline job can be matched to the complexity of the
oil column. The operator does not pay for unnecessary
analyses. Without DFA, there is no means to reveal fluid
complexities in real time; presumptions of fluid simplicity
often prevail which are often incorrect, leading to
subsequent problems. DFA measurements now include
GOR and some hydrocarbon composition, relative
asphaltene content, density, viscosity, fluorescence, and
CO2. More fluid chemical analytes are being added
regularly.

Compartmentalization. The costly, bad news of
compartmentalization is often easier to uncover with
adequate DFA data. Fluid density inversions (higher
density fluids higher in the oil column) immediately
reveal sealing barriers. Figure 7 shows just such a case.
Right above the depth of x400 meters, a low GOR is
found. Right below x400 meters a crude oil with ~4 times
the GOR is found. GOR is a reasonable proxy for density.
In this column, a high density crude oil is found right
above a low density crude oil. Clearly a sealing barrier is
indicated.

This was delineated with 11 DFA stations which were
needed due to the complexity of the crude oil column.
High GOR is consistent with large GOR gradients within
an equilibrium context.[1,2] In a section below, fluid
disequilibrium will also be considered and can contribute
significantly to fluid gradients.

GOR (scf/bbl

)

1000 2000 3000 4000 5000

x200

x300

x400

x500

x600

x700

x800

Dept

(m)

Figure 7. A low GOR (high density) fluid at x350 m is
found above a high GOR (low density) fluid at
x410m; a sealing barrier is indicated at x400 meters.
The complexity of the oil column with highly variable
GOR mandates a relatively large number of DFA
stations; in turn revealing fluid complexities and a
sealing barrier.

In some cases, the column does not exhibit a fluid density
inversion but does reveal an “asphaltene inversion”.
Asphaltenes are more dense than the crude oil. They do
not float in oil; if anything, they sink to lower points in
the column.

In Fig. 8, DFA color inversions (higher color fluid higher
in the oil column), thus asphaltene concentration
inversions are observed.[26] For example, crude oil A has
three times the color magnitude of crude oil B, thus three
times the asphaltene content.[27] Thus a sealing barrier is
between these zones. However, fluid densities are too
similar to make the same assessment. The low asphaltene
content (~1%) accounts for the low impact of asphaltenes
on overall fluid density.

2 SPE 146649

Color @ 815 nm Fluorescence Intensity

DFA

Color

DFA

Fluorescence

Natural
Gamma‐Ray

Formation
Pressure (psi)

Tr
ue

V
er
ti
ca
l D

ep
th
(f
ee
t)

Figure 8. Compartmentalization (sealing barriers) is
shown by asphaltene density inversions, higher
asphaltene content (and higher color, less
fluorescence) higher in the column. Also, the lack of
pressure communication indicates
compartmentalization which made this prospect
unattractive.[26]

The crude oils from the column in Fig. 8 were analyzed
by the highest resolution mass spectrometer on earth.
Tens of thousands of individual components were
resolved. However, no differences were noted from
asphaltenes in different zones.[26] The maltenes of these
crude oils were analyzed by two-dimensional gas
chromatography; again no differences amongst these
crude oils were discernable.[28] The chemical
composition of the different constituents of these crude
oils are the same. It is the concentration that differs and
thus concentration is most useful for compartment
identification. DFA methods are very sensitive to this
concentration.[3]

Reservoir Connectivity. It is far more valuable and
difficult to verify the good news of reservoir connectivity
versus the bad news of compartmentalization. The
significant expense of well testing precludes its use in
many settings. The difficulty of establishing connectivity
causes this attribute often to be the largest risk in
development.[3] Moreover, reservoirs that are not
conclusively established to be compartmentalized have
often been presumed to be connected and thus be large.
However, geostatistics teaches that small geophysical
objects are always much more numerous than large
geophysical objects.[3] Connectivity in reservoirs must be
proven, not presumed.

Pressure surveys have long been used to address
compartmentalization. The statement “if two zones are
not in pressure communication, they are not in flow
communication,” is true. However, the corresponding
statement “if two zones are in pressure communication,
they are in flow communication” does not follow from

the first statement and is often incorrect. Indeed,
presuming the two logical statements are self consistent
results from a fatal flaw in logic (see footnote). Pressure
communication can occur on geologic time with minimal
flow volumes while flow communication must occur on
production time; these constraints differ by six orders of
magnitude. Pressure communication can occur through
tiny permeability while flow communication requires
much larger permeability.[3,29] Pressure communication
is a necessary but insufficient condition for flow
communication. New methods to assess connectivity are
needed.

Determination of fluid equilibrium in a reservoir is a
stringent method to determine reservoir connectivity. If
the fluids are equilibrated throughout a reservoir, the
reservoir is very likely connected. This does not imply
that if there is disequilibrium, then there is no
connectivity; we shall examine disequilibrium in a
subsequent section. Reservoir fluids enter the reservoir
necessarily out of their final thermodynamic equilibrium.
For example, under a normal burial sequence, light fluids
enter the reservoir later in time. These lighter fluids must
then undergo the equilibration process. Typically, with
charging, the fluids exist initially in the reservoir as a
stratified sequence from lightest at the top to heaviest at
the bottom.[30] The process of equilibration then requires
massive fluid flow in the reservoir which takes a long
time, especially if by diffusion. If low permeability
barriers are present, then this process can be prohibitively
slow.

Figure 9. Time scales to achieve fluid equilibrium
and pressure equilibrium for a tilted sheet reservoir
with a barrier of poor permeability in the middle.
Reservoir modeling shows that fluid equilibration is
ten million times slower than pressure
equilibration.[29] Very good connectivity is needed
for fluid equilibration, not for pressure equilibration.

Footnote: The logical statement “If A, then B” implies “if
not B, then not A”. These statements are related as

SPE 146649 7

contrapositives and follow logically. Assigning flow
communication FC=A, and pressure communication
PC=B, we have “If FC, then PC” implies “If not PC, then
not FC.” This is logically true. However, the statement “if
A, then B” does not imply “if not A, then not B.” The 2nd
statement is called a nonsequitur. The statements “if not
PC, then not FC” and “if PC, then FC” are related as
nonsequiturs in logic; it is logically invalid to assume one
statement follows from the other.
Figure 9 shows that the measurement of fluid equilibrium
to evaluate reservoir connectivity is ten million times
better then the measurement of pressure equilibration for
the same purpose. Obviously, fluid equilibration should
now be used to assess reservoir connectivity as a
complementary measurement to pressure.

To assess fluid equilibrium, equations of state (EoS) are
needed. The Peng-Robinson equation is a “cubic equation
of state” that is effective for treating many properties of
crude oil. The Peng-Robinson equation, developed in
1976, is derived from the original cubic EoS, the van der
Waals equation, developed in 1873. The cubic EoS was
developed to treat gas-liquid equilibria and can be used on
gas-liquid fluid distributions to assess reservoir
connectivity. However, it is frequently the case that phase
separated gas does not equilibrate well with the liquid
column, thereby limiting somewhat the use of the cubic
EoS to assess reservoir connectivity. Moreover, for low
GOR black oils and heavy oils, there is very little GOR
gradient, so measurement of GOR gradients alone
provides little insight into reservoir architecture.
Especially for these cases, the primary gradient of interest
is the asphaltene gradient; crude oils generally contain
solids, the asphaltenes, and an appropriate EoS is need.
As depicted in Fig. 1, asphaltenes are often colloidal.
There is no provision in any cubic EoS to treat colloidal
solids. A different equation is needed.

The Flory-Huggins-Zuo Equation of State. In a general
sense, equations of state treat both phase behavior and
gradients. The cubic EoS does just this for gas-liquid
equilibria. For many years, the Flory-Huggins equation
has been used to treat the phase behavior of
asphaltenes.[31] For phase behavior, the solubility of
asphaltenes in the liquid phase is key and is represented
by the “solubility parameter”, a measure of the volumetric
density of chemical intermolecular interaction. This
theory quantifies the well known axiom “like dissolves
like” through the solubility parameter.

In order to extend this equation to treat gradients, it is
necessary to include the effect of gravity which depends
on the size of the particular asphaltene species in play.
This is now known for crude oils and laboratory solvents
(cf. Fig. 1). In addition, it is essential to understand how
GOR influences the solvation of asphaltenes. With the
gravity term included, and proper accounting of the
solvation term,[32,33] the Flory-Huggins-Zuo (FHZ)
equation results.[34]

( )
( )

( ) ( ) ( )[ ]
⎟
⎟

⎠

⎞

⎜
⎜

⎝

⎛ −−−
−⎟

⎠

⎞
⎜
⎝

⎛−⎟
⎠

⎞
⎜
⎝

⎛+
−Δ

==
RT

v
v

RT
hhgv

h
h

hOD
hOD hah

h
a
h
aa
a

a
2

2
1

2 12

exp
δδδδρ

φ
φ

Eq.
1.

where OD(hi) is the optical density (measured by DFA) at
a particular color channel at height hi in the oil column,
φa(hi) is the corresponding asphaltene concentration, va is
the molar volume of the asphaltene species (molecule,
nanoaggregate or cluster), v is the molar volume of the oil
phase, g is earth’s gravity, Δρ is the density difference
between asphaltenes and the liquid phase, R is the ideal
gas constant, T is temperature, and δa is the solubility
parameter of asphaltene, and δ the solubility parameter of
the oil.

The first term in the exponential includes Archimedes
buoyancy of an object in a liquid (perhaps more familiar
as mgh). Gravity tends to accumulate the asphaltenes
towards the base of the column; this is explicitly
counteracted by thermal energy, thus temperature is in the
denominator. This gravity term explicitly depends on the
molecular weight and colloidal size of the asphaltenes as
given in the Yen-Mullins model and is shown in Fig. 1.
For larger species such as clusters, the gravity term
becomes large giving rise to significant concentrations of
asphaltenes towards the base of the oil column. For low
GOR black oils and heavy oils, the other terms in Eq. 1
tend to be small, so still the simple gravity term
dominates.[33]

The second and third terms in Eq. 1 are simply the Flory-
Huggins entropy term. Entropy tends to randomize the
distribution counteracting any gradients. The entropy term
is not very large for asphaltenes in crude oils.[32]

The last term in the exponential is the solubility term as
discussed above. With the precept “like dissolves like”,
this term accounts for decreasing solubility of asphaltenes
with increasing chemical difference between the liquid
phase and the asphaltenes. In particular, large GOR gives
a low density, alkane rich liquid phase that is chemically
very different than asphaltenes, thus decreasing
asphaltene solubility.[35] If there is no GOR gradient,
then there is effectively no difference in the solubility
parameter from the top to the bottom in the oil column;
then this solubility term does nothing to create an
asphaltene gradient. Thus, for low GOR oils where small
GOR gradients are the norm, this term is unimportant. In
contrast, for large GORs, then GOR gradients become
large, and the solubility term contributes strongly to
create asphaltene gradients.[35]

There is a simple explanation why, for equilibrated oil
columns, low GOR yields low GOR gradients while high
GOR yields large GOR gradients.[2,3] The issue comes
down in large part to compressibility. For compressible
oils such as those with high GOR, the hydrostatic head
pressure of the oil column creates a density gradient in the

2 SPE 146649

fluid column. This density gradient then creates a
chemical compositional gradient forcing low density
species such as methane to the top of the column. In
contrast, low GOR black oils and heavy oils have very
low compressibility, thus the hydrostatic head pressure
does not create a density gradient. The lack of a density
gradient keeps the compositional gradient small; thus, for
this case, methane tends to be uniformly distributed. The
gravity segregation of colloidal asphaltene is a
fundamentally different type of effect and is related
simply to Archimedes buoyancy. Archimedes buoyancy
for methane molecules is small compared to asphaltenes.
Archimedes buoyancy depends on species molar volume
(cf. Eq. 1); asphaltene nanoaggregates have ~300 carbon
atoms, methane only 1.

DFA Field Studies of Connectivity via the FHZ EoS:
Condensates. For condensates, asphaltene concentrations
are low and thus asphaltenes are dispersed as molecules;
the small size means that the gravity term is responsible
for only a small part of the asphaltene gradient. However,
the large GOR of condensates is consistent with large
GOR gradients, meaning that the solubility term is
responsible for a large asphaltene gradient.

A condensate field was intersected by two wells and a
side track. The primary operator concern was reservoir
connectivity. The asphaltene concentrations in the
condensate were measured at different heights in three
wells using DFA; the asphaltenes found to be distributed
across the field according to a molecular dispersion of
asphaltenes in the crude oil and in accordance with the
FHZ equation. This is expected for a condensate.

For our purposes, it is not important whether the
condensate heavy ends are the lightest asphaltenes or the
heaviest resins. The chemical difference in these two
categories is largely definitional.[36] A salient question
for application of the FHZ EoS is whether the dispersion
is molecular or colloidal. For condensates, we find that
the colored heavy ends are molecularly dispersed.[33]

3660

3665

3670

3675

3680

3685

3690

3695

3700

3705

3710

De
pt

h
(T

VD
-m

)

Optical Density at 647 nm

CFA Well A CFA Well B LFA Well A LFA Well B LFA Well C CFA Well C

N
N
N
N

O
il Colum

Statoil

Nanoaggregate

Cluster

Molecule0 10.5

Figure 10. The DFA-measured asphaltene
distribution in three wells in the reservoir is largely

accounted for by the FHZ EoS (solid line).[33] The
molecularly dispersed asphaltenes are equilibrated
indicating the reservoir is connected. Subsequent
production proved this prediction correct.

Figure 10 shows DFA color (thus asphaltene) variations
in three wells. The data is in accordance with the FHZ
EoS for a molecular dispersion of asphaltenes with the
exception of one DFA station at x682 meters.[33] The
relatively large GOR gradient of the condensate [37]
creates the large asphaltene gradient. The equilibration of
the asphaltenes implies reservoir connectivity. This
reservoir has two separate gas caps with two different
GORs; the light ends are not equilibrated across the field.
Thus, the cubic EoS does not make a clear prediction
about reservoir connectivity. Since the asphaltenes do not
partition to the gas phase, they remain unperturbed from
the two different GOCs. Connectivity was proven in
production.
Connectivity: Black Oils. Black oils are often
characterized by low GOR. In such a case the cubic EoS
is not useful. Indeed the primary variation among black
oils is asphaltene content. The FHZ EoS is well suited to
handle black oils. Figure 11 shows DFA results from a
field study of a reservoir intersected by many wells. The
reservoir had two stacked sands that are not in pressure
communication, thus not in flow communication.
Inspection of Fig. 11 shows that the shallower sand has a
slightly higher asphaltene content especially in
conjunction with FHZ prediction, indicating
compartmentalization corroborating the pressure data.
The DFA color data from each sand show a gradient
across almost the entire field that matches the FHZ
equation; the asphaltenes are dispersed as nanoaggregates.
Due to the low GOR, the gravity term dominates so an
approximate analysis is very simple.[38] The ratio of
asphaltene concentrations at two heights is just given by
Archimedes buoyancy in the Boltzmann distribution:
exp{-ΔρVgΔh/RT). Equilibrated asphaltenes indicate
reservoir connectivity in each sand, which was
subsequently confirmed in production. Many other case
studies have exhibited nanoaggregate dispersion.[39,40]

0 2 3

TVD
Feet
103

DFA Color & Asphtene Content
1

Nanoaggregate

2~3 nm

Cluster

4~6 nm

~1.

5 nm

Molecule
O
il Colum
n
1

FHZ EoS

FHZ

Figure 11. Black oil reservoir with two stacked

SPE 146649 9

sands. The asphaltene content across almost all the
reservoir is accounted for by the FHZ EoS. The
prediction of connectivity based on asphaltene
equilibration throughout the reservoir was confirmed
in production.

Connectivity: Heavy Oil. Heavy oil has been a special
challenge to any EoS due to the very high asphaltene
content. Previously, there was not a clear method to
handle the large gradients in heavy oil columns. However,
with the nanoscience picture in Fig. 1 coupled with the
FHZ EoS, the modeling of heavy oil gradients is
straightforward.

Heavy oils that still flow are accounted for by the Yen-
Mullins model of colloidal structure. With increasing
asphaltene content or colder temperatures, the heavy oil
can become viscoelastic and will not flow. In this
situation, larger length scale structures than clusters are
present. Indeed, this rheological property is desired for
pavement; at high temperatures the organic component of
asphalt (with its high asphaltene content) can flow, thus
can be applied, and at low temperature asphalt gels and
becomes viscoelastic.[41]

2290

2295

2300

2305

2310
10 12 14 16 18 20

Asphaltene (wt%)

1
Nanoaggregate
2~3 nm
Cluster
5 nm
N

~1.5 nm

Molecule

TV
D
D
ep

th
(
m
et
er
s)

O
il Colum
n

Figure 12. Heavy oil column exhibiting a very large
asphaltene gradient. Asphaltenes are dispersed in
heavy oils as clusters due to the high asphaltene
concentration. The clusters being large yield very
large gravitational gradients of asphaltenes. The
vertical equilibration of asphaltenes in this single
well implies vertical connectivity, consistent with
production data.

The heavy oil in Fig. 12 gives a gradient 50 times larger
than the low GOR black oil in Fig. 11. In both cases, the
dominate term in the FHZ EoS is the gravity term; exp{-
ΔρVgΔh/RT). The only difference is that in the black oil,
the asphaltenes are present as nanoaggregates while in the
heavy oil, the asphaltenes are present as clusters. Even
though the diameter of the cluster is only about 2.5 times
bigger than that of the nanoaggregate, the volume
depends on the cube of this difference, and the volume

enters an exponential in the gravity term. This yields the
huge (x50) mulplicative difference in asphaltene gradient.
What was once considered to be a difficult problem –
understanding gradients in heavy oil columns – is now
seen to be founded on a very simple framework. Of
course, disequilibrium can occur in oil columns including
heavy oil columns. Nevertheless, the equilibrium case
provided by the FHZ EoS gives a starting point for any oil
column. Vertical and or lateral disequilibrium can then be
analyzed (see below).

Viscosity depends exponentially on asphaltene
content.[5,41] The viscosity gradient for the heavy oil
column in Fig. 11 is from 6 centipoise (cp) to 200 cp.
Clearly, productivity predictions depend on an accurate
assessment of asphaltene gradients particularly in heavy
oil.

Tar Mats. The term tar or “tar mat” is used inconsistently
and confusingly in the oil industry and whereas the term
tar commonly refers to pyrolysis product of organic
materials, “tar mats” in oil and gas industry usually refer
to zones of asphaltic phase or asphaltic oil in lighter oil
reservoirs. Asphaltic or asphaltene rich oil in a reservoir
can arise in many ways for example by phase instability
and in-reservoir precipitation [42], or by severe
biodegradation, or from primary oil charge at low
maturity from very sulfur rich source rocks. The
mechanism of formation of tar mats has long been a
puzzle in the oil industry. The Yen-Mullins model
provides a simple solution to this mechanism. Here, we
define tar mats as a large discontinuous increase in
asphaltene content from the oil leg to tar mat with tar
mats typically containing ca 50% asphaltenes compared
to much lower asphaltene content in their associated oil
legs. With this definition, the development of tar mats
likely corresponds to phase instability of asphaltene that
we address with our model.

The puzzle associated with this instability is as follows. If
the instability of asphaltenes occurs at the top of the oil
column, then how does the precipitated asphaltene move
through the porous medium to get to the base of the oil
column, the normal location of tar mats? If the asphaltene
phase instability of asphaltenes occurs at the base of the
oil column, then what process could give rise to this phase
instability? With water at the base of the column, some
have been tempted to claim a role for water in tar mat
formation.

Contrary to some suggestions in the literature,
biodegradation is not a likely candidate to induce phase
instability of asphaltenes. It is well known that microbes
preferentially remove n-alkanes and other alkanes from
crude oil.[43] This process is opposite to the standard
laboratory process to induce phase instability of
asphaltenes, the addition of n-alkanes. That is
biodegradation should 1) increase the asphaltene content
and 2) make asphaltenes less likely to undergo phase

2 SPE 146649

instability. There are no proper observations or examples
of classical tar mats in biodegraded oilfields.[42]

The solution to the puzzle of tar mat formation involves
having two, not one stable colloidal sizes of asphaltenes.
As we have seen, as the concentration of asphaltenes
increases, nanoaggregates form clusters. This is well
known in the laboratory.[6-8] The other similar
circumstance that can lead to cluster formation is the
reduction of solvancy of the liquid phase for asphaltenes.
That is, the reduction of asphaltene stability can lead to
the process going from nanoaggregates to clusters and
subsequently to flocs. Most importantly, clusters are
stably suspended in the liquid phase of the crude oil; they
are not seeking a solid surface to settle upon.

Figure 13 shows the result of a black oil column that was
subjected to a late gas and condensate charge.
Geochemical analysis established that this crude oil has
been gas washed.[44] This light hydrocarbon addition
destabilized the asphaltene thereby forming clusters.
Destabilized asphaltene was confirmed in a flow
assurance study of this crude oil. The clusters being larger
settle towards the base of the column. Some bitumen
deposition in core also confirmed asphaltene instability.
In this case, the operator checked all associated risk
factors for production, no significant problems were
found and production proceeded, with the knowledge that
large viscosity gradients characterize this oil column.

O
il C

o
lu
m
n

Nanoaggregate Cluster

N
Molecule

Figure 13. This is a black oil column that has both
nanoaggregates and clusters. The clusters formed
due to some asphaltene instability associated with a
late, light hydrocarbon charge into the reservoir. The
large clusters settled towards the base of the column
increasing the asphaltene gradient.

Excessive gas or condensate charge into the reservoir can
destabilize the asphaltene causing tar mat formation.[42]
This happened in a reservoir initially filled with crude
oil.[45] So much gas charged into the reservoir that the

asphaltenes were destabilized to flocs after accumulating
as clusters at the base of the oil column. A thin section of
core at the base of the oil column is shown in Fig. 14.
Most importantly, the tar mat in the core is seen to rest on
cement. The base of the oil column is not in contact with
water; this suggests that water had nothing to do with this
tar mat formation.[45]

TAR

Figure 14. Thin section of a core at the base of the
oil column where excessive gas charge caused a tar
mat to form. The tar mat has formed on cement;
water is not in contact with this oil at this point, this
suggests the process to form this tar mat did not
involve water.[45]

Asphaltene instability was confirmed by analyzing the
produced fluids, where asphaltene was found as a
separated phase in production oil.[45] A late gas charge
was confirmed by showing that methane isotope ratios
varied, methane is not equilibrated with biogenic methane
pooling updip.[45] In general, tar mats are found in fields
which frequently exhibit asphaltene instability issues on
production.[42]

Fluid Disequilibrium. Equations of state presume fluid
equilibration. Nevertheless, steady state or transient
process acting on reservoir fluids can be incorporated into
equations thereby accounting for disequilibrium in
reservoir oil columns. In one deepwater field, there is a
well known large asphaltene gradient that has been
captured in a photograph of 24 dead oils samples.[46] In
addition, this field has a large GOR gradient that is also
not equilibrated, again as shown by the variable methane
carbon isotope ratios throughout the field.[46] The
gigantic asphaltene gradient that is evident visually had
not previously been accounted for by any theory. A
simple model presuming methane diffusion into the top of
a black oil column, coupled with the FHZ theory has now
accounted for this oil column and for the array of dead
oils corresponding to this column.[47]

SPE 146649 11

Figure 15. The huge asphaltene gradient in a single
oil column from a deepwater field is now matched by
a simple theory presuming methane diffusion into
the top of an oil column from a late gas charge and
application of the Flory-Huggins-Zuo Equation.[47]
The modeling quantitatively matches the measured
GOR and asphaltene gradients.[47]

There are other mechanisms that can produce
disequilibrium. For example, significant asphaltene and
light (C6-C12) hydrocarbon gradients can also be caused
by biodegradation processes in very heavy oils, even with
low GOR gradients, whereby extremely low diffusive
mixing rates in very viscous oils allow the preservation of
large biodegradation induced compositional
gradients.[48]

Conclusions

Asphaltenes have long been known to be a flow assurance
concern. However, for the much more important issues of
reservoir and fluid complexities there simply had been no
way to treat or utilize asphaltenes from any first principles
approach. Asphaltene science had previously been
characterized by order of magnitude uncertainties that
precluded any first principles approach in development of
corresponding theoretical formalisms.

Asphaltene nanoscience has undergone a renaissance with
key results being confirmed by a plethora of methods.
Asphaltene nanoscience has been codified in a simple,
robust model known as the modified Yen model or the
Yen-Mullins model. The advances subsumed in this
model have enabled development of the simple, powerful
Flory-Huggins-Zuo equation of state to treat asphaltene
gradients in oil reservoirs. When combined with the new
technology downhole fluid analysis (DFA), a myriad of
reservoir complexities have come into the purview of
treatment by first principles. Assessment of reservoir
connectivity is dramatically improved as established in
several field studies. Large variations in the magnitude of
asphaltene gradients are now understood within a simple
framework. The foundation for the mechanism of tar mat
formation is now understood, and can be applied to many
reservoirs. Important origins of large scale disequilibrium
can be treated with simple transient concepts with
corresponding equations treating many chemical and
physical parameters of the reservoir fluids. As is always
the case, linking new science and new technology leads to
powerful new advances. In spite of the fact that the
advances in asphaltene science and in DFA are relatively
recent, a broad range of topics are already addressed. This
bodes well for gaining future insights about the
formidable complexities of reservoirs and their contained
fluids.

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[3] Høier L, Whitson C, (2001), Compositional Grading,
Theory and Practice, SPEREE, 525-535, (2001)

[3] Mullins OC, The Physics of Reservoir Fluids;
Discovery through Downhole Fluid Analysis,
Schlumberger Press, Houston, Texas, (2008)

[4] Larter SR, Aplin AC, Reservoir geochemistry:
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[5] Sirota EB, Lin, MY, The physical behavior of
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[6] Mullins OC, Sheu EY, Hammami A, Marshall AG,
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[7] Mullins OC, The modified Yen model, Energy &
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[8] Mullins OC, The Asphaltenes, Annual Reviews of
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[9] Sabbah H, Morrow AL Pomerantz AE, Zare RN,
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[10] Ruiz-Morales Y, Aromaticity in Pericondensed
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[11] Groenzin H, Mullins OC, Molecular sizes of
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[12] Pomerantz AE, Hammond MR, Morrow AL, Mullins
OC, Zare RN, Two step laser mass spectrometry of
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[13] Goncalves S, Castillo J, Fernandez A, Hung J,
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[14] Andreatta G, Bostrom N, Mullins OC, High-Q
Ultrasonic Determination of the Critical Nanoaggregate
Concentration of Asphaltenes and the Critical Micelle
Concentration of Standard Surfactants, Langmuir, 21,
2728, (2005)

2 SPE 146649

[15] Freed DE, Lisitza NV, Sen PN, Song YQ, A study of
asphaltene nanoaggregation by NMR. Energy Fuels 23,
1189–1193, (2009)

[16] Sheu EY, Long Y, Hamza H, Asphaltene self-
association and precipitation in solvents; AC conductivity
measurements. Chapter 10 in Ref. 6.

[17] Zeng H, Song YQ, Johnson DL, Mullins OC, Critical
nanoaggregate concentration of asphaltenes by low
frequency conductivity, Energy Fuels, 23, 1201–1208,
(2009)

[18] Goual L, Sedghi M, Zeng H, Mullins OC, Electrical
conductivity of n-alkane asphaltenes and its relation to
aggregation and clustering. Fuel, in press

[19] Mostowfi F, Indo K, Mullins OC, McFarlane R,
Asphaltene Nanoaggregates and the Critical
Nanoaggregate Concentration from Centrifugation,
Energy & Fuels, 23, 1194–1200, (2009)

[20] Barré L, Jestin, J, Morisset A, Palermo T, Simon S,
Relation between nanoscale structure of asphaltene
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[21] Eyssautier J, Levitz P, Espinat D, Jestin J, Gummel J,
Grillo I, Barré L, Insight into Asphaltene Nano-Aggregate
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[22] Barré L, Simon S, Palermo T, Solution properties of
asphaltenes, Langmuir, 24, 3709-3717, (2008)

[23] Yudin IK, Anisimov MA, Dynamic light scattering
monitoring of asphaltene aggregation in crude oils and
hydrocarbon solutions, Chapter 17 in Ref. 6.

[24] Private communication, Professor William W.
Mullins
(deceased)

[25] Andrews AB, McClelland A, Korkeila O, Krummel
A, Mullins OC, Demidov A, Chen Z, Sum frequency
generation studies of Langmuir films of complex
surfactants and asphaltenes, Accepted Langmuir

[26] Mullins OC, Rodgers RP, Weinheber P, Klein GC,
Venkatramanan L, Andrews AB, Marshall AG, Oil
Reservoir Characterization via Crude Oil Analysis by
Downhole Fluid Analysis in Oil Wells with Visible-Near-
Infrared Spectroscopy and by Laboratory Analysis with
ESI FT-ICR Mass Spectroscopy, Energy & Fuels, 21,
256, (2007)

[27] Ruiz-Morales Y, Wu X, Mullins OC, Electronic
Absorption Edge of Crude Oils and Asphaltenes

Analyzed by Molecular Orbital Calculations with Optical
Spectroscopy, Energy & Fuels, 21, 944, (2007)

[28] Mullins OC, Ventura GT, Nelson RK, Betancourt
SS, Raghuraman B, Reddy CM, Oil Reservoir
Characterization by coupling Downhole Fluid Analysis
with Laboratory 2D-GC Analysis of Crude Oils, Energy
& Fuels, 22, 496-503, (2008)

[29] Pfeiffer T, Reza Z, Schechter DS, McCain WD,
Mullins OC, Determination of Fluid Composition
Equilibrium under Consideration of Asphaltenes – a
Substantially Superior Way to Assess Reservoir
Connectivity than Formation Pressure Surveys, Denver
Colorado, SPE ATCE, (2011)

[30] Stainforth JG, “New Insights into Reservoir Filling
and Mixing Processes” in Cubit JM, England WA, Larter
S, (Eds.) Understanding Petroleum Reservoirs: toward
and Integrated Reservoir Engineering and Geochemical
Approach, Geological Society, London, Special
Publication, (2004)

[31] Buckley JS, Wang X, Creek JL, Solubility of the
Least-Soluble Asphaltenes. Chapter 16 in Ref. 6.

[32] Freed D, Mullins OC, Zuo JY, Asphaltene gradients
in the presence of GOR gradients, Energy & Fuels, 24 (7),
pp. 3942-3949, (2010)

[33] Zuo JY, Freed D, Mullins OC, Zhang D, Gisolf A,
Interpretation of DFA Color Gradients in Oil Columns
Using the Flory-Huggins Solubility Model, SPE 130305,
Int. Oil & Gas Conf. Beijing, China, June, (2010)

[34] This Flory-Huggins-Zuo name for the EoS has been
promulgated in many papers in spite of protests from Dr.
Zuo.

[35] Zuo JY, Elshahawi H, Dong C, Latifzai AS, Zhang
D, Mullins OC, DFA Assessment of Connectivity for
Active Gas Charging Reservoirs Using DFA Asphaltene
Gradients, accepted, SPE #145448, ATCE, (2011)

[36] Indo K, Ratulowski J, Dindoruk B, Gao J, Zuo JY,
Mullins OC, Asphaltene Nanoaggregates Measured in a
Live Crude Oil by Centrifugation, Energy & Fuels, 23,
4460–4469, (2009)

[37] Dubost FX, Carnegie AJ, Mullins OC, Keefe MO,
Betancourt SS, Zuo JY, Eriksen KO, Integration of In-
Situ Fluid Measurements for Pressure Gradients
Calculations, SPE 108494, Int. Oil Conf. Ex., Veracruz,
Mexico, (2007)

[38] Mullins OC, Betancourt SS, Cribbs ME, Creek JL,
Andrews BA, Dubost FX, Venkataramanan L, The
colloidal structure of crude oil and the structure of
reservoirs, Energy & Fuels, 21, 2785-2794, (2007)

SPE 146649 13

[39] Betancourt SS, Ventura GT, Pomerantz AE, Viloria
O, Dubost FX, Zuo JY, Monson G, Bustamante D, Purcell
JM, Nelson RK, Rodgers RP, Reddy CM, Marshall AG,
Mullins OC, Nanoaggregates of Asphaltenes in a
Reservoir Crude Oil, Energy & Fuels, 23, 1178–1188,
(2009)

[40] Pomerantz AE, Ventura GT, McKenna AM, Cañas
JA, Auman J, Koerner K, Curry D, Nelson RK, Reddy
CM, Rodgers RP, Marshall AG, Peters KE, Mullins OC,
Combining Biomarker and Bulk Compositional Gradient
Analysis to Assess Reservoir Connectivity, Org.
Geochem. 41 (8), pp. 812-821, (2010)

[41] Lin MS, Lumsford KM, Glover CJ, Davison RR,
Bullin JA, The effects of asphaltenes on the chemical and
physical characteristics of asphalt, Ch. 5 in Asphaltenes,
fundamentals and applications, Sheu EY, Mullins OC,
Eds. Plenum Press, New York, (1998)

[42] Wilhelms A, Larter SR, Origin of tar mats in
petroleum reservoirs. Part II: formation mechanisms for
tar mats, Mar. Petrol GeoL 11,442-456, (1994)

[43] Peters KE, Walters CC, Moldowan JM, The
Biomarker Guide, Cambridge University Press,
Cambridge, UK, (2005)

[44] Mullins OC, Freed DE, Zuo JY, Elshahawi H, Cribbs
ME, Mishra VK, Gisolf A, Downhole Fluid Analysis
coupled with Asphalene Nanoscience for Reservoir
Evaluation, Presented in Perth, Australia, SPWLA, (2010)

[45] Elshahawi H, Latifzai AS, Dong C, Zuo JY, Mullins
OC, Understanding Reservoir Architecture Using
Downhole Fluid Analysis and Asphaltene Science,
Presented, Colorado Springs, SPWLA, Ann., Symp.,
(2011)

[46] Elshahawi H, Dong C, Mullins OC, Hows M,
Venkataramanan L, McKinney D, Flannery M, Hashem
M, Integration of Geochemical, Mud Gas and Downhole
Fluid Analyses for the Assessment of Compositional
Grading – Case Studies, SPE 109684, ATCE, Anaheim,
CA, (2007)

[47] Zuo JY, Elshahawi H, Dong C, Latifzai AS, Zhang
D, Mullins OC, DFA Assessment of Connectivity for
Active Gas Charging Reservoirs Using DFA Asphaltene
Gradients, SPE 145438, ATCE, Denver, Colorado, (2011)
[48] Larter S, Adams J, Gates ID, Bennett B, Huang H,
The origin, prediction and impact of oil viscosity
heterogeneity on the production characteristics of tar sand
and heavy oil reservoirs. Journal Of Canadian Petroleum
Technology, 47(1), 52-61, (2008)

SPE 160891

Tar Characterization for Optimum Reservoir Management Strategy
Muhammad Al-Harthi, Mohammed Al-Ali, Ronny Gunarto/ Saudi Aramco

This paper was prepared for presentation at the SPE Saudi Arabia Section Technical Symposium and Exhibition held in Al-Khobar, Saudi Arabia, 8–11 April 2012.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper
have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position
of the Society of Petroleum Engineers, its officers, or members. Papers presented at the SPE meetings are subject to publication review by Editorial Committee of Society of
Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited.
Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of
where and whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

Abstract

Tar mats are extra-heavy bitumen that occur between
aquifers and overlaying oil columns. They seal either
partially or completely an oil reservoir from its
aquifer and reduces aquifer support. Tar
characterization includes evaluation of the tar
distribution and its sealing degree. It is an important
prerequisite to optimize the water injection well’s
requirement and placement to maximize sweep and
recovery.

This paper discusses a case study and demonstrates
an integrated methodology by using static and
dynamic data to determine the tar distribution and its
sealing degree. The study includes both early life data
before the subject field was put in production, as put
on production, and post-production data to refine the
characterization. Additionally, the use of formation
pressure while drilling and Pyrolytic Oil-Productivity
Index (POPI) analysis to optimize the injector’s
placement will also be highlighted. Moreover,
saturation and production logging tool analysis were
incorporated to determine if there is aquifer influx
across the tar mat. The degree of the aquifer influx is
also evaluated using material balance and reservoir
simulation.

Introduction

Field X is one of Saudi Aramco fields that was
developed with four producing reservoirs namely, 1,
2, 3 and 4 reservoirs. The producing reservoirs have

relatively good reservoir quality with average
permeability of 310 md. The value is only one-third
of average permeability from pressure build up which
indicates an existence of fractures. The reservoir
properties are relatively uniform across the
reservoires and vertical variation is considered
minimal with some higher permeability streak
observed from the core data up to 3 darcies.

The PVT data and numerous samples indicated oil
quality degrades from the crest to flank and All
producing erservoirs are confirmed to have tar in the
reservois boundary. The tar existence prevents an
effective aquifer support to the producing area thus
the water injection was chosen as part of the
development plan to effectively recover the oil.
Detail fractures study incorporating both static and
dynamic data shows that reservoir 3 and 4 are
interconnected with underlying undeveloped
reservoir 5 which has reasonable aquifer size without
tar existence. The reservoir 1 and 2 are also in
communication but they are not expected to be
connected with reservoir 3 and 4. The optimum
reservoir management strategy was formulated based
on the tar characterization. It is believed that not all
the tar section is fully impermeable. Understanding
the sealing degree of the tar is one of the key element
to optimize the field develoment plan such as
determine the optimum number and location of the
injectors taking into account the aquifer influx. The
tar is characterized with different techniques to
confirm the presence and the sealing degree using
static and dynamic data as follows :

Data Sources
POPI
Pyrolytic Oil Productivity Index is a novel
quantitative diagnosis developed by Saudi Aramco
and practically implemented in 2002. It utilizes
residual hydrocarbon staining on core and drill
cuttings to assess the reservoir quality. The method is
superior since it identifies the tar presence and
quantifies its percentage directly. Furthermore, it is
used in real time to enhance optimum well
placement. The injectors were placed slightly above
the Tar Oil Contact (TOC) to ensure sufficient
injectivity and provide effective pressure support and
reservoir sweep towards the producer wells (figure-
1).

Figure-1: Well placement above TOC.

POPI analysis generates a plot of Hydrocarbon yield
(mgHC/gRock) versus temperature (figure-2). This is
generated by heating a powdered rock sample at 180o
C for 3 minutes and then starting to increase the
temperature up to 600o C at a rate of 25o C per
minute. The end result is a plot with three peaks. The
first peak –at the start temperature (180o C) –
represents the amount of volatile hydrocarbons. The
second peak –usually between 180o C and 400o C-
represents the solvent extractable bitumen. The third
peak –above 500o C- represents the amount of
heavier hydrocarbons (e.g., asphaltenes,
pyrobitumen). The fraction of the heavier
hydrocarbon observed in real-time during wells
drilling will give indication whether the interval will
be effectively injected or not. Adjustment on the well
trajectory during drilling will be carried out as
deemed necessary based on POPI data and other
information such as NMR log. POPI results confirm
the tar existence around the wells and estimate the
depth of the TOC which could vary between areas.

Figure-2: Typical POPI plot.

Here is an example of one of the POPI analysis in
one of the flank wells. The apparent API gravity data
show an average gravity of 13.2° for the reservoir
(figure-3) with a range from 19.0° to 5.8°. The
apparent API gravity is lower than that reported for
produced oils in the field and it shows a clear
transition zone at x954.1’. Therefore, the reservoir at
the well area appears to contain medium gravity oil
and tar that is not producible.

Figure-3: Depth plot of apparent API gravity as
determined from pyrolysis using two different formulae
and total pyrolytic yield for core samples on of the wells
located in the flank area.

For the sake of this study, POPI is used to confirm
the existence of tar around the area of interest and
evaluate the possible different TOC and tar thickness
across the reservoir

MDT

MDT is a common tool used to evaluate the pressure
profile across the reservoir which can indicate the
formation pressure, mobile phase, the contacts and
the possible pressure depletion difference if it is run
post production period. It has been observed that the

MDT pressure points to be supercharged whenever it
is taken across the tarry interval. The fact that the tar
has extreme low viscosity, the mobility (k/m) in the
survey point will be very low and it would act as a
tight section with super charge behavior.

Pressure Performance

Pressure performance is the important dynamic data
which can indicate the tar sealing degree. Whenever
the field was shut-in in 1980s and 1990s, it was
clearly observed that the reservoir pressure built up in
each reservoir, indicating connectivity with the
aquifer. A material balance and simulation models
are used to estimate the degree of aquifer influx and
its connectivity to the producing area based on the
rate of reservoir pressure increase observed. A more
complex situation exists in reservoir 3 and 4 where
these reservoirs are inter-connected through fractures
system with underlying reservoir 5 which does not
have tar mat. Most of the external energy came from
reservoir 5 (not the aquifer itself) eventhough further
assessment shows that not all tar mats in reservoir 3
and 4 are fully sealing. The availability of aquifer
observation wells in specific locations aids the
interpretation of tar sealing degree. Should the tar be
fully sealing, the pressure in aquifer will not be
reduced at all whenever the field is produced.

Logs

Another approach used to evaluate the tar sealing
degree by analyzing open-hole and saturation logs.
Saturation logs are run periodically in the field to
monitor the flood front progression and confirm the
sweep efficiency. Carbon-Oxygen log is utilized as
saturation log as it has ability to determine water
saturation independent of salinity. In addition to the
C/O ratio, the log also works in the sigma mode
which can be used to identify high salinity aquifer
water encroachment. The relatively high salinity
aquifer water will be observed clearly as high sigma
reading if it is present within vicinity of the surveyed
wells. The open hole log of flank wells drilled post
production are also used to identify any possible
aquifer water encroachment. Should the wells
penetrate the impermeable tar, the open hole or
saturation logs show unchanged saturation compared
to the typical pre-production saturation profile.
PLT is another log that can be used to confirm tar
sealing degree. In a case of sealing tar penetrated by a
well, the PLT shows no injection or production
contribution across the tar section.

Geochemical Analysis

Additionally, geochemical analysis of produced
water has been utilized to determine the source of
water production in the producers. The salinity of
aquifer formation water is varying between 200,000
ppm to 240,000 ppm which is significantly higher
than the injected water salinity of 25,000 ppm.
Several wells at the flanks showed formation water
salinity which indicates aquifer influx and non-
sealing tar mat.

Injection Fall Off (IFO) Test

In IFO test, an injector well is shut in for a specific
amount of time. Meanwhile, a pressure gauge is
utilized to measure the pressure response. The
pressure is falling off when an injector is shut in. This
period is called the fall off period (see figure-4). The
falling trend is used to characterize the reservoir in
terms of both rock and fluid properties. For analysis,
different plots containing mainly calculated variables
from the pressure response versus time in different
scales. In this study, the pressure derivative will be
used to characterize tar. Basically, when fluid flow
disturbance propagation caused by shutting an
injector hits a sealing boundary (e.g. sealing fault),
the flow regime changes from an infinite acting radial
flow regime to linear flow regime. This is reflected
on the derivative as a change from zero slope state to
a half slope state (see figure-5). If tar is sealing, then
it will work as a sealing barrier and derivative will
follow the trend explained earlier. If otherwise the tar
is not sealing, the aquifer effect will be felt as
constant pressure boundary and the derivative will be
declining. The distance to the tar (no-flow boundary)
interpreted from the test is cross checked with the
estimated distance from geological map to confirm
the test result reliability. The interpretation is more
challenging in the case of horizontal injectors as the
linier flow will be observed due to the flow to the
horizontal section. The slope change as result of
boundary may not be detected clearly. All the tests
done in the study were carried out using real time
data to optimize the testing period and ensure the
required information has been collected from the test.

Figure-4: Fall of period.

Figure-5: Half slope state.

Material Balance
One of the indications of the tar is not fully sealing
around the reservoir is the increasing reservoir
pressure during field shut-in. Material balance model
was constructed to investigate the degree of aquifer
influx.

To match actual pressure history, an aquifer has to be
connected to the reservoir in addition to the gravity
water injection as energy source. The aquifer
contributed about 25% of total energy to produce the
reservoir. Further tar characterization to determine
the connectivity to the aquifer in each area is
performed using integrated static and dynamic data
(see figure-6).

Figure-6: Material balance model for Reservoir-1

Integration of Data Sources

Sealing Tar Case # 1
Reservoir 3 is developed using peripheral water
injection strategy. In traditional peripheral water
injection strategy, injectors are placed in flanks
directly below the oil water contact. However, in this
reservoir, injectors are placed directly above the tar
oil contact to ensure that injection effect is
transferred to the producing area in the crest.

Figure-7: Reservoir-3 structure map.

Well L is a vertical power water injector located and
completed in the east flank of Reservoir 3 as shown
in figure-7. The MDT results showed super charged
point at the lower part of the reservoir despite of
relatively uniform porosity, indicating tar existence
(see figure-8). The standard open hole log suite will
not be able to differentiate tar from light oil.

Figure-8: FAL log and MDT data for well-L.

A carbon-oxygen log was run across well L after two
years of injection (see figure-9). The dark blue color
in the 3rd track from the left shows the portion of the
rock filled by water when the well was drilled
whereas the light blue indicates the portion of water
based on CO-log. The calculated water saturation is
presented in the middle track between red (original)
and blue (current based on CO-log). The log showed
that oil in the upper section of the well was flushed
away leaving residual oil saturation around 25%. On
the other hand, the water saturation in the bottom
section did not change indicating that the tar around
the well is sealing. The injected water did not
displace this section. This was also confirmed by the
flow meter log shown in figure-10.

Figure-9: C/O log for well-L (2009).

Figure-10: PLT log for well-L.

In order to further confirm the results, an IFO test
was conducted on the subject well, as shown in figure
-11. The derivative plot shows a half slope indicating
a no flow boundary around the well. It is believed to
be the response from sealing tar. The increase in
derivative plot happens immediately after short radial
flow which suggests a very close location of no-flow
boundary. The behavior is consistent as the well did
penetrate the tar in the bottom section of the
reservoir.

Figure-11: IFO test of well-L.

The sealing tar condition is also inferred from the
historical pressure in the offset aquifer observation
well. Well F is an aquifer observation well located
downdip of well L. It shows that pressure in well F
was not affected by production since 1960s (see
figure-12). All of this information confirms that tar
is sealing around this well.

Figure-12: Reservoir-3 historical pressure data.

Sealing Tar Case # 2

Some areas had been evaluated with the integration
of more limited data. The tar sealing degree in
eastern part of Reservoir 3 has been evaluated with
C/O log of well-B. Similar to well-L in the previous
case, C/O of well-B confirms that there is no aquifer
influx at that area. Moreover, the top oil leg is almost
swept by injection water coming from Injector-W1
and W2. Also, the MDT data of well-B confirms the
tar oil contact at that area. (Figure-13, 14 and 15)

Figure-13: Reservoir-3 structure map.

Figure-14: C/O log for well-B (2010).

Figure-15: FAL log and MDT data for well-B.

Sealing Tar Case # 3

Reservoir 4 is developed using similar strategy to that
used in reservoir 3. Well A is a vertical power water
injector that is located and completed in the east flank

of Reservoir 4 .The well is placed in a close
proximity to tar area without cutting any tar section.
This case study has very limited data to investigate
about tar. However, with proper utilization of the
data, the tar sealing degree can be concluded.The
following figure-16 showed an injection fall off test
result. From the pressure derivative (shown in white)
a no-flow boundary was detected which is attributed
to tar effect. The estimated distance to the no-flow
boundary is 1,100 ft which matches the spacing
between the well and the tar-oil contact. This proves
that this area has a sealing tar.

Figure-16: IFO test of well-A.

Non-Sealing Tar Case # 1
The evaluation of the north east flank of reservoir 1
has been by the integration of the historical pressure
performance of Well M, geochemical analysis of the
well M and C/O log of well-I. The trend of the
pressure behavior of well-M, which is located in the
tarry area, is consistent with the pressure behavior in
well-J, H, E and well-Y (see figure-19). That is a
clear sign of reservoir aquifer connectivity on well-M
area. Also, this connectivity is confirmed by
geochemical analysis of well-M which is estimated
around 200,000 ppm. Aquifer water salinity is
varying between 200,000 ppm to 240,000 ppm.
Another source confirms the sealing degree of the tar
on the same area is C/O log of well-I which was run
in 2010(figure-18). There is a significant increase in
the sigma at the top layer of the reservoir. This sigma
increase represents the high salinity water
encroachment or aquifer influx form non sealing tar
at that area.

Figure-17: Reservoir-1 structure map.

Figure-18: C/O log for well-I (2010)

Figure-19: Reservoir-1 historical pressure performance.

Non-Sealing Tar Case # 2

In the south west flank in Arab-1 reservoir has, the
tar sealing degree on that area has been evaluated
based on C/O log of well-S, geochemical analysis of
the same well and by results of IFO test of the
horizontal injector Well-X (figure-20). C/O log run in
2010 of well-S indicated obvious aquifer influx
represented by both dark blue color (open hole log in
1983) and light blue color from CO-log result
(figure-21). The high sigma reading confirms the
source of encroached water. Aquifer influx is

confirmed also by the geochemical analysis of well-S
that had been taken before drilling injector well-II.
The estimated water salinity of well-S was estimated
by 196,769 ppm. Based on the IFO test of well-X, the
derivative plot shows radial flow infinite acting
which means there is no flow boundary in that area at
reasonable distance from the well based on Radius of
Investigation of the IFO test (figure-22). In other
words, the tar is not completely sealing for the flank
on that area.

Figure-20: Reservoir-1 structure map.

Figure-21: C/O log for well-S (2010).

Figure-22: IFO test of well-X

Optimum Reservoir Development

Having determined the location of the non-sealing tar
in each flank area, the simulation model is updated
with this information to provide more representative
model taking into account different and directional
aquifer support. The stream line simulation model
was further run to evaluate the injectors’ efficiency
and injectors’ allocation. The model is also used to
optimize the future injectors location and
requirement. In the area of non-sealing tar with more
significant aquifer influx, it is likely possible to leave
the area without injectors as the aquifer will provide
pressure support and sufficient sweep. On contrary,
the area with full sealing tar may need additional
injector(s) to improve the area sweep efficiency.
Depends on the geological heterogeneity, the sweep
pattern may have been already sufficient without
additional injectors.

Conclusion
Tar characterization is an important part to optimize
the reservoir development plan with water injection.
Integration both static and dynamic data will allow a
better tar characterization which covers both the tar
distribution and sealing degree. It is highly
recommended to optimize the location and number of
injectors required to ensure good areal sweep and
fully utilize the natural support from the aquifer if the
tar is not completely sealing.

References

1. Khalid M. Al-Salem, Said S. Al-Malki, Rabea A.

Ahyed, Peter J. Jones, Peter M. Neumann, Saudi
Aramco. ‘’Real Time Well Placement above a
Tar Mat, Leveraging Formation Pressure While
Drilling and Pyrolytic Oil-Productivity Index
Technologies’’.SPE-113550, SPE
Europec/EAGE Annual Conference and
Exhibition held in Rome, Italy, 9–12 June 2008.

2. Neumann, P. M, Salem, K. M., Tobert, G. P.,
Seifert, D. J., Dossary, S. M., Khaldi, N. A.,
Saudi Aramco, Shokeir, R. M., Halliburton. ‘’
Formation Pressure While drilling Utilized for
Geosteering”. SPE-110940, SPE Saudi Arabia
Technical Symposium held in Dhahran, Saudi
Arabia, 7–8 May 2007.

3. M.H Tobey, H.I. Halpern, G.A. Cole,J.D. Lynn,

J.M. Al-Dubaisi, and P.C. Sese, Saudi Aramco.
‘’ Geochemical Study of Tar in the Uthmaniyah

Reservoir’’. SPE 25609, SPE Middle East Oil
Technical Conference and Exhibition, held in
Bahrain, 3-8 April 1993.

4. A.S. Harouaka and H.K. Asar, KFUPM/RI; A.A.

Al-Arfaj and A.H. Al-Huasaini, KFUPM. ‘’
Characterization of Tar From a Carbonate
Reservoir in Saudi Arabia: Part I- Chemical
Aspect’’. SPE-21004, SPE International
Symposium on Oilfield Chemistry held in
Anaheim, California, February 20-122, 1991.

Acknowledgements

Special thanks go to Hassan Al-Mubarak and Udeh
Pius for their help in publishing this paper.

SPE 161144

Black Oil, Heavy Oil and Tar in One Oil Column Understood by Simple
Asphaltene Nanoscience
Douglas J. Seifert, Saudi Aramco, Murat Zeybek, Chengli Dong, Julian Y. Zuo, Oliver C. Mullins, Schlumberger

, Society of Petroleum Engineers

This paper was prepared for presentation at the Abu Dhabi International Petroleum Exhibition & Conference held in Abu Dhabi, UAE, 11–14 November 2012.

Abstract

A Jurrasic oilfield in Saudi Arabia is characterized by black oil in the crest and with mobile heavy oil underneath and all
underlain by a tar mat at the oil-water contact. The viscosities in the black oil section of the column are fairly similar and are
quite manageable from a production standpoint. In contrast, the mobile heavy oil section of the column contains a large
continuous increase in asphaltene content with increasing depth extending to the tar mat. The tar shows very high asphaltene
content but not monotonically increasing with depth. Because viscosity depends exponentially on asphaltene content in these
oils, the observed viscosity varies from several to ~ 1000 centipoise in the mobile heavy oil and increases to far greater
viscosities in the tar mat. Both the excessive viscosity of the heavy oil and the existence of the tar mat represent major,
distinct challenges in oil production. Conventional PVT modeling of this oil column grossly fails to account for these
observations. Indeed, the very large height in this oil column represents a stringent challenge for any corresponding fluid
model. A simple new formalism to characterize the asphaltene nanoscience in crude oils, the Yen-Mullins model, has enabled
the industry’s first predictive equation of state (EoS) for asphaltene gradients, the Flory-Huggins-Zuo (FHZ) EoS. For low
GOR oils such as those in this field, the FHZ EoS reduces to the simple gravity term. Robust application of the FHZ EoS
employing the Yen-Mullins model accounts for the major property variations in the oil column and by extension the tar mat
as well. Moreover, as these crude oils are largely equilibrated throughout the field, reservoir connectivity is indicated in this
field. This novel asphaltene science is dramatically improving understanding of important constraints on oil production in oil
reservoirs.

Introduction

Huge viscosity gradients in oil columns have an enormous impact on production. Oil flow rate depends inversely on visosity.
Water sweep efficiency is greatly reduced when the viscoity ratio between oil and water exceeds ~5 causing water fingering
instead of sweep. Tar mats at the OWC can preclude any aquifer support and any effectiveness of water injection in the
aquifer. In spite of this overriding impact of viscosity gradients in black oil, heavy oil and tar, there has been very little
understanding of the origin and distribution of these gradients. The reason for this glaring deficiency in petroleum science
and engineering is simple to understand. These viscosity gradients in black oil/heavy oil systems are dominated by asphaltene
gradients. Until recently, there has been no proper theoretical framework for understanding the distribution of asphaltene
gradients in oil reservoirs. For example, the ubiquitious use of the cubic equation of state in reservoir models traces back to
the Van der Waals Equation, which was developed to treat gas-liquid equilibria and has no provisions for handling colloidal
solids such as the asphaltenes. The reason for the inability to treat asphaltenes in thermodynamic models to give asphaltene
gradients is quite clear; there has been long-standing, orders-of-magnitude debate in the asphaltene science literature about
the size of asphaltene molecules (Mullins 2010). If the size is unknown, then the effects of gravity are indeterminate, thus
precluding the ability to model or predict gradients. In short, this deficiency has been resolved; the molecular and colloidal
sizes of asphaltenes in crude oil and in laboratory solvents has been resolved and codified in the Yen-Mullins model
(Mullins, in Press). Indeed, with the resolution, the Flory-Huggins-Zuo Equation of State (FHZ EoS) has been developed
(Freed 2010) and proven to give asphaltene gradients in heavy oils (Pastor 2012), black oils (Betancourt 2007) and
condensates (Elshahawi 2012).

2 SPE 161144

In this paper, a brief review of a new asphaltene formalism is given, and it is shown that the formalism is extremely simple
for low GOR fluids. This simple formalism is applied to a, double plunging anticlinal oil field (4 way closure) that has black
oil in the crest, mobile heavy oil in the flank, and a tar mat at the oil-water contact (OWC). (For this work mobile heavy oil is
defined to have viscosity less than ~1000 cP; in many fields such oil is produced conventionally.) It is shown that the simple
precepts herein properly account for detailed observations. Chemical analysis of the oils and tar show that the simple model
captures the primary features of the data. Indeed, the treatment of such important properties such as viscosity of such a large
volume of oil over such great distances with a simple, effective model might be called stunning. Certain unresolved issues are
discussed within the context of the new foundation of asphaltene science.

Asphaltene Nanoscience

The Yen-Mullins Model. After a lengthy literature debate, the centroid and distribution of asphaltene molecular weights and
sizes has largely been resolved by many different experimental methods and by many different groups around the world
(Mullins, in Press, 2007). In addition, there is now extensive consensus on the nanocolloidal picture of asphaltenes. Most
importantly, there are two, not one nanocolloidal species of asphaltenes, and this fact has a major bearing on asphaltene and
viscosity gradients in oil reservoirs. The dominant molecular and colloidal structures are represented in a model with
prototypical structures, now called the Yen-Mullins model (Sabbah 2011). (Professor Teh Fu Yen founded modern
asphaltene science.) A schematic of the model is shown in Figure 1.

Figure 1. The Yen-Mullins model of asphaltene science showing predominant molecular and colloidal structures
of asphaltenes (Mullins 2010). Left: At low asphaltene concentrations such as in condensates, asphaltenes are
dispersed as molecules. Center: At larger asphaltene concentrations such as in black oils, asphaltene molecules
self assemble forming nanoaggregates with about 6 molecules per nanoaggregate. Right: At even higher
asphaltene concentrations such as in (mobile) heavy oils, asphaltene nanoaggregates self assemble forming
asphaltene clusters with about 8 nanoaggregates.

Nominal sizes of molecules, nanoaggregates and clusters are shown in Figure 1. Generally different fields are seen to exhibit
these sizes within 10% variability. It is not currently know whether there are actual size differences of asphaltene
nanoaggregates from one oil to the next, or whether apparent differences are actually from errors in measurements. It is
important to note that asphaltene molecular properties from many different crude oils are seen to be rather uniform and not
dependent on the specific crude oil (Mullins 2007).

The salient components of this nanoscience model are as follows: asphaltene molecular weights of asphaltenes are ~750
g/mole with a range of 500 g/mole to 1000 g/mole. The predominant molecular achitecture has a large central ring system
with peripheral groups (Figure 1, Left). At low asphaltene concentrations, asphaltene molecules are not aggregated and
asphaltenes are dispersed as molecules; this applies to condensates (Elshahawi 2012). At higher concentrations such as in
black oils, asphaltene molecules self assemble into nanoaggregates (roughly six molecules) with a single, central, disordered
stack of aromatic groups (Figure 1, Center). At yet higher asphaltene concentrations for exampe found in mobile heavy oil,
asphaltene nanoaggregates self assemble into clusters of roughly eight nanoaggregates (Figure 1, Right). These structures
figure prominently when determining the direct effect of gravity on asphaltene gradients.

The Flory-Huggins-Zuo Equation of State. With the size known for these distinct asphaltene species, a 1st-principles model
can be developed for describing asphaltene gradients. The Flory-Huggins equation has been used extensively to describe
asphaltene solubility and asphaltene phase behavior (Buckley 2007). Adding the gravity term to the Flory-Huggins equation
enables calcualtion of asphaltene gradients in reservoirs. This modification yields the powerful Flory-Huggins-Zuo Equation
of State (Zuo 2010).

SPE 161144 3

where OD(hi) is the optical density or oil color typically measured by downhole fluid analysis at height hi in the oil column,
a(hi) is the asphaltene concentration at height hi, va is the molar volume of the asphaltene species of interest, either molecule,
nanoaggregate or cluster (cf. Figure 1), v is the molar volume of the crude oil, g is earth’s gravitational acceleration,  is the
density contrast between the asphaltene and the liquid crude oil, a is the solubility parameter of the asphaltene and  is the
solubility parameter of the crude oil, k is Boltzmann’s constant, and T is temperature. The color of the crude oil scales
linearly with asphaltene content as has been shown in numerous case studies.

The first term in the argument of the exponential is the gravity term. For low GOR black oils and heavy oils, the gravity term
dominates. This gravity term contains Archimedes buoyancy that has had two millenia of validation, vag. The asphaltenes
are negatively buoyant (more dense) than the liquid crude oil. Newton’s force (F=ma) is mass times acceleration. With
Archimedes buoyancy, it is not the total mass of the asphaltene species that matters but rather the effective buoyant mass,
va (volume times density is mass). This buoyant mass is multiplied by g to obtain the gravitational force on the asphaltene
particle. Of course, with larger asphaltene species (with larger volume va) the force is greater. In effect, the energy required to
lift an asphaltene particle off the base of the oil column to some height h equals the graviational force, vag, multiplied by
that height h.

If gravity were the only determinant for the asphaltene distribution, then all asphaltene would be at the base of the oil
column. As Boltzmann showed over 100 years ago, available thermal energy can lift particles to higher energy states. In a
gravitational field, this amounts to thermal energy lifting particles off the floor to some higher height. The Boltzmann
distribution describes the population distribution of ground (E=0) and excited (E) states and has the very simple form: exp{-
E/kT}; this applies to all systems. Most importantly, the Boltzmann distribution represents an equilibrated state. Having
particles in an excited state is not a transient condition; it is an equilibrium condition that will not change with time.

One system that clearly shows the Boltzmann distribution is the earth’s atmosphere. If gravity were the only determinant for
the distribution of air molecules, then all air molecules would be pulled to the surface of the earth and everyone would
sufficate. Thermal energy lifts air molecules to elevations above the earth’s surface. Because air molecules are small (two
heavy atoms in N2, and O2), then available thermal energy lifts air molecules to great height. Here, the air molecules are
suspended in a vaccum, so the Boltzmann distribution is simply exp{-mgh/kT} where m is the weighted molar mass of air
molecules, 80% N2 and 20% O2, and this is what is plotted in Figure 2 with T=298

o Kelvin. Such a simple prediction (Figure
2) closely matches observation.

Figure 2. Calculated atmospheric pressure from the equation exp{-mgh/kT} using the weighted average of the
molecular mass of air molecules (and 298oK) closely matches observations. The prediciton for Mount Everest is
slightly high because of the assumption of constant room temperature. Virtually the same equation applies to
mobile heavy oil gradients substituting the negative buoyancy of asphaltene particles for mass (Mullins 2012).

 
 

      












 
























v
v
v
v
kT

hhgv
exp

hOD

hOD
2
h

2
haa

h
a
h

2 12

4 SPE 161144

For asphaltenes, one replaces “m” by va, thereby using Archimedes buoyancy (essentially because the liquid is
incompressible so buoyancy is used) and the rest of the Boltzmann distribution expression remains the same as for the
atmosphere. For low GOR crude oils, the asphaltene gradient is predominatly just given by the gravity term with all variables
defined above.

Asphaltene molecules contain ~70 heavy atoms, nanoaggregates contain ~400 heavy atoms and clusters contain 3000 carbon
atoms. Consequently, the gravitation gradient of asphaltenes depends critically on the particular asphaltene species. For a
fixed thermal energy (temperature), asphaltene molecules are suspended to considerable height (but much less than air
molecules with only two heavy atoms), nanoaggregates less, and clusters with ~3000 heavy atoms, the least height. Figure 3
shows the gradients for asphaltenes presuming molecules, nanoaggregates and clusters in a crude oil of 0.90 g/cc liquid phase
density and T= 393o Kelvin.

Figure 3. The asphaltene gradient from the gravity term alone for the three asphaltene species from Figure 1 in
the Yen-Mullins model. The large clusters (5.0 nm) show a rapid decline of % asphaltene with height, while the
intermediate nanoaggregates (2.0 nm) and the small molecules (1.5 nm) show a very gradual decline. For low
GOR crude oils, the gravity term tends to dominate the asphaltene gradient, while for large GOR crude oils, the
solubility term in the FHZ EoS can dominate the asphaltene gradient (cf. Equation 1).

In Equation 1, the second and third terms in the argument of the exponential incorporate the effects of entropy. This term
tends to be small so can largely be ignored. The effect of entropy is to randomize or equally disperse the asphaltenes.

The last term in the argument of the expontial of Equation 1 is the solubility term. In chemistry, “like dissolves like” and this
chemical heristic is formalized in the solubility term. For example, water and alcohol are mututally soluble, both have OH
groups. In contrast, oil with its CH groups is dissimilar to water with OH groups; oil and water are not mutually soluble.
Here, being interested in gradients, it is the variation of the solubility term with height in the oil column that is important in
establishing asphaltene gradients. The asphaltene solubility parameter is determined by asphaltene chemical properties and is
invariant aside from a slight temperature dependence (Zuo 2010). If the composition of the liquid oil does not change in an
oil column, then there is no variation of the solubility parameter or solubility term in Equation 1 versus height in the oil
column, the gravity term still dominates.

The primary factor which determines whether or not there is a variation of the liquid oil solubility parameter (for equilibrated
oil columns) is the solution gas content. Solution gas is a colorless gas, asphaltenes are a dark brown solid – they are
chemically very different and don’t dissolve in each other. Asphaltene does not partition to gas making gas colorless.

Asphaltene does not dissolve well in crude oil with high solution gas. If there is a significant solution gas variation in an oil
column, then there will be a large variation of the liquid oil solubility parameter with height, and this can dominate creation
of an asphaltene gradient. Crude oils with low solution gas have largely homogeneous solution gas. For these crude oils the
gravity term dominates. For crude oils with high solution gas (>700 scf/bbl) then there is a signifcant solution gas variation
and the solubility parameter then becomes dominant creating the asphaltene gradient. The GOR variation is largely traceable

 
 
 
 

 







 






kT

hhgv
exp
h
h
hOD

hOD 12a

1a
2a
1
2

SPE 161144 5

to compressibility. Crude oils with high solution gas are compressible. The hydrostatic head pressure of the oil column
increases density at the base of the column; the light components get “squeezed out” of the base creating a solution gas
variation. Low solution gas crude oils are incompressible. For these oils, the hydrostatic head pressure does not increase the
oil density at the base of the column, thus there is no density gradient to drive a compositional gradient.

Black Oil, Heavy Oil and Tar in a Single Reservoir

Mobile Heavy Oil. A large anitclinal structure contains a black oil reservoir of low GOR (Mullins 2012). The asphaltenes
underwent some instability forming a mobile heavy oil section of the oil column and a tar mat at the oil-water contact. Here,
the focus is on the mobile heavy oil and tar mat in the field. A small fraction of the asphaltenes in the black oil were
destabilized possibly by a gas or condensate charge. The destabilized asphaltenes formed clusters, which then accumulated at
the base of the oil column. In a local section of the field spanning roughly 8 kilometers, the asphaltenes are in clusters and are
equilibrated as shown in Figure 4, in total agreement with the reservoir scenario just discussed (Mullins 2012).

Figure 4. A local section of a large anticline with fluid data from three wells. (Top) The asphaltene content versus
height agrees exactly with a simple equilibrium model with only one tightly constrained parameter, the size of the
asphaltene cluster, here determined to be 5.2 nm, closely matching the nominal 5.0 nm clusters size in Figure 1.
(Bottom) the viscosity matches a simple Pal-Rhodes model showing that viscosity is largely exponentially
dependent on asphaltene content.

Figure 4 shows that the simple gravity term of the FHZ EoS fits a huge increase in asphaltene content in a height of 120 feet.
Such a large height in the oil column, and the corresponding 6 fold increase in the asphaltene content from top to bottom
represents a stringent test of any model. The gravity term has only one tightly constrained parameter, the size of the
asphaltene cluster. The fitted data gives 5.2 nm which is a very close match to the nominal 5.0 nm cluster size shown in
Figure 1. Moreover, traditional modeling finds almost no asphaltene gradient because of the lack of any GOR gradient. That
is, traditionally fluid modeling of the mobile heavy oil fails miserably and here is all but useless.

Asphaltene data from eight wells around the entire circumference of the field is shown in Figure 5 (and includes the data
from Figure 4). The fit is very good indicating that the simple Boltzmann distribution of asphaltene clusters accounts for the
huge increase in asphaltene content in the height of the mobile heavy oil section for the entire circumference of the anticline.
The FHZ EoS with the Yen-Mullins model represents a dramatic improvement of understanding mobile heavy oil columns.
Moreover, the measured size of the asphaltene cluster closely matches that found in an Ecuador heavy oil column (5.0 nm)
(Pastor 2012) and in a Gulf of Mexico heavy oil column (5.2 nm) (Nagarajan 2012).

6 SPE 161144

Figure 5. Data from 8 wells shows that the mobile heavy oil column around the entire circumference of the field
matches the simple gravity term of the FHZ EoS with one tighly constrained parameter, the asphaltene cluster
size (here 5.2 nm versus the nominal 5.0 nm in Figure 1). Moreover, the large height of the column yields a factor
of 6 variation of asphaltene content. This field represents an extreme test of our simple model for mobile heavy oil
– and represents the best data set there is (to the knowledge of the authors) to test thermodynamic modeling of
mobile heavy oil.

Figure 5 provides dramatic confirmation that asphaltene clusters are in thermodynamic equilibrium as given by the FHZ EoS.
This fact indicates that this reservoir is in flow communication, that is, it is a connected reservoir (Pfeiffer 2011) Gross
differences in asphaltene concentration in crude oil versus height at different reservoir locations could trigger convection
which would then rapidly smooth out these differences. In additioin, it is plausible that distal parts of the field underwent
similar gravitation accumulations of asphaltene to arrive at current observations of substantial uniformity around the flank.
Asphaltene migration through reservoirs is a subject of current research, and the consequence of this migration is seen
repeatedly.

Above the mobile heavy oil section there is less data. Figure 5 shows that the asphaltene content of the highest samples
shown here is a few percent asphaltene. It is known that the oil in the crest at a much great height in the column is a black oil.
At the asphaltene concentration of a few percent (in this oil) is the point of transition from asphaltene cluster to asphaltene
nanoaggregate. At lower concentrations than a few percent asphaltene, the asphaltenes are dispersed as nanoaggregates.
Figure 3 shows that the gradient of nanoaggrgeates is not so great. Even at much greater heights in this oil column the oil
remains a black oil. If asphaltenes were still within clusters even at low concentrations, then, the huge reduction of asphaltene
concentration with height would continue until there would be almost no asphaltenes, as shown in Figure 3. In other words, if
the huge gradient of asphaltene concentration with height for clusters continued throughout the entire height of the oil
column, then there would be a condensate (no asphaltenes) practically on top of the mobile heavy oil section. This is not
correct and is avoided by asphaltenes being present as nanoaggregates at lower concentrations – thus yielding much small
gradients (cf. Figure 3).

A critical component of the model of gravitation accumulation of asphaltenes is that the ratio of other SARA components are
not changing or changing a rate an order of magnitude slower than the asphaltenes. Figure 6 shows lab data testing this idea.

SPE 161144 7

Figure 6. For the mobile heavy oils plotted in Figure 5, the primary variation is the asphaltene content. The
variation of the other SARA fractions is a factor of 5 to 10 smaller. This data shows consistency with the finding of
a simple gravitational equilibration of asphaltene clusters throught the height and circumference of the field.

There is signifcant scatter in the SARA data, which is not that unusual. Nevertheless, the trends are clear; the primary
variation in the mobile heavy oil samples is their asphaltene content. The variations of ratios of other SARA fractions are five
to ten times smaller. Indeed, if any other fraction were to associate with asphaltenes, one would expect that to be resins.
Clearly, bulk resins are not accompanying asphaltenes. This limits an age old model showing strong asphaltene-resin
association. Figure 6 shows that bulk resins do not associate with asphaltenes. Indeed, very similar results were obtained in a
lab centrifugation experiment of a live black oil.

Figure 7. Live, black oil centrifugation shows a similar result to that found in Figure 6.(Indo 2009) A giant
asphaltene gradient (10x) was formed by centrifuging a live black oil with moderate GOR so both the gravity term
and the solubility term contribute to the asphaltene gradient. Due to the lower asphaltene fracrtion in this black oil,
the asphaltenes are present as nanoaggregates.

8 SPE 161144

Figure 7 shows the results from centrifugation of a live black oil (Indo 2009). This oil had a GOR of 800 scf/bbl so both the
solubility term and the gravity term contribute to establishing the asphaltene and resin gradients. It took one month without
seal loss to achieve equilibrium in this spin. The asphaltene gradient is ~10x while the resin gradient is 25% relative. Thus,
bulk resins are not migrating with the asphaltenes. Analysis of the centrifugation results did conclude that a fraction of the
heaviest resins do associate with the asphaltenes. The picture that emerges is that there is a molecular continuum in going
from resins to asphaltenes. The criterion of n-heptane insolublity to define the asphaltenes captures most but not all of the
crude oil fraction that self-assembles into aggregates (cf. Figure 1) (Indo 2009). The field data presented in Figs. 5 & 6 is
consistent with the centrifugation data of Figure 7. The asphaltenes by far dominate the fraction of crude oil that self-
assembles. Moreover, mobile heavy oils such as those found in this study have large asphaltene fractions that are all in
asphaltene clusters. These clusters equilibrate in the gravitational field yeilding large gradient (cf. Figure 5).

Tar Mat. At the base of the mobile heavy oil section, Figure 5 indicates that a tar mat is found. Several wells were drilled in
order to interesect this tar mat for characterization. The organics were extracted from core sections at different depths in the
tar mat and characterized in terms of SARA fractions. Figure 8 shows an example of the asphaltene content in the extracted
tar versus depth for two separate wells on the same depth scale.

Figure 8. Asphaltene content versus depth for tar wells below the mobile heavy oil section in two wells (cf. Figure
5). The asphaltene content does not vary monotonically in even a single well. In addition, there is no lateral
correlation of asphaltene content in contrast to the mobile heavy oil sections. In the tar mat, there are large
increases and decreases of asphaltene with very small intervals of height.

Figure 8 shows that there is a nearly random variation of tar with height in each of the two “tar” wells. The asphaltenes are
not equilibrated versus height even in a single well, in huge contrast to the heavy oil sections where the asphaltene content is
(or appears) largely equilibrated over the circumference of the mobile heavy oil flank. Figure 8 shows that there is no
correlation of asphaltene concentration laterally for these two wells. The asphaltene content shows large increases and
decreases over very short vertical distances.

The mobile heavy oil section was shown to be characteized by a simple gravitational accumulation and equilibration of
asphaltene versus depth. Figure 8 shows that the asphaltene content of the tar is not even monotonic with depth, not even
approximating any equilibration. It is important to check whether the tar is simply an accumulation of asphaltene in oil or
whether other SARA fractions show large variations in the tar as well.

Figure 8 shows that there is huge variation of asphaltene content in the tar. Since the asphaltene content shows large
variations, the other SARA fractions must also show variations; the sum of all SARA fractions must add to 1. Thus, it is the
ratio of the other SARA fractions that is of interest. Figure 9 shows the ratio of asphaltenes to paraffins, aromatics to
paraffins and resins to paraffins. By far the largest change is in the asphaltene to paraffin ratio. That is, the tar is primarily an
addition of a variable amount of asphaltene to an oil with fixed ratios of paraffins (=saturates), aromatics and resins.

SPE 161144 9

Figure 9. The SARA fractions divided by paraffins versus asphaltene content for samples from two “tar” wells
(saturates = paraffins). By far the largest variation is in the asphaltene/paraffin ratio, the aromatic/paraffin ratio
and the resin/paraffin ratio exhibit much smaller changes. Consequently, the tar can largely be described as
having a large, variable asphaltene content in an oil of fixed composition.

Figure 9 shows that the tar is dominated by a change in asphaltene content in an oil of fixed SARA components. Indeed, the
variation of the asphaltene content is enormous, in one well changing from ~30% to 65%. This picture is consistent with the
origin of tar in this field being due to the gravitational accumulation of asphaltene at the base of the oil column and is
consistent with the same conlcusion drawn for the origin of the mobile heavy oil column immediately above the tar column.
The primary differences between the tar and the mobile heavy oil is that 1) the mobile heavy oils have asphaltene content less
than ~30% (cf. Figure 8) while the tar has asphaltene content greater than ~30% and 2) the mobile heavy oil is vertically and
laterally equilibrated while the tar is not equilibrated even over short vertical distances let alone large lateral distances. Two
factors play an important role in equilibration; distance and viscosity.

Figure 10 shows the viscosity as a function of asphaltene content in an oil phase of fixed composition (Lin 1995) This
viscosity profile is not for that of the oil and tar presented in this paper but nevertheless shows the dependence of viscosity on
asphaltene content.

Figure 10. Viscosity is shown to depend exponentially (or more) on asphaltene content for several different
carbonaceous systems (Lin 1995). For the range of %asphaltene relevant to the mobile heavy oil and tar sections
of the hydrocarbon column, the viscosity in this figure increases by a factor of 100,000,000. Note the hydrocarbon
system is not the crude oil and tar from this field, but the dependence of viscosity on asphaltene is similar.

Figure 10 illustrates a plausible reason why the tar is not equilibrated while the mobile heavy oil directly above the tar is
equilibrated. “Equilibrated” here means that the asphaltene content is varying monotonically versus depth according to
Equation 2. Figure 10 shows that the viscosity is high at 30% asphaltene content and that every 5% increase in asphaltene
content is associated with another huge increase in viscosity. In short, the viscosity in sections of the tar mat are
extraordinairly high precluding equilibration.

10 SPE 161144

Plausible Geoscenarios Matching field Overservation. This Jurrasic reservoir initially contained a black oil. A subsequent
charge of a lighter hydrocarbon could have occured; in a normal burial sequence, the kerogen generates lighter hydrocarbon
with longer times and greater temperature. The lighter hydrocarbon often goes to the top of the reservoir without good mixing
(Stainforth 2004). This lighter hydrocarbon (could even be gas) can diffuse into the oil column and causing instability of the
asphaltene (Elshahawi 2011; Zuo 2011). If the instability is not too great, the asphaltenes can migrate great distances in the
reservoir in some cases going to the base of the reservoir. High concentrations of asphaltenes at or near the oil-water contact
(OWC) can thus occur. One can imagine separate destablizing events yielding pulses of asphaltenes snowing down towards
the OWC. At high asphaltene concentrations, the viscosity increases, and if the viscosity increase is also associated with a
permeability restriction in the reservoir, then low viscosity tar can become trapped or “perched” below high viscosity tar. At
some high asphaltene concentration, there might also be a phase transition yielding a phase very rich in asphaltene phase that
might block pore throats. This is under investigation. If this occurs, this represents a second mechanism that can cause
trapped lower viscosity tar underneath higher viscosity tar. For asphaltene concentrations below 30%, the viscosity is
suffciently low that diffusion enables equilibration of the asphaltene in the mobile heavy oil section.

Conclusions

Traditional equation of state modeling of heavy oils has failed miserably due to 1) the former lack of knowledge about
asphaltene colloidal sizes and 2) the lack of a proper model to treat colloidal solids in crude oil. The Yen-Mullins model of
asphaltene nanoscience specifices the size of three distinct species of asphaltenes: molecules, nanoaagregates, and clusters.
This nanoscience model enables accounting for the effects of gravity which has been incorporated into the Flory-Huggins-
Zuo EoS for asphaltene gradients. Moreover, for mobile heavy oils, only the gravity term contrinbutes significantly to
asphaltene gradients. In a field in Saudi Arabia, a mobile heavy oil rim has been fit using a simple exponential equation (the
Boltzmann distribution) where the asphaltene content varies by a factor of 6 in the column. The simple Boltzmann
distribution of asphaltene clusters accounts for this entire volume of mobile heavy oil. SARA analysis of the crude oil
confirms that the mobile heavy oil column simply has added asphaltene into a crude oil of fixed composition. A tar mat
below the mobile heavy oil does not show monotonic increase of asphaltenes towards the base. This is linked to the
extraordinailry high viscosities within the tar mat. SARA analysis of the tar establishes that, similar to the mobile heavy oil,
there is variable asphaltene added to a crude oil of fixed composition. Gravitational accumulation of asphaltenes at the low
points of the reservoir is consistent with all observations. The application of new asphaltene science to heavy oils is seen to
greatly improve understanding and prediction of reservoir observations.

Refereneces

Betancourt, S.S., Dubost, F.X., Mullins, O.C., Cribbs, M.E., Creek, J.L., and Mathews, S.G. 2007. Predicting Downhole
Fluid Analysis Logs to Investigate Reservoir Connectivity. Paper IPTC 11488 presented at the IPTC Conference, Dubai,
UAE, 4-6 December.

Buckley, J.S. Wang, X., and Creek, J.L. 2007. Solubility of the least-soluble asphaltenes. In Asphaltenes, Heavy Oils and
Petroleomics, ed. O.C. Mullins, E.Y. Sheu, A. Hammami, and A.G. Marshall, New York, New York, Springer.

Elshahawi, H. Latifzai, A.S., Dong, C., Zuo, J.Y. and Mullins, O.C. 2011. Understanding Reservoir Architecture Using
Downhole Fluid Analysis and Asphaltene Science. Paper SPWLA-FF presented at the 52nd Annual Logging Symposium,
Colorado Springs, Colorado, 14-18 May.

Elshahawi, H., Shyamalan, R., Zuo, J.Y., Dong, C., Mullins, O.C., Zhang, D. and Ruiz-Morales, Y. 2012. Advanced
Reservoir Evaluation Using Downhole Fluid Analysis and Asphaltene Flory-Huggins-Zuo Equation of State. Paper prepared
for the 53rd Annual Logging Symposium, Cartagena, Colombia, 16-20 June.

Freed, D., Mullins, O.C., and Zuo, J. 2010. Asphaltene Gradients in the Presence of GOR gradients. Energy & Fuels 24 (7),
3942-3949.

Indo, K., Ratulowski, J., Dindoruk, B., Gao, J., Zuo, J.Y., and Mullins, O.C. 2009. Asphaltene Nanoaggregates Measured in a
Live Crude Oil by Centrifugation. Energy & Fuels 23, 4460–4469.

Lin, M.S., Lumsford, K.M., Glover, C.J., Davison, R.R., and Bullin, J.A. 1995. The Effects of Asphaltenes on the Chemical
and Physical Characteristics of Asphalt. In Asphaltenes: Fundamentals and Applications, ed. E.Y. Sheu and O.C. Mullins,
pp. 155–76. New York: Plenum Press.

Mullins, O.C., Sheu, E.Y., Hammami, A., and Marshall, A.G. ed. 2007. Asphaltenes, Heavy Oils and Petroleomics. New

SPE 161144 11

York, New York: Springer.

Mullins, O.C. 2010. The Modified Yen Model, Energy & Fuels 24, 2179–2207.

Mullins, O.C., Seifert, D.J., Zuo, J.Y., Zeybek, M., Zhang, D. and Pomerantz, A.E. 2012. Asphaltene Gradients and Tar Mat
Formation in Oil Reservoirs. Paper WHOC12-182 presented at World Heavy Oil Conference, Aberdeen, Scotland, 10-13
September.

Mullins, O.C., Sabbah, H., Eyssautier, J., Pomerantz, A.E., Barré, L., Andrews, A.B., Ruiz-Morales, Y., Mostowfi, F.,
McFarlane, R., Goual, L., Lepkowicz, R., Cooper, T., Orbulescu, J., Leblanc, R.M., Edwards, J., and Zare, R.N.. Advances in
Asphaltene Science and the Yen-Mullins Model. Energy & Fuels, in Press.

Nagarajan, N.R., Dong, C., Mullins, O.C. and Honarpour, M.M., Challenges of Heavy Oil Fluid Sampling and
Characterization, Paper SPE 158450 presentented at the SPE ATCE, San Antonio, Texas, 8-10 October.

Pastor, W., Garcia, G., Zuo, J.Y., Hulme, R., Goddyn, X., and Mullins, O.C. 2012. Measurement and EoS Modeling of Large
Compositional Gradients in Heavy Oils, , Paper SPWLA-T presented at the 53rd Annual Logging Symposium, Cartagena,
Colombia, 16-20 June.

Pfeiffer, T., Reza, Z., Schechter, D.S., McCain, W.D. and Mullins, O.C. 2011. Fluid Composition Equilibrium; a Proxy for
Reservoir Connectivity. Paper SPE 145703 presented at the SPE Offshore Europe Oil and Gas Conference and Exhibition,
Aberdeen, UK, 6-8 September.

Sabbah, H., Morrow, A.L., Pomerantz, A.E., and Zare, R.N. 2011. Evidence for Island Structures as the Dominant
Architecture of Asphaltenes. Energy & Fuels 25, 1597-1604, {This paper, among others, introduced the name Yen-Mullins
model after finding confirmation of a major component of the model.}

Stainforth, J.G. 2004. New Insights into Reservoir Filling and Mixing Processes. In Understanding Petroleum Reservoirs:
Toward and Integrated Reservoir Engineering and Geochemical Approach, ed. J.M. Cubit, W.A. England, and S. Larter,
Special Publication, Geological Society, London.

Zuo, J.Y., Mullins, O.C., Freed, D., and Zhang, D. 2010. A Simple Correlation for Solubility Parameters and Densities of
Live Reservoir Fluids. J. Chem. Eng. Data 55 (9), 2964-2969.

Zuo, J.Y., Elshahawi, H., Dong, C., Latifzai, A.S., Zhang, D., and Mullins, O.C. 2011. DFA Assessment of Connectivity for
Active Gas Charging Reservoirs Using DFA Asphaltene Gradients. Paper SPE 145438 presented at the SPE ATCE, Denver,
Colorado, 30 October – 2 November.

SPE 161626

Enhanced Assessment of the Distribution of Organic Matter in
Unconventional Plays and Tarmat in Reservoirs Using a New Laser
Pyrolysis Method on Core
D. Dessort, TOTAL; F. Gelin, TOTAL; D. Duclerc, TOTAL; R. Le-Van-Loi, TOTAL

This paper was prepared for presentation at the Abu Dhabi International Petroleum Exhibition & Conference held in Abu Dhabi, UAE, 11–14 November 2012.

Abstract

Estimation of the amount, the distribution and the quality of the sedimentary organic matter (kerogen) in unconventional plays or
tarmat in reservoirs are often key to a proper assessment of the viability technique and/or commercial of these plays.
Conventional core- or log-based methods for the evaluation of organic carbon content in various types of formation (e.g. tarmat
in reservoir, oil shale and gas shales) are not always satisfactory due to limited resolution and/or non-reliable data.

The acquisition of quantitative and high resolution (centimetric or sub-centimetric) logs of organic carbon on core can be
performed, at the labs or at the coring site, using a continuous power laser (LIPS: Laser Induced Pyrolysis System). The
technology was first developed to identify the presence of tarmat in carbonate reservoirs where the results impacted the
assessment of the reservoir quality, the GOIIP, the presence of permeability barriers and the response of electric logs.

More recently it has been successfully applied on various unconventional studies. For instance it was possible to have a high
resolution, accurate and quantitative measurement of total organic carbon in oil shale or gas shale plays. It then allowed (i)
to estimate the yield of petroleum which can be produced from oil shale Pyrolysis (ii) to extrapolate, model and map the quantity
of the remaining petroleum potential of oil shale deeper in the basin.

This technology will greatly benefit the mapping of these unconventional plays by providing, in particular, a very accurate tool to
calibrate conventional well logs with respect to the distribution of the organic matter.

Introduction:

Laser Pyrolysis on core

The LIPS (Laser Induced Pyrolysis System) is a new instrument being developed (fig.1). It performs the automatic acquisition
of high resolution logs (for instance, centimetric) of organic carbon on cores (e.g. tarmat, source rock, oil shale, shale gas).

The LIPS includes:
 One class IV laser (20 MegaW/m2) providing a continuous infra-red laser beam through an optical fiber.
 One or more detectors (mass spectrometer, PID, FID…).
 3D movable and programmable head which guides the laser beam and collects the material expelled from the core

during the laser shoot.
 An acquisition and data processing system.

The cleaning of the core surface is achieved by a preliminary laser impact at low power. The “L1” signal is produced during

2 SPE 161626

this impact.

A second, high intensity laser shoot impacts exactly the same place, pulverizes and pyrolyses the rock and the organic
matter. Nanoparticles and gases produced by the impact are continuously detected. The detector is not sensitive to the CO and
CO2 released during the thermal decomposition of carbonates. During this impact the “L2” signal is acquired. The intensity
of the signal increases with the quantity of organic material impacted by the laser. This signal is then calibrated with the Rock
Eval, by Fisher Assays or thin sections on limited number of samples.

Because the small size of the laser impact the LIPS preserves the core integrity. The typical duration of an acquisition cycle is
60 seconds. The data acquisition can be performed on hundredth meters of core.

The technology was first developed to identify the presence of tarmat in carbonate reservoirs where the results impacted the
assessment of the reservoir quality, the GOIIP, the presence of permeability barriers and the response of electric logs. More
recently it has been successfully applied on various unconventional studies. This technology will greatly benefit the mapping of
these unconventional plays by providing, in particular, a very accurate tool to calibrate conventional well logs with respect to the
distribution of the organic matter.

Application to tarmat in reservoir

Tarmats are a dark brown to black, thick, semisolid to viscous mixture of heavy hydrocarbons enriched in asphaltenes that
occurs naturally in reservoirs. One key concern regarding tar mats is their impact on reservoir connectivity and petrophysical
properties, their effect on electrical logs and on reserve calculation and water flooding. To gain full control of these issues, many
questions need to be answered:

o What are the processes behind their formation?

o Where are the tarmats located and how many are there?

o What is the effect of bitumen occurrence on reserve calculation, on petrophysical properties and on electric logs ?

o What is their horizontal continuity and can we predict and model tar mats distribution in a reservoir?

o Do they constitute strong permeability barriers or only partial ones?

Conventional methods are not always satisfactory in tar mat detection because:

o The log based methods and the seismic processing do not provide enough resolution and in addition they need to be
calibrated.

o The core based methods rely on analysis of selected core samples using Rock-Eval, Iatroscan and thin sections. The
results depend heavily on the sampling interval, i.e. the wider the sample spacing, the higher the chance of missing
small scale tar mats.

o The difference of color does not necessary indicate the presence of tarmat in carbonate reservoirs.

The fig. 2 shows selected results obtained on a carbonate reservoir in the Middle East. When the porosity is measured the % of
bitumen in rock porosity can be calculated. The study shows that the color of the rock is not always a reliable criterion to
characterize (and quantify) the tarmat. The difference in visual appearance can be the results of difference of mineralogy, rock
type, etc. In particular dolomite can show several different colors because it can include calcite, sulfide minerals, fluorite,
barite….

Application to unconventional resources
It is generally agreed that worldwide conventional petroleum supply will reach its productive limit, peak, and begin a long term
decline. This is one of the reasons why unconventional resources such as oil shale and shale gas and other should be
developed:

 Oil Shales are shallow organic-rich fine-grained sedimentary rock (shale or carbonate), containing significant amounts
of immature to marginally mature kerogen yielding oil in commercial amount upon pyrolysis.

 Shale Gas are fine-grained sedimentary rocks (shale to siltstone) containing a minimum of 0.5 wt % TOC. Gas shales
may be thermally marginally mature to mature and contain biogenic to thermogenic methane. Gas is generated and
stored in situ as both adsorbed (on organic matter) and free gas (in fractures and pores).

Each shale play is unique and heterogeneous. The assessment of unconventional shale resources can use Organic
Geochemistry. The accurate measurement of total organic carbon (TOC) as well as the remaining petroleum potential is
important for numerous reasons:

 For calculating the yield of petroleum which can be produced from oil shale pyrolysis,

 to estimate the quantity of gas adsorbed on organic matter in gas shales (the gas adsorbed onto organic material is

released as the formation pressure is drawn down by the well).

 To extrapolate, model and map the remaining petroleum potential of oil shale deeper in the basin.
Conventional methods for

TOC

and petroleum potential measurement are not always satisfactory because:

SPE 161626 3

 The log based methods and the seismic processing based methods do not provide enough resolution and in addition
they need calibration with conventional methods.

 The core based methods rely on analysis of selected core samples using Rock-Eval. The results depend heavily on the
sampling interval, i.e. the wider the sample spacing, the higher the chance of missing small scale organic-rich layers.

The fig.3 shows selected results obtained on 100 meters of an organic-rich oil shale formation. Owing to the great number of
data points uncertainties and statistics can be calculated as well as the averaged petroleum potential in every interval or facies.
In addition the high resolution allows displaying Milankowitch’s climatic cycles.

Conclusion
The automated Laser Pyrolysis can produce high resolution (centimetric) and quantitative logs of TOC or petroleum potential
over hundredth meters of core.

Fig.1: Laser Induced Pyrolysis System (LIPS)

4 SPE 161626

3808

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Control by
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2 samples / m

LIPS
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(~4500 data points

Z
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Stylolite

3832.5

3833.5

3834.5

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Fig.2: Application of high resolution TOC to tarmat in reservoir

SPE 161626 5

Lithology

~
1

0
0
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e
te

r
s

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Climatic
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Earth
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Eccentricity
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150 KY

2-Frequencies
Model

Fig.3: Application of high resolution TOC to oil shale.

TOC

SPE 163

Heavy Oil and Tar Mat Characterization Within a Single Oil Column Utilizing
Novel Asphaltene Science
Douglas J. Seifert (Saudi Aramco), Ahmed Qureshi, Murat Zeybek, Andrew E. Pomerantz, Julian Y. Zuo and
Oliver C. Mullins (Schlumberger)

This paper was prepared for presentation at the SPE Kuwait International Petroleum Conference and Exhibition held in Kuwait City, Kuwait, 10-12 December 2012.

ABSTRACT

A Jurassic oil field in Saudi Arabia is characterized by black oil in the crest, with heavy oil underneath and all underlain by a
tar mat at the oil-water contact (OWC). The viscosities in the black oil section of the column are similar throughout the field
and are quite manageable from a production standpoint. In contrast, the mobile heavy oil section of the column contains a
large, continuous increase in asphaltene content with increasing depth extending to the tar mat. Both the excessive viscosity
of the heavy oil and the existence of the tar mat represent major, distinct challenges in oil production. A simple new
formalism, the Flory-Huggins-Zuo (FHZ) Equation of State (EoS) incorporating the Yen-Mullins model of asphaltene
nanoscience, is shown to account for the asphaltene content variation in the mobile heavy oil section. Detailed analysis of the
tar mat shows significant nonmonotonic content of asphaltenes with depth, differing from that of the heavy oil. While the
general concept of asphaltene gravitational accumulation to form the tar mat does apply, other complexities preclude simple
monotonic behavior. Indeed, within small vertical distances (5 ft) the asphaltene content can decrease by 20% absolute with
depth. These complexities likely involve a phase transition when the asphaltene concentration exceeds 35%. Traditional
thermodynamic models of heavy oils and asphaltene gradients are known to fail dramatically. Many have ascribed this failure
to some sort of chemical variation of asphaltenes with depth; the idea being that if the models fail it must be due to the
asphaltenes. Our new simple formalism shows that thermodynamic modeling of heavy oil and asphaltene gradients can be
successful. Our simple model demands that the asphaltenes are the same, top to bottom. The analysis of the sulfur chemistry
of these asphaltenes by X-ray spectroscopy at the synchrotron at the Argonne National Laboratory shows that there is almost
no variation of the sulfur through the hydrocarbon column. Sulfur is one of the most sensitive elements in asphaltenes to
demark variation. Likewise, saturates, araomatics, resins and asphaltenes (SARA); measurements also support the application
of this new asphaltene formalism. Consequently, the asphaltenes are very similar, and our new FHZ EoS with the Yen-
Mullins formalism properly accounts for heavy oil and asphaltene gradients.

INTRODUCTION

Previously there have been no proper thermodynamic models for treating asphaltene gradients in reservoirs. The reason of
this deficiency is clear; nobody knew the size of asphaltene particles in oil. Without the size known (or mass, m), Newton’s
gravitational force (F=ma where a is earth’s gravitational acceleration) acting on the asphaltenes is unknown. And without
the ability to model the effect of gravity, one cannot model gradients in the oil reservoirs. This profound deficiency led to
improper understanding of low gas-oil ratio (GOR) black oils and mobile heavy oils. It is widely acknowledged that
condensates have large GOR gradients. That is, compressible reservoir fluids under the force of gravity exhibit density
gradients due to the hydrostatic head pressure squeezing the base of the oil column to higher density. In turn, this density
gradient of the compressible reservoir fluid provides the thermodynamic drive to yield a chemical compositional gradient and
is accurately modeled by cubic equations of state (EoS). Conceptually, one might view this as the methane being squeezed
out of the base of the compressible oil column.

In contrast, low GOR black oils and heavy oils are incompressible. The cubic EoS correctly predicts that the GOR gradients
for low GOR fluids are tiny. That is, the small methane fraction in these fluids is homogeneously distributed. The methane

2 SPE 163291

molecule is so small that thermal energy can lift it to great heights in the reservoir, in the same way that thermal energy can
lift atmospheric molecules diatomic nitrogen (N2) and oxygen (O2) to great heights in the earth’s atmosphere. Likewise, the
methane molecule is so small that Archimedes buoyancy forces are also very small precluding accumulation of dissolved
methane near the top of the column. The cubic EoS also correctly predicts that the GOR of low GOR black oils and heavy
oils is nearly homogeneous. Herein lays the source of the misunderstanding of black oils and heavy oils. The cubic EoS
predicts that the GOR is homogeneous in low GOR black oils and in heavy oils. Consequently, the gross misinterpretation
has been that low GOR black oils and heavy oils “should be” homogeneous (according to the cubic EoS); however, the cubic
EoS, which is derived from the Van der Waals cubic EoS (developed in 1873) is designed to handle gas-liquid equilibria
only. The cubic EoS is not designed to handle nanocolloidal solids of crude oil, the asphaltenes. (Nanocolloidal asphaltenes
means that the asphaltene molecules aggregate into species that are nanometer length scale in crude oils.) The cubic EoS
predictions for the asphaltenes are totally deficient. The reservoir engineering community has depended on the chemical
engineering community for a proper EoS for reservoir fluids. The cubic EoS works so well for gas-liquid equilibria that its
deficiency for solids has largely been ignored. In fact, it is not the gas content that defines black oils and heavy oils, it is the
asphaltene content, but this fact has been obscured due to the inability to model asphaltenes. The chemical engineering
community might have probed thermodynamic models for asphaltene gradients; except that the literature of the chemistry
community describing specific chemical properties of asphaltenes had been in disarray. Incredibly, even so basic a property
such as molecular weight of asphaltene has been the subject of recent debate, where it has varied over six orders of
magnitude (Mullins 2010; Mullins 2011; Mullins et al., 2012a). Fortunately, asphaltene science has undergone a renaissance
in recent years (Mullins 2010; Mullins 2011; Mullins et al., 2012a; Mullins et al., 2007). The molecular and colloidal sizes of
asphaltenes have been resolved, and the industry’s first predictive EoS for asphaltene gradients has been developed and is
discussed.

Asphaltene Nanoscience and Equation of State

In recent years, many of the molecular properties of asphaltenes, especially the distribution of asphaltene molecular weight,
have been resolved (Mullins 2010; Mullins 2011; Mullins et al., 2012a, Mullins et al., 2007). In addition, the aggregate
structures first found for asphaltenes in laboratory solvents are found to also apply to crude oils. In 2010, a simple
representation of the molecular and colloidal structures of asphaltenes in crude oils and laboratory solvents was first
published under the name “the modified Yen model.” Professor Teh Fu Yen was the founder of modern asphaltene science.
This published model has been renamed the Yen-Mullins model (Ruiz-Morales 2009; Sabbah et al., 2011) and is shown in
Fig. 1.

Fig. 1. The Yen-Mullins model of asphaltene science showing predominant molecular and colloidal structures of asphaltenes
(Mullins, 2010). At low concentrations as in condensates, asphaltenes are dispersed as a true molecular solution (left); for black
oils, asphaltenes are dispersed as nanoaggregates of molecules (center); for heavy oils, asphaltenes are dispersed as clusters of
nanoaggregates (right).

With the size known, the effect of gravity can be determined. For the asphaltene EoS, the gravity term is given by
Archimedes buoyancy in the Boltzmann distribution. That is, the asphaltene particles are negatively buoyant in the crude oil
as described by Archimedes buoyancy. Combining the gravity term, with a chemical solubility term and an entropy term we
have the EoS for asphaltene gradients, the Flory-Huggins-Zuo (FHZ) EoS. The Flory-Huggins theory has long been used to
describe polymer solubility, here we use this theory, but we also include a gravity term to treat asphaltene gradients.

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Where OD(hi) is the optical density (color) measured by downhole fluid analysis (DFA) of the fluids at height hi in the oil
column, a(hi) is the asphaltene fraction at that height, a is the molar volume of the relevant asphaltene species (cf. Fig. 1), 

SPE 163291 3

is the molar volume of the oil, R is the ideal gas constant, T is temperature, a is the solubility parameter of the asphaltene, 
is the solubility parameter of the oil, g is earth’s gravitational acceleration, a is the asphaltene density (~1.2g/cc),  is the oil
density. The solubility parameter of the asphaltene can be obtained from literature values, and, in an oil column, the solubility
parameter variation of the oil is primarily due to GOR variations. In the FHZ EoS, the first exponential factor is the solubility
term, the second is the gravity term and the third is the Flory-Huggins entropy term. For low GOR oils, the gravity term
dominates. For moderate GOR oils (1,000 scf/bbl), typically both the solubility term and the gravity term contribute to the
asphaltene gradient. With this foundation, the understanding of many reservoirs is dramatically improved. The FHZ EoS has
now been validated on light condensates to heavy black oil in many case studies. A review and expansion of the FHZ EoS for
reservoir fluids of all types is given by Zuo, et al., (in progress). The primary work flow is to measure the fluid gradient
accurately, especially within the solid, liquid and gas fractions of the reservoir fluids. This measurement is best performed
with downhole presssure measurements and DFA (Mullins, 2008). DFA is a relative new product line in the petroleum
industry. Once the gradients are accurately measured, the cubic EoS for gas-liquid gradients and the FHZ EoS for asphaltene
gradients are employed to understand the nature of the fluid column. By this means, a variety of issues can be addressed
including reservoir connectivity, viscosity profiles, and tar mat character.

One system that clearly shows the Boltzmann distribution is the pressure gradient of the earth’s atmosphere. If gravity were
the only determinant for the distribution of air molecules, then all air molecules would be pulled to the surface of the earth
and everyone would suffocate. Thermal energy lifts air molecules to elevations above the earth’s surface. Because air
molecules are small (two heavy atoms in N2 and in O2), then available thermal energy lifts air molecules to great heights.
Here, the air molecules are suspended in a vacuum, so the Boltzmann distribution is simply exp{-mgh/kT} where m is the
weighted molar mass of air molecules, 80% N2 and 20% O2, and this is what is plotted in Fig. 2 with T=298° Kelvin. Such a
simple prediction (Fig. 2) closely matches observation.

Fig. 2. Calculated atmospheric pressure from the equation exp{-mgh/kT} using the weighted average of the molecular mass of air
molecules (and 298 °K) closely matches observations. The prediction for Mount Everest is slightly high because of the assumption
of constant temperature. Virtually the same equation applies to mobile heavy oil gradients substituting the negative buoyancy of
asphaltene particles for mass.

For asphaltenes, one replaces “m” by va, thereby using Archimedes buoyancy (essentially because the liquid is
incompressible so buoyancy is used) and the rest of the Boltzmann distribution expression remains the same as for the
atmospheric pressure. For low GOR crude oils, the asphaltene gradient is predominantly just given by the gravity term with
all variables defined above.

(2)

Asphaltene molecules contain ~70 heavy atoms, nanoaggregates contain ~400 heavy atoms and clusters contain ~3,000
carbon atoms. Consequently, the gravitation gradient of asphaltenes depends critically on the particular asphaltene species.
For a fixed thermal energy (temperature), asphaltene molecules are suspended to considerable height (but much less than air
molecules with only two heavy atoms), nanoaggregates less, and clusters with ~3,000 heavy atoms, the least height. We are
discussing equilibrium distributions; this means the distribution doesn’t change with time (like the atmospheric pressure
gradient of the Earth), and the distribution does not change dramatically with a small change in applied conditions.

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4 SPE 163291

CASE STUDIES

Asphaltene Nanoaggregates. The first case study to prove the utility of Eq. 2 and ushered in the Yen-Mullins model and the
FHZ EoS was a reservoir depicted in Fig. 3 (Mullins et al., 2007). This field is tilted due to differential uplift from buoyant
salt, Fig. 3 left, and the reservoir contains a low GOR black oil. In the structuring process the reservoir was faulted and the
largest uncertainty in the reservoir is whether these faults are sealing or transmissive. The asphaltene gradient was measured
by DFA in the two primary stacked sands, the red and the blue sands and additionally in a section of the field with a different
sand, the green sand. Equation 2 (the gravity term only from the FHZ EoS) was used to fit the asphaltene gradient in each
sand. All data conformed to the asphaltenes being in the form of nanoaggregates (~2 nm particle size), the middle of the three
species shown in Fig. 1. Since the asphaltene nanoaggregates have a very small diffusion constant, the asphaltenes are
equilibrated (that is, they obey Eq. 2), then the conclusion is that reservoir must be connected in the sense of a production
time frame. Barriers that impede fluid flow would also impede equilibration of the reservoir fluids (Pfieffer et al., 2011).
Each sand, the red, blue and the green, contain equilibrated asphaltenes. Consequently, each of the sands are laterally
connected, but not connected to each other; this has been shown correct with production data (Mullins et al., 2012c). Other
case studies establish the existence of asphaltene nanoaggregates in black oils (Mullins et al., 2012c; Dong et al., 2012).

Fig. 3. Upper and lower horizons are depicted for a deepwater reservoir (Mullins et al., 2007). The stacked sands, the red and blue,
are not in pressure equilibration, therefore are not connected. Each sand (including the green sand) contains equilibrated
asphaltenes; they obey Eq. 2 for asphaltene nanoaggregates. Consequently, each sand is connected laterally and vertically, which
has been proven in production (Dong et al., 2012). This case study proved that the asphaltene nanoscience and thermodynamic
modeling presented herein are correct.

Asphaltene Clusters. The first study to prove the existence of asphaltene clusters in oil reservoirs was in Ecuador (Pastor et
al., 2012). Asphaltene clusters form at high concentration and therefore occur in heavy oils, Fig. 4. The clusters are large and
settle preferentially lower in the oil column, thereby yielding gigantic gradients.

Fig. 4. The asphaltene concentration gradient is about a factor of 2 in ~50 ft for samples from a single well in a field in Ecuador
(Pastor et al., 2012). Clusters form at high asphaltene concentration, here 10% to 20%. The relatively large cluster size, 5 nm, causes
preferential accumulation of these asphaltenes towards the base of the column in accord with predictions of Eq. 2. Here, vertical
connectivity is established and consistent production data.

SPE 163291 5

Recently, a similar heavy oil gradient was observed in deepwater Gulf of Mexico (Nagarajan et al., 2012) confirming the
observations from Ecuador that heavy oils can contain asphaltene clusters equilibrated according to Eq. 2. In addition, there
is a study that has shown the coexistence of nanoaggregates and clusters in a destabilized black oil (Mishra et al., 2012).
Nevertheless, the case study on asphaltene clusters with by far the most comprehensive data has been obtained in Saudi
Arabia (Seifert et al., 2012; Mullins et al., 2012b). The case study is important both from the vantage of understanding
reservoirs containing mobile heavy oil but also from the vantage of advancing petroleum science in many ways.

RESULTS AND DISCUSSION

The subject of this study is a Saudi Arabian oil field that is a doublely plunging anticlinal structure (4 way closure) of
Jurassic age that has black oil in the crest, mobile heavy oil along the peripheral flanks, and a tar mat at the oil-water contact
(OWC) (Seifert et al., 2012; Mullins et al., 2012b). Figure 5 is an illustration of the field and a plausible time evolution of the
fluid processes in the field to arrive at today’s observations. The sequence of events consistent with this scenario is: Initially
the reservoir was filled with black oil, then (1) Some asphaltene instability occurred in the crest possible due to a late gas or
condensate charge, (2) This instability event caused a fraction of the asphaltene nanoaggregates to form the 5 nm clusters.
The clusters, large compared to nanoaggregates, fell in the gravitational field yielding an asphaltic oil at the base of the crest,
(3) This heavy oil then underwent convective flow to the base of the oil column around the rim of the field, and (4) Diffusion
of clusters then enabled equilibrium of the asphaltenes to be attained, in particular, in the heavy oil rim of the field. It is very
important to note that this field has been shown to be connected through extensive production and well testing. Connectivity
is a requirement for true reservoir fluid equilibrium (Pfieffer et al., 2011).

Fig. 5. A large anticline has black oil in the crest, a large gradient of heavy oil in the rim underlain by a tar mat. A time sequence of
events consistent with this scenario is given.

Figure 5 presents a simple time sequence to account for the many varied observations in this field. One of the most important
observations is the equilibration of asphaltene clusters in the heavy oil rim of this field (cf. Fig. 1). In local sections of the
field, Fig. 6 shows the asphaltene content vs. height in the heavy oil section exactly matches expectations from the
Boltzmann distribution (Eq. 2).

Fig. 6. Two local regions of this giant field. Many fluid samples from three wells in the South (blue) exactly match the Boltzmann
distribution for asphaltene clusters with only one tightly constrained parameter, the cluster size, here 5.2 nm. Data from two wells
farther to the North (burgundy) show a consistent gradient but with less asphaltene than in the South. The crest of the field is
toward the South accounting for greater asphaltene accumulation in the Southern rim.

6 SPE 163291

Figure 6 shows excellent agreement of the asphaltene content with the simple Boltzmann distribution in spite of frequent
uncertainties in lab determinations of asphaltene content. The enormous gradient of a factor of six variation of asphaltene
content in 200 ft of height matches the cluster model exactly. Traditional equations of state purport that there is no asphaltene
gradient with height, therefore failing dramatically, with what is a routine occurrence in heavy oil.

This cluster distribution in Fig. 6 (and Fig. 4) is equilibrated. That is, it is not changing with time; gravity pulls the asphaltene
particles down but thermal energy lifts them up, the Boltzmann distribution of Eq. 2 gives the balance. The asphaltene
clusters of Fig. 6 are seen to yield a much bigger gradient (2x in asphaltene concentration in 50 ft) vs. the gradient of Fig. 3
due to asphaltene nanoaggregates (2x in 3,000 ft). For black oils and heavy oils, the viscosity depends exponentially on
asphaltene content, so these asphaltene gradients for heavy oils are very important for production considerations.

Plotting the asphaltene content vs. height for fluid samples from eight wells around the perimeter of the field, Fig. 7, one
finds that the entire heavy oil rim is equilibrated matching the Boltzmann distribution, although there is some localized
scatter in the data. That is, when using all field data from this giant field there is not total equilibrium, as shown in Fig. 6.
Nevertheless, the deviations are small from the equilibration curve depicted in Fig. 7. This may be due to a “recent”
geological structuring event.

Fig. 7. A large, Jurassic anticline oilfield in Saudi Arabia has black oil in most of the field, has a mobile heavy oil rim, which is
underlain by a tar mat (Seifert et al., 2012; Mullins et al., 2012b).The mobile heavy oil rim exhibits a gigantic asphaltene gradient
(10x) as shown. The asphaltene gradient fits the gravity term of the FHZ EoS, with one tightly constrained adjustable parameter, the
asphaltene cluster size, therefore the asphaltenes are equilibrated throughout this huge volume. The fitted data (above) gives a
cluster size of 5.2 nm, very close to the published nominal size of 5.0 nm (cf. Fig. 1).

This Jurassic field of Fig. 5 has experienced some asphaltene instability, most likely in the crest, but not too much instability
as substantial asphaltene remains in the crude oil in the crestal portion of the field. The destabilized asphaltene accumulated
in the flanks. The asphaltenes in the mobile heavy oil section equilibrated laterally over the entire circumference of the field
and in a height of approximately 150 ft. Equilibration ultimately does have a diffusive component; the simple diffusion
relation is Dt = x2 where D is the diffusion constant (which is very small for clusters), t is time, and x is mean distance of
displacement. This field has a large value of x, consequently there is no way this field could equilibrate simply by diffusion.
For example, it would take a trillion years for diffusion to equilibrate asphaltene clusters over 50 km! Figure 5 shows that
convective flow can transport asphaltenes laterally, which is by far the longest distance that must be traveled. Vertically the
asphaltenes need to diffuse hundreds of ft, which can be accomplished in the age of the reservoir. As noted above, convection
must also play a large role in equilibrating this field. The field is Jurassic, and being equilibrated, this field identifies what a
“long time” is for such reservoir process, that is ~200 million years.

Asphaltene Chemical Identity

Because of the former inability of petroleum models to treat asphaltene gradients, it has been claimed that large asphaltene
gradients originate from some as-yet ill-defined, large chemical variation of the asphaltenes from the top to the bottom of the
hydrocarbon column. In contrast, the model presented in the nanoscience section mandates that the chemical composition of
the asphaltenes is the same throughout the column; it is only the concentration of the asphaltenes that changes. That is, in this

SPE 163291 7

model, the asphaltenes are equilibrated according to Eq. 2; which stipulates a concentration variation without a chemical
compositional variation. This is in stark contrast to the expectation for the asphaltenes given the traditional view (with no
corresponding specifics to give the massive heavy oil gradients) vs. the approach using the Yen-Mullins model coupled with
the FHZ EoS that naturally gives large gradients.

One of the best ways to determine differences in asphaltene chemical identity is to characterize the sulfur chemical
speciation. Sulfur in asphaltenes, and in carbonaceous materials in general, can assume several different chemical forms. In
particular, asphaltene sulfur can be in the form sulfide, thiophene and sulfoxide. Other organic forms of sulfur can occur but
have not been observed in asphaltenes, these include sulfones and sulfonates. K-edge X-ray spectroscopy has been used in
the characterization of sulfur in carbonaceous materials (George and Gorbaty, 1989). Figure 8 shows the sulfur K-edge
spectra of several sulfur model compounds. The large single peak in each spectrum corresponds to the sulfur 1s-3p electronic
transition; the energy of this peak depends significantly on the formal oxidation state of sulfur (George and Gorbaty 1989;
Wiltfong, 2005).

Fig. 8. Sulfur X-ray spectra of various sulfur model compounds showing large and simple spectral differences for different types of
sulfur (George and Gorbaty, 1989).

Fig. 9. Typical sulfur X-ray spectra for three asphaltenes showing very different chemical speciation of sulfur among the three
chemical sulfur groups, sulfide, thiophene, and sulfoxide (Waldo et al., 1992).

Typical sulfur X-ray absorption near edge structure (XANES) spectra for asphaltenes are shown in Fig. 9. To perform these
measurements, it is required to have very high resolution data, such as that provided by synchrotrons. Sulfur is present in
relatively low mass percent in the asphaltenes, typically a few mass percent. The sulfur chemistry can be variable while not
impacting the asphaltene chemistry too much. For example, the Yen-Mullins model applies independent of the sulfur
speciation (Mullins, 2010; Mullins, 2011; Mullins et al., 2012). Figure 9 shows three different asphaltenes that exhibit
different sulfur speciation: Cal asphaltene; sulfide 16%, thiophene, 36%, sulfoxide 44%, Fr2 asphaltene; sulfide 20%,
thiophene, 67%, sulfoxide 11%, and Tex asphaltene, sulfide 38%, thiophene, 54%, sulfoxide 4% (Waldo et al., 1992). Other

8 SPE 163291

sulfur oxides contribute small amounts. If the asphaltenes from the Saudi Arabian field are chemically variable, as the old
view has claimed, then the sulfur XANES spectra could show this.

Several asphaltene samples from the heavy oil section of the Saudi Arabian reservoir were taken to the Advanced Photon
Source at the Argonne National Laboratory shown in Fig. 10 (left). The samples were mounted in the vacuum beamline
shown in Fig. 10 (right) (Pomerantz et al., 2012).

Fig. 10. Left, the Advanced Photon Source Synchrotron where sulfur X-ray spectra were acquired for the Saudi Arabian samples.
Right, the sample chamber, vacuum beamline and electronics utilized for acquisition of the sulfur X-ray spectral measurements
(Pomerantz et al., 2012).

Asphaltenes that were selected spanned almost the entire asphaltene concentration range in the heavy oil section, from ~2%
to ~35% asphaltene. The spectra are shown in Fig. 11 (left), and the analysis (right).

Fig. 11. Left, Sulfur X-ray spectra of the Saudi Arabian asphaltenes. Right, analyses showing that the sulfur speciation is almost
identical within error (of a few %). In all samples the thiophene sulfur dominates and with little chemical variation even though these
samples have highly variable asphaltene concentration, 5%-35%.

There is no evidence that the sulfur speciation is changing among the different asphaltene samples shown in Fig. 11. These
results support the simple model presented herein, that the gradients observed in the heavy oil rim of this field are due to the
concentration profile of asphaltene clusters as given by Eq. 2 and the Boltzmann distribution, and are not due to some
heretofore unidentified, unknown chemical variation of asphaltenes in the oil column.

Further chemical analysis of these samples is planned. We note that nitrogen XANES spectroscopy (Mitra-Kirtley et al.,
1993) and carbon X-ray Raman spectroscopy (Bergmann et al., 2003) on asphaltene samples are known to show less
chemical variation than sulfur, so sulfur is the element of choice for elucidating chemical variation of asphaltenes.

SARA Analysis

Heavy Oil. According to the simple model of the Boltzmann distribution, along with the simple description of events

SPE 163291 9

presented in Fig. 5, the heavy oil in the reservoir should simply be the black oil plus added asphaltene, where this addition
took place due to asphaltene instability forming clusters that then end up at the base of the reservoir. We can check the heavy
oil composition in terms of the SARA components: saturates, araomatics, resins and asphaltenes, shown in Fig. 12.

Fig. 12. For the heavy oil samples, on the Y-axis, the SARA ratios (aromatics vs. saturates, resins vs. saturates and asphaltenes vs.
saturates) are plotted vs. the %asphaltene of the sample on the X-axis. For the heavy oils, the only ratio that changes with the
%asphaltene is the asphaltene vs. saturate ratio, the other SARA ratios remain fixed. This means that the heavy oils are comprised
of oil+asphaltene without any additional aromatics or resins. This is exactly the expectation for our simple model of the formation of
heavy oil by asphaltene cluster addition to the black oil.

Figure 12 shows the analysis of the SARA data of the heavy oils from this field. The asphaltene content increases
dramatically in the heavy oil column from 2% to 35%. It is not sufficient to plot the different individual SARA percentages
against the asphaltene fraction because as the asphaltene fraction increases greatly the other SARA fractions must decrease,
thereby disguising dependencies. To illustrate dependencies of SARA fraction variation with asphaltene content, the ratios of
SARA fractions vs. the saturate fraction are plotted. The only significant variation of the SARA ratios vs. asphaltene content
is the only ratio involving the asphaltenes, the other ratios remained fixed. Consequently, the heavy oils do indeed appear to
be composed of black oil plus asphaltene. This is consistent with the simple model indicated in Fig. 5 where the heavy oil is
formed by accumulation of asphaltene clusters into black oil.

Tar Mat. When the asphaltene content exceeds 35%, the oil forms a tar mat. Unlike the mobile heavy oil, the asphaltene
content in the tar mat in this field is not even slightly equilibrated. The asphaltene content in the tar mat ranges from ~35% to
~60%, with large variations up and down in concentration within a few feet within individual wells, Fig. 13. The explanation
put forth is that the tar mat represents a phase transition; that the asphaltene is not soluble in crude oil in all proportions.

10 SPE 163291

Fig. 13. %Asphaltene vs. relative depth for both the mobile heavy oil (top) and tar mat (bottom). The blue arrows identify the same
depth in the two plots. The heavy oil shows a monotonic increase of asphaltene with depth around the entire periphery of the field,
while in the tar mat, the %asphaltene is not monotonic in individual wells (specific plot markers).

As the asphaltene continues to enter low points in the reservoir by accumulation of 5 nm asphaltene clusters, the crude oil can
become supersaturated in asphaltene content. The asphaltenes deposit out onto the grain surfaces, essentially as
heterogeneous nucleation. As this process continues, the pore throats become occluded and no further fluid exchange can take
place. That is, tar mats are not equilibrated in a couple of feet (vertical) because the carbonaceous grain coating in the tar mat
blocking the pore throats precludes any mass exchange necessary for equilibration, whereas the heavy oil is equilibrated over
many tens of km (lateral). Precepts in this explanation are under study.

Fig. 14. SARA ratios vs. %asphaltene for samples from two separate tar mat wells. As with heavy oils, the SARA ratios show that the
tar equals black oil plus asphaltene, exactly as expected for the simple scenario presented in Fig. 5.

The tar mat is similar to the mobile heavy oil in that it evidently equals asphaltene plus black oil as shown in Fig. 14. The tar
mat SARA analyses show that only the %asphaltene is variable, the other SARA fractions exhibit invariant ratios. This is
consistent with tar mat forming from the addition of asphaltenes in the form of clusters to the base of the oil column. The
mechanism shown in Fig. 5 is the most plausible scenario for this process.

CONCLUSIONS

Extensive fluid data from a Jurassic age Saudi Arabian oil field has been examined. The reservoir consists of a black oil in
the crest with a heavy oil rim around the entire periphery of the field and underlain by a tar mat. Live fluid data from multiple
depths in eight wells in the heavy oil section were analyzed along with fluid data from the tar mats in seven wells. The

SPE 163291 11

asphaltene gradient in the heavy oil column was found to be a factor of 10 in percentage content in 150 ft of height and
largely invariant laterally. The nanoscience model of asphaltenes, the Yen-Mullins model, coupled with the FHZ EoS for
asphaltene gradients accounts for this large asphaltene gradient with only one tightly constrained variable, the cluster size;
found here to be 5.2 nm vs. the nominal 5.0 nm. This case study is perhaps the most stringent test case imaginable,
encompassing an entire oil field, and the new nanoscience model and theory work very well. In addition, this equilibrium
model predicts that the asphaltene chemical composition to be the same throughout the reservoir. A detailed test of this
prediction was performed by examining the asphaltene sulfur chemical structures using X-ray spectra acquired at the APS
synchrotron at the Argonne National Labs. Sulfur chemistry, a known sensitive test, confirmed the asphaltene compositional
uniformity, that only the asphaltene concentration is variable, not its composition. Analyses of the SARA results also confirm
primary precepts of this new asphaltene formalism. The tar mat was also explored; corresponding data from the tar mat is
consistent with a simple asphaltene instability model to account for its basic measured properties. The confluence of new
asphaltene science and new understanding of reservoirs is advancing each discipline and presents new opportunities of cross
fertilization.

REFERENCES

Bergmann, U., Groenzin, H., Mullins, O.C., Glatzel, P., Fetzer, J. and Cramer, S.P. 2003. Carbon K-edge X-ray Raman
Spectroscopy Supports Simple yet Powerful Description of Aromatic Hydrocarbons and Asphaltenes. Chemical Physical
Letters, 369, 184-191.

Dong, C., Petro, D., Latifzai, A.S., Zuo, J.Y., Pomerantz, A.E. and Mullins, O.C. 2012. Evaluation of Reservoir Connectivity
from Downhole Fluid Analysis, Asphaltene Equation of State Model and Advanced Laboratory Fluid Analyses. Paper SPE
158838 presented at the SPE-ATCE, San Antonio, TX, 8-10 October.

George, G.N. and Gorbaty, M.L.J. 1989. K-edge X-ray Absorption Spectroscopy of Petroleum Asphaltenes and Model
Compounds. American Chemical Society, 111, 3182-3186.

Mishra, V., Hammou, N., Skinner, C., MacDonald, D., Lehne, E., Wu, J.L., Zuo, J.Y., Dong, C. and Mullins, O.C. 2012.
Downhole Fluid Analysis and Asphaltene Nanoscience Coupled with VIT for Risk Reduction in Black Oil Production. Paper
SPE 159857 presented at the SPE-ATCE, San Antonio, TX, 8-10 October.

Mitra-Kirtley, S., Mullins, O.C., Chen, J., van Elp, J., George, S.J. and Cramer, S.P. 1993. Determination of the Nitrogen
Chemical Structures in Petroleum Asphaltenes using XANES Spectroscopy. Journal American Chemical Society, Vol. 115,
252.

Mullins O.C., Betancourt, S.S., Cribbs, M.E., Creek, J.L., Andrews, B.A., Dubost, F. and Venkataramanan, L. 2007. The
Colloidal Structure of Crude Oil and the Structure of Reservoirs. Energy & Fuels, 21, 2785-2794.

Mullins, O.C., Sheu, E.Y., Hammami, A. and Marshall, A.G. ed. 2007. Asphaltenes, Heavy Oils and Petroleomics. New
York, New York: Springer.

Mullins, O.C. 2008. The Physics of Reservoir Fluids; Discovery through Downhole Fluid Analysis. Schlumberger Press,
Houston.

Mullins, O.C. 2010. The Modified Yen Model, Energy & Fuels, 24, 2179-2207.

Mullins, O.C. 2011. “The Asphaltenes.” Annual Review of Analytical Chemistry, Vol. 4, 393-418.

Mullins, O.C., Sabbah, H., Eyssautier, J., Pomerantz, A.E., Barré, L., Andrews, A.B., Ruiz-Morales, Y., et al. 2012.
Advances in Asphaltene Science and the Yen-Mullins Model. Energy & Fuels, 26, 3986-4003.

Mullins, O.C., Zuo, J.Y., Dong, C., Andrews, A.B., Elshahawi, H., Pfeiffer, T., Cribbs, M.E., Pomerantz, A.E. 2012.
Downhole Fluid Analysis Coupled with Asphaltene Nanoscience for Reservoir Evaluation. Paper SPWLA-CCC presented at
the 53rd Annual Logging Symposium, Cartagena, Colombia, 16-20 June.

12 SPE 163291

Nagarajan, N.R., Dong, C., Mullins, O.C. and Honarpour, M.M. 2012. Challenges of Heavy Oil Fluid Sampling and
Characterization. Paper SPE-158450 presented at the SPE-ATCE, San Antonio, TX, 8-10 October.

Pastor, W., Garcia, G., Zuo, J.Y., Hulme, R., Goddyn, X. and Mullins, O.M., 2012. Measurement and EoS Modeling of
Large Compositional Gradients in Heavy Oils, Cartagena, Colombia. SPWLA Paper T presented at the 53rd Annual Logging
Symposium, Cartagena, Colombia, 16-20 June.

Pfeiffer, T., Reza, Z., McCain, W.D., Schechter, D.S. and Mullins, O.C. 2011. Determination of Fluid Composition
Equilibrium – a Substantially Superior Way to Assess Reservoir Connectivity than Formation Pressure Surveys. Paper
SPWLA-EEE presented at the 52nd Annual Logging Symposium, Colorado Springs, Colorado, 14-18 May.

Pomerantz, A.E., Seifert, D.J., Qureshi, A., Bake, K., Craddock, P., Zeybek, M., Zuo, J.Y., Mitra-Kirtley, S., Kodalen, B.,
Bolin, T. and Mullins, O.C. Sulfur Speciation in Asphaltenes from a Compositionally Graded Oil Column. Manuscript in
preparation.

Ruiz-Morales, Y. 2009. Aromaticity in Pericondensed Cyclopenta-Fused Polycyclic Aromatic Hydrocarbons Determined by
Density Functional Theory Nucleus-Independent Chemical Shifts and the Y-rule; Implications in Oil Asphaltene Stability.
Canadian Journal of Chemistry, Vol. 87, 1280-1295.

Sabbah, H., Morrow, A.L., Pomerantz, A.E. and Zare, R.N. 2011. “Evidence for Island Structures as the Dominant
Architecture of Asphaltenes,” Energy Fuels, Vol. 25, 1597-1604.

Seifert, D.J., Zeybek, M., Dong, C., Zuo, J.Y. and Mullins, O.C. 2012. “Black Oil, Heavy Oil and Tar in One Oil Column
Understood by Simple Asphaltene Nanoscience,” Paper SPE 158838 presented at the Abu Dhabi International Petroleum
Exhibition & Conference, Abu Dhabi, U.A.E., 11-14 November.

Waldo, G.S., Mullins, O.C., Penner-Hahn, J.E. and Cramer, S.P., 1992. “Determination of the Chemical Environment of
Sulphur in Petroleum Asphaltenes by X-Ray Absorption Spectroscopy,” Fuel, Vol. 71, 53-57.

Wiltfong, R., Mitra-Kirtley, S., Mullins, O.C., Andrews, B.A., Fujisawa, G., Larsen, J.W. 2005. “Sulfur Speciation in
Different Kerogens by XNES Spectroscopy,” Energy & Fuels, Vol. 19, No. 5, 1971-1976.

Zuo, J.Y., Mullins, O.C., Freed, D.E., Elshahawi, H., Dong, C. and Seifert, D.J. “Advances in the Flory-Huggins-Zuo
Equation of State for Asphaltene Gradients and Reservoir Evaluation,” Submitted to Energy & Fuels.

SPE

163292

Permeable Tar Mat Formation Within the Context of Novel Asphaltene
Science
Hadrien Dumont, Vinay Mishra, Julian Y. Zuo, Oliver C. Mullins (Schlumberger)

This paper was prepared for presentation at the SPE Kuwait International Petroleum Conference and Exhibition held in Kuwait City, Kuwait, 10-12 December 2012.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of th e paper have not been
reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessar ily reflect any position of the Society of Petroleum Engineers, its
officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohi bited. Permission to
reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

ABSTRACT

Tar mats at the oil-water contact (OWC tar mats) in oilfield reservoirs can have enormous, pernicious effects on production

due to possibly preventing of any natural water drive and precluding any effectiveness of water injectors into aquifers. In

spite of this potentially huge impact, tar mat formation is only now being resolved and integrated within advanced asphaltene

science. Herein, we describe a very different type of tar mat which we refer to as a “rapid-destabilization tar mat”; it is the

asphaltenes that undergo rapid destabilization. To our knowledge, this is the first paper to describe such rapid-destabilization

tar mats at least in this context. Rapid-destabilization tar mats can be formed at the crest of the reservoir, generally not at the

OWC and can introduce their own set of problems in production. Most importantly, rapid-destabilization tar mats can be

porous and permeable, unlike the OWC tar mats. The rapid-destabilization tar mat can undergo plastic flow under standard

production conditions rather unlike the OWC tar mat. As its name implies, the rapid-destabilization tar mat can form in very

young reservoirs in which thermodynamic disequilibrium in the oil column prevails, while the OWC tar mats generally take

longer (geologic) time to form and are often associated with thermodynamically equilibrated oil columns. Here, we describe

extensive data sets on rapid-destabilization tar mats in two adjacent reservoirs. The surprising properties of these rapid-

destabilization tar mats are redundantly confirmed in many different ways. All components of the processes forming rapid-

destabilization tar mats are shown to be consistent with powerful new developments in asphaltene science, specifically with

the development of the first equation of state for asphaltene gradients, the Flory-Huggins-Zuo Equation, which has been

enabled by the resolution of asphaltene nanostructures in crude oil codified in the Yen-Mullins Model. Rapid-destabilization

tar mats represent one extreme while the OWC tar mats represent the polar opposite extreme. In the future, occurrences of tar

in reservoirs can be better understood within the context of these two end members tar mats. In addition, two reservoirs in the

same minibasin show the same behavior. This important observation allows fluid analysis in wells in one reservoir to indicate

likely issues in other reservoirs in the same basin.

INTRODUCTION

Tar mats are of critical importance in the oilfield; however, mechanisms of tar mat formation have not been well understood.

The confusion extends into terminology where terms bitumen and tar are sometimes meant to imply distinct yet ill-defined

provenances. In large part, this is due to the prior lack of detailed understanding of asphaltenes. Indeed, it is only recently that

the nanoscience of asphaltenes in crude oil has been largely resolved and codified in the “Yen-Mullins Model”.[1,2] In turn

this has led to the Industry’s first predictive equation for asphaltene gradients, the Flory-Huggins-Zuo Equation of State (FHZ

EoS).[3,4] With this new understanding, asphaltenes are now treated within standard methods of physical chemistry much as

gas-liquid equilibria of crude oils (GOR, bubble point, etc.) have long been treated with standard physical chemistry models

such as the cubic equation of state. The cubic EoS has been extended to try to treat the solid asphaltenes because there had

been no alternative. However, the cubic EoS was never designed to treat colloidal solids such as asphaltenes; consequently,

these cubic EoS methods to treat asphaltenes fail.[1-4] Just as gradients (e.g. GOR gradients) and phase behavior for gas

liquid equilibria are treated by the cubic EoS, we now have an equation, the FHZ EoS that treats both asphaltene gradients in

reservoir fluids as well as phase behavior of asphaltenes. Thus, asphaltenes whether in carbonaceous deposits or dissolved (or

colloidally suspended) in crude oil are now treated within one scientific framework. Consequently, tar mats are now

understood within the framework of reservoir fluids and corresponding geologic processes.

2 SPE

By definition, asphaltenes are destabilized by light alkane addition to a crude oil. (Asphaltenes are defined as being the

component of crude oil, or carbonaceous material that is insoluble in n-heptane and soluble in toluene.) As has long been

known, the lightest alkane, methane, causes just such asphaltene precipitation. Moreover, light ends are often added to the

reservoir late in the charge process. In a normal burial sequence, the lightest charge is expelled at the latest times from the

kerogen with longer catagenesis times, and higher catagenesis temperatures. This is similar to refining where higher

temperatures and longer times yields cracking to lighter hydrocarbons. Second, in colder reservoirs biogenic methane in

substantial quantities can enter the reservoir, again, after the oil charge, the food for the microbes. The light alkane or gas

enters the reservoir in high permeability streaks and goes right to the top of the fluid column without fluid mixing except in

the vicinity of the charge path or plane,[5] a schematic of this process is shown in Fig. 1.[6] In this process, the gas migrates

to the top of the oil column, then diffuses down from the top of the oil column, asphaltenes are destabilized at the gas-oil

contact and increasingly at lower points with time as the solution gas gradually increases with methane diffusion.

Figure. 1. Charge history mechanism determines the hydrocarbon distribution in the Stainforth model.[5] Initially,
the heaviest charge from the kerogen charges the reservoir. Later in the charging process, lighter hydrocarbons
are expelled from kerogen. These light hydrocarbons migrate to the top of the oil column through high
permeability streaks and do not mix with in situ reservoir fluids in this process. Given sufficient time, equilibration
can take place. Light ends can destabilize asphaltenes; this destabilization process is seen to occur at the top of
the oil column, not at its base where tar mats are frequently found.

The former lack of understanding of asphaltene nanoscience created considerable confusion as to how and where asphaltene

destabilization events take place. Specifically, it has long been assumed that asphaltene instability occurred where asphaltene

deposits are now found. Along these lines, in the laboratory, light alkane addition to crude oil destabilizes the asphaltenes

then and there. Thus, regional tar mats have been thought to form at the OWC due to gas entry at the oil-water contact

(OWC). This concept violates well known processes in reservoirs (cf. Fig. 1) and recent work has clarified actual reservoir

processes leading to tar mat formation. The reservoir is not like a distillation tower with bubble plates ensuring equal gas

entry everywhere at the OWC. This is in contrast to the laboratory where fluid mixing is simple and is part of asphaltene

flocculation procedures. The crucial point is that asphaltenes can migrate great distances in reservoirs even when partially

destabilized. Previously it had been thought that with asphaltene instability, there can only be floc formation, and all agree

that flocs cannot migrate through reservoirs. We now understand that there are two nanocolloidal asphaltene species, and that

instability can result in formation of the larger nanocolloidal asphaltene species; this nanocolloidal particle can migrate

through reservoirs.[7] This will be discussed in greater detail in the Asphaltene Nanoscience section.

16329

SPE 163292 3

In addition, tar mats can be as thick as 60 meters. Those who propose that the tar mat forms at the OWC because asphaltene

instability is mediated by a process involving water never explain how it is that the tens of meters of tar do not seal off the oil

from the water. (Many high-rise buildings have a thin layer, ≤ 1cm, of roofing tar sealing off water entry into the building,

tens of meters of tar are not needed for this purpose!) Once this sealing process takes place, the tar mat cannot grow further

IF tar mat growth depends on water. For example, if 3 meters of OWC tar mat creates this seal, then there should be no

further growth of the tar mat. In fact, this conventional explanation is incorrect; generally, tar mat growth is not mediated by

water. Frequently, tar mat growth occurs via asphaltene instability at the top of the oil column with asphaltene transport

through the reservoir to the flank. The thickness of tar mats in this process is rather unconstrained and is consistent with

thicknesses exceeding 30 meters that are frequently observed. The key improvement in understanding is the mechanism of

transport of unstable asphaltene through the reservoir. This is where new asphaltene science, the Yen-Mullins model, has had

a significant impact. Instability of asphaltene nanoaggregates can yield asphaltene clusters which are only 5 nm in size so

easily migrate through porous media, yet are large enough to accumulate at the base of the oil column.[1-4]

Case Studies. Gas addition to reservoirs mostly occurs through spatially restricted high permeability channels without

mixing with existing fluids in the reservoir. The added gas quickly finds its way to the top of the reservoir or at least to a

local high point in the reservoir. With new gas addition at the top of the reservoir, the gas diffuses down and expels

asphaltene from the oil in this process. Figure 2 shows a reservoir caught in the middle of such a process. One can visually

see lack of asphaltenes at the top of the reservoir, that is where asphaltene instability occurs.[8]

Figure. 2. A black oil reservoir had a substantial late gas charge which gave rise to a huge color gradient (see
actual dead oil bottles on the right), and a huge gradient in solution gas.[8] The late gas charge quickly went to
the top of the black oil column without mixing into this reservoir crude oil. Subsequently, the gas diffused down as
indicated by white arrows in the cartoon on the right. Substantial solution gas near the gas-oil contact caused the
asphaltenes to be expelled – thus the oil has very little color at the top of the column. The gas has not had time to
diffuse to the base of the column, thus, there, the solution gas is low, and the oil maintains a high concentration of
asphaltenes. In the figure, the calculated color (asphaltene) gradient is shown from the FHZ EoS coupled with a
methane diffusion term and agrees with the color gradient evident in the samples.[8]

Figure 2 shows a reservoir where substantial gas has diffused only partially into the oil column; thus expelling asphaltene

only towards the top of the column. It would take geologic time for this diffusion process to transport methane to the base of

the column. If the late gas charge has sufficient time to diffuse to the base of the oil column then the asphaltenes can become

very concentrated at the base and form a tar mat. Figure 3 shows just such a reservoir. In this case, large volumes of gas

entered the oil column expelling most of the asphaltenes. The asphaltenes migrated ahead of the gas front, most likely by

convective waves. The asphaltene rich fluid went to the base of the column, and was trapped there by cement underneath.

The gas caught up to the asphaltenes creating a condensate (high GOR, little asphaltene) and a tar mat underneath. This tar

mat is on cement so has nothing to do with water. The conventional explanation that the tar mat forms at the OWC because

that is the location of the asphaltene instability obviously does not apply here and is generally incorrect. For oil reservoirs at

or very close to surface, biodegradation and even evaporative and water washing processes can yield very viscous oil, such as

the Athabasca tar sands, but that is distinct from a reservoir with a tar mat.

4 SPE

Figure 3. A reservoir with a late gas charge. The gas went to the top of the reservoir and diffused down to the
base of the oil column (depicted by long white arrows in the left cartoon).[9] The expelled asphaltenes stayed
ahead of the diffusive gas front, presumably by convective currents. The tar mat is visible in core sections, the
fluorescence image of the core (right image in each of the six core panels) shows strong fluorescence of the
condensate immediately above the tar mat with no fluorescence (bottom two core panels with visible image, left;
fluorescence, right). This tar mat rests on cement, water had nothing to do with this tar mat formation.[9]

Equilibration of reservoir fluids is a geologically slow process.[10] Recently, a reservoir was reported to have two separate

gas caps that could each be seen in seismic data. Figure 4 shows an image of this reservoir.[11] Logging data from the two

wells established that the two separate gas-oil contacts (GOCs) differ by 20 meters in the true vertical depth (TVD).[11]

Either the reservoir is compartmentalized, each compartment with its own gas cap, or there is lateral disequilibrium in

solution gas in a single reservoir with two gas caps.

Figure 4. (Left) A reservoir with two separate gas caps (brown) over oil (green); the two gas-oil contacts differed
by 20 meters true vertical depth.[11] (Right) The continuous asphaltene gradient measured in three wells
indicated connectivity which was proven in production. There is lateral disequilibrium of solution gas; the diffusive
processes to cause equilibration in the reservoir are very slow even compared to geologic time.[10]

The downhole fluid analysis (DFA) data clearly established that there is a continuous color gradient across the reservoir (in

three wells including the two depicted in the Fig. 4).[11] This color gradient matched the FHZ EoS analysis and indicates that

the reservoir is connected. The heavy ends are equilibrated across the field – this requires connectivity. Production proved the

reservoir is connected.[11] Thus, the reservoir fluid is out of equilibrium for gas-liquid properties such as GOR. When gas

charges into the reservoir it can get stuck in local reservoir highs. To equilibrate the two gas caps, gas from the lower GOC

gas cap would have to dissolve in the oil, diffuse across the reservoir and release into the higher GOC gas cap. This process is

extremely slow, consequently the reservoir gas-liquid properties are out of equilibrium. In particular, if there is a late gas

phase charge into the reservoir, it is likely that gas would collect in local highs and would remain out of equilibrium for a

long time. In contrast, the asphaltenes only partition to the liquid phase so can equilibrate provided that the reservoir has good

connectivity. This is indeed what happened in the oilfield shown in Fig. 4.[11]

163292

SPE 163292 5

Another reservoir experienced only a small light end influx into a black oil reservoir. With just a slight destabilization event,

but with time for equilibration to take place, black oil can remain in the crest, whereas heavy oil and a tar mat can reside in

the low points or the flank or rim of the field. Figure 5 shows a cartoon of a very large Jurassic anticline field with a four way

dip closure. There was a slight asphaltene instability leading to asphaltene migration and accumulation of the asphaltenes in

the rim of the field. Most likely, this migration took place in convective waves of asphaltene rich oil (thus high density).

Diffusion would require on the order of one trillion years (much longer than the age of the universe), so diffusion alone

cannot account for asphaltene migration in this reservoir!

Figure 5. A large, Jurassic anticline oilfield in Saudi Arabia has black oil in most of the field, has a mobile heavy oil
rim which is underlain by a tar mat.[12,13] The mobile heavy oil rim exhibits a gigantic asphaltene gradient (6x) as
shown. The asphaltene gradient fits the gravity term of the FHZ EoS, with one tightly constrained adjustable
parameter, the asphaltene cluster size, thus the asphaltenes are equilibrated throughout this huge volume. The
fitted data (above) gives an asphaltene cluster size of 5.2 nm, very close to the published nominal size of 5.0 nm
(cf. Fig. 6).

This Jurassic field of Fig. 5 had experienced some asphaltene instability, but not too much as substantial asphaltene remains

in the crude oil, and the GOR is low – both conditions in contrast to conditions depicted in Fig. 3. The destabilized asphaltene

accumulated in the flank. The asphaltenes in the mobile heavy oil section equilibrated laterally over the entire tens of

kilometers circumference of the field and in height of 50 meters as shown in Fig. 5. Equilibration ultimately does have a

diffusive component; the simple diffusion relation is Dt =

2
where D is the diffusion constant (which is very small for

asphaltene clusters), t is time, and x is mean distance of displacement. This large field has a large value of x >>10 kilometers;

consequently, a very long time is needed to reach equilibration. As noted above, convection must also play a large role in

equilibrating this field. The field is Jurassic, and being equilibrated, this field identifies what a “long time” is for such

reservoir processes, it is ~150 million years.[13]

Where the asphaltene content exceeded 35%, this is tar mat. For asphaltene concentration above 4% and below 35%, this is

mobile heavy oil (viscosity < 1000 centipoise). We also note that unlike the mobile heavy oil, the asphaltene content in the

tar mat in this field is not even slightly equilibrated. The asphaltene content in the tar mat ranges from ~35% to ~60%, with

large variations up and down in concentration within a few meters within individual wells.[12,13] We propose that the tar

mat represents a phase transition; asphaltene is not soluble in crude oil in all proportions.[13] As the asphaltene continues to

enter low points in the reservoir by accumulation of 5 nm asphaltene clusters, the crude oil can become supersaturated in

asphaltene content. The asphaltenes then plate out on grain surfaces. As this process continues, the pore throats become

occluded, and no further fluid exchange can take place. That is, tar mats are not equilibrated in two meters (vertical) whereas

the heavy oil is equilibrated over many tens of kilometers (lateral) because the carbonaceous grain coating in the tar mat

precludes any mass exchange necessary for equilibration.[12,13] Precepts in this explanation are under study.

What had not been appreciated is that destabilized asphaltenes can migrate in reservoirs, but not when precipitated as flocs.

Asphaltenes in crude oils can exist as three distinct species as shown in Fig. 6.[1] All of these species are in the nanometer

size range so are tiny with respect to pore throats in rock in conventional reservoirs. When asphaltenes are slightly

destabilized for example by slow gas addition to a crude oil, asphaltene nanoaggregates form clusters (containing ~8

nanoaggregates). Clusters are relatively large compared to the other asphaltene species and consequently, they accumulate

6 SPE

towards the base of oil column by gravity, much more so than asphaltene nanoaggregates. By this means, asphaltenes that are

destabilized can migrate in reservoirs. The migration process most likely involves convective waves of asphaltene rich (and

cluster rich) fluids.

Asphaltene Nanoscience and Equation of State

Figure 6. The Yen-Mullins Model. Typical structures for asphaltene molecules, nanoaggregates (of molecules)
and clusters (of nanoaggregates). At low concentrations as in condensates, asphaltenes are dispersed as a true
molecular solution (left); for black oils, asphaltenes are dispersed as nanoaggregates of ~6 molecules (center); for
heavy oils, asphaltenes are dispersed as clusters of ~8 nanoaggregates (right).[1,2]

With the size known, the effect of gravity is determined. For the asphaltene equation of state, the gravity term is given by

Archimedes buoyancy in the Boltzmann distribution. That is, the asphaltene particles are negatively buoyant in the crude oil

as described by Archimedes buoyancy. Combining the gravity term, with a chemical solubility term and an entropy term we

have the equation of state for asphaltene gradients, the Flory-Huggins-Zuo (FHZ) Equation of state. The Flory-Huggins

theory has long been used to describe polymer solubility, here we use this theory but we also include a gravity term to treat

asphaltene gradients.

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haha
a

a
a

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where OD(hi) is the optical density (color) measured by DFA [6] of the fluids at height hi in the oil column, a(hi) is the

asphaltene fraction at that height, a is the molar volume of the relevant asphaltene species (cf. Figure 6),  is the molar

volume of the oil, R is the ideal gas constant, T is temperature, a is the solubility parameter of the asphaltene,  is the

solubility parameter of the oil, g is earth’s gravitational acceleration, a is the asphaltene density (~1.2g/cc),  is the oil

density. The solubility parameter of the asphaltene can be obtained from literature values, and, in an oil column, the solubility

parameter variation of the oil is primarily due to GOR variations. In the FHZ EoS, the first exponential factor is the solubility

term, the second is the gravity term and the third is the Flory-Huggins entropy term. For low GOR oils, the gravity term

dominates. For moderate GOR oils (700 scf/bbl), typically both the solubility term and the gravity term contribute to the

asphaltene gradient. With this foundation, the understanding of many reservoirs is dramatically improved.

In this study, we examine two Pliocene reservoirs each with multiple horizons with considerably smaller dimensions than the

reservoir of Figure 5. In the case herein, there is massive, recent gas addition to a black oil with dramatic but unusual effects

manifested in many ways. In particular, this reservoir is evidently grossly out of equilibrium, but not in a systematic way as

depicted in Fig. 2, but rather in a seemingly stochastic (random) disequilibrium variation of fluids properties. With the

reservoir rock being <5 million years, the black oil charge being younger than that and the gas charge more recent still, we

call this time frame roughly 1 million years; this defines what is very rapid regarding reservoir fluids. This case study and the

Saudi Aramco study (Fig. 5) bracket short and long times for reservoir fluids process, 1 million years to 150 million years.

RESULTS AND DISCUSSION

Two fields that share the same minibasin are probed herein; both fields exhibit similar characteristics of importance to this

paper. We will focus primarily on one reservoir with many details presented. These fields and the contained oil and gas are

quite young. Any fluid process that has occurred could take no more than a few million years. Frequently, reservoir processes

take longer than that, and often such young reservoirs have processes that are still ongoing, such as gas charging;

consequently fluid equilibration is not expected. Two horizons in each field are of interest. These reservoirs have substantial

structure containing many lobes. The wireline pressure survey along with some PVT data is shown in Fig. 7 for one reservoir.

163292

SPE 163292 7

x x

x
x
x
x
x x

Well 1

Well 2

Well 3

X,100

X,200

X,300

DEPTH
(TVD, feet)

Figure 7. Pressure survey and PVT data for one of the fields. The pressures in the one sand in three wells are
essentially on the same trend. The peculiar observation is that the GOR of one intermediate sampling point is
significantly different and smaller than all other samples. It is possible the fluids are grossly out of thermodynamic
equilibrium (but in pressure equilibrium) in a connected baffled reservoir.

Well Logging. The pressure data for the three wells are on the same trend. Prior to production, aligned pressure

measurements are not a strong indicator of connectivity; nevertheless, these pressure measurements are consistent with

connectivity. The GOR from the lab PVT reports is also shown in Fig. 7. The GOR from one sample (one well) is

substantially smaller than the others. Normally this could indicate compartmentalization. Here, there is a second explanation.

There could be ongoing massive gas influx into this reservoir, with gas pockets getting trapped in connected, lobate systems.

In this Pliocene reservoir with (likely) current gas charged, insufficient time has passed for equilibration. After production,

this reservoir was shut in and all measured pressures returned to virgin pressure indicating 1) excellent connectivity and 2)

strong aquifer support. This observation is consistent with connected but disequilibrium fluids as the possible origin of the

unusual GOR measurements in Fig. 7. Other unusual observations are consistent with this interpretation.

Figure 8. Log of an interval in one primary sand. A whole core was taken confirming the excellent porosity and
permeability obtained in wireline logging. Nevertheless, large sections of the producing interval exhibited no
fluorescence as indicated by two brown rectangles in the figure labeled “Non-fluorescing core”; this is traced to a
tar coating in these core sections.

Whole Core. In one of the wells, whole core was obtained for much of one of the target sands. Lab data confirmed excellent

porosity and permeability of the target sand. However, a surprising observation from the whole core is that large sections of

the oil bearing sand of the whole core did not fluoresce as indicated in Fig. 8, in spite of the crude oil being highly

8 SPE

fluorescent. The log data exhibits density–neutron crossover that is associated with gas (shaded yellow in Fig. 8). The gas is

towards and at the top of this interval which is what would be expected if this interval is vertically connected (but perhaps

around baffles). In addition, the reservoir pressure greatly exceeds saturation pressure of this oil. The observations of gas and

an extended section of tar are related as will be discussed.

Figure 9. Whole core (6 feet section in two contiguous 3 feet sections) from the well in Fig. 8 at the transition from
fluorescent to nonfluorescent sands under ultraviolet illumination. The core sections are displayed under visible
illumination (left in each 3 foot panel) and under UV illumination (right in each 3 foot panel). Visible light shows
increasing optical absorption in the nonfluorescent section. Laboratory analysis confirmed that tar in the core
produced little fluorescence and the dark color of the core. n.b. The core section with tar is permeable and porous.

The transition from fluorescent to nonfluorescent sand core is shown in Fig. 9. The nonfluorescent section is due to a coating

of tar in the core. This tar is not typical: it is not at the low point in the reservoir, it covers roughly ½ of the producing interval

(cf. Fig. 8) and most importantly the tar zone is porous and permeable. Moreover, this reservoir exhibits excellent

connectivity and natural aquifer support; shut-in resulted in virgin pressure. These properties are quite distinct from tar mats

routinely encountered in oilfields. Typically, tar mats are found at the base of the oil column, they cover a small overall

section of the producing interval, they are not permeable, and have not been considered porous. Tar mats at the OWC often

preclude natural aquifer support and preclude utility of water injectors.

To confirm that the tar mat herein is in fact permeable, it is desirable to perform a downhole flow test. That is, core properties

can be altered upon depressurization with subsequent lab measurements. To test the tar zone permeability, a one foot interval

in the tar zone was perforated and produced by straddling the perforation with the MDT Dual Packer. Figure 10 shows the

section of core from the well depth that was perforated.

Figure 10. Whole core including the one foot interval that was perforated and sampled using the MDT Dual
Packer. The entire permeable section of this core depicted here is coated with tar. (Shale at the base of this core
section is not permeable.) The sampled crude oil is less than 1% asphaltene while the tar is 35% asphaltene
confirming the coexistence in the reservoir or two immiscible hydrocarbon phases.

163292

SPE 163292 9

The tar zone allowed flow of a nice light crude oil in the wireline sampling test confirming that the tar zone is permeable.

The produced oil contains less than 1% asphaltene while the residual tar was found to have ~ 35% asphaltene. Consequently,

it is evident that there are two immiscible hydrocarbon phases present in the formation at the same depth, a light oil and a tar.

Indeed, four hydrocarbon phases have been experimentally determined to coexist in asphaltenic materials, a gas, two

immiscible liquids and a solid;[14] this reservoir is not violating any thermodynamic principles. Nevertheless, the unusual

nature of this tar mat must be explained.

Figure 11 shows a detailed analysis of the phase behavior of crude oils produced (Left) from the tar zone and
(Right) from a region not near the tar zone. The complex phase behaviors of these two crude oils are similar. The
tar mat does not signify a large change in properties of the local crude oil. The asphaltene onset pressure is near
or at reservoir pressure which is expected for a late gas charge into crude oil that resulted in tar.

Given that the tar represents a phase transition of heavy ends precipitated out of the oil, it is important to che ck the phase

behavior of crude oils, produced from the tar zone and produced from a point not so close to the tar. Figure 1 1 shows that the

overall complex phase envelops of these crude oils are similar, meaning that the tar mat is not associated with some dramatic

change of the corresponding crude oil. Nevertheless, there are some differences noted in bubble point and details of the

asphaltene onset. As expected for reservoirs with late gas charge and tar, the asphaltene onset pressure is near or at reservoir

pressure. Asphaltene is not a homogenous chemical substance. Some fractions of asphaltenes are more stable in solutions,

others less stable. When gas destabilizes asphaltene sufficiently, some fraction of asphaltene can for tar. Another fraction can

remain in the liquid phase, but very unstable such that any pressure reduction causes some of this fraction to precipitate as

shown in Fig. 11. The bubble points of the crude oils are not near reservoir pressure even though density neutron cross was

observed (cf. Fig. 9) in upper sections of the sand. This is another indicator that the reservoir fluids are very much out of

equilibrium.

Geoscenario. The explanation consistent with a broad array of observations is the following: the reservoir rock is of Pliocene

age. More recently, a black oil charged into the reservoir. More recently still, likely ongoing, the reservoir experienced a

massive gas influx. Roughly, this corresponds to processes occurring in the last 1 million years. The oil in proximity to the

gas experienced a large, rapid increase in GOR causing rapid destabilization of the asphaltenes. This destabilization was so

complete that the asphaltenes could fall only small distances and only vertically in the oil column before they stuck to

available surfaces, the grain surfaces of the rock. This rapid destabilization did not allow time for the asphaltenes to migrate

to the low points in the reservoir, the OWC. That migration process, for example that did occur in a large field in Saudi

Arabia, is primarily lateral.[12,13] This rapid destabilization event did not even allow time for the asphaltenes to fall

vertically all the way to a shale break at the base of the sand. That process would yield a relatively thin tar mat of no

permeability. Instead, the rapid destabilization of asphaltenes caused the asphaltenes to “paint” the rock surface over an

extended vertical interval; this occurring after the asphaltenes fell a short distance in the sand. An extended vertical tar mat

interval is consistent with only a thin layer of tar on the rock; there was not enough asphaltene in the oil to fill ½ the

producing interval with space-filling tar (cf. Fig. 8). This geoscenario is consistent with many observations: 1) the remaining

oil has contains very little asphaltenes, 2) reservoir pressure is the asphaltene onset pressure, 3) an extended vertical interval

of tar is permeable, 4) much tar is found up-structure and near gas bearing zones, 5) the evident lack of equilibrated GOR

indicates these processes are very recent; the large GOR variations indicate massive gas influx recently occurred 6) the

asphaltene destabilization was so dramatic that evidently even some resins of lower viscosity phase separated. The last point

has significant implications for production as discussed below in the Production Section.

10 SPE

This rapid reservoir process yielding a disequilibrated fluid column is essentially at one extreme with a very rapid time frame.

That is, the GOR and thus methane are not equilibrated, and the asphaltenes are plated out locally on reservoir rock up-

structure. This defines “very young” and is roughly 1 million years old. The massive Saudi Arabian reservoir with

equilibrated asphaltenes over a huge length scale is at the other extreme of time, essentially defining a “very old”. That is, the

asphaltenes are equilibrated over great distance in spite of their tiny diffusion constant. This Saudi Arabian reservoir defines

what is very slow – and is roughly 150 million years old. These two case studies, the one herein and the one from Saudi

Arabia [12,13] bracket reservoir fluid processes in time scale. Other reservoirs should be in between these two in terms of

time frame and thus in terms of observables that affect production, such as GOR distributions, asphaltene distributions, tar

location etc.

An important component of tar mats is their structure. Some tar mats appear to consist of both an immo vable carbonaceous

phase and a heavy oil phase.[13] This is expected from slow dynamics of asphaltene sedimentation. Tar mats produced in

rapid asphaltene destabilization can have fundamentally different properties. Asphaltenes from rapid destabilization can have

lower asphaltene content and higher mobility. That is, strong asphaltene destabilization that causes fast asphaltene deposition

also causes deposition of some heavy resin components that are more mobile than a higher purity asphaltene deposit. There

are important consequences of some mobility of tar, even if permeable.

A confusion can occur in evaluating OWC tar mats vs rapid destabilization tar mats. In both cases, the tar mats have >35%

asphaltene content (cf. Fig. 10, and Ref. [12, 13]). In both cases, thin sections exhibit porosity and exhibit a carbonaceous

coat on the grain surface. However, there are distinct differences. The OWC tar mats can go as high as 60% asphaltene. And

the OWC tar mats are not permeable, while the rapid destabilization tar mat is permeable. In both cases, there are two

immiscible hydrocarbon phases present. In the rapid destabilization tar mat, in addition to the tar, there is a light oil. In the

OWC tar mat, there is a heavy oil of ~35% asphaltene plus a carbonaceous coat of extremely high asphaltene content (≥60%

asphaltene). The OWC carbonaceous coat seals off pore throats trapping heavy oil, and precluding the ability to acquire pure

samples of the carbonaceous coating. The rapid destabilization tar mats are porous and allow easy isolation of the tar mat

from the light oil. The huge difference is that the rapid destabilization tar mat is not ultra-high viscosity and can flow (like a

heavy oil) while the 60% carbonaceous coat of the OWC tar mat is ultra-high in viscosity and cannot flow under any

conditions.

A thin section of the tar mat is shown in Figure 12.

Figure 12. Thin section of the tar mat. Black is the tar. The blue is epoxy that displaced movable fluids prior to
preparation of the thin section, and white is the sand grain. This image is consistent with significant porosity in the
tar zone.

Production. A well test following long term production was performed after perforation of an interval containing a

permeable tar. (This well test is different than the test presented in Fig. 10 where a one foot interval was flowed.) In this test,

significant and relatively low viscosity tar was obtained in tubulars during this test. Figure 13 shows viscous heavy ends

remaining in the tubulars after the well test. Obviously, flow of such a material is of major concern in production. As shown

herein, understanding the distribution of reservoir fluids and their organic solids alike within a single framework helps to

identify corresponding production issues that are significant.

163292

SPE 163292 11

Figure 13. Residual tar in tubulars after a well test. This tar is thought to arise from mobility of an existing tar mat.
This material differs from typical asphaltene deposits in flow assurance that have a physical consistency and
appearance similar to coal.

Figure 14. A cumulative-oil dependent skin in production is observed and is attributed to asphaltene concerns,
both mobile tar and asphaltene onset with pressure reduction. Xylene treatment of the producing well significantly
improves performance. With repeated xylene washes, the rate of skin deterioration can be reduced.

Consistent with this well test result, a skin that is dependent on cumulative produced oil has been observed in the formation

when analyzing extensive production data as shown in Fig. 14. Asphaltene concerns, including both mobile tar as well as

asphaltene deposition with pressure drop are considered responsible for this increasing skin. Xylene treatments are effective

in mitigating these production problems. In particular, repeated xylene treatments reduce the rate of skin deterioration.

Understanding these complex production issues at the outset is desirable in order to optimize production by dynamic

intervention.

12 SPE

Figure 15. Barrels of oil per day and the GOR of the produced oil over a multi-year period. It is uncommon to have
such large, seemingly random variations in GOR. The existence of pockets of connected, disequilibrium fluids in
this young reservoir is consistent with these observations.

Another observation that is not common is the large variation of the GOR of the produced oil. Figure 15 shows that the GOR

varies up and down by a factor of three in production from one well in one reservoir. Again, note that the reservoir appears to

be connected with a strong aquifer drive. The large, nonmonotonic variations of GOR coupled with many observations

discussed above suggest that there are pockets of significantly different fluids in the reservoir that have not had time to

equilibrate. Baffles but not barriers might be separating different fluids. Using literature diffusion constant (D where t=D/x
2
)

of methane through hydrocarbon filled porous rock of ~10
-5

cm
2
/sec, [15,16] one obtains that it takes a million years (t) to go

a distance (x) of 200 meters. It is plausible that reservoir fluid variations exist at that length scale being separated by baffles,

with current gas charging, and with no time to equilibrate.

Different Field, Same Observations. Another field in the same basin exhibits very similar behavior to the field discussed

above. There is tar deposition in a well near the crest of the field.

Figure 16. Core and logs from a well near the crest in another field in the same basin. Very similar observations
are made to the case study above; there is tar deposition up-structure that is porous and permeable. All
production issues discussed above apply to this field.

163292

SPE 163292 13

A question arises as to how common the above case study is. Figure 16 shows that another field in the same basin but a

significant distance away exhibits the same ‘unusual’ phenomena. There is tar deposition up-structure that is porous and

permeable. Other production issues such as a skin dependent on cumulative production and variable GOR in production are

also observed. It is evident that the phenomena discussed in this paper apply to a class of reservoirs, those with rapid and

recent gas charge into black oil.

CONCLUSIONS

A Pliocene reservoir study is presented with a variety of putatively unusual observations: there is tar deposition up-structure

that is porous and permeable. There are large, nonmonotonic variations in GOR obtained in wireline logging and with

production data over years. There is mobile tar as shown in photographs, yet the produced crude oil is rather light. A

consistent geoscenario is for a rapid and recent gas charge into black oil, the time frame being roughly one million years.

This short time is not sufficient for equilibration of reservoir fluids even though the reservoir exhibits excellent connectivity

and pressure build-up behavior in shut-in. The asphaltenes were knocked out of solution so rapidly and strongly that they did

not have time to descend in the reservoir to the OWC; rather, they made it only part way down the individual sand lobes

before sticking to and ‘painting’ the rock surface – thereby leaving permeability. This deposition process naturally leads to

somewhat higher mobility tar than typically found in OWC tar mats, enabling limited but important mobility of this tar.

Consequently, a production dependent skin develops and requires intervention via xylene treatment. These rapid ~1 million

year old processes are in distinct contrast to equilibrated asphaltenes in a giant, Jurassic Saudi Arabia field, thus old in a

geologic sense. Consequently, short and long time scales are establish as ~1 million years to ~150 million years for reservoir

fluid processes of interest to major production concerns. Many other reservoirs are intermediate in this time scale. These new

methods, particularly employing new asphaltene science and downhole fluid analysis technology are enabling significant

increases in efficiency as increasingly difficult reservoirs are exploited. Moreover, as shown herein, neighboring reservoirs

within a basin can exhibit very similar fluid variations and production concerns.

ACKNOWLEDGEMENTS The authors are deeply indebted to the technologists in the operating company. These
technologists recognized the origins of surprising reservoir complexities and performed definitive yet uncommon tests to

validate these physical origins. Simply stated, their skill and clarity of thought is inspiring. We are also indebted to these

technologists and the operating company for permitting this publication.

REFERENCES

[1] Mullins, O.C., “The Asphaltenes”, Annual Review of Analytical Chemistry, 2011 Vol. 4, page 393-418

[2] Mullins, O.C.; Sabbah, H.; Eyssautier, J.; Pomerantz, A.E.; Barré,

L.; Andrews, A.B.; Ruiz-Morales, Y.; Mostowfi, F.;

McFarlane, R.; Goual, L.; Lepkowicz, R.; Cooper, T.; Orbulescu, J.; Leblanc, R.M.; Edwards, J.; Zare, R.N.; Advances in

Asphaltene Science and the Yen-Mullins Model, Energy & Fuels, 26, 3986–4003, (2012)

[3] Freed, D.E., Mullins, O.C., Zuo, Y.J.: “Theoretical Treatment of Asphaltene Gradients in the Presence of GOR

Gradients”, Energy & Fuels, 24, 3942-3949, (2010)

[4] Zuo, J.Y.; Mullins, O.C.; Freed, D.; Elshahawi, H.; Dong, C.; Seifert, D.J.; Advances in the Flory-Huggins-Zuo Equation

of State for Asphaltene Gradients and Formation Evaluation, submitted, Energy & Fuels

[5] Stainforth, J.G., “New Insights into Reservoir Filling and Mixing Processes” in Cubit J. M., England, W.A., Larter, S.

(Eds.) Understanding Petroleum Reservoirs: toward and Integrated Reservoir Engineering and Geochemical Approach,

Geological Society, London, Special Publication, (2004)

[6] Mullins, O.C., The Physics of Reservoir Fluids; Discovery through Downhole Fluid Analysis, Schlumberger Press,

Houston, (2008)

[7] V. Mishra, N. Hammou, C. Skinner, D. MacDonald, E. Lehne, J.L. Wu, J.Y. Zuo, C. Dong, O.C. Mullins, Downhole

Fluid Analysis & Asphaltene Nanoscience coupled with VIT for Risk Reduction in Black Oil Production, Accepted, SPE

ATCE, (2012)

[8] Zuo, J.Y., Elshahawi, H., Dong, C., Latifzai, A.S., Zhang, D., Mullins, O.C., DFA Assessment of Connectivity for Active

Gas Charging Reservoirs Using DFA Asphaltene Gradients, SPE 145438, ATCE, (2011)

[9] Elshahawi, H., Latifzai, A.S., Dong, C., Zuo, J.Y., Mullins, O.C., Understanding Reservoir Architecture Using Downhole

Fluid Analysis and Asphaltene Science, Presented, Colorado Springs, SPWLA, Ann., Symp., (2011)

14 SPE

[10] Pfeiffer, T.; Reza, Z.; Schechter, D.S.; McCain, W.D.; Mullins, O.C.; Determination of Fluid Composition Equilibrium

under Consideration of Asphaltenes – a Substantially Superior Way to Assess Reservoir Connectivity than Formation

Pressure Surveys, SPE #145609 ATCE, (2011)

[11] Gisolf, A., Dubost, F.X., Zuo, J., Williams, S., Kristoffersen, J., Achourov, V., Bisarah, A., Mullins, O.C., SPE 121275,

SPE Europe/EAGE Ann. Conf. Ex., Amsterdam, The Netherlands, 8-11 June, (2009)

[12] Seifert, D.J., Zeybek, M., Dong, C., Zuo, J.Y., Mullins, O.C., Black Oil, Heavy Oil and Tar in One Oil Column

Understood by Simple Asphaltene Nanoscience, SPE ADIPEC 158838, Abu Dhabi (2012)

[13] Seifert, D.J., Qureshi, A., Zeybek, M., Zuo, J.Y., Pomerantz, A.E., Mullins, O.C., Mobile Heavy Oil and Tar Mat

Characterization Within a Single Oil Column Utilizing Novel Asphaltene Science, SPE KIPCE 163291, Kuwait International

Petroleum Conference and Exhibition, Kuwait City, Kuwait, Dec 10-12, (2012)

[14] Shaw, J.M., Zou, X.; Phase behavior of heavy oils, Ch. 19 in Asphaltenes, Heavy Oils and Petroleomics, Mullins, O.C.

Sheu, E.Y., Hamami, A., Marshall, A.G., Editors; Springer, New York, (2007)

[15] Chen, L.L., Katz, D.L., Tek, M.R., Binary gas diffusion of methane-nitrogen through porous solids, AICHE, 23, 336-

341, (1977)

[16] Ghorayeb, K., Firoozabadi, A., Modeling multicomponent diffusion and convection in porous media, SPE Journal, 5,

158-171, (2000)

163292

TAR MATS CHARACTERIZATION FROM NMR AND

CONVENTIONAL LOGS, CASE STUDIES IN DEEPWATER

RESERVOIRS, OFFSHORE BRAZIL
João de D. S. Nascimento and Ricardo M. R. Gomes – PETROBRAS

, held jointly by the Society of Petrophysicists and
Well Log Analysts (SPWLA) and the submitting authors.
This paper was prepared for presentation at the SPWLA 4

th
Annual Logging Symposium held in Noordwijk, The Netherlands,
June

–9, 2004.

ABSTRACT

Tar mats can be defined as hydrocarbon horizons
with high asphaltene concentrations (20% to 60%
in weigh) and high viscosity – typically more than

0,000 cp at reservoir conditions. As a
consequence of these characteristics, tar mats
represent a volume of hydrocarbon in place that
are very difficult or even impossible to be
produced and frequently form vertical
permeability barriers. The occurrence of these
high viscosity hydrocarbon layers is generally at
the bottom of the oil column. Therefore they can
isolate the oil leg from the aquifer. In these cases,
the producing drive mechanism will be by
expansion in a volumetric reservoir, instead of
water drive. So, a previous identification of tar
mats will help to correctly quantify reserves and
predict recovery with maximum efficiency.

Nuclear Magnetic Resonance logs in conjunction
with conventional logs can provide accurate
identification of tar mat levels and viscosity
estimation, from empirical relationships. In this
paper we present field examples of tar mat
characterization from NMR and conventional
logs, supported by formation pressure
measurements in the aquifer and in the oil leg.
Despite a very clear continuity of the reservoir all
along the aquifer and oil leg, with an obvious
oil/water contact, pressure data show evidence of
depletion by production in the oil column,
whereas in the water zone no pressure drop is
noted.

In the studied field examples, tar mat levels are
tens of meters thick and estimated viscosities are
around 20,000 cp. The NMR responses (total
porosity and T2 distribution) are very different in

the oil leg when compared to the tar mat
horizons, as a result of the low hydrogen index
levels and high viscosities in the tar mats,
compared to the hydrogen indexes and viscosities
of the medium/light oil. Also, because of the
hydrogen index, the total porosity values
measured by NMR and density logs are very
different in the tar mat levels, but they have good
agreement in the aquifer and oil zone. Neutron
porosity is also affected, in minor intensity, by the
low hydrogen index of tar mats. Additionally,
resistivity logs show different responses due to
the low mobility of tar mats when compared to
the oil leg. The non-consolidated characteristic of
the reservoirs in addition to the absence of mud
cake along the tar mat intervals due to low filtrate
invasion, result in caliper enlargement all along
these high viscosity levels but not in the oil zone
or in the aquifer.

INTRODUCTION

Tar mats in petroleum reservoirs are zones of
variable thickness – less than 1 meter to over 100
meters – containing extra heavy oil or bitumen,
typically with gravity under 10 °API and/or
viscosity in situ above 10,000 cp, generally at the
bottom of the oil column (Nascimento and Pinto,
200

). The high gravity and viscosity of tar mats
stems from the high asphaltene content, normally
20 to 60% weight (Wilhelms and Larter, 1994).

Asphaltenes are considered the highest molecular
weight hydrocarbon compounds in petroleum.
The chemical structure of these compounds is
mainly formed by carbon (100 to 300 atoms per
molecule), hydrogen, sulfur, nitrogen, oxygen and
minor proportions of nickel and vanadium
(Pineda-Flores, 2001).

Gravitational segregation is the main process
causing asphaltene enrichment and tar mat
formation in crude oil. It is governed by different
factors controlling the asphaltene stability in oil

solution, like the in situ oil composition, pressure
and temperature (Hirschberg, 19

4; Boer, 1992).
Tar mats at the bottom of oil reservoirs can be
expressed as the extreme manifestation of oil
compositional variation, caused by gravitational
segregation of asphaltenes (Hirschberg, 1988).

Tar mats identification in exploration wells is
crucial because this high viscous oil zones may
contain important volumes in place that are very
difficult or even impossible to be produced and
therefore must be considered as non-reserves.
Furthermore, tar mats may occur in large areas,
with high thickness, forming vertical permeability
barriers, isolating the oil leg from the aquifer and
therefore, preventing water drive production
mechanism.

TAR OR BITUMEN IDENTIFICATION
FROM RESISTIVITY LOGS

Because of the very low mobility of high
viscosity oil, such as tar or bitumen, resistivity
logs have been the main wireline devices used for
characterization of this type of hydrocarbon in
reservoirs. Arab (1990) reports the use of deep
(Rt) and shallow (Rxo) resistivity curves to
identify bitumen occurrence in Upper Zakum
Field (Abu Dhabi).

In Upper Zakum Field, with mud filtrate
resistivity (Rmf) higher than connate water
resistivity (Rw), the following typical responses
were achieved, according to Arab (1990):

• In the oil bearing zones ⇒ Rxo reads
less than Rt;

• In the water bearing zones ⇒ Rxo reads
higher than Rt;

• In the bitumen occurrence zones ⇒ Rxo
reads higher than Rt like in the water leg
but with higher resistivity values.

Arab (1990) explained the resistivity responses in
bitumen intervals by the mud filtrate ability to
flush formation water from nearby hole, while not
capable to flush the bitumen. Therefore, in the
invaded zone, Rxo reads bitumen resistivity plus
Rmf while in the virgin zone Rt reads bitumen
resistivity plus Rw. Since Rmf is higher than Rw
and bitumen resistivity is constant in both zones,

then Rxo will read higher than Rt in bitumen
zones, as verified in field case.

Also, according to Kopper (2001), in the Orinoco
Heavy Oil Belt in Venezuela, when Rxo reads
higher than Rt, means that no movable oil (or tar)
exists in the logged interval. A whole interval
core indicated that the zone was oil-satured,
however, it produced very little oil during the drill
stem test.

Some authors such as Wilhelms, Carpentier and
Huc (1994), report the comparison between deep
and shallow resistivity curves plus the Sw and
Sxo values, to recognize tar mats because of their
very low mobility when compared with
producible oil. These authors don’t use the Rxo
higher than Rt condition to characterize the tars.
The same values of resistivity curves, or same
water saturations, are considered enough to
identify tar levels.

TAR OR BITUMEN CHARACTERISTICS
AND VISCOSITY ESTIMATION FROM
NMR LOGS

The NMR porosity is derived from the signal
amplitude, which is proportional to the hydrogen
index (HI) of fluids in the porous rocks. The HI
of pure water is defined as 1 and it is used to
calibrate all the measurements. For alkanes,
which are the major constituents of light crude
oils, the HI is also equal to 1. So, light oils have
the same signal amplitude of water and,
consequently, same values of porosity are
obtained either in a light oil or in water-bearing
reservoir.

Because of the minor alkane constituents, higher
aromatic contents and non-hydrocarbon
components in heavy oils HI tends to decrease
with increment of oil density. The API gravity is
usually a good HI indicator in crude oil, with
accentuated HI reduction when API gravity
declines below 20. According to Kleinberg
(1996), for a 10 API gravity oil HI is close to 0,

.
Consequently, NMR measurements in heavy oil
zones will exhibit porosity deficit proportional to
the reduced hydrogen index.

Another characteristic of the NMR responses in
heavy oils is the short T2 caused by high

viscosity. Morris (1997) empirically found out
that viscosity (η) is a function of T2 log mean:

η0,9 = 1200/T2 log mean (1)

for η in centipoises and T2 log mean in
milliseconds.

Additionally, he noted that along with the
increment of oil viscosity, a tail of shorter
relaxation times in T2 distributions also increases,
representing the heavier components with minor
oil mobility.

To determine in situ oil viscosity by NMR logs
using equation (1) is necessary that oil and water
T2 distributions are not overlapping. In cases of
heavy oil viscosity near or greater than 100
centipoises, for example, the expected T2 log
mean is near or minor than 15 milliseconds and,
in addition, the tail originated from more
restricted motion nucleus spans for very short
relaxation times. In such cases, the oil and
irreducible water signals overlap and
consequently it is not possible to have direct
viscosity estimation.

To determine in situ viscosity of extra heavy and
high viscosity hydrocarbons, such as tar and
bitumen, using NMR logs, LaTorraca (1999)
proposed a empirical equation for indirect
determination based on one of the characteristics
of this type of hydrocarbon – the low hydrogen
index (HI) and therefore, the NMR porosity
deficit when compared with porosity
measurements from others logs unrelated to the
HI.

According to LaTarroca (1999), an apparent HI
(HIapp) can be estimated using as inputs the
porosity estimated from a log insensitive to the HI
of the oil (∅ ), the NMR porosity (∅ NMR) and oil
saturation (So) in the following equation:

HIapp = (So∅ _ ∆∅ )/So∅ (2)

where, ∆∅ is the difference between the porosity
not related to HI and the NMR porosity.

However, the HIapp of heavy oils from NMR
logs also depend on the echo spacing (TE) used in
T2 measurements. Because T2 signal is obtained
from samples at the echo peaks, TE is also the
sampling interval and NMR logging tools don’t

have sampling rates fast enough to detect all the
hydrogen in heavy oils (LaTorraca, 1999).

Correlations between HIapp and oil viscosity as a
function of TE have been established leading to
an equation for heavy oil viscosity (η) estimation:

ln(η)=(11+1.1/TE) _ (5.4+0.66/TE)∗ HIapp (3)

for η in centipoises and TE in milliseconds
(LaTorraca, 1999).

CASE STUDIES

Two fields examples from deep-water reservoirs
with tar mat occurrences at the bottom of oil
column are presented. In the first example (figure
1), well “A”, a tar mat about 40 meters thick
occurs above the aquifer. The top of tar in figure
1 is located approximately at xx52m and the base
is close to xx92m, in the same depth of
hydrocarbon/water contact.

One of the main tar mat characteristics from
NMR logs in well “A” (figure 1) is the unimodal
T2 distribution with shorter mean times, caused
by the high hydrocarbon viscosity, when
compared with T2 signal above xx52m, with
bimodal T2 distribution in the medium/light oil
leg, where capillary water and oil signals are
separated. In the tar mat interval T2 distributions
from hydrocarbon and water signals overlap, and
a pronounced tail of shorter signals is evident
below xx60 m, due to a more restricted motion tar
components (track 5, figure 1).

A good agreement between “total” NMR porosity
and density log porosity (track 4, figure 1) is
evident in the water zone (below xx92 m) and in
the oil leg (above xx52 m), whereas an obvious
porosity difference occurs in the tar mat zone,
where the NMR porosity shows about 8 p.u.
deficit compared to the density log porosity. This
feature is typical of hydrocarbons with short
hydrogen index. Using the porosity deficit (∆∅ )
plus other parameters, as ∅ , So and the
operational TE in equations 2 and 3 results in an
estimated viscosity of approximate 20,000
centipoises.

The resistivity curves response (track 2, figure 1)
corroborated another characteristic of very low
mobility hydrocarbons, in the cases when Rmf is

FF
3

greater than Rw, as already mentioned in previous
works. In the oil leg (above xx52 m) the Rxo
reads less than deep and medium resistivity
curves, while in the tar zone (xx52/xx92 m) the
Rxo reads higher than deep and medium
resistivity, similar to aquifer responses (below
xx92 m) but with greater resistivity values. The
differences in resistivity measurements along the
tar mat interval are caused by the lack of bitumen
displacement and probably because of the ionic
exchange between less salty mud filtrate and
more salty formation water.

The wash out in tar mat interval (track 1, figure 1)
makes clear a particular feature caused by the
insignificant bitumen mobility in this
unconsolidated reservoir. In the oil leg and in the
aquifer, where invasion is effective, mud cake is
formed in the well bore, keeping the caliper near
to the bit size diameter. Whereas, in the tar mat,
where filtrate invasion is more difficult, no mud
cake is formed and the well bore is enlarged by
erosion from mud circulation.

An additional indication of tar mat low hydrogen
index can be observed in neutron log porosity
(track 4, figure 1). Although neutron tools are
sensitive to all hydrogens, including that
associated with minerals – instead of NMR tools
that are only sensitive to hydrogen from fluids – a
minor neutron porosity deficit can also be
observed when compared to density porosity in
tar mat. In contrast, no neutron porosity deficit is
observed in the aquifer or in the oil leg.

Another interesting feature shown in figure 1 is a
vertical viscosity variation along the oil column.
The red flags in track 1 indicate that insufficient
wait time for adequate polarizations occurs at the
top of the oil column, caused by the largest T2
distribution in this zone, corresponding to the
lesser viscous oil in the reservoir. So, light oil
with low viscosity at the top of the reservoir
grades to oil with medium viscosity (xx10/xx52
m), ending in a tar mat occurrence, at the bottom.

The evidence of tar mat occurrence in well “A”
are validated from pressure measurements in the
oil and water zones. Although a very clear
continuity of the reservoir all along the aquifer
and oil column, with an obvious

hydrocarbon/water contact, pressure data show
evidence of depletion by production in the oil leg
whereas in the water zone no pressure drop is
noted.

Figure 2 shows a depth vs pressure crossplot from
wireline tests along the oil and water intervals.
The pressure gap between the oil column and
water zone is evident. Because of the enlarged
caliper and or very low fluid mobility, no pressure
was obtained in the tar mat zone. The expected
pressure in the hydrocarbon/water contact
projected from the oil pressure gradient is 150 psi
lesser than measured pressure at the top of aquifer
interval, characterizing the hydraulic
discontinuity between the aquifer and the oil leg.

The second field example (figure 3), well “B”, is
in the same area of well “A”. A tar mat near 55
meters thick and identical well log characteristics
also occurs below the oil column, but in this case,
no aquifer is present, instead the tar mat lies
directly on a shale sequence.

In this example it is not possible to confirm the
tar mat as a hydraulic seal because of the obvious
absence of a hydrocarbon/water contact.
Nevertheless, all well log characteristics noted in
xx52/xx92 m interval of well “A” are also present
in xx20/xx75 m interval of well “B”, which is a
relevant evidence of tar mat occurrence in the
second well.

In well “B” (figure 3), the end of bimodal T2
distribution and the start of overlapping short time
oil signals and water signals are around xx20 m
(track 5). At this depth, initiates the apparent
“total” NMR porosity deficit, compared to density
log porosity (track 4); the neutron porosity deficit,
compared to density log porosity (track 3); the
crossover of resistivity curves with Rxo reading
higher than Rt (track 2) and the washed out hole
section (track 1).

All the described characteristics of well “B” are
restricted to the interval xx20/xx75 m. Above
and below this interval occur respectively
medium viscous oil in a fine and laminated
reservoir and a shale sequence; both with their
peculiar log characteristics, very different from
tar mat log responses.

CONCLUSIONS

The tar mat resistivity log responses from field
examples presented in this paper are similar to the
ones described in previous works, for the
common condition of Rmf higher than Rw.
Additionally, another tar or bitumen log
characteristics derived from the low hydrogen
index and high viscosity of this type of
hydrocarbon were recognized in NMR logs and
also discussed. A particular characteristic of non-
invaded unconsolidated reservoirs was also
evidenced from wash outs in the tar mat intervals.

Original pressure in the aquifer and depletion in
oil column after production, confirmed from
wireline pressure data in well “A”, enabled a
validation of the well log indications and created
high confidence log response patterns to a reliable
tar mat identification, including for situations
where the aquifer is absent.

ACKNOWLEDGMENTS

The authors would like to thank PETROBRAS
for the support and permission to publish the data.
We also thank the geologist Almério Barros
França for his valuable help, revising the original
text.

REFERENCES

Arab, H., 1990, “Bitumen Occurrence and
Distribution in Upper Zakum Field”, Society of
Petroleum Engineers, Paper Number 21323.

Boer, R. B. de, et al., 1992, “Screening of Crude
Oils for Asphalt Precipitation: Theory, Practice
and the Selection of Inhibitors”, Society of
Petroleum Engineers, Paper Number 24987.

Hirschberg, A., et al., 1984, “Influence of
Temperature and Pressure on Asphaltene
Flocculation”, Society of Petroleum Engineers
Journal, June 1984, 283-294.

Hirschberg, A., 1988, “Role of Asphaltenes in
Compositional of Reservoir’s Fluid Column”,
Journal of Petroleum Technology, January 1988,
89-94.

Kleinberg, R. L., et al., 1996, “NMR Properties of
Reservoir Fluids”, The Log Analyst, November –
December 1996.

Kopper, R., et al., 2001, “Reservoir
Characterization of the Orinoco Heavy Oil Belt:
Miocene Oficina Formation, Zuata Field, Eastern
Venezuela Basin”, Society of Petroleum
Engineers, Paper Number 69697.

LaTorraca, G. A., et al., 1999, “Heavy Oil
Viscosity Determination Using NMR Logs”,
SPWLA 40th Annual Logging Symposium, May
30 – June 3, 1999.

Morriss, C. E., et al., 1997, “Hydrocarbon
Saturation and Viscosity Estimation from NMR
Logging in the Belridge Diatomite”, The Log
Analyst, March – April 1997.

Nascimento, J. de D. S., and Pinto, A. C. C.,
2003, “Tar Mats – Gênese, Caracterização e
Implicações em E&P”, Internal Report,
PETROBRAS.

Pineda-Flores, G., et al., 2001, “Petroleum
Asphaltenes: Generated Problematic and Possible
Biodegradation Mechanisms”, Revista
Latinoamericana de Microbiología, Volume 43,
Number 3, pp. 143-150.

Wilhelms, A., and Later, S. R., 1994, “Origin of
Tar Mats in Petroleum Reservoirs”, Marine and
Petroleum Geology, Volume 11, Number 4, pp.
418-456.

Wilhelms, A., Carpentier, B., and Huc, A. Y.,
1994, “New Methods to Detect Tar Mats in
Petroleum Reservoirs”, Journal of Petroleum
Science and Engineering 12, pp. 147-155.

ABOUT THE AUTHORS

João de D. S. Nascimento is a geologist at the
Exploration Department of PETROBRAS. He has
been working as log analyst ever since joining
PETROBRAS in 1976.

Ricardo M. R. Gomes is a geologist and
petrophysicist with PETROBRAS, where he has
been working as an exploration geologist since
1977. He holds a B.Sc. degree in geology from
the Universidade Federal do Rio de Janeiro,
Brazil (1976) and a M.Sc. degree in geology from
the Colorado School of Mines (1999).

FF
5

Figure 1 – Log responses in Well “A”. A tar mat was identified in the interval xx52/xx92 m, from T2
distribution (track 5), large total NMR porosity deficit (track 4), slight neutron porosity deficit (track 3), Rxo
higher than Rt (track 2) and wash out (track 1).

xx00

xx50

xx00
6

Figure 2 – Depth vs pressure cross plot in well “A”. Pressure distribution shows the evidence of a
permeability barrier between the oil column and the aquifer, given by the tar mat at the bottom of the oil
column, as indicated from log responses along xx52/xx92 m interval.

PRESSURE x DEPTH

3450

3500

3550

3600

3650

3700

3750

3800

3850
4750 4800 4850 4900 4950 5000 5050 5100 5150 5200 5250 5300 5350

PRESSURE (psi)

D
E

P
T

H
(

m
)

Pressures in Depleted Oil Column

Original Pressures in Aquifer

150 psi Gap at HC/Water Contact

Tar Mat Zone
(xx52/xx92 m)

xxxxxxxx xxxx xx xxxx xxxxxxxx
xx

xx
xx
xx
xx
xx
xx
xx

Well “A” – Pressure x Depth

PRESSURE x DEPTH

3450
3500
3550
3600
3650
3700
3750
3800
3850
4750 4800 4850 4900 4950 5000 5050 5100 5150 5200 5250 5300 5350
PRESSURE (psi)
D
E
P
T
H
(
m
)
Pressures in Depleted Oil Column
Original Pressures in Aquifer
150 psi Gap at HC/Water Contact
Tar Mat Zone
(xx52/xx92 m)
xx
xxxxxxxx xxxx xx xxxx xxxxxxxx
xx
xx
xx
xx
xx
xx
xx
xx
Well “A” – Pressure x Depth
Pressures in Depleted Oil Column
Original Pressures in Aquifer
150 psi Gap at HC/Water Contact
Tar Mat Zone
(xx52/xx92 m)
xx
xxxxxxxx xxxx xx xxxx xxxxxxxx
xx
xx
xx
xx
xx
xx
xx
xx

Well “A” – Pressure x Depth FF

Figure 3 – Log responses in Well “B”. A tar mat was recognized in xx20/xx75 m interval, with identical log
responses observed in well “A”. In this case no aquifer is present. The tar mat lies directly above a shale
sequence.

xx50
xx00
xx50
8

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LITERATURE REVIEW:

Tarmats is a topic that has not been studied very often, leading to a lack of published literature on the subject. The limited literature can very well be explained by the concentration of this problem in the Middle East.

One of the big contributors to the tarmat literature is Moor who studied tarmat presence, distribution and nature, as well as asphaltic sands and bitumens in reservoirs. He found four different organisms that contribute to the tarmats’ formation (Moor 1984).

(1)
Water Washing:
The removal of a portion of light hydrocarbons, and allowing the asphaltic fraction to locate itself at the foundation of oil accumulation.

(2)
Gravity Segregation:
In this procedure the resistance attracts the heavier hydrocarbons towards the foundation, and the lighter hydrocarbons move upwards.

(3)
Natural Deasphalting:
The entrance of natural gases from source rock and their rise through the hydrocarbon column due to buoyancy. Such an action would result in a lower solubility and case the asphaltic fraction to precipitate and rest at the foundation of the reservoir.

(4)
Biodegradation:
Meteoric water moves beneath the pooled reservoir, along transmitting bacteria use to metabolize crude oil’s lighter fraction. Thermal currents located in the reservoir would distribute lighter fraction to the oil/water located at the base where the bacteria is active. As a result, the formation of a tarmat is witnesses near the foundation of the reservoir.

Moor’s research extends to other areas, such as the five different groups of subsurface tar seal occurrences due to the level of concentration, continuation, and the structural position. The distribution of hydrocarbon within entire basis or individual traps is controlled by Tar seals associated with unconformities. Additionally, the tar seal that do occur at the unconformities are categorized in five different groups (Figure 2.1).

(i) Tar seals with four-way closure located above traps.

(ii) Tar seals located alongside the margins of overly matured basins

(iii) Oil first trapped by tar seals and then reallocated through basin deformation.

(iv) Trapped oil by tar seals and deeper structures.

(v) Tar seals advantageously traps the oil.

Those reservoirs which have many levels of these characteristics are known as tarmat reservoirs. Such type of reservoir is come across the World mainly in Middle East (Moor 1984).

Figure 2.1 Tar seal Classification (Moor 1984)

Abdul Aziz Al-Kaabi et al tell that there are so many searches done in WOR and oil recovery and many shapes of layers of tar are observed physically and also numerically in order to study the behaviour and working of WOR and recovery of oil. From all these researches four cases were found which are studied as square barrier beneath the well a disk beneath the well, a hollow square or disk beneath the well, and a half plane. The research conducted on these four cases shows that in hollow tarmat barrier case, the breakthrough time comes earlier, and if we consider the disk beneath the well case breakthrough time is delayed as well because WOR shoots very rapidly. No-barrier case got the highest recovery from all the cases discussed. And hollow tarmat barrier got the least recovery. Many of the major oil reservoirs in Middle East have the issue of tar barrier of oil zone and the underlying water zone, which have a very strong bottom water drive. Many investigations are done which can be used for increasing oil recovery from such type of reservoirs. There is no work published on this issue in Iraq, Kuwait and Saudi Arabia. Many models and schemes are made; initially three zones were set in the models namely oil zone which is at the top, water zone which is placed in the middle and tar zone which is placed at the bottom. The oil and tar zone have the thickness which is varied in order to fulfil the variety of conditions. And the water zone is protected with water drive. There are many different types of techniques which is done named internal water flood with bottom water drive, internal water flood without bottom water drive, injection of solvent, injection of steam into (a) water zone, (b)oil zone, (c)tar zone (Abdul Aziz Al-Kaabi et al 1988)

In Venezuela and North America many literatures were published on recovery of oil. Tarmats are introduced in reservoirs in Kuwait, South Iraq and Qatar. In Saudi Arabia huge accumulations of tar are reported in fields named Manifa, Khursaniyah and in many others fields. In Ghawar the tar zone exceeds from more than 15 miles and in Uthmaniya tar zone goes up to 500 ft with respect to its thickness ( Abdul Aziz Al-Kaabi et al 1988).

Osman during 1985, published a study regarding Minagish field located in Kuwait. The case of Minagish field in Kuwait represented a very typical case of tarmat reservoirs in which tar is in

cluded at the contact of oil-water and usually has a thickness that ranges between 30 feet and 115feet. In Figure 2.2 presents the average rock properties and the structural cross-section of the MN-26 injector showing the tarmat (Osman 1985).

Initially, the Minagish field was supposed to have water flooding below the tarmat. This was also the reason, whey the discussion of a possible tarmat breakdown due to the waterflood below the tar zone. Figure 2.3 demonstrate the graphical method that Osman used in order to predict the different pressure rates at the tarmats depending on the injection rate and time. In comparison, Figure 2.4 represents the curves of differential pressure of the water that was injected versus injection time depending on the distance of the injector. Osman’s study overall was fascinating; however one of the most important discoveries was that water injection was the main effect on differential pressure across tarmats, than the oil production. Lastly, Osman recommended a way of finding the response time at the well that can be observed, and allow for time to complete the switch injection from below to above the tarmat. (Osman 1985).

Regardless, of all the quantitative results that Osman presents, his model is very simplistic to represent the such a complicated problem. Osman made a few assumptions that were questionable, such as:

1) The consideration of a tarmat as a rigid barrier breaking at 15psi/foot as a pressure gradient.

2) The increase in pressure due to water injection is preeminent, while the decrease in pressure because of oil production is insignificant.

3) The way he applied the superposition theory is uncertain in this study at least.

4) Osman fails to mention the rheology and the characteristics of the tar.

5) Lastly, he fails to provide and discuss the geometric description of the tarmat that was broken.

An extension of Osman’s work examines the results from having a sealing fault close to the water injection and the influence of the sealing fault on the behaviour of the tarmat. This above mentioned study resulted in a technique that was able to calculate the time of the tarmat break down, what the response time was at the nearest well, and lastly the differential pressure at the tarmat located anywhere in the reservoir (Osman 1986).

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