Directory UMM :Data Elmu:jurnal:O:Organic Geochemistry:Vol31.Issue2-3.Feb2000:

Organic Geochemistry 31 (2000) 211±229
www.elsevier.nl/locate/orggeochem

A study of the composition of light hydrocarbons (C5±C13)
from pyrolysis of source rock samples
W. Odden a,*, T. Barth b
a
Statoil a.s., N-5020 Bergen, Norway
University of Bergen, N-5007 Bergen, Norway

b

Received 16 March 1999; accepted 6 January 2000
(returned to author for revision 21 September 1999)

Abstract
Pyrolysis±GC (open system) has been performed on a total of 30 source rock samples with quanti®cation of all
identi®ed components in the C5±C13 range. Ten samples are from the marine shales of the Upper Jurassic Spekk Formation and 10 samples from the coals and coaly shales of the Lower Jurassic AÊre Formation, from o€shore MidNorway. This sample set was expanded with 10 samples from the Danish sector, i.e. seven samples from the marine
shales of the Upper Jurassic Farsund Formation and three coal samples from the Middle Jurassic Bryne Formation.
The samples were selected to cover di€erent maturity levels and facies. The light hydrocarbon distribution generated by
pyrolysis shows a clear compositional di€erence between the di€erent source rock types. In general, the AÊre and Bryne

Formations were relatively enriched in mono-aromatics and naphthalenes, while the Spekk and Farsund Formations
were richer in n-alkenes and n-alkanes. Variations in terrestrial in¯uence of the marine shales of the Farsund and Spekk
Formation samples were also observed. It is found that the abundance of m ‡ p xylene most e€ectively distinguishes
between the di€erent source rock types. This is veri®ed by multivariate modelling. It is shown that the light hydrocarbon composition generated by pyrolysis of kerogen is more a€ected by source facies than maturity variations.
# 2000 Elsevier Science Ltd. All rights reserved.
Keywords: Pyrolysis±GC; Light hydrocarbons; Source rocks; Mid-Norway; Denmark; Multivariate modelling

1. Introduction
During the last 20 years, pyrolysis has been developed
into a versatile and powerful tool for source rock quality assesments. Numerous pyrolysis techniques in combination with gas chromatography (Py±GC), mass
spectrometry (Py±MS) and gas chromatography/mass
spectrometry (Py±GC±MS) have been developed and
applied in the characterization of source rocks and coals
(Larter and Douglas, 1980, 1982; Bjùrùy et al., 1984;

* Corresponding author. Tel.: +47-551-42744; fax: +47557-42050.
E-mail address: weod@statoil.com (W. Odden).

Hors®eld, 1984, 1989; Larter, 1984, 1985; Hors®eld et
al., 1989; Larter and Hors®eld, 1993 and references

therein). Most of the published works from Py±GC (see
references above) have concentrated on ®ngerprintbased qualitative comparison of pyrogram data on
whole rock or isolated kerogen. The whole gas chromatogram of n-alkenes and n-alkanes (C1±C33) have
usually been identi®ed together with some of the aromatics (such as benzene, toluene and the xylenes).
The composition of the produced pyrolysate is known
to be a function of maturity. Romovacek and Kubat
(1968) claimed that coals of increasing rank yielded
pyrolysates that were progressivily enriched in total and
low molecular weight aromatic compounds. This feature, which corresponds to the increasing maturity of

0146-6380/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PII: S0146-6380(00)00002-4

212

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

the kerogen, has also been documented by other workers using Py±GC (Hors®eld, 1984; Larter, 1984). Larter
and Douglas (1980) stated that the m+p xylene/noctene ratio increased with increasing rank for vitrinite
and sporinite, but they showed that this trend for alginites was rather doubtful. Sent¯e et al. (1986) also found

that the most abundant compounds from Py±GC of
vitrinite concentrates were benzene, C7 and C8 alkyl
benzenes, phenols and alkyl phenols. A study by Boreham and Powell (1991) of coals and carbonaceous
shales from Australia concluded that the proportion of
xylene remains constant up to a Tmax value of about
460 C, but they showed that the content of aromatics
increased relative to phenol and n-octane+n-octene
contents above this level.
The chain length distribution of normal paran precursors in kerogens and the way this changes as a result
of increasing maturity or in biological input has been
studied by several authors. éygard et al. (1988)
observed progressive chain shortening of the n-hydrocarbons with increasing maturity for a natural series of
kerogens consisting mainly of marine (type II) and coaly
(type III) kerogens. A study by Hors®eld (1989) with
kerogens from a wide range of depositional settings
showed that the chain length distribution in kerogens
with very similar maturities and bulk elemental compositions could be extremely variable. The latter work did
not indicate any progressive depletion in longer chain nalkyl pyrolysate with increasing maturity. van Graas et
al. (1981) observed a relative increase in the n-alkenes
and n-alkanes in the C10+ pyrolysate of immature to

early mature Toarcian shale kerogen.
Quanti®able pyrolysis products of kerogens under
Py±GC conditions (with a polymeric internal standard)
has also been applied for the characterization and typing of kerogens (Larter and Sent¯e, 1985; Sent¯e et al.,
1986; éygard et al., 1988). Hors®eld et. al. (1989) plotted the sums of the groups of n-alkanes in the ranges
C2±C5; C6±C14 and C15+ of arti®cially maturing samples in a ternary diagram for kerogen quality assesments. A similar diagram was calibrated with a series of
natural maturing samples (Hors®eld, 1989; Larter and
Hors®eld, 1993). This diagram included the total
resolved C1±C5 pyrolysate; the sum of the n-alkenes/nalkanes in the C6±C14 range and the total content of the
n-alkenes/n-alkanes in the C15+ range. The variations
in the abundance of aromatic versus aliphatic hydrocarbons in pyrolysates are also frequently used to distinguish between di€erent origins of the kerogens
(Larter, 1985; Hors®eld, 1989).
The e€ect of minerals in whole rock pyrolysates has
been discussed (Bjorùy et al., 1984; Hors®eld, 1984;
Larter, 1984; Solli et al., 1984). It is documented that
clay minerals produce a pyrolysate with a relatively
greater fraction of gaseous low-molecular-weight material and aromatic hydrocarbons than when mineral free

kerogens were pyrolysed. It is also found that the
mineral matrix of sediments may retain high-molecularweight hydrocarbons (C15+) thus a€ecting the composition of the pyrolysate. These mineral e€ects are most

pronounced in samples with low TOC value (about 1%
or lower). It is also shown that the relative abundance of
aromatic compounds is higher in whole rock pyrolysates
than isolated kerogens, while the abundance of C17±C19
hydrocarbons is lower in whole rock pyrolysates than
isolated kerogen. However, whole rock pyrolysates may,
in some cases, give a better indication of the type and
distribution of the hydrocarbons likely to be produced
under natural catagenetic conditions (Bjorùy et al., 1984).
The data sets generated by Py±GC of many samples
quickly become unmanageably large, and data analytical tools are necessary. Principal component (PC)
modelling has been used for the analysis of geochemical
data by, e.g. éygard et al. (1988), Barth (1991), Requejo
et al. (1994), Barth et al. (1996) and Odden et al. (1998).
In this work, PC modelling is applied to compare the
light hydrocarbon distribution of source rock pyrolysates by using quanti®ed peaks in the C5±C13 range
from the gas chromatograms. PC modelling can be used
either unsupervised, treating samples from di€erent
groups in a single model without utilizing a priori
information of group belonging, or supervised, with a

separate model for each group. The supervised approach
(SIMCA) has proven powerful in numerous chemical
and geochemical applications (Kvalheim, 1987; Petersen
et al., 1996; Skjevrak, 1997; Odden and Kvalheim, 2000).
In this study, the light hydrocarbon compositions of
kerogen pyrolysates from source rock samples from the
North Sea hydrocarbon province o€shore Mid-Norway
and Denmark are examined. Mid-Norway was initially
selected as the study area, since a comprehensive set of
source rock samples had been collected for earlier work
(Odden et al., 1998). In order to facilitate the correlation
with source rock samples from other locations, the data
set is expanded with samples from the Danish sector.
The light hydrocarbon fraction generated by pyrolysis
of kerogen has been analysed by gas chromatography
on-line, using the method described by Bjorùy et al.
(1984) and Solli et al. (1984). The individual components in the C5±C13 range have been quanti®ed and used
to discriminate between the di€erent source rock types.
The aim of this study is to use the compositional data
from pyrolysis to calibrate classi®cation diagrams for

optimal discrimination between the kerogens of marine
origin (type II±III) and the coals and coaly shales (type
III/IV) based on results from multivariate modelling
(PCA and SIMCA). The in¯uence of maturity on the
light hydrocarbon composition is also evaluated.
The identi®cation of the speci®c light hydrocarbon
components which contribute most signi®cantly to
discriminate between the source rocks is crucial both for
obtaining a simpli®ed classi®cation scheme and to explain

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

di€erences between them. Thus, the multivariate models
are used to de®ne suitable and simple parameters based
on the most common and abundant of the generated
light hydrocarbon components. These indicator components can then be used in simpli®ed applications, for
example by direct inspection of the pyrograms.

2. Sample set, experimental and data treatment
2.1. Sample set

In this study Py±GC has been performed on source
rock samples from the North Sea hydrocarbon province
o€shore Mid-Norway and Denmark (Table 1). The
samples are selected to cover di€erent maturity levels
and facies variations within the source rocks. The most
important hydrocarbon source rock in the Norwegian
and Danish sectors are the Upper Jurassic marine
shales, known as the Spekk Formation o€shore MidNorway and the Farsund Formation in the Danish sector (Fig. 1). The Spekk Formation is of marine origin
and contains kerogen which varies from type II to type
III (Whitley, 1992). Johannesen (1995) divided the
Spekk Formation chronostratigraphically into the
Upper Spekk Unit, which contains dominantly type II
to II/III kerogen and the Lower Spekk Unit with type
III to IV kerogen. The petroleum potential of the Farsund Formation is, as for the Spekk, highly variable.
The richest part of the formation is the upper part (``hot
unit'') which contains oil-prone type II kerogen. The
organic carbon content of the Farsund Formation generally decreases downwards and the organic matter
becomes more gas-prone, mixed type II/III and type III
kerogen (Damtoft et al., 1992). The organic matter of
the coals and shales of the Lower Jurassic AÊre Formation from Mid-Norway and the Middle Jurassic Bryne

Formation o€shore Denmark is dominated by terrestrially-derived humic material (vitrinite and inertinite)
and is classi®ed as a type III to type IV kerogen. The
petrographical composition of the AÊre and Bryne Formation coals and shales is rather similar. Vitrinite is the
major component, inertinite varies between 10 and 50%
and exinite from minor to 10% (Odden, 1986; Khorasani,
1989; Damtoft et al., 1992; Petersen et al., 1995, 1996).
Core samples were preferred where available, but in
many cases, cuttings had to be used to cover di€erent
maturity levels (Table 1).
2.1.1. Norwegian sector
A total of 10 samples from each of the Spekk and AÊre
Formations were collected for analysis (Table 1). The
Spekk Formation samples (SP1±SP10) have a maturity
level from 0.5 to 1.0±1.1% Ro (Tmax=415±453 C). Two
of the samples (SP1 and SP2) are immature (Ro=0.5%;
Tmax=415 and 417 C) with hydrogen indices of 500 and

213

273 mg HC/g TOC, respectively. The oil-window

mature samples (SP3±SP7) have hydrogen indices ranging from 191 to 393 mg HC/g TOC (Ro=0.7±0.8%;
Tmax=433±448 C) and the late mature samples (SP8SP10) from 71 to 125 mg HC/g TOC (Ro=1.0±1.1%;
Tmax=448±453 C). Of these, the only set of core samples from the Spekk (SP3±SP6) is from a single well
lying within the oil-window. The AÊre Formation samples (AR1±AR10) have a maturity level ranging from
Ro=0.5±1.2% (Tmax=428±470 C) and hydrogen indices from 103 to 288 mg HC/g TOC. The total organic
carbon (TOC) contents, Rock Eval-type pyrolysis data
and vitrinite re¯ectance measurements are taken from
Odden et al. (1998).
2.1.2. Danish sector
Seven samples from the Farsund Formation and three
samples from the Bryne Formation were collected for
analysis (Table 1). The core samples from the upper part
of the Farsund Formation (F1±F3) are: one early
mature sample and two oil-window mature samples, all
with high hydrogen indices, i.e. F1 (HI=611 mg HC/g
TOC; Tmax=427 C); F2 (HI=378 mg HC/g TOC;
Tmax=435 C) and F3 (HI=483 mg HC/g TOC;
Tmax=437 C). The cutting samples (F4±F7) from the
Farsund Formation are one from the upper part (F4)
and three from the lower part (F5±F7). Two of these

samples are early mature and two oil-window mature:
F4 (HI=357 mg HC/g TOC; Tmax=429 C); F5
(HI=156 mg HC/g TOC; Tmax=429 C); F6 (HI=125
mg HC/g TOC; Tmax=438 C) and F7 (HI=30 mg HC/
g TOC; Tmax=440 C). The Bryne Formation samples
(C1±C3) are all oil-window mature (Tmax=442±450 C)
and have hydrogen indices ranging from 216 to 224 mg
HC/g TOC and are classi®ed as type III to IV kerogen
(Petersen et al., 1995, 1996). The samples from the
Danish sector were screened for total organic carbon
(TOC) and by Rock Eval-type pyrolysis. The vitrinite
re¯ectance was not measured for these samples.
2.2. Experimental
2.2.1. Pyrolysis±gas chromatography
Up to 15 mg of the ®nely crushed whole rock samples
were heated in a GHM (Geo®na Hydrocarbon Meter)
at 330 C for 4 min, during which time thermal extraction occured (equivalent of the S1 peak of the Rock
Eval). Further analysis of the thermal extract was not
done in this application. The furnace temperature was
then increased to 550 C at 25 C/min with a ®nal hold
time of 3 min during the pyrolysis step (equivalent of the
S2 peak of the Rock Eval). The method is described by
Bjorùy et al. (1984).
The GC analyses were performed on a Varian 3400
Gas Chromatograph ®tted with a OV1 capillary column
(25 m  0.32 mm i.d.; 0.25mm ®lm thickness) with a

214

Table 1
Source rock samples used in this studya
Formation

Depth
(mRKB)

S1
(mg HC/g rock)

S2
(mg HC/g rock)

TOC
(wt%)

HI
(mg HC/g TOC)

PP
(mg HC/ g rock)

AR1
AR2
AR3
AR4
AR5
AR6
AR7
AR8
AR9
AR10
SP1
SP2
SP3
SP4
SP5
SP6
SP7
SP8
SP9
SP10
F1
F2
F3
F4
F5
F6
F7
C1
C2
C3

AÊre
AÊre
AÊre
AÊre
AÊre
AÊre
AÊre
AÊre
AÊre
AÊre
Spekk
Spekk
Spekk
Spekk
Spekk
Spekk
Spekk
Spekk
Spekk
Spekk
Upper Farsund
Upper Farsund
Upper Farsund
Upper Farsund
Lower Farsund
Lower Farsund
Lower Farsund
Bryne
Bryne
Bryne

2814.00
3529.00
4143.00
4278.00
4330.00
4539.79
4585.75
4613.70
4730.00
4795.00
2535.00
3103.00
3732.90
3742.75
3748.51
3754.07
3862.00
4170.00
4182.00
4209.00
2983.69
3434.55
4419.26
3992.90
3383.30
4024.90
4120.90
3571.77
3590.30
3597.46

4.00
4.90
12.90
3.14
7.35
1.88
11.50
0.33
1.98
9.43
2.43
1.70
3.37
3.18
3.22
2.34
3.45
1.10
3.20
2.50
3.33
0.84
2.61
2.14
0.89
1.30
0.96
16.60
9.16
21.81

105.00
66.50
116.00
26.85
159.05
20.00
128.30
7.37
26.34
116.03
37.80
18.41
12.69
7.29
12.23
6.54
13.85
2.20
6.70
3.20
39.33
10.86
21.32
12.33
3.65
3.52
0.88
120.56
92.29
166.18

48.20
24.70
59.20
26.00
81.00
7.00
70.50
5.16
14.40
57.90
7.60
6.80
3.30
2.15
3.10
2.02
7.25
2.50
5.30
4.50
6.44
2.87
4.41
3.45
2.34
2.82
2.92
55.70
42.80
74.20

218
269
196
103
196
288
182
143
183
200
500
273
388
339
393
324
191
86
126
71
611
378
483
357
156
125
30
216
216
224

109.00
71.40
128.90
30.00
166.40
21.90
139.80
7.70
28.30
125.50
40.20
20.10
16.10
10.50
15.40
8.90
17.30
3.20
9.80
5.70
42.70
11.70
23.90
14.50
4.50
4.80
1.80
137.20
101.50
188.00

PI

0.04
0.07
0.10
0.10
0.04
0.09
0.08
0.04
0.07
0.08
0.06
0.08
0.21
0.30
0.21
0.26
0.20
0.33
0.32
0.44
0.08
0.07
0.11
0.15
0.20
0.27
0.52
0.12
0.09
0.12

Tmax
( C)

%Ro

Sample
type

429
428
458
465
455
470
461
470
452
464
415
417
448
447
446
448
433
453
453
448
427
435
437
429
429
438
440
443
442
450

0.55
0.65
0.85
0.90
0.90
1.00
1.05
1.10
1.15
1.20
0.50
0.50
0.7±0.8
0.7±0.8
0.7±0.8
0.7±0.8
0.80
1.0±1.1
1.0±1.1
1.0±1.1
n.m.b
n.m.
n.m.
n.m.
n.m.
n.m.
n.m.
n.m.
n.m.
n.m.

Cut
Cut
Cut
Cut
Cut
Core
Core
Core
Cut
Cut
Cut
Cut
Core
Core
Core
Core
Cut
Cut
Cut
Cut
Core
Core
Core
Cut
Cut
Cut
Cut
Core
Core
Core

a
mRKB=meter Rotary Kelly Bushing; S1, free hydrocarbons in the sample; S2, hydrocarbons generated by thermal degradation of the kerogen; TOC, total organic carbon; HI, 100  (S2/
TOC); PP, (S1+S2); PI, S1/(S1+S2); Tmax, temperature of maximum S2 peak; % Ro, vitrinite re¯ectance measurements.
b
Not measured.

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

Sample
code

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

liquid nitrogen-cooled trap. The temperature program of
the gas chromatograph started at ÿ10 C, with a temperature gradient of 6 C/min to 290 C. The column was
connected to a ¯ame ionisation detector. Quanti®cation
of all components was based on peak areas. The determination of response factors was not considered necessary
for this kind of data. The compounds were identi®ed
through analyses of known standards (such as Black ven
Marl and SK142) and previous Py±GC±MS data.
Analyses of these standards show good reproducibility.
2.3. Data treatment
The data were interpreted through visual inspection
of the raw chromatograms from pyrolysis, cross-plots of
parameters and by multivariate data analysis. For the

215

latter, Principal Component Analysis (PCA) and Soft
Independent Modelling of Class Analogy (SIMCA)
classi®cation were applied. A brief summary of these
two methods are given below.
2.3.1. Principal component analysis
PCA gives compressed information of the total variation in a data table (Wold et al., 1984, 1987; Birks,
1987). Detailed description of the PCA technique is also
given in Jolli€e (1986). PCA is in this paper applied as
an exploratory data analysis tool, without any assumptions about the statistical distribution of the individual
components or possible inter-relations between them. In
traditional statistical analysis of independent variables
there is a requirement that the number of objects (samples)
should be signi®cantly larger than the number of variables.

Fig. 1. The two major source rock intervals o€shore Mid-Norway and Denmark.

216

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

However, this principle does not apply to PCA, since
PCA is a method for analysing data sets with strong
correlated variables (Wold et al., 1987; Kvalheim, 1988).
One major advantage of modelling a data set in terms of
principal components is the ease of visualising the
results with the ®rst two or three principal components.
PCA extracts systematic variation in the data matrix
related to a set of orthogonal vectors (principal components). The ®rst principal component (PC1) explains the
largest percentage of the total variation in the data set
(given as % variance explained by the component). The
second principal component (PC2) describes the next
most important direction, and the procedure continues
until no additional statistically signi®cant pattern can be
found in the data. This procedure reduces the data
matrix for the set of samples to a limited number of
principal components that contain all the systematic
variation in the data. The co-ordinates of each sample
projected on the axis de®ned by the principal components are termed ``scores'', while the coecients for each
variable direction in its linear expansion of the principal
components, is termed the ``loading'' of the variable.
These data are displayed in bivariate score plots, loading
plots (Miller and Miller, 1984; Wold et al., 1984, 1987)
and/or biplots (Gabriel, 1971; Birks, 1987). The latter
shows the objects (samples) and variables (individual
components) plotted on the same axis.
2.3.2. SIMCA analysis
Wold (1976), Wold and Sjùstrùm (1977) and Wold
(1978) developed the method called SIMCA to discriminate

between di€erent groups of samples as well as to quantify discrimination power. This procedure identi®es differences and similarities between groups based on
separate PCA of each group, and ®nds and uses regularities in the multivariate data by recognizing data patterns (Wold and Sjùstrùm, 1977; Albano et al., 1978,
1981; Wold et al., 1983; Kvalheim and Karstang, 1992).
This results in sub-grouping of samples with similar
variable patterns. If the groups are strongly overlapping
SIMCA will not give useful results. The separation may
be improved if variables with low discrimination power
are excluded (Albano et al., 1981). A detailed description of this multivariate method is given by Odden and
Kvalheim (2000).
When matching the PC model, one must determine
the appropriate number of components for each group.
Cross-validation can be used to select the number of
principal components that provide models with maximum predictive ability for each group of samples separately (Wold, 1978).
2.3.3. Data preprocessing
The principal component modelling and SIMCA
analysis were performed using the Sirius program running on personal computers (Kvalheim and Karstang,
1987). The total measured peak area of all the identi®ed
components of the C5±C13 fraction (Table 2) of the
pyrolysates were normalized to 100%, prior to the multivariate analyses, to compensate for di€erences in the
total amounts of the identi®ed compounds in each
chromatogram. Before the analysis was performed, all

Table 2
Codes for the components used in this study
Abbreviation

Hydrocarbon

Abbreviation

Hydrocarbon

C5-ene
nC5
2,3DMC4
CC5
2MC5
C6-ene
nC6
MCC5
Benz
CC6
2MC6
3MC6
C7-ene
nC7
MCC6
Tol
2MC7
3MC7
C8-ene
nC8
nPCC5

n-Pentene
n-Pentane
2,3-Dimethylbutane
Cyclopentane
2-Methylpentane
n-Hexene
n-Hexane
Methylcyclopentane
Benzene
Cyclohexane
2-Methylhexane
3-Methylhexane
n-Heptene
n-Heptane
Methylcyclohexane
Toluene
2-Methylheptane
3-Methylheptane
n-Octene
n-Octane
n-Propylcyclopentane

EBenz
m+p xyl
o xyl
C9-ene
nC9
A1
A2
O-ETol
C10-ene
nC10
A3
C11-ene
nC11
A4
Naph
C12-ene
nC12
2MNaph
1MNaph
C13-ene
nC13

Ethylbenzene
meta+para Xylene
ortho Xylene
n-Nonene
n-Nonane
Unidenti®ed aromatic
Unidenti®ed aromatic
ortho-ethyltoluene
n-Decene
n-Decane
Unidenti®ed aromatic
n-Undecene
n-Undecane
Unidenti®ed aromatic
Naphthalene
n-Dodecene
n-Dodecane
2-Methylnaphthalene
1-Methylnaphthalene
n-Tridecene
n-Tridecane

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

data sets were scaled to variance equal to 1.0 by division
with the standard deviation for each variable, so that
the magnitude of the variables did not in¯uence the
results. The e€ects of normalization and weighting of
chemical data prior to multivariate analysis have been
discussed by Johansson et al. (1984) and Kvalheim (1985).

3. Results and discussion
The sample set and codes for the components are
given in Tables 1 and 2, respectively. Table 2 contains
four aromatics, A1, A2, A3 and A4, with clearly aromatic structures from mass spectra which were impossible to identify more precisely.
Fig. 2 contains pyrograms of one sample from each of
the Spekk and AÊre Formations in full scale and on an
expanded scale for the AÊre sample only. The full scale
pyrograms shows the whole gas chromatogram of nalkenes and n-alkanes (up to C32 for the Spekk and C28
for the AÊre) together with three of the most abundant
aromatics (benzene, toluene and m+p xylene). The
partial pyrogram of the AÊre Formation sample shows
the peaks in the C5±C13 range which are identi®ed and
quanti®ed in this study (Table 2). The light hydrocarbon
components used are those which are present in relatively large proportions, because we believe that smaller
peaks are not reliable for interpretation.
3.1. Unsupervised PCA
Principal component modelling was ®rst performed
on the whole data set to detect irrelevant variables.
Based on measured peak areas of the C5±C13 fraction
(Table 2) and Tmax from Rock Eval pyrolysis, the systematic variation in the light hydrocarbon distributions
of the AÊre, Spekk, Farsund and Bryne samples were
modelled by PCA. The variables nC5, 2MC5, MCC6,
1c3DMCC5, 1t2DMCC5, 1t3DMCC5, EBenz, A3 and
A4 were removed as insigni®cant in a preliminary data
analysis. These variables were characetrised by a relatively high standard deviation (not explained by the three
major PCs) or that the peaks were so small that they
were not measured in all samples due to poor resolution.
An initial modelling with all samples and all variables
(except the nine above) showed a clear separation of the
source rocks. The coals and coaly shales of the AÊre and
Bryne Formation samples which represent the terrigenous composition plotted in one cluster of the score
plot, while the more oil-prone marine Spekk and Farsund Formation samples plotted in a separate cluster.
The loading plot indicated that the AÊre and Bryne samples were relatively enriched in aromatics and naphthalenes, while the Spekk and the Farsund samples (except
the two immature Spekk samples, discussed below) were
enriched in n-alkenes and n-alkanes with the highest

217

abundance in the ``hot'' shales from the upper part of
the Farsund Formation.
Inclusion of the total organic carbon content (TOC)
and other Rock Eval parameters (Table 1) in the analysis do not increase the explained total variance in the
model. Rather, additional ``noise'' is added to the data set.
New PC modelling was performed when all the
immature and marginally mature samples were excluded
(SP1±SP2, AR1±AR2, F1, F4, F5). The objective was to
detect the light hydrocarbon components which most
e€ectively distinguishes between mature source rock
samples. Five more variables (DMC4, 2MC7, 3MC7,
A2, C10-ene) in addition to the nine above were deleted,
as they were interpreted to carry little or no information
(high RSD) after preliminary data analysis. Thus, 23
samples with 32 variables remained (inclusive of Tmax).
The PCA model is shown in Fig. 3a and b. Fig. 3a,
the score plot, shows a clear separation of the di€erent
source rocks with the AÊre and Bryne samples plotting
on one side of the diagonal through origin, and the
Farsund and Spekk samples on the other side. In the
corresponding loading plot (Fig. 3b) the same tendencies as the modelling above are shown, i.e. the AÊre and
Bryne samples are relatively enriched in aromatics and
naphthalenes (lower left corner), while the Spekk and
Farsund samples, particularly the organic rich shales
from the upper part of the Farsund Formation (F2±F3),
contain a higher proportion of n-alkenes and n-alkanes
(right side). PC1 explains 57.4% of the variance of the
data set, and is mostly related to the separation of the
di€erent source rock types and to some extent maturity
caused by the late-mature AÊre samples. PC2 reveals
internal variations (possibly facies related) within the
source rocks and de®nes the next most important direction
of variation (20.6%). Thus, the two components together explain 78.0% of the total variance of the data set.
PCA was also performed when Tmax was excluded,
and the ®nal results were almost the same regardless
inclusion or exclusion of this maturity parameter. This
illustrates that Tmax does not in¯uence the separation
between the source rocks, and indicates that the light
hydrocarbon composition generated by pyrolysis is
strongly a€ected by source facies. The same conclusion
was also reached for the light fraction of thermal
extracts (Odden et al., 1998).
3.2. SIMCA
PC modelling was performed for the two groups of
samples (Spekk/Farsund and AÊre/Bryne) separately,
using cross validation. SIMCA is in this application
used to quantify discrimination power of the variables.
This makes possible to reveal samples which show a
poor match to their own model (outliers). If outliers are
not excluded they may in¯uence the model and induce
biased predictions.

218

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

Fig. 2. Pyrograms of an oil-window mature Spekk and AÊre Formation sample, at full scale and on an expanded scale where components
from C5 to C13 are identi®ed for the AÊre sample only.

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

219

Fig. 3. Principal component analysis on the mature samples and 32 selected variables: (a) scores; (b) loadings on PC1 vs. PC2. Sample
and variable codes as in Tables 1 and 2, respectively.

220

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

First, PCA of 12 Spekk (SP3±SP10) and Farsund
Formation samples (F2±F3; F6±F7) and the thirty-one
selected variables (Tmax excluded) using cross-validation
gave two signi®cant PCs accounting for 38.6% (PC1)
and 34.1% (PC2) of the total variance of the data set.
The score plot (not shown here) indicates that the samples from the Upper Farsund (F2±F3), Lower Farsund
(F6±F7) and Spekk plot separately, re¯ecting variations
in the extent of terrestrial in¯uence of the marine shales.
This is also indicated by the Rock Eval-type pyrolysis
data (Table 1).
PC modelling of the eleven AÊre (AR3±AR10) and
Bryne (C1±C3) Formation samples and the thirty-one
selected variables using cross validation gave two signi®cant components (PC1 and PC2) accounting for 65.4
and 14.9% of the total variance of the data set, respectively. The scattering of these samples (not shown here)
is most probably related to di€erences in the maceral
composition.
However, as it is important not to delete the samples
which represent the natural variations within the source
rocks, all samples were included in the ®nal analysis.
To select the individual components of the C5±C13
fraction with highest separating power, the two groups
of source rocks (AÊre/Bryne and Farsund/Spekk) were
subjected to SIMCA analysis.
From this analysis ten variables with decreasing discrimination power were identi®ed (such as m+p xyl >
2MNaph > C6-ene > C7-ene; see Table 3). The variables with discrimination power less than the average
for the group (in this case less than 3.3) were excluded,
because values below this suggest poor discrimination
(Albano et al., 1981).
This result shows that the content of m+p xylene
most e€ectively distinguishes between the light hydrocarbon composition generated by pyrolysis of the source
rocks. The same data analysis was also performed on
Spekk and AÊre samples only, and the ®nal result was
almost the same regardless inclusion or exclusion of the
Farsund and Bryne samples.
3.2.1. Variables with high separation power
PCA was performed on all 30 samples with the four
variables (Table 3) with highest separation power (m+p
xyl, 2MNaph, C6-ene and C7-ene). In Fig. 4, the biplot
shows an excellent separation of the two groups of
source rocks, even when the immature samples (SP1±
SP2; AR1±AR2) are included. The AÊre and Bryne coal
pyrolysates are enriched in m+p xylene and 2-methylnaphthene and the Spekk and Farsund pyrolysates give
a relatively higher proportion of the n-hexene and nheptene. PC1 explains 95.4% of the total variance of the
data set, i.e. showing a strong correlation between the
discriminating variables. This indicates that the four
individual components are related to the same geochemical characteristics. However, one oil-window

Table 3
Ranking of individual components after SIMCA analysis
(values above 3.3 only)
Variables

Discrimination power

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

10.2
6.5
4.8
4.6
3.9
3.7
3.6
3.5
3.4
3.3

m+p xyl
2MNaph
C6-ene
C7-ene
A1
nC8
nC7
C8-ene
Tol
C9-ene

mature Bryne sample (C2) plots away from the other
mature AÊre (AR3±AR10) and Bryne (C1, C3) samples.
The somewhat atypical behaviour of this sample is most
probably related to di€erences in the maceral composition.
3.3. Pyrograms of di€erent source rocks
As a consequence of the results from multivariate
modelling, direct inspection of pyrograms was performed, and the m+p xylene peak relative to nC8 and
nC9 is highlighted on those presented below (Figs. 5±7).
3.3.1. Spekk Formation pyrolysates
In Fig. 5, pyrograms of an immature and oil-window
mature Spekk Formation sample are shown on an
expanded scale. The pyrograms show an n-alkene/nalkene homology ranging from C5 to C13, together with
aromatics, naphthalenes and branched hydrocarbons.
Three of the unidenti®ed aromatics (A1, A2 and A3) are
present in the immature sample and two of them (A1,
A2) in the oil-window mature sample. The immature
Spekk Formation sample (SP1) contains a high proportion of benzene, toluene and m+p xylene relative to nalkenes/n-alkanes. The oil-window mature sample from
Spekk (SP5) shows that the relative proportions of aromatics is reduced resulting in a more dominant nalkene/n-alkane doublet homology as a result of
increasing maturity (van Graas et al., 1981). It is also
observed that the Spekk samples (SP8±SP10) with a
vitrinite re¯ectance above 1.0% contain a slightly lower
proportion of the n-alkene/n-akane doublet homology
relative to the aromatics than the oil-window mature
samples, which is a result of increasing maturity.
3.3.2. AÊre Formation pyrolysates
Fig. 6 displays partial pyrograms of an immature
(AR1) and oil-window mature (AR3) AÊre Formation
sample. The pyrograms show that the aromatics (as
benzene, toluene and m+p xylene) give the largest peaks
in both samples. The four unidenti®ed aromatics, A1,

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

A2, A3 and A4, are also present in the immature and
oil-window mature samples. The high proportion of
aromatics and methylnaphthalenes relative to n-hydrocarbons is typical for vitrinite rich coals (Larter and
Douglas, 1980; Bjorùy et al., 1984; Larter, 1984, 1985;
Solli et al., 1984; Sent¯e et al., 1986; Boreham and
Powell, 1991; Larter and Hors®eld, 1993). This shows
that the content of m+p xylene is strongly related to
source facies. Thus, the somewhat higher abundance of
m+p xylene in the oil-window mature sample (AR3)
compared with the immature sample (AR3) may be due
to di€erences in the maceral composition. The in¯uence
on maturity variations of this parameter is discussed below.
3.3.3. Farsund and Bryne Formation pyrolysates
In Fig. 7, pyrograms (expanded scale) of three oilwindow mature samples from Upper Farsund (F3),
Lower Farsund (F6) and Bryne (C3) Formations are
shown. The pyrograms show an n-alkene/n-alkane
homology ranging from C5 to C13, together with aromatics, naphthalenes and branched hydrocarbons. The
pyrogram from the ``hot'' upper part of the Farsund

221

Formation (F3) shows a dominant n-alkene/n-alkane
doublet homology relative to the aromatics, while that
from the more terrestrially in¯uenced lower part of the
Farsund Formation (F6) contains a somewhat higher
proportion of aromatics. The Bryne Formation sample is
very similar to the oil-window mature AÊre sample (Fig. 6,
bottom) with a high abundance of aromatics and methylnaphthalenes. These pyrograms show that the proportion of m+p xylene clearly separates between the source
rocks and increases with increasing terrigenous organic
matter.
The content of m+p xylene relative to n-hydrocarbons do not di€er as much between the marginally
mature and oil-window mature Farsund Formation
samples as for the immature and oil-window mature
Spekk (Fig. 5). One plausible explanation may be that the
Spekk samples are even less mature (Tmax=415±417 C)
than the Farsund samples (Tmax=427±429 C).
Visual inspection of the gas chromatograms from
pyrolysis thus con®rms the results from multivariate
data analysis that the content of m+p xylene is an
important indicator of source facies.

Fig. 4. Biplot; scores (samples) and loadings for four selected variables on PC1 vs. PC2. The variable loadings are marked with
ellipses.

222

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

Fig. 5. Pyrograms of an immature (SP1, top) and oil-window mature (SP5, bottom) Spekk Formation sample, on an expanded scale
where components from C5 to C13 are identi®ed.

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

223

Fig. 6. Pyrograms of an immature (AR1, top) and oil-window mature (AR3, bottom) AÊre Formation sample, on an expanded scale
where components from C5 to C13 are identi®ed.

224

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

Fig. 7. Pyrograms of Upper Farsund (F3, top), Lower Farsund (F6, middle) and Bryne (C3, bottom) Formation samples at oil-window
maturity, on an expanded scale where components from C5 to C13 are identi®ed.

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

3.3.4. Classi®cation diagrams based on individual
parameters
SIMCA analysis has detected that the two most discriminating individual variables are m+p xylene and 2methylnaphthalene. A cross plot of the percentages of
these two individual components is shown in Fig. 8, the
immature samples (SP1±SP2; AR1±AR2) are included.
This ®gure shows a clear separation between the two
groups of source rocks. The AÊre and Bryne samples
contain relatively more m+p xylene and 2-methylnaphthalene than the samples from the Spekk and Farsund Formations, the exceptions are the two immature
Spekk (SP1±SP2) samples with a relatively high abundance of m+p xylene. This ®gure con®rms that the
contents of m+p xylene and 2-methylnaphthalene are
mostly related to source facies. However, there is
observed an increase of these parameters with increasing
maturity for the late mature coal samples only, in contrast to the decrease from immature to mature Spekk
samples.

225

Crossplots of the contents of the other individual
components with high separation power (such as C6ene, C7-ene, nC8 and C8-ene) and m+p xylene have been
performed. These plots also clearly distinguish between
the two source rock groups with a much higher abundance of n-hydrocarbons in the Spekk and Farsund
samples than in the AÊre and Bryne.
Larter and Douglas (1980) introduced a plot of the
m+p xylene/n-octene ratio vs. maturity (vitrinite re¯ectance values normalised to % carbon content) for alginites, sporinites and vitrinites. Our data are plotted in a
similar diagram with Tmax (Fig. 9). The data are scattered, particularly the AÊre and Bryne samples. However,
the m+p xylene/n-octene ratio separates the two source
rock groups, with a higher abundance in the AÊre and
Bryne than in the Farsund and Spekk. The m+p xylene/
n-octene ratio increases in the late-mature AÊre samples
or when Tmax is about 465 C. The ratio seems to be
constant from immature to oil-window mature AÊre
samples, and the scattering of these samples may be

Fig. 8. Percentages of m+p xylene vs. 2-methylnaphthalene relative to the sum of all identi®ed components of the C5±C13 fraction of
the Spekk, Farsund, AÊre and Bryne Formation samples.

226

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

Fig. 9. m+p Xylene/n-octene concentration ratio vs. Tmax for the Spekk, Farsund, AÊre and Bryne Formation samples compared with
data from Larter and Douglas (1980).

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

facies related. This is in general agreement with Boreham and Powell (1991) who stated that the xylenes were
constant to a Tmax value above 460 C. The m+p xylene/
n-octene ratio decreases from the immature Spekk samples (SP1, SP2) with a Tmax below 420 C to the more
mature Spekk and Farsund samples. On the gas chromatograms it has been observed that the most mature
Spekk (SP8±SP10) samples are slightly depleted in nhydrocarbons. This should result in an increase of the
m+p xylene/n-octene ratio, but this increase is not signi®cant in the ®gure. The maturity levels of the Farsund
and Bryne samples do not vary as much as those of the
Spekk and AÊre, but they plot among the Spekk and AÊre
samples, respectively.

4. Conclusions
The data obtained by Py±GC show a clear compositional di€erence between the marine shales of the Spekk
and Farsund Formations and the coals and coaly shales
of the AÊre and Bryne Formations. The Farsund and
Spekk samples contain a higher proportion of n-alkenes
and n-alkanes than the AÊre and Bryne samples, which is
richer in mono-aromatics and naphthalenes. There are
also di€erences between the marine shales: the ``hot''
upper part of the Farsund Formation contains a relatively higher proportion of the n-alkene/n-alkane doublet homology than the Spekk samples and much more
than the samples from the terrestrially in¯uenced lower
part of the Farsund Formation.
Multivariate modelling (PCA and SIMCA) has
proved to be ecient for detecting an optimal subset of
individual components to discriminate between the different source rock types. Based on our data, the m+p
xylene signal is the dominant distinguishing factor
between the source rocks, i.e. those of marine origin
with variable input of terrigenous organic matter and
the coals and coaly shales. The abundance of this parameter increases with increasing terrestrial input. The
next most dominant individual components able to
separate the two source rock types are 2-methylnaphthalene, n-hexene and n-heptene.
Visual inspection of gas chromatograms of the pyrolysates con®rms that the content of m+p xylene is an
important indicator of source rock facies, and can be
used in simpli®ed applications.
The relative proportion of the n-alkene/n-alkane
doublet homology increases from immature to oil-window mature Spekk samples, but there is observed a
decrease when the maturity level exceeds a vitrinite
re¯ectance of 1.0% or Tmax above 450 C. For the coals
and coaly shales the content of m+p xylene seems constant to a Tmax value of about 465 C, but above this
maturation level there is observed an increase. However,
it has been demonstrated that source rock type is the

227

dominant factor controlling light hydrocarbons generated by pyrolysis of kerogen, but maturity variations
can result in sub-trends.

Acknowledgements
We thank Geolab Nor, Trondheim for the analytical
work (screening analysis and pyrolysis±GC) with a special thanks to M. éstbye-Hansen. GEUS with J. Bojesen-Koefoed is acknowledged for supplying the source
rock samples from the Danish sector. R.G. Schaefer and
T. Brekke are thanked for constructive reviewing and L.
Schwark for careful editorial handling. Statoil is
thanked for permission to publish this work.
Associate EditorÐL. Schwark

References
Albano, C., Dunn III, W., Edlund, U., Johansson, E., Norden,
B., Sjùstrùm, M., Wold, S., 1978. Four levels of Pattern
Recognition. Analytical Chimica Acta 103, 429±443.
Albano, C., Blomquist, G., Coomans, D., Dunn III, W.J.,
Edlund, U., Eliasson, B., et al., 1981. Pattern recognition by
means of disjoint principal components models (SIMCA).
Philosophy and methods. In: Proceedings Symposium on
Applied Statistics, NEUCC (Techn. Univ., Lyngby),
RECAU (Aarhus Univ.) and RECKU (Copenhagen Univ.)
in cooperation with the Danish Society of Theoretical Statistics, Copenhagen, pp.183±203.
Barth, T., 1991. Organic acids and ions in waters from petroleum reservoirs, Norwegian Continental shelf: a multivariate
statistical analysis and comparison with American reservoir
formation waters. Applied Geochemistry 6, 1±15.
Barth, T., Andersen, B., Iden, K., Johansen, H., 1996. Modelling source rock production potentials for short-chain
organic acids and CO2 Ð a multivariate approach. Organic
Geochemistry 25, 427±438.
Birks, H.J.B., 1987. Multivariate analysis in geology and geochemistry; an introduction. In: Kvalheim, O.M. (Ed.), Proceedings of the Multivariate Statistical Workshop of
Geologists and Geochemists 1986, Chemometrics and Intelligent Laboratory Systems, Vol. 2. Elsevier, Amsterdam, pp.
15±28.
Bjorùy, M., Solli, H., Hall, K., Leplat, P., 1984. Analysis of
source rocks, reservoir rocks and cap rocks by combined
thermal extraction and pyrolysis±gas chromatography. In:
Thomas, B.M. et al. (Eds.), Petroleum Geochemistry in
Exploration of the Norwegian Shelf. Graham & Trotman,
London, pp. 327±337.
Boreham, C.J., Powell, T.G., 1991. Variation in pyrolysate
composition of sediments from the Jurassic Walloon Coal
Measures, eastern Australia as function of thermal maturation. Organic Geochemistry 17, 723±733.
Damtoft, K., Nielsen, L.H., Johannesen, P.N., Thomsen, E.,
Andersen, P.R., 1992. Hydrocarbon plays of Danish central
trough. In: Spencer, A.M. (Ed.), Generation, Accumulation

228

W. Odden, T. Barth / Organic Geochemistry 31 (2000) 211±229

and Production of Europe's Hydrocarbons II (Special Publication of European Association of Petroleum Geoscientists
No. 2). Springer-Verlag, Berlin, pp. 35±58.
Gabriel, K.R., 1971. The biplot graphic display of matrices
with application to principal component analysis. Biometrika
58, 453±467.
Hors®eld, B., 1984. Pyrolysis studies and petroleum exploration.
In: Brooks, J., Welte, D. (Eds.), Advances in Petroleum Geochemistry. Academic Press, London, pp. 247±298, Vol. 1.
Hors®eld, B., 1989. Practical criteria for classifying kerogens:
some observations from pyrolysis-gas chromatography.
Geochimica et Cosmochimica Acta 45, 2465±2472.
Hors®eld, B., Disko, U., Leistner, F., 1989. The microscale
simulation of maturation: outline of a new technique and its
potential applications. Geologische Rundschau 78, 361±374.
Johannesen,J., 1995. Source rock characterization of the Spekk
Formation, Haltenbanken (o€shore Mid-Norway), Cand.
Scient Thesis, University of Bergen.
Johansson, E., Wold, S., Sjùdin, K., 1984. Minimizing e€ects of
closure on analytical data. Analytical Chemistry 56, 1685±1688.
Jolli€e, I.T., 1986. Principal Component Analysis. Springer,
New York.
Khorasani, G.K., 1989. Factors controlling source rock potential of the Mesozoic coal-bearing strata from o€shore central
Norway: application to petroleum exploration. Bulletin of
Canadian Petroleum Geology 37, 417±427.
Kvalheim, O.M., 1985. Scaling of analytical data. Analytica
Chimica Acta 177, 71±79.
Kvalheim, O.M., 1987. Oil-source correlation by the combined
use of principal component modelling, analysis of variance and
a coecient of congruence. In: Kvalheim, O.M. (Ed.), Proceedings of the Multivariate Statistical Workshop of Geologists
and Geochemists 1986, Chemometrics and Intelligent Laboratory Systems, Vol. 2. Elsevier, Amsterdam, pp. 127±136.
Kvalheim, O.M., 1988. Interpretation of latent-variable for
projection methods and their use and aims in the interpretation of multicomponent spectroscopic and chromatographic
data. Chemometrics and Intelligent Laboratory Systems 4,
11±25.
Kvalheim, O.M., Karstang, T.V., 1987. A general purpose
program for multivariate data analysis. In: Kvalheim, O.M.
(Ed.), Proceedings of the Multivariate Statistical Workshop
for Geologists and Geochemists 1986. Elsevier, Amsterdam,
pp. 235±239, Chemometrics and Intelligent Laboratory Systems, Vol. 2.
Kvalheim, O.M., Karstang, T.V., 1992. SIMCA± classi®cation
by means of disjoint cross validated principal component
models. In: Brereton, R.G. (Ed.), Multivariate Pattern
Recognition in Chemometrics. Data Handling in Science and
Technology 9. Elsevier, Amsterdam, pp. 209±249.
Larter, S.R., 1984. Application of analytical pyrolysis techniques
to kerogen characterization and fossil fuel exploration/exploitation. In: Voorhees, K. (Ed.), Analytical Pyrolysis± Methods
and Application. Butterworths, London, pp. 212±275.
Larter, S.R., 1985. Integrated kerogen typing in the recognition
and quantitative assesment of petroleum source rocks. In:
Thomas, B.M. et al. (Eds.), Petroleum Geochemistry in
Exploration of the Norwegian Shelf. Norwegian Petroleum
Society Graham and Trotman, London, pp. 269±286.
Larter, S.R., Douglas, A.G., 1980. A pyrolysis±gas chromatographic method for kerogen typing. In: Douglas, A.G.,

Maxwell, J.R. (Eds.), Advances in Organic Geochemistry
1979. Pergamon Press, Oxford, pp. 579±584.
Larter, S.R., Douglas, A.G., 1982. Pyrolysis methods in
organic geochemistry. An overview. Journal of Analytical
and Applied Pyrolysis 4, 1±19.
Larter, S.R., Hors®eld, B., 1993. Determination of structural
components of kerogen by the use of analytical pyrolysis
methods. In: Engel, M.H., Macko, S.A. (Eds.), Organic
Geochemistry, Principles and Applications. Plenum Press,
New York, pp. 271±287.
Larter, S.R., Sent¯e, J.T., 1985. Improved kerogen typing for
petroleum source rock analysis. Nature 318, 277±280.
Miller, J.C., Miller, J.N., 1984. Statistics for Analytical Chemistry. Wiley, New York pp. 59±62.
Odden, W., 1986. A study of coal from Haltenbanken, Cand.
Scient Thesis, University of Bergen.
Odden, W., Kvalheim, O.M., 2000. Application of multivariate
modelling to detect hydrocarbon components for optimal
discrimination between two source rock types. Applied Geochemistry 15, 611±627.
Odden, W., Patience, R.L., van Graas, G.W., 1998. Application of light hydrocarbons (C4-C13) to oil/source rock correlations: a study of the light hydrocarbon compositions of
source rocks and test ¯uids o€shore Mid-Norway. Organic
Geochemistry 28, 823±847.
éygard, K, Larter, S.R., Senftle, J.T., 1988. The control of
maturity and kerogen type on quantitative analytical pyrolysis data. Organic Geochemistry 13, 1153±1162.
Petersen, H.I., Bojesen-Koefoed, J.A., Thomsen, E., 1995.
Coal-derived petroleum in the Middle Jurassic Bryne Formation in the Danish North±Sea- a new type of play. In:
Grimalt, J.