A study of the composition of light hydrocarbons (C
5
±C
13
)
from pyrolysis of source rock samples
W. Odden
a,*, T. Barth
b aStatoil a.s., N-5020 Bergen, Norway bUniversity of Bergen, N-5007 Bergen, NorwayReceived 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±C13range. Ten samples are from the marine shales of the Upper Jurassic Spekk
For-mation and 10 samples from the coals and coaly shales of the Lower Jurassic AÊre ForFor-mation, from oshore Mid-Norway. 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 dierent maturity levels and facies. The light hydrocarbon distribution generated by pyrolysis shows a clear compositional dierence between the dierent 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 inn-alkenes andn-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 ofmpxylene most eectively distinguishes between the dierent source rock types. This is veri®ed by multivariate modelling. It is shown that the light hydro-carbon composition generated by pyrolysis of kerogen is more aected by source facies than maturity variations.
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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 qual-ity assesments. Numerous pyrolysis techniques in com-bination 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;
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 ®ngerprint-based qualitative comparison of pyrogram data on whole rock or isolated kerogen. The whole gas chroma-togram of n-alkenes and n-alkanes (C1±C33) have
usually been identi®ed together with some of the aro-matics (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 fea-ture, which corresponds to the increasing maturity of
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the kerogen, has also been documented by other work-ers using Py±GC (Hors®eld, 1984; Larter, 1984). Larter and Douglas (1980) stated that the m+p xylene/n -octene ratio increased with increasing rank for vitrinite and sporinite, but they showed that this trend for algi-nites 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 Bore-ham 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 paran pre-cursors 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 -hydro-carbons 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 compo-sitions could be extremely variable. The latter work did not indicate any progressive depletion in longer chainn -alkyl 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 typ-ing of kerogens (Larter and Sent¯e, 1985; Sent¯e et al., 1986; éygard et al., 1988). Hors®eld et. al. (1989) plot-ted the sums of the groups of n-alkanes in the ranges C2±C5; C6±C14 and C15+ of arti®cially maturing
sam-ples in a ternary diagram for kerogen quality asses-ments. 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±C5pyrolysate; the sum of then-alkenes/n
-alkanes in the C6±C14range and the total content of the
n-alkenes/n-alkanes in the C15+ range. The variations
in the abundance of aromatic versus aliphatic hydro-carbons in pyrolysates are also frequently used to dis-tinguish between dierent origins of the kerogens (Larter, 1985; Hors®eld, 1989).
The eect 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 mate-rial and aromatic hydrocarbons than when mineral free
kerogens were pyrolysed. It is also found that the mineral matrix of sediments may retain high-molecular-weight hydrocarbons (C15+) thus aecting the
compo-sition of the pyrolysate. These mineral eects 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 analy-tical 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 pyr-olysates by using quanti®ed peaks in the C5±C13range
from the gas chromatograms. PC modelling can be used either unsupervised, treating samples from dierent 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 oshore 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 compo-nents in the C5±C13range have been quanti®ed and used
to discriminate between the dierent 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.
dierences 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 compo-nents 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 oshore Mid-Norway and Denmark (Table 1). The samples are selected to cover dierent 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 oshore Mid-Norway and the Farsund Formation in the Danish sec-tor (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 Far-sund 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 gen-erally 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 Forma-tion from Mid-Norway and the Middle Jurassic Bryne Formation oshore Denmark is dominated by terrest-rially-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 For-mation 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 dierent 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±453C). Two
of the samples (SP1 and SP2) are immature (Ro=0.5%;
Tmax=415 and 417C) with hydrogen indices of 500 and
273 mg HC/g TOC, respectively. The oil-window mature samples (SP3±SP7) have hydrogen indices ran-ging from 191 to 393 mg HC/g TOC (Ro=0.7±0.8%;
Tmax=433±448C) and the late mature samples
(SP8-SP10) from 71 to 125 mg HC/g TOC (Ro=1.0±1.1%;
Tmax=448±453C). Of these, the only set of core
sam-ples from the Spekk (SP3±SP6) is from a single well lying within the oil-window. The AÊre Formation sam-ples (AR1±AR10) have a maturity level ranging from Ro=0.5±1.2% (Tmax=428±470C) and hydrogen
indi-ces 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=427C); F2 (HI=378 mg HC/g TOC;
Tmax=435C) and F3 (HI=483 mg HC/g TOC;
Tmax=437C). 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=429C); F5
(HI=156 mg HC/g TOC;Tmax=429C); F6 (HI=125
mg HC/g TOC;Tmax=438C) and F7 (HI=30 mg HC/
g TOC; Tmax=440C). The Bryne Formation samples
(C1±C3) are all oil-window mature (Tmax=442±450C)
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 330C for 4 min, during which time thermal extrac-tion 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 550C at 25C/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).
Table 1
Source rock samples used in this studya
Sample
AR1 AÊre 2814.00 4.00 105.00 48.20 218 109.00 0.04 429 0.55 Cut AR2 AÊre 3529.00 4.90 66.50 24.70 269 71.40 0.07 428 0.65 Cut AR3 AÊre 4143.00 12.90 116.00 59.20 196 128.90 0.10 458 0.85 Cut AR4 AÊre 4278.00 3.14 26.85 26.00 103 30.00 0.10 465 0.90 Cut AR5 AÊre 4330.00 7.35 159.05 81.00 196 166.40 0.04 455 0.90 Cut AR6 AÊre 4539.79 1.88 20.00 7.00 288 21.90 0.09 470 1.00 Core AR7 AÊre 4585.75 11.50 128.30 70.50 182 139.80 0.08 461 1.05 Core AR8 AÊre 4613.70 0.33 7.37 5.16 143 7.70 0.04 470 1.10 Core AR9 AÊre 4730.00 1.98 26.34 14.40 183 28.30 0.07 452 1.15 Cut AR10 AÊre 4795.00 9.43 116.03 57.90 200 125.50 0.08 464 1.20 Cut SP1 Spekk 2535.00 2.43 37.80 7.60 500 40.20 0.06 415 0.50 Cut SP2 Spekk 3103.00 1.70 18.41 6.80 273 20.10 0.08 417 0.50 Cut SP3 Spekk 3732.90 3.37 12.69 3.30 388 16.10 0.21 448 0.7±0.8 Core SP4 Spekk 3742.75 3.18 7.29 2.15 339 10.50 0.30 447 0.7±0.8 Core SP5 Spekk 3748.51 3.22 12.23 3.10 393 15.40 0.21 446 0.7±0.8 Core SP6 Spekk 3754.07 2.34 6.54 2.02 324 8.90 0.26 448 0.7±0.8 Core SP7 Spekk 3862.00 3.45 13.85 7.25 191 17.30 0.20 433 0.80 Cut SP8 Spekk 4170.00 1.10 2.20 2.50 86 3.20 0.33 453 1.0±1.1 Cut SP9 Spekk 4182.00 3.20 6.70 5.30 126 9.80 0.32 453 1.0±1.1 Cut SP10 Spekk 4209.00 2.50 3.20 4.50 71 5.70 0.44 448 1.0±1.1 Cut F1 Upper Farsund 2983.69 3.33 39.33 6.44 611 42.70 0.08 427 n.m.b Core F2 Upper Farsund 3434.55 0.84 10.86 2.87 378 11.70 0.07 435 n.m. Core F3 Upper Farsund 4419.26 2.61 21.32 4.41 483 23.90 0.11 437 n.m. Core F4 Upper Farsund 3992.90 2.14 12.33 3.45 357 14.50 0.15 429 n.m. Cut F5 Lower Farsund 3383.30 0.89 3.65 2.34 156 4.50 0.20 429 n.m. Cut F6 Lower Farsund 4024.90 1.30 3.52 2.82 125 4.80 0.27 438 n.m. Cut F7 Lower Farsund 4120.90 0.96 0.88 2.92 30 1.80 0.52 440 n.m. Cut C1 Bryne 3571.77 16.60 120.56 55.70 216 137.20 0.12 443 n.m. Core C2 Bryne 3590.30 9.16 92.29 42.80 216 101.50 0.09 442 n.m. Core C3 Bryne 3597.46 21.81 166.18 74.20 224 188.00 0.12 450 n.m. 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.
liquid nitrogen-cooled trap. The temperature program of the gas chromatograph started atÿ10
C, with a tempera-ture gradient of 6C/min to 290C. The column was connected to a ¯ame ionisation detector. Quanti®cation of all components was based on peak areas. The deter-mination 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
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 var-iation in a data table (Wold et al., 1984, 1987; Birks, 1987). Detailed description of the PCA technique is also given in Jollie (1986). PCA is in this paper applied as an exploratory data analysis tool, without any assump-tions 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.
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 compo-nents). 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 compo-nents are termed ``scores'', while the coecients for each variable direction in its linear expansion of the principal components, is termed the ``loading'' of the variable. These data are displayed in bivariatescore 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 dierent groups of samples as well as to quan-tify discrimination power. This procedure identi®es dif-ferences and similarities between groups based on separate PCA of each group, and ®nds and uses regula-rities in the multivariate data by recognizing data pat-terns (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 descrip-tion 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 max-imum predictive ability for each group of samples sepa-rately (Wold, 1978).
2.3.3. Data preprocessing
The principal component modelling and SIMCA analysis were performed using the Sirius program run-ning 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 mul-tivariate analyses, to compensate for dierences 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 n-Pentene EBenz Ethylbenzene
nC5 n-Pentane m+pxyl meta+paraXylene
2,3DMC4 2,3-Dimethylbutane oxyl orthoXylene
CC5 Cyclopentane C9-ene n-Nonene
2MC5 2-Methylpentane nC9 n-Nonane
C6-ene n-Hexene A1 Unidenti®ed aromatic
nC6 n-Hexane A2 Unidenti®ed aromatic
MCC5 Methylcyclopentane O-ETol ortho-ethyltoluene
Benz Benzene C10-ene n-Decene
CC6 Cyclohexane nC10 n-Decane
2MC6 2-Methylhexane A3 Unidenti®ed aromatic
3MC6 3-Methylhexane C11-ene n-Undecene
C7-ene n-Heptene nC11 n-Undecane
nC7 n-Heptane A4 Unidenti®ed aromatic
MCC6 Methylcyclohexane Naph Naphthalene
Tol Toluene C12-ene n-Dodecene
2MC7 2-Methylheptane nC12 n-Dodecane
3MC7 3-Methylheptane 2MNaph 2-Methylnaphthalene
C8-ene n-Octene 1MNaph 1-Methylnaphthalene
nC8 n-Octane C13-ene n-Tridecene
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 eects 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 aro-matic structures from mass spectra which were impos-sible 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 n -alkenes andn-alkanes (up to C32for 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±C13range which are identi®ed and
quanti®ed in this study (Table 2). The light hydrocarbon components used are those which are present in rela-tively 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±C13fraction
(Table 2) andTmax from Rock Eval pyrolysis, the
sys-tematic 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 rela-tively 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 terrige-nous composition plotted in one cluster of the score plot, while the more oil-prone marine Spekk and Far-sund Formation samples plotted in a separate cluster. The loading plot indicated that the AÊre and Bryne sam-ples were relatively enriched in aromatics and naphtha-lenes, 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
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 analy-sis 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 eectively 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 ofTmax).
The PCA model is shown in Fig. 3a and b. Fig. 3a, the score plot, shows a clear separation of the dierent 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 tenden-cies 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 ofn-alkenes andn-alkanes (right side). PC1 explains 57.4% of the variance of the data set, and is mostly related to the separation of the dierent 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 toge-ther 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 aected by source facies. The same conclusion was also reached for the light fraction of thermal extracts (Odden et al., 1998).
3.2. SIMCA
First, PCA of 12 Spekk (SP3±SP10) and Farsund Formation samples (F2±F3; F6±F7) and the thirty-one selected variables (Tmaxexcluded) 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 sam-ples 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 sig-ni®cant components (PC1 and PC2) accounting for 65.4 and 14.9% of the total variance of the data set, respec-tively. The scattering of these samples (not shown here) is most probably related to dierences 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 dis-crimination power were identi®ed (such asm+pxyl > 2MNaph > C6-ene > C7-ene; see Table 3). The
vari-ables 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 eectively distinguishes between the light hydro-carbon 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+pxylene and 2-methyl-naphthene and the Spekk and Farsund pyrolysates give a relatively higher proportion of the n-hexene and n -heptene. 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 geo-chemical characteristics. However, one oil-window
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 dierences in the maceral composition.
3.3. Pyrograms of dierent source rocks
As a consequence of the results from multivariate modelling, direct inspection of pyrograms was per-formed, and them+pxylene peak relative tonC8and
nC9is 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/n -alkene homology ranging from C5to 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 propor-tion of benzene, toluene andm+pxylene relative ton -alkenes/n-alkanes. The oil-window mature sample from Spekk (SP5) shows that the relative proportions of aro-matics is reduced resulting in a more dominant n -alkene/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 andm+pxylene) give the largest peaks in both samples. The four unidenti®ed aromatics, A1, Table 3
A2, A3 and A4, are also present in the immature and oil-window mature samples. The high proportion of aromatics and methylnaphthalenes relative ton -hydro-carbons 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+pxylene 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 dierences 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 oil-window 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
aro-matics, naphthalenes and branched hydrocarbons. The pyrogram from the ``hot'' upper part of the Farsund
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 methyl-naphthalenes. These pyrograms show that the propor-tion ofm+pxylene clearly separates between the source rocks and increases with increasing terrigenous organic matter.
The content of m+p xylene relative to n -hydro-carbons do not dier 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±417C)
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.
3.3.4. Classi®cation diagrams based on individual parameters
SIMCA analysis has detected that the two most dis-criminating individual variables arem+pxylene and 2-methylnaphthalene. 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-methyl-naphthalene than the samples from the Spekk and Far-sund Formations, the exceptions are the two immature Spekk (SP1±SP2) samples with a relatively high abun-dance 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 con-trast to the decrease from immature to mature Spekk samples.
Crossplots of the contents of the other individual components with high separation power (such as C6
-ene, C7-ene,nC8and C8-ene) andm+pxylene have been
performed. These plots also clearly distinguish between the two source rock groups with a much higher abun-dance 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+pxylene/n-octene ratio vs. maturity (vitrinite re¯ec-tance values normalised to % carbon content) for algi-nites, sporinites and vitrinites. Our data are plotted in a similar diagram withTmax (Fig. 9). The data are
scat-tered, particularly the AÊre and Bryne samples. However, them+pxylene/n-octene ratio separates the two source rock groups, with a higher abundance in the AÊre and Bryne than in the Farsund and Spekk. Them+pxylene/
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
facies related. This is in general agreement with Bore-ham and Powell (1991) who stated that the xylenes were constant to aTmaxvalue above 460C. Them+pxylene/
n-octene ratio decreases from the immature Spekk sam-ples (SP1, SP2) with a Tmax below 420C to the more
mature Spekk and Farsund samples. On the gas chro-matograms it has been observed that the most mature Spekk (SP8±SP10) samples are slightly depleted in n -hydrocarbons. This should result in an increase of the
m+pxylene/n-octene ratio, but this increase is not sig-ni®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 composi-tional dierence 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 ofn-alkenes andn-alkanes than the AÊre and Bryne samples, which is richer in mono-aromatics and naphthalenes. There are also dierences between the marine shales: the ``hot'' upper part of the Farsund Formation contains a rela-tively higher proportion of then-alkene/n-alkane doub-let 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 ecient for detecting an optimal subset of individual components to discriminate between the dif-ferent source rock types. Based on our data, them+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 para-meter increases with increasing terrestrial input. The next most dominant individual components able to separate the two source rock types are 2-methyl-naphthalene,n-hexene andn-heptene.
Visual inspection of gas chromatograms of the pyr-olysates con®rms that the content ofm+pxylene 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-win-dow mature Spekk samples, but there is observed a decrease when the maturity level exceeds a vitrinite re¯ectance of 1.0% orTmaxabove 450C. For the coals
and coaly shales the content ofm+pxylene seems con-stant to a Tmax value of about 465C, but above this
maturation level there is observed an increase. However, it has been demonstrated that source rock type is the
dominant factor controlling light hydrocarbons gener-ated 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 spe-cial thanks to M. éstbye-Hansen. GEUS with J. Boje-sen-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
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