geochemical truths in large data sets. i: geochemical screening data

~ Pergamon Org. Geochem. Vol. 29, No. 1 3, pp. 519 530, 1998

© 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain PII: S0146-6380(98)00189-2 0146-6380/98/$- see front matter

Geochemical truths in large data sets. I: Geochemical screening data

C H R I S C O R N F O R D * , PHILIP G A R D N E R and C H R I S B U R G E S S

Integrated Geochemical Interpretation Limited, Hallsannery, Bideford, Devon EX39 5HE, U.K.

Abstract~Organic geochemistry, especially when undertaken on a well by well or outcrop by outcrop basis, arguably lacks statistically significant numbers of analyses to establish reliable trends. Can a reliable vitrinite reflectance vs. depth trend be established from (say) 12 analysed samples? When the number of analysed samples are in the hundreds or even thousands, much clearer general trends are established and anomalous measurements more readily recognised. Interpretation of anomalies is itself highly instructive, but forms another paper. In this paper, a global data set of some 5000 source rock samples is assembled from the open literature and released data (e.g. open file reports) and the results discussed to produce practical oil-industry interpretations from basic screening data. A complex con- clusion can be extracted from simple data sets and are supported by mass balance and kinetic theory. It is concluded that interpretation always falls back on a comparison of new data with pre-existing knowledge in the form of tables and plots. Total organic carbon (TOC) values are generally log-normally distributed so that using mean + standard deviation values will under-estimate the upside potential when fed into risked (probabilistic) hydrocarbon charge prediction models. Kerogen type is initially best-determined using plots of Rock-Eval $2 vs. TOC rather than the hydrogen index (HI = S2/TOC) vs. oxygen index (OI = S3/TOC) or "pseudo-van Krevelen" plot. The intercept of the $2 vs. TOC plot predicts the average dead carbon (inertinite) content of the source rock set, with the slope corresponding to the hydrogen index of the labile component (HIE). Maturity trends of the hydrogen index (vs. Tma× or %Ro) show reversals at high maturities as supported by the kinetics of the thermal breakdown of the three standard kerogen types. At elevated maturities, Type III kerogens have higher hydrogen indices than Types I and II kerogens, a feature that must be taken into account when correcting mature kerogens to their original HI values (Hlo). The reduction in hydrogen index is a measure of generation with the corresponding increase in production index (PI = Sl/[Sl +$2]) being effectively a measure of hydrocarbon retention. A cross-plot of HI vs. PI can be interpreted in terms of expulsion efficiencies (expelled = generated minus retained hydrocarbon). Measured data support a pore saturation and/or generation pressure-driven process for expulsion and indicate a limiting pore saturation equivalent to a production index of 0.6 for rich (>2%TOC) source rocks. © 1998 Elsevier Science Ltd. All rights reserved

Key words geochemical interpretation, pyrolysis, screening geochemistry, organic petrography, kerogen type, hydrocarbon generation, expulsion efficiency

INTRODUCTION

This paper is not about the geochemistry of a single sample, well, stratigraphic unit or basin, but about large merged global data sets. The focus of the paper is to help an interested but non-specialist geoscientist (e.g. a petroleum geologist) to address the geochemical aspects of prospect generation or basin evaluation (Cornford, 1998). The important point is that once merged, the data in the form of graphs and tables may be simply grouped or ident- ified by symbol or colour in terms of geography, litho-, bio- or chrono-stratigraphy, lithology, depth, sample type, laboratory, date, well, etc. and the original data sets re-formed if desired.

With the ever-increasing size of corporate data- bases, the petroleum explorationist has access to and may manipulate large numbers of source rock and hydrocarbon analyses. These large data sets

*To whom correspondence should be addressed. E-mail: [email protected].

provide a firm basis for the use of "r isking" in exploration. Risking requires the knowledge of the spread of possible values (e.g. source rock yield in kg hydrocarbon per tonne of rock) in order to use Monte Carlo techniques to give probabilistic estimates of hydrocarbon volumes and properties char- ging traps (Cornford, 1993, 1998).

Petroleum geochemistry generally deals with analytical data from both source rocks and migrated hydrocarbons, with a general interpretation requir- ing an opinion on kerogen type, maturity, generation, migration and alteration, together with oil and source rock correlations. This paper restricts its discussion to source rock screening analyses such as TOC, Rock-Eval parameters (Horsfield, 1984; Peters, 1986), vitrinite reflectance and visual kerogen type and spore colour index (SCI) values. These data were organised and interpreted using the Integrated Geochemical Interpretation geochemical software package, p:IGI. Simple nomograms are discussed below to address aspects of hydrocarbon

519

520 Chris Cornford et al.

genesis based on commonly available screening data.

This geochemical interpretation package allows graphical presentation of data with discrimination of samples by depth, stratigraphy, lithology, sample type or any numeric variable such as TOC content, hydrogen index, etc. This discrimination is achieved by using both colour and symbol palettes, in addition to grouping data by sample sets (e.g. "all Palaeozoic samples from North America with < 30% liptinite and TOC values > 5%"). Such an approach allows the user to interrogate the raw data and define significant trends. Samples either following or falling away from these trends can be grouped and their geochemical or geological signifi- cance investigated. This ultimately leads to an understanding of the underlying variable(s) control- ling the data variability, and hence an ability to predict source rock or oil properties away from sampled locations. A number of trends relating to hydrocarbon source rock properties are considered in this paper.

PHILOSOPHY

While developing a geochemical interpretation software package capable of handling large diverse data sets, the practices adopted by petroleum geo-

Screening Geochemistry. Common Plots

Overview Multi-depth TOC Histogram Pseudo van Krevelen x-y plot %k, %V, %1 Triangle plot HI vs T-max x-y plot $2 vs TOC x-y plot T~,x %Ro vs Depth x-depth plot T~,x vs %Ro x-y plot HI vs PI x-y plot

x-de~th ~lot

{GI Ltd., 1991 No 07 I ~ k

Fig. 1. Common and novel ratios and plots used for source rock screening geochemistry.

chemists to arrive at their interpretations were pon- dered. In reviewing the literature of the last few years (or indeed, this volume), three approaches were (or are) apparent:

1. Visual inspection of pre-formed graphs and tables.

2. Use of artificial intelligence (AI). 3. Multivariate or other similar statistics.

The first approach is the traditional visual inspection of analytical data or derived ratios, plotted into familiar graphs or tables constructed from pre- viously interpreted data sets (Fig. 1). This is arguably a very conservative approach to interpretation, but, being internally consistent, is most appropriate for the non-specialist. Adopting visual inspection one is comparing the new data with pre-established trends, the approach being innovative only in as far as it identifies data falling outside existing patterns. As such it tends to confirm prejudices. This paper argues that standard cross plots based on large data sets and substantiated by overlays derived from mass balance and kinetic theory is the optimum practical approach for the non-specialist.

The second approach is to rely on rule-based or neural network decision-making to arrive at an interpretation. A number of companies have devel- oped in-house artificial intelligence (AI) software, with a semi-public example being Chevron's REESA (Rock-Eval Expert System Analyser) embedded in Platte River's BasinMod 1D maturity modelling software (Platte River Associates, 1996). This software, designed by Ken Peters and David Nelson, uses 25 rules and sub-rules such as "Reject Tmax if $2 is less than xxx (default = 0.2 mg HC/g rock)" in order to help identify three categories of values, viz. "good", "migrated" and "rejected". If 25 rules are required to attribute sample quality to the 5 primary and 4 derived Rock-Eval parameters, then it is clear that a much larger number of ad- ditional rules are needed if interpretations such as "if Tm~x>435 and <465 then flag sample as mature", are to be made. In addition, the number of rules increases approximately exponentially with the number of variables to be interpreted. For example, our attempt to introduce only vitrinite reflectance values into the REESA rules resulted in an increase to 47 rules. With this realisation, an AI approach has been rejected as unworkable for data sets where numerous variables contribute to the interpretation.

Facing this plethora of "rules", some authors (e.g. Huang and Williamson, 1996) have turned to neural networks as a basis for extracting geochemical knowledge via a logic network primed with a learning data set. The above authors had reasonable success in deriving TOC values from wire line logs. Work at the University of Newcastle (U.K.) has investigated the use of neural networks for both


Hydrogen Index (HI) = S2/TOC l Oxygen Index (01) = S ~ I ' O C

I Production Index (PI) = $1/($1 + $2) Transformation Index (TI) = S1/TOC Potential Yield (PY) = S~ + S 2

80 - 550°C @ 25°C/min

' F He L

ROCK EVAL I PYROLYSIS

'a

TCD

$2 Tmax (calculated

~ nfrOm time and eating rate)

n Time

S 3

m Time FID = Flame Ionization Detector; TCD = Thermal Conductivity Detector 97127:

Fig. 3. Cartoon of the Rock-Eval pyrolysis apparatus, measured parameters and derived ratios (FID = flame ionisation detector; TCD = thermal conductivity detector).

(Py = pyrolysate; %TOC = TOC expressed as weight percent of whole rock).

The gradient on the plot thus reflects kerogen quality as defined by hydrogen indices. Since HI values diminish with increasing maturity, samples should ideally be immature or early mature for this interpretation to be valid, though as shown by the maturity arrow in Fig. 5 the HI trend rotates about its intercept on the TOC axis with maturity. The

1000

800

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Rock-Eval Source Hydrccarbor Kerogen Types 0 r_ga,is_m sr~_.p o~n_fi_al_ _

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- - - . ~ - - - - - ~ - 1 - ugno- ~__ Gal- a ~[~.=1 ~ . cellulosic A r prone---

tissue,

• " " - I . . . . . . . . , wg~ I No ootenUal

50 100 150 Oxygen Index (mg/g TOC)

Changes with maturation

Fig. 4. Typical pseudo-van Krevelen diagram illustrating the confusion of kerogen type end members, mixes and

maturity effects with respect to hydrocarbon potential.

intercept on the TOC axis is thus essentially inde- pendent of sample maturity.

Most sets of immature-early mature samples generally produce S2/TOC trends that do not pass through the origin of the plot (Fig. 6), the trend projecting to a positive TOC value for zero $2 yield (Fig. 5, solid line). This intercept has been interpreted by Langford and Blanc-Valleron (1990) as the absorptive capacity of the mineral matter. However, in our data sets we note a strong correlation of the intercept TOC value with visual estimates of inertinite content taken as an area % of the microscope slide. This suggests that the intercept TOC value largely reflects the wt% dead carbon (DC), Here dead carbon is defined as organic matter that contributes nothing to the $2 pyrolysate yield and equates to Inertinite (from microscopy) or Type IV kerogen (from Rock-Eval pyrolysis). A re- calculated dead carbon-free or labile hydrogen index (Hit.) may then be defined:

Hi t = S2/(TOC - DC )

Eliminating the effects of dead carbon on hydrogen index thus allows an improved discrimination of kerogen type compared with the conventional pseudo-van Krevelen plot (Fig. 4).

As an example, a suite of rocks apparently containing Type II kerogen with a mean TOC of 5% and HI value of 500 mg/g TOC is shown to contain 0.3 fraction by weight of dead carbon from the TOC intercept (i.e. 30% of the total organic carbon comprises dead carbon, DC). Eliminating the 1.5%

Geochemical truths 523

4 0 1 ISLOPE = SJ(TOC-DC) = HI' = 585 (mgSJgAC) l

, . 3 0 1 . . . . . . . _T_,__. ~'~..~ ~ ~ l l " I Note Slope decreases with I

/ o~;':o"o~am%'=; ~-'PJ'-~,~'" I maturati°n'butocvalue I J . ~ . I remains constant I

20 4 .. '7 = - " / "H, = assi "1 " ........... "'".'-° / . ~ ~ Reworked carbon or

u~ 10 J ...'~" ' ~ ~ ~ 7 2"~'5 ! ~ inertinite rich / ... j l ~ .- ~ m - , l .Y ~ [ , INB: TOC = A C T I V E C A R B O N (AC)I

0 • .

0 2 4 6 8 10

TOC (wt %)

Fig. 5. A more detailed determination of kerogen type in immature and early mature kerogens using Rock Eval $2 vs. TOC plot to determine the amount of dead carbon, as a basis for calculating a dead

carbon-free hydrogen index values (HIL).

DC (0.3x5%TOC), produces a HIL value of 710 mg/g TOC. It is now clear that these rocks ap- proximate to a mix of Type I and Type IV kerogen (algal + inertinitic kerogen) and represent a different organo-facies than that implied by a Type II kerogen (bacterially degraded algal or exinitic kerogen).

Having eliminated the dead carbon (DC), the HIE value can be displayed as a line on a triangular plot of Type I, Type II and Type III kerogen based on end-member hydrogen indices (Fig. 7 upper). As presented, this plot is only valid for immature and early mature kerogens: an approach for more mature kerogens is discussed below.

The combination of kerogen types will decide exactly where on the HIE line the kerogen mix lies (i.e. pure Type II or a mix of Types I and III kerogens). This distinction may be best determined from the ratio of amorphous to particulate liptinites (from microscopy) or from pyrolysis gas-chroma- tography fingerprints (Burwood et al., 1995; Horsfield, 1984). Only organic petrography is routi- nely available as screening analyses (Fig. 7 lower), particularly in historical data sets. The equivalence of pyrolysis kerogen type and optical kerogen ter- minology inferred from a comparison of the upper and lower tri-plots in Fig. 7 is discussed in Cornford (1998).

MATURITY EFFECTS

Hydrogen index (HI) vs. Rock-Eval Tmax plots (Fig. 8) may be used to demonstrate kerogen type if maturity is known, or maturity if kerogen type is known. In practice, the trend of values reflects the residual hydrocarbon potential in terms of a

decrease in HI with increasing maturity. The decrease in hydrogen index values with increasing maturity thus reflects the generation of hydrocarbon in terms of the conversion of the $2 pyrolysate yield to the Sl free hydrocarbon yield plus expelled oil.

As shown in Fig. 8, Types I and II kerogen start with high hydrogen indices (e.g. 900 and 600 mg/g TOC, respectively) compared with Type III kerogens (e.g. HI = 180 mg/g TOC), but by late maturity (e.g. Tma×>460°C), the situation is strongly reversed. The exhausted Type II kerogen now has hydrogen indices of 50 mg/g TOC while the Type III kerogen with stronger bonds retains its hydrogen indices at about 100 mg/g TOC. This reversal is exactly what is expected based on the differences in activation energy (Ea) distributions. The higher Ea values of Type III kerogen predict the retention of higher hydrogen indices at elevated levels of maturity. A singular kerogen type is likely to require its own Tmax calibration: for example generation from a sulphur-rich Type IS (Sinninghe-Damste et al., 1993) or IIS kerogen (Orr, 1986) produces a reduction in hydrogen index at anomalously low maturity levels. An interpretational overlay to Fig. 8 may thus be derived from transformation ratios calculated from activation energy distributions and frequency factors via the Arrhenius equation (Cornford, 1998). Applying this overlay to the HI VS. Tma x plot containing the world-data set produces envelopes consistent with the cross-over of the Type I and II and Type III trends (Fig. 9).

Standard LLNL kerogen kinetics (activation energies and frequency factors), used in Platte Rivers BasinMod software (Platte River Associates, 1996; Waples and Marzi, 1998), can be used to cal- culate transformation ratios for the three kerogen


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'N RPRETING I DEAD C A R B O N FREE I l o / ~.9o

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10 20 30 40 50 60 70 80 90 % TYPE II

I PRESENTING I TYPE DATA

ORGANIC PETF

10 20 30 40 50 60 70 80 90 % I N E R T I N I T E

Fig. 7. Triangular plots of kerogen type based on pyrolysis HI values of immature-early mature end members with iso-HI lines (upper) and visual estimates of area % of kerogen particles from organic petrography (lower). An average HIL value represents a line on the upper plot, whilst most kerogens

plot as mixtures which can be related to survival during deposition (lower plot).

types at a burial/heating rate of approximately 2°C/ m.y. (Table 1). This is a geologically common heating rate, equating, for example, to a geothermal gradient of 40°C/m.y. and sedimentation rate of 50 m/na.y. The maturity-related increase in transformation ratios and hence theoretical decrease in predicted hydrogen indices are shown in Fig. 10. These trends form the basis for the three kerogen- type trends in Figs 8 and 9 and again confirm the reversal of HI for kerogen types at higher levels of maturity.

Thus when using Rock-Eval data from late- and post-mature samples as a basis for estimating the

original kerogen type, it must be borne in mind that the highest hydrogen indices represent the orig- inally least oil-prone kerogens. The triangular plot shown for low maturity kerogens in Fig. 7 can be modified to that shown in Fig. 11 for late mature kerogens (Tmax=465°C). The HI values for the apices of the triangle for any maturity level may be taken from Table 1. At high maturity levels, the variation of HI values for the kerogen end-members is small, and hence the sensitivity of the diagram is restricted. In this case, organic petrography descrip- tions may be of even greater assistance in establish- ing the original organic input since microscopy can


Rock-Eval Hydrogen Index (mg Py/g TOC) 0 100 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0

4 0 0 Ict at ~15°C ~peratures

4 2 0

O 9... 440

E I-- ¢o 4 6 0 > v ' o o 4 8 0 rY

500

520

Hydrogen Indices reversed 1 at high maturities J

a r e r T.,=

)~erature)

Fig. 8. Hydrogen index (HI) vs. T,,,x trends demonstrating kerogen type if maturity is known or maturity if kerogen type is known. Note the reversal of HI for kerogen types at higher levels of maturity.

define particle form and hence original type even at high levels of maturation.

E X P U L S I O N EFFICIENCY F R O M PYROLYSIS DATA

If, in a data set covering a range of maturities and with an homogenous kerogen type, the reduction in hydrogen index (S2/TOC) is a measure of generation and the corresponding increase in production index (St/Si + $2) is a measure of hydrocarbon retention, then a cross-plot of HI vs. PI can be interpreted in terms of expulsion efficiencies

(expelled = generated minus retained).

Generation = (HIinit - HIobs) x TOC kg/tonne,

Retention = (Sl(obs))

= Plobs x S2(obs))(l -- Plobs) kg/ tonne

Consider an initial production index (PIinit) of 0.05 for an immature kerogen with 6% TOC and initial

hydrogen index (HIinit) of 900 mg Py/g TOC. In a closed system this source rock will produce an $2 value of 54kg Py/tonne (900x0.06) and as the hydrogen index decreases with maturation will

0

400

420 i c ~

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480 - O ~O

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I H~ (mg/gTOC) I 100 200 300 400 500 600

I I r i I I J J I I ~ I I

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700 800 900 [ = I J

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$ 2 (kg/tonne)

• 0.0 - 10.0

• lO.O- 2o.o • 2o.o-3o.o O 30.0 - 40.0

40.0 - 50.0

Fig. 9. Large data set differentiated by kerogen type on a Hydrogen Index (HI) vs. Tin,× plot. The kerogen type overlay derived from kerogen kinetics.


Table 1. Relationship of vitrinite reflectance and transformation ratios based on Lawrence Livermore's standard kerogen types (Platte River Associates, 1996; Waples and Marzi, 1998) and predicted hydrogen indices

Maturity (%Ro) Transformation ratios (0 1) by kerogen type Hydrogen indices (S2/TOC) by kerogen type

Type I Type I1 Type III HI(I) HI(II) HI(Ill)

0.41 0 0 0 900 650 180 0.45 0 0 0 900 650 180 0.51 0 0 0 900 650 180 0.56 0.02 0.01 0 882 643.5 180 0.62 0.05 0.02 0.01 855 637 178.2 0.65 0.08 0.05 0.03 828 617.5 174.6 0.71 0.11 0.15 0.07 801 552.5 167.4 0.75 0.19 0.34 0.12 729 429 158.4 0.79 0.32 0.53 0.18 612 305.5 147.6 0.81 0.43 0.63 0.21 513 240.5 142.2 0.84 0.55 0.73 0.24 405 175.5 136.8 0.86 0.69 0.82 0.27 279 117 131.4 0.89 0.83 0.88 0.31 153 78 124.2 0.91 0.94 0.92 0.35 54 52 117 0.98 1 0.97 0.45 0 19.5 99 1.11 1 0.55 0 81 1.27 0.67 59.4 1.45 0.73 48.6 1.62 0.79 37.8 1.8 0.81 34.2 1.98 0.82 32.4 2.18 0.84 28.8 2.35 0.88 21.6 2.55 0.9 18

hence g e n e r a t e the d i f ference b e t w e e n the ini t ial a n d

o b s e r v e d $2[= ( H I i n i t - H I o b s ) x T O C kg P y / t o n n e o f

rock]. I f t he g e n e r a t e d p y r o l y s a t e is a d d e d to t he in-

itial Sl yield o f 2.84 kg b i t u m e n / t o n n e ( ca l cu la t ed

f r o m the ini t ia l $2 a n d PI va l ue s as 54 x 0 .0526 kg /

t onne ) , th is g ives the to ta l h y d r o c a r b o n p r e s e n t in

the rock as a c losed sy s t em. In a c losed s y s t e m , the

m e a s u r e d to t a l yield (SI + $2) will be c o n s t a n t . A n y

d e v i a t i o n f r o m c o n s t a n c y reflects the loss o f gene r -

a t ed h y d r o c a r b o n , a n d is a m e a s u r e o f the " o p e n -

n e s s " o f the sy s t em. T h i s c a n t h e n be u s e d to def ine

the e x p u l s i o n efficiency (EE):

1.2 - A A

-n A

2.2 ,A Z~ Lx

Transformation Ratios by Kerogen Type

0 0.2 0.4 0.6 0.8 1 0.2

I I I I I I I I I 3.2 0 200 400 600 800 1000

Hydrogen Index (mg Py/g TOC)

• Type I

, Type II

• Type III

o HI(I)

HI(II)

A HI(Ill)

Fig. 10. Increase in t ransformation ratios and hence decrease in predicted hydrogen indices as a function of maturi ty (vitrinite reflectance) based on standard L L N L kerogen kinetics used in Platte River's BasinMod ~" software (Platte River Associates, 1996). As in Fig. 8, note the reversal of HI for the 3

kerogen types at higher levels of maturity.


ESTIMATING ORIGINAL HYDROGEN INDEX AND

HENCE PYROLSIS YIELD / ~ FROM POST-MATURE l O / ~9o

KEROGEN 20 ~ -, \ 80 7 \- INe: rmax..48s'Cl

30.7. N..zo , , ,~ / ~ a n \ k , Rtcv 40 60 "z- o/

...k,o

~ xf I I I I I E r ~ ,o 20 30 40% #OE 1160 70 80 90 ~rt'o=*UUl I

Fig. 11. Triangular plots of kerogen type based on pyrolysis HI values of late mature end members (cf. Figure 7). Type I kerogen has the lowest HI value at this level of maturity. An average HIL value of

44 mg Py/g TOC represents a line of possible kerogen mixtures.

EE = Hydrocarbon expelled/initial plus generated

hydrocarbon = [Sl<init) - Sl(obs)] Jr- [ (HI in i t - H l o b s )

xTOC ]}/[Sl( ini t) -{- { (Hl in i t - Hlob~) x TOC }]

if the initial PI = 0.05, then

Sl(init) = ( 0 . 0 5 H I i n i t x T O C )/0.95

I EE = Expelled o g,°,r=ed, , - - E O,o0y,

+ o, \ \ \ \ ~ " { . O j l eLOSEO svsrEul I ~ ; ~ _ \ ~ ~ I LOW TOO I

HIGH TOC ~ ~ ~ . - , , , . ~ " ~ (pore saturation)l ~ ~

200 400 600 800

Hydrogen Index (SJTOC)

I GENERATION}

Fig. 12. Estimation of expulsion efficiency (EE) for a Type I kerogen with initial HI of 900 mg/g TOC and initial PI of 0.05. The form of the overlay is approximately common to all immature to early mature kerogen types adjusted for

different ranges of HI.

and

Sl(obs) = (Plobs × HIobs × TOC )/(1 - Plobs)

Thus the expulsion efficiency can be expressed in terms of the initial and observed hydrogen indices, the observed production index and TOC.

Accepting the approximation that the TOC (6% in the above example) remains constant indepen- dent of generation and expulsion (cf. Cornford, 1998). Figure 12 shows the results of calculating the PI as a function of decreasing HI for various values of EE with a limiting HI of 900 mg/g TOC. A similar theoretical set of curves based on a more appropriate limiting HI of 600 mg/g TOC is shown in Fig. 13. Here the overlay is compared with the pyrolysis results from a sub-set of 65 samples with TOC > 4% and mature Tma x values (> 430°C) differentiated by maturity (Tm,,x). This plot shows the samples to increase in expulsion efficiency with maturation, with the peak mature samples (large triangles) mostly falling between the 60 and 90% expulsion efficiency. The high maturity low-HI samples (star symbols) show a full range of PI values and high expulsion efficiencies. Taking a limiting PI of 0.6 for clean source rocks, samples with PI values > 0.6 indicate staining by influx of oil or contaminant. The comparison with the overlay lines is not totally direct since the measured data set con- tains kerogens of various types and hence initial hydrogen indices.


1.o-

0 . 8

~" 0.6-

~- 0.4

0.2-

0.0-

o

Oil-stained

200 400 600 HI (mg/gTOC)

EE : 20%

EE : 40%

EE = 60%

EE : 80%

EE : 90%

T-max (°C) • 430- 435 • 435-44O • 44o-445 • 445-450

I 450-455 800 ~5.4eo

-~. >460"C

Fig. 13. Plot of production index (PI) as a function of decreasing hydrogen index (HI) for 65 samples. This data set suggests EE values of 60 90% for fully mature samples with reference to the set of theor-

etical curves.

The combination of the theory of Fig. 12 and the observed data for Fig. 13 provides confirmatory evi- dence for a pore saturation or generation pressure driven model for oil expulsion from clastic source rocks (Mann et al., 1997; Cornford, 1998).

SUMMARY AND CONCLUSIONS

Complex conclusions can be extracted fi'om simple data sets and supported by theoretical con- siderations. In practice, interpretation always falls back on a comparison of new data with pre-existing knowledge (tables and plots of raw data and ratios). A global data set of published screening geochemistry from some 5000 source rock samples was used to establish general interpretation plots. The following conclusions were drawn:

• TOC values are generally log-normally distributed so that using mean+s tandard deviation under-estimates the upside potential when fed into risked (probabilistic) hydrocarbon charge prediction models.

• Kerogen type is best determined using plots of $2 vs. TOC rather than the HI vs. OI (pseudo-van Krevelen) plot. The intercept of the plot predicts the average dead carbon (inertinite) content of source rocks, with the slope the labile hydrogen index (HIL).

• Maturity trends of hydrogen index (vs. Tmax or %Ro) for Types I, II and III kerogens cross over at high maturities as supported by LLNL kinetics. At elevated maturities, Type III kerogens have higher hydrogen indices than Types I and II kerogens, a feature that must be taken into

account when estimating the original HI values (Hit) for mature kerogens

• The reduction in hydrogen index (S2/TOC) is a measure of generation and the corresponding increase in production index (S]/[SI+S2]) a measure of hydrocarbon retention in a source rock. A cross-plot of HI vs. PI can be interpreted in terms of expulsion efficiencies (expelled = generated minus retained). Measured data supports a rock pore saturation and/or pressure-driven process for expulsion and indicates a limiting pore saturation equivalent to a production index of 0.6.

Finally, it can be concluded that there is a vast amount of simple screening data in the literature that has barely been touched in terms of inter-pub- lication interpretation.

REFERENCES

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geochemical truths in large data sets. i: geochemical screening data

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