Model for prediction of organic carbon content in possible source rocks

Model for prediction of organic carbon content in possible source rocks Thomas A. Schwarzkopf* BP Research Centre, Sunbury-on-Thames, Chertsey Road, Middlesex TW16 7LN, UK

Received21 April 1992; revised 1 November 1992; accepted 7November 1992 A quantitative model has been developed to improve the prediction of the occurrence of marine siliciclastic source rocks and their likely organic carbon content. The input parameters of this quantitative model are: organic matter supply in the surface layer of the ocean (photic zone); water depth (calculation of the carbon flux); sedimentation rate; and preservation conditions (presence or absence of oxygen at the sediment-water interface). To handle the difficulties in the quantification of input parameters a probabilistic approach is applied. Depending on the available data or the interpretation of the kind of environment, different risk distribution functions are used to describe the possible range of each input parameter. The calculation is then carried out using the Monte Carlo simulation technique, resulting in a probability distribution of the total organic carbon content which forms the basis of the analysis. Different scenarios currently under debate, e.g. high primary productivity or the possible effects of enhanced preservation under anoxic bottom water conditions and the influence of varying water depth and sedimentation rate, can be tested very quickly. Using examples of recent (Peru continental margin) and ancient (Lower Toarcian Shale, Germany) organic-rich, marine shales, the potential of this tool is demonstrated, firstly to quantify souce rock quality (in this case total organic carbon content only), secondly to assess quantitatively the uncertainties in source rock prediction (e.g. evaluation of organic matter supply, water depth and sedimentation rates) and thirdly to determine source quality risk values to be used in the appraisal of sedimentary basins and prospects. Keywords: source rocks; organic matter preservation; oxic/anoxic sediments; upwelling; modelling

Introduction Predicting the presence and potential of source rocks in petroleum exploration are difficult tasks and must be addressed when appraising frontier areas and modelling amounts of generated and expelled hydrocarbons in sedimentary basins. The standard methods to answer these questions is to make predictions based on observations of the conditions under which organic-rich sediments are formed in recent environments and to use them, by analogy, to predict the occurrence of similar sediments in the past (Brooks and Fleet, 1987; Pelet, 1987; Huc, 1990). The result is typically qualitative and may at best lead to the assignment of average values of organic richness and organic matter composition to different classes of source rocks occurring in various depositional environments. This approach is in marked contrast with the highly sophisticated, quantitative geochemical computer models which make use of these 'predictions' in calculating the temporal and spatial distribution of generated and expelled hydrocarbons in petroliferous basins (Welte et a l . , 1983; Tissot e t a l . , 1987; Ungerer et al. , 1990). Apart from the lack of quantification in source rock *Present address: RWE AG, Research& DevelopmentDepartment, PO Box 103061, 45030 Essen, Germany

prediction, the fact that source rocks may be heterogeneous at a basin scale or within a sedimentary interval, reflecting lateral and vertical changes of the sedimentary facies, is often neglected. These heterogeneities have to be considered, and eventually explained and quantified, if we are to improve our ability to predict the petroleum potential of sedimentary basins (Huc, 1988), What are required are tools (computer modelling techniques) that enable the user to understand and quantitatively model the conditions under which source rocks form, and to assess, for example, the total organic carbon (TOC) content of these rocks under different environmental conditions. Other qualities of such models should be, if they are to be applied in the petroleum industry, simplicity and flexibility in handling incomplete input data sets. In trying to set up such models, the outcome can only be a compromise between the consideration of the latest scientific wisdom and the practical limitations of the user, for example, in the oil industry. In this paper such a tool, developed for siliciclastic marine sediments only, is presented. It takes into account organic matter supply in the surface layer of the ocean, carbon flux (decrease in organic matter with water depth), sedimentation rate and preservation conditions for the organic matter at the sediment-water interface and within the sediment. A deterministic solution was regarded as inadequate

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Prediction of organic carbon content: T. A. Schwarzkopf in the light of the many uncertainties involved in the input data. Therefore a probabilistic approach was ORGANIC MATTER SUPPLY favoured using risk analyses techniques (Monte Carlo), i.e. the model picks values at random from within the defined ranges. Hence the result (TOC content) is not a single value but a probability of a certain outcome WATER DEPTH which then forms the basis of the interpretation of the carbon flux calculation likelihood of the presence or absence of a source rock. The basic version of the proposed model presented here deals strictly with the prediction of organic matter concentration. By making assumptions about the initial composition of the organic matter in the ocean, the SEDIMENTATION RATE other aspect of source rock quality (namely organic matter composition) can be addressed indirectly. The main objective of the development of this tool is to improve the ability to predict source rock occurrence burial efficiency calculation and to add a quantitative dimension to the regional BOTTOM WATER source potential assessment. In addition, it contributes OXIC ODD to the ongoing fundamental discussion as to whether anoxia or productivity control the formation of organic-rich sediments (Demaison and Moore, 1980; Pedersen and Calvert, 1990). total organic carbon

Review of input parameters A primary objective was to reduce the number of input parameters of the source rock prediction model as far as possible. This is justified because in the exploration context limited data which can be used to calibrate and constrain a model are available. For example, often only vague ideas of the detailed depositional setting prevail. The proposed method forces the interpreter to focus on four key factors which form the input parameter of the model: (1) organic matter supply; (2) water depth (carbon flux = decrease of organic matter in the water column); (3) sedimentation rate; and (4) preservation conditions of the organic matter at the sediment-water interface and in the sediment. In the following text each of these essential parameters (Figure 1) is briefly reviewed in terms of the way they control the organic matter content of marine siliciclastic source rocks, how to derive the input data, the crucial points to be considered and how the input data fit into the overall model.

Organic matter supply One of the fundamental controls on source rock formation is the origin and amount of organic matter in the ocean (Figure 1). The two principal sources of organic matter are compounds formed in the ocean (primary productivity) and transported terrestrial organic matter. The regional variation of the primary productivity in the oceans (Figure 2) is in general the result of two factors: nutrient supply and radiant energy (Berger et al., 1989). Primary productivity is highest in areas of coastal upwelling where the zone of effective nutrient utilization (photic zone) is regularly replenished with nutrients. Similarly, production is generally higher in areas of seasonal water convection compared with areas where the water column has a high stability. Estuaries can represent local areas of high productivity. In contrast, low values are typical where a stable stratification between warm surface water and colder deeper water exists, limiting the nutrient supply and hence phytoplankton production (Berger et al., 1989). Thus over geological time, geological and climatic

Figure 1 General scheme of the source rock prediction model with the input parameters underlined. ODD = Oxygen depleted/deficient

changes that influence vertical mixing and advection in the oceans greatly affect the planktonic production processes. Figure 2 shows the great variability of phytoplankton productivty in the oceans at present. Organic-rich sediments (Figure 3) are found in areas of high productivity (upwelling areas, e.g. offshore Peru) or close to deltas (e.g. the Ganges delta), where there can be a significant local input of terrestrial organic matter and nutrients (Berger et al., 1989). Measurements of the primary productivity of the ocean have been carried out in abundance (Fogg, 1974; De Vooys 1979; Romankevich, 1984) (Table 1). It should be pointed out that larger errors are involved in determining these data. A lack of standardization in determining primary productivity hampers the comparability of different data sets. Another source of error is the extrapolation from single measurements which are biased by seasonal variations to an average lvalue of the annual primary productivity (gC m -2 yr- )In addition, oceanographic values generally only take into account the primary productivity of marine organisms (phytoplankton). This means that, in the end, the calculation of the TOC content in a source rock prediction model can only be related to the amount of marine, hydrogen-rich organic matter present, if only primary phytoplankton productivity is taken into account. The actual TOC content in the sediment might be higher due to a substantial input of terrigenous (hydrogen-poor) organic matter in certain marine environments. However, it could be argued that for a potential petroleum source rock the marine, hydrogen-rich organic matter is the more relevant part of the TOC content in terms of its oil-proneness. This should be kept in mind when using default values for organic matter supply representing only marine primary productivity.

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Table 1 shows data compiled from various sources illustrating the variability of phytoplankton productivity in different marine environments. These data can be used as input parameters to a source rock prediction model if comparable geological and oceanographic conditions can be assumed for the ancient source rock in question. Water depth (carbon flux) The carbon flux describes the transport of carbon, in this instance particulate organic matter (POC), per area and time (gC m -2 yr -~) from the photic zone to the seafloor. The starting point of carbon flux investigations is the surface layer of the ocean. Most of the particulate organic matter formed in the photic zone of the ocean is almost immediately recycled. This

part of the total production, which is based on the release of nutrients during the recycling process, is called regenerated production (Eppley and Peterson, 1979). Export production out of the photic zone is larger in coastal areas compared with the open ocean (Berger et al., 1989). Only a minor part of the global primary production is removed from the photic zone of the open ocean by sedimentation. Determining the carbon flux at any water depth forms an essential part of the proposed source rock prediction model because it provides the upper limit of organic matter content in the sediment. Several models have been published describing the decrease in organic matter concentration in the water with water depth (Hargrave, 1973; Suess, 1980; Betzer et al., 1984; Pace et al., 1987; Sarnthein et al., 1987; 1988). These

[ ~ <: 025 % J lt02% 025 to 05 % >2% F---105to1% Figure3 Global organic carbon content (wt.%) of seafloor sediments. Reproduced with permission from Pelet (1987) in Marine Petroleum Source Rocks, Spec. PubL Geol. Soc. London No. 26

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Prediction of organic carbon content: T. A. Schwarzkopf Table 1 Primary productivity data Area

Productivity (gCm 2yr 1)

Reference

Shallow siliciclastic seas Coastal zone Coastal zone Estuaries Tropical estuary Mississippi, coastal water

100 710 124 228

Nienhuis (1981) Woodwell etal. (1978) Fogg (1974) De Vooys (1979)

Shelf Upwelling zones Upwelling zones Continental shelf Continental shelf, New York

250 300 160 120

Woodwell et al. (1978) Nienhuis (1981) Woodwell etal. (1978) De Vooys (1979)

Closed/silled basins North Sea North Sea, coastal water Baltic Sea Baltic Sea Black Sea Red Sea Caspian Sea Mediterranean Sea Long Island Sound

45-110 160-180 59-67 130 112 200 227 40-60 205

Shallow water carbonate environments Algal bed/reef Co ra I reef

1200 1500-1800

Pelagic environments Open ocean Open ocean Subtropical oceans Arctic Arctic Arctic Antarctica Antarctica Subarctic North-east Pacific Pacific Ocean Atlantic Ocean Indian Ocean Continental slope Continental slope, New York

60 50 30 20 10 1 100 325 130 60 55 102 84 100 100

empirical models are not based on the actual rate laws and mechanisms that control organic matter consumption or the variations in sinking rates of POC. They are all characterized by a dramatic decrease in carbon flux at relatively shallow water depth due to various degradation processes being active, whereas at greater depth, below 400-600 m, only minor changes occur (Figure 4). One of the latest published calculations of the variation of carbon flux with depth was presented by Sarnstein et al. (1987; 1988) (Figure 4). Their data set includes the work of Suess (1980) and Betzer et al. (1984). They derived an equation which is fairly similar to that of Betzer et al. (1984). Carbon flux = 20.5631

x

PR °'664s x D -°5537

where PR = primary productivity (gC m -2 yr- l) and D = water depth (m). A general problem of all carbon flux models is their limited value in shallow and coastal waters. At shallow water depths the organic matter supply (primary productivity, allochthonous organic matter) and the carbon flux to the seafloor are dominated by other

Fogg (1974) De Vooys (1979) Fogg (1974) Romankevich (1984) Romankevich (1984) Romankevich (1984) Romankevich (1984) Romankevich (1984) De Vooys (1979) Woodwell eta/. (1978) De Vooys (1979) Woodwell etal. (1978) Nienhuis (1981 ) Nienhuis (1981 ) Fogg (1974) Nienhuis (1981) Eppley and Peterson (1979) Nienhuis (1981) Eppley and Peterson (1979) Fogg (1974) Fogg (1974) Eppley and Peterson (1979) Eppley and Peterson (1979) Eppley and Peterson (1979) Fogg (1974) De Vooys (1979)

processes than in the open ocean, e.g. the prevailing influence of lateral currents and a distinct change in the boundary conditions of phytoplankton productivity (Hedges et al., 1988). Therefore, carbon flux models are increasingly unreliable with decreasing water depth. At very shallow water depths (<50-100 m) they have to be substituted by a carbon flux estimate based on the regional knowledge of the depositional environment and from comparison with similar recent environments.

Preservation of organic matter at the sediment- water interface and within the sediment Following the fate of the organic matter from its origin in the surface layer of the ocean through the water column onto the seafloor, the next process to be discussed concerns the changes in the organic carbon content at the sediment-water interface and within the sediment (Figure 1). This stage is characterized by a great number of partly interdependent processes and parameters such as sedimentation rate, bottom water oxygenation, reactivity and concentration of organic matter, microbial activity, redox potential, pore water

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Prediction of organic carbon content: T. A. Schwarzkopf 12o1 intense microbial degradation and oxic decomposition. 110 With increasing sedimentation rate this residence time is reduced because the organic matter in the sediment 100 makes a more rapid passage through the near-seafloor col zone, hence leading to an increase in organic matter 801 content. '~ 70These statements, at best, represent rules of thumb. They do not take into account other effects which may completely invalidate these rules. The most important 60 / 300 Primary productivity (gCm-2yr"1) of these are: primary productivity, water depth and 40 oxygen concentration in the bottom water. The sol complex relationship between TOC content and 2oi /150 sedimentation rate was investigated and reinterpreted by Stein (1986). He pointed out the importance of the relationship between high primary productivity and O" ~' 200 ' 400 ' 600 ' 800 ' 1000 ' 1200 1 1400 ' triO0 ' 1 8 0 0 ' 2000 high sedimentation rates and the variation of carbon WATER DEPTH (m) flux with water depth. Finally, he emphasized the role Figure 4 Calculation of carbon flux. Based on calculations in of anoxic bottom waters, which was not thoroughly Sarnthein et al., 1988 addressed by Mfiller and Suess (1979). Their data set included only two samples from the Black Sea. As a chemistry, temperature and diffusion coefficients. result, the model by MOiler and Suess (1979) was net Given the complexity of these processes, which to a able to explain the formation of those marine great extent are not fully understood, it is not surprising siliciclastic organic-rich sediments in the geological that so far no theoretical model has been developed past, which are characterized by low sedimentation that takes into account all boundary conditions and rates and anoxic bottom water conditions. successfully predicts the formation of both recent and For his reinterpretation of the model by Miiller and ancient organic-rich sediments. The existing models Suess (1979), Stein (1986) extended his data set by can be subdivided into those which mainly deal with the including data from the Black Sea and Quaternary rates of the various degradation processes (Toth and Mediterranean sapropels. These data indicate no Lerman, 1977; Berner, 1980; Canfield, 1989) and correlation between the TOC content and empirical models purely based on observation and sedimentation rate; in fact, a negative correlation measurements on recent and ancient sediments probably caused by dilution with clastic sediments (Bralower and Thierstein, 1984; Henrichs and becomes apparent. These observations highlight the Reeburgh, 1987). The problem with the former is their problem that, for certain marine environments (e.g. requirement of a detailed knowledge of input anoxic bottom water), the likely TOC content of clays parameters such as the reaction rates of organic matter and shales cannot be predicted solely from and the concentration and diffusion coefficients of sedimentation rate. Instead, in the case of an oxygen and sulphate. In addition, the required organic-rich sediment, the key problem is whether the information is dynamic by nature and is difficult to enrichment in organic carbon is caused by either measure even in in situ conditions in the recent marine favourable preservation conditions (Demaison and environment. For ancient sediments, none of the Moore, 1980) or an increase in productivity of organic necessary calibration data is available. matter (Pedersen and Calvert 1990). The advantage of the empirical models is their Regardless of whether sedimentation rate represents simplicity. However, there is the danger that, without a a first, second or even third order effect in the theoretical underpinning, the application of these deposition of organic-rich marine sediments, it remains models could be misleading. an important parameter in any source rock prediction Different models will be discussed following a brief model. Determining or assessing a sedimentation rate review of the most important parameters in organic for a given marine mudrock presents a problem of its matter preservation. Owing to their significance and own and will not be discussed in this paper. However, because they form the only, to a certain extent, Table 2 provides a first attempt at quantifying typical assessable input parameters of a source rock prediction sedimentation rates in different marine environments. model, the role of varying sedimentation rates and The data should not be treated as default values in a bottom water oxygenation are dealt with separately. strict sense, but should indicate the order of magnitude in certain recent and ancient marine environments. The Relationship between TOC content and proposed source rock prediction model will require sedimentation rate. Several studies of recent marine more specific input data for each case. sediments have demonstrated the existence of an Relationship between T O C content and bottom empirical relationship between the TOC content and water oxygenation. Some investigations of recent sedimentation rate (Heath et al., 1976; Toth and marine sediments have shown an increase in the TOC Lerman, 1977; Miiller and Suess, 1979; Ibach, 1982). In content of sediments deposited under oxygen depleted the fundamental work by Miiller and Suess (1979) it bottom water conditions (Stein, 1986; Henrichs and was concluded that we can expect a doubling of Reeburgh, 1987; Suess et al., 1987). However, there the TOC content with each ten-fold increase in sedimentation rate, provided that other factors remain are only a few examples, which is due to the current state of the oceanic system. The validity of the data has constant. Low TOC values at low sedimentation rates were explained by a high residence time of the organic been questioned by Calvert (1991) among others. The sedimentological, palaeoecological and geochemical matter at the sediment-water interface in zones of 482

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Prediction of organic carbon content: T. A. Schwarzkopf Table 2 Sedimentation rates Recent marine depositional environments

Sedimentation rate (m/Ma)

Reference

Shallow siliciclastic seas Deltas Deltas Rh6ne delta, Holocene Rh6ne delta Nile delta Mississippi delta, Holocene Orinoco delta, Holocene

> 1000 100-600 700 660 1000 500-600

Stein (1986) Lisitzin (1972) Schwarzacher (1975) Schwarzacher (1975) Lisitzin (1972) Lisitzin (1972)

Shelf Coastal upwelling North-west Africa Atlantic shelf, Holocene (humid zone) Atlantic shelf, Holocene (arid zone)

50-1000 20-120 >100 1-30

Stein (1986) M6ller and Suess (1979) Lisitzin (1972) Lisitzin (1972)

Closed/silled basins Baltic Sea Black Sea Black Sea Black Sea Tyrrhenian Sea Gulf of California Caribbean Sea, Holocene Caspian Sea (pelagic)

140 50-1000 200 10-300 100-500 1000 270-330 20-40

Mfiller and Suess (1979) Stein (1986) Schwarzacher (1975) Degens and Ross (1974) Schwarzacher (1975) Schwarzacher (1975) Lisitzin (1972) Lisitzin (1972)

5-30 2-10 2-6 8-14 7-13 10-30 10-100 300-500

Stow and Piper (1984) Stein (1986) MQller and Suess (1979) Schwarzacher (1975) Schwarzacher (1975) Lisitzin (1972) Lisitzin (1972) Lisitzin (1972)

Pelagic environments Hemipelagic sediments Open ocean Pacific Pelagic ooze Red clay Pacific, Holocene Central Atlantic, Holocene Atlantic (base continental slope), Holocene

characteristics associated with these sediments have also been recognized in ancient marine mudrocks and hence were taken as an indirect indicator of the oxygen level in the bottom water at the time of deposition (Demaison and Moore, 1980) This has led to the well known, general assumption of dark coloured, marine organic-rich mudrocks being linked to 'anoxic' conditions and that oxygen concentrations somehow determine the rate of organic matter degradation. In a strict sense, both assumptions are probably wrong (see later discussion), or may at best represent an oversimplification of a much more complex relationship between different parameters. An important fact to be kept in mind is that the oxygen concentration is only one parameter forming part of the boundary conditions of organic matter preservation/degradation. The actual process causing the physical and chemical degradation of organic matter is mainly the activity of bacteria. Therefore it is the boundary conditions under which these are active that have to be considered and which largely determine the extent of organic matter degradation. A critical question in organic matter preservation is to what extent the degradation rate depends on the bottom water oxygenation. Observations in ancient environments suggest lower oxidation rates under oxygen deficient/depleted (ODD) or anoxic bottom water conditions (Stein, 1991). However, numerous, partly experimental, investigations, reviewed and compared by Henrichs and Reeburgh (1987) and Emerson and Hedges (1988) have shown that oxidation rates based on laboratory experiments differ widely

from field data. In addition, great variations were observed for different organic compounds. Several models have been proposed to explain these observations. First, organic materials which have had their natural structures altered, for example by bacteria, will be degraded slowly, if at all (Emerson and Hedges, 1988). Lower rates of metabolism under ODD or anoxic conditions may therefore result from the organic matter having already partially decomposed, e.g. in the oxic water column, and becoming less labile than the organic matter at the sediment surface (Henrichs and Reeburgh, 1987). Similarly, Canfield (1989) suggested a diminished ability of the anaerobic bacteria to oxidize a full complement of organic compounds. Other factors may be: the lack of bioturbation in the surface sediment layer, inhibiting the supply of oxidants into the sediment; the decrease of bacterial activity due to inappropriate physiochemical conditions, e.g. low pH, high redox potential or production of toxic compounds during bacterial metabolism; or humification of labile compounds (Demaison and Moore, 1980; Tissot and Welte, 1984). These investigations demonstrate the problems in defining the varying degradation rates of organic matter under different conditions. It seems to be difficult to establish a sequence of relative reactivities for different types of particulate organic compounds occurring within a variable sedimentary matrix and depositional environment.

Modelling preservation conditions within the sediment. Modelling measured and observed trends

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Prediction of organic carbon content: T. A. Schwarzkopf in the distribution and concentration of various does not prove that the mechanisms and processes are substances in the sedimentary column, by taking into sufficiently understood. Trying to apply this kind of model to the formation of account the basic physical, chemical and microancient source rocks has proved to be largely biological processes, is the main objective of diagenetic modelling. It is important to realize to what unsuccessful. This is mainly because of the time aspect extent diagenetic models can, or cannot, be used for and the present day ocean, which is in certain aspects the purpose of predicting the amount of organic carbon not representative of the geological past and therefore ill-suited for calibrating models predicting the in ancient marine siliciclastic rocks. Therefore the principles are briefly reviewed in the following, before formation of organic-rich sediments in the geological some of the more specific models are discussed which past. Consequently, it was decided here to base the have been developed solely to reconstruct and predict source rock prediction model on empirical observations, rather than having to assess a variety of the formation of organic-rich sediments in the poorly known parameters in ancient sediments. The geological past. Diagenetic modelling can provide a powerful tool for common feature of the so-called empirical models is the testing postulated mechanisms, for making up calculation of a preservation factor or the burial quantitative mass balances, or for investigating the efficiency of organic matter (Bralower and Thierstein, effect of some environmental parameters on the 1984; 1987; Henrichs and Reeburg, 1987). This factor dynamics of the sedimentary system. In particular, it is stands for all of the assumptions and unknowns that go powerful because it is commonly based on a more or into a diagenetic model of the type described above. less complete set of analyses of the concentration of Bralower and Thierstein (1987) defined the various compounds in the sedimentary column, preservation factor as the percentage of organic carbon supplemented by assumptions about, for example, produced in surface waters that accumulates in the diffusion rates or the reactivity of organic matter, which underlying sediment provide the possibility of calibrating and testing the model. organic carbon accumulation rate Preservation factor = Regarding the organic matter in the sediment, the primary production rate core problem is the poor understanding of the rate of organic matter degradation in different environments (Middleburg, 1989). In a simple model it is assumed RCorg [g cm -2 (1000 yrs-1)] x 1000 that the overall rate is directly proportional to the PF(%) = concentration of the reactive part of the organic matter PP(gC m -2 yr 1) (e.g. by first-order kinetics) (Toth and Lerman, 1977; Berner, 1978; 1980). This kind of model can be where RCorg (accumulation rate of organic carbon) = improved by allowing different degradation rates for [S(D(1-~))] x TOC and S = sedimentation rate different types of organic matter (Westrich, 1983; Aller (cm/1000 yrs), D -- dry density, • -- porosity and TOC and Mackin, 1984). Even then, other factors such as -- total organic carbon (%). microbial activity or bioturbation are difficult to take Figure 5 is a log-log plot of primary production rates into account. The sometimes reasonable agreement versus organic carbon accumulation rates for Holocene between modelled and measured concentration profiles sites. The diagonal lines give trends of equal organic • Analyses from oxic basins x Analyses from periodically anoxic basins carbon preservation factors. Laminated sediments Analyses from anoxic or euxinic basins / 10% deposited under anoxic or periodically anoxic / 10 ~ conditions show preservation factors > 2 % . This value / / a°/o was used by Bralower and Thierstein (1984) to /1% calculate the primary production rate for ancient waters with similar depositional features. One major drawback of this model is that it does not take into account the variation in organic carbon losses Preservation [octor Black Sea 0-1% at different water depths. This is, however, one of the 10-1 advantages of the latest published model on organic matter preservation by Henrichs and Reeburgh (1987), where carbon flux values are considered. It is much 0.01% more sensible to use the amount of organic carbon actually reaching the seafloor rather than using primary productivity data from the surface layer of the ocean. 0-001% Henrichs and Reeburgh (1987) quantified organic carbon preservation in sediments by calculating the a ~I. burial efficiency / // c3 n

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F i g u r e 5 L o g - l o g plot o f p r i m a r y p r o d u c t i v i t y rates v e r s u s organic carbon accumulation rates f o r H o l o c e n e sites. R e p r o d u c e d w i t h p e r m i s s i o n f r o m B r a l o w e r and Thierstein (1984) in Marine Petroleum Source Rocks, Spec. Pub/. GeoL Sac.

London No. 26

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Marine and Petroleum Geology, 1993, Vol 10, October

carbon flux E =

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Prediction of organic carbon content: T. A. Schwarzkopf where E = burial efficiency, %C~ = weight percentage of organic carbon below any surface zone of rapidly decreasing concentration, Q = sedimentation accumulation rate (cm yr-l), o = dry sediment density (g dry wt cm -3 dry sediment), q~ = porosity and fc -organic carbon flux to the sediment surface (gC cm -2 yr-1). The results of their calculation show that high sedimentation rates correspond to high burial efficiencies (Figure 6). The spread of the data points in Figure 6 is probably related to the uncertainties in the determination of sedimentation rate and carbon flux. Nevertheless, the model provides the theoretical background for observation by Mfiller and Suess (1979) and Stein (1986), showing the increase of the TOC content with increasing sedimentation rates under oxic open marine conditions. However, the independence of the TOC content from sedimentation rates under ODD conditions, pointed out by Stein (1986), was not corroborated by Henrichs and Reeburgh (1987). No significant difference in the burial efficiency was found between sediments deposited in oxic or anoxic bottom waters. This was explained mainly by the lack of data and the problem of finding sites in present day oceans that allow a study of the effects of anoxia. Nevertheless, the assumed increase in burial efficiencies at low sedimentation rates under anoxic conditions and the overall independence of the burial efficiency from sedimentation rates under anoxic bottom water conditions was later illustrated in a paper by Canfield (1989) using Black Sea sediments as an example. It follows from the discussions above that ODD

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Following the conclusions of Stein et al. (1986) and Canfield (1989), based on their investigations of Holocene Black Sea sediments, a direct relationship between burial efficiency and sedimentation rate under ODD conditions is regarded as invalid. To model a possible effect of increased preservation of organic matter under those conditions the following empirically derived distribution function for the burial efficiency, independent of sedimentation rate, was applied. Owing to great variations in the data from ancient organic-rich sediments and the uncertainties in deriving them, a broad triangular distribution function for the burial efficiency was chosen: minimum value 5%, most likely 20%, maximum value 60%. Methodology

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conditions in ancient sediments are probably bound up with high burial efficiencies, although the reasons are unclear, and that they will have a major effect on the amount of organic matter preserved in the sediment. High sedimentation rates, in the case of ODD conditions, only represent a second-order effect (dilution effect). Predicting ODD conditions is therefore of crucial importance in any source rock prediction model (Hallam and Bradshaw, 1979; Tourtelot, 1979; Demaison and Moore, 1980; Stein et al., 1986; Jenkyns 1988; Oschmann, 1988). At present, the model by Henrichs and Reeburgh (1987) seems to represent one of the best ways to assess reasonably the amount of organic matter preserved on reaching the seafloor. It does not require the numerous assumptions necessary in diagenetic modelling (Berner, 1980; 1982) and, in contrast with those diagenetic models, successfully describes the formation of recent and ancient organic-rich marine sediments. In the source rock prediction model under oxic bottom water conditions, burial efficiencies and hence the total organic carbon content of sediments are calculated from sedimentation rates (Henrichs and Reeburgh, 1987)

Rate

1 1

i 10

(cmlyr)

sedimentation

rates

(from

The development of the model has involved the application of risk analysis techniques to source rock prediction. This has an advantage over traditional deterministic models which combine single 'point' estimates of a model's variables to predict a single result. This in general, and in source rock prediction in particular, poses a problem because of the uncertainties and possible ranges of the input variables. The input parameters organic matter supply, water depth and sedimentation rate and the equations for calculating carbon flux and burial efficiency are implemented on a standard spreadsheet. Attached to the spreadsheet is a program that uses a simulation technique (Monte Carlo) to combine all the uncertainties identified in the model. It picks values at random from within the defined ranges/functions of the input parameters and calculates the risk of each possible outcome, in this instance the total organic carbon content of the sediment (Figure 7). The resulting probability distributions form the basis of the interpretation. Different scenarios and the influence of

Marine and Petroleum Geology, 1993, Vol 10, October

485

Prediction of organic carbon content: T. A. Schwarzkopf INPUT PARAMETER organic matter supply

waterdepth

sedimentation rate

V carbon flux calculation

burial efficiency calculation

MODEL Monte Carlo Simulation

RESULT 100

Probability

% 0 0

5 Total Organic Carbon %

Figure 7 M e t h o d o l o g y of the source rock prediction model

individual parameters can be tested quickly. The biggest advantage of using risk analysis techniques is the assignment of uncertainties to the input parameters. The nature of the uncertainties in variables is described by probability distributions, which give the range of values that the variable could take (e.g. minimum and maximum) and the likelihood of the occurrence of each value within the range. Depending on the state of knowledge of the depositional environment of a particular formation, values for organic matter supply, water depth and sedimentation rate can be incorporated with decreasing certainty, starting with discrete values to uniform distribution functions, triangular distribution functions and finally normal distribution functions (Figure 7).

Results The model was tested on recent and ancient marine sediments which were deposited under very different environmental conditions. Two examples are presented here: recent sediments from offshore Peru and organic-rich source rocks of the Lower Toarcian in north Germany.

Another important feature, typical of upwelling areas, is the occurrence of a distinct oxygen minimum layer in the ocean. This is the result of the microbial decomposition of large amounts of organic matter debris reducing the concentration of dissolved oxygen in the water column. Under these conditions laminated, diatomaceous, organic-rich sediments are formed on the outer shelf and upper slope. These sediments define a classical source rock type and the prediction of their occurrence and distribution in the geological past is a major research goal in the oil industry and the scientific community (Kruijs and Barren, 1990). One of the most debated questions is whether the formation of these organic-rich sediments mainly depends on increased preservation conditions at low oxygen concentrations near the sediment- water interface, or if high primary productivity is the dominant factor (Demaison and Moore, 1980; Pedersen and Calvert, 1990). Based on published data, in particular recent ODP reports (Reimers and Suess, 1983a; 1983b; 1983c; Suess et al., 1987; Von Huene et al., 1987; Suess and Von Huene, 1988), a set of input parameters was compiled for two sites. Location 1, on the upper slope at 300-450 m water depth, is characterized by high primary productivity in the surface water. Although the oxygen concentration of the bottom water is reduced, it was assumed firstly that this has no influence on the preservation of organic matter in the sediment. Location 2, further offshore, represents an open ocean, deep-water environment. The results of the simulation are shown in Figure 8. At both locations, on the upper slope and in the deep water of the Pacific, the predicted TOC concentrations in the surface sediments are in satisfactory agreement with the measured data. A closer look at the sedimentation history of location 1 reveals that the TOC concentration of the upper slope sediments has fluctuated considerably in the last 0.73 Ma as a result of sea-level changes induced by glacial conditions (Figure 9). At relatively high sea-level stands such as today, organic-rich clays and shales were formed when the upwelling situation was re-established in this area. On average 5-8% TOC was preserved in the sediment during these times. This range is well covered in the prediction by the results of the simulation. In addition, it covers the obviously exceptionally high TOC concentrations (10-12%) measured in the most recent sediments. However, it must be pointed out that these sediments close to the PERUCONTINENTALMARGIN Coastal

OpenOcean

Upwening

4

~-32 j m=e.asuredvelue$ ~

Continental margin o f Peru The upwelling area at the continental margin of Peru was selected as an example to address the problem of the formation of organic-rich sediments, being mainly a result of increased primary productivity and/or favourable preservation conditions due to low oxygen concentrations in the bottom water. The Peru continental margin represents one of the major coastal upwelling regions of the world's oceans. In this region, nutrient-rich subsurface waters are brought to the photic zone causing high phytoplankton productivity.

486

~o le t

~e~ted

, 0

: =m!gL-, 3 75

75 t 1.25 TOC(%)

15

Water depth: 300-450 m Organic matter supply: 400-600 gC/m2/yr Sedimentation rate: 50-150 m/Me Bottom water: reduced oxygen conc.,

no H~S

] lexpected . . . . It 0.3% TOC

result 7.1%

0,_ .ILl 0

0 75

,

,

15 TOC(%)

,

,

2 25

,

3

Water depth: 4100 m Organic matter supply: 10-20 gC/m2/yr Sedimentation rate: 1-8 m/Ma Bottom water: oxic

Figure 8 Comparison between measured and modelled TOC content of mudrocks deposited in an upwelling area (offshore Peru) and in the open ocean (south-west Pacific)

Marine and Petroleum Geology, 1993, Vol 10, October

TOC (%) 4

8

10

IZ

Prediction of organic carbon content: T. A. Schwarzkopf previous discussion on modelling preservation), which implies enhanced preservation of organic matter, the model predicts that the TOC content should be of the order of 20-30%. Such values have never been observed in these sediments. However, it must be highlighted that the oxygen concentrations in the bottom waters within the upwelling zone are relatively high. The low oxygen concentrations of less than 0.1 ml/1 which might influence organic matter preservation are not observed in this environment. Yet upwelling zones are not an adequate test to disprove the positive effect of oxygen deficiency on organic matter preservation.

Q

3O

~2

o.j j " 40

/ Line and data points

Brunhes/Matuyama magnetic boundary 0.73 Ma

Figure9 Fluctuations in the TOC content of Pliocene and Holocene sediments offshore Peru (DSDP site 680). Redrawn from Suess and von Huene (1988)

sediment-water interface are still undergoing some diagenetic modifications, reducing the TOC content. Additionally, this example serves well to demonstrate the crucial role of sedimentation rate as an input parameter. Sedimentation in this environment is characterized by periods of non-deposition alternating with periods of rapid sedimentation (Suess et al., 1987) This begs the question, what is the appropriate sedimentation rate to be used as an input parameter in the model? Measured sedimentation rates of Holocene sediments reach values of 2000-4000 m/Ma. Integrated rates over a time frame of several 10000 or 100000 years reduce those values by more than an order of magnitude. These average values (50-150 m/Ma) have been applied in the simulation. The results of this simulation are also significant to the research on marine source rock formation. They favour the view of several workers, e.g. Calvert (1987), that the combination of sedimentation rate and high organic productivity are the most important factors in upwelling areas. Enhanced" preservation of organic matter due to low oxygen concentrations in the bottom waters might be insignificant in this instance. The calculation carried out by the model supports this hypothesis. Applying the empirically derived function for the burial efficiency under ODD conditions (see

L o w e r Toarcian Shale ( G e r m a n y ) So far it has been demonstrated that the proposed model is a valuable tool to assess the influence of various environmental factors on the formation of organic-rich sediments. Its main application, however, lies in petroleum exploration, with the aim of predicting the occurrence of ancient marine siliciclastic source rocks and their likely TOC contents. One ideal candidate to test the model in this respect is the Lower Toarcian Shale in Germany because of a detailed knowledge of the sediment composition, regional distribution and organic geochemistry of this classic marine source rock. Two locations in south and north Germany, which were investigated among others by Littke et al. (1991), were modelled in this study to evaluate the facies difference observed and to develop different quantitative scenarios describing the environmental conditions under which these sediments were probably formed. In the following, the relevant observations and interpretations of Littke et al. (1991) are summarized briefly. In the north (Hils Mulde) compared with the south (Swabian Alb) the sequence is thicker, and the content and organic matter quality (as measured by the hydrogen index (HI)) are higher on average (Table 3). In total the sequence is more homogenous in the north whereas in the southern locations fluctuations in organic and sedimentological parameters indicate varying environmental conditions. The tentative interpretation by Littke et al. (1991) was that in the north the sediments were deposited in 'deeper' water and that a stable stratification of the water column resulting from a freshwater cap led to anoxic conditions in the bottom waters causing the continuous deposition of organic-rich shales. The formation of more heterogeneous shales in the south, slightly poorer in TOC content and organic matter quality, was explained by greater fluctuations of the oxygen content at or near the sediment-water interface caused by overall less stagnant conditions of the water column in 'shallower' water. Increased preservation of the organic matter under anoxic bottom water conditions, and not high primary productivity, was regarded as the major cause for the formation of organic-rich shales in the Lower Toarcian of both north and south Germany. The application of the proposed model required quantification of the observations and assumptions by Littke et al. (1991) and a re-evaluation of the measured data. During this process the difficulties in building up consistent quantitative models of the depositional environment became apparent and gave rise to alternative interpretations. The purpose of the following section is mainly to show in a condensed form

Marine and Petroleum Geology, 1993, Vol 10, October

487

Prediction of organic carbon content: T. A. Schwarzkopf Table 3 Compilation of geochemical data [average (standard deviation)] of organic-rich Lower Toarcian Shales from wells in north (Hils Mulde) and south (Swabian AIb) Germany (Littke et al., 1991) Thickness (m)

CaCO3 (%)

S(% )

North Germany Wickensen

27.4

42.9(15.1 )

South Germany 1026 1022 1003 1005

11.7 11.6 12.1 8.6

34.6(8.9) 33.9(15.3) 40.6(12.3) 30.9(14.0)

Location

Corg (%)

Hydrogen index [mgHC (g Corg ) 1]

3.6(1,2)

9.2(2,9)

698(70)

3.0(1.8) 2.9(1.1) 3.0(2.1 ) 2.5(1.1 )

5.8(2,2) 6.2(3,6) 5.2(2.7) 5.3(3.5)

548(74) 504(182) 485(160) 437(226)

resolution in this instance is limited. Applying different methods, values range between 10 and 100 m/Ma (see discussion in Littke et al., 1991). Therefore the calculation was carried out for a range of sedimentation rates: 5-10, 40-60, 90-110m/Ma (Figure 10). Estimates of water depth deduced from lithological and sedimentological features vary from 50-100 m to more than 500 m. Again, a range of values was modelled: 100-300, 400-600, 800-1000 m (Figure 10). Sedimentation took place under oxic bottom water conditions. Figures 10 and 12 show the results of the simulation. Two general trends can be observed: (1) TOC content decreases with increasing water depth due to the variations in carbon flux, provided that the OMS and sedimentation rate stay constant (Figure lOa, lOd and lOg); (2) the TOC content decreases with increasing sedimentation rate at similar water depth (Figure lOa, lOb and lOc). Here, dilution of organic matter is the overriding effect although the burial efficiency of organic matter increases with sedimentation rate. In the Lower Toarcian, shales below the organic-rich sequence contain much less than 1% TOC. Hence the results of the model suggest that the depositional environment was probably characterized by low organic matter supply in the surface water of the ocean (>30 gC m- 2 yr 1), moderate sedimentation rates

the major effects on the TOC content of marine shales if one or two factors of the model (organic matter supply, water depth, sedimentation rate, preservation conditions) are changed. A key to the understanding of the environment in which these sediments were deposited is the non-erosional contacts between organic-rich (>5% TOC) and organic-poor intervals (<1% TOC) at the Swabinan Alb locations, both at the base and within the Lower Toarcian Shales. One basic assumption is that for one particular site the environmental conditions, e.g. palaeogeography, sedimentation rate and water depth, did not drastically change although sediments with similar lithology but very different TOC contents were deposited. The first step is therefore to describe quantitatively the environment of the organic-lean 'normal' shales. Based on the palaeo-oceanographic situation (Riegraf, 1985) two scenarios with different organic matter supply (OMS) were modelled. The first scenario (Figure 10) assumes conditions similar to present day open ocean/shelf environments (OMS 10-30 gC m ~ yr-1). In the second (Figure 12) a higher OMS (50-70 gC m ~ yr -I) was used, taking into account possible nutrient supply by freshwater run-off from greater islands or the land mass in the east. The second factor which has to be constrained is the sedimentation rate. Unfortunately the stratigraphic

ORGANICMATTERSUPPLY(10-30g/m'2/yr"1) (oxicbottomwater) b 12 10 a ' 12 1.7% TOC 100 5 ~

Jk

3o0 r 0

r 15

3

45

6

d

12

400_

'

C

1.1%TOC

0

15

3

45

6 e

11

0

3

45

6

9

1.0%TOC

~

15

0.7%TOC

600

, k 0

15

3

11

45

6

g

8OO

0

15

3

45

6

h

12 0,7%

0

15

3

45

9

0.5%TOC

TOC

I

,L

000

l's

5- 10

;

4s

6

40-60

0

15

3

45

6

90- 110

SEDIMENTATIONRATE(m/Ma) Figure 10 Probability distributions of the total organic carbon content (TOC) of marine shales (OMS 10-30 gC m-2 yr 1, oxic bottom water) under different depositional boundary conditions

488

Marine and Petroleum Geology, 1993, Vol 10, October

Prediction of organic carbon content: T. A. Schwarzkopf ORGANIC MATTERSUPPLY(10-30 g/m'2/yr "1) (ODD bottom water) a 12 b 12

c

100

> 50% TOC 30O 0

,oo

IE~

12

0

.8% TOC

25

5o

75

12

d

100

5

10

12

15

1% TOC

Ilia_

0

5

10

15

g 12

20

0

5

10

15

e 12

20 f

2o 0

s

I0

15

h 12

2o i

"

000 0

25

/

50

75

~lOO J 0 I

5

10

5-10

15

20

0

i.

40-60

901

~10 15

2o

SEDIMENTATIONRATE (m/Me)

Figure 11 Probability distributions of the total organic carbon content (TOC) of marine shales (OMS 10-30 gC m 2 yr-1, ODD bottom water) under different depositional b o u n d a r y conditions (scales vary!)

(50-100 m/Ma) and a water depth roughly >300 m. If these are approximately the depositional boundary conditions of 'normal' shales then the next question is what are the most probable changes that occurred in this environment causing an abrupt increase in the TOC content of the sediment from less than 1% to 5-10%? The relatively stable tectonic situation in this region at that time makes it unlikely that a sudden shallowing of the water by several hundred metres or a dramatic decrease in sedimentation rate took place. Therefore to explain the formation of organic-rich shales other options, e.g. enhanced organic matter preservation at the sediment-water interface or increased organic matter supply, have to be addressed. The dark, pyrite-bearing, organic-rich shales indicate ODD bottom waters by the time of deposition. As

mentioned earlier, the assumed increased preservation of organic matter under these conditions was modelled by applying the following empirically derived distribution function for the burial efficiency: triangular distribution, minimum value 5%, most likely 20%, maximum value 60%. This variation corresponds directly to the relatively wide range of calculated TOC contents in the sediment by the model, e.g. Figure 11. However, the mean TOC value marked on the diagrams and the shape of the distribution still form a reasonable basis for the interpretation. The measured TOC contents of Lower Toarcian Shales deposited under ODD conditions vary around 5-8% in southern locations and 8-12% in the north (Table 3). Trying to quantitatively describe the changes in the depositional environment from organic-lean to

ooo

ORGANIC MAI-FER SUPPLY (50-70 g/m'2/yr "1) (oxic bottom water) 12

a 12 I

b 12

oo

c

2.4% TOC

30O 0

5

10

15

2O

d

12,

12

0

5

10

15

11i

2O

0

45

6

5

10

5- 10

15

2O

0

4'5

J i,

15

3

4,5

,i 15

3

45

6

f 1.4% TOC

6

0

15

3

45

12

1.5% TOC

I

1'51 ~ I L I

11

4.9% TOC

000

0

2.1% TOC

g Jlo

800

0

3

i

dL

600

15

J

6.8% TOC

400

/

0

1.0% TOC

6

40-60

0

i

15

3

45

90- 110

SEDIMENTATIONRATE (m/Ma)

Figure 12 Probability distributions of the total organic carbon content (TOC) of marine shales (OMS 5 0 - 7 0 gC m 2 y r - 1 oxic bottom water) under different depositional b o u n d a r y conditions (scales vary!)

Marine and Petroleum Geology, 1993, Vol 10, October

489

Prediction of organic carbon content: T. A. Schwarzkopf that would suggest regionally widespread high primary organic-rich shales, we could first assume constant productivity over several millions of years. However, boundary conditions apart from ODD bottom water. A the model enables its user to quickly check this comparison between Figure lOf and Figure l l f shows hypothesis as well and therefore assess the necessary that, although the TOC content increases due to boundary conditions. Figure 14 shows that at a high increased preservation, the increase is insufficient to level of organic matter supply (400 gC m -2 y r - ' ) , explain the observed TOC content. The oceanographic relatively shallow water and low sedimentation rates changes that caused ODD conditions to occur probably (Figure 14b, 14d and 14e) the conditions would be met had a simultaneous effect on other factors, most likely to form an organic-rich ( > 4 % TOC) shale in the the OMS and/or sedimentation rate. If ODD Lower Toarcian under oxic bottom water conditions. conditions, for example, have been the result of a The problem with this solution is the oceanographic freshwater cap, as proposed by Littke et al. (1991), primary productivity might have slightly increased by model for the Lower Toarcian Shale where high primary productivity (upwelling?) and a stable water increased nutrient supply (Figure 13f). A relatively stable stratification of the water column required for column (ODD conditions) are required simultaneously and which explains both the lateral and vertical changes ODD conditions argues for sluggish ocean currents and hence moderate to low sedimentation rates (Figure lle, of the TOC content in the Lower Toarcian shales Figure 13e). This again would favour the preservation on a basin scale. Littke et al. (1991) argue that neither the organic components nor mineralogical or sediof organic matter. mentological features support the assumption of The model offers various alternatives to explain the observed source facies differences of the Lower increased primary productivity in the Lower Toarcian Toarcian Shales in north and south Germany. shales. Assuming ODD conditions and constant but moderate OMS in both areas, water depth might have been Conclusions shallower in the north, resulting in a higher TOC A method has been presented that enables its user to content. This theory is in contrast to that of Littke et al. (1991). Similarly, a higher TOC content in the Hils quantitatively model the environmental conditions under which marine, organic-rich shales are deposited Mulde area could have been caused by a lower sedimentation rate. However, the thickness of the and to assess their likely organic carbon content. The sediments in the north compared with the south, input parameters of the model are organic matter assumed to have been deposited over the same period, supply, water depth, sedimentation rate and the is twice as high. This may therefore indicate increased presence or absence of oxygen at the sediment-water OMS in the north. interface. The uncertainty and the variation of each The results of the model discussed so far support the input parameter is taken into account by assigning widely acknowledged view that a switch from oxic to different risk distribution functions. The result is a anoxic bottom waters, increasing the preservation of probability distribution of the TOC content. Effects organic matter, could explain the observed increase in of individual parameters for different environmental TOC. Under this hypothesis the OMS is regarded as conditions on the TOC content can be tested very relatively low, typical for open ocean and shelf quickly. Further progress in the ongoing debate as to environments. This hypothesis is preferred partly whether anoxia or productivity control the formation of because of the difficulties in establishing an organic-rich sediments is expected if quantitative oceanographic model for the Lower Toarcian ocean source rock formation models, such as that proposed in ORGANIC MA1-FERSUPPLY (50-70 g/m'2/yr "1) (ODD bottom water) a 11

b

100 300 0

10

20

30

40

0

10

20

30

40

30

40

30

40

e 11

TOC

,

400

5.3% TOC

< < 50% TOC 600 0

10

20

30

40

h

0

20

12

TOC

800

10

1,8% TOC

< 50?/0TOC 1000 0

/ :

5- 10

10

20

30

40

40- 60

0

10

20

90- 110

/

SEDIMENTATION RATE (m/Ma) Figure 13 Probability d i s t r i b u t i o n s o f the total o r g a n i c carbon content (TOC) of marine shales (OMS 5 0 - 7 0 gC m -2 yr 1 , ODD b o t t o m water) u n d e r d i f f e r e n t d e p o s i t i o n a l b o u n d a r y c o n d i t i o n s (scales vary!)

498

Marine and Petroleum Geology, 1993, Vol 10, October

Prediction ORGANICMATTERSUPPLY(400g/m'2/yr-1)

(oxicbottomwater) b 8.5%TOC

a

13.0%TOC

_IL

100

3O0

0

5

10

15

1° I

C

4.2%TOC

,b 0

20

d 400

o f o r g a n i c c a r b o n c o n t e n t . " T. A. S c h w a r z k o p f

25

5

75

10

0

25

5

75

e 10

7.6%TOC

=.5%TOC

5.0%TOC

l

600 I

0

10

15

10

20

g

i

215

7.5

10

h

10

5.5%TOC

8oo

5

0

25

5

75

10

3.6% TOC

1 7% TOC

.

000 I 0

5

10

15

20

0

25

5

75

10

90- 110

40 - 60

0

25

5

75

11

300- 350

SEDIMENTATION RATE(m/Ma) Figure 14 Probability distributions of the total organic carbon content (TOC) of marine shales (OMS 400 gC m 2 yr 1, oxic bottom water) under different depositional boundary conditions (scales vary!)

this paper, are improved and applied. Demonstration of the model using the example of the Lower Toarcian source rocks highlights the need to quantify the relevant parameters in source rock formation to improve the ability to predict source rock occurrence and to incorporate calculated source quality risk values into the overall risk calculation of both a basin or a prospect. In this, source rock modelling follows the trend of quantifying geological processes as has been successfully demonstrated, for example, by the simulation of petroleum generation, tectonic events and basin filling.

Acknowledgements Permission by BP Research to publish this paper is gratefully acknowledged. The paper benefited from the constructive criticism of Richard Patience and Peter Haugan. The anonymous reviewers are thanked for their valuable comments on the manuscript.

References Aller, R. C. and Mackin, J. E. (1984) Preservation of reactive organic matter in marine sediments Earth Planet. Sci. Lett. 70, 260-266 Berger, W. H., Smetacek, V. S. and Wefer, G. (1989) Productivity of the Ocean: Present and Past, Wiley, Chichester Berner, R. (1978) Sulfate reduction and the rate of deposition of marine sediments Earth Planet. Sci. Lett. 37, 492-498 Berner, R. (1980) A rate model for organic matter decomposition during bacterial sulfate reduction in marine sediments Coll, Int. CNRS 293, 35-44 Berner, R. A. (1982) Burial of organic carbon and pyrite sulfur in the modern ocean: its geochemical and environmental significance Am. J. Sci. 282, 451-473 Betzer, P. R., Showers, W. J., Laws, E. A., Winn, C. D., Ditulluo, G. R. and Kroopnick, P. M. (1984) Primary productivity and particle fluxes on a transect of the equator at 153°W in the Pacific Ocean Deep-Sea Res. 31, 1-11 Bralower, T. J. and Thierstein, H. R. (1984) Low productivity and slow deep-water circulation in mid-Cretaceous oceans Geology 12, 614-618

Bralower, T. J. and Thierstein, H. R. (1987) Organic carbon and metal accumulation rates in Holocene and mid-Cretaceous sediments: palaeooceanographic significance. In: Marine Petroleum Source Rocks (Eds J. Brooks and A. Fleet), Spec. PubL Geol. Soc. London No. 26, pp. 345-369 Brooks, J. and Fleet, A. (1987) Marine Petroleum Source Rocks. Spec. Pub/. GeoL London Soc, No. 26 Calvert, S. E. (1987) Oceanographic controls on the accumulation of organic matter in marine sediments. In: Marine Petroleum Source Rocks (Eds J. Brooks and A. Fleet), Spec. PubL GeoL Soc. London No. 26, pp. 137-151 Calvert, S. E. (1991) Low organic carbon accumulation rates in Black Sea sediments Nature 353, 692 Canfield, D. E. (1989) Sulfate reduction and oxic respiration in marine sediments: implications for organic matter preservation in euxinic environments Deep-Sea Res. 36, 121-138 De Vooys, C. G. N. (1979) Primary production in aquatic environments. In: The Carbon Cycle (Eds B. Bolin et al.), SCOPE 13, 259-292 Degens, E. T. and Mopper, A. (1976) Organic material in marine sediments. In: Chemical Oceanography, Vol. 6 (Eds J. P. Riley and R. Chester), Academic Press, New York, pp. 59-113 Degens, E. T. and Ross, D. A. (1974) The Black Sea - - Geology, Chemistry and Biology. Am. Assoc. Petrol. Geol. Mem. No. 20 Demaison, G. and Moore, G. T. (1980) Anoxic environments of oil source bed genesis Am. Assoc. Petrol. Geol. Bull. 64, 1979-2109 Emerson, S. and Hedges, J. I. (1988) Processes controlling the organic carbon content of open ocean sediments Paleooceanography 3, 621-634 Eppley, R. W. and Peterson, B. J. (1979) Particulate organic matter flux and planktonic new production in the deep ocean Nature 282, 677-680 Fogg, G. E. (1974) Primary productivity. In: Chemical Oceanography, Vol. 2, 2nd edn, (Eds J. Riley and G. Skirrow), Academic Press, New York Hallam, A. and Bradshaw, M. J. (1979) Bituminous shales and oolitic ironstones as indicators of transgression and regressions J. GeoL Soc. London 136, 157-164 Hargrave, B. T. (1973) Coupling carbon flow through some pelagic and benthic communities J. Fish. Res. Board Can. 30, 1317-1326 Heath, G. R., Moore, T. C. and Dauphin , J. P. (1976) Organic carbon in deep-sea sediments Mar. Sci. 6, 605-625 Hedges, J. I., Clark, W. A. and Cowie, G. L. (1988) Fluxes and reactivities of organic matter in a coastal marine bay Limnol. Oceanogr. 33, 1137-1152

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