Influence of Maturation on the Pyrolysis Products from Coals and

26

Energy & Fuels 1996, 10, 26-38

Influence of Maturation on the Pyrolysis Products from Coals and Kerogens. 2. Modeling Sylvie Charpenay,* Michael A. Serio, Rosemary Bassilakis, and Peter R. Solomon Advanced Fuel Research, Inc., 87 Church Street, East Hartford, Connecticut 06108

Patrick Landais Centre de Recherches sur la Ge´ ologie des Matie` res Premie` res Mine´ rales et Energe´ tiques et Groupement Scientifique, CNRS CREGU No. 77, BP-23, F-54501 Vandoeuvre-le` s-Nancy Cedex, France Received July 19, 1995. Revised Manuscript Received September 28, 1995X

A methodology to determine the chemistry and kinetics of the multiple reactions during geological maturation was developed, with a special emphasis on the representation of diagenesis and oil formation processes. The methodology combines a unique macromolecular and kinetic model for hydrocarbon pyrolysis, the FG-DVC (functional groupsdevolatilization, vaporization, cross-linking) model, with a method of analysis based on thermogravimetric analysis with Fourier transform infrared spectroscopy (TG-FTIR). TG-FTIR pyrolysis data from several natural maturation series of coals and kerogens were measured, systematic trends with the degree of maturation were identified, and empirical processes and reaction kinetics during maturation necessary to induce these trends were estimated. This approach eliminates potential inaccuracies when extrapolating kinetic parameters obtained from laboratory experiments to geological conditions. The FG-DVC pyrolysis model was modified to include these maturation processes, with aqueous chemistry providing a guide for such modifications. The resulting FG-DVC maturation model was then used to predict the maturation of several immature samples through the well-known time/temperature history of the basin. The FG-DVC pyrolysis model was subsequently used to predict the open-system pyrolysis decomposition of the predicted maturation residues, and the predictions were compared to TG-FTIR data of the corresponding naturally matured samples. For most of the series investigated, the model gave good predictions of the variations in oxygenated gas precursors, tar Tmax, and extractable yield with maturation. Kinetics derived from open-system pyrolysis for bridge breaking were found to be applicable during maturation. However, faster kinetics were necessary to describe the removal of oxygenated gas precursors. In addition, the removal of methane and tar was found to be too slow during maturation when using open-system pyrolysis kinetics. Artificial maturation experiments using confined pyrolysis were also performed for comparison. While the evolution rates, during subsequent pyrolysis of the maturation residues, of oxygenated gas species are different from those obtained from samples naturally matured, the yields compare favorably with model predictions. The trends for pyrolysis tar and methane from artificially matured samples are similar to those of natural samples but suggest different kinetics.

Introduction Time-temperature modeling in relation to organic matter maturation has become an important tool in petroleum and gas exploration in recent years.1-8 Current models allow the prediction of the depth of oil and Abstract published in Advance ACS Abstracts, November 15, 1995. (1) Waples, D. W. AAPG Bull. 1980, 64, 916. (2) Waples, D. W. Adv. Pet. Geochem. 1984, 1, 7. (3) Yukler, M. A.; Kokesh, F. Adv. Pet. Geochem. 1984, 1, 69. (4) Wood, D. A. AAPG Bull. 1988, 72, 115. (5) Lewan, M. D. Philos. Trans. R. Soc. London, Ser. A 1985, 315, 123. (6) Ungerer, P. J.; Espitalie, J.; Marquis, F.; Durand, B. Thermal Modeling in Sedimentary Basins; Burris, J., Ed.; Edition Technip: Paris, 1986; p 531. (7) Naeser, N. D.; McCulloh, T. H. Thermal History of Sedimentary Basins, Methods and Case Histories; Springer-Verlag: New York, 1989; p 319. (8) Tissot, B. P.; Pelet, R.; Ungerer. Am. Assoc. Pet.. Geol. Bull. 1987, 71 (12), 1445-1466. X

0887-0624/96/2510-0026$12.00/0

gas formation (important information in drilling) and an assessment of overall basin potential oil and gas resources. The most serious difficulties in applying these models are (1) obtaining accurate downhole geological information, and (2) estimation of kinetic parameters. Single collective kinetic rates or a simplified set of kinetic rates are typically measured in the laboratory. However, generation of oil and gas from complex sedimentary organic matter (kerogen and coal) typically involves a complex series of parallel reactions which may not be described accurately by a simple set of kinetic rates. In addition, the determination of kinetic rates is usually performed using open pyrolysis involving short reaction times. The kinetics must then be applied on a much longer geological time scale. The validity of making these extrapolations in time is always questionable, considering the widely different experimental conditions. The resulting problems, which have © 1996 American Chemical Society

Pyrolysis Products from Coals and Kerogens. 2

been extensively discussed,1,3,4,9,10 are particularly acute for very old basins where the timing of oil and gas generation is currently not well predicted.4,9 Kinetic Models Background. The determination of detailed opensystem pyrolysis kinetics, using data from instruments such as Rock-Eval11 or Pyromat,12 has been performed in several laboratories.6,11-16 These instruments measure the evolution, as a function of pyrolysis temperature, of hydrocarbons and total pyrolyzable gases. The kinetics obtained can then be compared between the laboratory and the geological systems, in order to evaluate the effect of extrapolating rates at a different time/temperature scale. Tissot, Espitalie, Ungerer, and co-workers have pioneered the use of this approach6,8,11 and applied it to type II and type III kerogens and coals. In their work, a set of parallel first-order reactions having the same preexponential factor A is used. The A space is searched and the value of A thus is optimized. The advantages of this method include the rapidity of obtaining results and the ability to compare different samples on the basis of the width of their activation energy distribution. The results obtained for maturation predictions also often match overall oil and gas generation kinetics in a gross sense, but no attempt has been made to make the model generally useful in petroleum exploration by coupling these observations to an overall chemical structure. In addition, the process of diagenesis, i.e., the loss of oxygenated functionalities, is not accounted for in their model, which limits the choice of immature samples to a narrow window prior to the oil window. Another set of extensive studies involving the development of kinetics from parallel reaction models has been conducted by Burnham and co-workers.13-15 These authors have made excellent progress in obtaining kinetics which provide a good approximation of the geological system. There are, however, several concerns and there is need for further development, especially concerning the kinetics of maturation during diagenesis, which cannot be explained by open-system pyrolysis kinetics.11,15 Work by Sundararaman et al.16 emphasized the limitations involved when using a set of parallel reactions using a single preexponential factor and recommended to perform the pyrolysis experiments at widely different heating rates to obtain a robust solution. The authors also pointed out that a single preexponential factor may not be usable when the kerogens are composed of very different components which decompose in various pathways. A comprehensive model including detailed chemical reactions for applications in kerogen maturation has been developed by Freund.17 This model involves the (9) Hunt, J. M.; Lewan, M. K.; Hennet, R. AAPG Bull., in press. (10) Snowdon, L. R. AAPG Bull. 1969, 63, 1128. (11) Tissot, B. P.; Welte, D. H. Petroleum Formation and Occurrence; Springer-Verlag: New York, 1984. (12) Braun, R. L.; Burnham, A. K.; Reynolds, J. G.; Clarkson, J. E. Energy Fuels 1991, 5, 192-204. (13) Burnham, A. K.; Oh, M. S.; Crawford, R. W.; Samoun, A. M. Energy Fuels 1989, 3, 42. (14) Burnham, A. K.; Sweeney, J. J. Geochim. Cosmochim. Acta 1989, 53, 2649. (15) Burnham, A. K.; Sweeney, J. J. Geochim. Cosmochim. Acta 1991, 55, 643. (16) Sundararaman, P.; Merz, P. H.; Mann, R. G. Energy Fuels 1992, 6, 793-803.

Energy & Fuels, Vol. 10, No. 1, 1996 27

use of 171 elementary reactions, for which the kinetics were determined during laboratory high-temperature experiments and were extrapolated to geological conditions. The authors found good agreement with experimental results for a type I kerogen and concluded that laboratory kinetic data can be used to predict the kinetics of the maturation process. The conclusions of this study are promising in terms of predicting type I kerogens. However, the model, at this point, does not seem to be readily applicable to samples containing high oxygen content, i.e., type II and type III kerogens and coals. One major problem in using pyrolysis kinetics and processes and extrapolating to geological time scales is that some maturation processes have been found to differ from open pyrolysis processes.11,15 The differences in decomposition behavior between laboratory pyrolysis and natural maturation have been suspected to derive from different operating mechanisms and the presence of water in the seam.18-25 At high temperatures, radical reactions are preponderant, while at low temperatures ionic reactions may be dominant, especially if water is present. The presence of water has been observed in laboratory experiments to facilitate reactions such as ionic condensations, cleavages and hydrolyses.26 In order to estimate the importance of water chemistry, laboratory simulations of maturation have been performed.18-25 Among these techniques, experiments involving a large excess of water (“hydrous” pyrolysis),18,19 as well as others where the organic matter is confined under pressure and consequently in close contact with its own moisture and later pyrolytic water (“confined” pyrolysis)19-25 have been conducted. The latter technique, by keeping all the effluents in close contact with the coal, allows reaction of the early pyrolytic water with the coal, resembling a hydrous pyrolysis experiment without the problem of dilution. The comparison of residues from hydrous and confined pyrolysis seems to indicate that confined pyrolysis is a better representation of natural maturation than hydrous pyrolysis.19 However, in either case, the temperatures used are well above the ones operating during natural maturation, which raise concerns about the applicability of the derived kinetics. Finally, in the case of a failure of the kinetics derived by open pyrolysis or confined pyrolysis, an empirical method to determine maturation kinetics can be used. This method consists of investigating multiple natural series of samples, each containing samples from different maturity levels, and determining empirically the kinetics that would describe the changes in properties between the samples of each of the series. This approach eliminates potential inaccuracies when extrapolating kinetics from laboratory experiments to geological (17) Freund, H. Energy Fuels 1992, 6, 318-326. (18) Lewan, M. D. Organic Geochemistry; Engel, M. H., Macko, S. A., Eds.; Plenum: New York, 1993; Chapter 24. (19) Michels, R.; Landais, P. Fuel, 1994, 73 (11), 1691-1696. (20) Landais, P.; Monin, J. C.; Monthioux, M.; Poty, B.; Zaugg, P. C. R. Acad. Sci. Paris 1989, 308, Ser. II, 1161. (21) Monthioux, M.; Landais, P. Energy Fuels 1988, 2, 794. (22) Landais, P.; Monthioux, M. Fuel Process. Technol. 1988, 20, 123. (23) Michels, R.; Landais, P.; Torkelson, B. E.; Philp, R. P. Geochim. Cosmochim. Acta 1995, 59, 1589-1604. (24) Landais, P.; Michels, R.; Elie, M. Proc. 16th EAOG. Org. Geochem. 1994, 22, 617-630. (25) Landais, P.; Michels, R.; Poty, B. J. Anal. Appl. Pyrol. 1989, 16, 103. (26) Siskin, M.; Katritzky, A. R. Science 1991, 254, 231-237.

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Energy & Fuels, Vol. 10, No. 1, 1996

conditions and was used in the present work. In order to evaluate changes purely due to maturation, maturation series need to derive from a homogeneous precursor and possess a well-known thermal history. While the number of natural series responding to those criteria is limited, their study can lead to valuable kinetic information. Good estimates for maturation kinetics may be obtained only from a large number of series, as the thermal history of each series, and in particular the heat flows over the maturation time, are usually not precisely known. New Methodology. The present work involved the development of a combined theoretical and experimental methodology for determining empirically the chemistry and kinetics of the multiple reactions in macromolecular and functional group decompositions during the maturation of type II and type III kerogens. Special emphasis was made on the representation of diagenesis and oil formation processes. The methodology combines a macromolecular network model of hydrocarbon pyrolysis, the FG-DVC (functional groupsdepolymerization, vaporization, cross-linking) model27-31 with a method of analysis based on TG-FTIR (thermogravimetric analysis with Fourier transform infrared spectroscopy) analysis of the evolved products during pyrolysis.32,33 The TG-FTIR gives a quantitative analysis of the evolution of tar or oil, CH4, CO, CO2, H2O, and many other species as a function of temperature and time. The macromolecular network and multiple kinetic rates approach of the FG-DVC model holds an intermediate position between parallel reactions models and elementary reactions models. FG-DVC predicts the evolution of multiple species (oil, paraffins, CH4, H2O, CO2, CO, etc.) using independent first-order reaction rates (with different preexponential factor/activation energy A/E pairs, in contrast with parallel reaction models with a single A and multiple E’s) which are derived from TG-FTIR data at various heating rates. While elementary reactions are not modeled, reactions leading to gas evolution are included so that the evolution of each gas species is well described. The macromolecular network approach of the model can represent the changes in network properties of the organic matter undergoing maturation, as well as changes occurring during pyrolysis. In particular, the approach provides a means to describe the decomposition behavior of the coal or kerogen in terms of tar (oil) evolution, as related to depolymerization and crosslinking processes. This model has been successful in simulating laboratory coal pyrolysis experiments.27-31 The methodology used to apply the FG-DVC model to maturation processes was as follows. Samples from several natural maturation series of coals and kerogens as well as from artificial maturation of coals were (27) Solomon, P. R.; Hamblen, D. G.; Carangelo, R. M.; Serio, M. A.; Deshpande, G. V. Energy Fuels 1988, 2, 405. (28) Solomon, P. R.; Hamblen, D. G.; Yu, Z. Z.; Serio, M. A. Fuel 1990, 69, 754. (29) Hamblen, D. G.; Yu, Z. Z.; Charpenay, S.; Serio, M. A.; Solomon P. R. Int. Conf. Coal Sci., Proc., Banff, Canada 1993, 2, 401. (30) Solomon, P. R.; Hamblen, D. G.; Serio, M. A.; Yu, Z. Z.; Charpenay, S. Fuel 1993, 72, 469. (31) Solomon, P. R.; Best, P. E.; Yu, Z. Z.; Charpenay, S. Energy Fuels 1992, 6, 143. (32) Solomon, P. R.; Serio, M. A.; Carangelo, R. M.; Bassilakis, R.; Gravel, D.; Baillargeon, M.; Baudais, F.; Vail, G. Energy Fuels 1990, 4, 319. (33) Serio, M. A.; Solomon, P. R.; Charpenay, S.; Yu, Z. Z.; Bassilakis, R. Prepr. Pap.sAm. Chem. Soc., Div. Fuel Chem. 1990, 35 (3), 808.

Charpenay et al.

Figure 1. Summary of the methodology used for the maturation model development.

characterized using TG-FTIR analysis. Trends in openpyrolysis volatile evolution as a function of the degree of maturation were identified and are described in another publication.34 The original FG-DVC pyrolysis model was tested for its applicability to geological time scales. It was found that the processes and kinetic rates derived from pyrolysis data were not always applicable, as both the trends in open-pyrolysis gas evolution and yields with the degree of maturation were not well predicted.35 The FG-DVC model was consequently modified to predict the identified trends by including possible processes and kinetics occurring during maturation. Laboratory experiments on water chemistry26 provided a guide to make such modifications since hydrous pyrolysis better simulates some of the features of natural maturation than does open-system pyrolysis. The resulting FG-DVC-maturation model was then tested by predicting the aging, through the welldetermined time-temperature history of the basin, of the youngest sample of several maturation series. The predicted residue from maturation (obtained using the FG-DVC maturation model) was then used as an input for the FG-DVC pyrolysis model, and the predictions, obtained at the typical pyrolysis temperature profile of the TG-FTIR analyses, were compared with the TGFTIR results of the older samples of the series. Figure 1 summarizes the different steps of the procedure. A complete basin model can then be produced allowing the predictions the oil and gas potential of samples at different positions in the basin when the thermal history is known, as well as the predictions of the thermal history of the basin when several samples of different maturity are available. Experimental Section TG-FTIR. The apparatus used has been described extensively.32,33 The TG-FTIR gives a quantitative analysis of the evolution of tar or oil, CH4, CO, CO2, H2O, C2H4, SO2, NH3, HCN, COS, and many other species as a function of temperature and time during pyrolysis. The samples were heated in the TG-FTIR system using a helium flow and a heating rate of 30 °C/min up to 900 °C. Confined Pyrolysis Experiments. A procedure similar to the one described in ref 22 is used. Samples of powdered (34) Charpenay, S.; Serio, M. A.; Bassilakis, R.; Solomon P. R. Energy Fuels, preceding paper in this issue. (35) Solomon, P. R.; Serio, M. A.; Carangelo, R. M.; Bassilakis, R.; Yu, Z. Z.; Charpenay, S.; Whelan, J. J. Anal. Appl. Pyrolysis 1991, 19, 1.



Table 1. Original Sample Name, Collection Depth, Estimated Age, Estimated Maturation Heating Rate, and Estimated Maximum Temperature for the Coal and Kerogen Samples Investigated coal/kerogen series Mahakam coals

Ikpikpuk kerogens, Alaska Slope, type III

Monterey kerogens, type II-S Middle Valley, kerogens, type III

a

sample name

depth

32362 (1) 31590 (2) 31591 (3) 46156 (4) #1 #2 #5 #7 #10 KG-8 KG-24 17R 23R 25-26R 31R 51R

190 m 2000 m 2760 m 3100 m 4550 ft 7100 ft 7730 ft 8330 ft 9500 ft 1125 ft 780 ft 195 mbsfa 250 mbsf 270-275 mbsf 317-324 mbsf 424 mbsf

age, Ma 11 19 23.5 ∼29 ∼37 ∼46 6.1-7 11.4

0.12-0.25

heating rate, °C/Ma ∼5 (all) 1-10 (all)

7-10 (all) 0.1-0.2 (all)

max temp, °C 91-101 143 153 88 106 111 117 126 50 85-100 ∼120 150 160 180 225

Meters below sea floor.

Figure 2. TG-FTIR pyrolysis data and model predictions for the Mahakam coals for CO2, CO, and H2O evolution as a function of pyrolysis temperature: (a-c) experimental data of the natural samples; (d-f) experimental data of the sample No. 32362 confined-pyrolyzed up to the indicated temperatures; (g-i) model predictions for the natural samples. coal previously vacuum dried are sealed in gold cells under argon. The cells are isothermally heated at temperatures between 250 and 450 °C for 24 h in high-pressure autoclaves at 300 bar hydrostatic pressure. Using this procedure, the sample is pyrolyzed in a confined medium with a minimum dead volume in the gold cell. The consequent intimate contact between reactants is necessary for simulating natural maturation. On completion of the experiment, the gold cells are opened and the sample is recovered. Samples. Coal and kerogen series with presumed homogeneous precursors and a fairly well-defined thermal histories were studied. One coal series (Mahakam Delta36 ) and three isolated kerogen series (type II-S Monterey,37 type III Ikpikpuk,38 and type III Middle Valley39 ) were characterized by TG-FTIR. The complete TG-FTIR data appears in ref 34. (36) Boudou, J. P.; Durand, B.; Oudin, J. L. Geochim. Cosmochim. Acta 1984, 48, 2005. (37) Isaacs, C. M. AAPG Hedberg Conf. Proc., New Orleans 1993. (38) Farrington, J. W.; Davis, A. C.; Tarafa, M.; Whelan, J. K.; Hunt, J. R. Org. Geochem. 1988, 13, 303. (39) Whelan, J. K.; Seewald, J.; Eglinton, L.; Miknis, F. P. Proc. Ocean Drilling Program, Sci. Results 1994, 139.

Information about the samples of each series is given in Table 1. The most immature Mahakam sample, No. 32362, was subjected to confined pyrolysis.

Experimental Trends with Maturation Pyrolysis Products. Data for the Mahakam series are shown in Figures 2a-c and 3a,b and follow the general trends with maturation reported in ref 34. The trends observed are as follows: as a function of increasing rank, the yields of CO2, H2O, and CO during pyrolysis decrease, while the yields of CH4 and tar go through a maximum. In addition, the evolution rate of oxygenated gases appears to decrease in the whole pyrolysis temperature range; i.e., during maturation all the precursors are removed. The tar and CH4 evolution rates present a systematic shift toward high temperature when increasing maturity. Finally, at high maturity levels, the variations of pyrolysis gas evolution with maturity suggest that the maturation processes involved at that stage resemble more closely that of open-system

30


Charpenay et al.

Figure 3. TG-FTIR pyrolysis data and model predictions for the Mahakam coals for tar and CH4 evolution as a function of pyrolysis temperature: (a, b) experimental data of the natural samples; (c, d) experimental data of the sample No. 32362 confinedpyrolyzed up to the indicated temperatures; (e, f) model predictions for the natural samples.

Figure 4. Literature data for the extractables yields of various coals as a function of their carbon content, along with model predictions. The types of solvent used, the extraction conditions, and the corresponding references are as follows: (O) CS2-MP (ref 42); (]) pyridine at 400 °C (ref 43); (0) pyridine, single-step (ref 44); (9) pyridine, multistep (ref 44); ([) pyridine (ref 45); (4) pyridine, O-butylated coal (ref 46); (2) pyridine (ref 46); (3) pyridine (ref 47); (1) pyridine, C-alkylated coal (ref 47); (b) pyridine (ref 30); s theory.

pyrolysis. As described in ref 34, the coal samples show the trends described above very clearly and give very similar volatile yields for the same maturity level. The kerogen samples appear to follow the trends from the coal samples fairly well for the oxygenated gas species but differ for the evolution of CH4 and tar. Extractable Yields. Many investigations demonstrated that the extractable content has a strong dependence on the coal type as well as on the solvent and conditions used.40-47 General trends in coal solubility with rank can, however, be drawn from literature data with different coals, as shown in Figure 4. Some data points from the figure include coals that have undergone a chemical treatment which potentially (40) van Krevelen, D. W. Coal; Elsevier: New York, 1993. (41) van Bodegom, B.; Rob van Veen, J. A.; van Kessel, G. M. M.; Sinnige-Nijssen, M. W. A.; Stuiver, H. C. M. Fuel 1985, 64, 60. (42) Iino, M.; Takanohashi, T.; Ohsuga, H.; Toda, K. Fuel 1988, 67, 1639. (43) Ouchi, K.; Itoh, S.; Makabe, M.; Itoh, H. Fuel 1989, 68, 735. (44) Nishioka, M. Fuel 1991, 70, 1413. (45) Larsen, J. W.; Wei, Y. C. Energy Fuels 1988, 2, 345. (46) Mallya, N.; Stock, L. Fuel 1986, 65, 736. (47) Chatterjee, K.; Miyake, M.; Stock, L. Energy Fuels 1990, 4, 242.

removes secondary interactions but does not modify the structure of the coal.46,47 As seen in the figure, and despite the significant scatter in the data, an increase in solubility seems to happen after 73% C (“first” coalification jump), after which the solubility can be either constant or increasing slightly, followed by a large increase around 80-87% C and a sharp decrease in solubility after 87% C (“second” coalification jump). It is to be noted, however, that coals of identical carbon content can have widely different extractable yields. Effect of Cations. The effect of cations on the behavior of coal during pyrolysis has been studied extensively in our laboratory.48 It has been shown that exchanged cations on carboxyls (carboxylates) have an important role in retrogressive reactions occurring during the thermal degradation of coal and lead to low conversions. In this study, modified samples from Zap lignite were prepared. Specifically, a Zap lignite sample was demineralized and calcium loaded following standard procedures.49 The demineralization process, which removes the mineral phase, also removes the organically exchanged cations (mostly calcium), leaving free carboxyls behind. The subsequent calcium loading treatment then exchanges back the free carboxyls into carboxylates. The TG-FTIR analyses of the three samples (raw, demineralized, and demineralized followed by calcium exchange) are shown in Figure 5. It can be seen that the demineralized lignite produces significantly more tar and that the CO2 and CO evolutions are different from the raw lignite. When the demineralized lignite is subsequently calcium loaded, the CO2 and CO evolutions, as well as the tar evolution, resemble again the ones from the raw lignite. This suggests that the structure of the demineralized, calciumloaded lignite is very similar to that of the raw lignite. From these analyses, the high-temperature CO peak can be directly related to the presence of cations in the structure. Also, the CO2 evolution related to cations can be determined as the difference between the CO2 from raw (or cation loaded) and demineralized lignite CO2 evolution. The effect of cations on the starting network structure of lignite was also studied. The extractable amount and (48) Serio, M. A.; Kroo, E.; Teng, H.; Solomon, P. R. Prepr. Pap.sDiv. Fuel Chem. 1993, 38 (2), 577. (49) Bishop; Ward. Fuel 1958, 37, 191.


the volumetric swelling ratio for the demineralized Zap and the demineralized, calcium-loaded Zap were measured in a previous project.48 It was observed that the amount of extractables, the volumetric swelling ratio, and the tar yield are highest for the demineralized coal and approximately the same for the raw and demineralized, ion-exchanged lignite. This suggests that the presence of cations induces cross-links in the structure. This result is consistent with observations by van Bodegom et al.41 It is, however, not known whether these cross-links are stable with temperature or decompose at higher temperature. Confined Pyrolysis. TG-FTIR results for the residues from the artificial maturation of an immature Mahakam coal are given in Figures 2d-f and 3c,d. As in natural maturation, the yields of CH4 and tar (represented by the area under the evolution curve) increase and then decrease, and the yields of oxygenated gases decrease with increasing level of maturity. Artificial maturation also reproduces well the natural temperature evolutions for CH4 and tar but gives only approximate representations for the temperature evolutions of CO, H2O, and CO2. For these species, the loosely-bound precursors are preferentially removed during confined pyrolysis, in contrast with all precursors during natural maturation. From these results, it can be concluded that confined pyrolysis gives a reasonable representation of natural maturation. Previous studies involving the analysis of the maturation residues using other methods have also reached the same conclusion.19-25 However, the applicability of kinetics derived from confined pyrolysis maturation simulations still needs to be investigated. Mechanisms. The standard trends from the opensystem pyrolysis of an immature sample (containing high oxygen content and exchanged cations) pyrolyzed at various degrees are as follows: the gas precursors are removed starting from the loosely bound to the tightly bound, producing a steep gradient in the low-temperature shape of the evolution curve and a progressive reduction of pyrolysis gas yields; low-temperature crosslinking related to oxygen functionalities (in particular CO2 and possibly H2O) and cations occurs, extensively cross-linking the structure;27,30,50 bridge breaking at higher temperatures produces a small amount of low molecular weight pieces; condensation and aromatization occur in the later stages of the pyrolysis.40 From the experimental results described above, it appears that some processes occurring during maturation are different than the ones operating during opensystem pyrolysis. In particular, (1) the removal of oxygenated functionalities during maturation is performed regardless of the degree of stability of these species during open pyrolysis, i.e., in contrast to pyrolysis where the loosely bound precursors are removed first, (2) the number of CH4 precursors increases with the degree of maturation, and (3) there is a significant increase in extractability and pyrolysis tar yield from immature to moderately mature samples, suggesting that no significant cross-linking occurred during maturation. On the other hand, similarities of natural maturation with open pyrolysis include (1) the variations in gas evolution at high maturity levels, with the loosely-bound gas precursors also removed first during (50) Suuberg, E. M.; Lee, D.; Larsen, J. W. Fuel 1985, 64, 1668.


Figure 5. TG-FTIR pyrolysis data of CO2, CO, and H2O evolution as a function of pyrolysis temperature for raw Zap lignite (R), demineralized Zap lignite (D), and demineralized, ion-exchanged Zap lignite (DCa).

maturation,34 (2) to a certain extent, the increase, then decrease, in extractable yields with increasing degree of maturation at moderate to high maturity, and (3) the shift toward high temperatures of the maximum evolution of tar and CH4 when increasing maturation. A significant difference between pyrolysis in the laboratory and pyrolysis during maturation is the presence of water. As discussed by Siskin et al.,26 “in natural systems, water is ubiquitous and hot and usually contains salt and minerals. Reactions such as ionic condensations, cleavages, and hydrolyses are facilitated by changes in the chemical and physical properties of water as temperature increases. These changes make water more compatible with the reactions of organics”. These authors undertook an extensive experimental investigation on transformations of model compounds in the presence and absence of H2O and with salts and clays as catalysts.26,51-55 Their main conclusions relevant to the present work are as follows: (1) the removal of carboxyl groups in the presence of water is fast compared to the free-radical pathway available in pyrolysis,53 (2) aryl ethers are removed under hydrous conditions,54 and (3) phenols are one of the main products of reactions involving water but can be re(51) Siskin, M.; Brons, G.; Katritzky, A. R.; Balasubramanian, M. Energy Fuels 1990, 4, 475. (52) Siskin, M.; Brons, G.; Katritzky, A. R.; Murugan, R. Energy Fuels 1990, 4, 382. (53) Siskin, M.; Brons, G.; Vaugh, S. N.; Katritzky, A. R.; Balasubramanian, M. Energy Fuels 1990, 4, 488. (54) Siskin, M.; Katritzky, A. R.; Balasubramanian, M. Fuel 1993, 72 (10), 1435-1444. (55) Siskin, M.; Brons, G.; Vaughn, S. N.; Katritzky, A. R.; Balasubramanian, M.; Greenhill, J. V. Energy Fuels 1993, 7, 589-597.

32


moved under acid conditions.55 From these observations, it is expected that the kinetics of carboxyl (thought to be one of the CO2 precursors) removal during maturation would be faster than the corresponding pyrolysis kinetics. As free, non-hydrogen-bonded carboxyls have been found by Fourier transform infrared spectroscopy (FTIR) to decompose during pyrolysis at temperatures higher than 500 °C,56 their fast removal during maturation could explain why high-temperature CO2 precursors are removed. Also, as water may provide hydrogen to cap bonds, the removal of CO2 precursors may occur without the formation of crosslinks. The fast removal of the other oxygen functionalities (aryl ethers and to a certain extent phenols) can also explain the reduction, with maturation, of CO and/ or H2O during open-system pyrolysis. It should, however, be emphasized that while reactions of organic matter with water probably occur during maturation, these reactions may still be different than the ones determined in the laboratory where higher temperatures are used. As calcium cations are usually ion-exchanged on carboxylates, the removal of carboxyls and oxygen functionalities during maturation under hydrous conditions (i.e., without the formation of cross-links) may also lead to the removal of calcium cations, which could precipitate into calcite. This process would eliminate the presence of the cation-related cross-links, potentially increasing the extractables and tar yields. When increasing further the level of maturation, reactions of depolymerization of the organic matter start to occur. Bridges of increasing stability cleave, leading to the formation and expulsion of low molecular weight products, and forming a progressively more stable macromolecular network. This stage may not be significantly different in laboratory pyrolysis and natural maturation, as decomposition occurs in both cases. However, hydrogen seems to be more available during “hydrous” or natural maturation as it was observed that paraffins but few olefins are produced under these conditions, while olefins are present in significant quantities in open pyrolysis.11 Finally, past that stage, extensive reactions of condensation and aromatization during maturation occur.40 It is likely that water is less present at that point and that natural maturation reactions resemble more closely the reactions observed during laboratory open-system pyrolysis. The observation of pyrolysis gas trends with maturation is consistent with that assumption.34 The structure of highmaturity samples has been shown to be formed of large, mostly aromatic clusters, with no weak bridges left in the structure.57 At this stage of maturation, all the labile bridges have been removed, and condensation and aromatization processes have increased the average cluster size, thus limiting the tar amount and increasing the tar evolution temperature The variations of extractables yields with the degree of maturation can be explained as follows. It appears that the first increase may correspond to (1) the removal of cations, as a consequence of the removal of the functional groups they were associated with, i.e., carboxyls (see above), and/or (2) the removal of hydrolyz(56) Charpenay, S.; Bassilakis, R.; Kroo, E.; Serio, M. A.; Solomon, P. R. Final Report, NSF grant No. III-9203467, 1995. (57) Stock, L. M.; Muntean, J. V. Energy Fuels 1993, 7, 704.

Charpenay et al.

able bonds such as esters,41 although thought to be quite rare in coals. In any case, it is clear that cross-links related to oxygen functionalities are being removed from the coal network during maturation between approximately 73% C and ∼80% C. This is also consistent with nuclear magnetic resonance (NMR) data showing that Zap lignite has more bridges and loops than Illinois No. 6.58 The increase in solubility between 80% C and 87% C may be due to the onset of aliphatic bridge breaking. The sharp decrease in solubility after 87% C is attributed to an increased number of π-π bonds (or “polarization forces”) of increasing strength due to an increase in aromatization, as well as an increase in condensation. π-π bonds cannot be considered as true “cross-links” since they may break down with increasing temperature.59 This is especially true for coals in the range 85-89% C, which are assumed to be fairly depolymerized60 but which structure is held together by these bonds. However, in the case of very high rank coals, the breakdown temperature of π-π bonds is higher than the temperature of decomposition and significantly impacts fluidity properties.31 The experimental trends observed in the coal series for the increase in CH4 with maturation have been explained elsewhere.34 In brief, in the case of coal, the two main sources of CH4 during pyrolysis are proposed to be hydroaromatics and arylmethyls.60 At the lignite stage, few hydroaromatics and arylmethyls are present. When maturation is increased, ring closure of long-chain aliphatics occurs, increasing the concentration of hydroaromatics. These are subsequently consumed by either decomposition or aromatization, which explains the decrease in CH4 amount and the peak shift toward higher temperatures. It was found previously that differences in behavior exist between kerogens and coals.34 In the case of type III kerogens, the CH4 yields are found to be lower than in coals, and vary from kerogen to kerogen, which was attributed to a variable fixed-carbon content and/or a smaller aliphatic fraction in the case of kerogens. A more efficient removal of CH4 precursors in the case of kerogens was also observed,34 along with, in certain cases a more definite shift in the temperature of the maximum evolution. The type II-S Monterey kerogens have been found not to follow the trends described above. This is consistent with the fact that CH4 is assumed to primarily derive from arylmethyl groups and hydroaromatics61 which are less present in these kerogens than in type III kerogens or coals and may not be formed as readily during maturation. Pyrolysis Model FG-DVC Model. The FG-DVC model for pyrolysis has been described in detail in several publications.27-31 The rates and mechanisms in the FG and DVC models were derived for coal but are being generalized to other hydrocarbons. The FG model considers the parallel independent evolution of the light gas species formed by the decomposition of functional groups. A distribu(58) Pugmire, R. J.; Solum, M. S.; Grant, D. M.; Critchfield, S.; Fletcher, T. H. Fuel 1991, 70, 414. (59) Yun, Y.; Suuberg, E. M. Fuel 1993, 72 (8), 1246. (60) Stock, L. M.; Muntean, J. V. Energy Fuels 1993, 7, 704. (61) Stock, L. M. Report, TSR Program with Advanced Fuel Research, 1995.


Figure 6. Representation of a coal macromolecular network (a) and pyrolyzing network (c) in a percolation theory simulation and corresponding molecular weight distribution (b and d). The circles represent monomers (ring clusters and peripheral groups). Thin lines represent weak bridges, thick lines strong bridges, and double lines cross-links. The histogram is divided into pyridine-soluble and pyridine-insoluble fractions. The area under the histogram corresponds to the weight percent of the fractions.

tion of activation energies is used for each evolving gaseous species. Functional groups can also be released from the coal molecule attached to molecular fragments which evolve as tar. The DVC model describes the decomposition and condensation of a macromolecular network under the influence of bond breaking and cross-linking reactions in order to predict the molecular weight distribution of the network fragments. The cross-linking is related to the evolution of CO2 at low temperatures, CH4 at moderate temperatures, and H2 at high temperatures.27,56 Tar or oil formation is viewed as a combined depolymerization and transport process in which the pyrolytic depolymerization continuously reduces the weight of the coal macromolecular fragments through bond breaking and stabilization of free radicals, until the fragments are small enough to be transported out of the particle. This process continues until the donatable hydrogens are consumed. The model employs percolation theory28,29 to perform a computer simulation of the combined depolymerization, cross-linking, and transport events. The DVC model employs a sample macromolecular network consisting of aromatic ring clusters (monomers) linked by bonds. The bonds are either broken by scission reactions or are formed by cross-linking. A simple example of the model using percolation theory is shown in Figure 6. Figure 6a shows the starting molecule. It is created by using two independent Bethe lattice subnetworks, each having its own coordination number (i.e., number of maximum attachments per cluster) C1 and C2 for two independent types of connecting bonds considered in the model (bridges for network 1 and cross-links for network 2). The double network configuration was chosen in order to give similar results to a previous version of the model which used the Monte Carlo method.29 Each subnetwork has its own probability of occupation, p for the bridges and q for the cross-links. The bridges are either labile (weak), assumed to be ethylene bridges, or unbreakable (strong), assumed to be olefinic bridges. As pyrolysis proceeds, each time a labile bridge is broken, another labile bridge is assumed to provide hydrogen and turns into a “strong” bridge. The fraction of weak bridges is called psoft while the fraction of strong bridges is called ph, giving the total bridge fraction p defined as p ) ph + psoft. While four model parameters


are defined (p, q, ph, and psoft), only three are independent and need to be determined. The starting values of p, q, and psoft (po, qo, and psofto) are adjusted to fit three types of experimental data: (1) the pyridine extractables (defined at having a molecular weight less than 3000 amu), which mainly impacts on the starting fraction of cross-links qo, (2) the tar amount, which has an influence on the total and weak bridge fractions po and psofto, and (3) the amount of fluidity, which is determined by qo and to a lesser extend po and psofto. The values of p, ph, psoft, and q vary during pyrolysis following the processes of bridge breaking (decrease of psoft), condensation (increase in ph) and cross-linking (increase in q). An example of a starting molecular weight distribution is shown in Figure 6. When bonds are broken, more small molecules are formed as shown in Figure 6, c and d. The smallest pieces are evolved as tar or oil. The DVC subroutine is employed to determine the yield of tar and the molecular weight distribution of the tar and char. The FG subroutine is used to describe the gas evolution and the elemental and functional group compositions of the tar and char. The FG-DVC model predictions have been compared to the results obtained from a number of experiments from widely different conditions.27-31 For each coal, the composition and kinetic parameters are held constant and only the pyrolysis conditions (pressure, temperature, and heating rate) are varied. Determination of Kinetic Rates. The TG-FTIR apparatus has been applied to the analysis of the coal samples to determine the kinetic rate coefficients for the evolution of specific products. The kinetic rates are obtained by employing several heating rates between 0.05 and 1.67 K/s.33 The resulting kinetic parameters for each gas species are completely independent from one another, in contrast with the parallel reactions models described in the Background section. These rate coefficients are employed in the FG-DVC model for coal devolatilization30 to allow predictions of pyrolysis at other heating rates and temperatures. Maturation Model Model Development. The general goal of this work was to find “universal” empirical kinetics and processes which would explain the variations for gas and tar evolution with the degree of maturation for most of the natural series available. The changes in gas evolution were obtained through the removal or addition of gas precursors (FG model), and the changes in tar evolution and extractables amounts were obtained through changes in network parameters (DVC model). The underlying assumption of the model is that all the series mature similarly. Considering potential uncertainties on the time/temperature history, we attempted to find processes and kinetics that would fit in the range of the uncertainties in thermal histories. While this approach may not allow an accurate fit in the case of a small number of series, when increasing the number of series it should become fairly accurate. It was first assumed that pyrolysis processes and kinetic rates would apply in maturation. However, pyrolysis kinetics led to a severe underprediction of the removal of oxygen species, and, as mentioned above, a fundamental difference in the processes of removal of

34


Charpenay et al.

Table 2. Kinetic Parameters for the Series Predicted gas species

preexponential factor, 1/s

activation energy

CO2 CO H2O cations removal bridge breaking

4.0 × 1012 4.0 × 1012 4.0 × 1012 4.0 × 1012 1014

41000 44800 44800 32000 55000 (54000a)

σ 9000 9000 9000 4000 2800 (4800b) (same as in kinetic file)

a This value is used in the case of the type II-S kerogens from the Monterey series and the high-sulfur content type III kerogens from the Middle Valley series. b This value is used in the case of the type II-S kerogens from the Monterey series.

functional groups. It was then found necessary to develop new empirical kinetics and processes for the removal of oxygenated species, and implement a new process to describe the CH4 increase with maturation. Removal of Oxygen Functionalities. New kinetics for the removal of oxygen gas precursors were developed. In comparison to the uncertainties on the estimated maximum temperature and heating rate in the basins, the influence of the activation energy for the gas evolution is relatively small. Consequently, the preexponential factor was fixed to a reasonable value (i.e., in the range 1012-1014 s-1) and no attempt was made to determine a single, unique preexponential factor/activation energy (A/E) pair. Since the preexponential factor, and consequently the activation energy, was not varied, the width of the distribution of activation energies σ becomes an important factor, because it determines the range of temperatures (wide or narrow) in which the gas evolution is going to occur. In this case, the range of maturation temperature covers 70-225 °C and represents a wide range in evolution rate of at least 8 orders of magnitude. The parameters A, E, and σ of the first-order kinetic rate distribution used for the removal of the oxygen gas species during maturation were kept constant to the values presented in Table 2. The parameter A was taken to be the same for all the oxygenated gases, while E was adjusted for each species. The width of the activation energy range, σ, was adjusted to cover the range of temperatures in which the oxygenated gases evolve during maturation. It was observed that this range was very wide, since oxygen functionalities start decreasing at a relatively low temperatures (less than 80 °C) and survive until high temperatures (above 150 °C); i.e., the range covers at least 5 orders of magnitude in rate. Since only one value for the preexponential factor is used, a wide distribution of activation energies is required. In contrast with pyrolysis processes, the distribution of the oxygen functionalities was kept unchanged; i.e., it was assumed that all the functionalities were equally removed during maturation. In order to determine the optimum value for σ, predictions with various values were obtained, and the value providing the best results for all series was chosen. It was found, as expected, that higher values of σ than the value given in Table 2 would give gas yields which are in general too high at low maturation levels and too low at high maturation levels. Conversely, a lower value of σ would usually give too low values at low maturity and too high values at high maturity. In lignites, part of the high-temperature CO peak was found to correspond to the decomposition of calcite

derived from carboxylates. An extra gas pool, CO-XT cations, was introduced to describe this effect. Since this CO is related to carboxylates, its rate of removal should be the same as the one for the carboxylates and carboxyls, i.e., should be close to the rate of removal of early CO2. Since the kinetic parameters for CO2 removal include a wide activation energy distribution to represent CO2 evolution at both low and high maturity levels, only the first part of the distribution, relevant to early CO2, was used for the cations removal rate. The CO-XT cations pool was not used for kerogen modeling, as kerogens samples were separated from the rock matrix by acid treatment which also removes cations. Also, the Mahakam coals did not contain a significant amount of carboxylates. At high carbon contents, maturation processes similar to those of open-system pyrolysis (i.e., different than the processes involving reactions with water) are assumed to occur. Consequently, at high maturation levels, the FG-DVC-pyrolysis model is used instead of the FG-DVCmaturation model to represent maturation processes. The switch was set up to operate at a carbon content of 85% C. This value is a model parameter and can be adjusted. CH4 Precursors. As described above, two main CH4 precursors were proposed: hydroaromatics for the lowtemperature CH4 and arylmethyl groups for the hightemperature CH4. Unfortunately, the concentration of each of these precursors is not well-known and varies during maturation due to ring closure of aliphatic chains (formation of hydroaromatics) and chain breaking/ shortening processes (decomposition of hydroaromatics and formation of arylmethyls). Since the quantitative effect of each of these processes is difficult to estimate separately, a single, empirical mechanism which would represent the combined effect of all processes in the whole maturation range was considered. In particular, this single mechanism should introduce an increase of CH4 precursors with maturation, the decrease observed at high maturity being automatically accounted for by the FG model. The amount of oxygen removed in H2O was used as a measure of increasing maturity and related to an increase in CH4 precursors. Specifically, each time oxygen is removed as H2O during maturation, 1/ CH precursor is formed. While this is an empirical 4 4 relationship, it was found to reasonably predict the increase in CH4 with maturation. This mechanism was set up to operate only until a certain coal maturity (which was picked to be 85%, i.e., the end of “wet” maturation), as observed in the experimental data. As mentioned above, this single mechanism actually represents the combined effect of several more complex mechanisms and corresponds to a very simplified picture of these processes. Bridge Breaking. It appears from experimental observations that during natural maturation (as well as hydrous pyrolysis), more donatable hydrogen is available, as paraffins but few olefins are produced.11 This prompted a change in the FG-DVC model for the description of maturation concerning the breaking of bridges. During open-system pyrolysis, bridge breaking is assumed in the model to be accompanied by the transformation of labile (aliphatic) bridges into strong (olefinic) bridges, providing the necessary hydrogen.27 This assumption was relaxed in the maturation model;


i.e., the hydrogen was assumed not to come from the coal, but from other sources (possibly water). A single, distributed bridge breaking rate was used and is given in Table 2. Its value was chosen to be the same as the one used in open-system pyrolysis and was estimated from the rates determined previously for the various Argonne coals.30 The distribution of activation energies was chosen to be wide enough so that the changes, as a function of the degree of maturity of the sample, of the bridge breaking rate during pyrolysis would derive directly from the bridge distribution being consumed during aging. In other words, during maturation, the lower end of the bridge distribution is consumed. The resulting distribution for the remaining bridges is then used during open pyrolysis and leads to bridge breaking occurring at progressively higher temperatures when increasing maturation. This approach does not require any fitting parameter for the bridge breaking rate as a function of maturation and should be able to model the increase in tar peak temperature with maturation. For type II-S kerogens or type III kerogens with a high sulfur content, a slightly different value for the bridge breaking activation energy was used to represent the effect of more labile sulfur bonds (see Table 2). Network Parameters po, pho, psofto, and qo. While it is not straightforward to directly relate these model parameters to the actual coal structure, some changes in the coal structure with maturation can be reflected by specific variations of po, pho, psofto, and qo. The strong bonds (pho) can be taken as representative of the degree of condensation in the network and should increase with the level of maturation. The number of cross-links (qo) could describe the effect of oxygen bridges (related to either cations or esters bonds), which decrease with maturation, and could also describe π-π bonds, which become numerous at high maturity levels. The variations in weak bonds, psofto, should be consistent with the rate of bridge breaking during maturation. The parameter po, which is the fraction of bridges present, is defined from the two parameters pho and psofto as po ) pho + psofto, and should be chosen concurrently with qo to match the amount of extractables. However, the amount of extractables has been seen above from Figure 4 to be strongly dependent on the solvent and the extraction conditions. Consequently, the value of po can be only roughly estimated. When predicting maturation, processes that change the values of pho and qo with the degree of maturation following the trends described above have to be included. The first process implemented is the removal of crosslinks due to the removal of cations with maturation under wet conditions. Since part of the high-temperature CO peak has been found to be related to the decomposition of calcite formed from cations during open pyrolysis, a CO-XT cations pool has been introduced in the model and related to cross-links. As described above, this pool is removed at a rate similar rate to early CO2, since cations are thought to be initially found as carboxylates, and has a negative efficiency for cross-links to represent the fact that when cations are removed, cross-links are also removed. The amount of the CO-XT pool was estimated from the difference between the CO evolution of raw and demineralized lignite. The amount of cross-links necessary

Energy & Fuels, Vol. 10, No. 1, 1996 35 Table 3. Starting Network Parameters for the Series Predicted

series Mahakam Monterey Ikpikpuk Middle Valley Argonne a

pho po qo (starting (starting (starting cross-links) strong bonds) bonds) extractables 0.04 0.04 0.04 0.04 0.1

0 0 0 0.35 0

0.81 0.81 0.81 0.81 0.81

0.30a 0.30a 0.30a 0.30a 0.09

Estimated values.

to represent the difference in extractables between the raw and demineralized lignite was also separately estimated. The corresponding relationship between the amount of CO-XT cations and the amount of cross-links was found to be approximately 1, i.e., 1 cross-link per molecule of CO evolved. The efficiency used is then -1. At high carbon contents (>85% C), maturation processes similar to those of open-system pyrolysis are assumed to occur. In that case, the formation of strong bonds is assumed to occur when labile (weak) bridges are broken, thus increasing the value of pho with maturation. Below 85% C, the coal is assumed to undergo maturation in the presence of water, and weak bridges break without the formation of strong bonds since the hydrogen to cap bonds is assumed to come from another source, possibly water. The starting parameters were chosen to be, when possible, the same for all the series studied, in order to eliminate fitting parameters and are displayed in Table 3. However, in the case of the Middle Valley kerogens a higher fraction of starting strong bonds was found necessary as the amount of tar is very low. Table 4 summarizes the experimental trends observed with maturation, the suspected mechanisms, and the features of the FG-FVC-maturation and FG-DVC-pyrolysis models. Results Predictions for the Mahakam, Ikpikpuk, Middle Valley, and Monterey series were obtained using the thermal history known for each series. Information about the thermal history is summarized in Table 1. The time/temperature (t/T) profile for the Mahakam coals is not linear, and the heating rate varies between 2 and 20 °C/Ma.62 A thermal gradient with depth of 40 °C/ km is assumed. Only the age and the maximum temperature were available for the Monterey kerogens.37 The simulations were obtained using the corresponding average heating rate. The time/temperature history of one Ikpikpuk sample,38 concurrently with present temperatures, was used to determine the time/ temperature history of all the samples available. This approach assumes that the same differential of temperature has been kept over the entire maturation range. This series, however, did not contain an immature sample (of oxygen content above 20%) which can serve as a starting point for maturation simulations. In order to simulate aging, an immature sample was constructed so that its maturation over the known t/T profile would fit the first sample of the series. The kinetics of maturation were then tested by comparing the results of a simulation using the t/T profile of the (62) Landais, P., private communication.

36


Charpenay et al.

Table 4. Summary of the Experimental Trends Observed, the Suspected Mechanisms, the Mechanisms Present in the FG-DVC Pyrolysis Model and the Mechanisms Implemented in the FG-DVC Maturation Model exptl trends in natural maturation

suspected mechanisms

mechanisms in the original FG-DVC

mechanisms implemented in the FG-DVC matural model

for C e 85%, decrease of the CO2, H2O, CO precursors, regardless of their stability in pyrolysis for C g 85% removal of the early evolution precursors only

in the early stages of maturation, water reacts with oxygen functionalities without the formation of cross-links at high maturation temperature, maturation processes similar than in open-system pyrolysis

the loosely-bound CO2, H2O, and CO precursors are removed first, and cross-linking occurs with CO2 evolution

for C e 85%, all CO2, H2O, and CO precursors are removed (without formation of cross-links), without a change in the distribution of the gas pools for C g 85%, regular pyrolysis is performed

increase and subsequently decrease of CH4 precursors increased pyrolysis Tmax

increase in CH4 precursors (hydroaromatics and arylmethyls) decrease in hydroaromatics by aromatization and methyls through CH4 formation

CH4 precursors are continuously removed, producing an increase in Tmax for the pyrolysis CH4 evolution curve

the increase in CH4 precursors was empirically correlated with a rank-change indicator, the loss of oxygen in water the loss of CH4 is predicted as in the pyrolysis model

increase, then leveling-off, then sharp increase (the magnitude of the increase is strongly solvent-dependent), and finally sharp decrease of extractable yield

the first increase may be due to cation-related cross-links, which are removed during maturation the second increase may be due to depolymerization processes the last decrease may be related to an increase in π-π bonds with increased aromaticity

bridge breaking with the formation of strong bonds when labile bridges are formed to provide hydrogen cross-linking related to CH4 and H2 formation

cation-related cross-links are removed without the formation of other cross-links (first increase) bridge breaking (using pyrolysis kinetics) without formation of strong bonds when C < 85% (second increase) same as in pyrolysis above 85% C (leveling off) cross-linking related to CH4 and H2 same as in pyrolysis (decrease)

increase in aromatic content

aromatization processes

increase in strong bonds, occurring with bridge breaking cross-linking related to CH4 and H2

increase in strong bonds with bridge breaking when C g 85% cross-linking as in pyrolysis

lower extractables and tar yields, higher high-temperature CO peak when cations are present

cations “hold” the structure together (in particular oxygen functionalities)

none

correlation of the cation content with the high-T CO peak and the starting cross-link fraction cross-links are removed along with cations

increase of pyrolysis Tmax for tar evolution with maturation

weak bridges are broken in the early stages of maturation, the structure becoming more stable

the distribution of labile bridges is being consumed during pyrolysis

same as in pyrolysis; labile bridges are used up during maturation (using the pyrolysis kinetics), thus increasing the value of the tar peak temperature

early pyrolysis tar evolution for high-sulfur samples

C-S bridges break at lower temperatures than C-C bridges, and induce the formation of radicals

lower value for the activation energy is used

same as in pyrolysis

other samples of the series with the corresponding data. The final temperatures of the Middle Valley series have been estimated within ∼20 °C and the age within at least a factor of 2.39 However, as in the case of the Ikpikpuk samples, the series did not include an immature sample which could be used as the starting point for the maturation predictions. The same procedure was used as for the Ikpikpuk series was performed. While the Mahakam, Ikpikpuk, and Monterey samples have been produced at heating rates between 2 and 20 °C/Ma and at temperatures between 50 and 150 °C, the Middle Valley samples have been produced at a muchhigher heating rate (1000-2000 °C/Ma) and at much higher temperatures (120-225 °C). This allows for a check of the kinetics over a wide set of maturation conditions. An additional series of predictions using a typical heating rate of 10 °C/Ma was performed at various maturation temperatures. Each predicted matured

sample was then compared to the closest Argonne coal in terms of gas evolution, tar Tmax, and extractables. This allowed a more extensive comparison of the model predictions with data. Predictions of the evolution rates for the Mahakam samples are presented in Figures 2g-i and 3e,f. The parameters used are given in Table 3. As can be seen in the figures, fairly good predictions are obtained for all gas species. The model predicts the fast removal of oxygen species in the early stages of maturation. Figure 7 displays the predicted CO2, CO, H2O, tar, and CH4 gas yields versus the experimental values for all the series studied. Good agreement between data and predictions is generally obtained. The water plot is the most scattered as water was found to be usually difficult to measure accurately by FTIR, especially in the case of the kerogens.34 The Ikpikpuk series is the least well predicted. This may imply some inaccuracies in the thermal history. As the Middle Valley kerogens are reasonably well predicted, the kinetics used appear to



Figure 10. Comparison of the predicted versus measured gas and tar yields for confined pyrolyzed Mahakam samples: (a) CO2, CO, H2O, CH4; (b) tar.

Figure 7. Comparison of the predicted versus measured gas and tar yields: (a) CO2, (b) CO, (c) H2O, (d) tar, (e) CH4.

Figure 8. Comparison of the predicted versus measured tar Tmax.

Figure 9. Predicted extractables fraction as a function of the measured vitrinite reflectance Ro.

be applicable over very different thermal histories. However, the model underpredicts the rate of removal of tar and CH4 for both the Ikpikpuk and Middle Valley kerogens. The predicted versus measured Tmax values for tar evolution are shown in Figure 8. The good match between the theoretical and measured values clearly demonstrates the ability of the model to represent changes in bridge breaking rate with maturation. This result validates the assumption of an original distribution of bridges which gets consumed during maturation, as well as the value chosen for the width of the distribution (see Table 2). The predictions for the extractable yields using the generic 10 °C/Ma heating rate up to various temperatures are shown along with the data in Figure 4. Reasonable agreement is obtained. It should be noted that the predicted extractables yields depend on two processes: bridge breaking and cross-linking. The values of extractables are especially sensitive to a few cross-

links added to the network. This observation explains the dip seen in the theoretical curve around 85% carbon, which corresponds to cross-linking processes starting to operate (cross-linking is linked with CH4 and H2 evolution). A slight offset in the kinetic rates for the crosslinking processes (even at very low levels) can have a strong influence on the extractable yield and can lead to an underprediction of the extractables, as is probably occurring in this case. However, it is believed that the onset of the curve, when cross-linking processes have not started yet, gives a good indication of the magnitude of the bridge breaking rate. In the case of the predictions shown in Figure 4, the onset of the curve, occurring at a carbon content of about 82%, matches the general trends of the data. This result shows that the kinetic rate developed for bridge breaking (i.e., related to oil formation processes) from open-system pyrolysis experiments can be extrapolated to maturation conditions. Similar conclusions were drawn from other studies.12,17 In Figure 9, the predicted extractables yields are shown for the Mahakam coals and the Middle Valley and Ikpikpuk kerogens as a function of the measured vitrinite reflectance. It can be seen that the increase in extractables matches well the oil window, which is usually between Ro ) 0.5 and 1.2. Confined Pyrolysis. The maturation model was also compared with artificial maturation data from the confined pyrolysis system (see data in Figures 2d-f and 3c,d). The same time/temperature history as for the experiments was used in the modeling, and the pyrolysis decomposition behavior of the resulting predicted samples was compared with that of the confined pyrolysis residues. It was found that, while the pyrolysis evolution curves between the predicted samples and the confined-pyrolyzed samples are different (which is the result of the fact that the maturation model was developed following the trends from natural maturation, which are different from the ones from confined pyrolysis, as seen in Figures 2 and 3), the predicted gas yields, shown in Figure 10, are reasonably similar to the experimental values. This indicates that confined pyrolysis, while not representing well the temperatureresolved evolution of gas species, gives a reasonable value for the total yields. As for naturally matured samples, CH4 and tar removal during confined pyrolysis is underestimated by the model, as shown in Figure 10,

38


Figure 11. Predicted (empty symbols) and experimental (solid symbols) values of H/C and O/C on a van Krevelen diagram.

a and b, respectively. The kinetics for the removal of CH4 during artificial maturation were determined and found to have an apparent activation energy of 120 kcal/ mol with a preexponential factor of 1036 s-1. This last value has no chemical meaning and is probably the result of higher order reactions or multiple reactions mechanisms. Also, when these kinetics were used in the case of kerogens, the predictions were found to be as poor as, or even worse than, the original values (which were ∼60 kcal/mol for the activation energy and 3 × 1013 for the preexponential, with small variations from series to series).30 This result suggests that confined pyrolysis kinetics for CH4 removal (at least under a first-order reaction form), as well as opensystem pyrolysis kinetics, cannot be applied to natural maturation. Finally, in Figure 11 are plotted the predicted values of O/C and H/C for all the series studied on a van Krevelen diagram, along with experimental data. It can be seen that the predictions are fairly close to the data and that maturation trends for each kerogen type, as indicated by the thick lines, are followed. Conclusions The FG-DVC-maturation model was developed from the FG-DVC-pyrolysis model to include processes specific to long-time/low-temperature reactions in the presence of water, and with a special emphasis on the representation of diagenesis and oil formation processes. The processes implemented in the model were chosen to be consistent with experimental trends observed in several natural series of samples of various maturity, and include (i) the rapid removal of CO2, H2O, and CO precursors, as open-system pyrolysis kinetics for the evolution of oxygenated gases are too slow for the maturation processes, new kinetics being empirically estimated from several natural maturation series; (ii) the uniform removal of oxygenated precursors during maturation, regardless of their temperature of evolution during open-system pyrolysis; (iii) the removal of CO2 precursors without cross-linking (as opposed to opensystem pyrolysis), as a result of reactions where water may provide hydrogen to cap bonds; (iv) the removal of exchanged cations, along with a negative cross-link efficiency to represent the disappearance of cation-

Charpenay et al.

related cross-links which impact on the extractable yield at room temperature; (v) an increase in methane precursors, empirically connected with a maturation indicator (H2O precursors); (vi) the use of a single activation energy distribution for bridge breaking, progressively consumed during maturation; (vii) a switch from processes of maturation in the presence of water toward open-pyrolysis processes at a high maturity level. The FG-DVC-maturation model was used to predict the aging of immature samples of several natural series through the known thermal history of the basins. The predicted residue of the maturation model was then used as input of the FG-DVC-pyrolysis model and the predictions were compared to the actual pyrolysis decomposition data obtained by TG-FTIR. Predictions were obtained for four series of coals and kerogens with a well-known thermal history. It was found that 1. The model successfully predicted, in most cases, the open-pyrolysis evolution curves and final yields of the gases and tar of the matured samples. The Ikpikpuk series gave the least satisfying predictions. However, the reconstruction of the thermal history in that case contained several uncertainties. 2. The model successfully predicted the increase in Tmax for the tar evolution rate as a function of maturation. This result validates the assumption of an original distribution of bridges which gets consumed during maturation, as well as the chosen width of the distribution. 3. The rate of removal of CH4 and tar during maturation for the kerogen series is generally underestimated by the maturation model. 4. The model gave good predictions of the variations of extractable yields as a function of maturation. This result shows that the kinetic rate developed for bridge breaking (i.e., related to oil formation processes) from open-system pyrolysis experiments can be extrapolated to maturation conditions. 5. Confined pyrolysis maturation experiments provide reasonable predictions for the open-pyrolysis gas yields of the matured residues, but not for the evolution curves. Kinetics determined for the removal of CH4 precursors during maturation were found not to be applicable to natural series. Acknowledgment. This work was supported under grant No. III-9203467 from the National Science Foundation. The authors express appreciation to Total Indonesie, Pertamina, and Inpex for providing the Mahakam samples and to Dr. David Baskin of Chevron for providing the Monterey kerogens. Dr. Alan Burnham of the Lawrence Livermore National Laboratory supplied the San Juan samples and provided an extensive review of the manuscript. Dr. Jean Whelan of Woodshole Oceanographic Institute provided the Ikpikpuk and Middle Valley kerogen samples and helped in defining the direction of this work. The authors also thank Prof. Eric Suuberg for many useful discussions and insights, and Dr. Leon Stock of the Argonne National Laboratory for his important contribution to the identification of possible mechanisms for gas formation during maturation and pyrolysis. EF9501509

Influence of Maturation on the Pyrolysis Products from Coals and

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