This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

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1 This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier s archiving and manuscript policies are encouraged to visit:

2 Computers & Geosciences 45 (212) Contents lists available at SciVerse ScienceDirect Computers & Geosciences journal homepage: Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data: A case study of the South Pars Gas Field, Persian Gulf, Iran Bahram Alizadeh a,b,n, Saeid Najjari a, Ali Kadkhodaie-Ilkhchi c a Department of Geology, Faculty of Earth Sciences, S. Chamran University of Ahvaz, Ahvaz, Iran b Petroleum Geology and Geochemistry Research Centre (PGGRC), SCU, Ahvaz, Iran c Department of Geology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran article info Article history: Received 21 April 211 Received in revised form 21 November 211 Accepted 23 November 211 Available online 23 December 211 Keywords: Artificial neural network Cluster analysis Well log data Organic facies Burial history Rock-Eval pyrolysis abstract Intelligent and statistical techniques were used to extract the hidden organic facies from well log responses in the Giant South Pars Gas Field, Persian Gulf, Iran. Kazhdomi Formation of Mid-Cretaceous and Kangan-Dalan Formations of Permo-Triassic Data were used for this purpose. Initially GR, SGR, CGR, THOR, POTA, NPHI and DT logs were applied to model the relationship between wireline logs and Total Organic Carbon (TOC) content using Artificial Neural Networks (ANN). The correlation coefficient (R 2 ) between the measured and ANN predicted TOC equals to 89%. The performance of the model is measured by the Mean Squared Error function, which does not exceed.73. Using Cluster Analysis technique and creating a binary hierarchical cluster tree the constructed TOC column of each formation was clustered into 5 organic facies according to their geochemical similarity. Later a second model with the accuracy of 84% was created by ANN to determine the specified clusters (facies) directly from well logs for quick cluster recognition in other wells of the studied field. Each created facies was correlated to its appropriate burial history curve. Hence each and every facies of a formation could be scrutinized separately and directly from its well logs, demonstrating the time and depth of oil or gas generation. Therefore potential production zone of Kazhdomi probable source rock and Kangan- Dalan reservoir formation could be identified while well logging operations (especially in LWD cases) were in progress. This could reduce uncertainty and save plenty of time and cost for oil industries and aid in the successful implementation of exploration and exploitation plans. & 211 Elsevier Ltd. All rights reserved. 1. Introduction Jones (1987) defined an organic facies as a mappable subdivision of a stratigraphic unit distinguished by the character of its organic matter. Different organic facies generate and expel different amounts and types of oil and gas (Demaison, 1984). The quantity of the organic matter within rocks is usually expressed as total organic carbon (TOC) and the relative ability of a source rock to generate petroleum is defined by its kerogen quantity (TOC) and quality (Hunt, 1996). This parameter is best measured by Rock-Eval pyrolysis despite its cost and being time consuming. So to date, numerous researchers have tried to make n Corresponding author at: Department of Geology, S. Chamran University of Ahvaz, Faculty of Earth Sciences, Ahvaz, Khuzestan , Iran. Tel.: þ ; fax: þ addresses: alizadeh@scu.ac.ir (B. Alizadeh), ss.utgeo@gmail.com (S. Najjari), akadkhoda@khayam.ut.ac.ir (A. Kadkhodaie-Ilkhchi). a qualitative and quantitative correlation between well log responses (as common and cheaper data) and organic richness of rocks. Among them are Schmoker and Hester (1983), Meyer and Nederlof (1984), Fertle (1988), Hertzog et al. (1989), Passey et al. (199), Huang and Williamson (1996), Kamali and Mirshady (24), Alizadeh and Moradi (26). Kadkhodaie-Ilkhchi et al. (29) invented a committee machine with intelligent systems (CMIS), which combines the results of TOC predicted from intelligent systems including fuzzy logic, neuro-fuzzy and neural network. Current study creates both quantitative and qualitative correlation between petrophysics and geochemistry of formations using Artificial Neural Networks. Using the Cluster Analysis it proposes a method to classify a single source or reservoir unit into different specified zones as organic facies using well log-based neural network predictions, as well to specify burial/thermal history diagrams for each individual zone based on well log data. The methods used in this paper are well known in their own ways; the different point of this research is to combine these 98-34/$ - see front matter & 211 Elsevier Ltd. All rights reserved. doi:116/j.cageo

3 262 B. Alizadeh et al. / Computers & Geosciences 45 (212) Fig. 1. Location of the Persian Gulf (a) and the South Pars Gas Field (b) (Ghasemi-Nejad et al., 29). methods together to introduce a new, intelligent, quick, and completely economical approach for subsurface studies. In fact, for the first time, petrophysical data and intelligent techniques have directly been used to attain subsurface geochemical zonation information without needing for further laboratory analyses. The method is illustrated using a case study from the world s largest non-associated gas accumulation, South Pars Gas Field, located in the Persian Gulf, between Iran and Qatar (Fig. 1). Drill cutting samples for this study were collected from three formations: the late Albian to late Cenomanian gas-prone Kazhdomi Formation (equivalent of the Nahr Umar Formation) with shaly to marly composition, and the Upper Permian to Lower Triassic Dalan and Kangan Formations (equivalent of the Khuff Formation) with limestone, dolomite, dolomitic limestone and shaly interbeds, which are the two main condensate and gas-bearing reservoir units in this field (Ghasemi-Nejad et al., 29; Aali et al., 26; Rahimpour-Bonab, 27). 2. The methodology This study consists of five major steps: (a) Rock-Eval pyrolysis on drill cutting samples, (b) Formulating the TOC values to well logs using ANN and D log R methods, (c) Clustering the generated TOC data into specified zones by Cluster Analysis (CA), (d) Drawing the burial/thermal history diagrams for each zone, (e) Correlating the estimated with the achieved real data. The integrated technique described in this study could be considered as an efficient and instrumental way for predicting TOC parameter and extracting Organic facies by modern and intelligent methods Rock-Eval pyrolysis Rock-Eval pyrolysis provides information on the quantity and type of organic matter in a sedimentary rock, in addition to the level of organic maturation (Espitalié et al., 1977). This technique comprises two successive analytical stages of pyrolysis and oxidation and uses a small amount (7 mg) of crushed rock samples. The output comprises multiple parameters and TOC is one of the most important of them Artificial neural Networks Artificial neural network, ANN, is an intelligent system for solving non-linear complex problems. The back-propagation neural network (BP-NN), used in this study, is a supervised training technique that computes the difference between ANN-calculated output and corresponding desired output from the training dataset. The error is then propagated backward through the net and the weights are adjusted during a number of iterations, named epochs. The training ceases when the calculated output values best approximate the desired values (Bhatt and Helle, 22; Rumelhart et al., 1986) Cluster analysis Cluster analysis creates groups or clusters of data. Clusters are formed in such a way that objects in the same cluster are very similar and objects in different clusters are very distinct. Hierarchical Clustering, a kind of cluster analysis, which is used for this study, groups data over a variety of scales by creating a cluster tree or dendrogram. The dendrogram allows deciding the level or scale of clustering that is most appropriate for an application (MATLAB User s Guide, 29) Burial history Measured maturity values for possible source rocks are invaluable because they tell us much about the present status of hydrocarbon generation at the sample location. So, we still have no clue as to when oil generation eventuated, nor do we know at what temperature or depth it occurred. Lopatin (1971) described a simple method by which the effect of both time and temperature could be taken into account in calculating the thermal maturity of organic material in sediments. He developed a Time-Temperature Index (TTI) of maturity to quantify his method. The required input data are the time-stratigraphic data

4 B. Alizadeh et al. / Computers & Geosciences 45 (212) R² = GR (gapi) R² = SGR (gapi) R² = CGR (gapi) R² = THOR (ppm) R² = POTA (%) R² = NPHI (v/v) TOC (wt %) R² = DT (ms/ft) R² = LLD (ohm-m) R 2 = R 2 = RHOB (g/cm 3 ) PEF Fig. 2. Crossplots showing relationship between measured TOC and GR (a), SGR (b), CGR (c),pota (d), THOR (e), NPHI (f), DT (g), LLD (h), RHOB (i) and PEF (j) logs in wells SP-A, SPB,and SP-C of the South Pars Gas Field.

5 264 B. Alizadeh et al. / Computers & Geosciences 45 (212) (usually available as formation tops) and ages (obtained by routine biostratigraphic analysis of cuttings, seismic data or thicknesses of exposed sections nearby) (Waples, 1985). 3. The South Pars gas field ANN was used to predict TOC from petrophysical data including GR, SGR, CGR, THOR, POTA, NPHI and DT logs. For this purpose, 78 cutting samples were collected for Rock-Eval pyrolysis from about every 3 ft to 6 ft of well SP-A, SP-B and SP-C in the South Pars Gas Field (unsystematic sampling was chosen where necessary to include a wide range of lithology variations) Rock-Eval pyrolysis The Rock-Eval 6 was used for this study. In addition to TOC the output of this analysis includes several parameters such as S 1,S 2,HI, OI, PI and T max, which were used for thermal maturity modeling. Some of the hydrocarbon generated during pyrolysis (S 2 )is adsorbed by clay minerals and the host rock matrix. This phenomenon is called mineral matrix effect and must be corrected before interpreting the results. So, initially the mineral matrix effect was corrected according to the S 2 vs. TOC crossplot method expressed by Langford and Blanc-Valleron (199). The calculated correction values were.2,.42 and.28 mg HC/g rock for Kazhdomi, Kangan and Dalan formations, respectively Physical relationships between TOC and petrophysical data There is a logical relationship between the used petrophysical data and measured TOC content (Fig. 2). According to Fig. 2(a g) GR, SGR, CGR, THOR, POTA, NPHI and DT logs show a direct relationship with TOC. However, in this case study there is no clear relationship between LLD, RHOB and PEF logs with TOC (Fig. 2g, i, and j). These could occur due to rock heterogeneities, mineralogy changes, variations in the fluid content and saturation (Kadkhodaie-Ilkhchi et al., 29) Predicting TOC by ANN Using the MATLAB software, a three layered ANN with backpropagation algorithm was designed for TOC prediction. This ANN was trained using the Levenberg-Marquardt training algorithm whose details of computation process and training could be found in Boadu (1997, 1998) and Bishop (1995). The default Maen Squared Error performance function, MSE, was used to measure the performance of the model and the error goal was set to. The transfer function from layer one to two is TANSIG and from layer two to layer three is PURELIN. Based upon the qualitative and quantitative (R 2, correlation coefficient) relationships presented in Fig. 2, petrophysical data including GR, SGR, CGR, POTA, THOR, NPHI and DT logs were chosen as input data ;and the measured TOC values were used as desired output (or target). Some of the TOC values were eliminated because of repeated digits or unreliable well conditions. Indeed, a primary quality control was performed on the dataset to preprocess the noisy data. The input dataset was divided into three clusters including training (38 data points, to train the ANN), validation (8 data points, to control ANN s performance), and testing data (8 data points, to test the ANN). Various sets of input data, hidden layer neurons and epochs were examined to reach the optimum performance of the model (Table 1). In addition to MSE, several other criteria were used for selecting final neural network model; among them are: correlation coefficient between real and ANN predicted data in different steps of ANN preparation (train, validation, and test), the minimum deviation between regression line and R¼1 line in correlation coefficient of ANN preparation steps, number of epochs that will also affect training and operation period time, etc. Finally, a network with 7 neurons in the input layer and 5 hidden neurons was found as the optimal model. The correlation coefficient (R 2 ) of the model between the measured and ANN predicted TOC for training, validation and test stages equals 85%, 9% and 89%, respectively (Fig. 3). After adjusting the default weight and bias values, the performance of the model (MSE) is.73 (Fig. 4). The well log data of the intervals lacking the measured TOC values were feed to the optimized model and TOC was calculated. A graphical comparison between the measured and ANN predicted TOC value versus depth demonstrates a good agreement in the studied field (Fig. 5). The final modeled TOC as a geochemical log is of special importance since it saves thousands of dollars and several weeks of tedious lab work for sample analyses Cluster analysis The synthesized TOC logs could be divided into a few clusters (zones). Any remarkable change in the slope of the cumulative Table 1 Various sets of input data and hidden neurons for choosing optimal ANN model. Model no. 6 is the best model for predicting TOC. No. Input data Hidden Performance R 2 R 2 R 2 Epochs neurons (MSE) training validation test 1 GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI LLD, LLS, MSFL, GR, CGR, SGR, URAN, THOR, POTA, DT, PEF, RHOB, NPHI GR, CGR, SGR, URAN, THOR, POTA, DT, NPHI GR, CGR, SGR, URAN, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, THOR, POTA, DT, NPHI GR, CGR, SGR, DT, NPHI LLD, GR, CGR, SGR, URAN, THOR, POTA,DT, NPHI GR, CGR, SGR, DT, NPHI

6 B. Alizadeh et al. / Computers & Geosciences 45 (212) Fig. 3. Regression plots showing correlation coefficients between target and predicted TOC for training, validation and test stages. Fig. 4. Performance diagrams for Training (blue line), Validation (green line), and Test (red line) steps. The best validation performance is equal to.73 and reached at epoch 8. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) frequency plot of the TOC values refers to a new cluster. Accordingly, five clusters were determined for cluster analysis (Fig. 6). The Euclidian algorithm was used to calculate the similarity (distance) between every pairs of TOC values and the mean distance between elements of each cluster (also called average linkage clustering) was used to link between distance data.

7 266 B. Alizadeh et al. / Computers & Geosciences 45 (212) Depth (m) 27 Fig. 6. Cumulative frequency plot for predicted TOC values, and distinguished slope changes or possible cluster numbers layered ANN designed in the previous stage. A good agreement was found between the real and ANN-estimated organic facies. The overall estimation s accuracy was 84%. Fig. 8 shows an instance plots of well log data, estimated TOC, real organic facies from cluster analysis, and estimated organic facies from ANN at well SP-A. There is a clear relationship between statistical clusters and geological/geochemical facies, which are reflected in log data variations; for example zone 1 or 2 appear when GR, DT, NPHI, and finally TOC log increase and implies to organic enriched geochemical facies, and vice versa Burial history analysis of geochemical zones According to burial history diagrams of the studied formations it is possible to correlate each organic facies to its appropriate burial history curve. This enabled us to investigate geochemical potential of every facies in a single formation by their well logs. Fig. 9 demonstrates an example of the time and depth of oil and gas generation windows according to time-temperature index (TTI) as thermal maturity data D Log R method It is important to use a high accuracy method for prediction of TOC. In this section, a comparison is made between neural networks and empirical methods for TOC prediction. Passey et al. (199) presented and described the new Dlog R method, which employs the separation between porosity and resistivity logs for identifying and calculating total organic carbon. Fig. 1 shows a comparison among the measured TOC data, prediction results of ANN and DLog R methods. Despite the fact that DLog R method was unsuccessful in well SP-A, but in the other two wells it was satisfactory. In all, it can be concluded that the ANN estimations are much more accurate than Dlog R method Fig. 5. Comparison of measured (circles) and ANN predicted TOC (continuous lines) versus. depth for Kangan and Dalan Formations in well SP-A (a), well SP-B (b), well SP-C (c) and Kazhdomi Formation in well SP-C (d). Fig. 7 shows the created binary and hierarchical cluster tree. By applying a cutoff value as the number of optimal clusters on the extracted dendrogram, the TOC data were divided into 5 clusters representing organic facies (geochemical zones). Later, an intelligent relationship was developed between the created organic facies and well log responses using a similar three 4. Conclusion Artificial Neural Networks and Cluster analysis techniques were used for the estimation of TOC and organic facies from petrophysical data. The followings could be concluded from the results of the developed models: (a) ANN has been successful for making a quantitative and qualitative correlation between TOC and petrophysical data. The MSE of the ANN method for estimation of TOC in the test dataset is.73, which corresponds to the R 2 values of 85%, 9%, and 89% for training, validation and test steps, respectively.

8 B. Alizadeh et al. / Computers & Geosciences 45 (212) Fig. 7. Hierarchical and binary cluster tree extracted for TOC data. Numbers along the horizontal axis represent the indices of the objects in the original dataset. Links between objects are represented as upside-down U-shaped lines. The height of vertical axis indicates the distance between the objects. Dashed line is the cutoff distance for creating 5 clusters. Fig. 8. Correlation between well logs and ANN predicted TOC. Track titled Real Zones refers to organic facies as clustering result and track titled Predicted Zones refers to ANN predicted organic facies in Well SP-A, Kazhdomi Formation. (b) Clustering speeds up evaluation of a source rock potential. It increased the accuracy of investigation to minor thicknesses and to divide a formation into productive and nonproductive zones as well. Accordingly 5 organic facies were determined in the studied formations based on TOC log variations; and using an ANN model it was possible to distinguish these facies directly from well log data. (c) Burial history diagram for each organic facies and its correlation with their well logs enabled us to evaluate the maturity state of the drilled formations directly from well logs in a minimum time. (d) The Dlog R is a good method for predicting TOC data but it is not as accurate as ANN technique. In other words, neural network is still a fast, powerful and easy method for data calibration and prediction.

9 268 B. Alizadeh et al. / Computers & Geosciences 45 (212) Fig. 9. Burial history curves demonstrating oil and gas generation window for organic facies of Kazhdomi, Kangan and Dalan Formations (K1-2 & D1-5) in well SP-C using Time-Temperature Index as thermal maturity data Depth (m) 3 31 Depth (m) Measured TOC ANN predicted D log R predicted Fig. 1. Comparison between ANN derived and Dlog R derived TOC with measured values in SP-A (a) and SP-C (b) wells.

10 B. Alizadeh et al. / Computers & Geosciences 45 (212) Acknowledgments The authors are grateful to Petroleum Geology and Geochemistry Research Center of the S. Chamran University of Ahvaz for laboratory data analysis. Also extend their appreciation to the POGC (Pars Oil and Gas Company of Iran) for sponsoring, data preparation and permission to publish this paper. Last but not least we want to thank the editor and the reviewers for their useful and constructive comments, which significantly improved the paper. References Aali, J., Rahimpour-Bonab, H., Kamali, M.R., 26. Geochemistry and origin of natural gas in the world s largest non-associated gas field. Journal of Petroleum Science and Engineering 5, Alizadeh, B., Moradi, M., 26. Correlation of petrophysical and geochemical logs in Ahwaz and Ziloi oil fields. In: Proceedings of the 24th Symposium of Geological Society of Iran, Tehran. Bhatt, A., Helle, H.B., 22. Committee neural networks for porosity and permeability prediction from well logs. Geophysical Prospecting 5, Bishop, C.M., Neural Networks for Pattern Recognition. Clarendon Press, Oxford. 67 pp. Boadu, F.K., Rock properties and seismic attenuation: neural network analysis. Pure and Applied Geophysics 149, Boadu, F.K., Inversion of fracture density from field seismic velocities using artificial neural networks. Geophysics 63, Demaison, G.J., The generative basin concept. in: Demaison, G.J., Murris, R.J. (Eds.), Petroleum geochemistry and basin evaluation. American Association of Petroleum Geologists, memoir 35. American Association of Petroleum Geologists, Tulsa, pp Espitalié, J., Madec, M., Tissot, B., Menning, J.J., Leplat, P., Source rock characterization method for petroleum exploration. Proceedings 9th Annual Offshore Technology Conference 3, Fertle, H., Total organic carbon content determined from well logs. Society of Petroleum Engineers Formation Evaluation 15612, Ghasemi-Nejad, E., Head, M.J., Naderi, M., 29. Palynology and petroleum potential of the Kazhdumi Formation (Cretaceous: Albian Cenomanian) in the South Pars field, northern Persian Gulf. Marine and Petroleum Geology 26, Hertzog, R., Colson, L., Seeman, B., O Brian, M., Scott, H., Mckeon, D., Wraight, P., Grau, J., Schweitzer, J., Herron, M., Geochemical logging with spectrometry tools. Society of Petroleum Engineers Formation Evaluation 4, Huang, Z., Williamson, M.A., Artificial neural network modeling as an aid to source rock characterization. Marine and Petroleum Geology 13 (2), Hunt, John M., Petroleum Geochemistry and Geology, 2nd edition W.H. Freeman and Company. 743 pp. Jones, R.W., Organic Facies. in: Brooks, J., Welte, D. (Eds.), Advances in Petroleum Geochemistry, vol.2., Academic Press, London, pp Kadkhodaie-Ilkhchi, A., Rahimpour-Bonab, H., Rezaee, M.R., 29. A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data. Computers and Geosciences 35, Kamali, M.R., Mirshady, A.A., 24. Total organic carbon content determined from well logs using Dlog R and neuro fuzzy techniques. Journal of Petroleum Science and Engineering 45, Langford, F.F., Blanc-Valleron, M.M., 199. Interpreting Rock-Eval pyrolysis data using graphs of pyrolizable hydrocarbons vs. total organic carbon. American Association of Petroleum Geologists, Bulletin 6 (74), Lopatin, N.V., Temperature and geochemical time as factors of carbonifaction. Akad. Nauk SSSR, Izv. Ser. Geol. 3, MATLAB User s Guide, 29. Version 7.8, Statistics Toolbox. The MathWorks Inc. Meyer, B.L., Nederlof, M.H., Identification of source rocks on wireline logs by density/resistivity and sonic transit time/resistivity cross plots. American Association of Petroleum Geologists, Bulletin 68, Passey, O.R., Moretti, F.U., Stroud, J.D., 199. A practical modal for organic richness from porosity and resistivity logs. American Association of Petroleum Geologists Bulletin 74, Rahimpour-Bonab, H., 27. A procedure for appraisal of a hydrocarbon reservoir continuity and quantification of its heterogeneity. Journal of Petroleum Science and Engineering 58, Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning internal representations by error propagation. in: Rumelhart, D.E., McClelland, J.L. (Eds.), Parallel distributed processing, Vol. 1. Foundations, MIT Press, Cambridge, Massachusetts, pp Schmoker, J.W., Hester, T.C., Organic carbon in Bakken Formation, United States portion of Williston Basin. American Association of Petroleum Geologists, Bulletin 67, Waples, D.W., Geochemistry in Petroleum Exploration. International Human Resources Development Corporation, Boston. 215 pp.