Modeling of Lithology in South Pars Gas Field Using Artificial Neural Network

Document Type : Research Paper

Authors

1 Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Amirkabir University of Technology

Abstract

Coring from several wells, regardless of the oil field acreage, is an inevitable and necessary task in order to obtain general information of the region. Yet, coring in wells of huge fields is excessively costly. Therefore, finding a solution to avoid this excessive expense seems to be crucial. This work presents a type of artificial neural network modeling in order to use well bore logs in lithology prediction in one of the South Pars gas field reservoirs. Here, a network with three-layer back propagation (BP) method and Levenberg-Marquwardt algorithm has been used for lithology estimation. The network utilized density, neutron, gamma-ray and photoelectric effect (PEF) logs as inputs. Data from four wells in South Pars field has been used. Data from two wells (wells SPF1 and SPF2) having core analysis were used as network training, validation and testing. The network was then utilized to estimate the lithology in the two other wells (wells SPF3 and SPF4) and the results were compared with the core data (real lithology). The interval under investigation consists of Dolostone, Limestone, Dolomitic Limestone, Limy Dolostone, Anhydrite, Shale, Shaly Limestone and Shaly Dolostone. The mean square error (MSE) of rock types were 0.081 and 0.094 for SPF3 and SPF4 wells, respectively.

Keywords

References

منابع
[1] Mohaghegh S.D., “Chevron A., Gaskari R., Siegfreid R., Determining In-Situ Stress Profiles From Logs”, SPE., 90070, 2004.
[2] Duboisa K., Geoffrey C., Bohling S., “Comparison of four approaches to a rock facies classification problem”, Computers & Geosciences., Vol. 33, pp. 599-617, 2007.
[3] Siripitayananon P., Chen H., Hart B.S., A New Technique for Lithofacies Prediction: Back-Propagation Neural Network, Association for Computing Machinery., Inc, 2001.
[4] Nikravesh M., “Soft computing-based computational intelligent for reservoir characterization”, Expert Systems with Applications., Vol. 26, pp. 19-38, 2004.
[5] Nikravesh M., Aminzadeh F., “Mining and fusion of petroleum data with fuzzy logic and neural network agents”, Journal of Petroleum Science and Engineering., Vol. 29, pp. 221-238, 2001.
[6] Kadkhodaie Ilkhchi A., Rezaee M.R., Moallemi S.A., “A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field”, Journal of Geophysics and Engineering. Vol. 3, pp. 356-369, 2006.
[7] Rezaee M.R., Kadkhodaie A., Barabadi A., “Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia”, Journal of Petroleum Science and Engineering., Vol. 55, pp. 201-212, 2007.
[8] Taheri S.R., “Remote sensing, fuzzy logic and GIS in petroleum exploration”, SPE., 101040, 2006.
[9] Shiwei Y., Kejun Z., Fengqin D., “A dyanamic all parameter adaptive BP neural networks model and its application on oil reservoir prediction”, Applied mathematics and computation., Vol. 195, pp. 66-75, 2008.
[10] Hambalek N., Gozalez R., “Fuzzy logic applied lithofacies and permeability forecasting”, SPE., 81078, 2003.
[11] Cuddy S.L., “Lithofacies and permeability prediction from electrical logs using fuzzy logic”, SPE., 65411, 2000.
[12] Zhou Z.H., Chen Z.Q., Chen S., Neural networks based lithology identification, Proceedings of the International Conference on Intelligent Information Processing., Beijing, China, pp. 139-142, 2000.
[13] Chang H., Kopaska C., Chen H., “Identification of lithofacies using Kohonen self-organizing maps”, Computers & Geosciences., Vol. 28, pp. 223-229, 2002.
[14] Briqueu L., Gottlib-Zeh S., Ramadan M., Brulhet J., “Inferring lithology from downhole measurements using an unsupervised self-organising neural network: study of the Marcoule silty clayish Unit”, C. R. Geoscience., Vol. 334, pp. 331-337, 2002.
[15] Matlab User’s Guide, Neural Networks Toolbox for use with Matlab, by the Math Works, Ins., 2010.
[16] شرکت نفت و گاز پارس، گزارش نهایی حفاری و زمین شناسی میدان گازی پارس جنوبی، مدیریت مهندسی نفت و گاز، 1382.
[17] شرکت نفت و گاز پارس، گزارشات تکمیلی چاه‌های مورد مطالعه و نتایج عملیات مغزه گیری، بخش زمین‌شناسی و ژئوفیزیک، 1386.
Journal of Petroleum Research
Volume 21, Issue 66 - Serial Number 66
July and August 2011
Pages 12-22

Files

Share

How to cite

Statistics