Electrofacies Determination Using SOM Neural Network in Bangestan Reservoir, SW Iran

Document Type : Research Paper

Authors

1 Department of Geology, Chamran University, Ahvaz, Iran

2 گروه زمین‌شناسی، دانشکده علوم زمین، دانشگاه شهید چمران اهواز، ایران

3 National South Iranian Oil Company(NISOC), Ahvaz, Iran

Abstract

The reservoir electrofacies study is one of important subjects in hydrocarbon reservoirs scope now. Determination of the high reservoir quality zones can play an important role in production view of the hydrocarbon reservoir and their development. Electrofacies is defined on the basis of clustering which is grouping all similar log data in unique set and distinguished it from other sets.  In the present research at first using SOM, MRGC and DC methods, primary model of electrofacies in a number of field’s wells has been determined. Electrofacies have determined by different methods correlated with identified flow unit’s derived Core storage capacity (phie*h)-flow capacity(K*h) data, and SOM method has been chosen among them for clustering which had the highest accordance. 9 created Initial electrofacies reduced to 4 electrofacies according to the analogy of some parameter; such as, porosity and gamma logs. This electrofacies have been generalized for entire filed resulting in creation of a model with separation capability of the deferent reservoir zones. This model shows a decrease in reservoir quality from the upper part to the bottom of the reservoir also depicts reservoir quality changes whole the studied field.
 

Keywords

References

[1]. رحیمی‌بهار ع. ا.، "تفکیک زون‌های مختلف مخزن هیدروکربنب با کمک رخساره‌های الکتریکی،" پژوهش نفت، ص. 10، 1391.##
[2]. Serra O., and Abbot H., “The Contribution of Logging Data to Sedimentology and Stratigraphy,” SPE of AIME, Transaction 55th Annual Fall Technology Conference, 1980.##
[3]. رحیمی‌بهار ع. ا.، استفاده از رخساره‌های الکتریکی در شبیه‌سازی رخساره‌های رسوبی، مجله رخساره‌های رسوبی، دانشگاه مشهد، ص. 15، 1391.##
[4]. Kadkhodaie-Ilkchi A. and Amini A., “Afuzzy logic approach to estimating hydraulic flow units from well log data: a case study from the ahvaz oil field,” South of iran, Journal of Petroleum Geology, Vol. 32, No. 1, pp 1-12, 2009.##
[5]. Soto R. and Garcia J. C. “Permeability prediction using hydraulic flow units and hybrid soft computing systems,” SPE 71455, 2001.##
[6]. Shedid A. S. and Reyadh A. A., “A new approach of reservoir description of carbonate reservoirs,” SPE 74344, pp. 1- 10, 2002.##
[7]. Winland H. D., “Oil Accumulation in Response to Pore Size changes,” Weyburn field, Saskatchewan, Amoco Production Research Report, No. F72-G-25, pp. 197, 1972.##
[8]. Gunter G. W., Early determination of reservoir flow units using an integrated petrophysical method, SPE 38679, pp. 373-380, 1997.##
[9]. Amaefule J. O., Altunbay M., Tiab D., Kersey D. G. and Keeland D. K., “Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells,” SPE, P. 26436, pp. 1–16, 1994.##
[10]. Abbaszadeh M. D., Fujii F. and Fujimoto F., “Permeability Prediction by Hydraulic Flow Units-Theory and Applications,” SPE Formation Evaluation, Vol. 11, pp. 263-271, 1996.##
[11] Kohonen T., Kaski S. and Lappalainen H., “Self-organized formation of various invariant feature filters in the adaptive-subspace SOM.,” Neural Computation 9, pp. 1321-1344, 1997.##
[12]. Mukherjee A., “Self-organizing neural network for identification of natural modes,” The Journal of Computing in Civil Engineering, Vol. 11 No. 1, pp. 74-77, 1997.##
[13]. Sfidari E., Kadkhodaie-Ilkhchi A. and Najjari S., “Comparison of intelligent and statistical clustering approaches to predicting total organic carbon using intelligent systems”, Journal of Petroleum Science and Engineering, pp. 86-87, 2012.##
Journal of Petroleum Research
Volume 26, 95-2 - Serial Number 87
July and August 2016
Pages 64-74

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