Improving Electrofacies Analysis by Integrating Wireline and Image Logs in Asmari Formation Using MRGC

Document Type : Research Paper

Authors

1 Department of of Earth Scineces, University of Oslo, Oslo, Norway

2 Department of Reservoir Study and Field Development, Research Institute of Petroleum Industry (RIPI), Tehran

3 Department of Sedimentology and Petroleum Geology, University of Tehran

Abstract

In this paper, electrofacies in ASMARI formation in Gachsaran oil field were determined using multi resolution graph-based clustering method (MRGC), This oil field is located in the southwest of Iran. The determination of electrofacies in this field is performed by using the combination of image logs and other well logs obtained from one of the wells in the field. In order to obtain an exact evaluation, environmental corrections were performed on the logs. The comparison of lithology results, shale volume, porosity, and water saturation with determined facies, using clustering analysis by applying image logs, shows acceptable agreement between the obtained electrofacies and the corresponding lithological results, and represents a new categorization of the formation. This new categorization has reservoir aspects; the variations of the petrophysical properties in each facies are unique and the variations of these indices are determined in individual facies. Moreover, by considering the span of the identified electrofacies, the reservoir and non-reservoir layers are distinguished based on the performed zoning.
 

Keywords


[1]. خوشبخت، ف، شناخت ویژگی‌های شکستگی‌ها و پارامترهای پتروفیزیکی مخازن نفتی با استفاده از لاگ‌های تصویری، پایان نامه کارشناسی ارشد، دانشکده فنی دانشگاه تهران، ایران، 1384.##
[2]. Shin-Ju Y. J. and Rabiller Ph. “Automated electrofacies ordering,” Petrophysics 46.6, pp. 409-423, 2005.##
[3]. Serra O,. “Fundamentals of well-log interpretation,” Vol. 1, Elsevier, Amsterdam 1984.##
[4]. Gill D,. Shomrony A.,. and Fligelman H. “Numerical zonation of log suites and logfacies recognition by multivariate clustering,” AAPG bulletin, Vol. 77, No. 10, pp. 1781-1791, 1993.##
[5]. Sh. Ju Y., and Rabiller P. “A new tool for electro-facies analysis: multi-resolution graph-based clustering,” SPWLA 41st annual logging symposium. Dallas, Texas, pp. 14-27, 2000,##
[6]. Shin-Ju Y., Rabiller J., and Keskes N., “Automatic high resolution texture analysis on borehole imagery,” SPWLA 39th Annual Logging Symposium. Keystone, Colorado, pp. 14-27, 1998.##
[7]. Gagalowicz A., “Vers un modèle de textures,” PhD Thesis, Université Pierre et Marie Curie, Paris VI, France, 1983.##
[8]. Kohonen T., “Self-organization and associative memory,” Berlin, Springer-Verlag, Vol. 64, pp. 95-105, 1984##