Electro-facies determined based on sedimentary facies and rock types using clustering methods, wire-line logs and core data in the Kangan and Dalan Formations, Southern Pars gas field

Document Type : مقالات پژوهشی

Authors

1 Islamic Azad University, Damavand Branch

2 Research Institute of Petroleum Industry

Abstract

Electro-facies is a unit of the vertical sediments sequence on the wire-line logs that can be differentiated from the upper and lower units. For detection of the electro-facies interval in Kangan and upper Dalan formations in south Pars Gas Field, wire-line log and core data analysis (wells A, B, C and D) were used. Note, only core data analysis in well A were available. First, thin-Sections study core plugs from this well resulted in the identification of 12 micro-facies and 7 sedimentary environment. For recognition of electro-fasies based on these data, clustering method and core data were used to categories their homogenization of data. In this study, Rock-Types (R-T) that have similar reservoir characteristics were identified using porosity/permeability cross plot from core data; therefore based on these data, 6 Rock-Type were identified. After classification of Rock-Types and micro-facies from the core data, MRGC models of these facies were identified from the wire-line logs in well A. Optimized model in well A were applied in the wells B, C and D to recognized electro-fasies in these wells with no-core data. Average petro-physical properties of each facies in different wells were compared for quality controls of predicted facies. Also facies of 4 wells were correlated to see if they are comparable. As a result, 6 electro-facies were identified with different petro-physical characteristics and the poorest reservoir quality belong to the facies (1) with dominant anhydrite lithology and the best quality reservoir belong to the facies (6) with the dominant dolomite and limetone with grainstone textue. Due to the high accuracy of the results, the suggested model can be used in other wells in this field.

Keywords


امینی، ع.، ۱۳۸۸. مبانی چینه نگاری سکانسی. انتشارات دانشگاه تهران، ۳۲۴ ص.
آقانباتی، س.ع.، 1383. زمین شناسی ایران. سازمان زمین‌شناسی و اکتشافات معدنی کشور. 586ص.
رحیمی بهار، ع.ا.، پرهام، س.، ۱۳۹۱. تجزیه و تحلیل رخساره های الکتریکی بر اساس رخساره های رسوبی. رخساره های رسوبی، ۵ (۱): ۶۱-۷۴.
سروش نیا، م.، کدخدائی ایلخچی، ع.، نوری، ب.، ۱۳۹۱. بررسی روشهای خوشه سازی در تعیین الکتروفاسیسها و نیز میکروفاسیسهای مخزنی با استفاده از اطلاعات پتروفیزیکی و پتروگرافی در سازند آسماری در یکی از میادین نفتی خلیج فارس. سی و یکمین گردهمایی علوم زمین، سازمان زمین شناسی و اکتشافات معدنی کشور.
کدخدائی ایلخچی، ع.، رضائی، م.ر.، معلمی، س.ع.، شیخ زاده، ا.، ۱۳۸۴. تخمین گونه های سنگی و تراوایی در میدان گازی پارس جنوبی با استفاده از تکنیک خوشه سازی میان مرکز فازی و مدلسازی فازی. نهمین همایش انجمن زمین شناسی ایران، دانشگاه تربیت معلم تهران، ص۶۹۰- ۶۷۸.
کوت زاده، آ.، ۱۳۹۱. تعیین دسته بندی سنگی مخزن به کمک روشهای موجود در مهندسی نفت. سی و یکمین گردهمایی علوم زمین، سازمان زمین شناسی و اکتشافات معدنی کشور.
موحد، ب.، کهنسال قدیم وند، ن.، زمان نژاد، م.ر.، ۱۳۹۰. ارزیابی پتروفیزیکی سازندهای کنگان و دالان فوقانی ـ چاه Sp.x در میدان گازی پارس جنوبی. فصلنامه زمین، ۶ (۲۱)، ۱۸۵- ۱۶۹.
Alizadeh, B., Najjari, S., & Kadkhodaei-Ilkhchi, A., 2012. 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. Computers & Geosciences, Elsevier Press. 45: 261–269.
Askari, A.A., & Behrouz, T., 2011. A Fully Integrated Method for Dynamic Rock Type Characterization Development in One of Iranian Off-Shore Oil Reservoir. Journal of Chemical and Petroleum Engineering, University of Tehran, 45 (2): 83-96.
Bagheri, A.M., & Biranvand, B., 2006. Characterization of Reservoir Rock Types in a Heterogeneous Clastic and Carbonate Reservoir, JSUT, 32 (2): 29-38.
Gholizadeh, M.H., & Darand, M., 2009. Forecasting precipitation with artificial neural networks (case study: Tehran). Journal of Applied Sciences, 5: 23-32.
Khoshbakht, F., & Mohammadnia, M., 2012. Assessment of Clustering Methods for Predicting Permeability in a Heterogeneous Carbonate Reservoir. Journal of Petroleum Science and Technology, 2 (2): 50-57.
Kumar, B., Kishore, M., 2006. Electrofacies Classification–A Critical Approach. 6th International Conference & Exposition on Petroleum Geophysics, New Delhi, India, pp. 822-825.
Rabiller, P., 2005. Facies prediction and data modeling for reservoir characterization. 1th Ed., Rabiller Geo-consulting.
Schlumberger, 1989. Log Interpretation Principles/Applications. Schlumberger, Hoston, Texas, pp. 1-241.
Sefidari, E., Amini, A., Kadkhodaei, A., & Ahmadi, B., 2012. Electrofacies clustering and a hybrid intelligent based method for porosity and permeability prediction in the South Pars Gas Field, Persian Gulf, Geopersia, 2 (2): 11-23.
Selley, R.C., 1996. Ancient Sedimentary Environmentals and their Sub-Surface Diagnosis, 4th ed. Nelson Thornes (Publisher) Ltd., England, 315 pp.
Serra, O., & Abbott, H.T., 1980. The contribution of logging data to sedimentology and stratigraphic, SPE 9270, 55th Annual Fall Technical Conference and Exhibition, Dallas, Texas, pp. 19.
Serra, O., 1986. Fundamentals of Well-Log Interpretation 2. The Interpretation of Logging Data. Developments in Petroleum Science, 15 (B): 3-679
Sutadiwirya, Y., 2008. Using MRGC (Multi Resolution Graph-Based Clustering) Method to Integrate Log Data Analysis and Core Facies to Define Electrofacies, in the Benua Field, Central Sumatera Basin, Indonesia. International Gas Union Research Conference, IGRC, Paris, pp. 2-12.
Tvakoli, V., & Amini, A., 2006. Application of multivariate cluster analysis in logfacies determination and reservoir zonation, case study of Maroun Field, South of Iran. JUST, 32 (2): 173-180.
Xu, C., & Torres-Verdin, C., 2012. saturation-height and invasion consistent hydraulic rock typing using multi-well conventional logs. SPWLA, 53rd Annual Logging Symposium, pp. 1-16.
Ye, S.J., & Rabiller Ph., 2000. A new tool for electrofacies analysis: multi resolution graph based clustering. SPWLA, 41st Annual Logging Symposium.
CAPTCHA Image