Simulation of photoelectric log in oil-bearing formation using artificial neural network■

Document Type : Research


Faculty of Mining and Geophysics, Shahrood University of Technology, Shahrood, Iran


Estimating of various petrophysical parameters and determining of subsurface geology is very important in petroleum reservoir evaluation. Exploration drilling and various well logs normally provide this sort of information. Among the various well logs, the photoelectric (PEF) log is very important as it able to determine the lithology of the reservoir precisely. Therefore for those wells for which this log is not available it is necessary to predict them somehow. In this study it is aimed to use artificial neural network (ANN) ability to tackle this problem. To achieve the goals, a back-propagation ANN (BP-ANN) is planned to model the interrelationships between seven different well logs, and PEF logs. Data from three wells in the Ahvaz oil field (Asmari reservoir) are organized into training, testing and validation data sets for BP-ANN modeling. Data of the fourth and the fifth wells in the same field are retained as independent data sets for evaluating the ability of the network PEF prediction. Once the designed network has been trained properly, its performance has also been tested. When it has been found that the performance is satisfactory the data set of the fourth and the fifth wells are applied to the trained network. The results of the ANN modeling show that the designed network with three layers and architecture of 7-10-1 can produce the precise PEF log that compares well with the measured PEF logs. This means that the designed network is capable enough to predict the PEF logs for the required wells in the same area.


حبیبیان، ب.، نبی‌بیدهندی، م. و کاظم‌زاده، ع.، 1384، پیش‌بینی نفوذپذیری از روی داده‌های چاه‌نگاری با استفاده از شبکه‌های عصبی مصنوعی در یکی از مخازن کربناته جنوب ایران، مجلة فیزیک زمین و فضا، جلد 31، ص 79-86.
دشتی، ص.، 1369، مطالعه زمین‌شناسی مخزن آسماری میدان اهواز، گزارش شماره پ-4221، شرکت ملی نفت ایران، مناطق نفت‌خیز جنوب.
غفرانی، ا. و رضایی، م.، 1381، بررسی فرایند دولومیتی شدن و تأثیر آن بر کیفیت مخزن سازند آسماری در میدان اهواز، ششمین همایش انجمن زمین‌شناسی ایران، ص. 553-555 ، دانشگاه کرمان.
مرادزاده، ع. و قوامی ریابی، ر.، 1380، چاه‌پیمایی برای مهندسین. دانشگاه صنعتی شاهرود، ص 246.
منهاج، م. ب.، 1379، مبانی شبکه‌های عصبی مصنوعی، دانشگاه صنعتی امیر کبیر، ص 715.
Etnyre, L. M., 1992, Estimation of petrophysical parameters using a robust Levenberg-Marquardt procedure: Log Analyst, 33, 373-389.
Hampson, D., Schuelke, J., and Quieren, J., 2000, Use of multi-attribute transforms to predict log properties from seismic data. Geophysics, 66, 220-236.
Huang, Z., Shimeld, J., Williamson, M., and Katsube, J., 1996, Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada: Geophysics, 61, 422-436.
Liu, Z., and Liu, J., 1998, Seismic controlled nonlinear extrapolation of well parameters using neural networks: Geophysics, 63, 2035-2041.
Nikravesh, M., and Aminzadeh, F., 2001, Mining and fusion of petroleum data with fuzzy logic and neural network agents: J. Petrol. Sci. Eng. 29, 221-238.
Poulton, M. M., 2001, Computational neural networks for geophysical data processing. Pergamon, 335.
Poulton, M. M., 2002, Neural networks as an intelligence amplification tool: A review of application. Geophysics, 67, 979-993.
The Math Work, T., 2002, Manual of Matlab, The language of technical computing. The Math Work, Inc.
Walls, J., Taner, T., Taylor, G., Smith, M., Derzhi, N., Carr, M., Drummonds, J., McGuire, D., Morris, S., and Bregar, J., 2000, Seismic reservoir characterization of a mid-continent fluvial system using rock physics, post stack seismic attributes and neural networks: A case history. 69th Ann. Int. Mtg., Soc. Expl. Geophys., 1437-1439.
Zhang, Z., Zhou, Z., Frenkle, M., Chunduru, R., and Mezzatesta, A., 1999, Fast forward modeling simulation of resistivity logs using neural networks. 69th Ann. Internat. Mtg., Soc. Expl. Geophys., 124-127.
Zhang, Z., Zhou, Z., Frenkle, M., Chunduru, R., and Mezzatesta, A., 2000, Real time inversion of array resistivity logging data using dimensional reduction and neural network simulation. 70th Ann. Internat. Mtg., Soc. Expl. Geophys., 1802-1805.