مقالههای A Kamkar Rouhani
توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقالههای نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده میشوند.
۱Artificial intelligence: a proper approach for prediction of water saturation in hydrocarbon reservoir
اطلاعات انتشار: سومین کنگره ملی مهندسی نفت، سال ۱۳۹۰
تعداد صفحات: ۱۶
Water saturation (Sw) is a significant petrophysical parameter usually used for reservoir estimation and production. This parameter is one of the mostdifficult petrophysical properties to determine and predict. The conventional methods for water saturation determination are core analysis and well testdata. These methods are, however, very expensive and time–consuming. One of the comparatively inexpensive and readily available sources ofinferring Sw is from well logs. In recent decades, artificial Intelligent (AI) has many applications in the petroleum engineering as well as other areas ofresearch. The aim of this paper is to use two diverse machine learning technology named back–propagation neural network (BPNN) and generalregression neural network (GRNN) for predicting the water saturation of four wells in Burgan reservoir, south of Iran. Comparing the obtainedresults of these two methodologies has shown that BPNN is a faster and precious method than GRNN in prediction of water saturation.<\div>
۲Artificial Intelligence for prediction of porosity from Seismic Attributes: Case study in the Persian Gulf
اطلاعات انتشار: Iranian Journal of Earth Sciences، سوم،شماره۲، Oct ۲۰۱۱، سال ۰
تعداد صفحات: ۷
Porosity is one of the key parameters associated with oil reservoirs. Determination of this petrophysical parameter is an essential step in reservoir characterization. Among different linear and nonlinear prediction tools such as multi–regression and polynomial curve fitting, artificial neural network has gained the attention of researchers over the past years. In the present study, two–dimensional (2D) seismic and well logs data of the Burgan oil field were used for prediction of the reservoir porosity. In this regard, broad–band acoustic impedance was first extracted from 2D seismic dataset, as the attribute most related to porosity. Next, other optimum seismic attributes were selected using stepwise regression and cross validation techniques. At the end, three types of neural network were used for inversion of seismic attributes and prediction of reservoir porosity. The results show that probabilistic neural network (PNN) is the best one for prediction of the reservoir porosity using seismic attributes.
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