文章基本信息

标题：Prediction of permeability from reservoir main properties using neural network
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作者：Mostafa Mokhtari ; Hossein Jalalifar ; Hamid Alinejad-Rokny 等
期刊名称：Scientific Research and Essays
印刷版ISSN：1992-2248
出版年度：2011
卷号：6
期号：32
页码：6626-6635
DOI：10.5897/SRE11.686
语种：English
出版社：Academic Journals
摘要：Prediction on permeability is an essential task in reservoir engineering as it has great influences on oil and gas production, while porous media grain size, sorting, cementing, porosity, specific surface area, direction and location of grain and irreduction water saturation have effects on permeability. In this project we studied the effect of porosity, specific surface area and irreduction water saturation as main parameters on permeability distribution in the reservoir; the main goal of this research was permeability prediction in carbonat reservoir using neural network approach. Our studies showed a good agreement between our neural network model prediction and lab data or core analysis. This approach can be a useful tool for prediction permeability when core tests are not available.
关键词：Permeability prediction; neural network; specific surface area; irreducible water saturation; porosity