Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

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• 作者：Pengyu Gao^a ; ^{gaopengyu626@126.com" class="auth_mail" title="E-mail the corresponding author} ; Chong Jiang^a ; Qin Huang^a ; Hui Cai^a ; Zhifeng Luo^b ; Meijia Liu^a
• 关键词：Fluvial facies reservoir ; Productivity forecast ; Principal component analysis ; Artificial neural network
• 刊名：Petroleum
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：2
• 期：1
• 页码：49-53
• 全文大小：349 K

文摘

It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.