• 责任者：Luo Xin (College of Energy Resources ; Chengdu University of Technology ; Chengdu 610059 ; China) ; Kuang Jianchao
• 刊名：Computing Techniques for Geophysical and Geochemical Exploration
• 出版年：2009
• 卷期：31(3)
• 关键词：裂隙型储集层 ; fractured reservoirs ; 神经网络系统 ; neural network system ; 济阳坳陷 ; Jiyang Depression
• 页码：p.213-217
• 图表：2 illus., 3 tables, 15 refs.
• 分类号：1003
• issnNo：ISSN1001-1749
• isbnNo：CN51-1242/P

It is difficult to accurately identify reservoir, this paper proposes a model of wavelet neural networks based on PSO (PSO-WNN) to solve the problem. The activation function which is in the hidden layer is wavelet function. The fracture-caves reservoir identification model is formed based on PSO-based Wavelet Neural Network, and PSO is used to adjust the parameters of weight, stretching, the parallel move. The model has the advantage of simple algorithm, structural stability, fast speed of convergence, global optimization ability, high-accuracy of identification, generalization ability, etc. 108 samples of 9 testing wells from the reservoir of buried-hill in Zhuangxi-Chengdao, Jiyang sag were selected, and the input parameters are conventional logging parameters and the output parameter is the assignment of the type of reservoir.