Prediction of Hydrocarbon Reservoir Parameter Using a GA-RBF Neural Network

被引：2
|
作者：
Chen, Jing
Li, Zhenhua
Zhao, Dan
机构：
来源：
COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS | 2009年 / 51卷
关键词：
reservoir parameter prediction; RBF network; genetic algorithm; structure optimization;
D O I：
10.1007/978-3-642-04962-0_43
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Prediction of hydrocarbon reservoir characteristics using seismic at tributes is a very complicated problem with much nonlinear relation. The traditional BP neural network with a gradient decent approach may lead to local minima problem, resulting in the production of unstable and non-convergent solutions. To solve these problems and improve the precision, this paper introduces a GA-based optimized method of RBF neural network. A case studs shows that the GA-RBF algorithm not only works with high predicting precision comparable to real measured data in oil reservoir thickness, but also is superior to that of tradition BP neural network.
引用
收藏
页码：379 / 386
页数：8
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