Research on Logging Evaluation of Reservoir Contamination Based on PSO-BP Neural Network

被引：0

作者：

Li, Tao ^{[1
]}

Guo, Libo ^{[2
,3
]}

Wang, Yuanmei ^{[1
]}

Hu, Feng ^{[4
]}

Xiao, Li ^{[4
]}

Wang, Yanwu ^{[5
]}

Cheng, Qi ^{[1
]}

机构：

[1] Yangtze Univ, Coll Elect & Informat, Jinzhou 434023, Peoples R China

[2] Yangtze Univ, Sch Geosci, Jinzhou 434023, Peoples R China

[3] China Univ Geosci Beijing, Sch Energy Resources, Beijing 100083, Peoples R China

[4] Oil & Gas Storage & Transportat Co, Petrochina Xinjiang Oilfield Co, Changji 831100, North Korea

[5] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 2, PROCEEDINGS | 2009年 / 5552卷

基金：

中国国家自然科学基金;

关键词：

Skin-friction coefficient; Particle swarm optimize; BP neural network; Reservoir contamination; Prediction model;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The skin-friction coefficient which indicates the degree of the stratum damage and the loss of production is important for evaluating reservoir contamination. A skin-friction coefficient prediction model based on PSO-BP neural network is presented in this paper, which integrates PSO and BP algorithm and takes full use of the global optimization of PSO and local accurate searching of BP. The examples of skin-friction coefficient prediction show that the prediction model works with quicker convergence rate and higher forecast precision, and can be applied to evaluate the degree of reservoir contamination effectively.

引用

页码：839 / +

页数：2

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