测井技术2011，Vol.35Issue(2)：171-175,5.
遗传神经网络在煤质测井评价中的应用
Application of Genetic Neural Network to Coal Quality Evaluation Based on Log Data
摘要
Abstract
Coalbed methane reservoir log data interpretation results often show multi-solutions,ambiguity and uncertainty due to its heterogeneity and anisotropy. Put forward is a method to improve network training accuracy and coalbed methane reservoir evaluation accuracy by combining genetic algorithm and neural network. This method uses genetic algorithm to optimize neural network connection weights and threshold. It increases computing speed by avoiding its disadvantages that standard BP algorithm is apt to trap in local minimal solution, and genetic algorithm is weak at the locally searching capability. Introduced is the process for optimizing neural network connection weights and threshold and coal quality parameters forecast. Established is a coal quality log evaluation model based on GA-BP neural network, learning-samples selection and network structure determination, and data normalizing. Comparative analysis of 26 samples shows that this algorithm has higher accuracy and faster processing speed. Practical applications in more than 10 wells indicate that the prediction results of GA-BP method match well with coal core test data,and have good consistency with volume model calculation results.关键词
测井解释/煤层气/煤质分析/地球物理测井/BP神经网络/遗传算法Key words
log interpretation/ coalbed methane/ coal quality analysis/ geophysical logging/ BP neural network/ genetic algorithm分类
天文与地球科学引用本文复制引用
陈钢花,董维武..遗传神经网络在煤质测井评价中的应用[J].测井技术,2011,35(2):171-175,5.基金项目
十一五国家重大科技专项"煤层气地球物理勘探关键技术"(项目编号2008ZX05035) （项目编号2008ZX05035）
