PNN在煤田随钻测井岩性判识应用研究  

APPLICATION OF PNN IN THE LITHOLOGY IDENTIFICATION OF LOGGING WHILE DRILLING IN COAL FIELD

作　　者：陈刚 CHEN Gang;ZHANG Zhe(School o f Geosciences,China University of Petroleum(East China),Qingdao 266580,Shandong Province,China;X i'on Research Institute Co.,Ltd.o f China Coat Technology and Engineering Group,X i'on 710000,China)

机构地区：[1]中国石油大学(华东)地球科学与技术学院,山东青岛266580 [2]中煤科工集团西安研究院有限公司,陕西西安710077

出　　处：《地质与资源》2018年第1期103-106,共4页Geology and Resources

基　　金：国家十二五重大专项“煤与煤层气地质条件精细探测技术与装备”(2011ZX05040-002)

摘　　要：介绍了PNN方法原理及其算法训练学习过程,详细阐述了网络识别岩性参数的选取、岩性识别模型的建立过程.通过对比研究PNN与其他6种岩性识别方法,分析相同条件下预测结果,得到不同识别方法的优劣性.经研究发现,PNN概率神经网络方法在生产应用中效果更佳、训练识别用时最短.利用人工智能神经网络对测井数据进行自动解释分析,可满足随钻测井时效性及快速解释处理的地质导向需求.The principle of PNN(probabilistic neural network)method and its algorithm training processes,especially the selection of lithologic parameters and establishment of lithologic identification model,are discussed in detailinthe paper.The advantagesand disadvantages of different identification methods are analyzed with comparison between PNN and other six lithology identifications,as well as the prediction results under the same condition.The study shows that the PNN method is the best in production and application with the shortest training identification time.Theuse of artificial intelligence neural network for automatic interpretation and analysis of logging data can meet the needs of time liness and rapid interpretation of logging while drilling(LWD).

关 键 词：随钻测井 岩性识别 PNN 煤田 

分 类 号：P631.8[天文地球—地质矿产勘探]