Abstract:The study has been carried out for application of BP neural networks technique to lithologic recognition in logging interpretation of volcanic rocks in view of the speciality of volcanic reservoir (complexity.discreteness and stochastic property).The technical key of the method is to create the sample set and initial weight as well as to optimize the model.The paper presented a generation method of study samples based on crosspiot and multielement statistics, that is, building up sample set based on the study of geochemistry and lithology of cored samples and using cluster analysis and distance criterion to determine the initial weight.Application of studied method to lithologic recognition of volcanic rocks in Xin mountainous area of Songliao basin achieved good effects.The coincident rate of lithologic interpretation reaches to 90% and above.It is shown by correlation of four lithologic recognition patterns in the paper that weighted processing pattern is optimized processing model.In a predicting process of neural networks model,it needs to fully use available geologic experiments and logging information to create typical and reliable sample file and consider the influence of various factors in neural networks at the same time,so that the optimized model and calculated parameters can make predicted results be coincident with the objective reality
吴磊, 徐怀民, 季汉成. 基于交会图和多元统计法的神经网络技术在火山岩识别中的应用[J]. 石油地球物理勘探, 2006, 41(1): 81-86.
Wu Lei, Xu Huai-min, Ji Han-cheng. Application of neural networks technique based on crosspiot and multielement statistics to recognition of volcanic rocks. OGP, 2006, 41(1): 81-86.