石油地球物理勘探2026，Vol.61Issue(2)：294-302,9.DOI:10.13810/j.cnki.issn.1000-7210.20250083
应用BP神经网络的页岩气储层常规测井裂缝识别方法
Fracture identification in shale gas reservoirs using conventional logging data based on BP neural network
摘要
Abstract
Natural fracture systems serve as the storage space and seepage channel of shale gas reservoirs,and fracture identification of shale reservoirs is faced with such problems as high cost and low coverage of micro-re-sistivity imaging logging,as well as significant nonlinear characteristics of conventional logging data and strong subjectivity of manual interpretation.To this end,this paper screens four kinds of logging curves with high sensi-tivity to fractures of shale gas reservoirs,including the deep lateral resistivity,natural gamma ray,neutron po-rosity,and interval transit time via sensitivity analysis.Meanwhile,the first-order difference curve of resis-tivity and its product curve are introduced,and the complex temporal sequence relationship of conventional log-ging curves is transformed into a classifiable threshold discrimination problem.Subsequently,a BP neural net-work identification model for fractures in shale gas reservoirs is built based on the conjugate gradient descent op-timization algorithm.The results demonstrate that this model can effectively eliminate the subjective bias in manual interpretation.Compared with the actual fractures,the recall ratio of fracture identification in shale gas reservoirs reaches 90%,with a precision rate of 87%.This research provides a novel approach for fracture identi-fication of unconventional hydrocarbon reservoirs,effectively enhancing the identification efficiency of frac-tures in shale gas reservoirs.关键词
页岩气/裂缝识别/BP/神经网络/测井Key words
shale gas/fracture identification/BP/neural networks/logging分类
天文与地球科学引用本文复制引用
阎泽华,许巍,何浩然,季运景,郑旻千,王婷..应用BP神经网络的页岩气储层常规测井裂缝识别方法[J].石油地球物理勘探,2026,61(2):294-302,9.基金项目
本项研究受国家自然科学基金面上项目"复杂裂缝介质多尺度声波测井响应机理实验及智能评价方法研究"(42474177)资助. （42474177）
