应用BP神经网络的页岩气储层常规测井裂缝识别方法

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中图分类号：P631 文献标识码：A DOI：10.13810/j.cnki.issn.1000-7210.20250083

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-resistivity imaging logging，as wellas significant nonlinear characteristics of conventional logging data and strong subjectivity of manual interpretation.To this end，this paper screens four kindsoflogging curves with high sensitivity to fractures of shale gas reservoirs，including the deep lateral resistivity，natural gamma ray， neutron porosity，and interval transit time via sensitivity analysis.Meanwhile，the first-order difference curve of resistivity and its product curve are introduced，and the complex temporal sequence relationship ofconventional logging curves is transformed into a classifiable threshold discrimination problem.Subsequently，a BP neural network identification model for fractures in shale gas reservoirs is built based on the conjugate gradient descent optimizationalgorithm.Theresults demonstrate that this modelcan effectively eliminate thesubjective bias in manual interpretation.Compared with the actual fractures，the recallratio offracture identification in shale gas reservoirs reaches 90% ， with a precision rate of 87% . This research provides a novel approach for fracture identification of unconventional hydrocarbon reservoirs，effectively enhancing the identification efficiency of fractures in shale gas reservoirs.

eywords： shale gas， fracture identification，BP，neural networks， logging 阎泽华，许巍，何浩然，等，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. Yan Zehua， Xu Wei， He Haoran， et al. Fracture identification in shale gas reservoirs using conventional loggingdatabased on BP neural network[J].Oil Geophysical Prospecting，2026，61（2）：294-302.

0 引言

非常规油气资源已成为接替常规油气储量的重要领域，其中页岩气的勘探与开发呈现快速发展态势[1-3]。（剩余13464字）