应用BP神经网络的页岩气储层常规测井裂缝识别方法OA

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.