物探与化探2024，Vol.48Issue(4)：1045-1053,9.DOI:10.11720/wtyht.2024.1524
基于前馈神经网络井控多属性融合的断裂识别方法
A method for identifying faults based on well-controlled multi-attribute fusion using a feedforward neural network
摘要
Abstract
The fault-controlled fractured-vuggy carbonate reservoirs in the Tarim Basin exhibitconsiderable burial depths,complex structures,and highly developed faults.Faults serve asa dominant factor controlling oil and gas accumulation and possible hydrocarbon migration pathways in the study area.Hence,it is critical to predict their spatial distributions and sizes.There existvariousfault detec-tion attributes,which characterize fault scales and features differently due totheir different calculation methods.Moreover,conventional attribute detection ignores the use and constraints of logs.For more complete and accurate fault prediction results,this study selected multiple fault detection attributes for fusion using the feedforward neural network algorithm,with logs as prior information.First of all,a sample database for fault feature identification was established using fault attributes(like AFE,likelihood,and dip angle)with dis-tinct characteristics anddiscrimination criteria of fault types,including lost circulation data,imaging logs,and seismic event disloca-tions.The deep feedforward neural network was trained based on the sample database.A neural network prediction model with a mini-mum prediction error was obtained by comparing and testing the learning effects under different hidden layer depths.Finally,the neural network prediction model was applied to the fault prediction of the study area.The comparative analysis reveals thatthe fault prediction using deeplearning-based fused attributesyielded prediction results more consistent with the log-based interpretation results,and could synthesize the information of faults with different scale characteristics,thus effectively improving the prediction accuracy and reliability.关键词
断裂检测/井控/属性融合/前馈神经网络/缝洞型油气藏Key words
fault detection/well control/attribute fusion/feedforward neural network/fractured-vuggy reservoir分类
天文与地球科学引用本文复制引用
赵军,冉琦,朱博华,李洋,梁舒瑗,常健强..基于前馈神经网络井控多属性融合的断裂识别方法[J].物探与化探,2024,48(4):1045-1053,9.基金项目
中国石化科技攻关项目"顺北深层断溶体油藏描述及可采储量定量表征"(P21064-1) （P21064-1）
