Seismic-inversion method for nonlinear mapping multilevel well–seismic matching based on bidirectional long short-term memory networks
基于Bi-LSTM网络的非线性映射多级井震匹配的地震反演方法研究作者机构:Shandong Provincial Key Laboratory of Deep Oil&GasChina University of Petroleum(East China)Qingdao 266580China
出 版 物:《Applied Geophysics》 (应用地球物理(英文版))
年 卷 期:2022年第19卷第2期
页 面:244-257,308页
核心收录:
学科分类:081801[工学-矿产普查与勘探] 081802[工学-地球探测与信息技术] 08[工学] 0818[工学-地质资源与地质工程]
基 金:supported by the National Major Science and Technology Special Project(No.2016ZX05026-002)
主 题:bidirectional recurrent neural networks long short-term memory nonlinear mapping well–seismic matching seismic inversion
摘 要:In this paper,the recurrent neural network structure of a bidirectional long shortterm memory network(Bi-LSTM)with special memory cells that store information is used to characterize the deep features of the variation pattern between logging and seismic data.A mapping relationship model between high-frequency logging data and low-frequency seismic data is established via nonlinear *** seismic waveform is infinitely approximated using the logging curve in the low-frequency band to obtain a nonlinear mapping model of this scale,which then stepwise approach the logging curve in the high-frequency ***,a seismic-inversion method of nonlinear mapping multilevel well–seismic matching based on the Bi-LSTM network is *** characteristic of this method is that by applying the multilevel well–seismic matching process,the seismic data are stepwise matched to the scale range that is consistent with the logging ***,the matching operator at each level can be stably obtained to effectively overcome the problems that occur in the well–seismic matching process,such as the inconsistency in the scale of two types of data,accuracy in extracting the seismic wavelet of the well-side seismic traces,and multiplicity of *** test and practical application demonstrate that this method improves the vertical resolution of inversion results,and at the same time,the boundary and the lateral characteristics of the sand body are well maintained to improve the accuracy of thin-layer sand body prediction and achieve an improved practical application effect.