石油地球物理勘探2026，Vol.61Issue(1)：46-54,9.DOI:10.13810/j.cnki.issn.1000-7210.20250032
基于格兰杰因果图神经网络的测井曲线重构方法
Reconstruction method of logging curves based on GCGNN
摘要
Abstract
In geological exploration,density and acoustic time difference curves can reflect key physical param-eters,such as underground geological structure and reservoir porosity.However,due to the influence of com-plex geological conditions and other factors,logging data may be incomplete or missing.Therefore,this paper proposes a logging curve reconstruction method based on a Granger causality graph neural network(GCGNN).This method constructs a Granger causality graph by learning the Granger causality between logging curves and uses a graph convolutional network to process and predict missing data.The method is applied to the measured well data in the Gujing area and Jinjing area of the central depression of Songliao Basin in China.The correlation between the density and acoustic time difference curves of Well Gu204 and the original data is 71.70％and 83.76％,respectively,and that is 80.03％and 88.73％,respectively,for Well Gu432.The performance of GCGNN in the reconstruction experiment of the same well is better than that of the lightweight gradient boosting machine,time convolutional network,and long short-term memory network.The method is applied to the recon-struction experiment of different wells.The correlation between the density and acoustic time difference curves and the original data is 77.54％and 87.79％,respectively.Although the model obtained by GCGNN is not the op-timal model,the reconstruction effect is still good.The application results on measured data show that the pro-posed method can effectively predict the missing logging data.关键词
格兰杰因果图神经网络(GCGNN)/图卷积网络/曲线重构/密度测井/声波测井Key words
Granger causality graph neural network(GCGNN)/graph convolutional network/curve reconstruc-tion/density logging/acoustic logging分类
天文与地球科学引用本文复制引用
韩建,陈着,王业统,曹志民,叶林..基于格兰杰因果图神经网络的测井曲线重构方法[J].石油地球物理勘探,2026,61(1):46-54,9.基金项目
本项研究受海南省科技专项"海上油田精细分层注气工艺设计及智能气窜风险识别技术研究"(ZDYF2022GXJS220)、"海上油田高经济性高可靠地球物理测井数据人工智能合成与评价系统"(ZDYF2022GXJS222)联合资助. （ZDYF2022GXJS220）
