基于格兰杰因果图神经网络的测井曲线重构方法OA

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.