基于模态分解与LSTM注意表征的测井曲线重构研究OA北大核心CSTPCD

The well logging curve records the amplitude range of geophysical properties changing with depth and is the bond between well log and seismic data.It is also significant for reservoir lithology analysis and subsequent oil and gas exploration projects.However,instrument failure and other reasons will cause well-logging curves to be missing in the actu-al logging process.Re-logging is not only expensive but also difficult to achieve.Aiming at the problem that logging data is often missing during geological exploration,this paper proposes a logging curve reconstruction method based on the LSTM(Long Short-Term Memory)-attention model.At the same time,EMD-VMD(Empirical Mode Decomposition-Variational Mode Decomposition)decomposition is performed on the original logging signal and then the correlation between the com-ponents and the original curve is calculated.Some excrescent components are deletedto promote efficient and high-preci-sion manual completion.This proposed method is applied to reconstruct missing logs acoustic(AC)and density(DEN),and the prediction results are compared with those predicted by LSTM and BP(Back Propagation)neural network.The results show that the LSTM-attention model has a better prediction performance,and the correlations between predictive and the original curves can reach 86.8％(AC)and 74.8％(DEN),higher than the traditional LSTM and BP neural network.After re-moving redundant signal components,the correlation coefficients increased by 1.4％(AC)and 4.0％(DEN).At the same time,the logging curve predicted by the proposed method has the lowest prediction error.Therefore,the representation learning based on LSTM with an attention mechanism has better prediction accuracy for well-logging curve reconstruction.