Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction  

作　　者：Jiu-Qiang Yang Nian-Tian Lin Kai Zhang Yan Cui Chao Fu Dong Zhang 

机构地区：[1]College of Earth Sciences and Engineering,Shandong University of Science and Technology,Qingdao,266590,Shandong,China [2]Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao,266071,Shandong,China [3]Transportation Institute of Inner Mongolia University,Hohhot,010000,Inner Monggol,China [4]Key Laboratory of Gas Hydrate,Qingdao Institute of Marine Geology,Ministry of Natural Resources,Qingdao,266237,Shandong,China

出　　处：《Petroleum Science》2024年第4期2329-2344,共16页石油科学（英文版）

基　　金：funded by the Natural Science Foundation of Shandong Province (ZR2021MD061;ZR2023QD025);China Postdoctoral Science Foundation (2022M721972);National Natural Science Foundation of China (41174098);Young Talents Foundation of Inner Mongolia University (10000-23112101/055);Qingdao Postdoctoral Science Foundation (QDBSH20230102094)。

摘　　要：Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other in

关 键 词：Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TE37[自动化与计算机技术—控制科学与工程]