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

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

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Conventional machine learning(CML)methods have been successfully applied for gas reservoir pre-diction.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 multicom-ponent 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 opti-mization 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 individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.

Multicomponent seismic dataDeep learningAdaptive particle swarm optimizationConvolutional neural networkLeast squares support vector machineFeature optimizationGas-bearing distribution prediction

Jiu-Qiang Yang、Nian-Tian Lin、Kai Zhang、Yan Cui、Chao Fu、Dong Zhang

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College of Earth Sciences and Engineering,Shandong University of Science and Technology,Qingdao 266590,Shandong,China

College of Earth Sciences and Engineering,Shandong University of Science and Technology,Qingdao,266590,Shandong,China

Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao,266071,Shandong,China

Transportation Institute of Inner Mongolia University,Hohhot,010000,Inner Monggol,China

Key Laboratory of Gas Hydrate,Qingdao Institute of Marine Geology,Ministry of Natural Resources,Qingdao,266237,Shandong,China

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2024

10.1016/j.petsci.2024.02.012

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(4)