A Parametric Study of MPSO-ANN Techniques in Gas-Bearing Distribution Prediction Using Multicomponent Seismic Data

作     者：Yang, Jiuqiang Lin, Niantian Zhang, Kai Jia, Lingyun Zhang, Dong Li, Guihua Zhang, Jinwei 

作者机构：Shandong Univ Sci & Technol Coll Earth Sci & Engn Coll Geodesy & Geomat Qingdao 266590 Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266237 Peoples R China Shaanxi Xueqian Normal Univ Coll Econ & Management Xian 710100 Peoples R China Minist Nat Resources Qingdao Inst Marine Geol Key Lab Gas Hydrate Qingdao 266237 Peoples R China 

出 版 物：《REMOTE SENSING》 (遥感)

年 卷 期：2023年第15卷第16期

页      面：3987-3987页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：We would like to thank Xiucheng Wei  Hong Liu  Jianwen Chen  Jian Sun  and Chao Fu for their valuable contributions to this study 

主　　题：multicomponent seismic data artificial intelligence mutation particle swarm optimization artificial neural network hyperparameter analysis unconventional gas reservoir gas-bearing distribution prediction 

摘      要：Predicting the oil-gas-bearing distribution of unconventional reservoirs is challenging because of the complex seismic response relationship of these reservoirs. Artificial neural network (ANN) technology has been popular in seismic reservoir prediction because of its self-learning and nonlinear expression abilities. However, problems in the training process of ANNs, such as slow convergence speed and local minima, affect the prediction accuracy. Therefore, this study proposes a hybrid prediction method that combines mutation particle swarm optimization (MPSO) and ANN (MPSO-ANN). It uses the powerful search ability of MPSO to address local optimization problems during training and improve the performance of ANN models in gas-bearing distribution prediction. Furthermore, because the predictions of ANN models require good data sources, multicomponent seismic data that can provide rich gas reservoir information are used as input for MPSO-ANN learning. First, the hyperparameters of the ANN model were analyzed, and ANNs with different structures were constructed. The initial ANN model before optimization exhibited good predictive performance. Then, the parameter settings of MPSO were analyzed, and the MPSO-ANN model was obtained by using MPSO to optimize the weights and biases of the developed ANN model. Finally, the gas-bearing distribution was predicted using multicomponent seismic data. The results indicate that the developed MPSO-ANN model (MSE = 0.0058, RMSE = 0.0762, R-2 = 0.9761) has better predictive performance than the PSO-ANN (MSE = 0.0062, RMSE = 0.0786, R-2 = 0.9713) and unoptimized ANN models (MSE = 0.0069, RMSE = 0.0833, R-2 = 0.9625) on the test dataset. Additionally, the gas-bearing distribution prediction results were consistent overall with the actual drilling results, further verifying the feasibility of this method. The research results may contribute to the application of PSO and ANN in reservoir prediction and other fields.