Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

作     者：Kai Zhang Nian-Tian Lin Jiu-Qiang Yang Zhi-Wei Jin Gui-Hua Li Ren-Wei Ding Kai Zhang;Nian-Tian Lin;Jiu-Qiang Yang;Zhi-Wei Jin;Gui-Hua Li;Ren-Wei Ding

作者机构：College of Earth Sciences and EngineeringShandong University of Science and TechnologyQingdaoShandong266590China Laboratory for Marine Mineral ResourcesQingdao National Laboratory for Marine Science and TechnologyQingdaoShandong266237China 

出 版 物：《Petroleum Science》 (石油科学（英文版）)

年 卷 期：2022年第19卷第4期

页      面：1566-1581页

核心收录：

学科分类：081801[工学-矿产普查与勘探] 081802[工学-地球探测与信息技术] 081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程] 

基　　金：funded by the Natural Science Foundation of Shandong Province (ZR202103050722) National Natural Science Foundation of China (41174098) 

主　　题：Multi-component seismic exploration Tight sandstone gas reservoir prediction Deep neural network(DNN) Reservoir quality evaluation Fracture prediction Structural characteristics 

摘      要：The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are *** strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.