Application and challenges of deep learning technology in seismic data-based reservoir prediction
LUO Di1,2, WANG Hongbin1,2, CAI Feng1,2, WU Zhiqiang1,2, SUN Yunbao1,2, LI Qing1,2
1. Key Laboratory of Gas Hydrate, Ministry of Natural Resources, Qingdao Institute of Marine Geology, China Geological Survey, Qingdao, Shandong 266237, China; 2. Laboratory for Marine Mineral Resources, Laoshan Laboratory, Qingdao, Shandong 266237, China
Abstract：Traditional seismic data-based reservoir prediction technology fails to meet the demands of refined reservoir evaluation. Deep learning has strong feature extraction and high-dimensional data processing capabilities and has been extensively applied in seismic data-based reservoir prediction with promising results in recent years. This paper delved into the application and progress of deep learning technology in seismic data-based reservoir prediction, analyzed the challenges encountered during practical implementation, and proposed future research directions. The conclusions are as follows： ①In terms of qualitative hydrocarbon detection, deep learning technology facilitates the comprehensive utilization of multi-attribute seismic data to improve the efficiency and accuracy of prediction results. In terms of quantitative prediction, it enables a more precise approximation of the intricate nonlinear relationship between seismic data and targets, thereby achieving a refined quantitative evaluation of reservoirs. ②The application of deep learning technology faces several challenges. The issues such as insufficient label data and unbalanced samples lead to overfitting and poor generalization ability of the model； the complex model results in high computational costs； the “black box” feature of the model makes the prediction results lack physical interpretability； there is no evaluation criteria for qualitative prediction model and high-precision quantization algorithm for uncertainty. ③Future research should prioritize addressing challenges related to insufficient data availability and limitations of deep learning, such as constructing geophysical knowledge maps, effectively integrating and sharing multi-source data and knowledge, and combining deep learning with other machine learning algorithms such as feedback reinforcement learning, so as to provide more reliable technical support for hydrocarbon exploration and development.
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LUO Di, WANG Hongbin, CAI Feng, WU Zhiqiang, SUN Yunbao, LI Qing. Application and challenges of deep learning technology in seismic data-based reservoir prediction. Oil Geophysical Prospecting, 2024, 59(3): 640-651.
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