Application and challenges of deep learning technology in seismic data-based reservoir prediction
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 capabili-ties 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 effi-ciency and accuracy of prediction results.In terms of quantitative prediction,it enables a more precise approxi-mation of the intricate nonlinear relationship between seismic data and targets,thereby achieving a refined quan-titative evaluation of reservoirs.②The application of deep learning technology faces several challenges.The is-sues 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 geophysi-cal 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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