石油地球物理勘探2024，Vol.59Issue(3)：640-651,12.DOI:10.13810/j.cnki.issn.1000-7210.2024.03.027
深度学习技术在地震储层预测中的应用及挑战
Application and challenges of deep learning technology in seismic data-based reservoir prediction
摘要
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 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.关键词
地震储层预测/深度学习/地震反演/地震烃类检测/有监督学习/无监督学习Key words
seismic data-based reservoir prediction/deep learning/seismic inversion/seismic hydrocarbon detec-tion/supervised learning/unsupervised learning分类
天文与地球科学引用本文复制引用
骆迪,王宏斌,蔡峰,吴志强,孙运宝,李清..深度学习技术在地震储层预测中的应用及挑战[J].石油地球物理勘探,2024,59(3):640-651,12.基金项目
本项研究受崂山实验室科技创新项目"西太典型边缘海盆地水合物运聚成藏过程研究"(LSKJ202203501)和中国地质调查项目"渤海等海域新生界油气地质条件与碳封存选区"(DD20230401)联合资助. （LSKJ202203501）
