石油物探2026，Vol.65Issue(2)：308-322,15.DOI:10.12431/issn.1000-1441.2024.0134
机器学习方法在数字岩心研究中的应用进展
Application progress of machine learning to digital core research
孙朗秋 1闫博鸿 1马英珑 1任义丽 2马祯 1赵建国 1曹子雄3
作者信息
- 1. 中国石油大学(北京),北京 102249
- 2. 多资源协同陆相页岩油绿色开采全国重点实验室,黑龙江 大庆 163000||中国石油勘探开发研究院,北京 100083
- 3. 彗星科技公司,加拿大蒙特利尔 H3B1A7
- 折叠
摘要
Abstract
Digital core has become a widely used approach for quantitative characterization of oil and gas reservoirs,and it plays an important role in unconventional reservoir evaluation and modeling.However,there exist two inherent trade-offs at the core scale:one between the field of view and image resolution,and the other between computing cost and model size.These trade-offs have become increasingly prominent as digital core research progresses.In recent years,the closer integration of machine learning with digital core has partially addressed these cross-scale challenges and thereby expanded the application scenarios of digital core.This paper begins by outlining the workflow of digital core research,followed by the algorithm examples to illustrate the applications of deep learning across different stages,including image segmentation,image fusion,super-resolution,rock feature recognition,and rock physical property simulation.Compared with conventional methods,deep learning-assisted digital core modeling and numerical simulation offer higher accuracy and efficiency.This paper concludes by discussing the potential of machine learning in complex core feature recognition and numerical simulation.关键词
数字岩心/深度学习/机器学习/岩石物理Key words
digital core/deep learning/machine learning/rock physics分类
能源科技引用本文复制引用
孙朗秋,闫博鸿,马英珑,任义丽,马祯,赵建国,曹子雄..机器学习方法在数字岩心研究中的应用进展[J].石油物探,2026,65(2):308-322,15.
