摘要
油气藏是一个极其复杂的地下系统,油气藏表征是一个充满不确定性的科学与技术难题,主要基于知识驱动的传统油气藏表征理论与方法具有局限性,遇到众多难题和发展瓶颈.近年来,以深度学习为代表的人工智能技术广泛应用于油气藏表征问题研究中,并在特定问题上取得突破性进展,但是各方向研究相对分散,缺乏系统性理论认识,人工智能技术与专业领域知识的融合不足.本文提出知识与数据融合驱动的油气藏智能表征的理念与方法,基于油气藏数据特征,利用先进的人工智能技术,融合专业领域知识,充分挖掘油气藏大数据中隐含的有效信息,以实现更可靠、更高精度、更高效率的油气藏表征.同时对油气藏智能表征关键研究内容的最新进展综述,主要包括基于深度学习的测井智能储层评价、地震智能储层预测、储层智能随机建模等方面的研究进展,最后提出油气藏智能表征的发展展望.
Reservoir characterization is a scientific and technical problem full of uncertainty,since oil and gas reservoirs are extremely complex underground systems.The traditional theories and methods of reservoir characterization,mainly driven by knowledge,have inherent limitations and encounter numerous development bottlenecks.In recent years,artificial intelligence,especially deep learning,has been widely applied in reservoir characterization research,and breakthroughs have been made in specific problems.However,research in various problems is relatively dispersed,lacking systematic theory,and the integration of artificial intelligence and domain knowledge is insufficient.This paper proposes the concept and method of intelligent reservoir characterization driven by hybrid knowledge and data.Based on the characteristics of reservoir data,advanced artificial intelligence technology is used to integrate domain knowledge,fully mining the useful information hidden in big data,in order to achieve more reliable,high-precision,and efficient reservoir characterization.This paper summarizes the latest research progress in the key research content of reservoir intelligent characterization,mainly including deep learning-based reservoir logging evaluation,seismic reservoir prediction,and stochastic reservoir modeling.Finally,the development prospects of intelligent reservoir characterization are proposed.
作者
张国印
林承焰
王志章
任丽华
张宪国
曲康
张向博
ZHANG GuoYin;LIN ChengYan;WANG ZhiZhang;REN LiHua;ZHANG XianGuo;QU Kang;ZHANG XiangBo(School of Geosciences in China University of Petroleum(East China),Qingdao 266580,China;National Key Laboratory of Deep Oil and Gas,Qingdao 266580,China;Shandong Provincial Key Laboratory of Reservoir Geology,Qingdao 266580,China;School of Geosciences in China University of Petroleum(Beijing),Beijing 102249,China)
出处
《地球物理学进展》
CSCD
北大核心
2024年第1期119-140,共22页
Progress in Geophysics
基金
国家自然科学基金项目(42002144,42302153)
中央高校基本科研业务费专项资金资助(22CX06002A)
中石油重大科技项目(ZD2019-183-006)联合资助.
关键词
油气藏表征
油气藏大数据
知识驱动
数据驱动
人工智能
深度学习
Reservoir characterization
Reservoir big data
Knowledge-driven
Data-driven
Artificial intelligence
Deep learning
