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复杂油气藏储层预测方法综述

An overview of prediction methods for complex oil and gas reservoirs
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摘要 随着油气勘探不断深入,传统储层逐渐减少,使得岩溶储层、裂缝储层、页岩储层、煤层气等复杂储层成为重要勘探目标。这些复杂储层往往具有多种非均质性特征,包括岩性变化、孔隙裂缝、多介质分布等,导致储层的空间分布和特性变化极为复杂,深入研究复杂储层预测方法具有重要的理论和应用价值。本文重点分析了岩溶储层、裂缝储层、页岩储层和煤层气储层等复杂油气藏的形成机理和勘探难点;系统总结了相关研究的发展情况和应用方法,为相关领域研究人员提供了有益的参考。指出未来复杂储层预测的发展将集中在多源信息融合、数据集成、高分辨率地震技术和人工智能等方向,这些技术的应用有望提高预测精度和效率,为复杂气藏的勘探和开发提供更为全面和有效的支持。 With the continuous deepening of oil and gas exploration,traditional reservoirs are gradually decreasing,making complex reservoirs such as karst reservoirs,fractured reservoirs,shale reservoirs,and coalbed methane reservoirs important exploration targets.These complex oil and gas reservoirs often exhibit diverse heterogeneities,including lithological variations,pore-fissure systems,and multi-media distributions,resulting in intricate spatial distribution and property variations of reservoirs.Therefore,deepening the study of prediction methods for complex oil and gas reservoirs holds paramount theoretical and practical value.The formation mechanisms and exploration challenges of complex oil and gas reservoirs,encompassing karst reservoirs,fractured reservoirs,shale reservoirs,and coalbed methane reservoirs are analyzed,and the developmental status and application methods in relevant research are systematically summarized,providing valuable references for researchers in related fields.It is pointed out that the future development of complex reservoir prediction will focus on multi-source information fusion,data integration,high-resolution seismic technology,and artificial intelligence.The application of these technologies is expected to improve prediction accuracy and efficiency,providing more comprehensive and effective support for the exploration and development of complex oil and gas reservoirs.
作者 马晨 黄捍东 梁书义 吴亚宁 MA Chen;HUANG Handong;LIANG Shuyi;WU Yaning(State Key Laboratory of Petroleum Resources and Prospecting,College of Geophysics,China University of Petroleum,Beijing,Beijing 102249,China;China Petrochemical Company Limited Shengli Oilfield Branch,Dongsheng Company,Dongying,Shandong 257000,China)
出处 《中国海上油气》 CAS CSCD 北大核心 2024年第2期87-97,共11页 China Offshore Oil and Gas
基金 国家自然科学基金项目“裂缝性储层地震定量预测及流体识别方法研究(编号:ZX20190118)”部分研究成果。
关键词 复杂油气藏 储层预测方法 岩溶储层 裂缝储层 页岩储层 煤层气储层 人工智能 complex oil and gas reservoir reservoir prediction method karst reservoir fractured reservoir shale reservoir coalbed methane reservoir artificial intelligence
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