基于地震属性堆叠泛化集成学习的辫状河储层构型表征——以渤海湾盆地C-2油田为例  

Characterization of braided river reservoir architecture based on seismic attribute stacking ensemble learning:A case study of the C-2 oilfield in the Bohai Bay Basin

作　　者：张章 孟鹏 杨威 张小龙 黄奇 王浩然 ZHANG Zhang;MENG Peng;YANG Wei;ZHANG Xiaolong;HUANG Qi;WANG Haoran(CNOOC(China)Limited Tianjin Branch,Tianjin 300459,China;Wuhan Times GeoSmart Science and Technology Co.,Ltd.,Wuhan,Hubei 430000,China)

机构地区：[1]中海石油(中国)有限公司天津分公司,天津300459 [2]武汉时代地智科技股份有限公司,湖北武汉430000

出　　处：《油气藏评价与开发》2025年第1期64-72,共9页Petroleum Reservoir Evaluation and Development

基　　金：中国海洋石油有限公司“十四五”重大科技项目“海上‘双高-双特高’水驱油田提高采收率油藏关键技术”(KJGG2021-0501)。

摘　　要：C-2油田是位于渤海湾盆地的河流相海上油田,主要采用水平井开发,储层厚度薄、纵向多期砂体叠置、横向相变快,储层内部结构与连通关系复杂,受复杂的储层结构与井震资料的双重影响,稀疏井网条件下储层描述难度大,制约了油田精细挖潜效果,常规地震反演难以满足薄储层高分辨率预测与储层内部结构精细解剖的需要。针对研究油田辫状河储层结构特征,采用基于地震属性堆叠泛化集成学习的方法完成河流相复杂结构储层的预测,相较于单一的机器学习模型提高了预测精度。综合地质、地球物理与油藏动态等多维信息进行迭代优化,进一步降低地下储层预测与结构认识的不确定性,实现了研究区辫状河复杂结构储层的精细表征,为油田剩余油与潜力砂体精细挖潜提供了依据。研究表明:基于地震属性堆叠泛化集成学习的储层预测方法,不仅能有效提高地震纵向分辨率,同时具有较强的横向“相控”指示能力,预测结果包括砂体叠置关系与储层内部结构特征,更加适用于相变快、储层空间建筑结构复杂的陆相河流沉积体系储层预测与精细刻画,可为稀疏井网海上油田开发中后期河流相沉积储层构型精细表征提供借鉴。The C-2 oilfield,located in the Bohai Bay Basin,is a fluvial-facies offshore oilfield primarily developed with horizontal wells.It is characterized by thin reservoir layers,vertically stacked multi-phase sandbodies,and rapid lateral facies transitions,leading to complex internal reservoir structures and connectivity.The combined effects of complex reservoir structures and well-seismic data make reservoir characterization challenging under sparse well patterns,hindering refined development.Conventional seismic inversion fails to meet the requirements for high-resolution prediction of thin reservoirs and detailed characterization of internal reservoir structures.To study the structural characteristics of braided river reservoirs in the oilfield,a stacking ensemble learning method based on seismic attributes was applied to predict the complex fluvial-facies reservoir structures.This approach significantly improved prediction accuracy compared to a single machine learning model.By integrating multi-dimensional information such as geology,geophysics,and reservoir dynamics,iterative optimization was conducted to further reduce the uncertainty in subsurface reservoir prediction and structural understanding.This enabled the precise characterization of the complex braided river reservoir structures in the study area,providing a basis for refined exploitation of remaining oil and potential sandbodies in the oilfield.The study demonstrates that the reservoir prediction method based on stacking ensemble learning not only enhances seismic vertical resolution,but also exhibits strong horizontal phase-control capabilities.The prediction results include sandbody stacking relationships and internal reservoir structures,making it more suitable for the prediction and fine characterization of continental fluvial sedimentary systems with rapid facies transitions and complex spatial architectural structures.This method can serve as a reference for the detailed characterization of fluvial-facies reservoir configurations during the midd

关 键 词：机器学习 储层预测 辫状河 储层构型 水平井 

分 类 号：TE122[石油与天然气工程—油气勘探]