考虑孔隙形状的碳酸盐岩储层流体综合识别方法研究及应用--以鄂尔多斯盆地奥陶系马家沟组四段为例  

A comprehensive fluid identification method for carbonate reservoirs considering pore shapes:A case study of the fourth member of the Ordovician Majiagou Formation,Ordos Basin

作　　者：王永刚[1,2] 姚宗惠[1,2] 杨骐羽[3,4] 李景叶 宋炜 WANG Yong-Gang;YAO Zong-Hui;YANG Qi-Yu;LI Jing-Ye;SONG Wei(Research Institute of Exploration and Development,Changqing Oilfield Company,PetroChina,Xi'an 710018,China;National Engineering Laboratory for Exploration and Development of Low-Permeability Oil&Gas Fields,Xi'an 710018,China;State Key Laboratory of Petroleum Resources and Prospecting,Beijing 102249,China;College of Geophysics,China University of Petroleum(Beijing),Beijing 102249,China)

机构地区：[1]中国石油长庆油田分公司勘探开发研究院,陕西西安710018 [2]低渗透油气田勘探开发国家工程实验室,陕西西安710018 [3]油气资源与探测国家重点实验室,北京102249 [4]中国石油大学(北京)地球物理学院,北京102249

出　　处：《物探与化探》2025年第1期177-188,共12页Geophysical and Geochemical Exploration

基　　金：中国石油集团公司攻关性应用性科技专项“海相碳酸盐岩油气规模增储上产与勘探开发技术研究”课题4“复杂碳酸盐岩地球物理评价关键技术研究”(2023ZZ16YJ04)。

摘　　要：鄂尔多斯盆地马四段白云岩储层具有厚度较薄、致密、孔隙形状复杂、非均质性显著、地震响应相对较弱的特征。储层地震预测机理尚存疑,流体识别具有一定困难。传统的单一属性地球物理方法未能准确预测流体,因此我们以对孔隙形状与孔隙连通性的综合考虑为基础,借助岩石物理建模和分析,结合波动理论的叠前AVO反演、流体因子的频变AVO反演与基于PNN的孔隙结构参数预测,构建了一种新的综合流体识别方法。该方法全面考虑了弹性参数、物性参数与频散属性的影响,取得了显著的效果。相对于传统单一属性流体识别方法,综合流体识别方法展现出更高的精度,尤其在含气层区域表现出显著的指示效果,充分验证了其在流体识别领域的有效性,具备广泛推广和应用的潜力。The dolomite reservoirs of the fourth member of the Majiagou Formation in the Ordos Basin are characterized by thin layers,tightness,complex pore shapes,strong heterogeneity,and relatively weak seismic responses.The mechanisms behind the seismic prediction of the reservoirs remains uncertain,making fluid identification challenging.Traditional geophysical methods based on a single attribute fail to accurately predict fluids.Therefore,by comprehensively accounting for the pore shapes and connectivity,this study proposed a new fluid identification method using petrophysical modeling and analysis,as well as wave theory-based prestack AVO inversion,frequency-dependent AVO inversion of fluid factors,and probabilistic neural network(PNN)-based prediction of pore structure parameters.This method,comprehensively considering the impacts of elastic parameters,physical parameters,and dispersion properties,achieve remarkable results.Compared to traditional single-attribute fluid identification method,the proposed method demonstrates higher accuracy,particularly for gas-bearing areas,fully verifying its effectiveness in fluid identification and highlighting its potential for widespread application.

关 键 词：马家沟组四段 岩石物理模型 孔隙形状 频散属性 流体识别 

分 类 号：P631.4[天文地球—地质矿产勘探]