考虑孔隙结构的砾岩储层物性分类评价方法  

Physical property classification and evaluation method based on the pore structure for conglomerate reservoirs

作　　者：刘诗琼 刘向君[1] 孙杨沙 刘红岐 孔玉华[3] 李贤胜 LIU Shiqiong;LIU Xiangjun;SUN Yangsha;LIU Hongqi;KONG Yuhua;LI Xiansheng(State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum U-niversity,Chengdu,Sichuan 610500,China;Emergency Response Bureau,Zhongshan Dis-trict,Liupanshui,Liupanshui,Guizhou 553000,Chi-na;Research Institute of Exploration and Develop-ment of Xinjiang Oilfield Company,Karamay,Xin-jiang 834000,China;Geological Exploration&Development Re-search Institute,CNPC Chuanqing Drilling Engi-neering Company Limited(CCDC),Chengdu,Si-chuan 610051,China)

机构地区：[1]西南石油大学油气藏地质及开发工程国家重点实验室,四川成都610500 [2]六盘水市钟山区应急管理局,贵州六盘水553000 [3]新疆油田公司勘探开发研究院,新疆克拉玛依834000 [4]中国石油川庆钻探地质勘探开发研究院,四川成都610051

出　　处：《石油地球物理勘探》2022年第4期926-936,I0009,共12页Oil Geophysical Prospecting

基　　金：国家科技重大专项“准噶尔盆地低渗透储层分类评价方法与关键工程地质参数研究”(2017ZX05001-004-005);国家自然科学基金项目“微纳米孔致密岩石导电介电机理及全谱探测理论研究”(41974117)联合资助。

摘　　要：自新疆玛湖发现特大型砾岩油藏以来,砾岩油气藏的勘探与开发越来越受到重视。但砾岩油气藏具有很强的非均质性,导致储层识别和评价都非常困难。本次研究以HM工区上乌尔禾组为例,将砾岩储层分为五类,用球管模型对核磁回波进行优化反演,得到优化T谱,并采用12个谱形态参数刻画优化T谱。12个谱形态参数和谱特征值分别构成谱形态预测向量和联合预测向量,并用支持向量机(SVM)分别对该两个向量进行训练,建立储层物性分类参数的预测模型。将预测模型处理的结果与储层物性参数、油气产量进行对比、分析,发现储层物性分类参数与储层物性具有很好的相关性,与储层测试产量具有很好的一致性。对比结果表明,基于球管模型优化反演的T谱形态参数及特征值,可很好地刻画砾岩油气藏的物性特征。该研究成果有助于提高砾岩油气藏的分类评价质量和可靠性,并为高效开采砾岩油气藏提供测井技术支撑。Since the discovery of the giant conglomerate oil reservoir in Mahu,Xinjiang,the exploration and development of conglomerate oil and gas reservoirs have attracted increasing attention.Nevertheless,such reservoirs are highly heterogeneous,resulting in difficult reservoir identification and evaluation.Taking the upper Wuerhe Formation in the HM work area as an example,this study classifies conglomerate reservoirs into five categories.After the Sphere-Cylinder model is applied to optimize the inversion of NMR(nuclear magnetic resonance)echo,an optimized T2spectrum is obtained and then characterized by 12spectral shape parameters.A spectral shape prediction Vector and a Joint prediction Vector are formed,respectively,by the 12spectral shape parameters and the spectral eigenvalue.Then,they are trained by SVM(Support Vector Machine)to build a prediction model for reservoir physical property classification parameters.According to the comparison of the results of the prediction model with the reservoir physical property parameters and oil and gas production,the reservoir physical property classification parameters correlate well with the reservoir physical properties and are in good agreement with the test production of the reservoir.The comparison results show that the shape parameters of the T2spectrum obtained by optimized inversion with the Sphere-Cylinder model and the spectral eigenvalue can well characterize the physical properties of conglomerate oil and gas reservoirs.The results of this paper can help improve the quality and reliability of the classification and evaluation of conglomerate oil and gas reservoirs and provide logging technical support for the efficient exploitation of conglomerate oil and gas reservoirs.

关 键 词：砾岩储层 核磁共振 测井评价 孔隙结构 储层物性 回波反演 

分 类 号：P631[天文地球—地质矿产勘探]