基于机器学习的孔隙型碳酸盐岩油藏相控建模研究——以中东H油田白垩系Mishrif组油藏为例  

The phase control modeling of porous carbonate reservoir by machine learning for the Cretaceous Mishrif Formation reservoir of H Oilfield in the Middle East

作　　者：邵冠铭 乔占峰 尹楠鑫 曹鹏 孙晓伟 张宇 SHAO Guanming;QIAO Zhanfeng;YIN Nanxin;CAO Peng;SUN Xiaowei;ZHANG Yu(Hangzhou Research Institute of Geology,PetroChina,Hangzhou 310023,China;Key Laboratory of Carbonate Reservoirs,CNPC,Hangzhou 310023,China;School of Petroleumand Natural Gas Engineering,Chongqing University of Science and Technology,Chongqing 401331,China)

机构地区：[1]中国石油杭州地质研究院,杭州310023 [2]中国石油集团碳酸盐岩储层重点实验室,杭州310023 [3]重庆科技学院石油与天然气工程学院,重庆401331

出　　处：《海洋地质前沿》2023年第11期76-85,共10页Marine Geology Frontiers

基　　金：中国石油天然气股份有限公司科学研究与技术开发项目“ALBION碳酸盐岩储层联合研究”(2021DQ0407);中国石油天然气股份有限公司科学研究与技术开发项目“巨厚碳酸盐岩油藏注水开发关键技术研究与应用”(2023ZZ19-01)。

摘　　要：孔隙型碳酸盐岩储层的化学及机械沉积作用使得各沉积微相空间上不具备碎屑岩储层沉积微相明确的几何形态和外部结构,且不同成因储层的物性差异明显。依据常规的沉积微相建模方法难以如实地再现不同微相复杂的空间展布规律,进而也降低了相控属性建模的精度。本文以中东H油田白垩系Mishrif组生物碎屑灰岩为研究对象,通过开展波阻抗、孔隙度和渗透率的反演,利用机器学习的方法建立研究区的三维沉积微相模型。在此基础上,通过不同微相的变差函数分析,开展相控属性建模。结果表明,利用机器学习方法建立的沉积微相模型符合海相碳酸盐岩台地相序变化规律,充分体现了微相的空间形态和各微相间的接触关系,以沉积微相为约束条件建立的储层属性模型不仅满足了模拟结果与已知数据的概率一致性问题,又能分相带反映储层的空间变化特征。The chemical and mechanical sedimentation of porous carbonate reservoir blurs geometric shape and external structure of clastic reservoir sedimentary microfacies in space,and complicated the physical properties of reservoirs of different origins.Conventional sedimentary microfacies modeling methods are difficult to reproduce objectively the complex spatial distribution of different microfacies,thereby the accuracy of facies control attribute modeling could be reduced.Therefore,taking the Cretaceous Mishrif Formation bioclastic limestone in the Middle East H Oilfield as the research object,we established a three-dimensional sedimentary microfacies model in the study area by carrying out wave impedance inversion,porosity inversion,and permeability inversion,using machine learning methods.The phase-controlled attribute modeling was performed through the variogram analysis of different microfacies.The sedimentary microfacies model established using machine learning methods conforms to the changes in facies sequence of marine carbonate platforms,fully reflecting the spatial morphology of microfacies and the contact relationship among microfacies.The reservoir attribute model that established with the sedimentary microfacies as a constraint not only meets the requirement of probability consistency between simulation results and known data,but also reflects the spatial variation characteristics of the reservoir by phase zones.

关 键 词：Mishrif组 碳酸盐岩油藏 沉积微相 机器学习 相控建模 

分 类 号：P736[天文地球—海洋地质] P744[天文地球—海洋科学]