基于分频智能反演的曲流河点坝与废弃河道识别  

Identification of point bar and abandoned channel of meandering river by spectral decomposition inversion based on machine learning

作　　者：李洪辉 岳大力[1,2] 李伟[1,2] 郭长春[3] 李响 吕梅 LI Honghui;YUE Dali;LI Wei;GUO Changchun;LI Xiang;LYU Mei(State Key Laboratory of Petroleum Resources and Prospecting,Beijing 102249,China;College of Geosciences,China University of Petroleum(Beijing),Beijing 102249,China;Research Institute of Exploration&Development,SINOPEC Shengli Oilfield Company,Dongying,Shandong 257000,China)

机构地区：[1]油气资源与探测国家重点实验室,北京102249 [2]中国石油大学(北京)地球科学学院,北京102249 [3]中国石化胜利油田分公司勘探开发研究院,山东东营257000

出　　处：《石油地球物理勘探》2023年第2期358-368,共11页Oil Geophysical Prospecting

基　　金：国家自然科学基金项目“坳陷湖盆洪水型湖相重力流沉积演化机理及差异构型模式”(42272186);“坡度与水深主控的河流辫—曲转换机理及其沉积响应”(42202109);中石油战略合作专项“鄂尔多斯盆地致密油—页岩油储层非均质成因机理与表征技术”(ZLZX2020-02);中石油勘探开发研究院合作课题“典型低品位油藏储层定量刻画及不同流动单元表征研究”(2021DJ1101)联合资助。

摘　　要：曲流河点坝、废弃河道级次构型表征对丰富曲流河储层构型模式、指导油气田高效开发具有重要意义。为此,以渤海湾盆地孤岛油田中12—斜检3011井区馆陶组为例,基于分频智能反演技术识别曲流河点坝与废弃河道。首先,通过地震资料分频处理,依据振幅与砂体厚度之间的关系优选最佳频段地震资料,采用支持向量回归(SVR)的机器学习算法进行分频反演;其次,在利用反演数据体平面属性刻画复合河道砂体分布规律的基础上,根据河道边界的地震、测井等响应特征预测单一曲流带;最后,以废弃河道泥质半充填的样式为指导,选取目的层上、中、下部的反演属性切片进行RGB融合,建立废弃河道识别模板,并在定量模式约束下识别点坝和废弃河道。研究结果表明:(1)基于机器学习的分频反演技术能够充分利用不同频段地震信息与测井信息,提高了反演结果的分辨率,可指导河道边界识别;(2)采用RGB融合技术融合河道不同位置的反演属性切片,能够辅助判别砂体之间的空间组合关系,有助于井间废弃河道识别;(3)在地震资料主频为38 Hz的情况下,利用基于分频智能反演的曲流河点坝与废弃河道识别技术在研究区目的层复合曲流带中共识别了4个单一曲流带、13个废弃河道和15个点坝。开发动态资料验证了识别结果的准确性,该方法具有很好的应用前景。Characterizing the hierarchical architecture of point bars and abandoned channels of a meandering river is of great significance for enriching the architecture pattern of the meandering river reservoir and guiding the efficient development of oil and gas fields.Therefore,the Guantao Formation of Zhong12-Xiejian 3011 well block in Gudao Oilfield in Bohai Bay Basin is taken as an example,and the point bars and abandoned channels of the meandering river are identified based on spectral decomposition inversion technology.Firstly,with the seismic data processed by spectral decomposition,the optimal seismic data of frequency bands are selected according to the relationship between the amplitude and the thickness of the sand body,and the machine learning algorithm of support vector regression(SVR)is used for spectral decomposition inversion.Then,on the basis of describing the distribution law of sand bodies in composite channels by the plane attribute of inversed data volume,the single meander belt is predicted according to the seismic,well logging,and other response characteristics of channel boundaries.Finally,guided by the muddy semi-filling pattern of abandoned channels,this paper selects the inversion attribute slices of the upper,middle,and lower parts of the target layer for RGB fusion,so as to establish the identification pattern of abandoned channels and identify point bars and abandoned channels under the constraints of the quantitative model.The results show that:①The spectral decomposition inversion technology based on machine learning can make full use of seismic information and logging information of different frequency bands,which improves the resolution of inversion results and can guide channel boundary identification;②RGB fusion technology is used to fuse the inversion attribute slices at different positions of the channel,which can help recognize the spatial combination relationship between sand bodies and identify inter-well abandoned channels;③Based on the seismic data with a dominant frequenc

关 键 词：储层构型 分频反演 RGB融合 机器学习 曲流河 孤岛油田 

分 类 号：P631[天文地球—地质矿产勘探]