地震反演与属性耦合检测薄层含气砂岩  

Seismic inversion and detection of thin-layer gas-bearing sandstone by attributes coupling

作　　者：黄捍东[1,2] 张如伟[1,2] 赵迪[1,2] 陈丽华[3] 

机构地区：[1]油气资源与探测国家重点实验室(中国石油大学),北京102249 [2]地球探测与信息技术北京市重点实验室,北京102249 [3]中石化江汉油田分公司勘探开发研究院海相所,湖北潜江433124

出　　处：《石油地球物理勘探》2009年第2期185-189,共5页Oil Geophysical Prospecting

基　　金：国家973项目"中国西部典型叠合盆地油气成藏机制与分布规律"(项目编号:2006CB202306);国家863项目"深水海域油气与天然气水合物资源勘探开发关键技术"(项目编号:2006AA09A101-0103)联合资助

摘　　要：地震资料气层检测的常用方法是沿目的层拾取选定时窗内的地震属性进行属性分析,但该方法用于薄层砂岩储层的油气检测则比较困难。为此,本文利用非线性随机反演方法精细刻画砂体的空间展布,得到储层顶、底层位信息;再沿层提取各种地震属性,时窗随储层厚薄变化;分别应用基于RS理论的属性优化方法和SOM神经网络模式识别方法进行气层检测。将该方法应用于川西拗陷洛带气田,符合率达85%以上。Common-used method for gas reservoir detection by using seismic data is to pick up seismic attributes in fixed windows along the objective horizons and carry out attributes analysis,in which the oil/gas detection for thin-layer sandstone reservoir is more difficult.For that reason,the paper uses nonlinear random inversion method to finely describe the spatial distribution of sand body,getting the information of top and bottom horizons in reservoir;then picks up various seismic attributes along the horizons and windows change with the thickness variation of reservoir;and finally carries out gas-bearing formation detection by using RS theory-based attribute optimized method and SOM neural network mode recognition method respectively.The method was used in Luodai gasfield of Chuanxi depression,resulting in 85% and above of matched rate.

关 键 词：地震反演 地震属性 含气性检测 RS理论 神经网络 

分 类 号：P618.13[天文地球—矿床学] P631.443[天文地球—地质学]