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应用遗传神经网络研究低渗透储层成岩储集相--以胜利渤南油田三区沙河街组为例
李海燕 彭仕宓
摘 要:以渤南油田三区沙河街组为例,应用遗传人工神经网络模式识别方法,开展了低渗透储层成岩储集相的研究.该工作是在储集层沉积相、成岩作用研究的基础上,选用流动层带指标、孔隙度、渗透率、粒度中值、泥质含量、孔喉半径均值和变异系数等7项参数,采用神经网络模式识别方法,通过建立遗传神经网络的学习及预测模型,对渤南油田三区沙河街组进行了成岩储集相识别,识别出4类成岩储集相:不稳定组分强溶解次生孔隙成岩储集相、碳酸盐胶结物溶解次生孔隙成岩储集相、强压实强胶结残余粒间孔成岩储集相和极强压实强胶结致密成岩储集相.Ⅰ类储集相的储集性能最好,Ⅳ类最差,为非储层或差储层.
关键词:神经网络;低渗透;模式识别;成岩储集相;渤南油田
分类号:TE112.2 文献标识码:A
文章编号:0253-9985(2006)01-0111-07
Study diagenetic reservoir facies of low permeability reservoir with genetic neural network: take Shahejie Fm in block 3 of Bonan oilfield as an example
Li Haiyan Peng Shimi
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基金项目:国家重点基础研究发展规划(973)项目(2002CCA00700)
作者简介:李海燕(1973-),女,博士后,油藏开发地质学
作者单位:李海燕(中国石油大学,北京,102249)
彭仕宓(中国石油大学,北京,102249)
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