新场气田须二气藏单井气水层识别模型研究  

Study on Gas-water Layer Identification Model in the Single Well of Xu 2 Gas Reservoir of Xinchang Gas Field

作　　者：庞河清[1] 匡建超[2,3] 蔡左花[4] 廖开贵[4] 王众[2,3] 

机构地区：[1]中国石化西南油气分公司博士后科研工作站,四川成都610041 [2]成都理工大学能源学院,四川成都610059 [3]成都理工大学管理科学学院,四川成都610059 [4]中国石化西南油气分公司勘探开发研究院,四川成都610041

出　　处：《西南石油大学学报（自然科学版）》2014年第2期71-78,共8页Journal of Southwest Petroleum University(Science & Technology Edition)

基　　金：教育部规划基金项目(11YJAZH043);四川石油天然气研究中心项目(川油气科SKA09–01)

摘　　要：川西新场气田须二气藏为典型的低渗致密碎屑岩气藏,由于地质条件复杂,储层非均质性严重,气水分布十分复杂,束缚水含量较高,气层、气水同层电阻率界限模糊不清,测井解释往往造成很大误判。针对这一难点,应用基于粒子群算法(PSO)的核主成分分析与支持向量机(KPCA–SVM)模型进行气水层识别。模型先通过核主成分分析(KPCA)进行非线性属性变量提取,再将提取的属性变量作为支持向量机(SVM)的输入变量,在识别过程中利用粒子群算法(PSO)寻优,最终实现气水层识别。将模型应用于新场气田须二气藏气水层识别,识别结果符合研究区的实际情况。Xu 2 Gas Reservoir,which is in Xinchang Gas Field in western Sichuan Basin,is a typical low-permeability and tight clastic gas reservoir. Due to the complicated geological conditions and serious heterogeneity in this area,the gas-water layer distribution is very complicated,and the bound water’s content is high. The boundaries of resistivity between gas reservoir and gas-water layer are blurred,so that some mistakes arise in log interpretation. We use kernel principal component analysis and support vector machine,also known as KPCA-SVM model,which is based on particle swarm optimization（PSO）,to solve the problem. Firstly,the model extracts non-linear properties of variables by kernel principal component analysis（KPCA）, and then inputs the properties of a variable into the support vector machine（SVM）. And in the identification process,we use the particle swarm optimization（PSO）to seek the optimization algorithm. Finally,the gas-water layer identification is implemented in the SVM. We applied this model to gas＆amp;water layer prediction of Xu 2 Member gas reservoir of Xinchang Gas Field,and the recognition result is in line with the actual situation of the study area.

关 键 词：粒子群算法 核主成分分析 支持向量机 气水层识别 新场须二气藏 

分 类 号：TE122[石油与天然气工程—油气勘探]