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.
出处
《西南石油大学学报（自然科学版）》
CAS
CSCD
北大核心
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)
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
particle swarm optimization
kernel principal component analysis
support vector machine
gas-water layer iden-tification
Xu 2 Member gas reservoir of Xinchang Gas Field
