中文摘要：

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。

英文摘要：

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.