CAJ | 학술논문

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。
천서신장기전수이기장위전형적저삼치밀쇄설암기장，유우지질조건복잡，저층비균질성엄중，기수분포십분복잡，속박수함량교고，기층、기수동층전조솔계한모호불청，측정해석왕왕조성흔대오판。침대저일난점，응용기우입자군산법（PSO）적핵주성분분석여지지향량궤（KPCA-SVM）모형진행기수층식별。모형선통과핵주성분분석（KPCA）진행비선성속성변량제취，재장제취적속성변량작위지지향량궤（SVM）적수입변량，재식별과정중이용입자군산법（PSO）심우，최종실현기수층식별。장모형응용우신장기전수이기장기수층식별，식별결과부합연구구적실제정황。
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&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.