西南石油大学学报(自然科学版)

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

Pang Heqing1, Kuang Jianchao2,3, Cai Zuohua4, Liao Kaigui4, Wang Zhong2,3   

  1. 1. Post-doctoral Research Station,SWPB,SINOPEC,Chengdu,Sichuan 610041,China
    2. College of Energy Resources,Chengdu University of Technology,Chengdu,Sichuan 610059,China
    3. College of Management Science,Chengdu University of Technology,Chengdu,Sichuan 610059,China
    4. Exploration and Development Institute,SWPB,SINOPEC,Chengdu,Sichuan 610041,China
  • Online:2014-04-01 Published:2014-04-01

Abstract:

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.

Key words: particle swarm optimization, kernel principal component analysis, support vector machine, gas-water layer identification,
Xu 2 Member gas reservoir of Xinchang Gas Field