• DocumentCode
    3290769
  • Title

    Modeling of Oil and Water Migration Based on Chaos Genetic Algorithm Neural Network

  • Author

    Tian, Jingwen ; Zhou, Shiru ; Gao, Meijuan

  • Author_Institution
    Coll. of Autom., Beijing Union Univ., Beijing, China
  • fYear
    2009
  • fDate
    16-17 May 2009
  • Firstpage
    763
  • Lastpage
    766
  • Abstract
    An actual physical simulation model was constructed to simulate the course of oil and water migration. Under certain physical property conditions, we simulated the water injection well and the oil well on the physical simulation model, and continuous measured online the oil and water content of different area of model in three-dimensional space using the 512 routes resistivity measuring circuit, then we can obtain large numbers of simulation samples. Considering the issues that the relationship between the remaining oil and every parameters of water displacing oil is a complicated and nonlinear and the chaos genetic algorithm neural network has the ability of strong nonlinear function approach and global optimization, in this paper, the chaos genetic algorithm neural network was used to establish the oil and water migration model. We construct the structure of chaos genetic algorithm neural network. The experimental results show that this method is feasible and effective.
  • Keywords
    chaos; crude oil; genetic algorithms; neural nets; production engineering computing; chaos genetic algorithm neural network; crude oil; nonlinear function approach; oil field; oil migration; oil saturation; oil well; water injection well; water migration; Area measurement; Automation; Chaos; Circuit simulation; Educational institutions; Genetic algorithms; Neural networks; Petroleum; Production; Water; chaos genetic algorithm; modeling; neural network; oil and water migration; water displacing oil;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits, Communications and Systems, 2009. PACCS '09. Pacific-Asia Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-3614-9
  • Type

    conf

  • DOI
    10.1109/PACCS.2009.196
  • Filename
    5232437