• DocumentCode
    3407125
  • Title

    Research on Modeling Method of Water Displacing Oil Physical Simulation Based on Wavelet Neural Network

  • Author

    Gao, Meijuan ; Tian, Jingwen ; Zhou, Hao

  • Author_Institution
    Beijing Union Univ., Beijing
  • fYear
    2007
  • fDate
    5-8 Aug. 2007
  • Firstpage
    2109
  • Lastpage
    2114
  • Abstract
    An actual physical simulation model was constructed to simulate the course of water displacing oil. 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, the wavelet neural network was used to establish the water displacing remaining oil model. We adopt a method of reduce the number of the wavelet basic function by analysis the sparsity property of sample data, and use the learning algorithm based on gradient descent to train network. The experimental results show that this method is feasible and effective.
  • Keywords
    gradient methods; learning (artificial intelligence); neural nets; wavelet transforms; gradient descent algorithm; learning algorithm; resistivity measuring circuit; sparsity property; water displacing oil physical simulation; water injection well; wavelet basic function; wavelet neural network; Area measurement; Artificial neural networks; Circuit simulation; Hydrocarbon reservoirs; Neural networks; Petroleum; Predictive models; Production; Solid modeling; Water resources; Modeling; Physical simulation; Water displacing oil; Wavelet neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics and Automation, 2007. ICMA 2007. International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-1-4244-0828-3
  • Electronic_ISBN
    978-1-4244-0828-3
  • Type

    conf

  • DOI
    10.1109/ICMA.2007.4303877
  • Filename
    4303877