• DocumentCode
    2209166
  • Title

    A hybrid system for well test analysis

  • Author

    May, Edward A. ; Dagli, Cihan H.

  • Author_Institution
    Smart Eng. Syst. Lab., Missouri Univ., Rolla, MO, USA
  • Volume
    1
  • fYear
    1998
  • fDate
    4-8 May 1998
  • Firstpage
    295
  • Abstract
    Petroleum well test analysis is a tool for estimating the average properties of the reservoir rock. It is a classic example of an inverse problem. Visual examination of the pressure response of the reservoir to an induced flow rate change at a well allows the experienced analyst to determine the most appropriate model from a library of generalized analytical solutions. Rock properties are determined by finding the model parameters that best fit the observed data. This paper describes a framework for hybrid network to assist the analyst in selecting the appropriate model and determining the solution. The hybrid network design offers significant advantages by reducing training time and allowing incorporation of both symbolic and numeric data. The network structure is described and the advantages and disadvantages compared to previous approaches are discussed
  • Keywords
    geology; geophysical prospecting; geophysical techniques; geophysics computing; inverse problems; neural nets; parameter estimation; petroleum industry; rocks; generalized analytical solution library; geology; hybrid neural network; hybrid system; induced flow rate change; inverse problem; measurement technique; numeric data; oil well; petroleum well test analysis; pressure response; reservoir rock; symbolic data; visual examination; well test analysis; Boundary conditions; Inverse problems; Laboratories; Logic; Neural networks; Petroleum; Predictive models; Reservoirs; System testing; Systems engineering and theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-4859-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1998.682280
  • Filename
    682280