Dew point pressure model for gas condensate reservoirs based on multi-gene genetic programming approach

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@Article{Kaydani:2016:ASC,
  author =       "Hossein Kaydani and Ali Mohebbi and Ali Hajizadeh",
  title =        "Dew point pressure model for gas condensate reservoirs
                 based on multi-gene genetic programming approach",
  journal =      "Applied Soft Computing",
  volume =       "47",
  pages =        "168--178",
  year =         "2016",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2016.05.049",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494616302599",
  abstract =     "One of the most critical parameters in
                 characterization of gas condensate reservoirs is dew
                 point pressure (DPP), and its accurate determination is
                 a challenging task in development and management of
                 these reservoirs. Experimental measurement of DPP is a
                 costly and time consuming method. Therefore, searching
                 for a quick, reliable, inexpensive, and robust
                 algorithm for determination of DPP is of great
                 importance. In this paper, first, a new approach based
                 on multi-gene genetic programming (MGGP) to determine
                 DPP of gas condensate reservoirs is presented. Then, a
                 correlation for DPP calculation using MGGP has been
                 developed for gas condensate reservoirs. Finally, the
                 efficiency of the proposed DPP model has been validated
                 by comparing its predictions with the results of other
                 conventional models. It is found that the correlation
                 developed in this work is capable of predicting more
                 accurate values of DPP, with the lowest average
                 relative and absolute errors with respect to the
                 experimental results, and also higher correlation
                 coefficient among the results of all the evaluated DPP
                 correlations. Therefore, it is suggested that the
                 proposed model can be applied effectively for DPP
                 prediction for a wide range of gas properties and
                 reservoir temperatures.",
  keywords =     "genetic algorithms, genetic programming, Gas
                 condensate reservoir, Dew point pressure, PVT data",
}

Genetic Programming entries for Hossein Kaydani Ali Mohebbi Ali Hajizadeh Moghaddam

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