Estimation of dynamic viscosity of natural gas based on genetic programming methodology

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  author =       "Danial Abooali and Ehsan Khamehchi",
  title =        "Estimation of dynamic viscosity of natural gas based
                 on genetic programming methodology",
  journal =      "Journal of Natural Gas Science and Engineering",
  volume =       "21",
  pages =        "1025--1031",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming, Natural gas,
                 Dynamic viscosity, Correlation",
  ISSN =         "1875-5100",
  DOI =          "doi:10.1016/j.jngse.2014.11.006",
  URL =          "",
  abstract =     "Investigating the behaviour of natural gas can
                 contribute to a detailed understanding of hydrocarbon
                 reservoirs. Natural gas, alone or in association with
                 oil in reservoirs, has a large impact on reservoir
                 fluid properties. Thus, having knowledge about gas
                 characteristics seems to be necessary for use in
                 estimation and prediction purposes. In this project,
                 dynamic viscosity of natural gas (mu_g), as an
                 important quantity, was correlated with pseudo-reduced
                 temperature (Tpr), pseudo-reduced pressure (Ppr),
                 apparent molecular weight (Ma) and gas density (rhog)
                 by operation of the genetic programming method on a
                 large dataset including 1938 samples. The squared
                 correlation coefficient (R2), average absolute relative
                 deviation percent (AARDpercent) and average absolute
                 error (AAE) are 0.999, 2.55percent and 0.00084 cp,
                 respectively. The final results show that the obtained
                 simple-to-use model can predict viscosity of natural
                 gases with high accuracy and confidence.",
  notes =        "GPTIPS",

Genetic Programming entries for Danial Abooali Ehsan Khamehchi