A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming

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@Article{Kaydani:2014:JNGSE,
  author =       "Hossein Kaydani and Mohammad Najafzadeh and 
                 Ali Hajizadeh",
  title =        "A new correlation for calculating carbon dioxide
                 minimum miscibility pressure based on multi-gene
                 genetic programming",
  journal =      "Journal of Natural Gas Science and Engineering",
  volume =       "21",
  pages =        "625--630",
  year =         "2014",
  ISSN =         "1875-5100",
  DOI =          "doi:10.1016/j.jngse.2014.09.013",
  URL =          "http://www.sciencedirect.com/science/article/pii/S187551001400273X",
  abstract =     "Miscible gas injection is one of the most efficient
                 enhanced oil recovery (EOR) methods in petroleum
                 industry. Minimum miscibility pressure (MMP) is a key
                 parameter in any gas injection design project.
                 Experimental Measurement of MMP is a costly and
                 time-consuming method; so searching for a quick, not
                 expensive and reliable method to determine gas-oil MMP
                 is inevitable. This paper Present a fast and vigorous
                 method using a new approach based on multi-gene genetic
                 programming (MGGP) to determine carbon dioxide minimum
                 miscibility pressure (CO2 MMP) for carbon dioxide
                 injection processes. Then, new correlations for MMP
                 calculation of both pure and impure CO2 streams using
                 the MGGP, have been developed. Consequently, the MGGP
                 models have been validated and compared with the other
                 conventional model results, to evaluate different
                 techniques. It was founded that the new developed
                 correlations predict accurate values of CO2 MMP compare
                 with the experimental slim-tube CO2 MMP test results,
                 with the lowest average relative and absolute error and
                 also higher correlation coefficient among all evaluated
                 CO2 MMP correlation results.",
  keywords =     "genetic algorithms, genetic programming, Carbon
                 dioxide injection, Minimum miscibility pressure,
                 Empirical correlations",
}

Genetic Programming entries for Hossein Kaydani Mohammad Najafzadeh Ali Hajizadeh Moghaddam

Citations