On the determination of CO2-crude oil minimum miscibility pressure using genetic programming combined with constrained multivariable search methods

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@Article{Fathinasab:2016:Fuel,
  author =       "Mohammad Fathinasab and Shahab Ayatollahi",
  title =        "On the determination of CO2-crude oil minimum
                 miscibility pressure using genetic programming combined
                 with constrained multivariable search methods",
  journal =      "Fuel",
  volume =       "173",
  pages =        "180--188",
  year =         "2016",
  ISSN =         "0016-2361",
  DOI =          "doi:10.1016/j.fuel.2016.01.009",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0016236116000181",
  abstract =     "In addition to reducing carbon dioxide (CO2) emission,
                 the high oil recovery efficiency achieved by CO2
                 injection processes makes CO2 injection a desirable
                 enhance oil recovery (EOR) technique. Minimum
                 miscibility pressure (MMP) is an important parameter in
                 successful designation of any miscible gas injection
                 process such as CO2 flooding; therefore, its accurate
                 determination is of great importance. The current
                 experimental techniques for determining MMP are
                 expensive and time-consuming. In this study, multi-gene
                 genetic programming has been combined with constrained
                 multivariable search methods, and a simple empirical
                 model has been developed which provides a reliable
                 estimation of MMP in a wide range of reservoirs,
                 injection gases and crude oil systems. The experimental
                 data for developing the proposed correlation consists
                 of 270 data points from twenty-six authenticated
                 literature sources. This model uses reservoir
                 temperature, molecular weight of C5+, volatile (N2 and
                 C1) to intermediate (H2S, CO2, C2, C3, C4) ratio and
                 pseudo critical temperature of the injection gas as
                 input parameters. Both statistical and graphical error
                 analyses have been employed to evaluate the accuracy
                 and validity of the proposed model compared to the
                 pre-existing correlations. The results showed that the
                 new model provides an average absolute relative error
                 of 11.76percent. Moreover, the relevancy factor
                 indicated that the reservoir temperature has the
                 greatest impact on the minimum miscibility pressure.",
  keywords =     "genetic algorithms, genetic programming, Minimum
                 miscibility pressure, Carbon dioxide, Constrained
                 multivariable search methods",
}

Genetic Programming entries for Mohammad Fathinasab Shahab Ayatollahi

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