Novel method for estimation of gas/oil relative permeabilities

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{MohamadiBaghmolaei:2016:JMLa,
  author =       "Mohamad Mohamadi-Baghmolaei and Reza Azin and 
                 Zahra Sakhaei and Rezvan Mohamadi-Baghmolaei and 
                 Shahriar Osfouri",
  title =        "Novel method for estimation of gas/oil relative
                 permeabilities",
  journal =      "Journal of Molecular Liquids",
  volume =       "223",
  pages =        "1185--1191",
  year =         "2016",
  ISSN =         "0167-7322",
  DOI =          "doi:10.1016/j.molliq.2016.08.096",
  URL =          "http://www.sciencedirect.com/science/article/pii/S016773221630318X",
  abstract =     "As the ages of most oil fields fall in the second half
                 of their lives, many attempts have been made to enhance
                 oil recovery in an efficient way. Gas injection into
                 oil reservoirs for enhanced oil recovery (EOR) purposes
                 requires relative permeability as a crucial issue in
                 reservoir engineering. In this study, a new method is
                 applied to predict relative permeabilities of gas/oil
                 system related to various rock and fluid types. For
                 this reason, a soft computing technique - multi-gene
                 genetic programming (MGGP) is employed to develop tools
                 for prediction of relative permeability. The new
                 methods are evaluated by experimental data extracted
                 from open literature and are validated by extensive
                 error analysis. The generated smart mathematical
                 equations are able to predict relative permeabilities
                 of gas/oil system with high accuracy and are applicable
                 for various types of rock and fluid as well. In
                 contrary to other reported correlations, the new novel
                 equations require oil API and gas molecular weight as
                 extra input variables to improve their estimating
                 ability for every type of rock and fluid. The proposed
                 technique is promising and encouraging for petroleum
                 and reservoir engineers to be implemented for other
                 gas/oil petro-physical properties.",
  keywords =     "genetic algorithms, genetic programming, Reservoir
                 engineering, Relative permeability, Gas injection,
                 Empirical correlation",
}

Genetic Programming entries for Mohamad Mohamadi-Baghmolaei Reza Azin Zahra Sakhaei Rezvan Mohamadi-Baghmolaei Shahriar Osfouri

Citations