Development of a robust model for prediction of under-saturated reservoir oil viscosity

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@Article{Hajirezaie:2017:JML,
  author =       "Sassan Hajirezaie and Amin Pajouhandeh and 
                 Abdolhossein Hemmati-Sarapardeh and Maysam Pournik and 
                 Bahram Dabir",
  title =        "Development of a robust model for prediction of
                 under-saturated reservoir oil viscosity",
  journal =      "Journal of Molecular Liquids",
  volume =       "229",
  pages =        "89--97",
  year =         "2017",
  ISSN =         "0167-7322",
  DOI =          "doi:10.1016/j.molliq.2016.11.088",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0167732216320608",
  abstract =     "Fluid viscosity is considered as one of the most
                 important parameters for reservoir simulation,
                 performance evaluation, designing production
                 facilities, etc. In this communication, a robust model
                 based on Genetic Programming (GP) approach was
                 developed for prediction of under-saturated reservoir
                 oil viscosity. A third order polynomial correlation for
                 prediction of under-saturated oil viscosity as a
                 function of bubble point viscosity, pressure
                 differential (pressure minus bubble point pressure) and
                 pressure ratio (pressure divided by bubble point
                 pressure) was proposed. To this end, a large number of
                 experimental viscosity databank including 601 data sets
                 from various regions covering a wide range of reservoir
                 conditions was collected from literature. Statistical
                 and graphical error analyses were employed to evaluate
                 the performance and accuracy of the model. The results
                 indicate that the developed model is able to estimate
                 oil viscosity with an average absolute percentage
                 relative error of 4.47percent. These results in
                 addition to the graphical results confirmed the
                 robustness and superiority of the developed model
                 compared to the most well-known existing correlations
                 of under-saturated oil viscosity. Additionally, the
                 investigation of relative impact of input parameters on
                 under-saturated reservoir oil viscosity demonstrates
                 that bubble point viscosity has the greatest impact on
                 oil viscosity.",
  keywords =     "genetic algorithms, genetic programming,
                 Under-saturated reservoir oil viscosity, Statistical
                 and graphical error analyses, Relevancy factor",
}

Genetic Programming entries for Sassan Hajirezaie Amin Pajouhandeh Abdolhossein Hemmati-Sarapardeh Maysam Pournik Bahram Dabir

Citations