Improved oil formation volume factor (Bo) correlation for volatile oil reservoirs: An integrated non-linear regression and genetic programming approach

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@Article{Fattah:2016:JKSUES,
  author =       "K. A. Fattah and A. Lashin",
  title =        "Improved oil formation volume factor (Bo) correlation
                 for volatile oil reservoirs: An integrated non-linear
                 regression and genetic programming approach",
  journal =      "Journal of King Saud University - Engineering
                 Sciences",
  note =         "In Press",
  ISSN =         "1018-3639",
  DOI =          "doi:10.1016/j.jksues.2016.05.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1018363916300198",
  abstract =     "In this paper, two correlations for oil formation
                 volume factor (Bo) for volatile oil reservoirs are
                 developed using non-linear regression technique and
                 genetic programming using commercial software. More
                 than 1200 measured values obtained from PVT laboratory
                 analyses of five representative volatile oil samples
                 are selected under a wide range of reservoir conditions
                 (temperature and pressure) and compositions. Matching
                 of PVT experimental data with an equation of state
                 (EOS) model using a commercial simulator (Eclipse
                 Simulator), was achieved to generate the oil formation
                 volume factor (Bo). The obtained results of the Bo as
                 compared with the most common published correlations
                 indicate that the new generated model has improved
                 significantly the average absolute error for volatile
                 oil fluids. The hit-rate (R2) of the new non-linear
                 regression correlation is 98.99percent and the average
                 absolute error (AAE) is 1.534percent with standard
                 deviation (SD) of 0.000372. Meanwhile, correlation
                 generated by genetic programming gave R2 of
                 99.96percent and an AAE of 0.3252percent with a SD of
                 0.00001584. The importance of the new correlation stems
                 from the fact that it depends mainly on experimental
                 field production data, besides having a wide range of
                 applications especially when actual PVT laboratory data
                 are scarce or incomplete.",
  keywords =     "genetic algorithms, genetic programming, Oil formation
                 factor correlation, Volatile oil, PVT, Non-linear
                 regression, Black oil simulation",
}

Genetic Programming entries for Khaled Abdel Fattah Elshreef A Lashin

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