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",
• year = "2018",
• volume = "30",
• number = "4",
• pages = "398--404",
• keywords = "genetic algorithms, genetic programming, Oil formation factor correlation, Volatile oil, PVT, Non-linear regression, Black oil simulation",
• 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.",

}

Genetic Programming entries for Khaled Abdel Fattah Elshreef A Lashin

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