Estimation of dynamic viscosity of natural gas based on genetic programming methodology

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@Article{Abooali:2014:JNGSE,

• author = "Danial Abooali and Ehsan Khamehchi",
• title = "Estimation of dynamic viscosity of natural gas based on genetic programming methodology",
• journal = "Journal of Natural Gas Science and Engineering",
• volume = "21",
• pages = "1025--1031",
• year = "2014",
• keywords = "genetic algorithms, genetic programming, Natural gas, Dynamic viscosity, Correlation",
• ISSN = "1875-5100",
• DOI = "doi:10.1016/j.jngse.2014.11.006",
• URL = "http://www.sciencedirect.com/science/article/pii/S1875510014003394",
• abstract = "Investigating the behaviour of natural gas can contribute to a detailed understanding of hydrocarbon reservoirs. Natural gas, alone or in association with oil in reservoirs, has a large impact on reservoir fluid properties. Thus, having knowledge about gas characteristics seems to be necessary for use in estimation and prediction purposes. In this project, dynamic viscosity of natural gas (mu_g), as an important quantity, was correlated with pseudo-reduced temperature (Tpr), pseudo-reduced pressure (Ppr), apparent molecular weight (Ma) and gas density (rhog) by operation of the genetic programming method on a large dataset including 1938 samples. The squared correlation coefficient (R2), average absolute relative deviation percent (AARDpercent) and average absolute error (AAE) are 0.999, 2.55percent and 0.00084 cp, respectively. The final results show that the obtained simple-to-use model can predict viscosity of natural gases with high accuracy and confidence.",
• notes = "GPTIPS",

}

Genetic Programming entries for Danial Abooali Ehsan Khamehchi

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