A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming

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@Article{Kaydani:2014:JNGSE,

• author = "Hossein Kaydani and Mohammad Najafzadeh and Ali Hajizadeh",
• title = "A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming",
• journal = "Journal of Natural Gas Science and Engineering",
• volume = "21",
• pages = "625--630",
• year = "2014",
• ISSN = "1875-5100",
• DOI = "doi:10.1016/j.jngse.2014.09.013",
• URL = "http://www.sciencedirect.com/science/article/pii/S187551001400273X",
• abstract = "Miscible gas injection is one of the most efficient enhanced oil recovery (EOR) methods in petroleum industry. Minimum miscibility pressure (MMP) is a key parameter in any gas injection design project. Experimental Measurement of MMP is a costly and time-consuming method; so searching for a quick, not expensive and reliable method to determine gas-oil MMP is inevitable. This paper Present a fast and vigorous method using a new approach based on multi-gene genetic programming (MGGP) to determine carbon dioxide minimum miscibility pressure (CO2 MMP) for carbon dioxide injection processes. Then, new correlations for MMP calculation of both pure and impure CO2 streams using the MGGP, have been developed. Consequently, the MGGP models have been validated and compared with the other conventional model results, to evaluate different techniques. It was founded that the new developed correlations predict accurate values of CO2 MMP compare with the experimental slim-tube CO2 MMP test results, with the lowest average relative and absolute error and also higher correlation coefficient among all evaluated CO2 MMP correlation results.",
• keywords = "genetic algorithms, genetic programming, Carbon dioxide injection, Minimum miscibility pressure, Empirical correlations",

}

Genetic Programming entries for Hossein Kaydani Mohammad Najafzadeh Ali Hajizadeh Moghaddam

Citations