A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming
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- @Article{Kaydani:2014:JNGSE,
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author = "Hossein Kaydani and Mohammad Najafzadeh and
Ali Hajizadeh",
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title = "A new correlation for calculating carbon dioxide
minimum miscibility pressure based on multi-gene
genetic programming",
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journal = "Journal of Natural Gas Science and Engineering",
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volume = "21",
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pages = "625--630",
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year = "2014",
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ISSN = "1875-5100",
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DOI = "doi:10.1016/j.jngse.2014.09.013",
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URL = "http://www.sciencedirect.com/science/article/pii/S187551001400273X",
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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.",
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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