Dew point pressure model for gas condensate reservoirs based on multi-gene genetic programming approach
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- @Article{Kaydani:2016:ASC,
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author = "Hossein Kaydani and Ali Mohebbi and Ali Hajizadeh",
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title = "Dew point pressure model for gas condensate reservoirs
based on multi-gene genetic programming approach",
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journal = "Applied Soft Computing",
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volume = "47",
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pages = "168--178",
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year = "2016",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2016.05.049",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494616302599",
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abstract = "One of the most critical parameters in
characterization of gas condensate reservoirs is dew
point pressure (DPP), and its accurate determination is
a challenging task in development and management of
these reservoirs. Experimental measurement of DPP is a
costly and time consuming method. Therefore, searching
for a quick, reliable, inexpensive, and robust
algorithm for determination of DPP is of great
importance. In this paper, first, a new approach based
on multi-gene genetic programming (MGGP) to determine
DPP of gas condensate reservoirs is presented. Then, a
correlation for DPP calculation using MGGP has been
developed for gas condensate reservoirs. Finally, the
efficiency of the proposed DPP model has been validated
by comparing its predictions with the results of other
conventional models. It is found that the correlation
developed in this work is capable of predicting more
accurate values of DPP, with the lowest average
relative and absolute errors with respect to the
experimental results, and also higher correlation
coefficient among the results of all the evaluated DPP
correlations. Therefore, it is suggested that the
proposed model can be applied effectively for DPP
prediction for a wide range of gas properties and
reservoir temperatures.",
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keywords = "genetic algorithms, genetic programming, Gas
condensate reservoir, Dew point pressure, PVT data",
- }
Genetic Programming entries for
Hossein Kaydani
Ali Mohebbi
Ali Hajizadeh Moghaddam
Citations