Dew point pressure model for gas condensate reservoirs based on multi-gene genetic programming approach

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@Article{Kaydani:2016:ASC,

• author = "Hossein Kaydani and Ali Mohebbi and Ali Hajizadeh",
• title = "Dew point pressure model for gas condensate reservoirs based on multi-gene genetic programming approach",
• journal = "Applied Soft Computing",
• volume = "47",
• pages = "168--178",
• year = "2016",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2016.05.049",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494616302599",
• 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.",
• 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