GA-RBF model for prediction of dew point pressure in gas condensate reservoirs

被引:18
|
作者
Najafi-Marghmaleki, Adel [1 ]
Tatar, Afshin [2 ]
Barati-Harooni, Ali [1 ]
Choobineh, Mohammad-Javad [3 ]
Mohammadi, Amir H. [4 ,5 ,6 ]
机构
[1] Islamic Azad Univ, Ahvaz Branch, Young Researchers & Elite Club, Ahvaz, Iran
[2] Islamic Azad Univ, North Tehran Branch, Young Researchers & Elite Club, Tehran, Iran
[3] Univ Tehran, Fac Engn, Inst Petr Engn, Tehran, Iran
[4] IRGCP, Paris, France
[5] Univ KwaZulu Natal, Sch Engn, Discipline Chem Engn, Howard Coll Campus,King George V Ave, ZA-4041 Durban, South Africa
[6] Univ Laval, Dept Genie Mines Met & Mat, Fac Sci & Genie, Quebec City, PQ G1V 0A6, Canada
关键词
Dew point pressure; Gas condensate; Model; Genetic algorithm; Radial basis function; DEWPOINT PRESSURE; NETWORKS; TEMPERATURE; SOLUBILITY;
D O I
10.1016/j.molliq.2016.08.087
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
This study presents the application of an intelligent algorithm in estimation of dew point pressure (DPP) of gas condensate systems. Radial Basis Function (RBF) network in conjunction with Genetic Algorithm (GA) as an optimization algorithm was utilized for this aim. The performance of the proposed GA-RBF model was investigated by statistical and graphical analysis of results. The analysis show that the implemented model is precise and robust in prediction of experimental DPP data. Furthermore, the GA-RBF model was compared with a literature intelligent approach (GEP model) as well as three well-known correlations. The comparison reveals that the GA-RBF model is superior to other model and correlations and successfully improves the predictions. (C) 2016 Published by Elsevier B.V.
引用
收藏
页码:979 / 986
页数:8
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