Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

•1009 record date of from the Iranian condensate fields (Marun-Khami, Aghajari-Khami and Ahvaz-Khami).•New hybrid machine learning technique accurately predicts gas flow rate through wellhead choke in gas condensate reservoirs.•MELM-PSO model constructs the most accurate condensate gas flow rate pre...

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Veröffentlicht in:	Fuel (Guildford) Jg. 308; S. 121872
Hauptverfasser:	Abad, Abouzar Rajabi Behesht, Ghorbani, Hamzeh, Mohamadian, Nima, Davoodi, Shadfar, Mehrad, Mohammad, Aghdam, Saeed Khezerloo-ye, Nasriani, Hamid Reza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 15.01.2022 Elsevier BV
Schlagworte:	Accuracy Algorithms Artificial intelligence Artificial neural networks Chokes (restrictions) Computer applications Condensates Correlation coefficient Correlation coefficients Datasets Empirical equations Flow rates Flow velocity Fluid dynamics Fluid flow Gas flow Gas flow rate Gas wells Genetic algorithms Hybrid machine learning algorithms Learning algorithms Least squares support vector machine Machine learning Multi-hidden layer extreme learning machine Oil and gas fields Optimization Performance prediction Predictions Pressure Recombination Reservoir management Reservoirs Root-mean-square errors Support vector machines Swarm intelligence Wellhead choke Gas flow rate Hybrid machine learning algorithms Least squares support vector machine Wellhead choke Multi-hidden layer extreme learning machine
ISSN:	0016-2361, 1873-7153
Online-Zugang:	Volltext
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