Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

Gas condensate reservoirs display unique phase behavior and are highly sensitive to reservoir pressure changes. This makes it difficult to determine their PVT characteristics, including their condensate viscosity, which is a key variable in determining their flow behavior. In this study, a novel mac...

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Veröffentlicht in:	Journal of natural gas science and engineering Jg. 95; S. 104210
Hauptverfasser:	Behesht Abad, Abouzar Rajabi, Mousavi, Seyedmohammadvahid, Mohamadian, Nima, Wood, David A., Ghorbani, Hamzeh, Davoodi, Shadfar, Alvar, Mehdi Ahmadi, Shahbazi, Khalil
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2021
Schlagworte:	Gas condensate viscosity Hybrid machine learning algorithms Least squares support vector machine Multi-layer extreme learning machine Multilayer perceptron Multilayer perceptron Hybrid machine learning algorithms Gas condensate viscosity Multi-layer extreme learning machine Least squares support vector machine
ISSN:	1875-5100
Online-Zugang:	Volltext
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