2015
DOI: 10.2516/ogst/2015019
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CFD Applied to Process Development in the Oil and Gas Industry – A Review

Abstract: -Computational Fluid Dynamics (CFD) is increasingly used in the oil and gas industry. The present article aims to show how CFD can be used at all steps of the development of a new process, with a focus on refining technologies. Those different steps consist first of setting up tools that will be used during the development phase, second of obtaining data in complement with experiments required for the process development, and finally, of troubleshooting actions or technology developments that will make the pro… Show more

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Cited by 32 publications
(23 citation statements)
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“…For the past three decades, there has been an increasing tendency to use the Computational Fluid Dynamics (CFD) methods for computational justifications of engineering solutions in designing various industrial facilities (Spalart and Venkatakrishnan 2016, Raynal et al 2016, Mizzi et al 2015, Joshi and Nayak 2019. Using modern CFD codes enables modeling heat and mass transport, hydro-and gas dynamics hard to reproduce experimentally.…”
Section: Introductionmentioning
confidence: 99%
“…For the past three decades, there has been an increasing tendency to use the Computational Fluid Dynamics (CFD) methods for computational justifications of engineering solutions in designing various industrial facilities (Spalart and Venkatakrishnan 2016, Raynal et al 2016, Mizzi et al 2015, Joshi and Nayak 2019. Using modern CFD codes enables modeling heat and mass transport, hydro-and gas dynamics hard to reproduce experimentally.…”
Section: Introductionmentioning
confidence: 99%
“…[10] Computational fluid dynamics (CFD) has been used in the oil and gas industry. [4] For instance, the drilling and refinement of heavy oil have been evaluated using CFD. [5,6] Previous studies have dealt with vertical or horizontal flow in tubes or pipes.…”
Section: Introductionmentioning
confidence: 99%
“…Support Vector Machine (SVM) has been emerged as a proven technology which offers an alternative way to address problems with no specific relationship between input and output parameters. The main advantage of such models over existing approaches is the capability of learning and generalizing data, fault tolerance and inherent contextual information processing in addition to fast computation potential (Raynal et al, 2016). Such characteristics make them perfect candidates for applications where the complexity of the data or task demands high computational costs (Haghbakhsh et al, 2012;Adib et al, 2013Adib et al, , 2015Moradi et al, 2016).…”
Section: Introductionmentioning
confidence: 99%