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Journal article

Association models for petroleum applications

From

Center for Energy Resources Engineering, Centers, Technical University of Denmark1

Department of Chemical and Biochemical Engineering, Technical University of Denmark2

CERE – Center for Energy Ressources Engineering, Department of Chemical and Biochemical Engineering, Technical University of Denmark3

Thermodynamics plays an important role in many applications in the petroleum industry, both upstream and downstream, ranging from flow assurance, (enhanced) oil recovery and control of chemicals to meet production and environmental regulations. There are many different applications in the oil & gas industry, thus thermodynamic data (phase behaviour, densities, speed of sound, etc) are needed to study a very diverse range of compounds in addition to the petroleum ones (CO2, H2S, water, alcohols, glycols, mercaptans, mercury, asphaltenes, waxes, polymers, electrolytes, biofuels, etc) within a very extensive range of conditions, up to very high pressures.

Actually, the petroleum industry was one of the first industrial sectors which used extensively thermodynamic models and even contributed to the development of several of the most popular and still widely used approaches. While traditional thermodynamic models like cubic equations of state have been the dominating tools in the petroleum industry, the focus of this review is on the association models.

Association models are defined as the models of SAFT/CPA family (and others) which incorporate hydrogen bonding and other complex interactions. Such association models have been, especially over the last 20 years, proved to be very successful in predicting many thermodynamic properties in the oil & gas industry.

They have not so far replaced cubic equations of state, but the results obtained by using these models are very impressive in many cases, e.g., for gas hydrate related systems, CO2/H2S mixtures, water/hydrocarbons and others. This review highlights both the major advantages of these association models and some of their limitations, which we believe should be discussed in the future.

Language: English
Year: 2013
Pages: 63-79
ISSN: 10248579
Types: Journal article
ORCIDs: Kontogeorgis, Georgios

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