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Abstract

Summary

A new approach based on the statistical associating fluid theory (SAFT) is presented here to model eight light crudes, with the SARA analysis as the only input for the model. Within the characterization procedure of Punnapala and Vargas (2013), the aromaticity parameter and the asphaltene molecular weight were fixed to all crude oil samples, while the asphaltene aromaticity is the only fitted parameter of the model. A correlation for this parameter with the flashed gas molecular weight allows full predictions of the phase behavior without the need of any asphaltene onset data. The predictive molecular model was used to study asphaltene instability as a function of injected CO and natural gas concentration. The model can also accurately reproduce routine PVT experiments such as constant composition expansion, differential vaporization and multi-stage separation tests performed on the crude oils, thereby providing a unified framework for phase behavior studies.

© 2018 Society of Exploration Geophysicists
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/content/papers/10.1190/RDP2018-41463673.1
2018-05-09
2024-04-23

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http://instance.metastore.ingenta.com/content/papers/10.1190/RDP2018-41463673.1
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Development of a Predictive Molecular Model for Abu Dhabi Crude Oils Phase Behavior
Conference Proceedings 2018, 73 (2018); https://doi.org/10.1190/RDP2018-41463673.1
/content/papers/10.1190/RDP2018-41463673.1
/content/papers/10.1190/RDP2018-41463673.1
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