--> Development of a predictive molecular model for Abu Dhabi crude oils phase behavior

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Development of a predictive molecular model for Abu Dhabi crude oils phase behavior

Wael A. Fouad1, Mohammed I. L. Abutaqiya2, Kristian Mogensen3, Yit Fatt Yapd, Afshin Goharzadeh4, Francisco M. Vargas2, and Lourdes F. Vega1


1Gas Research Center and Department of Chemical Engineering, Khalifa University of Science and Technology, Petroleum Institute, P.O. Box 2533, Abu Dhabi, United Arab Emirates
2Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
3Abu Dhabi National Oil Company (ADNOC), P.O. Box 898, Abu Dhabi, United Arab Emirates
4Department of Mechanical Engineering, Khalifa University of Science and Technology, Petroleum Institute, P.O. Box 2533, Abu Dhabi, United Arab Emirates


May 9-10, 2018 – AAPG/SEG/EAGE/SPE Middle East Region RDPETRO, Abu Dhabi, UAE

Posted: August 26, 2018

Abstract

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 CO2 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.

AAPG Datapages/Search and Discovery Article #90329©2018 AAPG/SEG/EAGE/SPE Middle East Region RDPETRO, Abu Dhabi, UAE, May 9-10, 2018