Phase Behavior Investigation of a Live Presalt Crude Oil from Short-Wave Infrared Observation, Acoustic Wave Sensing, and Equation of State Modeling

In this work, the phase behavior of a live presalt crude oil characterized by a high CO2 content was investigated by combining measurement and modeling techniques. Because of the complexity of the fluid–fluid phase transitions observed within this system, a combination of indirect and direct detecti...

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Bibliographic Details
Published in:Energy & fuels Vol. 35; no. 22; pp. 18504 - 18517
Main Authors: Romero Yanes, Jose F, Lin, Chih-Wei, de Sant’Ana, Hosiberto B, Pujol, Magali, Collell, Julien, Pauly, Jérôme, Fleming, Felipe P, Montel, François, Daridon, Jean-Luc
Format: Journal Article
Language:English
Published: American Chemical Society 18-11-2021
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Summary:In this work, the phase behavior of a live presalt crude oil characterized by a high CO2 content was investigated by combining measurement and modeling techniques. Because of the complexity of the fluid–fluid phase transitions observed within this system, a combination of indirect and direct detection methods was necessary to determine the phase diagram of this reservoir fluid. A shortwave infrared camera was used for direct observation of the phase transitions occurring in an oil sample placed in either a full visibility cell or a high-pressure microscopy device. In addition, an acoustic sensor working in the thickness-shear mode was used to probe phase changes during constant mass expansion experiments. The phase transitions of the live crude oil were measured from temperatures of 310 to 383 K and reported in the form of a (p, T) diagram. The obtained diagram was modeled using the Peng–Robinson equation of state with a lumping procedure that allowed representing the oil in 8 cuts including carbon dioxide. Additional measurements were carried out in a pseudobinary oil + gas model system composed of the dead oil with a synthetic gas from an equimolar content of methane and carbon dioxide to validate the experimental observations and the model predictions.
ISSN:0887-0624
1520-5029
DOI:10.1021/acs.energyfuels.1c02980