This study aims to evaluate the performance of the four most known screening methods used to predict the risk of asphaltene precipitation in crude oil employing a large database from the literature. The selection of these methods was based on the amount of property data required for their application. Most methods reported in the literature use saturate, aromatic, resin, and asphaltene (SARA) analysis as the property to monitor the stability of the oil with respect to asphaltene precipitation. Other methods require the temperature, pressure, and oil density data to indicate the risk of asphaltene precipitation. Results showed inconsistency for two of the four screening methods selected, and improvements were proposed and successfully validated.

No abstract

No abstract

No abstract

ABSTRACT:We modeled solid-liquid equilibria (SLEs) in polyethylene and polypropylene solutions with a Soave-Redlich-Kwong (SRK) cubic equation of state (EOS) and a perturbed-chain statistical associating fluid theory (PC-SAFT) EOS. Two types of mixing rules were used with SRK EOS: The Wong-Sandler mixing rule and the linear combination of the Vidal and Michelsen mixing rules (LCVM), both of which incorporated the Bogdanic and Vidal activity coefficient model. The performance of these models was evaluated with atmospheric-pressure and high-pressure experimental SLE data obtained from literature. The basic SLE equation was solved for the equilibrium melting temperature instead of for the composition. The binary interaction parameters of SRK and PC-SAFT EOS were estimated to best describe the experimental equilibrium behavior of 20 different polymer-solvent systems at atmospheric pressure and 31 other polymer-solvent systems at high pressure. A comparison with experimental data showed that SRK-LCVM agreed very well with the atmospheric SLE data and that PC-SAFT EOS was more efficient in highpressure conditions.