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An improvement of thermodynamic micellization model for prediction of asphaltene precipitation during gas injection in heavy crude
Tavakkoli Mohammad
Masihi Mohsen
Ghazanfari Mohammad Hossein
Kharrat Riyaz
Fluid Phase Equilibria, 2011, 308(1-2): 153-163.
Thermodynamic micellization model is known as an appropriate approach for prediction of asphaltene precipitation. However, the reliability (i.e. accuracy) of this model for whole range of pressure or injected as mole percent must be checked. In practice, the accuracy can be improved by using a suitable characterization method. In this research, a computer code for implementing the thermodynamic micellization model has been developed. Having used this program, we make the prediction of asphaltene precipitation by using data reported in the literature as well as the experimental data obtained from high pressure, high temperature asphaltene precipitation experiments under gas injection conditions. An enhancement ID the thermodynamic micellization model has been proposed by applying the characterization method taken from the thermodynamic solid model. This new approach introduces a new matching parameter representing the interaction coefficients between the asphaltene component and light hydrocarbon components. Sensitivity analysis has emphasized that the thermodynamic micellization model is highly sensitive to this new matching parameter, the resin interaction energy parameter (Delta U(r)), the interfacial tension between the asphaltene micellar core and the crude (sigma(0)), and the concentration of asphaltene monomers in the crude which is in equilibrium with the pure solid asphaltene phase (X(a1)(ons)). Finally, the predictions obtained from this approach under gas/solvent injection conditions, resulted in a good agreement with experimental data which shows a significant improvement in comparison to the other matches in the available literature.
Asphaltene precipitation; Thermodynamic modeling; Thermodynamic micellization model with a new approach; Gas injection; Characterization methods; Sensitivity analysis
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