Asphaltene, the heaviest and most polarizable fraction of crude oil, has a potential to precipitate, deposit and plug pipelines, causing considerable production costs. The main objective of this study is to contribute to the thermodynamic and transport modeling of asphaltene in order to predict its precipitation, segregation and deposition. Potential calculation of some thermophysical properties of asphaltene is also explored.

Predicting the flow assurance issues caused by asphaltene requires the ability to model the phase behavior of asphaltene as a function of pressure, temperature and composition. It has been previously demonstrated that the Perturbed Chain form of Statistical Association Fluid Theory (PC-SAFT) equation of state can accurately predict the phase behavior of high molecular weight compounds including that of asphaltene. Thus, a PC-SAFT crude oil characterization methodology is proposed to examine the asphaltene phase behavior under different operating conditions.

With the fluid being well characterized at a particular reservoir depth, a compositional grading algorithm can be used to analyze the compositional grading related to asphaltene using PC-SAFT equation of state. The asphaltene compositional grading that can lead in some cases to the formation of a tar mat is studied using the same thermodynamic model.

Quartz crystal microbalance experiments are performed to study the depositional tendency of asphaltene in different depositing environments. The possibility of simulating asphaltene deposition in a well bore is discussed by modeling the capillary data, which simultaneously accounts for asphaltene precipitation, aggregation and deposition.

The work presented is expected to contribute to the calculation of thermophysical properties of hydrocarbons and in particular of asphaltene, characterization of crude oils, improve tools to model asphaltene phase behavior, check the quality of fluid samples collected and the accuracy of (pressure, volume and temperature) PVT tests, reduce the uncertainties related to reservoir compartmentalization, optimize the logging during data acquisition, prediction of tar mat occurrence depths, improved understanding of the asphaltene deposition process, and finally optimize the wellbore operating conditions to reduce the asphaltene deposit.