Destabilization and Aggregation Kinetics of Asphaltenes.

Abstract

Asphaltenes are the heaviest fraction of crude oil and can precipitate due to changes in temperature, pressure or composition. It was previously shown that the precipitation and aggregation of asphaltenes is a kinetic phenomenon. In this thesis, it is demonstrated that the kinetics of aggregation is universal among many different crude oils, model oils and precipitants. The factors that can play a role in controlling this kinetic behavior are investigated in this study.

It is shown that the rate of asphaltene aggregation strongly depends on the type of solvent (i.e. crude oil) used for stabilizing asphaltenes. A new universal mathematical model relating the aggregation rate to the solution viscosity and the solubility parameters of the solution and asphaltenes is developed. With this model, all aggregation curves collapse onto a single master curve.

The asphaltene content and the chemical identity of the precipitant are also shown to play important roles in controlling the aggregation rates. The findings for the effect of asphaltene content challenge the intuitive expectation that higher asphaltene concentration would lead to accelerated aggregation kinetics. Instead, two distinct regimes are identified for the effect of asphaltene concentration: 1) Below 1 wt%, the aggregation rate of asphaltenes increases, 2) Above 1 wt%, the aggregation rate decreases. The new universal model successfully predicts this behavior after accounting for the contribution of soluble asphaltenes to the solubility parameter of the solution.

In addition, the polydispersity of asphaltenes is shown to be important in dictating their kinetic behavior in different n-alkane precipitants. The universal model quantitatively accounts for the asphaltenes polydispersity using a solubility parameter distribution and can successfully predict the precipitation rate of asphaltenes in the blends of up to five different precipitants.

Finally, our findings reveal that asphaltenes with the highest solubility paramenter precipitate first and have the tendency to form the largest nano-particles in toluene. The findings from this thesis leads to a better understanding of the factors that govern the kinetics and can in turn give rise to new predictive models to foresee precipitation and aggregation kinetics under different operational conditions.