Recent developments in the deposition of colloidal asphaltene in capillary flow : experiments and mesoscopic simulation

The aggregation and deposition of asphaltenic material in reservoir rock are significant problems in the oil industry and can adversely affect the producibility of a given reservoir. To obtain a fundamental understanding of this phenomenon, we have studied the deposition and aggregation of colloidal asphaltene in capillary flow by experiment and simulation. For the simulation, we have used the stochastic rotation dynamics (SRD) method, in which the solvent hydrodynamics emerge from the collisions between the solvent particles, while the Brownian motion emerges naturally from the interactions between the colloidal asphaltene particles and the solvent. We compare our simulation results with flow experiments in glass capillaries where we use extracted asphaltenes only in toluene, re-precipitated with n-Heptane and also asphaltenes precipitated from the whole oil. In the experiments, the asphaltene precipitation and deposition dynamics were monitored in a slot capillary using optical microscopy under flow conditions similar to those used in the simulation. Maintaining a constant flow rate of 5µL/min, we found that the pressure drop across the capillary first increased slowly, followed by a sharp increase, corresponding to a complete local blockage of the capillary. Subsequently the pressure fell sharply as asphaltenes were re-entrained. This condition was confirmed by the visual observations that showed the slow build-up of asphaltenes deposit followed by the sudden erosion of a channel through the deposit at the time when the pressure suddenly decreased. We calculate the change in the dimensionless permeability as a function of time for both experiment and simulation. By matching the experimental and simulation results, we obtain information about 1) the interaction potential well depth for the particular asphaltenes used in the experiments, and 2) the flow conditions associated with the asphaltene deposition process. The data obtained will also be used as input parameters for a deep-bed filtration model