Mechanistic study of nanoparticles deposition and release in porous media

Abstract

Physicochemical interaction between the nanoparticles and the pore walls can cause significant retention of nanoparticles in porous media. The objective here is to provide mechanistic model based on Extended DLVO theory to study the rate of deposition and release of nanoparticles in porous media at different temperature, ionic strength, and pH. Empirical equation has been derived to calculate zeta potential at different temperature, ionic strength, and pH. The interaction energy can be with/without energy barrier between the nanoparticles and the pore surface. The rate of deposition and release of nanoparticles in each case has been derived. Numerical model has been used to compare the theoretically calculated rates with several experimental data. Increasing the temperature decreases the energy barrier height and increases the rate of deposition. With increasing the ionic strength, the thickness of the electrostatic double layer decreases and hence the rate of deposition increases. The effect of pH on the rate of deposition depends on the location of environment pH with respect to the isoelectric point of the nanoparticles and rock. For the extreme values of pH, energy barrier exists and rate of deposition is low. However, when the pH of the solution is between the isoelectric points of the nanoparticles and rock, the energy barrier decreases and the rate of deposition increases. The rate of deposition is time dependent as it decreases with increasing the covered rock surface. The effect of surface roughness has been included in the model using the effective height and density of the surface roughness distribution. Finally, these theoretically calculated rate values are used in a numerical model of the advection-dispersion equation with source/sink term. Several experimental results have been perfectly matched that validate the theoretical calculations of the rate of deposition. The new mechanistic model for nanoparticles can be used to determine the fate of nanoparticles in porous media under different conditions of temperature, ionic strength, concentration, and pH. This model can help to understand the nanoparticles transport in porous media and effectively design nanoparticles fluid for injection into oil and gas reservoirs.