Fines migration and clogging under pH alteration: Stochastic model and its upscaling

Abstract

Profound understanding of fine particle detachment, mobilisation, migration and consequent porous media clogging is essential for optimal design and clean-up of filter beds in water purification and wastewater treatment processes. In particular, changes in the solution pH affect the electrostatic force between particles and rocks which regulates particle detachment induced by viscous forces; fines detachment and migration usually leads to serious well rate decrease. The change in the concentration of attached particles when changing pH is quantified using a maximum retention function (MRF). Currently, this function can only be determined empirically from experiments despite strong theoretical foundations for predicting particle detachment. In this work, the microscale description of particle detachment is linked with macroscale predictions of particle detachment by averaging the condition of mechanical equilibrium over the distributions of microscale parameters. Heterogeneities of attached particles and pore space are reflected in probability distributions of relevant parameters. A Monte Carlo algorithm is implemented to combine the parameter distributions and detachment condition to calculate the MRF. The methodology allows for predictive modelling of particle detachment, and inverse modelling of the microscale parameter distributions. Inverse modelling is performed on two coreflooding tests in which the injected pH was varied. Fitting shows good agreement with the detachment model and the lever arm and aspect ratios determined during fitting are within commonly reported intervals. The model is used to recalculate the MRF versus velocity, which is then used to predict formation damage for production and injection wells at different values of pH.