Volume 57

A Statistical Characterization Method for the Migration of CO2-Responsive Nanoparticle in Porous Media Gang Huang, Bin Yuan, Wei Zhang

Abstract

CO2-responsive nanoparticles are injected into the formation to enhance oil recovery by improving reservoir heterogeneity and increasing macro sweep efficiency. As the particles migrate within the pore space, their size increases with the rise in the pH of the aqueous phase. Consequently, particles cause plugging when the pH decreases. However, the lack of a suitable model for particle migration limits the optimization of particle injection. An easy-to-understand and effective description of the particle migration process is essential for making accurate predictions.
This research introduces a novel model for particle migration. First, a pore network model is used to represent the pore structure. Then, by considering changes in particle size due to pH and treating the total particle concentration as an independent variable, Markov chain theory is applied to develop quantitative relationships between the amount of plugged particles and the total number of particles. Finally, a new control model of particle migration is established, and a comparison between theoretical predictions and experimental results is provided to validate its accuracy.

Keywords CCUS, CO2-responsive nanoparticle, compositional simulation, Statistical characterization method