Enhanced oil recovery with CO2 based nanofluid: A Multiscale modelling scheme for Nanoparticle interaction with liquid CO2

Residual oil saturation not accessible to primary and secondary recovery can be recovered using Enhanced Oil Recovery (EOR) methods. Water-Alternating-Gas (WAG) technique involving gas (e.g., CO₂) injection to increase displacement efficiency followed by water injection to increase sweep efficiency is an emerging EOR method that has attracted significant attention. This technique has high microscopic sweep efficiency; however, it suffers from poorer macroscopic sweep efficiency (e.g., channeling, gravity instability) due to the large density difference and also adverse mobility ratio between displacing fluid (CO₂) and displaced fluid (oil).

An increase in CO₂ viscosity which results in lower CO₂ mobility could reduce problems with poor macroscopic sweep efficiency. Therefore, design of an economic CO₂ thickener remains an extremely relevant research topic for CO₂-EOR. Besides their high cost, efficiency of thickeners such as polymer, foam and gel are not satisfactory due to long-term stability which is difficult to maintain. The use of nanoparticles instead of surfactant to stabilize CO₂ by forming a nanofluid may overcome the long-term instability and surfactant adsorption loss and high cost issues that affect efficiency of CO₂-EOR processes.

The objective is to develop a new method for thickening of CO₂ using nanoparticle and forming a CO₂ based nanofluid which can improve volumetric sweep efficiency compared to conventional CO₂-EOR method. In this project, we use ab initio - quantum mechanics guided nanoparticle selection combined with atomistic simulation for optimization of the size and shape of the nanoparticles. We study the interaction of CO₂ molecules with different nanoparticles by solving the quantum mechanics equations of the electronic structure that so called ab initio calculations. This reveals the most promising compositions for the nanoparticles to make the CO₂ based nanofluid. Atomistic simulations of liquid CO₂ interaction with nanoparticles (i.e., most promising compositions) perform using Molecular Dynamics (MD) and Dissipative Particle Dynamics (DPD) methods to optimize the size and shape of the nanoparticles. The results attained apply for reservoir-scale modeling to better understanding of the performance of the CO₂ based nanofluid at larger-scale of a reservoir.