CIRA: Maximizing Recovery in CO2-EOR by a Holistic, Bottom-Up, and Multi-Scale Experimental and Simulation Approach involving Machine Learning Optimization

Nowadays, carbon dioxide-enhanced oil recovery (CO2-EOR) has gained global attention in light of declining oil reserves. The potential of combining CO2-EOR technology with carbon capture and storage (CCS) has the benefits of enhanced oil recovery and permanent storage of an amount of injected CO2 in the depleted reservoir.
This project is funded by Khalifa University of Science and Technology (Abu Dhabi, UAE) in collaboration with NCSR “Demokritos” and University of Crete. The project aims to develop novel experimental tools applied in CO2-EOR methodology coupled to artificial intelligence (AI) for maximizing oil recovery. The project involves the performance of in situ neutron scattering experiments upon supercritical CO2 injection in reservoir rocks in order to monitor the oil recovery process at the nanoscale and to investigate the structural properties of pore-confined CO2 as well as the additives performance. The project also involves core-flooding experiments, rheology studies and studies on rock physicochemical characteristics and pore structure. All experimental data will be integrated into the development of Machine Learning (ML) simulations and optimization algorithms. All efforts are in continuous synergy with mutual feedback and response, so as to achieve the optimization provided by the proposed toolset, which is applied for the first time in CO2-EOR, aiming to resolve the yet persistent challenges in maximizing oil recovery.