Optimization of miscible CO2 EOR and storage using heuristic methods combined with capacitance/resistance and Gentil fractional flow models

Abstract Ongoing increase in worldwide oil demand and emission of greenhouse gases persuades engineers to utilize new approaches to optimize enhanced hydrocarbon recovery operations so that the concentration of such gases in atmosphere is reduced, simultaneously. CO 2 injection into geological formations could be a promising alternative for enhancing oil recovery and lessening anthropogenic CO 2 emissions. It is our objective to employ Capacitance-Resistance Model (CRM) for characterization of inter-well interactions in two reservoir models, which experience miscible CO 2 injection for combined EOR and storage purposes. Then, an efficient model is developed, on the basis of Gentil fractional flow model, coupled with CRM. The introduced strategy is applied to optimize miscible CO 2 injection scenarios. The main goal in this research work is to minimize the fraction of cumulatively produced CO 2 to cumulatively produced oil by varying injection rates pattern with the same total injection as history. Minimizing the produced CO 2 ensures the increase of stored CO 2 through the formation. The developed methodology is validated through comparison with the results obtained from reservoir production history. Three heuristic optimization methods utilized in this work are Artificial-Bee-Colony (ABC), Particle-Swarm-Optimization (PSO), and Genetic-Algorithm (GA). According to results of several simulations and optimizations and compared to reservoir history, amounts of stored CO 2 and recovered oil increased, remarkably, for a real geological formation. In general, all optimization techniques result in favorable outcomes; however, ABC exhibits better performance, followed by PSO and GA. It was also found that well transmissibility is a vital factor to satisfy desired conditions for optimization process.