Optimization of Improved Oil Recovery through CO2-WAG Injection

Abstract

Many newly discovered reservoirs in Brazil have a high content of CO2 and the distance from the coast, make impossible the flow through pipelines. These facts, added to environmental causes, opened the possibility of recovery through CO2-WAG injection. However, a methodology that quantifies the advantages of this recovery stopped, in many times, due to complexity of the problem, since the operation of many wells involves a large number of control variables in the optimization process and, added to the high processing time of compositional simulation, increases significantly the time spent on the process, forcing a search for solutions through simplified procedures. To circumvent this problem, this project proposes the development of a methodology to assess quickly and efficiently, increasing the reservoir recovery under CO2-WAG injection, through the combination of decreased computational simulation time with increased efficiency in the optimization process. For this, the proposed method involves developing a compositional simulator of research, in order to integrate the reduced order model and modeling the physical phenomena that are not present in commercial simulators. Then, we propose the use of optimization method called simultaneous perturbation with stochastic approximation (SPSA). Thus, once achieved high speed on the simulation, we can use more complex and efficient optimization methods, and the combination of both, could result in better solutions, generating in this integration the increase of the management of the pre-salt reservoirs. The final result can fill an important gap in the literature and in practical applications, which makes it cannot quickly and clearly assess the real benefits and advantages of CO2-WAG injection. Moreover, this project aims to consolidate the collaboration between USP and two other institutions, internationally renowned in petroleum engineering, UNICAMP and Texas A&M, bringing benefits for the technical merit of this research, and for the students training in optimization methods, mathematical for simulation of CO2 and reduced models. (AU)