Summary A pragmatic method has been developed to efficiently design the production-injection parameters to optimize the water-alternating-gas (WAG) performance in a field-scale CO2-miscible flooding project. The net present value (NPV) is selected as the objective function, while the controlling variables are chosen to be the injection rates, ratios of gas slug size to water slug size (WAG ratio) and cycle time (i.e., the injection time for each gas or water slug) for the injectors and bottomhole pressures (BHPs) for the producers. A hybrid technique, which integrates the orthogonal array (OA) and Tabu technique into the genetic algorithm (GA), is then developed and employed to determine the optimum WAG production-injection parameters. Sensitivity analysis of the WAG parameters on oil recovery is conducted and a field case is finally presented to demonstrate the successful application of the newly developed technique.

CO2  flooding has gained momentum in the oil and gas industry and might be suitable for approximately 80% of oil reservoirs worldwide based on the oil recovery criteria alone. In addition to miscibility, production performance needs to be optimized to achieve higher sweep efficiency and oil recovery. Although many techniques have been made available for production optimization in the upstream oil and gas industry, it is still a challenging task to optimize production performance in the presence of physical and/or financial uncertainties. In this paper, a new technique is developed to optimize production performance in a CO2 flooding reservoir under uncertainty. More specifically, potential uncertainties influencing production performance are analyzed and assessed by using the geostatistical technique. This enables us to integrate the available information within a unified and consistent framework and to generate multiple geological realizations accounting for uncertainty and spatial variability. Subsequently, the net present value (NPV) is selected as the objective function to be optimized by using the genetic algorithm, while well rates of the injectors and the flowing bottomhole pressure for the producers are chosen as the controlling variables. In addition, corresponding modifications have been made to accelerate the convergence speed of the genetic algorithm. A field case is used to demonstrate the procedures of the newly developed technique and the optimized results show that the oil recovery and the NPV can be increased by 6.4% and 9.2%, respectively. It is also found that the genetic algorithm is a powerful and reliable search method to optimize production performance of reservoirs with complex structures. Introduction CO2 flooding is considered as a promising and practical enhanced oil recovery (EOR) process because it not only increases oil recovery, but also reduces greenhouse gas emissions by sequestrating CO2 in the depleted reservoirs. In practice, CO2 flooding performance can be greatly affected by the reservoir heterogeneity, which can severely reduce the sweep efficiency, result in early CO2 breakthrough at the producers, and thus, leave a large amount of bypassed oil in the reservoir(1). Therefore, it is of fundamental and practical importance to optimize production performance of a CO2 flooding reservoir.

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