In a gas miscible flooding reservoir, injection of gas, such as CO2, is often alternating with water to reduce the mobility contrast between gas and reservoir fluids as well as the degree of viscous fingering. Performance of a water-alternating-gas (WAG) process is largely affected not only by the injection parameters including water-gas ratio, injection rate and cycle period, but also by the production rate and bottomhole pressure (BHP) at the producer. Inappropriate selection of parameters for the WAG process can lead to unstable pressure distribution, early gas breakthrough, and low ultimate oil recovery. Previous studies for achieving the optimum WAG performance are mostly limited to a certain well pattern or a small-scale problem. It is essential to conduct optimal parametric design to optimize the WAG performance for a field-scale problem. In this study, a pragmatic method is developed to efficiently design the production-injection parameters for optimizing the WAG performance in a fieldscale 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, WAG ratios, cycle periods and BHPs. A hybrid technique which integrates the orthogonal array (OA) and Tabu technique into genetic algorithm (GA) is then developed and employed to determine the optimum WAG production-injection parameters. Sensitivity analysis of the WAG parameters on the objective function is conducted and a field case is finally presented to demonstrate the successful application of the newly developed technique.
Water-alternating-gas (WAG) is a tertiary oil recovery process that has been implemented successfully in a number of oilfields around the world. About 55% of the total oil productions by enhanced oil recovery (EOR) methods in the United States are resulted from gas-injection methods, most of which are WAG processes. As for the WAG process, water and gas, such as CO2, can be injected either simultaneously or alternatively. The water is used to control the mobility of the gas for achieving higher macroscopic sweep efficiency, while gas injection, especially miscible gas injection, provides higher microscopic sweep efficiency. The WAG process also improves the economic benefits by reducing the volume of gas that needs to be injected into the reservoir.
The WAG performance is significantly affected by reservoir heterogeneity, rock wettability, fluid properties, miscibility conditions, trapped gas, injection techniques and operational parameters. In a field application, the WAG operational parameters need to be optimized to achieve the maximum net present value (NPV) Despite of the striking growth of the computer memory and speed, optimizing production performance is still expensive, to the point that it may not be feasible to consider all alternative WAG injection schemes. Optimization methodologies need to be developed to obtain the most profitable solution of WAG project management. Among all the optimization techniques, genetic algorithm (GA) has gained great popularity in the petroleum industry. Although GA is regarded as one of the most robust and powerful searching approaches, it suffers greatly from low convergence speed.