Huazhou Li

Optimal Parametric Design for Water-Alternating-Gas (WAG) Process in a CO2-Miscible Flooding Reservoir

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 CO 2 -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.

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

CO 2 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 CO 2 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.

An Efficient Methodology for Performance Optimization and Uncertainty Analysis in a CO2 EOR Process

Abstract A pragmatic technique is proposed and successfully applied to determine the optimal production–injection scheme in a CO2 flooding reservoir under uncertainty. Well rates of injectors and bottomhole pressures of producers are chosen as the controlling variables. Geological uncertainty is accounted for using the multiple reservoir models. An objective function associated with net present value (NPV) is defined, and a modified genetic algorithm is employed to determine the optimal production–injection scheme. It is shown from a field case study that the optimized scheme can not only increase the expected oil recovery and NPV by 7.8 and 6.6%, respectively, but can also achieve a considerably small range of possible NPVs.