Performance Comparison of Gradient-Free Optimization Methods for Well Placement and Well Controls Optimization for Geologic CO2 Storage

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Summary In this study, we investigate the use of three different gradient-free population-based optimization methods—namely, iterative Latin hypercube sampling (ILHS), particle swarm optimization (PSO), and genetic algorithm (GA)—for the well placement and well controls optimization problem of CO2 underground storage in a 2D saline aquifer with bound constraints on the design variables. We also consider both simultaneous and sequential optimizations of well locations and well controls using these gradient-free methods. The optimization problem involves finding the optimal well types, well locations, or well controls for a CO2 injection problem in a synthetic saline aquifer compositional model built in a commercial simulator. The objective function formulated for the CO2 injection problem is the net present environmental value (NPEV) involving discrete design variables (well types and well locations) and continuous design variables (well controls). We observe that for simple well-location optimization problems involving one or two wells, all three algorithms obtained comparable results, given the same number of samples and number of iterations. However, for more complex cases such as the sequential or simultaneous optimization problems, when multiple wells that may include injectors and producers are present, we observe significant differences in the selection of the optimal well types and well locations among the three optimizers. In the simultaneous optimization of well types, well locations, and well controls, when both injectors and producers are bottomhole pressure (BHP) controlled, the results show that it is more optimal for at least one producer to be present, and for the injectors to be operated at the upper bound of the injector BHP, while the producers are operated at the lower bound of the producer BHP to maximize the NPEV.