A general framework for optimizing the locations and time-varying injection rates of a set of monobore wells for geological carbon storage is presented and applied. Two objective functions, minimization of mobile CO2 fraction at the end of the operation, and maximization of storage efficiency, are considered. Appropriate linear and nonlinear constraints, involving the geometry of the well configuration, injection rates, and injected mass (for pressure management), are specified. Two derivative-free algorithms, particle swarm optimization (PSO) and differential evolution (DE), are applied and assessed. The various constraints are treated using a preprocessing repair procedure, penalty functions, and a filter method. The framework utilizes multifidelity optimization, in which increasing levels of grid resolution are applied during the course of the optimization run. For the minimization of mobile CO2 fraction, the multifidelity approach is compared with high-resolution optimization. This treatment is shown to outperform high-resolution PSO and DE optimization in terms of both solution quality and computational requirements. The multifidelity DE optimization case provides the best (feasible) solution, with 0.090 mobile CO2 fraction at 200 years, which represents a 68% improvement over a heuristic base-case. For the second objective function, multifidelity PSO provides a design that results in a storage efficiency of 0.074, which is about double the base-case value. For both objective functions, the optimized solutions contain horizontal and deviated wells placed near the bottom of the storage aquifer. The well configurations are much different for the two objective functions, with wells more closely spaced, resulting in a single merged plume, for the storage efficiency maximization case. For the mobile CO2 minimization case, by contrast, wells are separated and pulsed, which facilitates dissolution and residual trapping.

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