Smart well technology is attracting increasing attention because it promises to add operation flexibility and potentially increases oil recovery. However, it remains difficult to find the best strategy for production optimization. One challenge is to find a good optimization algorithm, as some optimization algorithms require prohibitive work to compute the gradients of the objective function with respect to the well controls. These methods also require access to the simulator code, which makes them difficult to use with commercial software. Another challenge is to find a reasonable frequency for well control adjustment: adjusting well controls too frequently imposes unrealistic control burdens on operations, increasing well management cost. Moreover, high-frequency control adjustment increases the risk of optimization algorithms being trapped at local optima as the problem is more under-determined. On the other hand, excessively low-frequency control adjustment may not truly optimize oil recovery.

To address these issues, two simulator-independent optimization algorithms were investigated: ensemble-based optimization (EnOpt) and bound optimization by quadratic approximation (BOBYQA). Multiscale regularization was applied to both to find appropriate frequencies for well control adjustment. In a synthetic case study, if multiscale regularization was not used, then EnOpt converged to a higher net value of production than BOBYQA —even though BOBYQA uses second order Hessian information (EnOpt uses first order gradients). BOBYQA performed comparably only if multiscale regularization was used. After multiscale regularization, both methods obtained net value of production (NVP) that equalled or exceeded unregularized optimization, with simpler well control strategies and convergence in fewer iterations.

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