In this paper, we develop an efficient algorithm for production optimization under linear and nonlinear constraints and an uncertain reservoir description. The linear and nonlinear constraints are incorporated into the objective function using the the augmented Lagrangian method and the bound constraints are enforced using a gradient-projection trust-region method. Robust long-term optimization maximizes the expected life-cycle net-present value (NPV) over a set of geological models, which represent the uncertainty of reservoir description. As the life-cycle optimal controls may be in conflict with the operator's objective of maximizing short-time production, the method is adapted to maximize the expectation of short-term NPV over the next one or two years subject to the constraint that the life-cycle NPV will not be substantially decreased. The technique is applied to synthetic reservoir problems to demonstrate its efficiency and robustness. Experiments show that the field can not always achieve the optimal NPV using the optimal well controls obtained based on a single, but uncertain reservoir model, whereas the application of robust optimization significantly reduces this risk. Experimental results also show robust sequential optimization on each short-term period is not able to achieve an expected life-cycle NPV as high as the one obtained with robust long-term optimization.

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