Embedding structural information in simulation-based optimization

This paper proposes and explores an algorithm designed to find optimal settings for a process network. Emphasis is put on the system being divisible into components, as this underlying assumption motivates the algorithm in its entirety in that rather simple relations between the system components are modeled as explicit structural constraints, while the significantly more complex relations within each component are approximated based on the underlying simulator data. Although the approach taken in this paper is rather broadly applicable we are, in particular, interested in its application to production optimization problems in the oil and gas industry. We give limited numerical results for one such example that clearly indicates the advantages of our approach. We show the advantages of both decomposing the problem of interest and accounting for the structure from the point of view of exploiting, where ever possible, the explicitly analytic aspects of the problem. The advantage of doing the former is that the considered subproblems are significantly smaller than the overall problem. The advantage of the latter is that one can use derivatives for the analytic parts whereas they are unavailable for the simulators. The underlying approach is a trust-region one with a mixed integer nonlinear program formulation. There are some significant differences in the details of the algorithm from those generally available for such problems.

► Optimization has great unused potential in petroleum production.
► Simulators are essential in petroleum production optimization problems.
► Combining simulators and gradient based optimization techniques in an structured framework has great potential in process systems.