Efficient optimization for Model Predictive Control in reservoir models
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The purpose of this thesis was to study the use of adjoint methods for gradient calculations in Model Predictive Control (MPC) applications. The goal was to find and test efficient optimization methods to use in MPC on oil reservoir models. Handling output constraints in the optimization problem has been studied closer since they deteriorate the efficiency of the MPC applications greatly. Adjoint- and finite difference approaches for gradient calculations was tested on reservoir models to determine there efficiency on this particular type of problem. Techniques for reducing the number of output constraints was also utilized to decrease the computation time further. The results of this study shows us that adjoint methods can decrease the computation time for reservoir simulations greatly. Combining the adjoint methods with techniques that reduces the number of output constraints can reduce the computation time even more. Adjoint methods require some more work in the modeling process, but the simulation time can be greatly reduced. The principal conclusion is that more specialized optimization algorithms can reduce the simulation time for reservoir models.