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Efficient optimization for Model Predictive Control in reservoir models

By Jørgen Frenken Borgesen, Supervisor Bjarne, Anton Foss, Co-supervisor John and Petter Jensen


Practical use of optimization, for MPC or parameter estimation, requires a large number of gradient calculations. These gradients are used to compute search directions, for instance in a SQP algorithm. Computing gradients is time-consuming and limits the use of for instance MPC to small and medium-sized systems. Gradients are usually computed by finite difference methods. Adjoint-based methods is an alternative since these are efficient for problems with many decision variables and few outputs. Efficiency may however deteriorate in cases with output constraints which typically are present in MPC. In this project, which continues earlier work, the use of adjoints as a means to increase efficiency of MPC for large scale reservoir models is further studied. Tasks: 1. Present adjoint-based methods and review central literature. The presentation shall focus on MPC, with a sequential approach, applied to reservoir models. 2. Output constraints can be detrimental to the efficiency of adjoint-based methods. Hence, optimization algorithms where output constraints can be removed, as for instance in barrier methods, or the number of output constraints are significantly reduced are approaches to exploit the efficiency of adjoint-based methods. Discuss and propose such methods. 3. Evaluate the methods above by comparing them with forward methods. This should be done on suitable reservoir examples, preferably available benchmark cases, using realistic test scenarios

Year: 2011
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