A new method for reservoir history matching and dynamic optimization of water flooding with smart wells is presented. For the dynamic optimization of water flooding case, we solve a constrained optimization problem where the net present value is maximized and the reservoir flow equations are considered as constraints. Similarly for the history matching, we recover the permeability field from measurements of pressures and saturations. In both cases the problem is formulated as finding a saddle point of the associated augmented Lagrangian functional. We compare the method with a more traditional optimal control method, based on solving the adjoint system of equations. In the examples tested the new method obtains approximately the same results using the same or less computational effort compared to the adjoint method. An advantage of the new method is that we do not solve the flow equations exactly at each iteration. As the optimization proceeds, the flow equations will be fulfilled at convergence. Thus, each iteration of the new minimization algorithm is cheaper than for the adjoint method.

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