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There is a potential for large improvements in reservoir management by using optimization and model updating techniques in a closed-loop fashion. Here we demonstrate how the combination of the ensemble Kalman filter technique for continuous model updating with an automated adjoint-based water flood optimization algorithm leads to significant improvements in net present value (NPV) of the water flooding process.

Using the ensemble Kalman filter, both static parameters (permeabilities) and dynamic variables (pressures and saturations) are updated in the reservoir model as new production measurements become available. Other properties are assumed known in advance. At the start of the production process, in the absence of information on the permeability distribution, an optimal control strategy based on a homogeneous reservoir is used. Subsequently production data are at regular intervals …