Improved Reservoir Management Through Optimal Control and Continuous Model Updating

Abstract

Abstract 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 assimilated with the ensemble Kalman filter, resulting in an updated estimate of the reservoir pressures, saturations and permeability field. Based on these updated parameters an optimal water flooding strategy is determined for the remainder of the production process. This process of model updating and optimization is continued over the life of the reservoir. The methodology is applied to two synthetic examples, enabling comparison with traditional production strategies. Significant improvement in NPV, acceleration of oil production, cumulative oil recovery, and reduction of water production were realized. Results were close to those obtained with water flood optimization based on an a-priori known reservoir description. For one example the improvement in cumulative oil recovery is about 44 %, which is quite close to the improvement obtained for an a-priori known reservoir description.