Closed-loop reservoir management is a combination of model-based optimization and data assimilation (computer-assisted history matching), also referred to as ‘real-time reservoir management’, ‘smart reservoir management’ or ‘closed-loop optimization’. The aim is to maximize reservoir performance, in terms of recovery or financial measures, over the life of the reservoir by changing reservoir management from a periodic to a near-continuous process. The key sources of inspiration for our work are measurement and control theory as used in the process industry and data assimilation techniques as used in meteorology and oceanography. We present results of a numerical example to illustrate the scope for closed-loop water flooding using real-time production data under uncertain reservoir conditions. The example concerns a 12-well water flood in a channelized reservoir. Optimization was performed using a reservoir simulator with functionality for adjoint-based life cycle optimization under rate and pressure constraints. Data assimilation was performed using the ensemble Kalman filter. Applying an optimization frequency of respectively once per 4 years, once per 2 years, once per year and once per 30 days resulted in an increase of net present value (NPV) with 6.68, 8.29, 8.30 and 8.71% compared to a conventional reactive control strategy. Moreover, the results for the 30-day cycle were very close (0.15% lower NPV) to those obtained by open-loop optimization using the ‘true’ reservoir model. We illustrate that for closed-loop reservoir management with a fixed well configuration, the use of considerably different reservoir models may lead to near-identical results in terms of NPV. This implies that in such cases the essential information may be represented with a much less complex model than suggested by the large number of grid blocks in typical reservoir models. We also illustrate that the optimal rates and pressures as obtained by open- or closed-loop optimization are often too irregular to be practically applicable. Fortunately, just as is the case for the data assimilation problem, the flooding optimization problem usually contains many more control variables than necessary, allowing for optimization of long-term reservoir performance while maintaining freedom to perform short-term production optimization.

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