Model updating and optimum control for water coning: An application of ensemble Kalman filter

Recent advances in downhole data acquisition facilitate continuous updating of the reservoir model and optimum control of well operations. Ensemble Kalman Filter is a model updating algorithm that permits rapid assimilation of production response in reservoir models and thus renders the process feasible in a real time setting. This paper demonstrates the feasibility of implementing a feedback optimum control procedure for controlling water production due to coning. Optimal production rate setting for a reservoir with strong bottom water influence is demonstrated. The critical rate at which a well can be produced without inducing coning is strongly influenced by the reservoir permeability in the vicinity of the well. Since there is uncertainty in the reservoir permeability distribution, there is uncertainty in the prediction of critical rate. Continuous updating of the reservoir model reduces the uncertainty in critical rate estimates. This paper demonstrates the worth of periodic model updating in terms of optimized production and oil recovery. A synthetic reservoir with a horizontal well over an active aquifer is first presented. Continuous updating of the permeability field using the ensemble Kalman Filter results in better prediction of production profile. The approach is then implemented in a laboratory setting using a sandpack with the main features of the synthetic reservoir, and monitoring production pressure and rates that were used for updating sandpack permeability. A novel feedback control scheme for efficient operation of wells is demonstrated with an example of a vertical well affected by water coning.