Near-Well Reservoir Monitoring Through Ensemble Kalman Filter

Abstract

Abstract In the management of reservoirs it is an important issue to utilize the available data in order to make accurate forecasts. In this paper a novel approach for frequent updating of the near-well reservoir model as new measurements becomes available is presented. The main focus of this approach is to have an updated model usable for forecasting. These forecasts should have initial values that are consistent with recent measurements. The novel approach is based on utilizing a Kalman filter technique. The idea behind the Kalman filter is to incorporate the information from the measurements into the current estimate of the state of the model, taking into account the uncertainty that belongs both to the state of the model and the measurements. The uncertainty of the model is updated simultaneously with the model itself. A benefit of this approach compared to usual history matching is that the initial values for the forecasts will be in better agreement with the current measurements. Originally, the Kalman filter had shortcomings for large, non-linear models. During the last decade, however, Kalman filter techniques has been further developed, and applied successfully for such models within oceanographic and hydrodynamic application. This work is based on use of the ensemble Kalman filter. The ensemble Kalman filter is easy to implement, and have some good properties for non-linear problems. Here, we demonstrate the use of this technique within near-well reservoir monitoring, focusing on its performance in forecasting the future production.