History matching of petroleum reservoir models by the Ensemble Kalman Filter and parameterization methods

History matching of petroleum reservoir models by the Ensemble Kalman Filter and parameterization methods

The Ensemble Kalman Filter (EnKF) has been successfully applied in petroleum engineering during the past few years to constrain reservoir models to production or seismic data. This sequential assimilation method provides a set of updated static variables (porosity, permeability) and dynamic variables (pressure, saturation) at each assimilation time. However, several limitations can be pointed o...

Reservoir Multiscale Data Assimilation Using the Ensemble Kalman Filter

In this paper we propose a way to integrate data at different spatial scales using the ensemble Kalman filter (EnKF), such that the finest scale data is sequentially estimated, subject to the available data at the coarse scale (s), as an additional constraint. Relationship between various scales has been modeled via upscaling techniques. The proposed coarse-scale EnKF algorithm is recursive and...

Real - Time Reservoir Model Updating Using Ensemble Kalman Filter

This paper was selected for presentation by an SPE Program Committee following review of information contained in a proposal submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum ...

Near-Well Reservoir Monitoring Through Ensemble Kalman Filter

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...

Estimating Reservoir Permeabilities with the Ensemble Kalman Filter: The Importance of Ensemble Design