Reservoir Monitoring and Continuous Model Updating Using Ensemble Kalman Filter

Abstract

Reservoir Monitoring and Continuous Model Updating Using Ensemble Kalman Filter Nævdal Geir; Nævdal Geir RF-Rogaland Research Search for other works by this author on: This Site Google Scholar Liv Merethe Johnsen; Liv Merethe Johnsen Norsk Hydro Search for other works by this author on: This Site Google Scholar Sigurd Ivar Aanonsen; Sigurd Ivar Aanonsen Norsk Hydro Search for other works by this author on: This Site Google Scholar Erlend H. Vefring Erlend H. Vefring RF-Rogaland Research Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. Paper Number: SPE-84372-MS https://doi.org/10.2118/84372-MS Published: October 05 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Geir, Nævdal, Johnsen, Liv Merethe, Aanonsen, Sigurd Ivar, and Erlend H. Vefring. "Reservoir Monitoring and Continuous Model Updating Using Ensemble Kalman Filter." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. doi: https://doi.org/10.2118/84372-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractThe use of ensemble Kalman filter techniques for continuous updating of reservoir model is demonstrated. The ensemble Kalman filter technique is introduced, and thereafter applied on two 2-D reservoir models. One is a synthetic model with two producers and one injector. The other model is a simplified 2-D field model, which is generated by using a horizontal layer from a North Sea field model.By assimilating measured production data, the reservoir model is continuously updated. The updated models give improved forecasts. Both dynamic variables, as pressure and saturations, and static variables as the permeability are updated in the reservoir model.IntroductionIn the management of reservoirs it is important to utilize all available data in order to make accurate forecasts. For short time forecasts, in particular, it is important that the initial values are consistent with recent measurements. The ensemble Kalman filter1 is a Monte Carlo approach, which is promising with respect to achieving this goal through continuous model updating and reservoir monitoring.In this paper, the ensemble Kalman filter is utilized to update both static parameters, such as the permeability, and dynamic variables, such as the pressure and saturation of the reservoir model. The filter computations are based on an ensemble of realizations of the reservoir model, and when new measurements are available new updates are obtained by combining the model predictions with the new measurements. Statistics about the model uncertainty is built from the ensemble. While new measurements become available, the filter is used to update all the realizations of the reservoir model. This means that an ensemble of updated realizations of the reservoir model is always available.The ensemble Kalman filter has previously been successfully applied for large-scale nonlinear models in oceanography2 and hydrology3. In those applications only dynamic variables were tuned. Tuning of model parameters and dynamic variables was done simultaneously in a well flow model used for underbalanced drilling4. In two previous papers5,6, the filter has been used to update static parameters in near-well reservoir models, by tuning the permeability field. In this paper, the filter has been further developed to tune the permeability for simplified real field reservoir simulation models.We present results from a synthetic model as well as a simplified real field model. The measurements are well bottomhole pressures, water cuts and gas/oil ratios. A synthetic model gives the possibility of comparing the solution obtained by the filter to the true solution, and the performance of the filter can be evaluated. It is shown how the reservoir model is updated as new measurements becomes available, and that good forecasts are obtained. The convergence of the reservoir properties to the true solution as more measurements becomes available is investigated.Since the members of the ensemble are updated independently of each other, the method is very suitable for parallel processing. It is also conceptually straightforward to extend the methodology to update other reservoir properties than the permeability.Based on the updated ensemble of models, production forecasts and reservoir management studies may be performed on a single "average" model, which is always consistent with the latest measurements. Alternatively, the entire ensemble may be applied to estimate the uncertainties in the forecasts. Keywords: state variable, noise, injector, deviation, forecast, kalman filter, reference solution, permeability, upstream oil & gas, initial ensemble Subjects: Reservoir Simulation This content is only available via PDF. 2003. Society of Petroleum Engineers You can access this article if you purchase or spend a download.