Real-Time Reservoir Model Updating Using Ensemble Kalman Filter

Abstract

Real-Time Reservoir Model Updating Using Ensemble Kalman Filter X.-H. Wen; X.-H. Wen ChevronTexaco Energy Technology Company Search for other works by this author on: This Site Google Scholar W.H. Chen W.H. Chen ChevronTexaco Energy Technology Company Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, January 2005. Paper Number: SPE-92991-MS https://doi.org/10.2118/92991-MS Published: January 31 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Wen, X.-H., and W.H. Chen. "Real-Time Reservoir Model Updating Using Ensemble Kalman Filter." Paper presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, January 2005. doi: https://doi.org/10.2118/92991-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 Reservoir Simulation Conference Search Advanced Search AbstractEnsemble Kalman Filter (EnKF) has been reported to be very efficient for real-time updating of reservoir model to match the most current production data. Using EnKF, an ensemble of reservoir models assimilating the most current observations of production data are always available. Thus the estimations of reservoir model parameters, and their associated uncertainty, as well as the forecasts are always up-to-date.In this paper, we apply the EnKF for continuously updating an ensemble of permeability models to match real-time multiphase production data. We improve the previously EnKF by resolving the flow equations after Kalman filter updating so that the updated static and dynamic parameters are always consistent. By doing so, we show that the production data are also better matched for some cases. We investigate the sensitivity of using different number of realizations in the EnKF. Our results show that a relatively large number of realizations are needed to obtain stable results, particularly for the reliable assessment of uncertainty. The sensitivity of using different covariance functions is also investigated.The efficiency and robustness of EnKF is clearly demonstrated using an example. By assimilating more production data, new features of heterogeneity in reservoir model can be revealed with reduced uncertainty, resulting in more accurate predictions.IntroductionThe reliability of reservoir models increases as more data are included in their construction. Traditionally, static (hard and soft) data, such as geological, geophysical, and well log/core data are incorporated into reservoir geological models through conditional geostatistical simulation1. Dynamic production data, such as historical measurements of reservoir production, account for the majority of reservoir data collected during the production phase. These data are directly related to the recovery process and to the response variables that form the basis for reservoir management decisions. Incorporation of dynamic data is typically done through a history matching process.Traditionally, history matching adjusts model variables (e.g., permeability, porosity, and transmissibility, etc.) so that the flow simulation results using the adjusted parameters match the observations. It requires repeated flow simulations. Both manual and (semi)automatic history matching processes are available in the industry2–12. Automatic history matching is usually formulated in the form of minimization problem in which the mismatch between measurements and computed values is minimized13–14. Gradient-based methods are widely employed for such minimization problems, which require the computation of sensitivity coefficients15–16. In recent decade, automatic history match has been a very active research area with significant progress reported17. However, most approaches are either limited to small and simple reservoir models or computationally very intensive. Under the framework of traditional history matching, the assessment of uncertainty is usually through repeated history matching process with different initial models, which makes the process even more CPU demanded. In addition, the traditional history matching methods are not designed in such a fashion that allows for continuous model updating. When new production data are available and are required to be incorporated, the history matching process has to be repeated using all measured data. These limit the efficiency and applicability of the traditional automatic history matching techniques.On the other hand, during the recent years, more and more permanent sensors are deployed for monitoring pressure, temperature, or flow rates. The data output frequency in this case is very high. It has become important to incorporate the data as soon as they are available so that the reservoir model is always up-to-date. Traditional history matching is not suitable for such purpose because of the heavy computational burden and the high data sampling frequency. A new kind of history matching method that can use all recorded data for fast and continuous model updating is needed. Keywords: ensemble, prediction, upstream oil & gas, assimilation, spe 92991, ensemble size, ensemble kalman filter, real-time reservoir model, machine learning, kalman filter Subjects: Reservoir Simulation, History matching This content is only available via PDF. 2005. Society of Petroleum Engineers You can access this article if you purchase or spend a download.