Real-Time Reservoir Model Updating Using Ensemble Kalman Filter

The ensemble Kalman Filter technique (EnKF) has been reported to be very efficient for real-time updating of reservoir models 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 upto-date. In this paper, we apply the EnKF for continuously updating an ensemble of permeability models to match realtime multiphase production data. We improve the previous 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.

[1]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[2]  A. S. Cullick,et al.  High Resolution Reservoir Models Integrating Multiple-Well Production Data , 1997 .

[3]  Dean S. Oliver,et al.  Critical Evaluation of the Ensemble Kalman Filter on History Matching of Geologic Facies , 2005 .

[4]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[5]  A. S. Cullick,et al.  Integrating Pressure and Fractional Flow Data in Reservoir Modeling With Fast Streamline-Based Inverse Method , 1998 .

[6]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[7]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[8]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[9]  Jef Caers,et al.  Geostatistical History Matching Under Training-Image Based Geological Model Constraints , 2002 .

[10]  Sigurd Ivar Aanonsen,et al.  Reservoir Monitoring and Continuous Model Updating Using Ensemble Kalman Filter , 2005 .

[11]  A. S. Cullick,et al.  Inversion of dynamic production data for permeability: fast streamline-based computation of sensitivity coefficients of fractional flow rate , 2003 .

[12]  Trond Mannseth,et al.  NEAR-WELL RESERVOIR MONITORING THROUGH ENSEMBLE KALMAN FILTER , 2002 .

[13]  Jan Dirk Jansen,et al.  Improved Reservoir Management Through Optimal Control and Continuous Model Updating , 2004 .

[14]  Dean S. Oliver,et al.  The Ensemble Kalman Filter for Continuous Updating of Reservoir Simulation Models , 2006 .

[15]  Dean S. Oliver,et al.  History Matching of the PUNQ-S3 Reservoir Model Using the Ensemble Kalman Filter , 2004 .

[16]  Martin J. Blunt,et al.  Streamline-Based Method With Full-Physics Forward Simulation for History-Matching Performance Data of a North Sea Field , 2003 .

[17]  A. S. Emanuel,et al.  History Matching Finite Difference Models With 3D Streamlines , 1998 .

[18]  Akhil Datta-Gupta,et al.  Integrating dynamic data into high-resolution reservoir models using streamline-based analytic sensitivity coefficients , 1998 .

[19]  D. Entekhabi,et al.  Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment , 2001 .

[20]  Akhil Datta-Gupta,et al.  Field Experiences with Assisted and Automatic History Matching Using Streamline Models , 2004 .

[21]  A. Tarantola.  Inverse problem theory : methods for data fitting and model parameter estimation , 1987 .

[22]  Dean S. Oliver,et al.  Three-dimensional reservoir description from multiwell pressure data and prior information , 1997 .

[23]  L. Hu,et al.  Gradual Deformation of Continuous Geostatistical Models for History Matching , 1998 .

[24]  Peter Jan van Leeuwen,et al.  Comment on ''Data Assimilation Using an Ensemble Kalman Filter Technique'' , 1999 .

[25]  M. L. Wasserman,et al.  A New Algorithm for Automatic History Matching , 1974 .

[26]  Roland N. Horne,et al.  A Procedure to Integrate Well Test Data, Reservoir Performance History and 4-D Seismic Information into a Reservoir Description , 1997 .

[27]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .