Efficient and Robust Reservoir Model Updating Using Ensemble Kalman Filter With Sensitivity-Based Covariance Localization

2007 ◽  
Author(s):  
Deepak Devegowda ◽  
Elkin Arroyo ◽  
Akhil Datta-Gupta ◽  
Sippe G. Douma
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Model Updating ◽  
Reservoir Model

Streamline Assisted Ensemble Kalman Filter for Rapid and Continuous Reservoir Model Updating

2006 ◽  
Author(s):  
Elkin Arroyo ◽  
Deepak Devegowda ◽  
Akhil Datta-Gupta ◽  
Jonggeun Choe
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Model Updating ◽  
Reservoir Model

Gravimetric Monitoring of Water Influx Into a Gas Reservoir: A Numerical Study Based on the Ensemble Kalman Filter

SPE Journal ◽  
2011 ◽  
Vol 17 (01) ◽  
pp. 163-176 ◽  
Author(s):  
M.. Glegola ◽  
P.. Ditmar ◽  
R.G.. G. Hanea ◽  
F.C.. C. Vossepoel ◽  
R.. Arts ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Gravity Data ◽  
Added Value ◽  
Reservoir Model ◽  
Gas Reservoir ◽  
Gas Field ◽  
Water Influx ◽  
Reservoir Permeability

Summary Water influx into gas fields can reduce recovery factors by 10–40%. Therefore, information about the magnitude and spatial distribution of water influx is essential for efficient management of waterdrive gas reservoirs. Modern geophysical techniques such as gravimetry may provide a direct measure of mass redistribution below the surface, yielding additional and valuable information for reservoir monitoring. In this paper, we investigate the added value of gravimetric observations for water-influx monitoring into a gas field. For this purpose, we use data assimilation with the ensemble Kalman filter (EnKF) method. To understand better the limitations of the gravimetric technique, a sensitivity study is performed. For a simplified gas-reservoir model, we assimilate the synthetic gravity measurements and estimate reservoir permeability. The updated reservoir model is used to predict the water-front position. We consider a number of possible scenarios, making various assumptions on the level of gravity measurement noise and on the distance from the gravity observation network to the reservoir formation. The results show that with increasing gravimetric noise and/or distance, the updated model permeability becomes smoother and its variance higher. Finally, we investigate the effect of a combined assimilation of gravity and production data. In the case when only production observations are used, the permeability estimates far from the wells can be erroneous, despite a very accurate history match of the data. In the case when both production and gravity data are combined within a single data assimilation framework, we obtain a considerably improved estimation of the reservoir permeability and an improved understanding of the subsurface mass flow. These results illustrate the complementarity of both types of measurements, and more generally, the experiments show clearly the added value of gravity data for monitoring water influx into a gas field.

scholarly journals Ensemble Kalman filter for model updating – A special issue

2011 ◽  
Vol 15 (2) ◽  
pp. 223-224 ◽  
Author(s):  
Geir Nævdal ◽  
Remus G. Hanea ◽  
Dean S. Oliver ◽  
Brice Vallès
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Model Updating ◽  
Special Issue
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