History Matching with an Ensemble Kalman Filter and Discrete Cosine Parameterization

2007 ◽  
Author(s):  
Behnam Jafarpour ◽  
Dennis Mclaughlin
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
History Matching

History Matching Using the Ensemble Kalman Filter on a North Sea Field Case

2006 ◽  
Author(s):  
Vibeke Eilen Jensen Haugen ◽  
Lars-Jorgen Natvik ◽  
Geir Evensen ◽  
Aina Margrethe Berg ◽  
Kristin Margrethe Flornes ◽  
...  
Keyword(s):  
Kalman Filter ◽  
North Sea ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Field Case

scholarly journals Implementation of Region-Based Covariance Localization Ensemble Kalman Filter in History Matching Workflow

2017 ◽  
Vol 10 (1) ◽  
pp. 177-194
Author(s):  
Fajril Ambia ◽  
Tutuka Ariadji ◽  
Zuher Syihab ◽  
Agus Y. Gunawan
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Spatially Correlated ◽  
Reservoir Performance ◽  
Matching Process ◽  
True Model ◽  
Inherent Nature ◽  
The Many ◽  
Region Information

Background:History matching is an indispensable phase in the workflow of reservoir analysis. Nevertheless, there is a considerable challenge in performing the procedure in a proper scientific manner due to the inherent nature of non-unique solutions from the many-unknown variables with limited known equations.Objective:In this study, we introduce the Ensemble Kalman Filter (EnKF) method complemented by the Region-Based Covariance Localization (RCL) scheme to address the aforementioned issue.Method:The algorithms work initially by modifying the covariance localization generated by Gaussian correlation model using region information such as facies or flow unit, in which the area within a region is spatially correlated. Subsequently, the correlation between distant areas in the region is eliminated, hence promoting better modification of the distribution of the parameters while maintaining the characteristics of the predefined geological model of the reservoir.Result:Result shows that RCL scheme is capable of enhancing the performance of EnKF procedure and produce parameter distributions that is close to the true model of the reservoir.Conclusion:Implementation of the proposed methodology ameliorates the accuracy and reliability of the history matching process, thus establishing better consideration in predicting reservoir performance.

Sign in / Sign up

Export Citation Format

Share Document