Authors
Reza Tavakoli, Gergina Pencheva, Mary F Wheeler, Benjamin Ganis
Publication date
2013/2
Journal
Computational Geosciences
Volume
17
Pages
83-97
Publisher
Springer Netherlands
Description
We present a parallel framework for history matching and uncertainty characterization based on the Kalman filter update equation for the application of reservoir simulation. The main advantages of ensemble-based data assimilation methods are that they can handle large-scale numerical models with a high degree of nonlinearity and large amount of data, making them perfectly suited for coupling with a reservoir simulator. However, the sequential implementation is computationally expensive as the methods require relatively high number of reservoir simulation runs. Therefore, the main focus of this work is to develop a parallel data assimilation framework with minimum changes into the reservoir simulator source code. In this framework, multiple concurrent realizations are computed on several partitions of a parallel machine. These realizations are further subdivided among different processors, and …
Scholar articles
R Tavakoli, G Pencheva, MF Wheeler, B Ganis - Computational Geosciences, 2013