TY  - JOUR
T1  - Improvement of Localization Effect on Region Based Covariance Localization
Ensemble Kalman Filter Method using Dynamic Parameters
AU - Ambia, Fajril AU - Ariadji, Tutuka AU - Syihab, Zuher AU - Gunawan, Agus Yodi 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 23
SP  - 7339
EP  - 7344
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.7339.7344
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.7339.7344
KW  - Dynamic parameter
KW  -ensemble kalman filter
KW  -region based covariance localization
KW  -history matching
KW  -explore
KW  -improve the area
AB  - Region based covariance localization ensemble Kalman filter is a method that incorporating the
information of region to ensure that the updated parameters honor the region models such as facies, flow unit,
rock type model, etc. Since, the model updated under specified regions, the adjacent parameters would not
maintain its spatial correlation if it is under different regions. Therefore, the algorithm could freely update the
parameters within the region without considering the values in another region. This approach would fit best
in history matching that target reservoir-wide area. On the contrary, the significance of the fluid dynamics rarely
follows such regions. The affected areas that influenced the production data is governed by the physics of fluid
flow which incorporate the fluid types, relation of rock-fluid properties and so on. Since, history matching use
production data as a measurement data, the parameters should only occur in the areas that affected by fluid flow
in reservoir. These areas usually smaller than the area provided by regions model. Thus, it could be used to
improve localization effect. In this study, we explore the formulation of localization based on the behavior of
pressure and fluid flow combined with region based covariance localization ensemble kalman filter. The results
show that, the combination of both methods could improve the localization effect while maintaining the defined
regions. This method could be useful to improve the area within the wells that affects directly to the production
forecast.
ER  -