History matching using traditional and finite size ensemble Kalman filter

This paper focuses on comparison of traditional ensemble Kalman filter (EnKF) and a relatively new form of ensemble filters called finite size ensemble transform Kalman filter on a small size benchmark model. This study tries to estimate the porosity and permeability fields by assimilating production data in 13 time steps. A weighting approach is used as modification to filter procedure. The results show traditional ensemble Kalman filter with localization has a better performance in a problem with a small ensemble but this superiority comes with more computational cost.