History matching using traditional and finite size ensemble Kalman filter

Abstract

Generally reservoir simulation results show considerable distance from actual behavior of system which is mainly due to poor initial estimates of model parameters. The problem of improving the property estimates using reservoir observations is called history matching which is an essential and inseparable part of reservoir studies. In recent years a promising tool from control engineering called ensemble filtering is used in history matching problems which could be used to quantify the uncertainty in predictions.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.