Abstract:
The present work evaluates the use of hierarchical ensemble Kalman filter (HEnKF) for updating real size reservoir models. It is tested on the large scale Brugge field SPE benchmark study and we chose to revisit the first cycle of this case study (Peters et al., 2009). The Brugge field is a synthetic reservoir built by TNO that has the size of a real field and has been purposely built to mimic a real reservoir with a 30 years leasing life. The HEnKF method is an automated generic localisation approach that computes at each assimilation time a damping factor, for each state variable, aimed at minimising sampling errors estimated from a group of sub-ensembles. It differs from the ensemble Kalman filter (EnKF) algorithm in the implementation of its analysis step. The results are compared against those obtained when using the traditional EnKF approach on two different ensembles. The members of the first ensemble are generated based on empirical variograms while the second ensemble is composed of all the 104 original realisations provided by TNO. The results show that spurious correlations are avoided when using HEnKF as do distance dependent localisation approaches. Our best results are obtained when using HEnKF and are the second best when compared to all the results presented in the original Brugge field study (Peters et al., 2009). The main advantages of HEnKF is that it is not limited to spatially distributed variables and it is simpler and more straightforward to use than localisation functions. In addition we observed that the best results were obtained using an initial ensemble with more higher diversity in the types of reservoir models included. This was somewhat surprising since the alternative was an initial ensemble not only built to take into account prior geostatistical knowledge, but also to satisfy the assumption of Gaussianity that lies behind the EnKF approach.