Ensemble Kalman filter

Assisted history matching using ensemble based filters and in particular the ensemble Kalman filter (EnKF)

Assisted history matching with EnKF opened a whole new tool kit with possibilities that traditional  manual history matching or gradient based methods could not provide. IRIS pioneered this in petroleum with Lorentzen et al. (SPE71384) in 2001 and Nævdal et al. (SPE75235) in 2002. The EnKF is easy to implement and updates an ensemble of reservoir models sequentially taking into account production data and measurements when they become available in time. The updated ensemble provides uncertainty estimates for production forecasts.
IRIS has in particular contributed with evaluation of performance and robustness of the methodology revealing strengths and weaknesses. In continuation of this we pointed out future directions for the research and developed variants of the filter to remedy shortcomings. Lately we have been focusing on iterative filters,  facies modelling, updating of models with complex geology, localization of the filter to avoid spurious correlations and development of the Adaptive Gaussian Mixture filter (AGM).
IRIS initiated and organizes the yearly International EnKF Workshop together with CIPR and NERSC. The workshop has been organized since 2006 and brings together experts and practitioners from both industry and academia.
IRIS uses the competence on advanced filtering techniques not only for updating reservoirs but also in parameter estimation in various other applications (e.g. well flow modelling, geosteering and production optimization).