The ensemble Kalman filter (EnKF) has become a popular method for history matching production and seismic data in petroleum reservoir models. However, it is well known that EnKF may fail to give acceptable data matches especially for highly nonlinear problems. This reseach project focus on the development of iterative forms of EnKF and ensemble smoother (ES) for application in highly nonlinear systems. This reseach includes adjoint-based and adjoint-free iterative forms of EnKF and ES.
Example illustrating the improved data-match obtained with an iterative ES developed in this research project
Recent publications:
- Emerick, A.A. and Reynolds, A.C.: Investigation on the Sampling Performance of Ensemble-based Methods – submitted to Computational Geosciences, 2012.
- Emerick, A.A. and Reynolds, A.C.: Ensemble Smoother with Multiple Data Assimilation – Computers & Geosciences, 2012.
- Emerick, A.A. and Reynolds, A.C.: History Matching Time-lapse Seismic Data Using the Ensemble Kalman Filter with Multiple Data Assimilations – Computational Geosciences, 2012.
- Wang, Y.; Li, G. and Reynolds, A.C.: Estimation of Depths of Fluid Contacts by History Matching Using Iterative Ensemble Kalman Smoothers, SPE Journal, 2009.
- Li, G. and Reynolds, A.C.: Iterative Ensemble Kalman Filters for Data Assimilation, SPE Journal, 2009.
