Constraining 3D Petroleum Reservoir Models to Petrophysical Data, Local Temperature Observations, and Gridded Seismic Attributes with the Ensemble Kalman Filter (EnKF)

A methodology based on the Ensemble Kalman Filter (EnKF) is proposed for petroleum reservoir characterization, using continuous integration of petrophysical core data, reservoir temperature observations, and gridded time-lapse acoustic impedances. The localization of updating and covariance matrices is performed to assimilate exhaustive seismic data. A shortcut based on propagation of the ensemble mean and co-simulation of the ensemble variations is implemented to reduce computational cost of the forecast step. The integration of additional data from multiple sources and time steps improves the estimates of porosity and permeability. This methodology is applied to a synthetic 2D steam assisted gravity drainage (SAGD) case study to examine the ability of the EnKF to constrain spatial distributions of porosity and permeability. A realistic 3D SAGD case study is used to demonstrate the applicability of this methodology to a real industrial problem. Obtained results show effective application of the EnKF to petroleum reservoir characterization.