Ensemble Kalman Filter Data Assimilation to Condition a Real Reservoir Models to Well Test Observation

Recently a significant effort has been made to characterize reservoir models benefiting from Ensemble Kalman filter as data assimilation technique. EnKF proved to be a powerful tool to deal with almost any sort of measurement also to be capable of handling different type of uncertainty in the simulation models and and being affordable from the computational point of view. Lately the technique has been deployed to assimilate on pressure transient and production logging data to update permeabilities and estimate layer skin factor. In the present paper EnKF methodology was used to characterize an offshore reservoir model against the well test pressure data as well as the pressure derivative to adjust cell by cell petrophysical properties, and the skin factor in each well perforation. The results showed that using the derivative observations to calibrate the uncertain parameters helps improving the quality of the match not only in the predicted derivative but also in better forecasting the pressure measurements. The importance of assimilation on skin as well as recalculation of well connection factors revealed. Moreover a new distance based localisation scheme based on the well drainage zone has been introduced to help reducing unnecessary changes in the model.