In this work, a new history matching methodology is proposed, coupling within the same framework the advantages of using geostatistical sequential simulation and the principles of ensemble Kalman filters: history matching based on ensemble updating.  The main idea of this procedure is to use simultaneously the relationship between the petrophysical properties of interest and the dynamical results to update the static properties at each iteration, and to define areas of influence for each well. This relation is established through the experimental non-stationary covariances, computed from the ensemble of realizations. A set of petrophysical properties of interest is generated through stochastic sequential simulation. For each simulated model, we obtain its dynamic responses at the wells locations by running a fluid flow simulator over each single model. Considering the normalized absolute deviation between the dynamic responses and the real dynamic response in each well as state variables, we compute the correlation coefficients of the deviations with each grid cell through the ensemble of realizations. Areas of high correlation coefficients are those where the permeability is more likely to play a key role for the production of that given well. Using a local estimation of the response of the deviations, through a simple kriging process, we update the subsurface property of interest at a given localization.