Sequential assimilation of 4D seismic data for reservoir description using the ensemble Kalman filter

Automatic history matching of both production data and time-lapse seismic data to achieve reservoir characterization with reduced uncertainty has been extensively studied in recent years. Feasible applications, however, require either the adjoint method or the gradient simulator method to compute the gradient/Hessian matrix of the objective function for the minimization algorithm. Both methods are computationally expensive when either the number of model parameters or the number of observed data is large. s paper, the ensemble Kalman filter (EnKF) is used to history match both production data and time-lapse seismic impedance data. EnKF uses a set of reservoir models as input; continuously updates the models by assimilating observation data whenever they are available; and outputs a number of “history-matched” models that are suitable for uncertainty analysis. Since EnKF does not require the adjoint code, it is independent of reservoir simulators. A small synthetic case study was conducted, which shows the possibility of integrating both time-lapse seismic data and production data using the EnKF for reservoir characterization. The observed data are matched very well, and the true model features are recovered. The estimated porosity field is better than the estimated permeability field because seismic data are directly sensitive to porosity but only indirectly sensitive to permeability. The improved initial member sampling algorithm helps to keep large variance space within ensemble members, ensuring stable filter behavior.