Study On Application Of Ensemble Kalman Filter On Production Seismology

The ensemble Kalman Filter techniche is a new automatic history matching technique. Recently Ensemble Kalman Filter has been used to update reservoir model in automatic history matching. It is proved that EnKF is an effective method. Relatively large ensemble realizations are required for EnKF. However, generally only one reservoir model is available in the reality. In this case, it is a key in practice to update reservoir model using EnKF based on a single starting model. It is important to integrate other data, such as time-lapse seismic data, for reservoir characterization. In this paper, the ensemble Kalman filter is used to update reservoir model by integrating both production data and time-lapse seismic impedance data.For the case of only one reservoir model, we propose a method (Random Pilot Point Method) which can generate multi-realizations with the single model for EnKF. Furthermore, we try to update reservoir model using EnKF based on the secondary derived models. Results from the example demonstrate that the result using this method is valid. We improve the Random Pilot Point Method by adding seismic data to the initial realizations. Results from the example in this paper demonstrate that this method performs better with more accurate predictions and less uncertainty than the conventional methods.In this paper, we also present a new approach to reservoir characterization by integrating time-lapse seismic and production data. A small synthetic case study is conducted, which shows the possibility of integrating both time-lapse data and production data using the EnKF for reservoir characterization. Finally, we investigate the weights for both mismatches of the time-lapse data and production data respectively.