DEVELOPMENT OF REGION BASED COVARIANCE LOCALIZATION ON ENSEMBLE KALMAN FILTER FOR HISTORY MATCHING PROCESS IN RESERVOIR SIMULATION - AMBIA (NIM: 32212009), FAJRIL
The Ensemble Kalman Filter (EnKF) method provides ability to fine tune static parameter distribution of reservoir, such as permeability. The distribution of permeability could be corrected along with the addition of new production data; therefore, the model could be updated continuously. This algorithm is also very efficient since it only needs the information from the previous timestep to update the model. Hence, reducing the computational time. However, the difficulty of using this EnKF algorithm is that the number unknown variables are significantly more than the available equations and turns the case into underdetermined problem. As a result, the obtained solution may not be good enough compared to the model yang sebenarnya. In order to reduce the effect of underdetermined system, the EnKF algorithm needs to be modified by imposing additional boundary conditions. One of the proposed boundary condition that could be used is facies information which guiding the Kalman Gain calculation, combined with implementation of spatial correlation model. The combination would give a correlation matrix that represents facies model. Additional effort to improve the localization effect is by introducing modifier of dynamic parameters to modify Kalman Gain calculation. It is implemented through selecting parameters which related to the production, such as pressure and fluid flux. The result shows that region based covariance localization could improve the updating process without altering geological concept which makes it suitable to be implemented near well area.