Modified Ensemble Kalman filter for reservoir parameter and state estimation in the presence of model uncertainty

摘要: Abstract Ensemble Kalman filter (EnKF) is widely used in reservoir modelling for on-line history matching. Typically, it assumed that structurally the model an accurate representation of and uncertainty exists only parameters. This paper focuses on estimating static parameters (i.e., porosity permeability) dynamic states using EnKF presence mismatch between model. An depth investigation application challenges reported. Two modifications are introduced joint state-parameter estimation: i) addition error to represent predictive real system, ii) introduction a tuning parameter called ‘forcing data’ perturbation variable dealing with noisy system. A benchmark problem defined as ‘tank series model’ has been designed verification algorithm. Using simplified mathematical formulation state estimation combined calibration presented systematically. Later similar approach applied nonlinear two-dimensional under water flooding operation. To assess performance matching, sensitivity analysis conducted. It was observed due forcing data perturbation, about 13.6% 9% improvement possible matching tank cases respectively when measurement high.