Accurate assessment of uncertainty of production performance is critical for successful planning and assets development. Particularly in deepwater scenarios, where the uncertainty in the fluid and reservoir characterization is of high level for the difficulties for well testing, fluid and core sampling. Therefore, specific methods are needed for efficient uncertainty quantification of production for deepwater reservoirs with limited information.
In this paper, we introduce a history matching method to assimilate static geological data and production data base on the ensemble Kalman filter (EnKF). The EnKF is independent of simulators, and is suitable for uncertainty assessment, reservoir monitoring and performance prediction. We tested this method with a deepwater case. We analyzed the effects of initial ensembles and production history. After that, the uncertainty of production prediction is quantified and the posterior distribution of cumulative production can be estimated.
The EnKF is shown to be efficient in updating fluid and reservoir heterogeneity. By sequentially assimilating observed data, the EnKF is suitable for reservoir monitoring and performance prediction. The results indicate that history match with plausible geology can be improved with the improvement in the generation of the initial ensemble. History matching with longer history can narrow the range of ultimate recovery distribution, thus the uncertainty can be decreased. And the results also show that the updated amounts of parameters in the ensemble is larger in the first iteration, and it will get smaller gradually with more data been assimilated. After the history matching, the ultimate recovery can be obtained with each set of parameters in the ensemble, the uncertainty can be quantified with the statistical frequency distribution of recovery.
The proposed methodology provides a practical means to assess uncertainty in history matching for deepwater fields. This method can also be used to project and risk management.