Probabilistic forecasts can be obtained by taking into account different sources of uncertainty in the history matching process. When a non-unique model is used for prediction, the problem of finding the best development plan to maximize the Net Present Value can be extremely time consuming. Nevertheless, taking into account uncertainty can be critical to take better decisions reducing investments risks. The approach proposed uses response surface approximation based on Gaussian process to find the solution of the probabilistic inverse problem, thus reducing considerably the number of required simulations. Adaptive sampling strategies are used to obtain predictive response surface models in both the probabilistic history matching and in the forecasting problem.
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