The application of statistical methods such as response surface methodology and experimental design theory enable us to effectively deal with the uncertainties in the reservoir simulation. The method was successfully applied to the field case. First, sensitivity analysis was performed from the simulation results, which ranks the influence of the uncertain parameters regarding any simulation data, like the cumulative oil production. The method also provides the possible interactions between the parameters. Second, the most influential uncertain parameters from the previous step were considered as history matching parameters to match the simulated data with the observed data. Then the experimental design approach was used, with a fewer number of parameters, to perform a risk analysis. The result is an accurate and predictive quantification of the studied response (cumulative oil production) as a function of uncertain parameters. This function is then used to compute distribution of the studied response from some probability distributions of the uncertain parameters, using the Monte-Carlo sampling technique. The results from sensitivity and risk analysis showed that the cumulative oil production strongly depends on well locations. The Pareto Plots indicated that there exists an optimal set of well locations for each matched model.


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