The appraisal phase in a petroleum field is characterized by several uncertainties, high investment and critical decisions, which are always strongly related to risk. In the past, it was usual to realize production forecast based on a deterministic simulation model. However, production forecast obtained by a probabilistic approach allows the quantification of uncertainty in the reservoir performance by numeric flow simulation of several possible models. Current hardware permits to incorporate more accurate production prediction in the decision processes. A probabilistic approach requires the definition of a methodology. The objective of this work is to develop a methodology to improve the performance of the risk analysis process, trying to get the best accuracy with the lowest number of simulation runs, using an automated process and parallel computing to accelerate the process. The methodology is based on simulation of several flow models representing possible scenarios of the reservoir, through the combination of the uncertain attributes. As simplification, sensitivity analysis is made to reduce the number of uncertain attributes. The simulation models are built through the derivative tree using only the critical attributes. To reduce the simulation time, parallel computing is also applied. After simulation of the models, a statistic treatment is used to obtain the risk curve of the production forecasts and of the net present value. Representative models are selected to integrate the analysis with economic uncertainties. The methodology is applied in petroleum fields and the advantages of the automated process and the simplified procedure are discussed.

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