It was shown recently that it is important to evaluate risk through probabilistic methodologies that involve a high number of simulation models because of the number of uncertain attributes. Geological modeling yields reservoir models that are represented through fine grids with millions of blocks. A probabilistic risk evaluation based on such grids would require a very high computational effort. Therefore, an upscaling procedure is necessary to reduce the grid size but it is difficult to select a grid size that could represent an adequate balance between precision of risk assessment and computational effort. The methodology applied to quantify risk involves a sensitivity analysis in order to reduce the number of critical attributes and the simulation of reservoir models obtained through all possible combinations of these attributes. After the simulation of the models, a statistic treatment is used to evaluate the risk involved in the process. Several procedures can be used to speedup the process; however the number of simulation runs maybe very high. Upscaling of the simulation models can decrease significantly the computational effort and global time of the risk analysis process but it can also yield an inadequate risk assessment. In this paper the effect of the grid size on the process is evaluated. It was developed a methodology (1) to select an adequate grid size and (2) to speed up the risk analysis process. The choice of geological representative models from coarse grid risk evaluation can to be useful to represent the risk on fine model, avoiding the simulation of all fine models, yielding a significant speedup up of the process. Practical applications of upscaling in a probabilistic risk assessment, that use the concept of representative models selected to characterize geological uncertainties, are shown through calculations performed in a petroleum field represented by a fine grid simulation model with geological uncertainties. 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