This paper presents an extension of the RMFinder technique, previously proposed to identify representative models (RMs) within the decision-making process in oil fields. As there are several uncertainties associated with this decision-making process, a large number of scenarios are supposed to be analyzed, so that high-quality production strategies can be defined. Such broad analysis is often unfeasible, so techniques to automatically identify RMs are particularly relevant. The original RMFinder does not consider the individual probability of each RM, which may not be accurate when the risk curves of the problem are estimated. Therefore, a mechanism to calculate the individual probability of each RM was developed here, together with a graphical way to visualize different proposals of RMs. To automatically identify the optimal probability of each RM, this new version of RMFinder minimizes the deviation between the risk curves generated with the selected RMs and the original risk curves of the problem. The graphical approach automatically exhibits, in a single page per solution, the RM dispersion in the scatter plots, the resulting risk curves and the differences between attribute-level distributions. This helps the decision makers to visualize and compare different sets of RMs. The proposed methodology was applied to a small synthetic problem and to three reservoir models based on real-world Brazilian fields: (i) UNISIM-I-D, a benchmark case based on the Namorado field; (ii) UNISIM-II-Dβ, a benchmark case based on a highly fractured pre-salt carbonate reservoir; and (iii) ST001a, a highly heterogeneous heavy oil offshore field. The obtained sets of RMs were evaluated by experts and considered appropriate to the studied problems, being adopted as the standard models in the following steps of the decision-making process to define the production strategies under uncertainties.

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