Clustering techniques applied to a large number of scenarios under uncertainty allows the selection of a reduced, however, representative set of the complete set of scenarios. In other words, it allows to select a sample that contains a smaller amount of elements to the point of sufficiently reducing the total data volume and obtaining efficiency gains in data processing. The challenge is that the sample must, above all, be able to preserve the characteristics of the stochastic process that originated it. To this end, this study proposes a methodology for selecting stochastic scenarios using the classic Set Covering model, inspired by the forward selection method proposed by Heitsch and Romisch (2003). Applied in the calculating of stochastic demand for tools and services for the construction of offshore oil exploration wells, this approach presents a different scenario conception from the one used by the authors. The set of scenarios consists of activity schedules generated from the introduction of uncertainties in the planning of each activity, which are static, independent and with multiple attributes. A sensitivity analysis compares the results of the demands calculated with the scenarios selected by the Set Covering Problem (SCP) and the demand calculated with all the universe of scenarios. The SCP was solved, in this application, in its classic version using an exact algorithm and a heuristic algorithm. The results appoint na unexpressive loss in the final result of the demand calculated with reduced scenarios and with the complete set of scenarios. The simple first solution heuristic presented a satisfactory result in relation to the performance gain versus reliability, and indicates the potential of the method if solved with metaheuristic and local search algorithms.