The successful characterization and management of petroleum fields depends strongly on the knowledge of the hydrocarbons volumes in place and the flow conditions of the phases (water, oil and gas). These data are the support for the economic and strategic decisions, like drilling new wells or the field abandonment. Several analytical models are available, but its applicability is restricted to small models, due to the complexity and mathematic effort required in most of the practical applications. So the solution for intermediate and large models is the numerical simulation. Other requirements remain with the simulation of large models: the need of adequate computation resources and the very long simulation time. Models with more than 100,000 blocks need a big amount of memory and usually require a large wall clock time. Another critical situation is when a specific procedure demands a huge number of simulations like history matching, uncertainty analysis and optimization of production strategy. So, the total computational time to solve these procedures could be very large. Parallel computing could handle both situations: large execution wall clock time and procedures with several simulations without losing information and avoiding expensive cost o
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