This work presents the development and evaluation of a hybrid intelligent system to optimize oil fields development. This system employs the following techniques: evolutionary algorithms to optimize the positioning and characteristics of wells in a reservoir; distributed processing to perform simultaneous reservoir simulations; function approximation models as simulator proxies; and quality maps to use some reservoir information to improve the optimization process.

This work represents the first stage in the application of modern methodologies for the analysis of alternatives of oil field development under uncertainties, where no uncertainties are considered. In this sense, the optimization consists in finding wells positioning, type and geometry in a delimited petroleum field, in order to maximize the NPV of alternative, considering some technical constrains as the minimum wells distance and maximum wells trajectory.

The problem approached in this work is considered of up most importance and it is recognized as a complex optimization problem, since the benefit of the option to develop an oil field depends on investments which in turn depend on the alternative chosen. The combination with other aspects makes this problem even more complex, yet properly optimized by Evolutionary Algorithms.

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