In many stages of the work chain of Oil & Gas Industry, activities of petroleum engineering demand processes that involve optimization. More specifically, in the reservoirs management, the methodologies for decision making by using intelligent wells involve optimization processes. In those processes, usually, the goal is to maximize the NVP (Net Present Value), which is calculated through the curves of oil, gas and water production, supplied by a reservoir simulator. Such simulations require high computational costs, therefore in many cases the optimization processes become unfeasible. Techniques of computational intelligence are applied in this study - artificial neural networks and neuro-fuzzy models - for building proxies for reservoirs simulators aiming at to reduce the computational cost in a decision support system for using, or not, intelligent wells within oil reservoirs. In order to reduce the number of samples needed for build the models, it was used the Fractional Factorial Design of Experiments. The proxies have been tested in two oil reservoirs: a synthetic one, very sensitive to changes in the control of intelligent wells and another one with real characteristics. The replacement of the simulator by the reservoir proxy, in an optimization process, indicates a good result in terms of performance - low errors and significantly reduced computational costs. Moreover, tests demonstrate that the total replacement of the simulator by the proxy, turned out to be an interesting strategy for using the optimization system, which provides to the users a very fast tool for decision support.