In this work we investigate the reduction of the computational cost of the calculus of statistical moments of simulator s output in uncertainties propagation s models. For do that, we present an alternative s implementation to the traditional Monte Carlo s Method, called Polynomial Chaos; that is adequate to problems where the number of uncertain variables is not so high. In the Polynomial Chaos method, the expectation and the variance of the simulator s output are directly estimated, as functions of the probability distribuition of the uncertain variables in simulator input. The great advantage of Polynomial Chaos is that number of points necessary for a good estimation of the output statistics have smaller magnitude, compared to the Monte Carlo Method. Applications of Polynomial Chaos on oil reservoir simulations will be presented. As it is just a preliminar implementation, we just treat propagation s problems with at most four uncertainties variables, despite of the method being applicable to problems with more dimensions. Our main results are applied to two models of synthetic oil reservoirs.