Production forecasting is one of the essential parameters to measure the economics of an oil field. There are several conventional techniques in petroleum engineering to estimate the production curve. They range from simplified analytical models to complex numerical simulations. This study proposes the use of Artificial Neural Networks (ANN) to predict an oil production curve that approximates to a numerical simulator curve. The methodology consists of using a feedforward neural network to predict the initial flow and the production forecast over ten years of an oil well. This methodology has practical application in the exploration area, since, at this stage, there is still much uncertainty about the oil accumulation, so the reservoir models tend not to be complex. The results were obtained from the ANN training with data collected from the numerical simulator IMEX, whose outputs were later compared with the original data of the numerical simulation. It was possible to get an estimate for the oil initial flow forecast with an accuracy of 97 percent. The production forecast had a mean absolute percentage error of less than 10 percent in the first two years. Despite the increasing error values over the years, they are smaller when compared to those obtained from the exponential decline and multiple linear regression.