Abstract We see traditional material balance calculations as an old technique when we face the challenging issue of forecasting reservoir performance, numerical simulation is by far the selected method used by reservoir engineers; but, what happen in an early development phase of an oilfield, when we have scarcely data and a high degree of uncertainty associated?. The aim of this work is to show that methods based on MBE can be successfully integrated with Monte Carlo technique in order to predict reservoir performance under these circumstances. We have selected two methodologies; the first one was proposed by Tarner, designed to predict the Cumulative Oil production as a function of the average reservoir pressure (reservoir depletion). Tarner uses simultaneously the MBE and the GOR definition to estimate the value of the Cumulative Gas Production, through the convergence of both values. The second method was proposed by Tracy, in which a trial and error procedure is followed to converge to the instantaneous Gas Oil Rate, which in turns, allows us to estimate the incremental cumulative oil production. Special attention was given to the ratio of relative permeability, because both methods are quite sensitive to it, and usually this ratio is calculated by correlations (in absence of special core analysis). Flow rate forecasts were calculated using Vogel approach and regarding decline rates of local oilfields. A macro in excel was plugged into a commercial risk analysis software in order to combine the material balance approach with Monte Carlo simulation, so as to address simultaneously to reservoir properties uncertainty and limited production records, instead of getting on with complex numerical simulation models. A small mature area in Talara Basin producing from Mogollón formation was successfully tested with this integrated tool; in contrast to the conventional decline curve forecasts, a sound set of analytical predictions weighed by certainty were prepared, which is indeed more reliable for a decision-making process.