The San Jorge Basin is one of the most prolific hydrocarbon bearing zones in Argentina.The Cañadon Seco Formation is characterized by sandstones with thickness usually bellow the tuning lenght, under these conditions high uncertainty exists in sandstone prediction when seismic amplitudes are analyzed, due to interference and tuning effects. This paper presents an estimation procedure that overcomes these limitations based on the extrapolation of the well data further away from its location by Geostatistical Inversion of 3D seismic data. Although vertical sampling of the seismic data is much less dense than the one from logging, its higher lateral resolution is used to complement the high vertical resolution of the well data. Unlike conventional trace based inversion in which the source is the seismic data in Geostatistical Inversion the data source is the well, and the estimated acoustic impedance values satisfy the seismic data, in this way driving resolution to an intermediate range between the well and seismic data. With this procedure stochastic simulations of the petrophysical variables are generated, (density and lithology) that not only honour the hard data of the wells but also the seismic data, and minimize the residual error between the Synthetic model and the real 3D-Seismic trace. Each simulation (SGS-Simulated Annealing and SIS) is itself a possible solution to the problem and it is called a realization, wich is then integrated into an average to optimize the certainty of the predictions. The Acoustic Impedance, density and lithology models derived from GI have a resolution of 1ms. This allowed discriminations of lateral variations in the petrophysics of the reservoir (density porosity) and the delineation of sand packets of up to 6 meters thick. The Geostatistical Inversion turned out to be a very powerful tool for the static reservoir modelling wich can function itself as an input to the dynamic model (reservoir simulation), guide the development of the block and assists to the EOR.