In this paper, stochastic simulations and some multiple-point geostatistical principles are developed using a comparative methodology consisting of two phases. First, a traditional methodology based on variogram concepts called Conditional Stochastic Simulation (CSS) and more specifically the Sequential Indicator Simulation (SISIM) is applied to the aquifer modeling. Secondly, modern algorithm based on multiple-point geostatistics called SNESIM [1] and ζ Model [4] are implemented to integrate information available in the definition of Morroa aquifer facies. The simulations are realized by using algorithms constructed in references [1, 2] and S-Gems software prepared at Stanford University[3]. Results show the convenience of employing training images with soft and hard data conditioning integrated by ζ Model, and CSS for configuring hydrogeological models when barely deficient information is available.