Since the 80 s, data-driven approaches have been used for fluids identification and reservoir characterization of siliciclastic and carbonate rocks mainly regarding seismic amplitudes analyses. However, techniques successfully applied for siliciclastic rocks, such as: AVO analysis, seismic inversions and DHI (Direct Hydrocarbon Indicators) ranking revealed not have achieved the same outstanding and reliable results for heterogeneous carbonate rocks. On the other hand, several articles demonstrate that reservoir characterization workflows with rock physics models embedded have been reaching a robust success in order to obtain petrophysical properties and elastic attributes of both rocks, from the seismic and well logs, in a model-driven approach focused on the reservoirs microstructural information. In this way, taking into account the importance of applying rock physics models in the scope of reservoir characterization, two rock physics models - Xu and Payne and T-Matrix - were applied, compared and their parameters were stochastically evaluated and optimized in a Bayesian framework. Through this approach, it was possible to estimate, in a reliable manner, the elastic attributes of a carbonate reservoir (coquinas) taking into consideration different kinds of uncertainties. Furthermore, after the calibration in the well location and validation of both rock physics models for other wells, sensitivity analyses were conducted in order to quantitatively understand how the coquinas elastic attributes behave regarding the variations in the reservoir mineralogical content, pore shapes and fluids.