A well-succeeded fracturing stimulation depends on conductivity creation, which is ruled by the propping agent in sustained fracturing. These agents are characterized by uniform spherical materials of high compressive resistance, besides being capable of keeping conductivity, even under high stress state. Acid fracture conductivity depends on uneven etching on the surface of the fracture wall, as well as on the mechanical capacity of the rock to support a continuous flow after the fracture is closed. Reservoir performance predictions require, among other parameters, complete information about the flow model and about the pressure decreased around the well. The resulting geometry and conductivity after fracturing have great influence on these predictions. The aim of the present study is to experimentally investigate the behavior of acid fractures supported by different stress stages according to deep carbonate samples, as well as to develop a calibration proposition to the empirical model developed by Nierode and Kruk, in order to estimate the resulting conductivity by using data logging based on dynamic properties and mineralogical compositions. Moreover, it aims at assessing the impact of conductivity on the productivity of the well. Results of the herein performed analysis allowed concluding that it is possible to get an acid fracture under the confining stress expected for deep carbonates, and that sustained fractures have higher conductivity, which is set by the characteristics of the adopted propping agent. Therefore, different from the acid fracturing, these sustained fractures are not significantly influenced by higher stress. Based on the indirect data collected from the profile of the well, it was possible estimating acid fracture conductivity distribution, without the need of samples. The simulated productivity increase based on the different variables available for each of the assessed stimulation techniques made it possible ratifying that there is no single solution recommended for carbonates, and that the medium permeability is the main factor influencing the decision making process.