The main considerations for well planning and hydraulic fracturing in unconventional resources plays include the amount of total organic carbon and how much hydrocarbon can be extracted. Brittleness is the direct measurement of a formation about the ability to create avenues for hydrocarbons when applying hydraulic fracturing. Brittleness can be directly estimated from laboratory stress-strain measurements, rock-elastic properties, and mineral content analysis using petrophysical analysis on well logs. However, the estimated brittleness using these methods only provides “cylinder” estimates near the borehole. We proposed a workflow to estimate brittleness of resource plays in 3D by integrating the petrophysics and seismic data analysis. The workflow began by brittleness evaluation using mineral well logs at the borehole location. Then, we used a proximal support vector machine algorithm to construct a classification pattern between rock-elastic properties and brittleness for the selected benchmark well. The pattern was validated using well-log data that were not used for constructing the classification. Next, we prestack inverted the fidelity preserved seismic gathers to generate a suite of rock-elastic properties volumes. Finally, we obtained a satisfactory brittleness index of target formations by applying the trained classification pattern to the inverted rock-elastic-property volumes.