Technologies such as horizontal drilling and multistage hydraulic fracturing are central to ensuring the viability of shale oil and gas resource development by maximizing contact with the most productive reservoir volumes. However, characterization efforts based on the use of well logs and cores, although very informative, may be associated with substantial uncertainty in interwell volumes. Consequently, this work is centered around the development of a predictive tool based on surface seismic data analysis to rapidly demarcate the most prolific reservoir volumes, to identify zones more amenable to hydraulic fracturing, and to provide a methodology to locate productive infill wells for further development. Specifically, we demonstrate that surface seismic attributes such as / crossplots can successfully be employed to quantitatively grade reservoir rocks in unconventional plays. We also investigate the role of seismically inverted Poisson’s ratio as a fracability discriminator and Young’s modulus as an indicator of total organic carbon richness and porosity. The proposed predictive tool for sweet spot identification relies on classifying reservoir volumes on the basis of their amenability to fracturing and reservoir quality. The classification scheme is applied to a field case study from the Lower Barnett Shale and we validate these results using production logs recorded in four horizontal wells and microseismic data acquired while fracturing these wells. The integration of seismic data, production logs, and microseismic data underscores the value of shale reservoir characterization with a diverse suite of measurements to determine optimal well locations and to locate hydraulic fracture treatments. A key advantage of the methodology developed here is the ease of regional-scale characterization that can easily be generalized to other shale plays.