As oil and gas exploration and production occur in deeper basins and more complex geologic settings, accurate characterization and modeling of reservoirs to improve estimated ultimate recovery (EUR) prediction, optimize well placement and maximize recovery become paramount. Existing technologies for reservoir characterization and modeling have proven inadequate for delivering detailed 3D predictions of reservoir architecture, connectivity and rock quality at scales that impact subsurface flow patterns and reservoir performance. Because of the gap between the geophysical and geologic data available (seismic, well logs, cores) and the data needed to model rock heterogeneities at the reservoir scale, constraints from external analog systems are needed. Existing stratigraphic concepts and deposition models are mostly empirical and seldom provide quantitative constraints on fine-scale reservoir heterogeneity. Current reservoir modeling tools are challenged to accurately replicate complex, nonstationary, rock heterogeneity patterns that control connectivity, such as shale layers that serve as flow baffles and barriers.