The vertical resolution of most existing downhole log measurement data is readily acknowledged to be insufficient in providing a representative characterization of thin bed properties that are common to unconventional reservoirs and can potentially have a significant influence on well performance. This data resolution blind spot can be mitigated by core analysis, but coring introduces an associated operational risk and additional cost to data acquisition programs. By leveraging the enhanced vertical resolution and wellbore coverage of borehole images, an innovative new workflow demonstrates how high-resolution characterization of thin bedded reservoirs can be achieved in a cost-effective manor with lower operational risk. The outputs of this workflow aim to enhance geological understanding, petrophysical evaluation and drilling & completion optimization. This case study shows how borehole image data is used to provide thin bed characterization of true stratigraphic thickness-corrected bed lamination density, average bed resistivity, adjacent bed resistivity contrast and per-depth bed thickness classification. In addition, the workflow also provides a dip-corrected borehole image-based, high-resolution facies model that captures thin bed lithology variations through the section. Finally, by integrating the borehole image data with an acoustically derived sonic stress profile, considering anisotropic properties, the workflow also generates high-resolution outputs of the closure gradient and unconfined compressive strength (including geomechanical property borehole images). These high-resolution geomechanical property outputs help to support drilling optimization of horizontal wellbores and more representative hydraulic fracture stimulation modeling. Together, these outputs provide a more representative subsurface characterization of unconventional reservoirs with respect to both vertical resolution (thin beds) and facies heterogeneity observed around the wellbore with the borehole images. Ultimately, the workflow aims to support optimized lateral target selection through an enhanced understanding of how thin bed rock properties impact reservoir quality and drilling & completion performance. By integrating the results of this new workflow with other available downhole data and calibration to core, it is considered that the existing data resolution blind spot encountered with characterizing many unconventional reservoirs can be significantly reduced.