Evaluating uncertainty in karst pore volume (K_PV) is a current industry challenge that is critical for field development planning and optimizing recovery. Hydrocarbon pore volume in karst can be significant in large super-giant fields. Although a wide variety of karst features and the geologic processes that describe their morphology have previously been described in many studies, understanding exactly how to translate this knowledge of karst into practical guidelines for the assessment of pore volume in carbonate reservoirs remains an industry challenge. In this paper, we present a robust model-assisted characterization workflow that integrates well data, seismic data (when available), drilling data, geologic concepts from modern and ancient outcrop analogs, and the application of discrete fracture network (DFN) technology to explicitly model karst features. These DFN models of karst serve as powerful visualization and communication tools in addition to quantifying the K_PV. The model-assisted characterization workflow presented is specifically designed for the rapid evaluation of multiple viable geologic scenarios in recognition of the inherent uncertainty in karst morphology, fill, and sampling bias. We present nomograms to facilitate fast practical estimates of karst abundance and porosity, as well as cave area estimates from volumes lost while drilling to help condition and validate the morphometric inputs used for modeling karst. A synthetic reservoir case study with varying degrees of karst that is interpreted to be coastal in origin is used to demonstrate the workflow.