Terrestrial light detection and ranging (LIDAR) surveys offer potential enrichment of outcrop-based research efforts to characterize fracture networks and assess their impact on subsurface fluid flow. Here, we explore two methods to extract the three-dimensional (3-D) positions of natural fractures from a LIDAR survey collected at a roadcut through the Cretaceous Austin Chalk: (1) a manual method using the University of California, Davis, Keck Center for Active Visualization in the Earth Sciences and (2) a semiautomated method based on mean normal and Gaussian curvature surface classification. Each extraction method captures the characteristic frequencies and orientations of the primary fracture sets that we identified in the field, yet they extract secondary fracture sets with varying ability. After making assumptions regarding fracture lengths and apertures, the extracted fractures served as a basis to construct a discrete fracture network (DFN) that agrees with field observations and a priori knowledge of fracture network systems. Using this DFN, we performed flow simulations for two hypothetical scenarios: with and without secondary fracture sets. The results of these two scenarios indicate that for this particular fracture network, secondary fracture sets marginally impact (similar to 10% change) the breakthrough time of water injected into an oil-filled reservoir. Our work provides a prototype workflow that links outcrop fracture observations to 3-D DFN model flow simulations using LIDAR data, an approach that offers some improvement over traditional field-based DFN constructions. In addition, the techniques we used to extract fractures may prove applicable to other outcrop studies with different research goals.