Understanding natural fracture networks in the subsurface is highly challenging, as direct one-dimensional borehole data are unable to reflect their spatial complexity, and three-dimensional seismic data are limited in spatial resolution to resolve individual meter-scale fractures.
Here, we present a prototype workflow for automated fracture detection along horizontal scan lines using terrestrial light detection and ranging (t-LIDAR). Data are derived from a kilometer-scale Pennsylvanian (locally upper Carboniferous) reservoir outcrop analog in the Lower Saxony Basin, northwestern Germany. The workflow allows the t-LIDAR data to be integrated into conventional reservoir-modeling software for characterizing natural fracture networks with regard to orientation and spatial distribution. The analysis outlines the lateral reorientation of fractures from a west–southwest/east–northeast strike, near a normal fault with approximately 600 m (∼1970 ft) displacement, toward an east–west strike away from the fault. Fracture corridors, 10–20 m (33–66 ft) wide, are present in unfaulted rocks with an average fracture density of 3.4–3.9 m−1 (11.2–12.8 ft−1). A reservoir-scale digital outcrop model was constructed as a basis for data integration. The fracture detection and analysis serve as input for a stochastically modeled discrete fracture network, demonstrating the transferability of the derived data into standard hydrocarbon exploration-and-production-industry approaches.
The presented t-LIDAR workflow provides a powerful tool for quantitative spatial analysis of outcrop analogs, in terms of natural fracture network characterization, and enriches classical outcrop investigation techniques. This study may contribute to a better application of outcrop analog data to naturally fractured reservoirs in the subsurface, reducing uncertainties in the characterization of this reservoir type at depth.