Abstract We present the Point cLoud Algorithm for NEtwork Extraction of Discrete Fracture Networks ( PLANE‐DFN ), a point cloud–based algorithm for automatic fracture network extraction designed to support discrete fracture network (DFN) modeling workflows. PLANE‐DFN segments three‐dimensional fracture planes from raw point cloud data using RANdom SAmple Consensus coupled with statistical outlier removal and density‐based clustering to isolate individual fracture features. Each candidate plane is constrained against site‐specific structural constraints based on strike and dip. After segmentation, each fracture is converted into a 2‐D convex polygon suitable for meshing and simulation. The PLANE‐DFN algorithm is validated by comparing geometric and flow and transport data against data from dfnWorks simulations with ensembles of plane‐fit networks. We find that the flow and transport in plane‐fit networks are comparable to dfnWorks‐generated networks when realistic network geometry is maintained. The PLANE‐DFN algorithm provides an automated and streamlined workflow to transform point clouds of data into DFN network geometry.
Sutton et al. (Wed,) studied this question.