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 (RANSAC) 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.

ABSTRACT:

Fractures significantly influence flow and transport in subsurface geological systems. Coupled processes such as colloid transport, water-rock interactions, and fracture-matrix exchange complicate flow and transport modeling. Quantifying these coupled processes remains difficult due to the discrete and variable fracture geometry across rock lithologies and limitations of traditional experimental techniques. This study addresses these challenges by integrating pulse-tracer experiments, positron emission tomography (PET) imaging, and graph-based modeling to characterize colloid transport, radioactive decay, and fracture-matrix diffusion. Fluorodeoxyglucose (18F-FDG) was utilized as a radiotracer for flow-through experiments in fractured Niagaran dolomite, Berea sandstone, and Sierra granite cores under single-phase flow conditions. Radiolabeled 64Cu-kaolinite was then used for quantifying colloid transport in the fractured Sierra granite. PET imaging provided high-resolution, spatially resolved observations of tracer and colloid distributions within the cores at the millimeter scale. Experimental data were subsequently used to validate a discrete graph fracture network (DGFN) model that relies on a modified time domain random-walk (TDRW) particle tracking algorithm to incorporate matrix diffusion and first-order kinetics. Results demonstrate that the model is capable of accurately representing fracture-matrix interactions and first-order kinetics such as radioactive decay and irreversible colloid attachment. The approach efficiently captures complex transport phenomena without requiring a high-fidelity representation of fracture geometry, highlighting its potential as a computationally effective alternative to conventional simulation methods. While previous studies validated graph-based modeling for conservative solute transport, this study represents one of the first comparisons with observational data, highlighting the capability of representing coupled transport processes involving fracture-matrix exchanges, radioactive decay, and colloidal transport.

ABSTRACT:

Fractures are a primary feature controlling flow, transport, and coupled processes in geologic systems. To date, experimental image-based observations of these processes have been challenging. Here, we successfully demonstrate the use of a graph-based, laboratory-validated flow and transport model for conservative solute transport in a natural fracture. Pulse-tracer experiments with a conservative radiotracer ([18F]-FDG) spanning multiple flow regimes with simultaneous positron emission tomography (PET) imaging are used to characterize transport in a two-inch fractured Sierra granite core. Model network complexity, determined by the number of nodes and edges, significantly impacts model fit to observed data. Large graphs over-describe a fracture plane and act similarly to a porous medium while small graphs oversimplify the solute transport behavior. This work provides the first validation of graph-based flow and transport models across a range of experimental conditions and sets the groundwork for upscaling to more complex and computationally efficient fracture network models.