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Data and code repository for Experimental validation of graph-based modeling for coupled transport in fractured rocks

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Created:	Jun 06, 2025 at 6:48 p.m. (UTC)
Last updated:	Oct 15, 2026 at 7:39 p.m. (UTC) (Metadata update)
Published date:	Oct 15, 2026 at 7:39 p.m. (UTC)
DOI:	10.4211/hs.b42bb8bcb54c4df3b1678557121a71c7
Citation:	See how to cite this resource

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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.

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README.md

Model and Figure Data — Experimental validation of graph-based modeling for coupled transport in fractured rocks

This archive contains the model outputs, experimental comparison data, source imaging data, and figure-generating notebooks associated with the manuscript [Experimental validation of graph-based modeling for coupled transport in fractured rocks]. It accompanies the open-source dgfn package, which implements the graph-based flow and transport modeling methods used here.

Software: dgfn (Discrete Graph Fracture Network), available on PyPI.
Install: pip install dgfn
Project page: https://pypi.org/project/dgfn/
Version used for this study: dgfn 0.1.0

What's in this archive

Figure and analysis notebooks

Notebook	Purpose	Requires `dgfn`?
`DGFN_core_fits.ipynb`	The core-flood fitting workflow: builds graph networks for each rock core, calibrates apertures, runs TDRW transport, and fits modeled breakthrough curves (BTCs) to the experimental data. Produces the `*_results.csv` files.	Yes
`DGFN_method_figures.ipynb`	Generates the methodology / schematic figures illustrating the graph construction, flow solution, and transport algorithm.	Yes
`Paper_figures_of_model_data_comparison.ipynb`	Reads the result CSVs from the core fitting notebook and produces the exact experiment-vs-model comparison figures in the paper. Does not call `dgfn` directly — it operates on the precomputed result files so the user can avoid installing `dgfn` if desired.	No

Model–experiment comparison data (breakthrough curves)

Each of these CSVs contains one experimental breakthrough curve and its fitted model, with the following columns:

Column	Description
`Time`	Time since injection start [seconds].
`Inj_norm`	Normalized injected (inlet) concentration, C/C0 [-].
`Prod_norm`	Normalized produced (outlet) concentration, C/C0 [-].
`model`	Modeled outlet concentration from the dgfn TDRW fit, interpolated onto the experimental `Time` grid [-].

File	Experiment
`granite_point5ml_exp_model_results.csv`	Granite fracture, 0.5 mL/min, conservative tracer.
`granite_1ml_exp_model_results.csv`	Granite fracture, 1.0 mL/min, conservative tracer.
`granite_kao_exp_model_results.csv`	Granite fracture with kaolinite colloids, 0.5 mL/min.
`dolomite_exp_model_results.csv`	Dolomite core, conservative tracer.
`dolomite_exp_model_w_decay_results.csv`	Dolomite core including first-order (18F) decay.
`berea_exp_model_results.csv`	Berea sandstone core, conservative tracer.
`berea_exp_model_w_decay_results.csv`	Berea sandstone core including first-order (18F) decay.

These files are produced with the DGFN_core_fits.ipynb notebook.

Particle deposition data

File	Description
`granite_kao_deposition.csv`	Spatial map of kaolinite colloid deposition locations from the granite-kaolinite simulation. Columns: `time` [s], `x`, `y`, `z` [m] — the time and 3-D coordinate at which each deposited particle was filtered out of the flow field. Used to generate the deposition-map figure.

Source imaging data

File	Description
`22Nov2022_90min_90frames_60s_1mL_loop_graniteKaolinite2_64Cu_68_44_159_90_32b.raw`	Raw 4-D PET imaging dataset (64Cu tracer) of the granite-kaolinite core flood from which the granite graph network was derived. Dimensions encoded in the filename: 68 x 44 x 159 voxels x 90 time frames, 32-bit; 90 min acquisition, 60 s/frame, 1 mL injection loop. This is a headerless binary; see "Reading the .raw file" below.

How to reproduce the figures

Install the model code and dependencies. Python 3.9+ recommended.

bash pip install dgfn cmcrameri jupyter

dgfn pulls in its own dependencies (numpy, scipy, pandas, networkx, matplotlib, scikit-learn). cmcrameri provides the scientific colormaps used in the figure notebooks, and jupyter is needed to run them.

To regenerate the comparison figures only (fastest path — uses the precomputed result CSVs, no modeling required): launch Jupyter and run Paper_figures_of_model_data_comparison.ipynb. It reads the *_results.csv files in this archive.
To re-run the full modeling workflow (rebuilds the fits from scratch): run DGFN_core_fits.ipynb. Note that this notebook requires the files in the core_breakthrough_curve_data folder (the experimental tracer CSVs with raw rad2/rad3 columns) and the graph_node_data_cores.xlsx graph-definition spreadsheet.
To regenerate the method/schematic figures: (requires dgfn install) run DGFN_method_figures.ipynb.

Citation

If you use these data or the dgfn software, please cite Sutton, Duvvuri, and Zahasky (2026) Experimental validation of graph-based modeling for coupled transport in fractured rocks in Water Resources Research.

Credits

Funding Agencies

This resource was created using funding from the following sources:

Agency Name	Award Title	Award Number
National Science Foundation	None	EAR 2437912
American Chemical Society	None	66040-DNI9
National Science Foundation	None	2002412
Geological Society of America	Graduate Student Research Grant	None

Contributors

People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name	Organization	Address	Phone	Author Identifiers
Christopher Zahasky	UW-Madison Department of Geoscience	Wisconsin, US

How to Cite

Sutton, C. (2026). Data and code repository for Experimental validation of graph-based modeling for coupled transport in fractured rocks, HydroShare, https://doi.org/10.4211/hs.b42bb8bcb54c4df3b1678557121a71c7

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

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