voids 0.1.5

Scientific Python package for pore network modeling (PNM)

voids

voids is a scientific Python package for pore network modeling (PNM) aimed at research workflows where reproducibility, explicit assumptions, and validation matter. The current project emphasis is a clean canonical network model, interoperability with PoreSpy/OpenPNM-style data, and a validated single-phase workflow that now includes shape-aware conductance, pressure-dependent thermodynamic viscosity, and nonlinear solve options before expanding to more complex multiphase physics.

Goals

The intended direction of voids is:

• provide a rigorous internal representation of pore-throat networks
• preserve sample geometry and provenance information needed for reproducible studies
• support import and normalization of extracted networks from external tools
• expose well-scoped physics modules with diagnostics and regression tests
• build confidence on single-phase transport first, then expand toward richer models

This is a research codebase, not a GUI application or a full image-to-network extraction pipeline. Raw segmentation and extraction are intentionally delegated to upstream tools such as PoreSpy.

Current Scope

The current v0.1.x implementation includes:

• canonical Network, SampleGeometry, and Provenance data structures
• import of PoreSpy/OpenPNM-style dictionaries into the canonical model
• geometry normalization helpers for extracted networks
• static petrophysics:
  • absolute porosity
  • effective porosity
  • connectivity metrics
• single-phase incompressible flow with directional permeability estimation
• shape-aware valvatne_blunt_throat and valvatne_blunt conductance closures
• pressure-dependent water viscosity via thermo and CoolProp
• Picard and damped-Newton nonlinear solves for variable-viscosity problems
• Krylov linear solvers with optional pyamg preconditioning
• HDF5 serialization
• optional Plotly and PyVista network visualization
• interoperability cross-checks against OpenPNM
• optional direct-image permeability benchmarking against XLB
• synthetic and manufactured examples for regression and tutorials

Important boundaries:

• multiphase flow is not implemented yet
• production image acquisition and fully automated "push-button" extraction pipelines are out of scope
• controlled grayscale preprocessing, segmentation helpers, and snow2-based extraction helpers are available in voids.image
• synthetic mesh/manufactured examples are controlled validation cases, not realistic rock reconstructions

For a more formal statement of scope and assumptions, see spec_v0_1.md.

The rendered documentation is intended to live alongside the repository at https://geomech-project.github.io/voids/.

Installation

Install from PyPI

If you want the published package rather than a local editable checkout:

pip install voids

PyPI package page: https://pypi.org/project/voids/

Recommended: Pixi

This repository is configured for Pixi and exposes four main environments:

• default: core runtime + notebooks + plotting + PyVista + thermodynamic backends
• test: everything in default plus test-only dependencies
• lbm: test environment plus the optional XLB stack
• docs: MkDocs, Material for MkDocs, and mkdocstrings

pixi install
pixi run -e default python -c "import voids; print(voids.__version__)"

Pixi activation also provides project path variables used by notebooks:

• VOIDS_PROJECT_ROOT
• VOIDS_NOTEBOOKS_PATH
• VOIDS_EXAMPLES_PATH
• VOIDS_DATA_PATH

Editable pip install

If you prefer a plain Python environment from the repository checkout:

python -m pip install -e .

Optional extras:

python -m pip install -e ".[dev,viz,test,lbm,docs]"

Assumption to keep in mind: the notebooks are exercised primarily through the Pixi environments, so the most reliable setup is still Pixi.

Quick Start

from voids.examples import make_linear_chain_network
from voids.physics.petrophysics import absolute_porosity
from voids.physics.singlephase import FluidSinglePhase, PressureBC, solve

net = make_linear_chain_network()

result = solve(
    net,
    fluid=FluidSinglePhase(viscosity=1.0),
    bc=PressureBC("inlet_xmin", "outlet_xmax", pin=1.0, pout=0.0),
    axis="x",
)

print("phi_abs =", absolute_porosity(net))
print("Q =", result.total_flow_rate)
print("Kx =", result.permeability["x"])
print("mass_balance_error =", result.mass_balance_error)

There is also a small workflow entry point:

pixi run examples-singlephase

Examples And Notebooks

The repository includes paired notebooks and py:percent scripts under notebooks/:

• 01_mwe_singlephase_porosity_perm
  • minimal single-phase solve, porosity, and permeability
• 02_mwe_openpnm_crosscheck_optional
  • roundtrip and OpenPNM cross-check workflow
• 03_mwe_pyvista_visualization
  • optional PyVista-based network rendering
• 04_mwe_manufactured_porespy_extraction
  • manufactured 3D void image, PoreSpy extraction, import into voids, and serialization
• 05_mwe_cartesian_mesh_network
  • configurable 2D/3D mesh-like pore networks, flow solve, Plotly visualization, and HDF5 export
• 06_mwe_real_porespy_extraction
  • real segmented Ketton image, PoreSpy extraction, voids import, solve, and diagnostics
• 07_mwe_synthetic_vug_case
  • grayscale synthetic vug volume preprocessing, extraction, solve, and pruning comparison
• 08_mwe_image_based_vug_shape_sensitivity
  • controlled baseline vs spherical/ellipsoidal vug study with porosity, Kabs, and network statistics
• 09_mwe_image_based_vug_sensitivity_2d
  • simplified 2D image-based baseline vs circular/elliptical vug study with porosity, Kabs, and K/K0 distributions
• 10_mwe_lattice_based_vug_sensitivity
  • lattice-based stochastic baselines with spherical/ellipsoidal vug insertion, Kabs/porosity sensitivity, and K/K0 distributions
• 11_mwe_lattice_based_vug_sensitivity_2d
  • simplified 2D lattice counterpart with circular/elliptical vugs, multi-baseline sensitivity, and K/K0 frequency distributions
• 12_mwe_synthetic_volume_openpnm_benchmark
  • synthetic spanning volumes, synthetic grayscale segmentation, snow2 extraction, and Kabs cross-checks between voids and OpenPNM
• 13_mwe_synthetic_volume_xlb_benchmark
  • synthetic segmented volumes, direct-image XLB solves, extracted-network voids solves, and Kabs comparison between voxel-scale LBM and PNM
• 14_mwe_shape_factor_conductance_comparison
  • synthetic and extracted-network comparison of conductance closures (generic_poiseuille, valvatne_blunt_throat, and valvatne_blunt) and permeability sensitivity to shape factors
• 15_mwe_external_pnflow_benchmark
  • committed external pnextract/pnflow reference cases compared against the current voids extraction + solve workflow
• 16_mwe_viscosity_model_kabs_benchmark
  • benchmark of Kabs using constant viscosity versus pressure-dependent thermodynamic viscosity
• 17_mwe_solver_options_benchmark
  • benchmark of the available linear and nonlinear solver options, including pyamg-preconditioned Krylov solves
• 18_mwe_drp317_berea_raw_porosity_perm
  • DRP-317 Berea validation notebook against experimental porosity and permeability
• 19_mwe_drp317_bentheimer_raw_porosity_perm
  • DRP-317 Bentheimer validation notebook against experimental porosity and permeability
• 20_mwe_drp317_banderagray_raw_porosity_perm
  • DRP-317 Bandera Gray validation notebook against experimental porosity and permeability
• 21_mwe_drp317_banderabrown_raw_porosity_perm
  • DRP-317 Bandera Brown notebook against the Table 1 experimental porosity and permeability values
• 22_mwe_drp317_bereasistergray_raw_porosity_perm
  • DRP-317 Berea Sister Gray notebook against the Table 1 experimental porosity and permeability values
• 23_mwe_drp317_bereauppergray_raw_porosity_perm
  • DRP-317 Berea Upper Gray notebook against the Table 1 experimental porosity and permeability values
• 24_mwe_drp317_buffberea_raw_porosity_perm
  • DRP-317 Buff Berea notebook against the Table 1 experimental porosity and permeability values
• 25_mwe_drp317_castlegate_raw_porosity_perm
  • DRP-317 Castlegate notebook against the Table 1 experimental porosity and permeability values
• 26_mwe_drp317_kirby_raw_porosity_perm
  • DRP-317 Kirby notebook against the Table 1 experimental porosity and permeability values
• 27_mwe_drp317_leopard_raw_porosity_perm
  • DRP-317 Leopard notebook against the Table 1 experimental porosity and permeability values
• 28_mwe_drp317_parker_raw_porosity_perm
  • DRP-317 Parker notebook against the Table 1 experimental porosity and permeability values

Example data under examples/data/ includes a deterministic manufactured void image and generated artifacts from the extraction/mesh notebooks.

Verification & Validation

The project documentation now separates two kinds of evidence:

• Verification: benchmarks against software references and controlled numerical workflows
• Validation: benchmarks against experimental data

Software-verification reports live under docs/verification/. Experimental-validation reports for the DRP-317 sandstones live under docs/validation/.

DRP-317 Data Citation

The DRP-317 notebooks and validation reports use the following sources:

• Dataset: Neumann, R., ANDREETA, M., Lucas-Oliveira, E. (2020, October 7). 11 Sandstones: raw, filtered and segmented data [Dataset]. Digital Porous Media Portal. https://www.doi.org/10.17612/f4h1-w124
• Experimental reference paper: Neumann, R. F., Barsi-Andreeta, M., Lucas-Oliveira, E., Barbalho, H., Trevizan, W. A., Bonagamba, T. J., & Steiner, M. B. (2021). High accuracy capillary network representation in digital rock reveals permeability scaling functions. Scientific Reports, 11, 11370. https://doi.org/10.1038/s41598-021-90090-0

The full Table 1 sample references used by the DRP-317 notebooks are committed in examples/data/drp-317/drp317_experimental_references.csv.

Scientific Notes

Several assumptions are deliberate and should be stated explicitly:

• extracted-network predictions depend strongly on upstream segmentation and extraction quality
• imported geometry fields may be incomplete or model-dependent across tools
• single-phase OpenPNM cross-checks compare solver/assembly consistency, not universal physical truth
• throat visualization may use arithmetic averaging of pore scalars when no throat scalar field is provided; that is a visualization choice, not a constitutive model

If any of those assumptions are inappropriate for a study, the corresponding workflow should be tightened before using results quantitatively.

Development

Useful commands:

pixi run test
pixi run test-cov
pixi run lint
pixi run typecheck
pixi run precommit
pixi run notebooks-smoke

Version updates are handled with:

pixi run bump-version <new-version>

Status

voids is still pre-alpha. The codebase is already useful for controlled PNM experiments, solver validation, and interoperability studies, but it should not be described as a complete pore-network simulation platform yet.

Institutional Support

voids receives institutional support from the Laboratório Nacional de Computação Científica (LNCC), a research unit of the Ministério da Ciência, Tecnologia e Inovação (MCTI), Brazil.