Netflow Python library - A free software tool for the generation and analysis of pore or flow networks.

Area of Science:

• Earth Sciences
• Computational Science
• Geophysics

Background:

• Tomographic scanning provides detailed pore-space geometries of natural porous media for subsurface flow studies.
• Current pore network models struggle with complex heterogeneous rocks and become computationally expensive for large scales.
• Existing methods often fail to preserve key pore clusters characteristic of heterogeneous rock types.

Purpose of the Study:

• To introduce the netflow Python library for generating large, computationally efficient, and representative pore network models.
• To address limitations in existing methods for modeling complex heterogeneous rocks and large-scale applications.
• To enable the generation of periodic networks that minimize boundary effects.

Main Methods:

• The netflow library extracts dendrograms from experimental data and perturbs them to generate larger networks.
• It preserves pore or node clusters, crucial for accurately representing heterogeneous natural rock types.
• Methods are implemented for computationally efficient generation of large, boundary-free periodic networks.

Main Results:

• The netflow library successfully generates large irregular networks that preserve pore clusters found in natural rocks.
• It enables the creation and analysis of periodic networks, effectively eliminating boundary effects.
• The library includes functionalities to convert periodic networks into conventional cubical ones.

Conclusions:

• The netflow Python library offers a significant advancement in creating accurate and efficient pore network models for heterogeneous porous media.
• It provides a scalable solution for subsurface flow and transport studies relevant to engineering applications.
• The library's ability to preserve geological complexity and minimize boundary effects enhances the reliability of large-scale simulations.