Investigation of multiphase fluid seepage behaviour in abandoned mines: Insights from single fracture to network scale

Abstract

Quantifying two-phase fluid flow in fractured rocks is essential for resource reutilization in abandoned mines, subsurface energy recovery and underground waste isolation. This study develops a mathematical framework for predicting the permeability of rough fracture networks by integrating fractal geometry with single-phase and two-phase seepage theory. A permeability model for rough fracture networks is first established, and its sensitivity to key geometric parameters is analyzed. A second model is then formulated to relate water-phase saturation to measurable variables, enabling the estimation of two-phase permeability from Reynolds number and aperture. Model predictions show deviations of less than 10% from numerical simulations for both single-phase and two-phase flow, demonstrating the accuracy and robustness of the proposed approach. The results highlight the dominant roles of fracture number, tortuosity and aperture in controlling permeability, as well as the influence of flow regimes on relative permeability. The proposed framework provides a practical and physically based method for analyzing multiphase seepage in fractured rock and offers a foundation for further applications to field-scale fractured systems.

Keywords

Rough-walled fracture network / Gas-water two-phase flow / Relative permeability / Sensitivity analysis / Fractal permeability model / Reynolds number mapping

CRediT authorship contribution statement

Kangsheng Xue: Writing – original draft, Software, Methodology, Funding acquisition, Conceptualization. Hai Pu: Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization. Ming Li: Visualization, Validation, Resources, Investigation. Lulu Liu: Validation, Software, Investigation, Formal analysis. Xiaoyan Liu: Visualization, Validation, Resources, Methodology, Funding acquisition. Dejun Liu: Writing – review & editing, Visualization, Formal analysis, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 52504155 and 52374147) and National Key Research and Development Program of China (No. 2023YFC3804204) and China Postdoctoral Science Foundation (No. 2024M753531) and Jiangsu Funding Program for Excellent Postdoctoral Talent (No. 2024ZB853).

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