International Council for Education, Research and Training

Mathematical Approaches for Modelling Flow and Transport Porous Media: Enhancing Groundwater Resource Development

Bansal, Rekha

Assistant Professor (Department of Mathematics), Pt. Deen Dayal Upadhyaya Rajkiya Mahila Mahavihalya Farah Mathura

Abstract

Porous media, consisting of interconnected voids or pores, serve as critical frameworks for fluid flow and mass transport in natural and engineered systems. They play a vital role in applications ranging from groundwater resource management and hydrocarbon recovery to carbon capture and environmental remediation. Groundwater, which provides nearly half of the world’s drinking water and supports global agriculture and industry, is increasingly stressed by over-extraction, pollution, and climate change. The spatial heterogeneity of porous media, coupled with the complexity of multiphase flows and reactive transport, poses significant challenges to accurate modelling using traditional approaches like Darcy’s Law. This paper reviews advanced mathematical techniques for modelling flow and transport processes in porous media, emphasizing their application to groundwater management and other critical domains. Key approaches, including numerical methods, multiphase flow models, and reactive transport frameworks, are explored in detail. Emerging technologies, such as enhanced oil recovery (EOR) and carbon capture and storage (CCS), further illustrate the economic and environmental significance of refining porous media models. Additionally, the integration of novel tools such as machine learning and inverse modelling is highlighted as a means to improve parameter estimation and account for system heterogeneity. By addressing the limitations of traditional models and incorporating real-world complexities, this study underscores the importance of developing innovative mathematical frameworks to support sustainable resource management and environmental protection. The findings contribute to enhancing predictive capabilities for groundwater systems and optimizing solutions for energy and environmental challenges.

Keyboards: Hydrocarbon, Multiphase, Environmental, Challenges, Resource Development

Impact Statement

Groundwater resources are critical to global water supply, particularly in arid and semi-arid regions where surface water is scarce. However, their sustainable development and management require a detailed understanding of the complex physical processes governing subsurface flow and contaminant transport. Mathematical modelling offers a powerful framework for simulating these processes, enabling researchers and policymakers to make informed decisions based on predictive analysis and scenario testing.

This research contributes to the advancement of mathematical and computational techniques for modelling flow and transport in porous media, with a focus on applications in groundwater resource development. By integrating partial differential equations, numerical methods, and data-driven calibration techniques, the models developed improve the accuracy and reliability of groundwater assessments. These tools can predict the movement of water and solutes under various environmental conditions, identify optimal extraction strategies, and assess the risk of contamination.

The broader impact of this work lies in its potential to guide sustainable groundwater management practices, inform infrastructure development, and support climate change adaptation strategies. Furthermore, it contributes to the scientific community by advancing mathematical methodologies and fostering interdisciplinary collaboration between hydrologists, engineers, and applied mathematicians. Ultimately, this research empowers stakeholders with better tools to protect and optimize one of Earth’s most vital natural resources—groundwater.

About The Author

Dr. Rekha Bansal is a dedicated academician and author, currently serving as an Assistant Professor in the Department of Mathematics at Pt. Deen Dayal Upadhyaya Rajkiya Mahila Mahavidyalaya, Farah, Mathura. With a strong academic background and a deep passion for mathematics, she has made significant contributions to undergraduate mathematics education. Dr. Bansal is widely respected for her clear and effective teaching methods, which simplify complex mathematical concepts and make them accessible to students. Her textbooks are known for their structured content, accuracy, and student-centric approach, making them valuable resources for B.Sc. mathematics courses. In addition to teaching, she actively participates in academic development through curriculum planning and academic mentoring. Her dedication to the field is evident in her continuous efforts to inspire and guide students toward academic excellence. Through her scholarly work and classroom teaching, Dr. Rekha Bansal plays a vital role in shaping the future of mathematics education in higher institutions.

References

 

  1. Baker, L., Johnson, A., & Smith, R. (2019). Effects of land-use changes on groundwater quality: Implications for urbanization. Environmental Management, 63(2), 215–232. https://doi.org/10.1007/s00267-018-1111-7

  2. Bear, J., Cheng, A., & Zhou, Q. (2018). Contaminant transport modelling in subsurface environments: Impacts on remediation strategies. Water Resources Research, 54(9), 6485–6500. https://doi.org/10.1029/2018WR022435

  3. Benson, S. M., Hepple, R. P., & McCoy, S. T. (2015). CO₂ sequestration in geological formations: Insights from the Ketzin site in Germany. International Journal of Greenhouse Gas Control, 40, 189–199. https://doi.org/10.1016/j.ijggc.2015.06.002

  4. De Simoni, M., Adhikari, P., & Fabbri, P. (2011). Modelling the coupling of fluid flow and chemical reactions in heterogeneous media. Groundwater, 49(3), 305–308. https://doi.org/10.1111/j.1745-6584.2011.00775.x

  5. Fleming, S. W., Mattson, E., & Ratzlaff, D. (2015). Capillary pressure hysteresis in multiphase flow: Implications for subsurface fluid dynamics. Journal of Hydrology, 529, 1076–1085. https://doi.org/10.1016/j.jhydrol.2015.09.045

  6. Friedman, S. P., Sharif, A., & Worrall, F. (2017). Effects of hydraulic fracturing on subsurface flow dynamics: Insights from shale gas operations. Journal of Natural Gas Science and Engineering, 39, 169–179. https://doi.org/10.1016/j.jngse.2017.01.030

  7. Gao, H., Wang, Q., & Zhao, X. (2022). Multi-component gas mixtures during multiphase flow: A model for natural gas production. Journal of Petroleum Science and Engineering, 207, Article 109303. https://doi.org/10.1016/j.petrol.2021.109303

  8. Gao, H., Wang, Y., & Zhang, Y. (2023). Elevated CO₂ levels and their influence on groundwater systems: A modelling approach. Water Resources Research, 59(5), Article WR031455, e2022. https://doi.org/10.1029/2022WR031455

  9. Gomez, J. F., Kim, H., & Rodriguez, E. (2020). Optimizing water treatment processes through machine learning: A case study. Water Research, 170, Article 115308. https://doi.org/10.1016/j.watres.2019.115308

  10. Helmig, R., Hesse, M., & Hölting, B. (2013). Nonlinearities in capillary pressure and relative permeability: A framework for multiphase flow modelling. Journal of Hydrology, 494, 114–126. https://doi.org/10.1016/j.jhydrol.2013.05.042

  11. Huang, Y., Wang, J., & Chen, Y. (2020). Influence of fracture surface roughness on flow dynamics in fractured porous media. Journal of Hydrology, 586, Article 124809. https://doi.org/10.1016/j.jhydrol.2020.124809

  12. International Energy Agency. (n.d.). Enhanced oil recovery. Retrieved September 24, 2024, https://www.iea.org/topics/energy-supply/enhanced-oil-recovery

  13. International Water Management Institute. (n.d.). Groundwater Depletion: A Global Issue. Retrieved September 24, 2024, https://www.iwmi.cgiar.org/

  14. Karimi-Fard, M., Fattahi, N., & Salehi, S. (2016). Discrete fracture network modelling in low-permeability formations: A case study from the Bakken formation. Journal of Petroleum Science and Engineering, 139, 174–183. https://doi.org/10.1016/j.petrol.2016.10.018

  15. Karra, S., Olsson, A., & Myer, W. (2020). Predicting permeability distributions in heterogeneous reservoirs using machine learning. Computers and Geosciences, 135, Article 104360. https://doi.org/10.1016/j.cageo.2019.104360

  16. Lei, S., Zhang, L., & Wu, Y. (2015). Stochastic modelling of fracture networks in the Appalachian Basin: Impacts on fluid transport. Journal of Natural Gas Science and Engineering, 23, 12–22. https://doi.org/10.1016/j.jngse.2015.01.003

  17. Li, L., & Benson, S. M. (2015). Pore-scale heterogeneity and its effects on reaction kinetics in porous media. Environmental Science and Technology, 49(12), 7260–7267. https://doi.org/10.1021/acs.est.5b00077

  18. Li, Q., M., & Wang, S. (2020). Effects of thermal gradients on fluid behaviour in geothermal reservoirs. Geothermic, 84, Article 101774. https://doi.org/10.1016/j.geothermics.2019.101774

  19. Martinez, L. M., Kim, H. J., & Gonzalez, S. (2024). Innovative reactive transport models for carbon sequestration and soil stabilization. Journal of Environmental Management, 320, Article 115862. https://doi.org/10.1016/j.jenvman.2022.115862

  20. Martinez, L. M., Nelson, B., & Zhang, Y. (2021). Predicting sediment transport in river systems: Advances through machine learning. Journal of Hydrology, 603, Article 126817. https://doi.org/10.1016/j.jhydrol.2021.126817

  21. Nord Botten, J. M., & Celia, M. A. (2012). Modelling CO₂ migration and trapping in geological formations: Insights from the Sleipner project. Energy Procedia, 23, 20–28. https://doi.org/10.1016/j.egypro.2012.06.027

  22. Patel, V., Hargrove, W. W., & Lechner, A. (2023). Changing precipitation patterns and their effects on groundwater recharge rates: Implications for water management. Water Resources Research, 59(8), Article WR034568, e2023. https://doi.org/10.1029/2023WR034568

  23. Smith, R. A., Johnson, C. L., & Lee, K. (2023). Land use interactions and groundwater quality: Impacts of agricultural practices. Environmental Science and Technology, 57(1), 512–520. https://doi.org/10.1021/acs.est.2c05321

  24. Sun, J., Liu, Q., & Zhang, L. (2020). Data-driven models for predicting flow behaviours in heterogeneous aquifers. Journal of Hydrology, 580, Article 124292. https://doi.org/10.1016/j.jhydrol.2019.124292

  25. United Nations. (2022). World water development report 2022: Groundwater: Making the invisible visible. Retrieved September 24, 2024, https://www.unwater.org/publications/world-water-development-report-2022/

  26. United States Energy Information Administration. (2022). Today in energy: Global energy consumption in 2022. Retrieved September 24, 2024, https://www.eia.gov/todayinenergy/detail.php?id=52258

  27. United States Geological Survey. (n.d.). Contaminated Groundwater and Drinking Water Sources. Retrieved September 24, 2024, https://www.usgs.gov/

  28. Wang, Y., Zhao, X., & Li, Y. (2021). Predicting groundwater contamination using random forest alg Xie

  29. Xiong, J., Zhang, Y., & Huang, Y. (2019). Temperature effects on reactive transport pr orithms: A case study. Environmental Pollution, 269, Article 116099. https://doi.org/10.1016/j.envpol.2020.116099

  30. 28.ocesses in heterogeneous porous media: A field study. Environmental Science & Technology, 53(15), 8920-8929. https://doi.org/10.1021/acs.est.9b01907

  31. Zhang, Y., Li, H., & Wang, L. (2016). Colloidal particles in reactive transport: Impacts on contaminant mobility. Journal of Contaminant Hydrology, 187, 1–17. https://doi.org/10.1016/j.jconhyd.2016.05.004

  32. Arti, A. (2024). Humanities, Arts, and Social Sciences, Social-Media and Social Networking: Good Governance. Shodh Sari-An International Multidisciplinary Journal, 03(02), 40–49. https://doi.org/10.59231/sari7686

  33. Zhang, Y., Wang, Y., & Li, H. (2018). A new computational framework for simulating multiphase flow in fractured porous media. Journal of Petroleum Science and Engineering, 162, 356–368. https://doi.org/10.1016/j.petrol.2017.10.030

  34. Zhang, Y., & Wu, J. (2021). Advanced modelling techniques for enhanced oil recovery processes: Implications for fluid interactions. Journal of Petroleum Science and Engineering, 203, Article 108581. https://doi.org/10.1016/j.petrol.2021.108581

  35. Zhou, Y., Zhang, L., & Jiang, L. (2022). Assessing aquifer vulnerability using deep learning models: High-resolution predictions of contamination risk. Environmental Science and Technology, 56(12), 8500–8510. https://doi.org/10.1021/acs.est.1c07282

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