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Comparative Machine Learning Framework for Permeability Prediction in a Heterogeneous Carbonate Reservoir Cover

Comparative Machine Learning Framework for Permeability Prediction in a Heterogeneous Carbonate Reservoir

Open Access
|May 2026

References

  1. Tali, A., Abdulridha, S., Khamees, L., Humadi, J., Farman, G., & Naser, S. (2023). Permeability estimation of Yamama formation in a Southern Iraqi oil field, case study. AIP Conference Proceedings, 2806(1), Article 030003. AIP Publishing. https://doi.org/10.1063/5.0163281
  2. Okon, A. N., Adewole, S. E., & Uguma, E. M. (2021). Artificial neural network model for reservoir petrophysical properties: Porosity, permeability and water saturation prediction. Modeling Earth Systems and Environment, 7(4), 2373–2390. https://doi.org/10.1007/s40808-020-01012-4
  3. Tiab, D., & Donaldson, E. C. (2024). Petrophysics: Theory and practice of measuring reservoir rock and fluid transport properties. Elsevier.
  4. Ganguli, S. S., & Dimri, V. P. (2023). Reservoir characterization: State-of-theart, key challenges and ways forward. In Developments in structural geology and tectonics (Vol. 6, pp. 1–35). Elsevier. https://doi.org/10.1016/B978-0-323-99593-1.00015-X
  5. Nabawy, B. S. (2025). New approaches in reservoir characterization utilizing conventional and special core analyses: A comprehensive review. Journal of Umm Al-Qura University for Applied Sciences, 1–32. https://doi.org/10.1007/s43994-025-00225-6
  6. Khamees, L. A., Abdulrazzaq, F. N., & Humadi, J. (2024). Predicting reservoir or non-reservoir formations by calculating permeability and porosity in an Iraqi oil field. Journal of Chemical and Petroleum Engineering, 58(1), 115–129. https://doi.org/10.22059/jchpe.2024.367201.1459
  7. Kaura, A. M., Andrawus, Y., & Ibrahim, A. Y. (2025). Geophysics, geology, formation evaluation, and reservoir characterization in unconventional resources. In Unconventional resources (pp. 40–122). CRC Press. https://doi.org/10.1201/9781003319955-2
  8. Hosseinzadeh, S., Mollajan, A., Akbarzadeh, S., & Kadkhodaie, A. (2024). Rock type based-estimation of pore throat size distribution in carbonate reservoirs using integrated analysis of well logs and seismic attributes. Carbonates and Evaporites, 39(2), Article 46. https://doi.org/10.1007/s13146-024-00954-5
  9. Mehrabi, H., & Bagherpour, B. (2022). Scale, origin, and predictability of reservoir heterogeneities in shallow-marine carbonate sequences: A case from Cretaceous of Zagros, Iran. Journal of Petroleum Science and Engineering, 214, Article 110571. https://doi.org/10.1016/j.petrol.2022.110571
  10. Li, W., Duan, J., Zhu, D., & Wu, J. (2025). The research progress on carbonate reservoir evaluation: Technical applications, challenges, and future development directions. Advances in Resources Research, 5(3), 1177–1198. https://doi.org/10.50908/arr.5.3_1177
  11. Harrison, M. J. (2023). Uncertainty quantification and propagation of hydraulic conductivity fields across scales and their effect on flow and solute transport [Doctoral dissertation, University of Warwick]. Warwick Research Archive Portal.
  12. Khamees, L., & Abdulrazzaq, F. N. (2024). Evaluation uncertainty in the volume of oil in place in Mishrif Reservoir. Journal of Chemical and Petroleum Engineering, 58(2), 243–254. https://doi.org/10.22059/jchpe.2024.373776.1491
  13. Rehman, M., Hafeez, M. B., & Krawczuk, M. (2024). A comprehensive review: Applications of the Kozeny–Carman model in engineering with permeability dynamics. Archives of Computational Methods in Engineering, 31(7), 3843–3855. https://doi.org/10.1007/s11831-024-10094-7
  14. Chen, J., Tong, H., Yuan, J., Fang, Y., & Gu, R. (2022). Permeability prediction model modified on Kozeny‑Carman for building foundation of clay soil. Buildings, 12(11), Article 1798. https://doi.org/10.3390/buildings12111798
  15. Schuler, T., Weber, C. C., Wrubel, J. A., Pivovar, B., Gubler, L., Buchi, F. N., & Bender, G. (2024). Ultrathin microporous transport layers: Implications for low catalyst loadings, thin membranes, and high current density operation for proton exchange membrane electrolysis. Advanced Energy Materials, 14(7), Article 2302786. https://doi.org/10.1002/aenm.202302786
  16. Li, H., Yang, Y., Zhang, Q., Shang, Z., Liang, X., Zhang, L., Sun, H., Zhong, J., Zhang, K., & Yao, J. (2026). 3D pore-scale study of dynamic interfacial interactions effects in natural porous rocks with various pore structures. SPE Journal, 31(3), 1950–1971. https://doi.org/10.2118/231837-PA
  17. Zhang, P., Hu, S., Xiao, Y., Chen, P., & Sun, D. (2025). Application of stacked bidirectional LSTM neural networks in reservoir porosity prediction. Scientific Reports, 15(1), Article 39333. https://doi.org/10.1038/s41598-025-23095-8
  18. Ampomah, W., Huang, L., Bratton, T., El-Kaseeh, G., Will, R., Cather, M., … & Lee, D. (2023). Improving subsurface stress characterization for carbon dioxide storage projects by incorporating machine learning techniques. New Mexico Institute of Mining and Technology. https://doi.org/10.2172/1964119
  19. Zhang, W., Gu, X., Tang, L., Yin, Y., Liu, D., & Zhang, Y. (2022). Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge. Gondwana Research, 109, 1–17. https://doi.org/10.1016/j.gr.2022.03.015
  20. Zhao, T., Wang, S., Ouyang, C., Chen, M., Liu, C., Zhang, J., ... & Wang, L. (2024). Artificial intelligence for geoscience: Progress, challenges, and perspectives. The Innovation, 5(5), Article 100691. https://doi.org/10.1016/j.xinn.2024.100691
  21. Takaew, P., Xia, J. C., & Doucet, L. S. (2024). Machine learning and tectonic setting determination: Bridging the gap between Earth scientists and data scientists. Geoscience Frontiers, 15(1), Article 101726. https://doi.org/10.1016/j.gsf.2023.101726
  22. Orujov, K. N. (2025). Machine learning-based reservoir performance modelling and optimization for improved production forecasting and decision-making [Master’s thesis, Khazar University].
  23. Rahmanifard, H., & Gates, I. (2024). A comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: Best practices and future directions. Artificial Intelligence Review, 57(8), Article 213. https://doi.org/10.1007/s10462-024-10865-5
  24. Mehrabi, A. R., Bagheri, M., Nabi Bidhendi, M., Biniaz Delijani, E., & Behnood, M. (2025). Enhancing porosity prediction accuracy in oil reservoirs: Evaluating hybrid machine learning approaches integrating well log and core data. Journal of the Earth and Space Physics, 50(4), 91–113. https://doi.org/10.22059/jesphys.2025.377351.1007611
  25. Biswakalyani, C., Samantaray, S., & Satpathy, D. P. (2026). Application of hybrid machine learning for groundwater level prediction: A comprehensive review. Archives of Computational Methods in Engineering. Advance online publication. https://doi.org/10.1007/s11831-025-10447-w
  26. Mahetaji, M., & Brahma, J. (2025). Enhancing wellbore stability through machine learning for sustainable hydrocarbon exploitation. Scientific Reports, 15(1), Article 35268. https://doi.org/10.1038/s41598-025-17588-9
  27. Alagoz, E., Dündar, E. C., Tangirala, S., Oskay, M. M., Canbaz, C. H., Saputelli, L., … & Temizel, C. (2026). AI applications in unconventionals. In C. Temizel, S. Tutun, C. H. Canbaz, E. Alagoz, E. C. Dündar, M. M. Sari, … & M. M. Oskay (Eds.), Artificial intelligence in the energy industry: Theory, case studies, and applications (pp. 478–557). CRC Press. https://doi.org/10.1201/9781003617327-8
  28. Delpisheh, M., Ebrahimpour, B., Fattahi, A., Siavashi, M., Mashhadimoslem, H., Leary, P., … & Mostaghimi, P. (2024). Leveraging machine learning in porous media. Journal of Materials Chemistry A, 12(32), 20717–20782. https://doi.org/10.1039/D4TA00251B
  29. Zarin, T., Eshkaftaki, H. A., & Sharifi, A. (2025). Machine learning-based prediction of oil-water relative permeability using core flooding and CT‑scan data. Results in Engineering, 27, Article 105735. https://doi.org/10.1016/j.rineng.2025.105735
  30. Hou, R., Lo, J. Y., Marks, J. R., Hwang, E. S., & Grimm, L. J. (2024). Classification performance bias between training and test sets in a limited mammography dataset. PLOS ONE, 19(2), Article e0282402. https://doi.org/10.1371/journal.pone.0282402
  31. Ahmadi, M. (2024). Advancing geotechnical evaluation of wellbores: A robust and precise model for predicting uniaxial compressive strength (UCS) of rocks in oil and gas wells. Applied Sciences, 14(22), Article 10441. https://doi.org/10.3390/app142210441
  32. Kůdela, J. (2022). A critical problem in benchmarking and analysis of evolutionary computation methods. Nature Machine Intelligence, 4(12), 1238–1245. https://doi.org/10.1038/s42256-022-00579-0
  33. Naser, M. Z., Al-Bashiti, M. K., Tapeh, A. T. G., Naser, A., Kodur, V., Hawileh, R., … & Eslamlou, A. D. (2025). A review of benchmark and test functions for global optimization algorithms and metaheuristics. Wiley Interdisciplinary Reviews: Computational Statistics, 17(2), Article e70028. https://doi.org/10.1002/wics.70028
  34. Martínez-Salvador, B., Marcos, M., Palau, P., & Domínguez Mafé, E. (2023). A model-driven transformation approach for the modelling of processes in clinical practice guidelines. Artificial Intelligence in Medicine, 137, Article 102495. https://doi.org/10.1016/j.artmed.2023.102495
DOI: https://doi.org/10.2478/lpts-2026-0023 | Journal eISSN: 2255-8896 | Journal ISSN: 0868-8257
Language: English
Page range: 90 - 105
Published on: May 27, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year

© 2026 F. N. Abdulrazzaq, L. A. Khamees, published by Institute of Physical Energetics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.