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Storage efficiency prediction for feasibility assessment of underground CO2 storage: Novel machine learning approaches

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  • Sadeghpour, Farshad

Abstract

Underground CO2 storage is a critical strategy for reducing atmospheric greenhouse gases and mitigating climate change by sequestering carbon emissions in various geological formations. Machine learning has emerged as a pivotal tool in enhancing the efficiency and accuracy of CO2 storage, particularly in the context of carbon capture and storage (CCS) projects. In this study, data from various CO2 storage sites (including salt caverns, saline aquifers, depleted oil and gas reservoirs, coal seams, and basalts) was used to predict storage efficiency. The data comprises a comprehensive set of parameters including technical, economic, safety, environmental, and other aspects that are effective in CCS projects. Powerful machine learning and novel hybrid methods were employed to predict storage efficiency. According to the results, the methods achieved high accuracy. The analysis revealed that the storage depth parameter has the least effect on the algorithms, while the CO2 storage capacity parameter has the greatest importance score on the results. The study's findings demonstrated the high accuracy of intelligent methods, including machine learning, in predicting the efficiency of CO2 storage. These results are used to assess the feasibility of CO2 storage in various types of sites, helping to mitigate risks and minimize costs.

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  • Sadeghpour, Farshad, 2025. "Storage efficiency prediction for feasibility assessment of underground CO2 storage: Novel machine learning approaches," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225016822
    DOI: 10.1016/j.energy.2025.136040
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    1. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Abdulwahab Alqahtani & Xupeng He & Bicheng Yan & Hussein Hoteit, 2023. "Uncertainty Analysis of CO 2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization," Energies, MDPI, vol. 16(4), pages 1-16, February.
    3. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    4. Taofik H. Nassan & Carsten Freese & Dirk Baganz & Hakan Alkan & Oleksandr Burachok & Jonas Solbakken & Nematollah Zamani & Morten Gunnar Aarra & Mohd Amro, 2024. "Integrity Experiments for Geological Carbon Storage (GCS) in Depleted Hydrocarbon Reservoirs: Wellbore Components under Cyclic CO 2 Injection Conditions," Energies, MDPI, vol. 17(12), pages 1-26, June.
    5. Manuel Casal-Guisande & Alberto Comesaña-Campos & Alejandro Pereira & José-Benito Bouza-Rodríguez & Jorge Cerqueiro-Pequeño, 2022. "A Decision-Making Methodology Based on Expert Systems Applied to Machining Tools Condition Monitoring," Mathematics, MDPI, vol. 10(3), pages 1-30, February.
    6. Kevin McDonnell & Levente Molnár & Mary Harty & Fionnuala Murphy, 2020. "Feasibility Study of Carbon Dioxide Plume Geothermal Systems in Germany−Utilising Carbon Dioxide for Energy," Energies, MDPI, vol. 13(10), pages 1-24, May.
    7. Vo Thanh, Hung & Lee, Kang-Kun, 2022. "Application of machine learning to predict CO2 trapping performance in deep saline aquifers," Energy, Elsevier, vol. 239(PE).
    8. Cheng Cao & Hejuan Liu & Zhengmeng Hou & Faisal Mehmood & Jianxing Liao & Wentao Feng, 2020. "A Review of CO 2 Storage in View of Safety and Cost-Effectiveness," Energies, MDPI, vol. 13(3), pages 1-45, January.
    9. Rashid Mohamed Mkemai & Gong Bin, 2020. "A modeling and numerical simulation study of enhanced CO2 sequestration into deep saline formation: a strategy towards climate change mitigation," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(5), pages 901-927, May.
    10. Ezekiel, Justin & Vahrenkamp, Volker & Hoteit, Hussein A. & Finkbeiner, Thomas & Mai, P. Martin, 2024. "Techno-economic assessment of large-scale sedimentary basin stored–CO2 geothermal power generation," Applied Energy, Elsevier, vol. 376(PB).
    11. Bai, Mingxing & Sun, Jianpeng & Song, Kaoping & Li, Lili & Qiao, Zhi, 2015. "Well completion and integrity evaluation for CO2 injection wells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 556-564.
    12. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    13. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.
    14. Chaitanya B. Pande & N. L. Kushwaha & Israel R. Orimoloye & Rohitashw Kumar & Hazem Ghassan Abdo & Abebe Debele Tolche & Ahmed Elbeltagi, 2023. "Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1367-1399, February.
    15. Eigbe, Patrick A. & Ajayi, Olatunbosun O. & Olakoyejo, Olabode T. & Fadipe, Opeyemi L. & Efe, Steven & Adelaja, Adekunle O., 2023. "A general review of CO2 sequestration in underground geological formations and assessment of depleted hydrocarbon reservoirs in the Niger Delta," Applied Energy, Elsevier, vol. 350(C).
    16. Muhammad Hammad Rasool & Maqsood Ahmad & Muhammad Ayoub, 2023. "Selecting Geological Formations for CO 2 Storage: A Comparative Rating System," Sustainability, MDPI, vol. 15(8), pages 1-39, April.
    17. Aminu, Mohammed D. & Nabavi, Seyed Ali & Rochelle, Christopher A. & Manovic, Vasilije, 2017. "A review of developments in carbon dioxide storage," Applied Energy, Elsevier, vol. 208(C), pages 1389-1419.
    18. Laleh Parviz & Kabir Rasouli & Ali Torabi Haghighi, 2023. "Improving Hybrid Models for Precipitation Forecasting by Combining Nonlinear Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3833-3855, August.
    19. Hou, Lei & Elsworth, Derek & Wang, Jintang & Zhou, Junping & Zhang, Fengshou, 2024. "Feasibility and prospects of symbiotic storage of CO2 and H2 in shale reservoirs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    20. Nassabeh, Mehdi & You, Zhenjiang & Keshavarz, Alireza & Iglauer, Stefan, 2024. "Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection," Energy, Elsevier, vol. 305(C).
    21. Li, Peng & Li, Yinping & Shi, Xilin & Zhu, Shijie & Ma, Hongling & Yang, Chunhe, 2024. "Gas tightness around salt cavern gas storage in bedded salt formations," Renewable Energy, Elsevier, vol. 233(C).
    22. Waleed Dhhan & Habshah Midi & Thaera Alameer, 2017. "Robust Support Vector Regression Model in the Presence of Outliers and Leverage Points," Modern Applied Science, Canadian Center of Science and Education, vol. 11(8), pages 1-92, August.
    23. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    24. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    25. Tianran Ma & Jonny Rutqvist & Weiqun Liu & Li Zhu & Kunhwi Kim, 2017. "Modeling of CO 2 sequestration in coal seams: Role of CO 2 ‐induced coal softening on injectivity, storage efficiency and caprock deformation," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 7(3), pages 562-578, June.
    26. Yi Zhang & Wenjing Li & Guodong Chen, 2022. "A Thermodynamic Model for Carbon Dioxide Storage in Underground Salt Caverns," Energies, MDPI, vol. 15(12), pages 1-20, June.
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