Machine Learning Applications in CO 2 Geological Sequestration: A Review of Pre-Injection Evaluation, Injection Optimization, and Post-Injection Monitoring.
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| Title: | Machine Learning Applications in CO 2 Geological Sequestration: A Review of Pre-Injection Evaluation, Injection Optimization, and Post-Injection Monitoring. |
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| Authors: | Al-Mudhafar, Watheq J.1 (AUTHOR) watheq.almudhafar@utexas.edu, Alsubaih, Ahmed2 (AUTHOR), Sepehrnoori, Kamy1,2 (AUTHOR) |
| Source: | Energies (19961073). Jul2026, Vol. 19 Issue 13, p3104. 53p. |
| Subject Terms: | *Machine learning, *Geological carbon sequestration, *Deep learning, *Geological surveys, *Carbon sequestration, *Leak detection, *Process optimization |
| Abstract: | Rising atmospheric CO2 levels pose a critical challenge to achieving global sustainability targets. Geological carbon sequestration (GCS) offers a long-term solution for reducing greenhouse gas emissions, but its large-scale deployment faces limitations in cost, uncertainty, and operational risk. Recent advances in machine learning (ML) present transformative opportunities to enhance every stage of the carbon capture and storage (CCS) lifecycle, from pre-injection evaluation to post-injection monitoring. This review systematically examines ML integration in CCS applications, emphasizing roles in geological characterization, injection optimization, plume prediction, and leakage detection. It provides a structured overview of ML methodologies including Random Forest, Support Vector Regression, and XGBoost, along with emerging deep learning models used for anomaly detection and uncertainty quantification. Experimental insights, monitoring techniques, and real-time data applications are summarized to illustrate ML's capability in accelerating simulations, reducing costs, and increasing safety assurance. Furthermore, real-world case studies such as Sleipner (Norway), Illinois Basin–Decatur (USA), Boundary Dam (Canada), Gorgon (Australia), and Quest (Canada) demonstrate how ML has enhanced performance, predictive accuracy, and storage reliability in field-scale CCS projects. The review concludes by identifying existing challenges, data scarcity, interpretability, and regulatory integration, and proposes a unified ML framework for scalable, autonomous, and secure CO2 storage. Overall, this study provides a comprehensive roadmap for leveraging artificial intelligence to achieve reliable, cost-effective, and sustainable carbon management solutions aligned with global net-zero objectives. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 195441252 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine Learning Applications in CO 2 Geological Sequestration: A Review of Pre-Injection Evaluation, Injection Optimization, and Post-Injection Monitoring. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Al-Mudhafar%2C+Watheq+J%2E%22">Al-Mudhafar, Watheq J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> watheq.almudhafar@utexas.edu</i><br /><searchLink fieldCode="AR" term="%22Alsubaih%2C+Ahmed%22">Alsubaih, Ahmed</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sepehrnoori%2C+Kamy%22">Sepehrnoori, Kamy</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jul2026, Vol. 19 Issue 13, p3104. 53p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Geological+carbon+sequestration%22">Geological carbon sequestration</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Geological+surveys%22">Geological surveys</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon+sequestration%22">Carbon sequestration</searchLink><br />*<searchLink fieldCode="DE" term="%22Leak+detection%22">Leak detection</searchLink><br />*<searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Rising atmospheric CO2 levels pose a critical challenge to achieving global sustainability targets. Geological carbon sequestration (GCS) offers a long-term solution for reducing greenhouse gas emissions, but its large-scale deployment faces limitations in cost, uncertainty, and operational risk. Recent advances in machine learning (ML) present transformative opportunities to enhance every stage of the carbon capture and storage (CCS) lifecycle, from pre-injection evaluation to post-injection monitoring. This review systematically examines ML integration in CCS applications, emphasizing roles in geological characterization, injection optimization, plume prediction, and leakage detection. It provides a structured overview of ML methodologies including Random Forest, Support Vector Regression, and XGBoost, along with emerging deep learning models used for anomaly detection and uncertainty quantification. Experimental insights, monitoring techniques, and real-time data applications are summarized to illustrate ML's capability in accelerating simulations, reducing costs, and increasing safety assurance. Furthermore, real-world case studies such as Sleipner (Norway), Illinois Basin–Decatur (USA), Boundary Dam (Canada), Gorgon (Australia), and Quest (Canada) demonstrate how ML has enhanced performance, predictive accuracy, and storage reliability in field-scale CCS projects. The review concludes by identifying existing challenges, data scarcity, interpretability, and regulatory integration, and proposes a unified ML framework for scalable, autonomous, and secure CO2 storage. Overall, this study provides a comprehensive roadmap for leveraging artificial intelligence to achieve reliable, cost-effective, and sustainable carbon management solutions aligned with global net-zero objectives. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=195441252 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19133104 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 53 StartPage: 3104 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Geological carbon sequestration Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Geological surveys Type: general – SubjectFull: Carbon sequestration Type: general – SubjectFull: Leak detection Type: general – SubjectFull: Process optimization Type: general Titles: – TitleFull: Machine Learning Applications in CO 2 Geological Sequestration: A Review of Pre-Injection Evaluation, Injection Optimization, and Post-Injection Monitoring. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Al-Mudhafar, Watheq J. – PersonEntity: Name: NameFull: Alsubaih, Ahmed – PersonEntity: Name: NameFull: Sepehrnoori, Kamy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 13 Titles: – TitleFull: Energies (19961073) Type: main |
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