Journal of Natural Science Research and Review (JNSRR) · Research article · Open access
Advances in Ai-Powered Analytics for Carbon Capture and Storage (CCS) in Reservoir Management
Lymmy Ogbidi, Benneth Oteh
31 December 2025 · Vol. 1, Issue 6, pp. 221-225
DOI: 10.65150/EP-jnsrr/V1E6/2025-11
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What is this article about?
The escalating challenge of mitigating carbon emissions has positioned Carbon Capture and Storage (CCS) as a critical technology for combating climate change. This paper explores the transformative role of artificial intelligence (AI) in enhancing CCS's efficiency, safety, and scalability, focusing on reservoir management.
Article at a glance
- Published
- 31 December 2025
- Volume / issue / pages
- Vol. 1, Issue 6, pp. 221-225
- ISSN
- 3051-3766 (online) · 3051-3758 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
The escalating challenge of mitigating carbon emissions has positioned Carbon Capture and Storage (CCS) as a critical technology for combating climate change. This paper explores the transformative role of artificial intelligence (AI) in enhancing CCS's efficiency, safety, and scalability, focusing on reservoir management. Key advancements include the application of machine learning for precise reservoir characterization, predictive analytics for optimizing injection and storage processes, and neural networks for accurate subsurface behavior predictions. The discussion extends to data acquisition systems and advanced monitoring techniques that enable real-time tracking and improved decision-making. AI-driven risk assessment tools are also highlighted for their role in identifying and mitigating leakage and instability risks while optimizing storage site selection and operations. The paper addresses current limitations, recommends further integration of explainable AI and interdisciplinary collaboration, and identifies future research directions to ensure long-term storage security and scalability.
Keywords: Carbon Capture and Storage, Artificial Intelligence, Reservoir Management, Machine Learning, Risk Assessment, Predictive Analytics
Cite this article
Lymmy Ogbidi, & Benneth Oteh (2025). Advances in Ai-Powered Analytics for Carbon Capture and Storage (CCS) in Reservoir Management. Journal of Natural Science Research and Review, 1(6), 221-225. https://doi.org/10.65150/EP-jnsrr/V1E6/2025-11
@article{Lymmy2025,
title = {Advances in Ai-Powered Analytics for Carbon Capture and Storage (CCS) in Reservoir Management},
author = {Lymmy Ogbidi and Benneth Oteh},
journal = {Journal of Natural Science Research and Review},
year = {2025},
volume = {1},
number = {6},
pages = {221-225},
doi = {10.65150/EP-jnsrr/V1E6/2025-11},
url = {https://doi.org/10.65150/EP-jnsrr/V1E6/2025-11}
}Questions about this article
Who wrote this article?
Lymmy Ogbidi, Benneth Oteh.
Where and when was it published?
In Journal of Natural Science Research and Review, Volume 1, Issue 6, pages 221-225, published by EP Journals Group on 31 December 2025.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-jnsrr/V1E6/2025-11 (https://doi.org/10.65150/EP-jnsrr/V1E6/2025-11).
How do I cite this article?
APA: Lymmy Ogbidi, & Benneth Oteh (2025). Advances in Ai-Powered Analytics for Carbon Capture and Storage (CCS) in Reservoir Management. Journal of Natural Science Research and Review, 1(6), 221-225. https://doi.org/10.65150/EP-jnsrr/V1E6/2025-11
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