Journal of Natural Science Research and Review (JNSRR)

Advances in Ai-Powered Analytics for Carbon Capture and Storage (CCS) in Reservoir Management

Ogbidi, Lymmy, Oteh, Benneth

31 December 2025 · Vol. 1, Issue 6, pp. 221-225

DOI: 10.65150/EP-jnsrr/V1E6/2025-11

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

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Ogbidi, Lymmy, Oteh, & Benneth (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{Ogbidi2025,
  title   = {Advances in Ai-Powered Analytics for Carbon Capture and Storage (CCS) in Reservoir Management},
  author  = {Ogbidi and Lymmy and Oteh and Benneth},
  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}
}

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