Advances in Ai-Powered Analytics for Carbon Capture and Storage (CCS) in Reservoir Management
DOI:
https://doi.org/10.65150/EP-jnsrr/V1E6/2025-11Keywords:
Carbon Capture and Storage, Artificial Intelligence, Reservoir Management, Machine Learning, Risk Assessment, Predictive AnalyticsAbstract
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.
References
1) Ajayi, T., Gomes, J. S., & Bera, A. (2019). A review of CO 2 storage in geological formations emphasizing modeling, monitoring and capacity estimation approaches. Petroleum Science, 16, 1028-1063.
2) Aminu, M., Akinsanya, A., Dako, D. A., & Oyedokun, O. (2024). Enhancing cyber threat detection through real-time threat intelligence and adaptive defense mechanisms. International Journal of Computer Applications Technology and Research, 13(8), 11-27.
3) AMINU, M., AKINSANYA, A., OYEDOKUN, O., & TOSIN, O. (2024). A Review of Advanced Cyber Threat Detection Techniques in Critical Infrastructure: Evolution, Current State, and Future Directions.
4) Aminu, M. D., Nabavi, S. A., Rochelle, C. A., & Manovic, V. (2017). A review of developments in carbon dioxide storage. Applied Energy, 208, 1389-1419.
5) Asante, J., Ampomah, W., Tu, J., & Cather, M. (2024). Data-driven modeling for forecasting oil recovery: A timeseries neural network approach for tertiary CO2 WAG EOR. Geoenergy Science and Engineering, 233, 212555.
6) Bui, M., Adjiman, C. S., Bardow, A., Anthony, E. J., Boston, A., Brown, S., . . . Hackett, L. A. (2018). Carbon capture and storage (CCS): the way forward. Energy & Environmental Science, 11(5), 1062-1176.
7) Canadell, J. G., Monteiro, P. M., Costa, M. H., Cotrim da Cunha, L., Cox, P. M., Eliseev, A. V., . . . Koven, C. (2023). Intergovernmental Panel on Climate Change (IPCC). Global carbon and other biogeochemical cycles and feedbacks. In Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change (pp. 673-816): Cambridge University Press.
8) Digitemie, W. N., & Ekemezie, I. O. (2024). Enhancing carbon capture and storage efficiency in the oil and gas sector: an integrated data science and geological approach. Engineering Science & Technology Journal, 5(3), 924-934.
9) Elete, T. Y., Nwulu, E. O., Omomo, K. O., & Emuobosa, A. (2022a). Data analytics as a catalyst for operational optimization: A comprehensive review of techniques in the oil and gas sector.
10) Elete, T. Y., Nwulu, E. O., Omomo, K. O., & Emuobosa, A. (2022b). A generic framework for ensuring safety and efficiency in international engineering projects: Key concepts and strategic approaches.
11) Elete, T. Y., Nwulu, E. O., Omomo, K. O., & Emuobosa, A. (2023). Alarm rationalization in engineering projects: analyzing cost-saving measures and efficiency gains.
12) Erofeev, A., Orlov, D., Ryzhov, A., & Koroteev, D. (2019). Prediction of porosity and permeability alteration based on machine learning algorithms. Transport in Porous Media, 128, 677-700.
13) Esiri, A. E., Jambol, D. D., & Ozowe, C. (2024). Best practices and innovations in carbon capture and storage (CCS) for effective CO2 storage. International Journal of Applied Research in Social Sciences, 6(6), 1227-1243.
14) Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98-115.
15) Ikpe, A. E., Ekanem, I., & Ekanem, K. R. (2024). Conventional trends on carbon capture and storage in the 21st century: a framework for environmental sustainability. Journal of environmental engineering and energy, 1(1), 1-15.
16) Liu, B., Yasin, Q., Sohail, G. M., Chen, G., Ismail, A., Ma, Y., & Fu, X. (2023). Seismic characterization of fault and fractures in deep buried carbonate reservoirs using CNN-LSTM based deep neural networks. Geoenergy Science and Engineering, 229, 212126.
17) Misra, S., Li, H., & He, J. (2019). Machine learning for subsurface characterization: Gulf Professional Publishing.
18) Nwaiwu, U., Leach, M., & Liu, L. (2023). Development of an Improved Decision Support Tool for Geothermal Site Selection in Nigeria Based on Comprehensive Criteria. Energies, 16(22), 7602.
19) Nwulu, E. O., Elete, T. Y., Aderamo, A. T., Esiri, A. E., & Erhueh, O. V. (2023). Promoting plant reliability and safety through effective process automation and control engineering practices.
20) Nwulu, E. O., Elete, T. Y., Aderamo, A. T., Esiri, A. E., Omomo, K. O., & Nigeria, L. Optimizing shutdown and startup procedures in oil facilities: A strategic review of industry best practices.
21) Nwulu, E. O., Elete, T. Y., Omomo, K. O., & Emuobosa, A. (2023). Revolutionizing turnaround management with innovative strategies: Reducing ramp-up durations post-maintenance.
22) Okedele, P. O., Aziza, O. R., Oduro, P., & Ishola, A. O. (2024a). Assessing the impact of international environmental agreements on national policies: A comparative analysis across regions.
23) Okedele, P. O., Aziza, O. R., Oduro, P., & Ishola, A. O. (2024b). Carbon pricing mechanisms and their global efficacy in reducing emissions: Lessons from leading economies.
24) Okedele, P. O., Aziza, O. R., Oduro, P., & Ishola, A. O. (2024c). Climate change litigation as a tool for global environmental policy reform: A comparative study of international case law.
25) OYEDOKUN, O., Ewim, S. E., & Oyeyemi, O. P. (2024a). A Comprehensive Review of Machine Learning Applications in AML Transaction Monitoring. Retrieved from https://www.ijerd.com/paper/vol20-issue11/2011730743.pdf
26) Oyedokun, O., Ewim, S. E., & Oyeyemi, O. P. (2024b). Leveraging advanced financial analytics for predictive risk management and strategic decision-making in global markets. Global Journal of Research in Multidisciplinary Studies, 2(02), 016-026.
27) Ramirez-Corredores, M. M., Goldwasser, M. R., & Falabella de Sousa Aguiar, E. (2023). Carbon dioxide and climate change. In Decarbonization as a Route Towards Sustainable Circularity (pp. 1-14): Springer.
28) Ritchie, H., & Roser, M. (2017). CO₂ and other greenhouse gas emissions. Our world in data.
29) Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804-818.
30) Sahith, J. K., & Lal, B. (2024). Artificial Intelligence for Enhanced Carbon Capture and Storage (CCS). Gas Hydrate in Carbon Capture, Transportation and Storage: Technological, Economic, and Environmental Aspects, 159.
31) Saxena, A., Prakash Gupta, J., Tiwary, J. K., Kumar, A., Sharma, S., Pandey, G., . . . Raghav Chaturvedi, K. (2024). Innovative Pathways in Carbon Capture: Advancements and Strategic Approaches for Effective Carbon Capture, Utilization, and Storage. Sustainability, 16(22), 10132.
32) Uchendu, O., Omomo, K. O., & Esiri, A. E. The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models.
33) Uchendu, O., Omomo, K. O., & Esiri, A. E. Conceptual advances in petrophysical inversion techniques: The synergy of machine learning and traditional inversion models. Engineering Science & Technology Journal, 5(11).
34) Uchendu, O., Omomo, K. O., & Esiri, A. E. (2024a). Conceptual Framework for Data-driven Reservoir Characterization: Integrating Machine Learning in Petrophysical Analysis. Comprehensive Research and Reviews in Multidisciplinary Studies, 2(4), 001–013. doi:DOI:10.57219/crmms.2024.2.2.0041
35) Uchendu, O., Omomo, K. O., & Esiri, A. E. (2024b). Theoritical Insights into Uncertainty Quantification in Reservoir Models: A Bayesian and Stochastic Approach. International Journal of Engineering Research and Development, 20(11), 987–997.
36) Zhang, J. (2021). Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey. Wiley Interdisciplinary Reviews: Computational Statistics, 13(5), e1539.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Lymmy Ogbidi, Benneth Oteh (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
