Fracture Networks Imaging in CO2 Injection Zones in IBDP Site: An Unsupervised Machine Learning Application with Multiple Datasets

Conference · Sun Oct 20 00:00:00 EDT 2024

DOI:https://doi.org/10.2172/2483868· OSTI ID:2483868

Kumar, Abhash ^[1]; Harbert, William ^[2]; Myshakin, Evgeniy ^[1]; Liu, Guoxiang ^[3]; Siriwardane, Hema ^[3]

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1. NETL Site Support Contractor, National Energy Technology Laboratory
2. Oak Ridge Institute for Science and Education (ORISE)
3. NETL

Poster presented at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. This poster highlights the integration of unsupervised machine learning (ML) techniques as a transformative tool for advancing understanding of CO2 injection into reservoirs that could potentially contribute to optimizing injection strategies and reservoir management, ultimately bolstering the efficacy and sustainability of CO2 storage.

• Research Organization: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy and Carbon Management (FECM)
• OSTI ID: 2483868
• Country of Publication: United States
• Language: English