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

Conference · Sun Oct 20 04:00:00 EDT 2024

OSTI ID:2483869

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

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1. NETL Site Support Contractor, National Energy Technology Laboratory
2. NETL

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. This work 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: 2483869
• Country of Publication: United States
• Language: English