Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights

Conference · Wed Sep 10 04:00:00 EDT 2025

DOI:https://doi.org/10.2172/2998634· OSTI ID:2998634

Bauer, Jennifer ^[1]; Dyer, Alec ^[2]; Zaengle, Dakota ^[2]; Schooley, Catherine ^[2]; Malay, Caleb ^[2]; Pfander, Isabelle ^[2]; Leveckis, Stephen ^[2]; Gao, Michael ^[2]; Houghton, Benjamin ^[2]; Rose, Kelly ^[1]

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

GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.

• 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); USDOE Office of Fossil Energy and Carbon Management (FECM), Office of Carbon Management (FE-20); USDOE Office of Fossil Energy and Carbon Management (FECM), Office of Resource Sustainability (FE-30)
• OSTI ID: 2998634
• Country of Publication: United States
• Language: English

Related Subjects

AIIM
Advanced Infrastructure Integrity Model (AIIM)
EDX
EDX Spatial
artifical intelligence / machine learning (AI/ML)
data-driven model
energy infrastructure
geospatial
model
pipelines
spatial