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Carbon Capture and Storage (CCS) is vital for reducing emissions from hard-to-decarbonize industries such as oil & gas, chemicals, steel, and cement production. Large-scale CCS implementation requires a comprehensive understanding of potential storage sinks, connectivity between emission sources and storage sites, and the infrastructure needed for CO2 capture, transport, and injection. While existing industry reports and research studies provide rich insights into geological and operational parameters, manual consolidation of these vast data sets is inefficient, error-prone, and limits traceability and validation.
To address these challenges, we propose a Data Fabric framework that transforms multi-modal data into a unified, queryable graph-like representation (semantic graph). This graph links pre-defined concepts, such as emission sources, geological formations, and infrastructure systems, by identifying and semantically evaluating their co-occurrence in a specific context. This framework is applied to CCS-related research data from two geologically different regions for rapid knowledge discovery to support effective CCS project planning and deployment.