#6371. Distilling data to drive carbon storage insights

September 2026publication date
Proposal available till 22-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computers in Earth Sciences;
Information Systems;
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Abstract:
Wide-spread implementation of carbon capture and storage has the potential to decrease carbon emissions and aid in meeting global climate change mitigation goals. Data availability is one of the biggest challenges faced by the carbon capture and storage (CCS) community for modeling risks associated with CCS, necessary for wide-spread implementation in coming years. Collecting, integrating, and intuitively managing data is a time-consuming process, but one which is fundamental to establishing necessary access to carbon storage data. The US Department of Energy (US DOE) has been a major supporter of energy research in the US, including significant investment into carbon capture and storage research and technology development over the last ten years. The US DOE investments into the Regional Carbon Sequestration Partnerships, the National Risk Assessment Partnership, and other CCS related research has resulted in a large volume of data, of which much has been made public through the National Energy Technology Laboratories data repository, the Energy Data eXchange (EDX). Researchers at the National Energy Technology Laboratory have developed workflows, tools, and other methods that leverage EDX, open-source software, machine learning, and natural language processing to discover, curate, label, organize and visualize available data. This paper describes the available data on EDX for carbon storage applications, describes the results of a spatial and temporal analysis of the data, describes where it is most geographically available, makes a general assessment of the quality of the available data, and discusses visualization tools and natural language processing tools developed for understanding, discovering and reusing the data.
Keywords:
Data availability; Energy data exchange; FAIR (Findable, Accessible, Interoperable, Reusable); Geologic carbon sequestration; Natural language processing; Spatial data density analysis

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