Tools for Data Collection, Curation, and Discovery to Support Carbon Sequestration Insights
Abstract
Carbon capture and geologic sequestration has the potential to decrease carbon emissions and aid in meeting climate change mitigation goals set out by the global community. Since 1997, the US Department of Energy's Carbon Storage program has led an effort to fund carbon sequestration demonstration projects and subsurface modeling tool development for the US and parts of Canada. From this effort formed the regional carbon sequestration partnership initiative (RCSP) to conduct small- and large-scale carbon sequestration test projects, and formed the national risk assessment partnership (NRAP) to put forth comprehensive effort on risk assessment associated with geologic carbon sequestration through development of a suite of tools to analyze seismic, leakage, and ground water contamination risks. Since the formation of these partnerships, a large quantity of data has been created, collected, and curated, including field test data, spatial data layers, text-based documents, power point presentations, and model outputs. In recent years, the partnerships have transitioned from housing data on private servers and public websites, to using the Energy Data Exchange (EDX). The use of EDX, a data curation and collaboration platform that conforms to FAIR data principles, has enabled publishing of data into public domain for open user access and virtualization of spatial data assets for interactive exploration and visualization within the platform's web mapping tool, Geocube. A suite of machine learning/artificial intelligence (ML/AI) enhanced tools, including SmartSearch, SmartParse, and GeoCube were also utilized to support data tagging, enhance discoverability and improve accessibility. Ultimately, over 630,000 attributes from over 3000 data resources have been acquired, curated, and assigned enhanced metadata and citations through this geo-data science enabled, data management approach on EDX for the carbon storage community. The combination of ML/AI tools trained for this community supports smarter, more efficient implementation of relevant resources. The goal of this implementation is to enhance data access and use for the carbon storage community for decision support, enabling a larger number of carbon storage projects in the future.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMIN0140002M
- Keywords:
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- 1920 Emerging informatics technologies;
- INFORMATICS;
- 1938 Knowledge representation and knowledge bases;
- INFORMATICS;
- 1954 Natural language processing;
- INFORMATICS;
- 1958 Ontologies;
- INFORMATICS