Carbon Storage Research Portfolio
Carbon Storage Data2021-05-05T16:11:22+00:00

Carbon Storage Data

Overview

The objective of the Carbon Storage Program (CSP) is to create a portfolio of carbon management options to manage emission levels and develop and advance carbon capture, utilization, and storage (CCUS) technologies for widespread commercial deployment by the 2025-2035 timeframe. Multiple projects, including NETL’s intramural research as well as its extramurally funded efforts, are underway, spanning field characterization, laboratory studies, numerical modeling, and analytics to accelerate large-scale demonstration and deployment of carbon capture and sequestration (CCS) technology. This work relies upon the generation and use of significant volumes of data.

The Carbon Storage Data Field Work Proposal (FWP) is one part of this multi-faceted DOE Carbon Storage research and development (R&D) effort, aiming to assist and expedite the Program’s goals by developing and deploying data computing capabilities and enhanced data science solutions tailored to improve collection, organization, and long-term curation of Carbon Storage Program data and knowledge products on NETL’s Energy Data eXchange (EDX). EDX is DOE-FE’s online data library and virtual collaboration platform, providing public access to FE R&D products while also offering capabilities and resources to foster the next generation of data-driven research breakthroughs for the DOE-FE community. The ongoing machine learning (ML)/artificial intelligence (AI) revolution has emphasized the need for improved access to documented and labeled R&D data and knowledge products. EDX and the novel CSP assets it hosts encourage effective and cost-efficient reuse of project-generated datasets and results, benefitting carbon storage researchers and external stakeholders through FAIR (findable, accessible, interoperable, and reusable) data practices and capabilities.

Approach

Carbon storage frequently relies on the use of multi-source data, or data originating from sources ranging from basic science and characterization studies to industry and regulatory resources. The variety, variability, and volume of these data often present a labor- and time-intensive challenge to collect, integrate, label, and format to use to drive advanced analyses and insights in data-driven models and tools. The process of collecting and processing data alone can take up the majority of a project’s time and can impede the timeliness and accuracy of project outcomes. The goal of the Carbon Storage Data portfolio is to curate data produced by, and relevant to the work of, the Carbon Storage Program, while also creating interoperability tools and technologies that help the broader CCS community rapidly and effectively simulate, model, predict, and address CCS technology challenges and accelerate widespread commercial-scale deployment.

Anticipated Outcomes

The projects within this portfolio focus on the development of geospatial and R&D data management platforms, such as EDX and National Carbon Sequestration Database (Natcarb), to ensure the effective use of and access to CS-related data. Current projects center around:

  • Supporting the development of data, materials, maps, analyses, and figures for the Carbon Storage Atlas, Natcarb Viewer, and Natcarb Database. The outcomes of this project will include the release of new data and insights to the Geologic Carbon Storage (GCS) community.
  • Collecting, curating, and cataloging existing data from CSP R&D projects in a user-friendly format by utilizing geo-data science and advanced data computing solutions. The outcome of this project is the development of a virtual carbon storage data library, granting users access to original data products as well as more structured databases with key carbon storage attributes and features.
  • Developing novel, tailored AI/ML and deep learning tools to help the broader CCS community with more efficient discovery, collection, exploration, transformation, and integration of data needed to support efficient and effective forecasting from CCS models and analytical tools.
  • Delivering and utilizing geo-data science and advanced data computing expertise to support the efficient acquisition, integration, and release of all CS-related RIC research products (publications, tools, data, presentations, etc.) using the EDX Carbon Storage Portfolio site. The outcome of this project is a technical, public portal to all NETL CSP public information, project background, products, etc., to ensure stakeholder access to all carbon storage public data.

Find, connect, curate, use and re-use data to advance energy, environmental, and innovative R&D.

Offshore geologic storage in the United States is currently being investigated for its potential to serve as one of the options for safe, long-term CO2 storage.

Themes

SMART Curation of FE Carbon Storage R&D Products – The innovative R&D performed by the research team under the CS Data FWP aims to develop custom AI/ML and Natural Language Processing (NLP) methods to integrate disparate data, producing structured, labeled big data sets that improve discovery, transformation, and reuse of relevant data for the broader carbon storage community’s benefit, ultimately, ensuring “smart,” enduring, and efficient access to these resources to meet a range of stakeholder needs.

Innovation of AI/ML-Enhanced Data Management Solutions- The projects within this theme focus on establishing data management and storage workflows, including those enhanced with NLP and ML algorithms, and providing guidance for data generated as part of the DOE CSP projects. Using NLP and ML techniques coupled with big data computing algorithms, NETL presents updated capabilities emerging through EDX to mature the Virtual Subsurface Data Framework and enhance data-driven R&D and analytics for the range of end-user needs.

Recent Research Products

A Knowledge-Data Framework and Geospatial Fuzzy Logic-Based Approach to Model and Predict Structural Complexity

December 1st, 2020|

Justman, D., Creason, C. G., Rose, K., & Bauer, J. (2020). A Knowledge-Data Framework and Geospatial Fuzzy Logic-Based Approach to Model and Predict Structural Complexity. Journal of Structural Geology, 141. https://doi.org/10.1016/j.jsg.2020.104153

Reframing Resources: Offshore CO2 Storage in the Gulf of Mexico

July 14th, 2020|

Romeo, L., Bean, A., Mark-Moser, M., Thomas, R.B., & Rose, K., (2020, July 14). Reframing Resources: Offshore CO2 Storage in the Gulf of Mexico. Esri Users Conference, San Diego, CA.

Exploring Beneath the Basemap. GIS for Science

January 1st, 2020|

Romeo, L., & Rose, K., et. al. (2020). Exploring Beneath the Basemap. GIS for Science. https://www.gisforscience.com/chapter5/

Moving Data “Rocks” Out of Hard Places: Adapting and Innovating Data Science Tools to Improve Geoscience Analytics

December 9th, 2019|

Yeates, D., Walker, S., Fillingham, J., Sabbatino, M., Suhag, A., Rose, K., Mark-Moser, M., Creason, C.G., & Baker, V. (2019, December 9–13). Moving Data “Rocks” Out of Hard Places: Adapting and Innovating Data Science Tools to Improve Geoscience Analytics. American Geophysical Union Annual Fall Meeting, San Francisco, CA.  

Back to the Future: Rescue, Curation, and Transformation of a Corpus of Carbon Storage Data

December 9th, 2019|

Sabbatino, M., Baker, V., Bauer, J., Creason, C., Romeo, L., Rose, K., Rowan, C., and Zoch, G., “Back to the Future: Rescue, Curation, and Transformation of a Corpus of Carbon Storage Data,” submitted, American Geophysical Union (AGU) Annual Fall Meeting, Session: AGU Dirty Stories of Data Rescue, San Francisco, CA, December 9–13, 2019, https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/600855.

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