Carbon Storage Wellbore Materials Data Tool
For presentation at the Carbon Management Review Meeting 2023, Pittsburgh, PA, August 28-September 1, 2023.
Engineering topics
Publications and source records attributed to Rose, Kelly.
For presentation at the Carbon Management Review Meeting 2023, Pittsburgh, PA, August 28-September 1, 2023.
For presentation at the 2023 FECM / NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 30-Septamber 1, 2023.
For presentation at the FECM/ NETL Carbon Management Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
For presentation at the FECM / NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
Presentation for Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
Presentation for the National Energy Technology Laboratory's Carbon Management Review Meeting 2023 on the EDX4CCS FWP efforts under Task 21.
Poster presentation for 2023 NETL Carbon Management Review Meeting
For presentation at the 2023 FECM / NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023
For presentation at the 2023 FECM/NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
For presentation at the 2023 FECM / NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
For presentation at the Carbon Management Project Review Meeting, Pittsburgh, PA, August 28--September 1, 2023.
The Geospatial and Information Substitution and Anonymization Tool (GISA) incorporates techniques for obfuscating identifiable information from point data or documents, while simultaneously maintaining chosen variables to enable future use and meaningful analysis. This approach promotes collaboration and data sharing while also reducing the risk of exposure to sensitive information. GISA can be used in a number of different ways, including the anonymization of point spatial data, batch replacement/removal of user-specified terms from file names and from within file content, and aid with the selection and redaction of images and terms based on recommendations using natural language processing. Version 1 of the tool, published here, has updated functionality and enhanced capabilities to the beta version published in 2023. Please see User Documentation for further information on capabilities, as well as a guide for how to download and use the tool. If there are any feedback you would like to provide for the tool, please reach out with your feedback to edxsupport@netl.doe.gov. Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The Geospatial and Information Substitution and Anonymization Tool (GISA) was developed jointly through the U.S. DOE Office of Fossil Energy and Carbon Management’s EDX4CCS Project, in part, from the Bipartisan Infrastructure Law.
The Carbon Storage Technical Viability Approach (CS TVA) database was developed to support the implementation of the CS TVA Matrix to a national data availability assessment for technically viable carbon storage. This database leverages the efforts of multiple adjacent and overlapping databases by non-redundantly combining the databases into a single database along with additionally providing tags facilitating the CS TVA. The non-redundant aspect of the database permits an accurate assessment of the concentration of available data, aiding in spatial and categorical data gaps analysis relative to the individual CS TVA Matrix Components. Version 2.0 of the database is an expansion of Version 1.0. Version 2.0 was created to include additional data gathered to fill gaps in the existing data set. Downloading the CS TVA v2.0 database will result in two separate databases, the version 1.0 original .gdb, and a second addendum .gdb with the new data gathered, together these two databases make up v2.0. Please see the ReadMe file below for full details, metadata information, use disclaimer, and attributions.
Carbon Storage BIL Workshop, Santa Fe, NM, May 1-3, 2023
Carbon Storage BIL Workshop, Santa Fe, NM, May 1-3, 2023
Carbon Storage BIL Workshop, Santa Fe, NM, May 1-3, 2023
Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.