Justman, D., Creason, C. G., Pantaleone, S., Amrine, D., Rose, K. (2023, October 15-18). Developing a National Structural Complexity Database for U.S. Saline Basins [Conference presentation]. Geological Society of America Annual Meeting. Pittsburgh, PA. https://gsa.confex.com/gsa/2023AM/meetingapp.cgi/Paper/391762
disCO2ver
Unlocking data-driven capabilities for the entire CCS community
Bibliographies
Developing a National Structural Complexity Database for U.S. Saline Basins
Carbon Storage Open Data Geospatial Curation and Accessibility
Choisser, A., Morkner, P., Sabbatino, M., Bauer, J., Rose, K. (2023, October 16-18). Carbon Storage Open Data Geospatial Curation and Accessibility [Conference presentation]. Geological Society of America Annual Meeting. Pittsburgh, PA. https://community.geosociety.org/gsa2023/home
RokBase: Digital Rock Visualization and Exploration Web Application
Sharma, M. Paronish, T. Crandall, D. Naberhaus, T. Nakacwa, S. (2023, October 16). RokBase: Digital Rock Visualization and Exploration Web Application [Conference presentation]. GSA Connects Conference 2023. https://gsa.confex.com/gsa/2023AM/meetingapp.cgi/Paper/394714
CO2-Locate: A National Oil & Gas Wellbore Database and Visualization Tool to Support Geological and Environmental Assessment
Sharma, M. Romeo, L. Bauer, J. Amrine, D. Pfander, I. Sabbatino, M. Rose, K. (2023, October 15) CO2-Locate: A National Oil & Gas Wellbore Database and Visualization Tool to Support Geological and Environmental Assessment [Conference presentation]. GSA Connects Conference 2023. https://gsa.confex.com/gsa/2023AM/meetingapp.cgi/Paper/395013
Understanding Federal Data Curation Requirements and EDX++ Tool to Serve CS Data Curation Needs
Rowan, C. Sinclair, J. (2023, August 31). Understanding Federal Data Curation Requirements and EDX++ Tool to Serve CS Data Curation Needs [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Rowan.pdf
DOE’s Carbon Matchmaker
Sharma, M. Dooley, K. (2023, August 31). DOE’s Carbon Matchmaker [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Sharma.pdf
Carbon Storage Bipartisan Infrastructure Law Communications and Stakeholder Engagements
Wanosky, G. Sinclair, J. (2023, August 31). Carbon Storage Bipartisan Infrastructure Law Communications and Stakeholder Engagements [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Wanosky.pdf
SMART Site-Specific Visualization and Decision Support
Bacon, D. Morgan, D. Mudunuru, M. (2023, August 31). SMART Site-Specific Visualization and Decision Support [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Bacon2.pdf
Advanced Machine Learning and Computational Methods
Schuetter, J. Tartakovsky, A. Shih, C. (2023, August 31). Advanced Machine Learning and Computational Methods [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Schuetter.pdf
Overview of SMART Initiative
Siriwardane H. Mishra, S. (2023, August 31). Overview of SMART Initiative [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Siriwardane.pdf
Extensive Pipeline Location Data Resource: Integrating Reported Incidents, Past Environmental Loadings, and Potential Geohazards for Integrity Evaluations in the U.S. Gulf of Mexico
Isabelle Pfander, Lucy Romeo, Rodrigo Duran, Alec Dyer, Catherine Schooley, Madison Wenzlick, Patrick Wingo, Dakota Zaengle, Jennifer Bauer. Extensive pipeline location data resource: Integrating reported incidents, past environmental loadings, and potential geohazards for integrity evaluations in the U.S. Gulf of Mexico, Data in Brief, Volume 55, 2024, 110728, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110728.
High-Resolution CT Scan Dataset of Lower Mount Simon Sandstone Samples from the Illinois Basin
Magdalena Gill, Mathias Pohl, Sarah Brown, Karl Jarvis, Dustin Crandall, High-resolution computed tomography scan dataset of lower Mount Simon Sandstone samples from the Illinois Basin, Data in Brief, Volume 55, 2024, 110643, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110643.
Scoping Review of Global Offshore Geologic Carbon Storage Activities
Choisser, A., Mark-Moser, M., Mulhern, J., Rose, K. (2023) Scoping Review of Global Offshore Geologic Carbon Storage Activities. National Energy Technology Laboratory Technical Report Series, DOE/NETL-2024/4798 https://edx.netl.doe.gov/dataset/scoping-review-of-global-offshore-geologic-carbon-storage-activities
Computed Tomography Scanning and Petrophysical Measurements of Illinois Basin Coal Wells
Paronish, T.; Crandall, D.; Jarvis, K.; Workman, S.; Drosche, J.; Pohl, M.; Mckisic, T.; McLaughlin P.; Friedberg, J.; Delpomdor F. Computed Tomography Scanning and Petrophysical Measurements of Illinois Basin Coal Wells; DOE/NETL-2024/4799; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2024; p 56. http://edx.netl.doe.gov/dataset/computed-tomography-scanning-and-petrophysical-measurements-of-illinois-basin-coal-wells. DOI: 10.2172/2282147.
A Curated Data Resource to Support Safe Carbon Dioxide Transport-Route Planning
Catherine Schooley, Lucy Romeo, Isabelle Pfander, Maneesh Sharma, Devin Justman, Jennifer Bauer, Kelly Rose. A curated data resource to support safe carbon dioxide transport-route planning. Data in Brief, Volume 52, 2024, 109984, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.109984.
Enhancing Knowledge Discovery from Unstructured Data Using a Deep Learning Approach to Support Subsurface Modeling Predictions
Hoover B, Zaengle D, Mark-Moser M, Wingo P, Suhag A and Rose K. (2023) Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions. Front. Big Data 6:1227189. doi: https://doi.org/10.3389/fdata.2023.1227189
Dynamic risk assessment for geologic CO2 sequestration
Chen, B.; Harp, D. R.; Zhang, Y.; Oldenburg, C. M.; Pawar, R. J. (in Press, Corrected Proof). Dynamic risk assessment for geologic CO2 sequestration. Gondwana Research 2022. https://doi.org/10.1016/j.gr.2022.08.002.
Integrating Risk Assessment Methods for Carbon Storage: A Case Study for the Quest Carbon Capture and Storage Facility
Brown, C. F., G. Lackey, N. Mitchell, S. Baek, B. Schwartz, M. Dean, R. Dilmore, H. Blanke, S. O’Brien, and C. Rowe. 2023. “Integrating Risk Assessment Methods for Carbon Storage: A Case Study for the Quest Carbon Capture and Storage Facility.” International Journal of Greenhouse Gas Control 129: 103972. https://doi.org/10.1016/j.ijggc.2023.103972.
A Project Lifetime Approach to the Management of Induced Seismicity Risk at Geologic Carbon Storage Sites
Dennise C. Templeton, Martin Schoenball, Corinne E. Layland‐Bachmann, William Foxall, Yves Guglielmi, Kayla A. Kroll, Jeffrey A. Burghardt, Robert Dilmore, Joshua A. White; A Project Lifetime Approach to the Management of Induced Seismicity Risk at Geologic Carbon Storage Sites. Seismological Research Letters 2022;; 94 (1): 113–122. https://doi.org/10.1785/0220210284
A Framework to Simulate the Blowout of CO2 Through Wells in Geologic Carbon Storage
Bhuvankar, P.; Cihan, A.; Birkholzer, J. A Framework to Simulate the Blowout of CO2 Through Wells in Geologic Carbon Storage. International Journal of Greenhouse Gas Control, 2023, 127, Article 103921, ISSN 1750-5836. https://doi.org/10.1016/j.ijggc.2023.103921.
Possible Controls on Porosity Preservation in the Andaman Forearc Gas Hydrate System
Johnson, J., Rose, K., Torres, M. (2020, Jan). Possible controls on porosity preservation in the Andaman forearc gas hydrate system: OSR, AOM, and/or marine silicate weathering [Conference presentation]. Geologic Society of America Meeting 2020, Session: T99. Records of Early Diagenesis in Modern and Ancient Sediments. https://community.geosociety.org/gsa2020/program/technical
Back to the Future: Rescue, Curation, and Transformation of a Corpus of Carbon Storage Data
Sabbatino, M., Baker, V., Bauer, J., Creason, C., Romeo, L., Rose, K., Rowan, C., Zoch, G., submitted, Back to the Future: Rescue, Curation, and Transformation of a Corpus of Carbon Storage Data, Annual Meeting 2019, Session: AGU Dirty Stories of Data Rescue. https://www.osti.gov/servlets/purl/1778129
Developing a Virtual Subsurface Data Framework: Transforming DOE’s EDX data lake using ML/NLP
Rose, R. Rowan, C., Sabbatino, M., Baker, V., Bauer, J., Creason, C.G., Jones, T.J., Justman, D., Romeo, L., Suhag, A., Yeates, D., and Walker, S., submitted, Developing a Virtual Subsurface Data Framework: Transforming DOE’s EDX data lake using ML/NLP, Annual Meeting 2019, Session: IN020 – Data Integration: Enabling the Acceleration of Science Through Connectivity, Collaboration, and Convergent Science. https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/596761
Moving data “rocks” out of hard places: adapting and innovating data science tools to improve geoscience analytics
Yeates, D., Walker, S., Fillingham, J., Sabbatino, M., Suhag, A., Rose, K., Mark-Moser, M., Creason, C.G., Baker, V., submitted, Moving data “rocks” out of hard places: adapting and innovating data science tools to improve geoscience analytics, AGU Annual Meeting 2019, Session IN005 – AI for Model and Data Integration in the Geosciences. https://ui.adsabs.harvard.edu/abs/2019AGUFMIN32B..09Y/abstract
Subsurface Trend Analysis
Rose, K., Mark-Moser, M., Suhag, A. Subsurface Trend Analysis: A methodical framework for artificial intelligence subsurface property prediction. Machine Learning for Unconventional Resources, Nov. 18th 2019, University of Houston, Texas. https://www.osti.gov/servlets/purl/1778138
Putting Data to Work: Transforming Disparate Open-Source Data for Engineered-Natural Systems and Models
Creason, C.G., Romeo, L., Bauer, J., Rose, K., Rowan, C., and Sabbatino, M., 2019, Putting Data to Work: Transforming Disparate Open-Source Data for Engineered-Natural Systems and Models, AGU Annual Meeting 2019, Session: IN020 – Data Integration: Enabling the Acceleration of Science Through Connectivity, Collaboration, and Convergent Science. https://www.osti.gov/biblio/1778210
