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
disCO2ver
Unlocking data-driven capabilities for the entire CCS community
Bibliographies
Developing a Virtual Subsurface Data Framework: Transforming DOE’s EDX data lake using ML/NLP
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
Cumulative spatial impact layers: A novel multivariate spatio‐temporal analytical summarization tool
Romeo, L., Nelson, J., Wingo, P., Bauer, J., Justman, D., Rose, K. 2019. Cumulative spatial impact layers: A novel multivariate spatio‐temporal analytical summarization tool. Transactions in GIS.00:1–29. https://doi.org/10.1111/tgis.12558
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
Estimating Carbon Storage Resources in Offshore Geologic Environments
Cameron, E.; Thomas, R.; Bauer, J.; Bean, A.; DiGiulio, J.; Disenhof, C.; Galer, S.; Jones, K.; Mark-Moser, M.; Miller, R.; Romeo, L.; Rose, K. Estimating Carbon Storage Resources in Offshore Geologic Environments; NETL-TRS-14-2018; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Albany, OR, 2018; p 32. DOI: 10.18141/1464460 https://edx.netl.doe.gov/dataset/estimating-carbon-storage-resources-in-offshore-geologic-environments
Variable Grid Method: An Intuitive Approach for Simultaneously Quantifying and Visualizing Spatial Data and Uncertainty
Bauer, J. R., and Rose, K., 2015, Variable Grid Method: an Intuitive Approach for Simultaneously Quantifying and Visualizing Spatial Data and Uncertainty, Transactions in GIS. 19(3), p. 377-397. https://doi.org/10.1111/tgis.12158
Automatic Waveform Quality Control for Surface Waves Using Machine Learning
Chai, C., Kintner, J.A., Cleveland, K.M., Luo, J., Maceira, M., and Charles J. Ammon, C.J., “Automatic Waveform Quality Control for Surface Waves Using Machine Learning,” Seismological Research Letters, 93(3), 1683-1694, (2022) https://doi.org/10.1785/0220210302.
NRAP-Open-IAM: Generic Aquifer Component Development and Testing
Bacon, D. H. NRAP-Open-IAM: Generic Aquifer Component Development and Testing. PNNL-32590, 2022, Pacific Northwest National Laboratory, Richland, WA. https://doi.org/10.2172/1845855.
Machine Learning Enhanced Seismic Monitoring at 100 km and 10 m Scales
Chai, C., Maceira, M., and EGS Collab Team, “Machine Learning Enhanced Seismic Monitoring at 100 km and 10 m Scales,” in Proceedings, 47th Workshop on Geothermal Reservoir Engineering, edited, Stanford University, Stanford, California, 47, 635–645, (2022) https://www.osti.gov/biblio/1845768.
Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes
Gasperikova, E.; Appriou, D.; Bonneville, A.; Feng, Z.; Huang, L.; Gao, K.; Yang, X.; Daley, T. Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes. International Journal of Greenhouse Gas Control 2022, 114, Article 103585. https://doi.org/10.1016/j.ijggc.2022.103585.
Scaling Behavior of Thermally Driven Fractures in Deep Low-Permeability Formations: A Plane Strain Model with 1-D Heat Conduction
Chen, B.; Zhou, Q. Scaling Behavior of Thermally Driven Fractures in Deep Low-Permeability Formations: A Plane Strain Model with 1-D Heat Conduction. Journal of Geophysical Research – Solid Earth 2022, Research Article. https://doi.org/10.1029/2021JB022964.
Distilling Data to Drive Carbon Storage Insights
Morkner, P.; Bauer, J.; Creason, C.; Sabbatino, M.; Wingo, P.; Greenburg, R.; Walker, S.; Yeates, D.; Rose, K. Distilling Data to Drive Carbon Storage Insights. Computers & Geosciences 2022, 158, Article 104945. https://doi.org/10.1016/j.cageo.2021.104945.
Deep Learning Inversion of Gravity Data for Detection of CO2 Plumes in Overlying Aquifers
Yang, X.; Chen, X.; Smith, M.M. Deep Learning Inversion of Gravity Data for Detection of CO2 Plumes in Overlying Aquifers. Journal of Applied Geophysics 2022, 196(104507). https://doi.org/10.1016/j.jappgeo.2021.104507.
A Review of Well Integrity Based on Field Experience at Carbon Utilization and Storage Sites
Iyer, J.; Lackey, G.; Edvardsen, L.; Bean, A.; Carroll, S.A.; Huerta, N.; Smith, M.M.; Torsaeter, M.; Dilmore, R.M.; Cerasi, P. A Review of Well Integrity Based on Field Experience at Carbon Utilization and Storage Sites. International Journal of Greenhouse Gas Control 2022, 113(103533). https://doi.org/10.1016/j.ijggc.2021.103533
A Review of Well Integrity Based on Field Experience at Carbon Utilization and Storage Sites
Iyer, J.; Lackey, G.; Edvardsen, L.; Bean, A.; Carroll, S.A.; Huerta, N.; Smith, M.M.; Torsaeter, M.; Dilmore, R.M.; Cerasi, P. A Review of Well Integrity Based on Field Experience at Carbon Utilization and Storage Sites. International Journal of Greenhouse Gas Control 2022, 113(103533). https://doi.org/10.1016/j.ijggc.2021.103533
Impact of time-dependent deformation on geomechanical risk for geologic carbon storage
Bao T.; Burghardt, J. A.; Gupta, V.; White, M. D. Impact of time-dependent deformation on geomechanical risk for geologic carbon storage. International Journal of Rock Mechanics and Mining Sciences 2021, 148, 104940. PNNL-SA-161528. https://doi.org/10.1016/j.ijrmms.2021.104940.
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
Assessing Risks of Rapid Commercial-Scale Deployment of Geologic Carbon Storage
Bacon, D. Camargo, J. Kirol, A. Haagenson, R. Creason, C. Lackey, G. Morkner, P. Zhou, Q. Cihan, A. Eier, J. Schmidt, B. (2023, August 31). Assessing Risks of Rapid Commercial-Scale Deployment of Geologic Carbon Storage [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Bacon1.pdf
Developing a Tool to Quantify Liability of Geologic Carbon Storage
Morgan, D. Bello, K. Burghardt, J. Creason, C. Dilmore, R. Gasperikova, E. Grant, T. Huerta, N. Liu, G. Kroll, K. Mark-Moser, M. Oldenburg, C. Rasouli, P. Smith, M. Strazisar, B. Vasylkivska, V. Vikara, D. Warner, T. Wilson, K. (2023, August 31). Developing a Tool to Quantify Liability of Geologic Carbon Storage [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Morgan.pdf
Adaptive, Risk-Based Monitoring of Geologic Carbon Storage
Gasperikova, E. Vasylkivska, V. Yang, X. Huang, L. Hanna, A. Chen, B. Creasy, N. Li, D. Blatter, D. Kumar, A. Dilmore, R. Harbert, B. Morgan, D. Iyer, J.K. Smith, M. Kirol, A. Appriou, D. (2023, August 31). Adaptive, Risk-Based Monitoring of Geologic Carbon Storage [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Gasperikova.pdf
NRAP Phase III Overview: Objectives and Progress
Dilmore, R. (2023, August 31). NRAP Phase III Overview: Objectives and Progress [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS31_Dilmore.pdf
Anonymizing Sensitive Carbon Storage Data Tool
Morkner, P. Bauer, J. Wingo, P. Gao, M. Sharma, M. Hoover, B. Neumann, C. Johnson, C. Schuetter, J. Rose, K. (2023, August 29). Anonymizing Sensitive Carbon Storage Data Tool [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS29_Morkner.pdf
A Comprehensive Dashboard for CS Planning
Justman D. Pantaleone, S. Sharma, M. Romeo, L. Morkner, P. Bauer, J. (2023, August 29). A Comprehensive Dashboard for CS Planning [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS29_Justman.pdf
EDX Cloud Optimization for Carbon Management
Rose, K. Baker, V. (2023, August 28). EDX Cloud Optimization for Carbon Management [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS28_Rose.pdf
Licensing and Virtualizing Carbon Storage Models and Tools via EDX disCO2ver
Zaengle, D. Sinclair, J. Wingo, P. Rowan, C. Rose, K. (2023, August 28). Licensing and Virtualizing Carbon Storage Models and Tools via EDX disCO2ver [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS28_Zaengle.pdf
