Wenzlick, M., Devanathan, R., Mamun, O., Rose, K., Hawk, J., 2021. Incorporating historical data & past analyses for improved tensile property prediction of 9Cr steel. 2021 TMS Annual Meeting & Exhibition, AI/Data informatics: Design of Structural Materials, Orlando, FL, March 2021. https://www.researchgate.net/publication/349544140_Incorporating_Historical_Data_and_Past_Analyses_for_Improved_Tensile_Property_Prediction_of_9_Cr_Steel
disCO2ver
Unlocking data-driven capabilities for the entire CCS community
Bibliographies
Incorporating Historical Data and Past Analyses for Improved Tensile Property Prediction of 9% Cr Steel
Aseismic deformations perturb the stress state and trigger induced seismicity during injection experiments
Duboeuf, L.; De Barros, L.; Kakurina, M.; Guglielmi, Y.; Cappa, F.; Valley, B. Aseismic deformations perturb the stress state and trigger induced seismicity during injection experiments. Geophysical Journal International 2021, 224(2), 1464-1475. doi: 10.1093/gji/ggaa515. https://academic.oup.com/gji/article-abstract/224/2/1464/5974524?redirectedFrom=fulltext
Tools for Data Collection, Curation, and Discovery to Support Carbon Storage Insights
Mark-Moser, M., Rose, K., Baker, V. D. (2020, December 17). Tools for Data Collection, Curation, and Discovery to Support Carbon Storage Insights. [Conference presentation]. Session: IN042 – Utilizing unstructured data in Earth Science Poster Session. https://ui.adsabs.harvard.edu/abs/2020AGUFMIN0140002M/abstract
NRAP-Open-IAM: A New, Open-Source Code for Integrated Assessment of Geologic Carbon Storage Containment Effectiveness and Leakage Risk
Vasylkivska, V., Bacon D., Chen, Bailian, Dilmore R., Harp D., King S., Lackey G., Lindner E., Liu Guoxiang, Mansoor K., Zhang Yingqi. NRAP-Open-IAM: A New, Open-Source Code for Integrated Assessment of Geologic Carbon Storage Containment Effectiveness and Leakage Risk. AGU Annual Fall Meeting (Virtual), 2020 Session: GC110. Advances in Computational Methods for Geologic CO2 Sequestration I eLightning. https://ui.adsabs.harvard.edu/abs/2020AGUFMGC110..10V/abstract
Developing a structured seafloor sediment database from disparate datasets using SmartSearch
Mark-Moser, M., Rose, K., Baker, V. D. 2020. Developing a structured seafloor sediment database from disparate datasets using SmartSearch. AGU Annual Fall Meeting (Virtual), 2020. Session: IN042 – Utilizing unstructured data in earth science https://www.osti.gov/servlets/purl/1776797
Probabilistic Machine Learning for Integrated Social-Natural-Physical Assessment
Ghanem, R., Zhang, R., Rose, K., invited talk, Probabilistic Machine Learning for Integrated Social-Natural-Physical Assessment, AGU Annual Meeting 2020, Session: H027 – Artificial Intelligence and Machine Learning for Multiscale Model-Experimental Integration https://agu.confex.com/agu/fm20/prelim.cgi/Session/103051
Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry
Dyer, A., Zaengle, D., Mark-Moser, M., Duran, R., Suhag, A., Rose, K., Bauer, J. Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry. AGU Annual Fall Meeting (Virtual), 2020. Session: NH007 – Data Science and Machine Learning for Natural Hazard Sciences II Posters. https://www.osti.gov/servlets/purl/1779617
A knowledge-data framework and geospatial fuzzy logic-based approach to model and predict structural complexity
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, 104153. https://doi.org/10.1016/j.jsg.2020.104153
A Geospatial Analytical Framework to Identify Seafloor Geohazards in the Northern Gulf of Mexico
Duran, R., Dyer, A., Mark-Moser, M., Bauer, J., Rose, K., Zaengle. D., Wingo, P. 2020. A Geospatial Analytical Framework to Identify Seafloor Geohazards in the Northern Gulf of Mexico. AGU Annual Meeting 2020, Session: NH010 – Geohazards in Marine and Lacustrine Environments. https://ui.adsabs.harvard.edu/abs/2020AGUFMNH004..08D/abstract
Optimizing Prediction of Reservoir Properties with Artificial Intelligence, Big Data, and the Subsurface Trend Analysis Method
Mark-Moser, M., Suhag, A., Rose, K., Wingo, P. (2020, November 9). Optimizing prediction of reservoir properties with artificial intelligence, big data, and the Subsurface Trend Analysis method [Conference presentation]. Machine Learning for Oil and Gas 2020, Nov. 9-11, Virtual. https://edx.netl.doe.gov/sites/offshore/optimizing-prediction-of-reservoir-properties-with-artificial-intelligence-big-data-and-the-subsurface-trend-analysis-method/
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
