Amrine, D. Justman, D. Creason, C. Pantaleone, S. Gordon, A. Rose, K. (2023, August 28). Developing a National Structural Complexity Database for U.S. Saline Basins [Poster presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTSp_Amrine.pdf
disCO2ver
Unlocking data-driven capabilities for the entire CCS community
Bibliographies
Developing a National Structural Complexity Database for U.S. Saline Basins
Managing Carbon Storage Data With a Living Database
Sabbatino, M. Morkner, P. Choisser, A. Leveckis, S. Bauer, J. Rose, K. (2023, August 28). Managing Carbon Storage Data With a Living Database [Poster presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTSp_Sabbatino.pdf
Machine Learning Based Fracture Network Quantification at the IBDP CO2 Sequestration Site
Kumar, A. Harbert, W. Liu, G. Myshakin, E.(2023, August 28). Machine Learning Based Fracture Network Quantification at the IBDP CO2 Sequestration Site [Poster presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTSp_Kumar.pdf
Class II to Class IV Operations – Insights from Simulation-Based Investigation of a CO2-EOR to Dedicated Storage Scenario
Liu, G. Dilmore, R. Strazisar, B. Lackey, G. (2023, August 28). Class II to Class IV Operations – Insights from Simulation-Based Investigation of a CO2-EOR to Dedicated Storage Scenario [Poster presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTSp_Liu.pdf
Application of NRAP Risk Assessment Tools in the Context of Bowtie Risk Management Framework
Brown, C. Lackey, G. Mitchell, N. Baek, S. Schwartz, B. Dean, M. Dilmore, R. Blanke, H. Rowe, C. (2023, August 28). Application of NRAP Risk Assessment Tools in the Context of Bowtie Risk Management Framework [Poster presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTSp_Brown.pdf
Conceptualizing Data Availability and Technical Viability Methods within the Carbon Storage Technical Viability (CS TVA) Approach
Mulhern, J. Mark-Moser, M. Creason, C. Shay, J. Rose, K. (2023, August 28). Conceptualizing Data Availability and Technical Viability Methods within the Carbon Storage Technical Viability (CS TVA) Approach [Poster presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTSp_Mulhern.pdf
Carbon Storage Program Data Curation, Transformation and Reuse
Morkner, P. Bauer, J. Choisser, A. Sabbatino, M. Leveckis, S. Rose, K. (2023, August 28) Carbon Storage Program Data Curation, Transformation and Reuse [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS28_Morkner.pdf
EJ/SJ Dynamic Datasets for CCS Systems and the Energy Transition Web Atlas Tool
Sharma, M. White, C. Cleaveland, C. Romeo, L. Bauer, J. Rose, K. (2023, August 28). EJ/SJ Dynamic Datasets for CCS Systems and the Energy Transition Web Atlas Tool [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS28_Sharma.pdf
Developing a National Structural Complexity Database for U.S. Saline Basins
Justman, D. Creason, C. Pantaleone, S. Gordon, A. Amrine, D. Rose, K. (2023, August 28). Developing a National Structural Complexity Database for U.S. Saline Basins [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS28_Justman.pdf
Carbon Storage Technical Viability Approach
Mark-Moser, M. Creason, C. Mulhern, J. Shay, J. Lara, A. Rose, K. (2023, August 28). Carbon Storage Technical Viability Approach [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS28_Mark-Moser.pdf
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.
Development of Machine Learning Models for Full Field Reservoir Characterization
Wu, X., Shih, C., Mark-Moser, M., and Wingo, P., 2021. Development of machine learning models for full field Reservoir Characterization. AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session H34D – Application of Multimodal Physics-Informed Machine Learning/Deep Learning in Subsurface Flow and Transport Modeling. https://www.osti.gov/servlets/purl/1846178
Science-based Artificial Intelligence and Machine Learning (AI/ML) Institute (SAMI) – Accelerating Cross-Disciplinary AI/ML for Applied Geoscience, Energy, and Environmental Challenges
Shih, C., Thornton, J., Rose, K., Syamlal, M., Bromhal, G., Guenther, C., Pfautz, J., Van Essendelft, D., and Bauer, J., 2021, Science-based Artificial Intelligence and Machine Learning (AI/ML) Institute (SAMI) – accelerating cross-disciplinary AI/ML for applied geoscience, energy, and environmental challenges. AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session: IN12A – Growing Opportunities for Multiparty Collaborations in Artificial Intelligence and Machine Learning for Science Research. https://ui.adsabs.harvard.edu/abs/2021AGUFMIN12A..05S/abstract
Improving Prediction of Subsurface Properties Using a Geoscience Informed, Multi-Technique, Artificial Intelligence Approach
Rose, K., Mark-Moser, M., Suhag, A., and Bauer, J. 2021. Improving prediction of subsurface properties using a geoscience informed, multi-technique, artificial intelligence approach (Invited). AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session H33C – Application of Multimodal Physics-Informed Machine Learning/Deep Learning in Subsurface Flow and Transport Modeling. https://ui.adsabs.harvard.edu/abs/2021AGUFM.H33C..01R/abstract
Leveraging Data Ecosystems to Support Earth Science Research for Decarbonization
Morkner, P., Mark-Moser, M., Justman, D., Rowan, C., Bauer, J., and Rose, K., 2021. Leveraging Data Ecosystems to Support Earth Science Research For Decarbonization. AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session U21A-07 – How Earth Science Research Can Help Accelerate the Transition to a Decarbonized Economy. https://ui.adsabs.harvard.edu/abs/2021AGUFM.U21A..07M/abstract
Exploring Subsurface Data Availability on the Energy Data eXchange (EDX)
Morkner, P., Bean, A., Bauer, J., Barkhurst, A., and Rose, K.. 2021. Exploring subsurface data availability on the Energy Data eXchange. AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session: SY039 – Subsurface Storage of Natural Gas, CO2, and Hydrogen: Key Learnings and Future Opportunities. https://www.osti.gov/servlets/purl/1846774
AI/ML Integration for Accelerated Analysis and Forecast of Offshore Hazards
Mark-Moser, M., Wingo, P., Duran, R., Dyer, A., Zaengle, D., Suhag, A., Hoover, B., Pantaleone, S., Shay, J., Bauer, J., and Rose, K. 2021. AI/ML integration for accelerated analysis and forecast of offshore hazards. AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session: EP027 – Proven AI/ML applications in the Earth Sciences. https://www.osti.gov/servlets/purl/1846789
On the Predictability of Loop Current Eddy Shedding Events and Unexpected Links to the Brazil and Guiana Currents
Duran, R., Liang, X.S., Allende-Arandia, M.E., Appendini, C.M., Mark-Moser, M., Rose, K., Bauer, J. 2021. On the predictability of Loop Current Eddy Shedding events and unexpected links to the Brazil and Guiana Currents. AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session: OS45D – Ocean Dynamics of the Gulf of Mexico III Poster. https://www.osti.gov/servlets/purl/1846777
Evaluating the Effects of a Low-Carbon Energy Transition on Existing U.S. Fossil Energy Communities
Bauer, J., Rose, K., Romeo, L., Justman, D., Hoover, B., and B. White. 2021. Evaluating the effects of a low-carbon energy transition on existing U.S. fossil energy communities. AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session GC25G: Environmental Justice/Equity and Global Change: Methodologies, Frameworks, and Results II Poster. https://ui.adsabs.harvard.edu/abs/2021AGUFMGC25G0722B/abstract
Forecasting 3D Structural Complexity with AI/ML method: Mississippi Canyon, Gulf of Mexico
Pantaleone, S., Mark Moser, M., Bean, A., Walker, S., Rose, K., 2021, “Forecasting 3D Structural Complexity with AI/ML method: Mississippi Canyon, Gulf of Mexico”. AAPG/SEG IMAGE conference, Denver, Colorado, September 26, 2021 October 1, 2021. https://edx.netl.doe.gov/sites/offshore/forecasting-3d-structural-complexity-with-ai-ml-method-mississippi-canyon-gulf-of-mexico/
Assessing Current & Future Infrastructure Hazards: Forecasting Integrity using Machine Learning & Advanced Analytics
Romeo, L. (2021, August 9). Assessing Current & Future Infrastructure Hazards: Forecasting Integrity using Machine Learning & Advanced Analytics [Conference presentation]. Carbon Management and Oil and Gas Research Project Review Meeting. https://netl.doe.gov/sites/default/files/netl-file/20VPRONG_26_Romeo.pdf
