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
disCO2ver
Unlocking data-driven capabilities for the entire CCS community
Bibliographies
Assessing Risks of Rapid Commercial-Scale Deployment of Geologic Carbon Storage
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
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
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
EDX++: Migrating EDX to the Cloud, Unlocking Next-Generation Data Infrastructure
Baker, V. Rose, K. Obradovich, J. McFarland, D. Jones, TJ. Mondello, J. Dean, E. Sarle, J. (2023, August 28). EDX++: Migrating EDX to the Cloud, Unlocking Next-Generation Data Infrastructure [Conference presentation]. FECM/NETL Carbon Management Meeting 2023. https://netl.doe.gov/sites/default/files/netl-file/23CM_CTS28_Baker.pdf
Regulatory Considerations for Geologic Storage of Carbonated Brine Streams. 16th International Conference on Greenhouse Gas Control Technologies
Van Voorhees, R.; Thomas, R. B.; Schwartz, B.; Dilmore, R.; Hamling, J.; Klapperich, R.; Taunton, M. Regulatory Considerations for Geologic Storage of Carbonated Brine Streams. 16th International Conference on Greenhouse Gas Control Technologies, GHGT-16, 23-24th October 2022, Lyon, France. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4285028
Enhancing High-Fidelity Nonlinear Solver with Reduced Order Model
Kadeethum, T., O’Malley, D., Ballarin, F., Ang, I., Fuhg, J.N., Bouklas, N., Silva, V.L.S., Salinas, P., Heaney, C.E., Pain, C.C., Lee, S., Viswanathan, H.S., and Yoon, H., “Enhancing High-Fidelity Nonlinear Solver with Reduced Order Model,” Scientific Reports, 12, Article 20229. (2022) https://doi.org/10.1038/s41598-022-22407-6.
A Quantitative Comparison of Risk-based Leak Mitigation Strategies at a Geologic Carbon Storage Site
Lackey, G.; Mitchell, N.; Schwartz, B.; Liu, G.; Vasylkivska, V. S.; Strazisar, B.; Dilmore, R. M. A Quantitative Comparison of Risk-based Leak Mitigation Strategies at a Geologic Carbon Storage Site. 16th International Conference on Greenhouse Gas Control Technologies, GHGT-16, 23-24th October 2022, Lyon, France. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4271578
Continuous Conditional Generative Adversarial Networks for Data-Driven Solutions of Poroelasticity with Heterogeneous Material Properties
Kadeethum, T., O’Malley, D., Choi, Y., Viswanathan, H.S., Bouklas, N., and Yoon, H., “Continuous Conditional Generative Adversarial Networks for Data-Driven Solutions of Poroelasticity with Heterogeneous Material Properties,” Computers & Geosciences, Vol. 167, 105212, (2022), https://doi.org/10.1016/j.cageo.2022.105212.
TOUGH3-FLAC3D: a modeling approach for parallel computing of fluid flow and geomechanics
Rinaldi, A. P.; Rutqvist, J.; Luu, K.; Blanco-Martin, L.; Hu, M. et al. TOUGH3-FLAC3D: a modeling approach for parallel computing of fluid flow and geomechanics. Computational Geosciences 2022, 26, 1563–1580. https://doi.org/10.1007/s10596-022-10176-0.
Data-driven offshore CO2 saline storage assessment methodology
Romeo, L., Thomas, R., Mark-Moser, M., Bean, A., Bauer, J. and Rose, K., 2022. Data-driven offshore CO2 saline storage assessment methodology. International Journal of Greenhouse Gas Control, 119, p.103736. https://www.sciencedirect.com/science/article/pii/S1750583622001542
Data-driven offshore CO2 saline storage assessment methodology
Romeo, L., Thomas, R., Mark-Moser, M., Bean, A., Bauer, J. and Rose, K., 2022. Data-driven offshore CO2 saline storage assessment methodology. International Journal of Greenhouse Gas Control, 119, p.103736. https://www.sciencedirect.com/science/article/pii/S1750583622001542
3D Visualization of Integrated Geologic and Geophysical Subsurface Data Using Open-Source Programming: A Case Study Using Data from the MSEEL Project
Panetta, B., Carr, T., and Fathi, E., “3D Visualization of Integrated Geologic and Geophysical Subsurface Data Using Open-Source Programming: A Case Study Using Data from the MSEEL Project,” AAPG and SEG Second International Meeting for Applied Geoscience & Energy, August 14-15, 2022, Houston, TX, expanded abstract, https://doi.org/10.1190/image2022-3746025.1
Deep Learning Multiphysics Network for Imaging CO2 Saturation and Estimating Uncertainty in Geological Carbon Storage
Um, E.S., Alumbaugh, D., Commer, M., Feng, S., Gasperikova, E., Li, Y., Lin, Y., and Samarasinghe, S., “Deep Learning Multiphysics Network for Imaging CO2 Saturation and Estimating Uncertainty in Geological Carbon Storage;” Geophysical Prospecting, (2022) https://doi.org/10.1111/1365-2478.13257.
Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring
Liu, G., Kumar, A., Zhao, S., Shih, C., Vasylkivska, V., Holcomb, P., Hammack, R., Ilconich, J., and Bromhal, G., “Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring,” presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA, (June 2022) https://doi.org/10.15530/urtec-2022-3723466.
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
