Liu, Y., Feng, S., Tsvankin, I., Alumbaugh, D., and Lin, Y., “Joint Physics-Based and Data-Driven Time-Lapse Seismic Inversion: Mitigating Data Scarcity,” Geophysics, (2022) doi.org/10.1190/geo2022-0050.1.
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Bibliographies
Joint Physics-Based and Data-Driven Time-Lapse Seismic Inversion: Mitigating Data Scarcity
NRAP Recommended Practices for Containment Assurance and Leakage Risk Quantification
Thomas, R. B.; Schwartz, B.; Oldenburg, C.; Bacon, D. H.; Gasperikova, E.; Lackey. G.; Appriou, D.; Harp, D.; Chen, B.; Doughty, C.; Burghardt, J.; Pawar, R. J.; Brown, C. F.; Smith, M. M.; Van Voorhees, R.; Strazisar, B. R.; Dilmore, R. M. NRAP Recommended Practices for Containment Assurance and Leakage Risk Quantification; NRAP-TRS-I-002-2022; DOE.NETL-2022.3344; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Pittsburgh, PA, 2022; p 76. DOI: 10.2172/1906399 https://www.osti.gov/biblio/1906399/
Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership
Dilmore, R. M.; Appriou, D.; Bacon, D.; Brown, C.; Cihan, A.; Gasperikova, E.; Kroll, K.; Oldenburg, C. M.; Pawar, R. J.; Smith, M. M.; Strazisar, B. R.; Templeton, D.; Thomas, R. B.; Vasylkivska, V. S.; White, J. A. Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership. 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=4298480
Extended Abstract to: Integrating Qualitative and Quantitative Risk Assessment Methods for Carbon Storage: A Case Study for the Quest Carbon Capture and Storage Facility
Brown, C. F.; Lackey, G.; Schwartz, B.; Deane, M.; Dilmore, R.; Blanke, H.; O’Brien, S.; Rowe, C. O’Brien, S.; Rowe, C. Extended Abstract to: Integrating Qualitative and Quantitative Risk Assessment Methods for Carbon Storage: A Case Study for the Quest Carbon Capture and Storage Facility. 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=4297575
High-Quality Fracture Network Mapping Using High Frequency Logging While Drilling (LWD) Data: MSEEL Case Study
Fathi, E., Carr, T.R., Adenan, M.F., Panetta, B., Kumar, A., and Carney, B.J., ”High-Quality Fracture Network Mapping Using High Frequency Logging While Drilling (LWD) Data: MSEEL Case Study,” Machine Learning with Applications, Vol. 10 (2022), https://doi.org/10.1016/j.mlwa.2022.100421.
Reduced Order Modeling for Flow and Transport Problems with Barlow Twins Self-Supervised Learning
Kadeethum, T., Ballarin, F., O’Malley, D., Choi, Y., Bouklas, N., and Yoon, H., “Reduced Order Modeling for Flow and Transport Problems with Barlow Twins Self-Supervised Learning,” Scientific Reports, 12, Article 20654 (2022), https://doi.org/10.1038/s41598-022-24545-3.
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.
Rules and Tools Crosswalk: A Compendium of Computational Tools to Support Geologic Carbon Storage Environmentally Protective UIC Class VI Permitting
Lackey, G.; Strazisar, B. R.; Kobelski, B.; McEvoy, M.; Bacon, D. H.; Cihan, A.; Iyer, J.; Livers-Douglas, A.; Pawar, R.; Sminchak, J.; Wernette, B.; Dilmore, R. M. Rules and Tools Crosswalk: A Compendium of Computational Tools to Support Geologic Carbon Storage Environmentally Protective UIC Class VI Permitting; NRAP-TRS-I-001-2022; DOE.NETL-2022.3731; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Pittsburgh, PA, 2022; p 120. DOI: https://doi.org/10.2172/1870412
Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks
Dyer, A.S., Zaengle, D., Nelson, J.R., Duran, R., Wenzlick, M., Wingo, P.C., Bauer, J.R., Rose, K., and Romeo, L. (2022). Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks, Marine Structures, Volume 83, 103152. https://doi.org/10.1016/j.marstruc.2021.103152.
Bayesian Approach for In-Situ Stress Prediction and Uncertainty Quantification for Subsurface Engineering
Bao, T.; Burghardt, J. A. Bayesian Approach for In-Situ Stress Prediction and Uncertainty Quantification for Subsurface Engineering. Rock Mechanics and Rock Engineering 2022, 55, 4531–4548. https://doi.org/10.1007/s00603-022-02857-0.
Thermal and solubility effects on fault leakage during geologic carbon storage
Meguerdijian, S.; Pawar, R. J.; Harp, D. R.; Jha, B. Thermal and solubility effects on fault leakage during geologic carbon storage. International Journal of Greenhouse Gas Control 2022, 116, Article 103633. https://doi.org/10.1016/j.ijggc.2022.103633.
Leakage from Coexisting Geologic Forcing and Injection-Induced Pressurization: A Semi-Analytical Solution for Multilayered Aquifers with Multiple Wells
Cihan, A.; Oldenburg, C. M.; Birkholzer, J. T. Leakage from Coexisting Geologic Forcing and Injection-Induced Pressurization: A Semi-Analytical Solution for Multilayered Aquifers with Multiple Wells. Water Resources Research 2022, 58 (5), e2022WR032343. https://doi.org/10.1029/2022WR032343.
Coupled hydromechanical modeling of induced seismicity from CO2 injection in the Illinois Basin
Luu, K.; Schoenball, M.; Oldenburg, C. M.; Rutqvist, J. Coupled hydromechanical modeling of induced seismicity from CO2 injection in the Illinois Basin. Journal of Geophysical Research: Solid Earth 2022, 127(5), e2021JB023496. https://doi.org/10.1029/2021JB023496.
Monitoring Geologic Carbon Sequestration Using Electrical Resistivity Tomography
Yang, X.; Carrigan, C. Monitoring Geologic Carbon Sequestration Using Electrical Resistivity Tomography, Geophysical Monitoring for Geologic Carbon Storage, Huang, L. (Ed.), 2022, 255-271. https://doi.org/10.1002/9781119156871.ch16.
Monitoring Carbon Storage Sites With Time-Lapse Gravity Surveys. Geophysical Monitoring for Geologic Carbon Storage
Appriou, D.; Bonneville, A. (2022). Monitoring Carbon Storage Sites With Time-Lapse Gravity Surveys. Geophysical Monitoring for Geologic Carbon Storage, Huang, L. (Ed.), 2022, 211-232. https://doi.org/10.1002/9781119156871.ch14.
Fundamentals of Electrical and Electromagnetic Techniques for CO2 Monitoring
Gasperikova, E.; Morrison, H. F. Fundamentals of Electrical and Electromagnetic Techniques for CO2 Monitoring. Geophysical Monitoring for Geologic Carbon Storage, Huang, L. (Ed.), 2022, 233-253. https://doi.org/10.1002/9781119156871.ch15.
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.
Data Curation for Basin-Scale Modeling in NRAP Phase III
Morkner, P., and Zhou, Q. Data Curation for Basin-Scale Modeling in NRAP Phase III. National Risk Assessment Partnership Annual Technical Meeting, May 2022. Oral Presentation. https://www.osti.gov/servlets/purl/1891859
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/