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
disCO2ver
Unlocking data-driven capabilities for the entire CCS community
Bibliographies
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
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.
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
Transient evolution of permeability and friction in a slowly slipping fault activated by fluid pressurization
Cappa, F.; Guglielmi, Y.; De Barros, L. Transient evolution of permeability and friction in a slowly slipping fault activated by fluid pressurization. Nature Communications, 2022, 13, 3039 (2022). https://doi.org/10.1038/s41467-022-30798-3.
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.
Possible Controls on Porosity Preservation in the Andaman Forearc Gas Hydrate System
Johnson, J., Rose, K., Torres, M. (2020, Jan). Possible controls on porosity preservation in the Andaman forearc gas hydrate system: OSR, AOM, and/or marine silicate weathering [Conference presentation]. Geologic Society of America Meeting 2020, Session: T99. Records of Early Diagenesis in Modern and Ancient Sediments. https://community.geosociety.org/gsa2020/program/technical
Back to the Future: Rescue, Curation, and Transformation of a Corpus of Carbon Storage Data
Sabbatino, M., Baker, V., Bauer, J., Creason, C., Romeo, L., Rose, K., Rowan, C., Zoch, G., submitted, Back to the Future: Rescue, Curation, and Transformation of a Corpus of Carbon Storage Data, Annual Meeting 2019, Session: AGU Dirty Stories of Data Rescue. https://www.osti.gov/servlets/purl/1778129
Developing a Virtual Subsurface Data Framework: Transforming DOE’s EDX data lake using ML/NLP
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
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
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
