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.
disCO2ver
Unlocking data-driven capabilities for the entire CTS community
Bibliographies
Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring
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.
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
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.
A curated data resource to support safe carbon dioxide transport-route planning
Schooley, C., Romeo, L., Pfander, I., Sharma, M., Justman, D., Bauer, J. and Rose, K., 2024. A curated data resource to support safe carbon dioxide transport-route planning. Data in Brief, 52, p.109984.
Computed Tomography Scanning and Petrophysical Measurements of Oriskany Cores Across Eastern Ohio
Pohl, M., Paronish, T., Mitchell, N., Jarvis, K., Sharma, M., Moore, J., Crandall, D., Danielsen, E.M., and McDonald, J., “Computed Tomography Scanning and Petrophysical Measurements of Oriskany Cores Across Eastern Ohio,” NELT-PUB-4800, NETL Technical Report Series, U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, 2024, p. 46, https://edx.netl.doe.gov/dataset/computed-tomography-scanningand-petrophysical-measurements-of-oriskany-cores-across-eastern-ohio, DOI: 10.2172/2322546.
Developing a Prototype Methodology to Rank Class II CO2-EOR Wells and Assess Reuse Potential for Geologic Sequestration
McElroy P.D., Zaengle, D.J., Tetteh, D.A., Bauer, J., and Rose, K., “Developing a Prototype Methodology to Rank Class II CO2-EOR Wells and Assess Reuse Potential for Geologic Sequestration,” NELT-PUB-XXXX, NETL Technical Report Series, U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, 2024.
Extensive Pipeline Location Data Resource: Integrating Reported Incidents, Past Environmental Loadings, and Potential Geohazards for Integrity Evaluations in the U.S. Gulf of Mexico
Isabelle Pfander, Lucy Romeo, Rodrigo Duran, Alec Dyer, Catherine Schooley, Madison Wenzlick, Patrick Wingo, Dakota Zaengle, Jennifer Bauer. Extensive pipeline location data resource: Integrating reported incidents, past environmental loadings, and potential geohazards for integrity evaluations in the U.S. Gulf of Mexico, Data in Brief, Volume 55, 2024, 110728, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110728.
Computed Tomography Scanning and Geophysical Measurements of the UW Enterprises Well in Southwestern Indiana
Hu, L., Paronish, T., Crandall, D., Jarvis, K., Mitchell, N., Brown, S., Workman, S., Douds, A., and Mastalerz, M., “Computed Tomography Scanning and Geophysical Measurements of the UW Enterprises Well in Southwestern Indiana,” DOE/NETL-2024/4803, NETL Technical Report Series, U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, 2024, https://doi.org/10.2172/2371703.
Offshore Geologic Carbon Storage Data Collection and Data Gaps Analysis
Mulhern, J.S., Mark-Moser, M., and Rose, K., 2024. Offshore Geologic Carbon Storage Data Collection and Data Gaps Analysis. DOE.NETL-2024.4804; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Albany, OR, 2024; p 24. https://doi.org/10.2172/2382659
High-Resolution CT Scan Dataset of Lower Mount Simon Sandstone Samples from the Illinois Basin
Magdalena Gill, Mathias Pohl, Sarah Brown, Karl Jarvis, Dustin Crandall, High-resolution computed tomography scan dataset of lower Mount Simon Sandstone samples from the Illinois Basin, Data in Brief, Volume 55, 2024, 110643, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110643.
Scoping Review of Global Offshore Geologic Carbon Storage Activities
Choisser, A., Mark-Moser, M., Mulhern, J., Rose, K. (2023) Scoping Review of Global Offshore Geologic Carbon Storage Activities. National Energy Technology Laboratory Technical Report Series, DOE/NETL-2024/4798 https://edx.netl.doe.gov/dataset/scoping-review-of-global-offshore-geologic-carbon-storage-activities
Computed Tomography Scanning and Petrophysical Measurements of Illinois Basin Coal Wells
Paronish, T.; Crandall, D.; Jarvis, K.; Workman, S.; Drosche, J.; Pohl, M.; Mckisic, T.; McLaughlin P.; Friedberg, J.; Delpomdor F. Computed Tomography Scanning and Petrophysical Measurements of Illinois Basin Coal Wells; DOE/NETL-2024/4799; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2024; p 56. http://edx.netl.doe.gov/dataset/computed-tomography-scanning-and-petrophysical-measurements-of-illinois-basin-coal-wells. DOI: 10.2172/2282147.
A Curated Data Resource to Support Safe Carbon Dioxide Transport-Route Planning
Catherine Schooley, Lucy Romeo, Isabelle Pfander, Maneesh Sharma, Devin Justman, Jennifer Bauer, Kelly Rose. A curated data resource to support safe carbon dioxide transport-route planning. Data in Brief, Volume 52, 2024, 109984, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.109984.
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/