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.
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Bibliographies
High-Resolution CT Scan Dataset of Lower Mount Simon Sandstone Samples from the Illinois Basin
Advanced Offshore Hazard Forecasting to Enable Resilient Offshore Operations
Mark-Moser, M. K., Romeo, L., Duran, R., Bauer, J., Rose, K., (2024, May 6). Advanced Offshore Hazard Forecasting to Enable Resilient Offshore Operations. [Conference presentation] Offshore Technology Conference 2024. Houston, TX. https://www.osti.gov/biblio/2352616
EDX disCO2ver, Fostering Public-Private Partnerships for Accelerated Advancements in AI for Energy
Rose, K., 2024, “EDX disCO2ver, Fostering Public-Private Partnerships for Accelerated Advancements in AI for Energy”, AI in Oil & Gas Conference, April 9-10, 2024. Invited.
Web-Based GIS Data: Observation of Differences in Performance and Management
Pramuk, J., McFarland, D., Chittum, J., Bauer, J., and Rose, K., “Web-Based GIS Data: Observation of Differences in Performance and Management,” Esri Developer Summit, Palm Springs, CA, March 12–15, 2024.
Carbon Storage Technical Viability Approach and National Data Assessment
Mark-Moser, M., Creason, C.G., Mulhern, J., Shay, J., Lara, A., and Rose, K., “Carbon Storage Technical Viability Approach and National Data Assessment,” presented at the CCUS 2024 SPE AAPG SEG, Houston, TX, March 11–13, 2024.
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.
Enhancing Knowledge Discovery from Unstructured Data Using a Deep Learning Approach to Support Subsurface Modeling Predictions
Hoover B, Zaengle D, Mark-Moser M, Wingo P, Suhag A and Rose K. (2023) Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions. Front. Big Data 6:1227189. doi: https://doi.org/10.3389/fdata.2023.1227189
Developing a National Oil & Gas Wellbore Database and Visualization Tool to Support Locating and Characterizing Undocumented Wells
Bauer, J., Romeo, L., Sharma, M., Amrine, D., Pfander, I., Sabbatino, M., and Rose, K., “Developing a National Oil & Gas Wellbore Database and Visualization Tool to Support Locating and Characterizing Undocumented Wells,” American Geophysical Union (AGU) Fall Meeting 2023, San Francisco, CA, December 11–15, 2023.
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.
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.
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/