Wenzlick, M., Devanathan, R., Mamun, O., Rose, K., Hawk, J., 2021. Incorporating historical data & past analyses for improved tensile property prediction of 9Cr steel. 2021 TMS Annual Meeting & Exhibition, AI/Data informatics: Design of Structural Materials, Orlando, FL, March 2021. https://www.researchgate.net/publication/349544140_Incorporating_Historical_Data_and_Past_Analyses_for_Improved_Tensile_Property_Prediction_of_9_Cr_Steel
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Bibliographies
Incorporating Historical Data and Past Analyses for Improved Tensile Property Prediction of 9% Cr Steel
Aseismic deformations perturb the stress state and trigger induced seismicity during injection experiments
Duboeuf, L.; De Barros, L.; Kakurina, M.; Guglielmi, Y.; Cappa, F.; Valley, B. Aseismic deformations perturb the stress state and trigger induced seismicity during injection experiments. Geophysical Journal International 2021, 224(2), 1464-1475. doi: 10.1093/gji/ggaa515. https://academic.oup.com/gji/article-abstract/224/2/1464/5974524?redirectedFrom=fulltext
Tools for Data Collection, Curation, and Discovery to Support Carbon Storage Insights
Mark-Moser, M., Rose, K., Baker, V. D. (2020, December 17). Tools for Data Collection, Curation, and Discovery to Support Carbon Storage Insights. [Conference presentation]. Session: IN042 – Utilizing unstructured data in Earth Science Poster Session. https://ui.adsabs.harvard.edu/abs/2020AGUFMIN0140002M/abstract
NRAP-Open-IAM: A New, Open-Source Code for Integrated Assessment of Geologic Carbon Storage Containment Effectiveness and Leakage Risk
Vasylkivska, V., Bacon D., Chen, Bailian, Dilmore R., Harp D., King S., Lackey G., Lindner E., Liu Guoxiang, Mansoor K., Zhang Yingqi. NRAP-Open-IAM: A New, Open-Source Code for Integrated Assessment of Geologic Carbon Storage Containment Effectiveness and Leakage Risk. AGU Annual Fall Meeting (Virtual), 2020 Session: GC110. Advances in Computational Methods for Geologic CO2 Sequestration I eLightning. https://ui.adsabs.harvard.edu/abs/2020AGUFMGC110..10V/abstract
Developing a structured seafloor sediment database from disparate datasets using SmartSearch
Mark-Moser, M., Rose, K., Baker, V. D. 2020. Developing a structured seafloor sediment database from disparate datasets using SmartSearch. AGU Annual Fall Meeting (Virtual), 2020. Session: IN042 – Utilizing unstructured data in earth science https://www.osti.gov/servlets/purl/1776797
Probabilistic Machine Learning for Integrated Social-Natural-Physical Assessment
Ghanem, R., Zhang, R., Rose, K., invited talk, Probabilistic Machine Learning for Integrated Social-Natural-Physical Assessment, AGU Annual Meeting 2020, Session: H027 – Artificial Intelligence and Machine Learning for Multiscale Model-Experimental Integration https://agu.confex.com/agu/fm20/prelim.cgi/Session/103051
Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry
Dyer, A., Zaengle, D., Mark-Moser, M., Duran, R., Suhag, A., Rose, K., Bauer, J. Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry. AGU Annual Fall Meeting (Virtual), 2020. Session: NH007 – Data Science and Machine Learning for Natural Hazard Sciences II Posters. https://www.osti.gov/servlets/purl/1779617
A knowledge-data framework and geospatial fuzzy logic-based approach to model and predict structural complexity
Justman, D., Creason, C.G., Rose, K., & Bauer, J., 2020. A knowledge-data framework and geospatial fuzzy logic-based approach to model and predict structural complexity. Journal of Structural Geology, 104153. https://doi.org/10.1016/j.jsg.2020.104153
A Geospatial Analytical Framework to Identify Seafloor Geohazards in the Northern Gulf of Mexico
Duran, R., Dyer, A., Mark-Moser, M., Bauer, J., Rose, K., Zaengle. D., Wingo, P. 2020. A Geospatial Analytical Framework to Identify Seafloor Geohazards in the Northern Gulf of Mexico. AGU Annual Meeting 2020, Session: NH010 – Geohazards in Marine and Lacustrine Environments. https://ui.adsabs.harvard.edu/abs/2020AGUFMNH004..08D/abstract
Optimizing Prediction of Reservoir Properties with Artificial Intelligence, Big Data, and the Subsurface Trend Analysis Method
Mark-Moser, M., Suhag, A., Rose, K., Wingo, P. (2020, November 9). Optimizing prediction of reservoir properties with artificial intelligence, big data, and the Subsurface Trend Analysis method [Conference presentation]. Machine Learning for Oil and Gas 2020, Nov. 9-11, Virtual. https://edx.netl.doe.gov/sites/offshore/optimizing-prediction-of-reservoir-properties-with-artificial-intelligence-big-data-and-the-subsurface-trend-analysis-method/
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.
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.
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
Dynamic risk assessment for geologic CO2 sequestration
Chen, B.; Harp, D. R.; Zhang, Y.; Oldenburg, C. M.; Pawar, R. J. (in Press, Corrected Proof). Dynamic risk assessment for geologic CO2 sequestration. Gondwana Research 2022. https://doi.org/10.1016/j.gr.2022.08.002.
Integrating Risk Assessment Methods for Carbon Storage: A Case Study for the Quest Carbon Capture and Storage Facility
Brown, C. F., G. Lackey, N. Mitchell, S. Baek, B. Schwartz, M. Dean, R. Dilmore, H. Blanke, S. O’Brien, and C. Rowe. 2023. “Integrating Risk Assessment Methods for Carbon Storage: A Case Study for the Quest Carbon Capture and Storage Facility.” International Journal of Greenhouse Gas Control 129: 103972. https://doi.org/10.1016/j.ijggc.2023.103972.
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
A Framework to Simulate the Blowout of CO2 Through Wells in Geologic Carbon Storage
Bhuvankar, P.; Cihan, A.; Birkholzer, J. A Framework to Simulate the Blowout of CO2 Through Wells in Geologic Carbon Storage. International Journal of Greenhouse Gas Control, 2023, 127, Article 103921, ISSN 1750-5836. https://doi.org/10.1016/j.ijggc.2023.103921.
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
