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/
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
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.
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
