Romeo, L., Leveckis, S., Houghton, B., Gao, M. C., Zaengle, D., Schooley, C., Rose, K., and Bauer, J., “An Open-Source, Machine Learning-Informed, Geospatial-Driven Tool for Identifying and Evaluating CO2 Transport Routes,” SPE AAPG SEG CCUS 2025, Houston, TX, March 3–5, 2025.
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Bibliographies
An Open-Source, Machine Learning-Informed, Geospatial-Driven Tool for Identifying and Evaluating CO2 Transport Routes
Analog Selection and Project Comparison Using Offshore Carbon Storage Project Inventory and Data
Mulhern, J.S., Mark-Moser, M., and Rose, K., “Analog Selection and Project Comparison Using Offshore Carbon Storage Project Inventory and Data,” accepted for SPE AAPG SEG CCUS 2025, Houston, TX, March 3–5, 2025.
Extending Compliance Inspection Data with Predictive Modeling for Marginal Conventional Wells with Emissions in New York State
Dyer, A., Schooley, C., White, C., Wise, J., and Lackey, G., “Extending Compliance Inspection Data with Predictive Modeling for Marginal Conventional Wells with Emissions in New York State,” conference abstract from AGU Annual Meeting 2024, Washington, DC, December 9–13, 2024.
CO2-Locate: A Dynamic Database and Tool for Accessing National Oil and Gas Well Data to Inform Carbon Storage Projects
Dyer, A., Pfander, I., Tetteh, D., Cleaveland, C., Sabbatino, M., Romeo, L., Bauer, J., and Rose, K., “CO2-Locate: A Dynamic Database and Tool for Accessing National Oil and Gas Well Data to Inform Carbon Storage Projects,” conference abstract from AGU Annual Meeting 2024, Washington, DC, December 9–13, 2024.
The Carbon Storage Planning Inquiry Tool (CS PlanIT)
Morkner, P., Pantaleone, S., Rich, M., Justman, D., and Rose, K. “The Carbon Storage Planning Inquiry Tool (CS PlanIT)”. US Energy Administration Seminar. November, 2024. Online.
Carbon Storage Technical Viability Approach (CS TVA) Matrix: Integrating Multiple Components for Comprehensive Scoping and Data Availability Assessments
Mulhern, J.S., Mark-Moser, M., Creason, C.G., Maymi, N., Shay, J., Lara, A., and Rose, K., “Carbon Storage Technical Viability Approach (CS TVA) Matrix: Integrating Multiple Components for Comprehensive Scoping and Data Availability Assessments,” AAPG Rocky Mountain Elevating Energy Section Meeting, Park City, UT, October 6–8, 2024.
Where are the Data? Automating a Workflow for Carbon Storage Data Gap Analysis
Creason, C.G., Mulhern, J.S., Cordero Rodriguez, N., Mark-Moser, M., Lara, A., Shay, J., and Rose, K. Where are the Data? Automating a Workflow for Carbon Storage Data Gap Analysis, Geological Society of America Connects Annual Meeting. Anaheim, CA. September 22-25, 2024.
Carbon Storage Technical Viability Approach (CS TVA) Matrix: Integrating Multiple Components for Comprehensive Scoping
Mulhern, J.S., Mark-Moser, M., Creason, C.G., Maymi, N., Shay, J., Lara, A., and Rose, K., “Carbon Storage Technical Viability Approach (CS TVA) Matrix: Integrating Multiple Components for Comprehensive Scoping,” Geological Society of America CONNECTS Annual Meeting, Anaheim, CA, September 22–25, 2024.
Offshore Carbon Storage Data Collection and International Offshore Carbon Storage Project Inventory
Mulhern, J.S., Mark-Moser, M., and Rose, K., “Offshore Carbon Storage Data Collection and International Offshore Carbon Storage Project Inventory,” Geological Society of America CONNECTS Annual Meeting, Anaheim, CA, September 22–25, 2024.
International Offshore Geologic Carbon Storage Project Inventory and Data Collection
Mulhern, J.S., Mark-Moser, M., and Rose, K. “International Offshore Geologic Carbon Storage Project Inventory and Data Collection”. Seventh International Offshore Geologic CO2 Storage Workshop. Port Arthur, Texas. September 17-19, 2024. Invited.
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.
Automatic Waveform Quality Control for Surface Waves Using Machine Learning
Chai, C., Kintner, J.A., Cleveland, K.M., Luo, J., Maceira, M., and Charles J. Ammon, C.J., “Automatic Waveform Quality Control for Surface Waves Using Machine Learning,” Seismological Research Letters, 93(3), 1683-1694, (2022) https://doi.org/10.1785/0220210302.
Building Regional Baselines and a Suite of Spatial Tools to Better Prepare for Oil Spills
Romeo, L., Dyer, A., Nelson, J., Bauer, J., Rose, K., Dao, A., Wingo, P., Creason, C.G., and Sabbatino, M. Building Regional Baselines and a Suite of Spatial Tools to Better Prepare for Oil Spills, AGU Ocean Sciences Meeting 2020, Poster Presentation. https://www.osti.gov/biblio/1787016
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