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.
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Bibliographies
Monitoring Carbon Storage Sites With Time-Lapse Gravity Surveys. Geophysical Monitoring for Geologic Carbon Storage
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.
NRAP-Open-IAM: Generic Aquifer Component Development and Testing
Bacon, D. H. NRAP-Open-IAM: Generic Aquifer Component Development and Testing. PNNL-32590, 2022, Pacific Northwest National Laboratory, Richland, WA. https://doi.org/10.2172/1845855.
Machine Learning Enhanced Seismic Monitoring at 100 km and 10 m Scales
Chai, C., Maceira, M., and EGS Collab Team, “Machine Learning Enhanced Seismic Monitoring at 100 km and 10 m Scales,” in Proceedings, 47th Workshop on Geothermal Reservoir Engineering, edited, Stanford University, Stanford, California, 47, 635–645, (2022) https://www.osti.gov/biblio/1845768.
Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes
Gasperikova, E.; Appriou, D.; Bonneville, A.; Feng, Z.; Huang, L.; Gao, K.; Yang, X.; Daley, T. Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes. International Journal of Greenhouse Gas Control 2022, 114, Article 103585. https://doi.org/10.1016/j.ijggc.2022.103585.
Scaling Behavior of Thermally Driven Fractures in Deep Low-Permeability Formations: A Plane Strain Model with 1-D Heat Conduction
Chen, B.; Zhou, Q. Scaling Behavior of Thermally Driven Fractures in Deep Low-Permeability Formations: A Plane Strain Model with 1-D Heat Conduction. Journal of Geophysical Research – Solid Earth 2022, Research Article. https://doi.org/10.1029/2021JB022964.
Distilling Data to Drive Carbon Storage Insights
Morkner, P.; Bauer, J.; Creason, C.; Sabbatino, M.; Wingo, P.; Greenburg, R.; Walker, S.; Yeates, D.; Rose, K. Distilling Data to Drive Carbon Storage Insights. Computers & Geosciences 2022, 158, Article 104945. https://doi.org/10.1016/j.cageo.2021.104945.
Deep Learning Inversion of Gravity Data for Detection of CO2 Plumes in Overlying Aquifers
Yang, X.; Chen, X.; Smith, M.M. Deep Learning Inversion of Gravity Data for Detection of CO2 Plumes in Overlying Aquifers. Journal of Applied Geophysics 2022, 196(104507). https://doi.org/10.1016/j.jappgeo.2021.104507.
A Review of Well Integrity Based on Field Experience at Carbon Utilization and Storage Sites
Iyer, J.; Lackey, G.; Edvardsen, L.; Bean, A.; Carroll, S.A.; Huerta, N.; Smith, M.M.; Torsaeter, M.; Dilmore, R.M.; Cerasi, P. A Review of Well Integrity Based on Field Experience at Carbon Utilization and Storage Sites. International Journal of Greenhouse Gas Control 2022, 113(103533). https://doi.org/10.1016/j.ijggc.2021.103533
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
Computed Tomography Scanning and Geophysical Measurements of Appalachian Basin Core from the Jones and Laughlin #1 Well, Beaver County, PA
Sharma, M., Paronish, T., Mitchell, N., Crandall, D., Zerbe, S., Pyle, S.J., Howard, C.M., Haldeman, A., and Neubaum, J., “Computed Tomography Scanning and Geophysical Measurements of Appalachian Basin Core from the Jones and Laughlin #1 Well, Beaver County, PA,” NETL-PUB-3889, NETL Technical Report Series, U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, 2023, p. 36, https://edx.netl.doe.gov/dataset/ct-scanning-and-gm-of-appalachian-basin-core-from-the-jonesand-laughlin-1-well-beaver-county-pa, DOI: 10.2172/1995971.
Computed Tomography Scanning and Petrophysical Measurements of the Lively Grove #1 Well Core
Crandall, D., Paronish, T., Mitchell, N., Jarvis, K., Brown, S., Moore, J., Gill, M., Blakley, C., Okwen, R., Korose, C., and Carman, C., “Computed Tomography Scanning and Petrophysical Measurements of the Lively Grove #1 Well Core,” NETL-PUB-3877, NETL Technical Report Series, U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, 2023, p. 60, https://edx.netl.doe.gov/dataset/computed-tomography-scanning-and-petrophysicalmeasurements-of-the-lively-grove-1-well-core, DOI: 10.2172/1989188.
Computed Tomography Scanning and Geophysical Measurements of the CarbonSAFE Seal Integrity Wells in the Illinois Basin
Paronish, T., Mitchell, N., Brown, S., Pohl, M., Crandall, D., Blakley, C., Korose, C., and Okwen, R., “Computed Tomography Scanning and Geophysical Measurements of the CarbonSAFE Seal Integrity Wells in the Illinois Basin,” DOE/NETL-2023/4323; NETL Technical Report Series, U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, (2023), p. 68, DOI: https://doi.org/10.2172/1962306.
Computed Tomography Scanning and Geophysical Measurements of the One Earth Energy Well #1 Core
Crandall, D., Gill, M., Paronish, T., Brown, S., Mitchell, N., Jarvis, K., Moore, J., Blakley, C., Okwen, R., Korose, C., and Carman, C., “Computed Tomography Scanning and Geophysical Measurements of the One Earth Energy Well #1 Core,” DOE.NETL-2023.3847; NETL Technical Report Series, U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, (2023), p 60. https://doi.org/10.2172/1963265.
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.
Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-Fidelity Model for Unconventional Oil and Gas Wells
Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O’Malley, D., Marquis, M., Vactor, R.T., and Cunha, L., “Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-Fidelity Model for Unconventional Oil and Gas Wells,” Gas Science and Engineering, (2023) https://doi.org/10.1016/j.jgsce.2023.204972.
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