Sequestration Site Monitoring: Difference between revisions
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ML can help monitor and maintain active sequestration sites. Noisy sensor measurements must be translated into inferences about subsurface CO<sub>2</sub> flow and remaining injection capacity <ref>{{Cite journal|last=Celia|first=M. A.|last2=Bachu|first2=S.|last3=Nordbotten|first3=J. M.|last4=Bandilla|first4=K. W.|date=2015|title=Status of CO2 storage in deep saline aquifers with emphasis on modeling approaches and practical simulations|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2015WR017609|journal=Water Resources Research|language=en|volume=51|issue=9|pages=6846–6892|doi=10.1002/2015WR017609|issn=1944-7973}}</ref>; recently, <ref>{{Cite journal|last=Mo|first=Shaoxing|last2=Zhu|first2=Yinhao|last3=Zabaras|first3=Nicholas|last4=Shi|first4=Xiaoqing|last5=Wu|first5=Jichun|date=2019|title=Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023528|journal=Water Resources Research|language=en|volume=55|issue=1|pages=703–728|doi=10.1029/2018WR023528|issn=1944-7973}}</ref> found success using convolutional image-to-image regression techniques for uncertainty quantification in a global CO2 storage simulation study. Additionally, it is important to monitor for CO<sub>2</sub> leaks <ref>{{Cite journal|last=Moriarty|first=Dylan|last2=Dobeck|first2=Laura|last3=Benson|first3=Sally|date=2014-01-01|title=Rapid surface detection of CO2 leaks from geologic sequestration sites|url=http://www.sciencedirect.com/science/article/pii/S1876610214022425|journal=Energy Procedia|series=12th International Conference on Greenhouse Gas Control Technologies, GHGT-12|language=en|volume=63|pages=3975–3983|doi=10.1016/j.egypro.2014.11.427|issn=1876-6102}}</ref>. ML techniques have recently been applied to monitoring potential CO<sub>2</sub> leaks from wells <ref>{{Cite journal|last=Chen|first=Bailian|last2=Harp|first2=Dylan R.|last3=Lin|first3=Youzuo|last4=Keating|first4=Elizabeth H.|last5=Pawar|first5=Rajesh J.|date=2018-09-01|title=Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach|url=http://www.sciencedirect.com/science/article/pii/S0306261918307372|journal=Applied Energy|language=en|volume=225|pages=332–345|doi=10.1016/j.apenergy.2018.05.044|issn=0306-2619}}</ref>; computer vision approaches for emissions detection (see <ref>{{Cite journal|last=Chen|first=Bailian|last2=Harp|first2=Dylan R.|last3=Lin|first3=Youzuo|last4=Keating|first4=Elizabeth H.|last5=Pawar|first5=Rajesh J.|date=2018-09-01|title=Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach|url=http://www.sciencedirect.com/science/article/pii/S0306261918307372|journal=Applied Energy|language=en|volume=225|pages=332–345|doi=10.1016/j.apenergy.2018.05.044|issn=0306-2619}}</ref> and [[Greenhouse Gas Emissions Detection]]) may also be applicable. |
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==Background Readings== |
==Background Readings== |
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Revision as of 00:44, 9 December 2020
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This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
ML can help monitor and maintain active sequestration sites. Noisy sensor measurements must be translated into inferences about subsurface CO2 flow and remaining injection capacity [1]; recently, [2] found success using convolutional image-to-image regression techniques for uncertainty quantification in a global CO2 storage simulation study. Additionally, it is important to monitor for CO2 leaks [3]. ML techniques have recently been applied to monitoring potential CO2 leaks from wells [4]; computer vision approaches for emissions detection (see [5] and Greenhouse Gas Emissions Detection) may also be applicable.
Background Readings
Conferences, Journals, and Professional Organizations
Libraries and Tools
Data
Future Directions
Relevant Groups and Organizations
References
- ↑ Celia, M. A.; Bachu, S.; Nordbotten, J. M.; Bandilla, K. W. (2015). "Status of CO2 storage in deep saline aquifers with emphasis on modeling approaches and practical simulations". Water Resources Research. 51 (9): 6846–6892. doi:10.1002/2015WR017609. ISSN 1944-7973.
- ↑ Mo, Shaoxing; Zhu, Yinhao; Zabaras, Nicholas; Shi, Xiaoqing; Wu, Jichun (2019). "Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media". Water Resources Research. 55 (1): 703–728. doi:10.1029/2018WR023528. ISSN 1944-7973.
- ↑ Moriarty, Dylan; Dobeck, Laura; Benson, Sally (2014-01-01). "Rapid surface detection of CO2 leaks from geologic sequestration sites". Energy Procedia. 12th International Conference on Greenhouse Gas Control Technologies, GHGT-12. 63: 3975–3983. doi:10.1016/j.egypro.2014.11.427. ISSN 1876-6102.
- ↑ Chen, Bailian; Harp, Dylan R.; Lin, Youzuo; Keating, Elizabeth H.; Pawar, Rajesh J. (2018-09-01). "Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach". Applied Energy. 225: 332–345. doi:10.1016/j.apenergy.2018.05.044. ISSN 0306-2619.
- ↑ Chen, Bailian; Harp, Dylan R.; Lin, Youzuo; Keating, Elizabeth H.; Pawar, Rajesh J. (2018-09-01). "Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach". Applied Energy. 225: 332–345. doi:10.1016/j.apenergy.2018.05.044. ISSN 0306-2619.