Sequestration Site Monitoring
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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
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Data
Future Directions
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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.