气候变化领域集成服务门户(CCPortal): Toward Automated Early Detection of Risks for a CO2 Plume Containment From Permanent Seismic Monitoring Data

CCPortal

DOI	10.1029/2020JB021087
	Toward Automated Early Detection of Risks for a CO2 Plume Containment From Permanent Seismic Monitoring Data
	Glubokovskikh S.; Wang R.; Ricard L.; Bagheri M.; Gurevich B.; Pevzner R.
发表日期	2021
ISSN	21699313
卷号	126 期号:5
英文摘要	Permanent reservoir surveillance is an invaluable monitoring tool for CO2 storage projects, because it tracks spatial-temporal evolution of the injected plume. The frequent images of CO2 plumes will facilitate history-matching of the reservoir simulations and increase confidence of early leakage detection. However, continuous data acquisition and real-time interpretation require a new approach to data analysis. Here, we propose a data-driven approach to forecasting future time-lapse seismic images based on the observed past images and test this approach on the Otway Stage 2C data. The core component of the predictor is a convolutional neural network, which considers subsequent plume maps as color layers, similar to standard red-green-blue blending. Based on the extent of the past plumes, we may predict the future contour of the seismically resolvable portion of the plume. The neural networks reproduce the dynamics of CO2 migration after training on reservoir simulations for a wide range of injection scenarios and subsurface models. Extensive testing shows that realistic plumes for Stage 2C are too complicated and the neural network should be pretrained on simpler reservoir simulations that include only one or two geological features, such as: faults, spill-points, etc. Such staged training can be seen as a gradual descent of the neural network optimization to a global minimum. The approach is practical, because each CO2 storage project requires extensive preinjection reservoir simulations. Once the predictor has been trained, it can forecast plume evolution near real-time and adapt efficiently to changing dynamics of CO2 migration. © 2021. American Geophysical Union. All Rights Reserved.
英文关键词	CO2 storage; neural networks; seismic monitoring
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187092
作者单位	Curtin University, Perth, WA, Australia; CSIRO, Kensington, WA, Australia; CO2CRC Ltd., Carlton, VIC, Australia; Rio Tinto Brisbane, Brisbane City, QLD, Australia; Lawrence Berkeley National Laboratory, Berkeley, CA, United States
推荐引用方式 GB/T 7714	Glubokovskikh S.,Wang R.,Ricard L.,et al. Toward Automated Early Detection of Risks for a CO2 Plume Containment From Permanent Seismic Monitoring Data[J],2021,126(5).
APA	Glubokovskikh S.,Wang R.,Ricard L.,Bagheri M.,Gurevich B.,&Pevzner R..(2021).Toward Automated Early Detection of Risks for a CO2 Plume Containment From Permanent Seismic Monitoring Data.Journal of Geophysical Research: Solid Earth,126(5).
MLA	Glubokovskikh S.,et al."Toward Automated Early Detection of Risks for a CO2 Plume Containment From Permanent Seismic Monitoring Data".Journal of Geophysical Research: Solid Earth 126.5(2021).