1887
Volume 40, Issue 6
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Summary

Planning and managing operations of subsurface reservoir assets in terms of conformance is crucial for responsible CO storage. One important component of conformance management is the monitoring of the reservoir dynamics in response to the implemented operational strategies, particularly for early detection of deviations from intended behaviour (i.e., non-conformance). In recent work, we have introduced a model-based quantitative workflow to objectively assess the usefulness of monitoring for conformance verification in CO storage and shown how to use state-of-the-art supervised learning techniques to achieve a more practical workflow. In the present work, we investigate the use of a semi-supervised anomaly detection approach based on auto-encoder neural networks as an alternative to circumvent limitations of the supervised classification approaches explored so far. The results of our case study of a real storage aquifer show that auto-encoders trained on simulated time-lapse seismic data from (only) conformance scenarios can be used to accurately detect scenarios where the migration of CO deviates from the desired range of behaviours. These promising results confirm that the proposed approach can be used to derive efficient conformance classification workflows without an explicit finite dataset representing non-conformance to be defined in advance.

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2022-06-01
2022-06-27
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References

  1. Barros, E.G.D., Leeuwenburgh, O. and Boullenger, B. [2020]. Practical quantitative monitoring strategy assessment for conformance verification of CO2 storage projects. Paper presented at the EAGE 2020 Annual Conference Online, December 8–11. https://doi.org/10.3997/2214-4609.202011450.
    [Google Scholar]
  2. Barros, E.G.D., Leeuwenburgh, O. and Szklarz, S.P. [2021]. Quantitative assessment of monitoring strategies for conformance verification of CO2 storage projects. IJGGC, 110, 103403. https://doi.org/10.1016/j.ijggc.2021.103403.
    [Google Scholar]
  3. Barros, E.G.D. and Boullenger, B. [2020]. Quantitative Conformance Assessment in CO2 Storage Reservoirs under Geological Uncertainties Using Convolutional Neural Network Classifiers. Paper presented at the 1st EAGE Geoscience and Engineering in Energy Transition Conference (online), Strasbourg, France, November 16–18. https://doi.org/10.3997/2214-4609.202021062.
    [Google Scholar]
  4. Russo, S., Disch, A., Blumensaat, F. and Villez, K. [2019]. Anomaly detection using deep autoencoders for in-situ wastewater systems monitoring data. Proceedings of the 10th IWA Symposium on Systems Analysis and Integrated Assessment (Watermatex 2019), Copenhagen, Denmark, September 1–4.
    [Google Scholar]
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  • Article Type: Research Article
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