1887

Abstract

Summary

In this paper we demonstrate how microseismic networks have been multi-purposed to identify variations in fluid saturation in the context of CO2 sequestration and underground gas storage. In traditional microseismic monitoring, only the waveforms from actual seismic events are processed, and most of the recordings are discarded after the detection process.

The full waveform data is however rich in information that can be extracted by waveform interferometry and used for the direct monitoring of saturation changes or fluid movements. This approach is based on investigating the variations of the properties of surface waves reconstructed through interferometry, which are much sensitive to the fluid types (water, gas, oil…) and their spatial distribution within the subsurface.

The use of permanent microseismic monitoring stations presents advantages in comparison to temporary networks as they are designed and carefully setup for long recording times in virtually anthropogenic noise-free environments. The flexibility in the network design allows to use any existing microseismic monitoring network, even if it has not been specifically optimized for the waveform interferometry technique, and this added value of the network comes at little incremental deployment cost.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202321090
2023-11-14
2025-11-14
Loading full text...

Full text loading...

References

  1. Boschi, L., Magrini, F., Cammarano, F., & van Der Meijde, M. (2019). On seismic ambient noise cross-correlation and surface-wave attenuation.Geophysical journal international, 219(3), 1568–1589.
    [Google Scholar]
  2. Brenguier, F., Shapiro, N. M., Campillo, M., Nercessian, A., & Ferrazzini, V. (2007). 3‐D surface wave tomography of the Piton de la Fournaise volcano using seismic noise correlations.Geophysical research letters, 34(2).
    [Google Scholar]
  3. Draganov, D., Campman, X., Thorbecke, J., Verdel, A., & Wapenaar, K. (2009). Reflection images from ambient seismic noise.Geophysics, 74(5), A63–A67.
    [Google Scholar]
  4. Kremer, T., Mouquet, P., Kasantsev, A., Grauls, A., & Voisin, C. (2022a, June). Improved Reconstruction and Coherence Analysis of a Wave-Field Extracted from Ambient Seismic Noise Cross-Correlations. In 83rd EAGE Annual Conference & Exhibition (Vol. 2022, No. 1, pp. 1–5). European Association of Geoscientists & Engineers.
    [Google Scholar]
  5. Kremer, T., Mouquet, P., Kasantsev, A., Grauls, A., & Voisin, C. (2022b, November). Monitoring geological gas storage sites with ambient noise interferometric methods: focus on seismic attenuation changes for gas movement detection.Proceedings of the 16th Greenhouse Gas Control Technologies Conference (GHGT-16)23–24 Oct 2022.
    [Google Scholar]
  6. Shapiro, N. M., & Campillo, M. (2004). Emergence of broadband Rayleigh waves from correlations of the ambient seismic noise.Geophysical Research Letters, 31(7).
    [Google Scholar]
  7. Vardy, M., & Pinson, L. (2018, September). Seismic Attenuation-Friend or Foe. In 3rd Applied Shallow Marine Geophysics Conference (Vol. 2018, No. 1, pp. 1–5). European Association of Geoscientists & Engineers.
    [Google Scholar]
/content/papers/10.3997/2214-4609.202321090
Loading
/content/papers/10.3997/2214-4609.202321090
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error