1887

Abstract

Summary

Ideally, time-lapse seismic data from different vintages should be identical except at the target area (i.e., the reservoir). However, it is almost impossible to have identical data because of many factors, such as different positioning of the sources and receivers and near-surface velocity variation, which result in 4D noise and reduce the repeatability of the data. To increase the 4D signal and reduce the noise, time-lapse cross equalization methods aim to match the monitor data to the baseline. Here, we propose to implement the cross equalization intelligently using deep learning models. We specifically use a convolutional autoencoder trained on the base data to later predict the matching using another fully connected neural network in the latent space. We implement the approach on a synthetic data and show an improvement in the repeatability by imaging the reservoir and computing the normalized root mean square.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202011720
2020-12-08
2024-04-19
Loading full text...

Full text loading...

References

  1. Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T.
    [2018] Deep-learning tomography.The Leading Edge, 37(1), 58–66.
    [Google Scholar]
  2. Bakulin, A., Burnstad, R., Jervis, M. and Kelamis, P.
    [2012] The feasibility of permanent land seismic monitoring with buried geophones and hydrophones. In: 74th EAGE Conference and Exhibition incorporating EUROPEC 2012.
    [Google Scholar]
  3. Glubokovskikh, S., Pevzner, R., Dance, T., Caspari, E., Popik, D., Shulakova, V. and Gurevich, B.
    [2016] Seismic monitoring of CO2 geosequestration: CO2CRC Otway case study using full 4D FDTD approach.International Journal of Greenhouse Gas Control, 49, 201–216.
    [Google Scholar]
  4. Kazei, V., Ovcharenko, O., Plotnitskii, P., Peter, D., Zhang, X. and Alkhalifah, T.A.
    [2019] Mapping full seismic waveforms to vertical velocity profiles by deep learning.submitted to Geophysics.
    [Google Scholar]
  5. Kragh, E. and Christie, P.
    [2002] Seismic repeatability, normalized rms, and predictability.The Leading Edge, 21(7), 640–647.
    [Google Scholar]
  6. Liu, M. and Grana, D.
    [2019] Time-lapse seismic history matching with iterative ensemble smoother and deep convolutional autoencoder.Geophysics, 85(1), 1–63.
    [Google Scholar]
  7. Nguyen, P.K., Nam, M.J. and Park, C.
    [2015] A review on time-lapse seismic data processing and interpretation.Geosciences Journal, 19(2), 375–392.
    [Google Scholar]
  8. Ovcharenko, O., Kazei, V., Kalita, M., Peter, D. and Alkhalifah, T.A.
    [2019] Deep learning for low-frequency extrapolation from multi-offset seismic data.
    [Google Scholar]
  9. Ovcharenko, O., Kazei, V., Peter, D. and Alkhalifah, T.
    [2017] Neural network based low-frequency data extrapolation. In: 3rd SEG FWI workshop: What are we getting.
    [Google Scholar]
  10. Rickett, J. and Lumley, D.
    [2001] Cross-equalization data processing for time-lapse seismic reservoir monitoring: A case study from the Gulf of Mexico.Geophysics, 66(4), 1015–1025.
    [Google Scholar]
  11. Robinson, E.A. and Treitel, S.
    [2000] Geophysical signal analysis. Society of Exploration Geophysicists.
    [Google Scholar]
  12. Shulakova, V., Pevzner, R., Dupuis, J.C., Urosevic, M., Tertyshnikov, K., Lumley, D.E. and Gurevich, B.
    [2014] Burying receivers for improved time-lapse seismic repeatability: CO2CRC Otway field experiment.Geophysical Prospecting, 63(1), 55–69.
    [Google Scholar]
  13. Sun, B. and Alkhalifah, T.
    [2019] ML-descent: an optimization algorithm for FWI using machine learning. In: SEG Technical Program Expanded Abstracts 2019, Society of Exploration Geophysicists, 2288–2292.
    [Google Scholar]
  14. Sun, H. and Demanet, L.
    [2019] Extrapolated full waveform inversion with deep learning.arXiv preprint arXiv:1909.11536.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202011720
Loading
/content/papers/10.3997/2214-4609.202011720
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