1887

Abstract

Summary

This study investigates the impact of data preprocessing techniques on microseismic event detection using Distributed Acoustic Sensing (DAS) data. DAS technology uses fiber optic cables as seismic sensor arrays, crucial for monitoring subsurface activity in various industries. The research utilizes a dataset of microseismic events and background noise from a shale reservoir.

The data was processed in three categories: normalization only, median removal followed by normalization, and median removal, band-pass filtering, and normalization. A convolutional neural network was trained on this data to classify events. The results indicate that while minimal preprocessing can achieve high accuracy, more extensive preprocessing, particularly with band-pass filtering, improves generalization and reduces overfitting. The study concludes that the optimal level of preprocessing depends on the specific application and that longer training can enhance model performance, even with minimal preprocessing.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2025641032
2025-09-29
2026-02-07
Loading full text...

Full text loading...

References

  1. Given, P., F.Huot, A.Lellouch, B.Luo, R. G.Clapp, B. L.Biondi, T.Nemeth, and K.Nihei, 2022, Automatic microseismic event detection in downhole das data through convolutional neural networks: A comparison of events during and post-stimulation of the well: Second International Meeting for Applied Geoscience & Energy, Society of Exploration Geophysicists and American Association of Petroleum …, 1966–1969.
    [Google Scholar]
  2. Huot, F., A.Lellouch, P.Given, B.Luo, R. G.Clapp, T.Nemeth, K. T.Nihei, and B. L.Biondi, 2022, Detection and characterization of microseismic events from fiber-optic das data using deep learning: Seismological Society of America, 93, 2543–2553.
    [Google Scholar]
  3. Lellouch, A., S.Horne, M. A.Meadows, S.Farris, T.Nemeth, and B.Biondi, 2019, Das observations and modeling of perforation-induced guided waves in a shale reservoir: The Leading Edge, 38, 858–864.
    [Google Scholar]
  4. Lellouch, A., M. A.Meadows, T.Nemeth, and B.Biondi, 2020, Fracture properties estimation using distributed acoustic sensing recording of guided waves in uncon ventional reservoirs: Geophysics, 85, M85–M95.
    [Google Scholar]
  5. Stork, A., A.Baird, S.Horne, G.Naldrett, S.Lapins, J.Kendall, J.Wookey, J.Verdon, A.Clarke, and A.Williams, 2020, Application of machine learning to microseismic event detection in distributed acoustic sensing data, geophysics, 85, ks149–ks160.
    [Google Scholar]
  6. Verdon, J. P., S. A.Horne, A.Clarke, A. L.Stork, A. F.Baird, and J.-M.Kendall, 2020, Microseismic monitoring using a fiber-optic distributed acoustic sensor array: Geophysics, 85, KS89–KS99.
    [Google Scholar]
  7. Willis, M. E., 2022, Distributed acoustic sensing for seismic measurements–what geophysicists and engineers need to know: Society of Exploration Geophysicists.
    [Google Scholar]
/content/papers/10.3997/2214-4609.2025641032
Loading
/content/papers/10.3997/2214-4609.2025641032
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