1887

Abstract

Summary

Seismic-while-drilling (SWD) provides a cost-effective solution to subsurface imaging by utilizing the drill-bit noise as a source of seismic energy. However, retrieving an accurate virtual reflection data from SWD waveforms is challenging due to the erratic and unknown nature of the source signature. We propose a novel approach for multi-dimensional deconvolution (MDD) of SWD data that generates data free of surface-related multiples, corresponding to virtual sources located on the Earth’s surface. A key component of our approach is the direct arrival estimation and removal process based on the particle swarm optimization algorithm, which optimizes an initial traveltime curve by maximizing the energy of a flattened and stacked seismic recording. Moreover, to keep the computational cost of MDD to a reasonable level, the continuous SWD data is divided into smaller segments along the time axis, correlated, and stacked; in other words, we propose to form and solve the normal equations of the MDD problem. Validation on a synthetic dataset demonstrates that the proposed method can produce accurate virtual data and images of the subsurface. The proposed method is finally successfully applied to field dataset acquired in KAUST during a recent drilling campaign.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202510409
2025-06-02
2026-02-08
Loading full text...

Full text loading...

References

  1. Asgharzadeh, M., Grant, A., Bona, A. and Urosevic, M. [2019] Drill bit noise imaging without pilot trace, a near-surface interferometry example. Solid Earth, 10(4), 1015–1023.
    [Google Scholar]
  2. Boiero, D., Mahat, S., Bagaini, C. and Ortin, M. [2023] True-amplitude multiple prediction in sparse ocean-bottom acquisitions using a multidimensional deconvolution approach. 84th EAGE Annual Conference and Exhibition, 2023(1), 1–5.
    [Google Scholar]
  3. Bonyadi, M.R. and Michalewicz, Z. [2017] Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review. Evolutionary Computation, 25(1), 1–54.
    [Google Scholar]
  4. Goertz, A., Atkinson, B., Thiem, T. and Bergfjord, E.V. [2020] Reservoir imaging-while-drilling with PRM arrays. First Break, 38(11), 77–83.
    [Google Scholar]
  5. Poletto, F.B. and Miranda, F. [2022] Seismic While Drilling - Fundamentals of Drill-Bit Seismic for Exploration, 2nd Edition. Elsevier.
    [Google Scholar]
  6. Ravasi, M. and Vasconcelos, I. [2021] An open-source framework for the implementation of largescale integral operators with flexible, modern high-performance computing solutions: Enabling 3D Marchenko imaging by least-squares inversion. GEOPHYSICS, 86(5), WC177–WC194.
    [Google Scholar]
  7. Vidal, C.A. and Wapenaar, K. [2014] Passive seismic interferometry by multi-dimensional deconvolution-decorrelation. SEG Technical Program Expanded Abstracts, 2224–2228.
    [Google Scholar]
  8. Wapenaar, K., van der Neut, J., Ruigrok, E., Draganov, D., Hunziker, J., Slob, E., Thorbecke, J. and Snieder, R. [2011] Seismic interferometry by crosscorrelation and by multidimensional deconvolution: A systematic comparison. Geophysical Journal International, 185(3), 1335–1364.
    [Google Scholar]
/content/papers/10.3997/2214-4609.202510409
Loading
/content/papers/10.3997/2214-4609.202510409
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