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Adding Prior Information in FWI through Relative Entropy.

Danilo Santos Cruz1, João M de Araújo1,2, Carlos A N da Costa2

  • 1Programa de Pós-Graduação em Ciência e Engenharia do Petróleo, Universidade Federal do Rio Grande do Norte, Natal 59064-741, Brazil.

Entropy (Basel, Switzerland)
|June 2, 2021
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Summary

This study introduces relative entropy to full waveform inversion, enhancing subsurface resolution in petroleum reservoir characterization. This method effectively reduces ambiguity and avoids local minimums for more accurate geological models.

Keywords:
entropyfwiinverse problemsprior informationregularization

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Area of Science:

  • Geophysics
  • Computational Seismology

Background:

  • Full waveform inversion (FWI) is crucial for high-resolution subsurface imaging.
  • Ambiguity in FWI results, particularly in reservoir characterization, often necessitates incorporating well log data as prior information.

Purpose of the Study:

  • To integrate relative entropy as a deterministic regularization term into the full waveform inversion (FWI) formalism.
  • To explore novel methods for incorporating prior information via relative entropy to improve FWI convergence and resolution.

Main Methods:

  • A deterministic application of relative entropy is proposed for FWI, avoiding complex statistical formulations.
  • Three distinct approaches for integrating prior information using relative entropy with a dynamic weighting scheme are presented.
  • The impact of logarithmic weighting (entropy) on suppressing low-intensity ripples and sharpening point events is analyzed.

Main Results:

  • The addition of relative entropy significantly improves the resolution of FWI results.
  • Prior information incorporated through entropy aids in guiding the inversion towards the global minimum, especially in the initial stages.
  • Synthetic data tests on the BP 2004 model, particularly in salt-rich regions, demonstrated substantial improvements.

Conclusions:

  • Relative entropy serves as an effective regularization technique in FWI, enhancing subsurface model accuracy.
  • This entropy-based approach provides a robust method for avoiding local minimums and achieving desired solutions in inverse problems.
  • The integration of relative entropy leads to sharper geological features and higher-resolution subsurface information.