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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Solid Earth Geophysics
Volume 12 - 2024 | doi: 10.3389/feart.2024.1493749
This article is part of the Research Topic Advances and New Methods in Reservoirs Quantitative Characterization Using Seismic Data View all 5 articles

A nonlinear inversion method for Young's modulus and shear modulus based on the exact Zoeppritz equations

Provisionally accepted
Yao Cheng Yao Cheng 1,2*Sha Song Sha Song 1*Dan Li Dan Li 3
  • 1 School of Geology Engineering and Geomatics, Chang'an University, Xi'an, Shaanxi, China
  • 2 Shaanxi Yellow River Science Research Institute, Xi’an, China
  • 3 International College, Northwestern Polytechnical University, Xi'an, China

The final, formatted version of the article will be published soon.

    Young's modulus and shear modulus are essential mechanical parameters for evaluating subsurface rocks, playing a pivotal role in the exploration and development of unconventional resources. Young's modulus indicates the brittleness of the reservoir, while shear modulus deter-mines the ease of fracturing rock layers. Traditional methods estimate these moduli through indirect calculations and approximate expressions, which are prone to cumulative errors and rely on multiple assumptions, reducing inversion accuracy. This paper presents a direct inversion method for Young's modulus and shear modulus using the exact Zoeppritz equations, integrated within a Bayesian framework for prestack inversion. By introducing the quantum particle swarm optimization (QPSO) algorithm, the paper achieves a nonlinear solution to the objective function. Tests on synthetic and actual field data demonstrate the proposed method's feasibility and effectiveness, yielding more accurate inversion results compared to traditional methods. These findings provide valuable insights for predicting reservoir brittleness and characterizing reservoirs in unconventional shale gas exploration and development.

    Keywords: Exact Zoeppritz equations, Young's modulus, shear modulus, Pre-stack inversion, Bayesian framework, Shale gas exploration, Quantum particle swarm optimization (QPSO)

    Received: 09 Sep 2024; Accepted: 08 Oct 2024.

    Copyright: © 2024 Cheng, Song and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yao Cheng, School of Geology Engineering and Geomatics, Chang'an University, Xi'an, Shaanxi, China
    Sha Song, School of Geology Engineering and Geomatics, Chang'an University, Xi'an, Shaanxi, China

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