Abstract:
Rocks with a set of vertical fractures embedded in a vertical transverse isotropic (VTI) background can be regarded as an effective long-wavelength orthorhombic (ORT) med...Show MoreMetadata
Abstract:
Rocks with a set of vertical fractures embedded in a vertical transverse isotropic (VTI) background can be regarded as an effective long-wavelength orthorhombic (ORT) medium, common in fractured reservoirs. Seismic inversion using wide-azimuth data is an effective tool for fracture detection. However, azimuthal seismic inversion in ORT medium is inherently ill-posed and uncertain, largely due to the considerable number of parameters involved in the reflection coefficients and the relatively minor contribution of fracture parameters to the reflection coefficients. Therefore, in this article, a modified PP-wave reflection coefficient equation was derived through parameter combination, comprising only five model parameters and exhibiting near-equivalent accuracy to the existing equation. Furthermore, each parameter in the equation has a clear physical meaning. These include attribute A (P-wave impedance), attribute B (anisotropic shear modulus), attribute C (horizontal P-wave phase velocity), normal fracture weakness, and tangential fracture weakness. Subsequently, an azimuthal seismic inversion method was developed to estimate the above five model parameters based on Bayesian inference, combined with Cauchy prior information and low-frequency regularization constraints. The method was validated through synthetic tests, which demonstrated its efficacy and feasibility even with moderate noise. Field data also illustrated the stability and effectiveness of the method. Finally, based on field data, the interpretation capability of attribute parameters was discussed. The modified reflection coefficient equation and the inversion method proposed in this article serve to enhance the inversion robustness of the ORT medium, which in turn guides the prediction of the orthogonally fractured reservoir using azimuth seismic data.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 63)