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Bayesian Deterministic Inversion Based on the Exact Reflection Coefficients Equations of Transversely Isotropic Media With a Vertical Symmetry Axis

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Unconventional reservoirs usually have strong anisotropy. Generally, they can be regarded as transversely isotropic media with a vertical symmetry axis [(VTI) media] in the absence of fractures. Therefore, it is… Click to show full abstract

Unconventional reservoirs usually have strong anisotropy. Generally, they can be regarded as transversely isotropic media with a vertical symmetry axis [(VTI) media] in the absence of fractures. Therefore, it is of great significance to develop high-accuracy inversion method for VTI media. The three elastic parameters and two Thomsen anisotropy parameters of VTI media are usually obtained by indirect calculation or inversion methods based on the approximate formulas. However, the cumulative errors caused by indirect calculation and low calculation accuracy of the approximate formulas limit the estimation accuracy of these parameters. In this article, we propose a new method based on the exact reflection coefficients equations (ERCEs) of VTI media to improve the inversion accuracy of these elastic and anisotropy parameters. The new method adopts the Bayesian deterministic inversion (BDI) strategy to solve the inversion problem, which becomes highly nonlinear when using the ERCEs. We analyze the feasibility of the new method using the residual function maps (RFMs) and eigenvalue analysis of Hessian matrix. The analysis results show that the BDI strategy can well solve a series of problems brought by the ERCEs for nonlinear inversion, such as strong nonlinearity and more target parameters need to invert. Both synthetic and field data examples show that the proposed method can accurately estimate the elastic and anisotropy parameters, which verifies the feasibility and effectiveness of the method.

Keywords: media vertical; vertical symmetry; inversion; transversely isotropic; method; isotropic media

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2022

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