Conventional reservoir-characterization techniques utilize amplitude-variation-with-offset (AVO) analysis to invert for the elastic parameters or directly for the physical properties of reservoirs. However, the quality of AVO inversion is degraded by errors in the velocity model, inaccurate amplitudes, and structural complexity. Whereas full-waveform inversion (FWI) potentially represents a much more powerful tool for reservoir characterization. FWI strongly relies on the accuracy of the initial model and suffers from parameters trade-offs. Here, we use a probabilistic Bayesian framework to supplement data fitting with rock-physics constraints based on geologic facies obtained from borehole information (well logs). The advantages of the facies-based FWI are demonstrated on a structurally complex isotropic elastic model and on a 3D layered VTI (transversely isotropic with a vertical symmetry axis) medium. In particular, the tests show that our algorithm can operate without ultra-low-frequency data required by conventional FWI and can reduce crosstalk between the medium parameters.

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