An investigation performed with a Bayesian approach illustrates that the introduction of prior information may increase the inversion sensitivity to water saturation. This can be explained with radiation pattern analysis, in which very low relative scattering amplitudes from saturation perturbations are observed. Water saturation tends to be the most difficult property to recover in all situations examined. However, the response of the inversion to the range of possible numerical optimization and frequency selections, as well as acquisition geometries, varies widely. Significant variability in inversion fidelity is visible from one rock-physics model to another. Numerical examples suggest that conditioning issues, which make a sequential inversion (in which velocities and density are first determined through EFWI, followed by PCS parameters) unstable, are avoided in this direct approach. We select a three-parameter porosity, clay content, and water saturation (PCS) parameterization, and we link them to elastic properties using three representative rock-physics models: the Han empirical model, the Voigt-Reuss-Hill boundary model, and the Kuster and Toksöz inclusion model. Within a suitably formed multiparameter EFWI, in this case a 2D frequency-domain isotropic-elastic full-waveform inversion with a truncated Gauss-Newton optimization, any rock-physics model with a well-defined mapping between its parameters and seismic velocity/density can be examined. Building on recent work on high-resolution multiparameter inversion for reservoir characterization, we have constructed a rock-physics parameterized elastic full-waveform inversion (EFWI) scheme. Most current seismic workflows in this field are based on amplitude variation with offset.

Quantitative estimation of rock-physics properties is an important part of reservoir characterization.

Bosch al 1450 dv manual transfer