Applications of deterministic and stochastic rock physics modelling to anisotropic velocity model building

Abstract

A B S T R A C TModern anisotropic depth imaging requires transverse isotropy velocity models. Because the surface seismic experiment alone cannot uniquely determine all transverse isotropy parameters, additional information is required. Traditional methods that use checkshots can only constrain anisotropy locally near the well and thus model building requires 3D extrapolation of well data. We propose to use rock physics. Rock physics compaction modelling of shales and sandy shales can be used to constrain and predict anisotropic parameters of sediments whose depositional environment is governed by compaction. In this study, we present several applications of rock physics modelling to anisotropic velocity model building. We show how both deterministic and stochastic rock physics modelling can be used for initial anisotropic velocity model building and as a constraint for anisotropic parameter estimation. In all cases, all predicted anisotropy models are physical and realizable in terms of effective medium theory and do not violate elasticity theory.

Shale Anisotropy Estimation From Logs in Vertical Wells

“…Compared with previously proposed methods (Bachrach et al, 2013;Bandyopadhyay, 2009), our method eliminates the need of estimating parameters that have little practical guidance and does not depend on a fixed assumption of the relationship between P and S wave anisotropy, as in the ANNIE model (Schoenberg et al, 1996). It represents a formalized workflow with less input parameters, each with clear physical meanings.…”

“…Therefore, most of the rock physics models describe the qualitative depth trends of anisotropy, without providing quantitative information. Bachrach et al (2013) and Li et al (2014) managed to utilize stochastic rock physics modeling schemes to capture the uncertainties in the modeling process and demonstrated that the rock physics modeling results can be used to constrain seismic imaging and inversion. However, due to the arbitrary choices of the parameter distribution and their independency, the resulting rock physics models might be inconsistent across different realizations of the input parameters.…”

Error estimates of elastic components in stress-dependent VTI media

“…The independence is important because the stochastic models help to explain the measurement error and/or heterogeneity in the sample. Previously published work to constrain the range of VTI stiffnesses or anisotropy parameters includes the ANNIE approximations (Schoenberg et al, 1996) and the use of stochastic rock-physics modeling for velocity modeling (Bachrach et al, 2013). However, the consideration of multiple possible models within some range of laboratory measurement uncertainty has not been performed to any extent.…”