Bayesian framework for elastic full-waveform inversion with facies information

Abstract

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.

Facies‐based full‐waveform inversion for anisotropic media: a North Sea case study

“…Constraining the model-updating procedure by incorporating prior information about the subsurface (e.g. geologic facies) has proven promising in mitigating the problems mentioned above (Guitton et al, 2012;Asnaashari et al, 2013;Singh et al, 2018;Zhang et al, 2018;Singh et al, 2020b). The primary source of this geologic information is available well logs.…”

“…Therefore, here we perform FWI for tilted TI media with the axis orientation estimated from the structural dips (Audebert et al (2006)). Also, we employ a velocity-based parameterization, shown to be particularly effective in facies-based FWI (Singh et al, 2018(Singh et al, , 2020b.…”

“…However, density is a crucial physical parameter in reservoir characterization and lithologic interpretation. Also, an accurate density model is beneficial for the velocity inversion (Guitton and Alkhalifah, 2017;Pratt and Smithyman, 2018;Singh et al, 2018Singh et al, , 2020bSingh et al, , 2021. Therefore, here we invert for the 2D density field and update it independently of the other parameters with the corresponding inversion gradient.…”

Elastic full waveform inversion with probabilistic petrophysical clustering

“…Zhang et al . (), Singh, Tsvankin and Naeini () and Zhang and Alkhalifah () use seismic facies as a priori information to regularize elastic FWI and derive higher resolution isotropic and anisotropic models. They achieve this goal by defining a model regularization related to the facies.…”

“…() and Singh et al . () propose to update the facies at each iteration using a Bayesian inversion workflow, while Zhang and Alkhalifah () build the distribution of facies in the subsurface by training deep neural networks.…”