Multiparameter elastic full waveform inversion with facies-based constraints

Abstract

Full waveform inversion (FWI) incorporates all the data characteristics to estimate the parameters described by the assumed physics of the subsurface. However, current efforts to utilize full waveform inversion beyond improved acoustic imaging, like in reservoir delineation, faces inherent challenges related to the limited resolution and the potential trade-off between the elastic model parameters. Some anisotropic parameters are insufficiently updated because of their minor contributions to the surface collected data. Adding rock physics constraints to the inversion helps mitigate such limited sensitivity, but current approaches to add such constraints are based on including them as a priori knowledge mostly valid around the well or as a global constraint for the whole area. Since similar rock formations inside the Earth admit consistent elastic properties and relative values of elasticity and anisotropy parameters (this enables us to define them as a seismic facies), utilizing such localized facies information in FWI can improve the resolution of inverted parameters. We propose a novel approach to use facies-based constraints in both isotropic and anisotropic elastic FWI. We invert for such facies using Bayesian theory and update them at each iteration of the inversion using both the inverted models and a prior information. We take the uncertainties of the estimated parameters (approximated by radiation patterns) into consideration and improve the quality of estimated facies maps. Four numerical examples corresponding to different acquisition, physical assumptions and model circumstances are used to verify the effectiveness of the proposed method.

Elastic full waveform inversion with probabilistic petrophysical clustering

“…As the spatial distribution of the facies is generally unknown, Zhang et al . () 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.…”

“…; Zhang et al . ). One can perform regularization by adding the first‐order Tikhonov regularization term into the objective function in order to provide smoothness to the estimated models, such that where ∇ represents the first‐order spatial derivative operator.…”

“…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.…”

Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

“…The exact TV regularization (ϵ ¼ 0 in equation 6) is implemented by an efficient primal-dual hybrid gradient algorithm (Zhu and Chan, 2008). A more advanced facies-based regularization is also applicable if the prior information is available (Zhang et al, 2017(Zhang et al, , 2018b.…”

“…Besides, due to the varying wavefield wavelengths, the estimated P-and S-wave velocities can have different spatial resolutions. Common ways to reduce the null space in multiparameter inversion include choosing the proper parameterization (Alkhalifah and Plessix, 2014;Oh and Alkhalifah, 2016) and adding additional constraints to the inversion (Asnaashari et al, 2013;Zhang et al, 2017Zhang et al, , 2018b). The first-or second-order spatial derivatives (i.e., Laplacian) can be used to generate a smooth estimation of the subsurface and also can suppress some short-wavelength artifacts in the estimate of S-wave velocity.…”