Facies-constrained FWI: Toward application to reservoir characterization

Kamath, Nishant; Tsvankin, Ilya; Naeini, Ehsan Zabihi

doi:10.1190/tle36110924.1

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Attempts to make FWI more robust by including a facies distribution have shown some promise (Zhang et al, 2017). In particular, Kamath et al (2017) use a two-stage process to incorporate facies information into FWI. The results of the conventional FWI (first inversion stage) are employed to build the facies which are compared to the prior facies model obtained from a well log.…”

Section: Introductionmentioning

confidence: 99%

“…Both techniques operate with relatively simple layered media that have mild lateral heterogeneity. Because Kamath et al (2017) incorporate information from a single borehole, the wrong facies can be assigned to certain grid points if the model is structurally complicated with pronounced lateral gradients. For the same reason, the inversion results of Zhang et al (2017) include artifacts (e.g., edge effects).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, our algorithm employs image-guided interpolation (Hale, 2010) to reconstruct the model parameters between the available boreholes. Because the 3D elastic VTI medium used in another example later has a simpler structure, the prior model for it is built by a technique similar to that of Kamath et al (2017).…”

Section: Introductionmentioning

confidence: 99%

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Bayesian framework for elastic full-waveform inversion with facies information

Singh

Tsvankin

Naeini³

2018

The Leading Edge

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian framework for elastic full-waveform inversion with facies information

Singh

Tsvankin

Naeini³

2018

The Leading Edge

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“…This makes the method suitable for low‐frequency data which is not able to resolve all properties. Facies‐based constraints impose regularization and reduce the non‐uniqueness in seismic inversion (Kamath et al., 2017; Zhang & Alkhalifah, 2019). The proposed method inverts all traces independently using a convolutional forward model based on one‐dimensional layered earth without taking lateral correlation into account.…”

Section: Discussionmentioning

confidence: 99%

Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

Dhara

Sen

Biswas

2023

Geophysical Prospecting

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We present a methodology for seismic inversion that generates high-resolution models of facies and elastic properties from pre-stack data. Our inversion algorithm uses a transdimensional approach where, in addition to the layer properties, the number of layers is treated as unknown. In other words, the data itself determine the correct model parameterization, that is, the number of layers. The reversible jump Markov Chain Monte Carlo method is an effective tool to solve such transdimensional problems as it generates models of reservoir properties along with uncertainty estimates. However, current implementations of the reversible jump Markov Chain Monte Carlo algorithms do not account for the non-Gaussian and multimodal nature of model parameters. The target elastic reservoir properties generally have multimodal and non-parametric distribution at each location of the model. The number of modes is equal to the number of facies.Taking these factors into account, we extend the reversible jump Markov Chain Monte Carlo algorithm to simultaneously invert for discrete facies and continuous elastic reservoir properties. The proposed extension to the algorithm iteratively samples the facies, by moving from one mode to another, and elastic properties by sampling within the same mode. The integration of facies classification within the inversion reduces nonuniqueness, improves convergence speed and produces geologically consistent results.The workflow uses machine learning to generate probabilistic priors for the model parameters. We validate our approach by applying it to a synthetic dataset generated from a well log with two facies and then to a complex synthetic two-dimensional model involving three facies having overlapping elastic property distribution. Finally, we apply our algorithm to a field dataset acquired over an unconventional reservoir. Our algorithm demonstrates the usefulness of incorporating facies information in seismic inversion and also the feasibility of inverting for facies from seismic data.

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“…The Bayesian theory provides a natural platform to incorporate available prior information of the model for many geophysical inversion problems (Buland & Omre 2003). A more advanced type of constraint, namely per-facies rock physics constraints, has been proved to be very effective in optimizing seismic inversion (Zabihi Naeini & Exley 2017;Kamath et al 2017). Zabihi Naeini et al (2016 discussed the main components of FWI as a potential reservoir characterization tool and one of their suggestions was to use facies based rock physics constraints in FWI.…”

Section: Introductionmentioning

confidence: 99%

Multiparameter elastic full waveform inversion with facies-based constraints

Zhang

Alkhalifah

Naeini³

et al. 2018

Geophysical Journal International

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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.

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Facies-constrained FWI: Toward application to reservoir characterization

Cited by 9 publications

References 13 publications

Bayesian framework for elastic full-waveform inversion with facies information

Bayesian framework for elastic full-waveform inversion with facies information

Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

Multiparameter elastic full waveform inversion with facies-based constraints

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