Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Li, Yunyue Elita; Biondi, Biondo; Clapp, Robert G.; Nichols, David M.

doi:10.1190/geo2015-0592.1

Cited by 37 publications

(5 citation statements)

References 39 publications

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“…Estimated anisotropy parameters can be used (after proper filtering and calibration) as initial solutions Journal of Geophysical Research: Solid Earth 10.1029/2019JB017572 to seismic imaging and amplitude-versus-angle inversion algorithms. Multiple realizations and uncertainty analysis of the anisotropy parameters can also be used to construct the covariances among the elastic parameters for seismic inversion (Li et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, Stoneley wave's sensitivity to the SH wave reduces when the formation S wave velocity is faster than the borehole fluid P wave velocity (Cheng & Toksöz, 1983). In any case, we do not have direct P wave anisotropy measurements from the well logs in a vertical well, which is strongly needed by seismic imaging and lithological inversion (Li et al, 2011(Li et al, , 2016.…”

Section: Research Articlementioning

confidence: 99%

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Shale Anisotropy Estimation From Logs in Vertical Wells

Cheng

You

2019

JGR Solid Earth

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Anisotropic elastic parameters for shales are widely needed in seismic imaging, reservoir characterization, and carbon sequestration monitoring. Unlike other elastic parameters such as vertical P and S wave velocities, anisotropy parameters are not measured directly from the acoustic well logs due to the single-directional nature of a well. We assume that shale anisotropy is induced by thin cracks that are filled with liquid in a background isotropic medium, whose bulk and shear moduli are obtained from the vertically measured P and S wave velocities, density, and porosity from corresponding well logs through a formalized inversion scheme. We show that the estimated anisotropy using the proposed method is consistent with the mineralogy and agrees with the published laboratory measurements. This framework allows us to quantify the uncertainties in the anisotropy parameters estimated from the inversion, which can be used as a measure to evaluate the validity of the chosen rock physics model. Plain Language SummaryThe differences in the wave speeds as they propagate in different angles, defined as anisotropy, are widely needed for imaging the subsurface and understanding the tectonic processes. However, measurements made in a vertical borehole cannot provide this directional information. In this study, we assume a rock physics model for shales that adds anisotropy-inducing thin inclusions in an isotropic background, the elastic and fluid properties of which are inverted formally from the measured well logs. The proposed method generates anisotropy estimates that agree with published laboratory measurements and that are consistent with the mineralogy log. Moreover, the proposed method quantifies the uncertainties in the estimated anisotropy, which can be used to evaluate the applicability of the chosen model on a particular rock formation.

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Section: Discussionmentioning

confidence: 99%

Section: Research Articlementioning

confidence: 99%

Shale Anisotropy Estimation From Logs in Vertical Wells

Cheng

You

2019

JGR Solid Earth

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“…In order to properly image the subsurface, we need to take into account of the effects of anisotropy on seismic wave propagation, thus the need for an anisotropic Earth model. Contrarily, ignorance of seismic anisotropy Journal of Geophysical Research: Solid Earth 10.1029/2019JB019042 can lead to poor seismic imaging, mispositioning of the seismic reflectors, inaccurate well ties, and incorrect interpretation of lithology and fluid content (Bandyopadhyay, 2009;Li et al, 2016aLi et al, , 2016b. Recent developments in seismic acquisition which involve longer offsets, wider azimuths, and multiple components result in wave propagation from larger apertures and more varied angles, making anisotropy a critical component of any Earth model.…”

Section: Introductionmentioning

confidence: 99%

Shale Anisotropy Model Building Based on Deep Neural Networks

You

Cheng

2020

JGR Solid Earth

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Seismic anisotropy parameters are essential in the processing and interpretation of modern array data with multicomponent, long offsets and wide azimuth acquisitions. Traditional well logs do not measure anisotropy in a vertical well and thus cannot provide the needed information. Conventional calibration‐based as well as recent inversion‐based rock physics modeling methods involve tuning parameters and subjective choices that are largely empirical and difficult to generalize. Here we present a machine learning approach to alleviate these problems. Since it is impossible to collect massive labeled field well log data, we generate paired synthetic data of features (porosity, density, vertical P and S wave velocities, P wave and shear moduli) and labels (bulk and shear moduli of rock matrices and aspect ratio of ellipsoidal cracks). By tuning hyperparameters we obtain an optimal fully connected neural network with four hidden layers that fits well with the synthetic data. The neural network is applied to published laboratory measurements and field well log data from a Chinese well and a U.S. well without any modification. We show that anisotropy models estimated by the deep neural network agree well with the inversion results and with the laboratory measurements. The neural network optimized by extensive training based on massive synthetic data removes the subjectivity in parameter selection, generalizes to different geological environments, and has the potential to provide real‐time anisotropy estimation while logging.

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“…Current implementations of IDT (Y. Li et al . ; V. Li et al . ) are limited to TI media with a vertical symmetry axis (VTI).…”

Section: Introductionmentioning

confidence: 99%

Image‐domain wavefield tomography for tilted transversely isotropic media

Tsvankin

Guitton

et al. 2019

Geophysical Prospecting

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Transversely isotropic models with a tilted symmetry axis have become standard for imaging beneath dipping shale formations and in active tectonic areas. Here, we develop a methodology of wave‐equation‐based image‐domain tomography for acoustic tilted transversely isotropic media. We obtain the gradients of the objective function using an integral wave‐equation operator based on a separable dispersion relation that takes the symmetry‐axis tilt into account. In contrast to the more conventional differential solutions, the integral operator produces only the P‐wavefield without shear‐wave artefacts, which facilitates both imaging and velocity analysis. The model is parameterized by the P‐wave zero‐dip normal‐moveout velocity, the Thomsen parameter δ, anellipticity coefficient η and the symmetry‐axis tilt θ. Assuming that the symmetry axis is orthogonal to reflectors, we study the influence of parameter errors on energy focusing in extended (space‐lag) common‐image gathers. Distortions in the anellipticity coefficient η introduce weak linear defocusing regardless of reflector dip, whereas δ influences both the energy focusing and depth scale of the migrated section. These results, which are consistent with the properties of the P‐wave time‐domain reflection moveout in tilted transversely isotropic media, provide important insights for implementation of velocity model‐building in the image‐domain. Then the algorithm is tested on a modified anticline section of the BP 2007 benchmark model.

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Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Cited by 37 publications

References 39 publications

Shale Anisotropy Estimation From Logs in Vertical Wells

Shale Anisotropy Estimation From Logs in Vertical Wells

Shale Anisotropy Model Building Based on Deep Neural Networks

Image‐domain wavefield tomography for tilted transversely isotropic media

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