Building Anisotropic Velocity Model Using Rock Physics and Local Tomography – A North Sea Case Study

We propose a fast and efficient method to build anisotropic seismic velocity models by combining forward modeling and traveltime inversion of 2-D walkaway VSP data. Applying this approach, we derived anisotropic seismic velocity models for a 1200 m thick reservoir overburden consisting of horizontally layered shales and shaley sands in the Western Canadian basin. The models were determined by iteratively minimizing the misfit between the measured and modeled VSP first break times. The modeled times were calculated by 2-D ray tracing through isotropic and anisotropic velocity models. Once we achieved a fairly good match between the data and the model we then used inversion for fine-tuning. The proposed new method can help to provide good anisotropic velocity models for both surface and borehole seismic anisotropic migration.

Anisotropic models are recognized as a more realistic representation of the subsurface where complex geological environment exists. These models are widely needed by all kinds of migration and interpretation schemes. However, anisotropic model building is still a challenging problem in the industry. In this paper, we propose an approach to building anisotropic models from surface seismic data based on the theory of WaveEquation Migration Velocity Analysis (WEMVA). Because of the ambiguity between depth and δ , we parametrize our model space using only NMO velocity (Vnmo) and the anellipticity parameter η . We present a synthetic data example of anisotropic WEMVA applied to the shallow part of the Hess anisotropic synthetic VTI model. The results show that anisotropic WEMVA is effective in resolving the anisotropic perturbation; however, a unique solution to the inversion requires additional constraining information.

As anisotropic depth imaging is widely used in the industry, anisotropic velocity models are routinely being constructed and applied. However, the degree of accuracy of the anisotropic velocity model needed to correctly position the horizons is not well defined. To that end, we tested four anisotropic parameter estimation scenarios and performed depth imaging using a realistic synthetic sedimentary seismic data that included an irregularly shaped salt body. Although similar images derived from the data using those four different anisotropic input models were obtained, a quantitative analysis indicates that systematic differences do exist. They enable us to relate the degree of accuracy needed in the image to the effort needed to build the anisotropic velocity model.

Combining PP-waves with mode-converted PS reflections in migration velocity analysis (MVA) can help build more accurate VTI (transversely isotropic with a vertical symmetry axis) velocity models. To avoid problems caused by the moveout asymmetry of PS-waves and take advantage of efficient MVA algorithms designed for pure modes, here we generate pure SS-reflections from PP and PS data using the PP þ PS ¼ SS method. Then the residual moveout in both PP and SS common-image gathers is minimized during iterative velocity updates. The model is divided into square cells, and the VTI parameters VP0, VS0, e, and δ are defined at each grid point. The objective function also includes the differences between the migrated depths of the same reflectors on the PP and SS sections. Synthetic examples confirm that 2D MVA of PP- and PS-waves may be able to resolve all four relevant parameters of VTI media if reflectors with at least two distinct dips are available. The algorithm is also successfully applied to a 2D line from 3D ocean-bottom seismic data acquired at Volve field in the North Sea. After the anisotropic velocity model has been estimated, accurate depth images can be obtained by migrating the recorded PP and PS data.

In this study we develop a practical method for the location of microseismic events in anisotropic media. The velocity anisotropy is taken into account in the additional step after the conventional location procedure. In this step, the isotropic velocity model and corresponding estimated locations serve as an initial model, and the anisotropy parameters as well as resulting location misfits are inverted simultaneously due to the coupling of velocity model and hypocenter parameters in calculation. After obtaining the anisotropic velocity model, one can relocate all the events around their isotropic velocity based locations. The method is ray theory based and only consider transversely isotropic layered model. The nonlinear inversion problem is solved by using Gauss-Newton method. The results of synthetic example closely fit with the true values and event depths are better determined than radial distances. Real data test shows that the estimated Thomsen parameters are consistent with previous study, and the isotropic velocity based locations are significantly corrected with large decrease of average misfit from 52m to 6m.

Summary In 2014 we proposed a new technique that used well information to correlate anisotropy with velocity for localized lithology dependent anomalies (Birdus at al., 2014). It is based on the assumption that in appropriate geological settings localized variations in both velocity and anisotropy are caused by changes in the lithology. This results in some correlation between anisotropy and velocity anomalies. We used well information to establish such a correlation for tomographic PSDM imaging anisotropic velocity models. In this paper we extend our approach to high resolution FWI depth velocity modeling. We use a real 3D seismic dataset from the NW Australian shelf to illustrate how our technique produces more realistic anisotropic velocity models and reduces depth misties.

This paper compares rock physics trends in the Vp-vs.-Vs crossplot and the Vp/Vs-vs.-compressional slowness crossplot. Trend uncertainties are presented with laboratory data from the Bakken, Bazhenov, Monterey, and Niobrara shales; departures from expected trends attributed to kerogen, hydrocarbon, anisotropy, and well deviation are discussed. Anisotropy models, such as ANNIE (Schoenberg et al.1996), are presented for computing Thomsen parameters and stiffness coefficients. Castagna mudrock line (Castagna et al. 1993) derived from in-situ sonic and seismic measurements provided an average linear relationship between compressional and shear-wave velocities. Brie (1995) extended previous work to include predicting gas saturation from a Vp/Vs-vs.-compressional slowness crossplot. Projecting the Castagna mudrock line onto a shear-vs.-compressional slowness crossplot proved useful for interpreting sonic data. Classical-rock physics equations were used to model compressional and shear velocities as a function of well deviation. Laboratory anisotropy models allowed for characterizing the effects of dispersion, anisotropy, and well deviation. Anisotropic-rock physics models for the Bakken, Bazhenov, Monterey, and Niobrara organic shales are presented and compared in terms of the Thomsen parameters, ε, γ, and δ, and stiffness coefficients, Cij. These models are first transposed to trend curves in Vp-vs.-Vs, Vp/Vs-vs.-compressional slowness, and shear-vs.-compressional slowness crossplots and then compared to the Castagna mudrock trend curve. The anisotropy models are then applied to characterize the effect of well deviation on these trend curves. Results are also presented for well log data from the Eagle Ford, Haynesville, and Bakken formations in the context of the discussed crossplots and then compared with the expected trend curves. The Eagle Ford data satisfies the carbonate Vp-vs.-Vs trend predicted in previous literature by Castagna et al. (1993), and the Haynesville data clearly satisfies the gas effect predicted by the Brie Vp/Vs-vs.-compressional slowness crossplot discussed. In theory, the models describing anisotropy with the Thomsen parameters or stiffness coefficients are equivalent. In practice, not all the parameters for either model can be measured from log data. Preferentially, anisotropic models should be derived by combining log data from multiple vertical, deviated, and horizontal wells for each shale reservoir.

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2001Anisotropic migration and model building for 4C seismic data: A case study from AlbaAuthors: Hengchang DaiXiang‐Yang LiHengchang DaiEdinburgh Anisotropy Project, British Geological Survey and Xiang‐Yang LiEdinburgh Anisotropy Project, British Geological Surveyhttps://doi.org/10.1190/1.1816753 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1816753FiguresReferencesRelatedDetailsCited byLarge-offset P-wave traveltime in layered transversely isotropic mediaMohammad Mahdi Abedi and David Pardo11 March 2021 | GEOPHYSICS, Vol. 86, No. 3Reduced memory implementation of a local elastic finite-difference solverLigia Elena Jaimes-Osorio, Alison Malcolm, Polina Zheglova, Erik F. M. Koene, and Henrik R. Thomsen11 March 2021 | GEOPHYSICS, Vol. 86, No. 3Converted wave anisotropic correction technology based on azimuth scanningRuyi Zhang*, Qingfeng Kong, and Jianxin Shi19 August 2015Pre-stack time migration for PS-wave data in orthorhombic symmetry mediaZhong-Yu Huang* and Yu-Jing Wang5 August 2014An OpenCL Approach of Prestack Kirchhoff Time Migration Algorithm on General Purpose GPUComputing prestack Kirchhoff time migration on general purpose GPUComputers & Geosciences, Vol. 37, No. 10Four-parameter velocity analysis and its applications to the Ken-71 area converted-wave data13 May 2008 | Applied Geophysics, Vol. 5, No. 1Three‐component converted‐wave data processing and its application in a complicated areaGuo Xiangyu, Jiang Xiaosong, Zhang Guojuan, Hou Yan, Feng Xukui, and Tian Chengchun15 December 2008Converted-wave imaging in anisotropic media: theory and case studiesGeophysical Prospecting, Vol. 55, No. 3The effects of migration velocity errors on traveltime accuracy in prestack Kirchhoff time migration and the image of PS converted wavesHengchang Dai and Xiang-Yang Li6 March 2006 | GEOPHYSICS, Vol. 71, No. 2Parallel processing of Prestack Kirchhoff Time Migration on a PC ClusterComputers & Geosciences, Vol. 31, No. 7Converted‐wave imaging in anisotropic media: An overviewXiang‐Yang Li, Hengchang Dai, and Fabio Mancini3 January 2005 SEG Technical Program Expanded Abstracts 2001ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2001 Pages: 2135 publication data© 2001 Copyright © 2001 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 03 Jan 2005 CITATION INFORMATION Hengchang Dai and Xiang‐Yang Li, (2001), "Anisotropic migration and model building for 4C seismic data: A case study from Alba," SEG Technical Program Expanded Abstracts : 795-798. https://doi.org/10.1190/1.1816753 Plain-Language Summary PDF DownloadLoading ...

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Anisotropic depth migration (ADM) has become more commonplace over the past four years. The data-processing examples detailed here illustrate the robustness of the method in a variety of structured settings in the Alberta Foothills. The public-domain structural line, the Husky/Talisman dataset, illustrates subtle improvements in imaging with a dramatic improvement in accuracy of horizon depths when ADM is applied to these data. A 3D survey in a difficult imaging area, Nordegg/Chungo, shows significant healing of broken basement reflectors when we correct for anisotropy in the complex-dipping clastic overburden. Finally, in the Blackstone area where we observe intense folding in the near surface, comparisons between poststack time migration, prestack time migration, prestack depth migration, and prestack anisotropic depth migration show the similar step-change improvements as we imrove the technology of our algorithm. Introduction Traditional depth migration corrects only for lateral velocity heterogeneity. Current anisotropic depth migration algorithms also correct for velocity changing with direction in the dipping clastic overburden. Seismic anisotropy is a well-quantified physical characteristic of clastic rocks. Studies of anisotropic behaviour in clastic rocks include Thomsen (1986), Vernik and Liu (1997), Leslie et al (1997). Several papers over the past seven years illustrate the improvements in imaging when we stop ignoring anisotropy and begin to correct for it (Ball, 1995; Vestrum and Muenzer, 1997; Vestrum, et al., 1998; Ferguson and Margrave, 1998; Schmid et al., 1998; Dai et al., 1998; Fei et al., 1998; Vestrum et al., 1999, Kirtland Grech et al, 2001; etc.). The seismic data processing case histories presented here outline the improvements in seismic imaging and structural positioning when we correct for seismic anisotropy in depth migration. Benjamin Creek The Husky/Talisman dataset is the focus of our first processing case history. In this example, I illustrate the improvement in correlation between well depths and seismic depths. Figure 1 shows this improvement. Isotropic depth migration for this line (Figure 1a) shows a typical result (Shultz and Canales, 1997; Etris et al., 2001) of isotropic depth migration where the seismic depths are deeper than the well depths. When we ignore anisotropy, we need to use a velocity faster than the vertical velocity if we are to flatten the image gathers and obtain a quality image. The anisotroic migration (Figure 1b), models the velocity behavour changing with direction, so it produces a clear image and correlates to the well depths. One can see in Figure 1b that there is still some uncertainty in the correlation, but we have removed a systematic error from the depth correlation. We could now go back and adjust our imaging velocities so that our final depth migration matched perfectly with well depths, without needing a separate depth-conversion step after the depth migration. We can now consider well depths as a constraint on our imaging velocity model. There are also several imaging improvements on the anisotropic depth migration over the isotropic depth migration. Figure 1: (a) isotropic depth migration and (b) anisotropic depth migration and overlain well depths to top and bottom of the carbonate sheet. Nordegg/Chungo The Nordegg/Chungo 3D is a Time Seismic data volume acquired in the Alberta Foothills. A more difficult imaging area than the Benjamin Creek area, this volume shows imaging problems that arise when the anisotropic overburden has dramatically changing dip. Figure 2 shows the comparison between isotropic and anisotropic depth migrations for a line out of this volume. Note the improvement in basement continuity beneath the steeply dipping near-surface when we use anisotropic depth migration (Figure 2b). Several other potential hydrocarbon-target events show similar improvement. Note that the target event, as indicated by the red arrow, shows more consistent imaging over the range of dips from the back limb over the crest of the structure and into the forelimb. The anisotropic velocity model more accurately represents the velocity structure of the overburden. With more accurate raytracing over a larger range of reflector angles, the migration is better able to image the range of reflector dips on this structure. Blackstone This Talisman dataset is a very dramatic anisotropic imaging example. The extensive folding of the clastic overburden creates difficulties imaging structures below. Figure 3 shows a comparison between poststack time migration (Figure 3a), prestack time migration (Figure 3a), prestack depth migration (Figure 3c), and prestack anisotropic depth migration (Figure 3d). Each step forward in complexity of the migration

Recent seismic acquisition and anisotropic velocity modeling has resulted in improved salt definition and sub‐salt imaging at Puma and Mad Dog Fields in the Gulf of Mexico. For these fields, wide‐azimuth towed‐streamer (WATS) surveys, isotropic imaging, vertical transverse isotropy (VTI) imaging, and tilted transverse isotropy (TTI) imaging were carried out sequentially. This series offers a great opportunity to understand the impact of different levels of anisotropy approximation and why TTI reverse time migration (RTM) should be used for future WATS imaging.

The coupling–ray–theory tensor Green function for electromagnetic waves or elastic S waves is frequency dependent, and is usually calculated for many frequencies. This frequency dependence represents no problem in calculating the Green function, but may represent a great problem in storing the Green function at the nodes of dense grids, typical for applications such as the Born approximation. This paper is devoted to the approximation of the coupling–ray–theory tensor Green function, which practically eliminates this frequency dependence within a reasonably broad frequency band. In the vicinity of a given prevailing frequency, we approximate the frequency–dependent frequency–domain coupling–ray–theory tensor Green function by two dyadic Green functions corresponding to two waves described by their travel times and amplitudes calculated for the prevailing frequency. We refer to these travel times and amplitudes as the coupling–ray–theory travel times and the coupling–ray–theory amplitudes. This “prevailing–frequency approximation” of the coupling ray theory for electromagnetic waves or elastic S waves allows us to process the coupling–ray–theory wave field in the same way as the anisotropic–ray–theory wave field. This simplification may be decisive when storing the tensor Green function at the nodes of dense grids, which is typical for applications such as the Born approximation. We test the accuracy of the proposed prevailing–frequency approximation of the coupling ray theory numerically using elastic S waves in eight anisotropic velocity models. The additional inaccuracy introduced by the prevailing–frequency approximation is smaller than the inaccuracy of the standard frequency–domain coupling ray theory, and smaller than the additional inaccuracy introduced by many other approximations of the coupling ray theory.

As the leading edge of the Indian–Asian collision, the eastern Himalayan syntaxis region has experienced extensive tectonic activities, resulting in complex crustal uplift and deformation in the corner area of the southeastern pathway for the extrusion of Tibetan plateau materials. Despite considerable efforts, the corresponding deformation mechanisms remain uncertain. This study presents a new 3D high-resolution azimuthal anisotropic shear-wave velocity model in the crust and uppermost mantle derived from ambient noise dispersion data. Results show that the upper crustal anisotropy aligns with the geological boundaries and major faults nearby, suggesting shape-preferred orientations. The upper crustal low velocity and weak anisotropy beneath the core of the eastern Himalayan syntaxis (EHS) are closely associated with the high fragmentation of shallow rocks and the upwelling of hot materials during the ongoing subduction of the Indian plate. Our model also reveals relatively complex anisotropic patterns in the midlower crust. The eastern Lhasa terrane, in particular, exhibits low velocity and strong anisotropy with a northwest–southeast-oriented fast axis, supporting the local scale midlower crustal “channel flow” model. In addition, a conspicuous, elongated low-velocity zone along the northwest–southeast direction is observed in the midlower crust and uppermost mantle beneath the Bangong–Nujiang suture. The anisotropy in this region increases with depth, and the fast directions are consistently parallel to the northeast subduction of the Indian plate. We infer that this low-velocity zone may result from partial melting under local compression driven by the Indian–Asian collision. On the basis of newly revealed anisotropic model and previous studies, we construct a new dynamic model, which reveals that the migration of mechanically weak material in the midlower crust and the significant contribution of the northeast subduction of the Indian plate jointly control the crustal deformation of the EHS region.

Because anisotropic models are unconstrained by surface-seismic data alone, we must learn to incorporate other non-seismic measurements and knowledge into the model building process. For this purpose, we demonstrate the regularization of anisotropic tomography with a preconditioning method which smooths updates along geological dip and constrains cross-property correlations to follow the predictions of rock-physics for compacting shales. The method is applied to the Green Canyon area in the Gulf of Mexico.

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 and wave velocities, 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.