Azimuthal Anisotropy at Valhall: the Helmholtz Equation Approach

International audienceWe used 6 hours of continuous vertical records from 2320 sensors of the Valhall Life of Fields Seismic network to compute 2 690 040 cross-correlation functions between the full set of sensor pair combinations. We applied the 'Helmholtz tomography' approach combined with the ambient noise correlation method to track the wave front across the network with every station considered as a virtual source. The gradient of the interpolated phase travel time gives us an estimate of the local phase speed and of the direction of wave propagation. By combining the individual measurements for every station, we estimated the distribution of Scholte's wave phase speeds with respect to azimuth. The observed cosine pattern indicates the presence of azimuthal anisotropy. The elliptic shape of the fast anisotropy direction is consistent with results of previous shear wave splitting studies and reflects the strong seafloor subsidence due to the hydrocarbon reservoir depletion at depth and is in good agreement with geomechanical modeling

Helmholtz Tomography of ambient noise surface wave data to estimate Scholte wave phase velocity at Valhall Life of the Field

International audienceWe applied the Helmholtz tomography technique to 6.5 hours of continuous seismic noise record data set of the Valhall Life of Field network. This network, that has 2320 receivers, allows us to perform a multifrequency, high-resolution, ambient-noise Scholte wave phase velocity tomography at Valhall. First, we computed crosscorrelations between all possible pairs of receivers to convert every station into a virtual source recorded by all other receivers. Our next step was to measure phase traveltimes and spectral amplitudes at different periods from crosscorrelations between stations separated by distances between two and six wavelengths. This is done in a straightforward fashion in the Fourier domain. Then, we interpolated these measurements onto a regular grid and computed local gradients of traveltimes and local Laplacians of the amplitude to infer local phase velocities using a frequency dependent Eikonal equation. This procedure was repeated for all 2320 virtual sources and final phase velocities were estimated as statistical average from all these measurements at each grid points. The resulting phase velocities for periods between 0.65 and 1.6 s demonstrate a significant dispersion with an increase of the phase velocities at longer periods. Their lateral distribution is found in very good agreement with previous ambient noise tomography done at Valhall as well as with a full waveform inversion P-wave model computed from an active seismic data set. We put effort into assessing the spatial resolution of our tomography with checkerboard tests, and we discuss the influence of the interpolation methods on the quality of our final models

Application of 2D acoustic frequency-domain full-waveform inversion to OBC wide-aperture data from the Valhall field

International audienceWe present an application of 2D acoustic frequency-domain Full Waveform Inversion (FWI) to the hydrophone component of 4-C ocean bottom cable (OBC) data recorded from the Valhall field in North sea. The starting model for FWI was built by reflection traveltime tomography (RTT). Although this starting model leads to flat common-image gathers (CIGs), it does not allow us to match first-arrival traveltimes of diving waves from above the gas layers. This mismatch between vertical and horizontal velocities is likely the footprint of anisotropy. We updated the RTT model by first-arrival traveltime tomography (FATT) to build a new starting model for FWI. The velocities above the gas layers of the updated model are significantly higher than velocities from in-well seismic (VSP) data. FWI models were computed from the two starting models just mentioned. More stable results were obtained with the starting model updated by FATT. The resulting FWI model shows a reasonable agreement with a former model developed by 3D FWI. A reasonable match of both short-aperture and wide- aperture components of the data was obtained by isotropic FWI. This might indicate that layer-induced anisotropy was created by FWI in the gas layers to balance the increase of the shallow velocities created by the inversion of the wide-aperture data components. ©2010 Society of Exploration Geophysicist

Two-dimensional Anisotropic Visco-elastic Full Waveform Inversion of Wide-aperture 4C OBC Data from the Valhall Field

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On the footprint of anisotropy on isotropic full waveform inversion: the Valhall case study

International audienceThe validity of isotropic approximation to perform acoustic full waveform inversion (FWI) of real wide-aperture anisotropic data can be questioned due to the intrinsic kinematic inconsistency between short- and large-aperture components of the data. This inconsistency is mainly related to the differences between the vertical and horizontal velocities in vertical-transverse isotropic (VTI) media. The footprint of VTI anisotropy on 2-D acoustic isotropic FWI is illustrated on a hydrophone data set of an ocean-bottom cable that was collected over the Valhall field in the North Sea. Multiscale FWI is implemented in the frequency domain by hierarchical inversions of increasing frequencies and decreasing aperture angles. The FWI models are appraised by local comparison with well information, seismic modelling, reverse-time migration (RTM) and source-wavelet estimation. A smooth initial VTI model parameterized by the vertical velocity V0 and the Thomsen parameters δ and ε were previously developed by anisotropic reflection traveltime tomography. The normal moveout (inline image) and horizontal (inline image) velocity models were inferred from the anisotropic models to perform isotropic FWI. The VNMO models allows for an accurate match of short-spread reflection traveltimes, whereas the Vh model, after updating by first-arrival traveltime tomography (FATT), allows for an accurate match of first-arrival traveltimes. Ray tracing in the velocity models shows that the first 1.5 km of the medium are sampled by both diving waves and reflections, whereas the deeper structure at the reservoir level is mainly controlled by short-spread reflections. Starting from the initial anisotropic model and keeping fixed δ and ε models, anisotropic FWI allows us to build a vertical velocity model that matches reasonably well the well-log velocities. Isotropic FWI is performed using either the NMO model or the FATT model as initial model. In both cases, horizontal velocities are mainly reconstructed in the first 1.5 km of the medium. This suggests that the wide-aperture components of the data have a dominant control on the velocity estimation at these depths. These high velocities in the upper structure lead to low values of velocity in the underlying gas layers (either equal or lower than vertical velocities of the well log), and/or a vertical stretching of the structure at the reservoir level below the gas. This bias in the gas velocities and the mispositioning in depth of the deep reflectors, also shown in the RTM images, are required to match the deep reflections in the isotropic approximation and highlight the footprint of anisotropy in the isotropic FWI of long-offset data. Despite the significant differences between the anisotropic and isotropic FWI models, each of these models produce a nearly-equivalent match of the data, which highlights the ill-posedness of acoustic anisotropic FWI. Hence, we conclude with the importance of considering anisotropy in FWI of wide-aperture data to avoid bias in the velocity reconstructions and mispositioning in depth of reflectors. Designing a suitable parameterization of the VTI acoustic FWI is a central issue to manage the ill-posedness of the FWI