Philippe Herrmann

De‐aliased, high‐resolution radon transforms

Multiple elimination methods based on the move-out discrimination between primaries and multiples rely heavily on the focusing of seismic events in the parabolic Radon domain. This focusing, however, is affected both by the finite spatial aperture and sampling of the data. As a consequence of the resulting smearing, multiple energy may be mapped into the primary model and conversely primary energy may be mapped into the multiple model. This leads to poor multiple removal and to the nonpreservation of the primary amplitudes. To overcome these pitfalls one has to make use of De-aliased, HighResolution Radon transforms. High-resolution Radon transforms have already been proposed by some authors. Here we present a novel approach that simultaneously tackles the aliasing and resolution issues in a non-iterative way.

Elimination of Surface-related Multiply Reflected And Converted Waves

A new method is introduced for eliminating all surface related multiples in both acoustic and elastic media. This pre-stack multiple elimination procedure does m require any knowledge about the subsurface structure but only about the reflection characteristics at the surface and the source wave field. The procedure is carried out in the space-frequency domain and can therefore handle data from laterally inhomogeneous ubsurfaces. The multiple elimination method is based on the wave equation. The main characteristic is the fact that the data itself is used as an multiple prediction operator. This in contrary to other wave equation based multiple suppressing schemes which assume a subsurface model and generate this operator in a synthetic way. The main operations in the algorithm are matrix multiplications, which makes it very suitable for vector computers. In the case of elastic data (multi component recording) a decomposition is applied to acquire true P and S wave responses. After this decomposition converted multiples can be handled as well. Applying this multiple elimination procedure on synthetic data, both acoustic and elastic, yields good results.

Non-linear Tomography for Time Imaging

Velocity model building for time imaging generally involves an iterative process involving several loops of PreSTM, picking, and velocity update. We propose a new approach allowing the full interpretation of kinematic information picked on PreSTM gathers

Processing Solutions For Wide Azimuth Data: Outcome From a WATS Field Experiment In Deep Water Gulf of Mexico

Wide Azimuth Towed Streamer (WATS) acquisition has already proved to be a key technique in improving seismic imaging, especially in complex areas such as sub-salt plays. In 2006, a WATS field experiment was conducted in a deep water area of the Gulf of Mexico. The main purpose was to challenge recently developed 3D processing algorithms and find the most suitable processing strategy for a wide azimuth dataset. The results indicate that a 3D shot based processing sequence is an effective solution that accommodates the effects related to the multi-pass acquisition method and realizes the full benefit of the recorded 3D wide azimuth wave field.

Shot-based pre-processing solutions for wide azimuth towed streamer datasets

Wide azimuth towed streamer (WATS) acquisition has been shown to provide improved seismic imaging, especially in areas with complex 3D structures. By making use of additional source vessels shooting into the streamer array from large lateral offsets, a dataset is created with a large cross-line aperture, higher fold, and a broader offset-azimuth distribution than conventional (narrow azimuth) streamer datasets. The improved imaging provided by WATS acquisition geometry has been well illustrated as this method is being more widely adopted. An example which illustrates the superiority of WATS data for imaging was given by Michell et al. (2006). There, a straightforward depth migration of WATS data with limited pre-processing was shown to provide a significantly improved image compared to the results obtained with conventional streamer acquisition. However, to realize the full potential of WATS data, preprocessing (the processing sequence prior to imaging) is essential, especially for applications which depend on the quality of the pre-stack gathers such as: N Velocity model building and update N Pre-stack time and depth imaging N 4D/time lapse processing N AVO analysis and reservoir characterization N Quantitative analysis

A Regularization Workflow for the Processing of Cross-spread COV Data

Data regularization is critical for the suppression of Kirchhoff migration noise and the production of a clean migration image. We introduce a workflow utilizing Fourier reconstruction in two spatial dimensions to fully regularize cross-spread COV data i

From Time to Depth Imaging: an Accurate Workflow

We present a new strategy for depth velocity model building from pre-stack time migrated gathers. It is based on a dense volumetric dip and residual move-out (RMO) picking in the prestack time migrated domain. The kinematic information is demigrated to compute multioffset un-migrated attributes – called seismic invariants – used as input data for a multi-offset depth tomography. Compared to the corresponding existing strategy based on picking in the depth migrated domain, our new strategy allows to fully take advantage of a previous time imaging project. It bypasses the initial depth model building and initial prestack depth migration required for picking RMO in depth. Furthermore it takes advantage of the quality of a picking phase carried out on already reasonably focused time migrated dataset, with no compromise on the accuracy. We demonstrate the relevance of the approach on a real dataset.

Multi-Dimensional Data Regularization for Modern Acquisition Geometries

P143 Multi-Dimensional Data Regularization for Modern Acquisition Geometries G. Poole* (CGGVeritas) & P. Herrmann (CGGVeritas) SUMMARY Data regularization is critical for the suppression of Kirchhoff migration noise and the production of a clean migration image. Techniques that perform data regularization simultaneously along two axes provide a solution where multiple passes of a 1D approach fail. We introduce a versatile two-dimensional Fourier reconstruction algorithm that regularizes the input data as well as filling gaps in the coverage. We validate the algorithm on a synthetic cross-spread gather example as well as demonstrating the technology on a real offset volume dataset. The results

Common-angle Processing Using Reflection Angle Computed By Kinematic Pre-stack Time Demigration

Recent works have shown the benefit of seismic data processing in common angle domain especially for AVA studies and stack resolution enhancement. When wide angle datasets are exploited, difficulties and reservations come with the genuine signification and the validation of the reflection angle value. The workflow presented includes two original methods providing accurate angle CDP gather from time migrated offset CDP gather. The dependencies offset, angle and time are computed through a kinematic pre-stack time demigration process taking into account local dips, the effective velocity and anelipticity model. The kinematic demigration takes advantage of the existing relationships between the reflection angle and the differential stretch in time of the wavelet for different offsets. This approach gives a better angle estimation than conventional methods especially for angles larger than 40 and for dipping events. In addition to the mapping of offset, angle and time correspondences, a direct (and reverse) angle transform is defined in order to provide seismic traces regularly distributed in angle. The “angle regularization” is performed by an iterative Fourier reconstruction minimizing the spatial frequency leakage. Because the amount of stretch is nearly stationary in time (if there are no variations of dip in time) for a common angle time-migrated trace, a spectral harmonization along angles can be easily implemented by single spectral matching operators. This stretch compensation impacts on the stack quality and resolution.

Statistical properties of seismically derived AVO attributes

AVO analysis relies on characteristic AVO attributes derived from the amplitude behavior of the back scattered seismic wave field recorded over a range of offsets (or angles: AVA). An early established practice involves the production of two stacks: a near offset stack (with the first half range of offsets) and a far offset stack (with the second half offset range). These two AVO attributes fulfill two important requirements: High signal-to-noise ratio: compared to the individually recorded traces, the S/N ratio is enhanced by the partial stacking. Statistical independence: the near and far offset stacks involve distinct data samples. Therefore, by construction, any observed correlation between them is solely induced by the data and not by the way in which the AVO attributes are derived.