Pre-Stack Enhancement: Technical Details

Process 1

Demultiple, Noise Reduction and gather flattening.

Process 2

Spectral Balancing

Process 3

AVA Attributes and Inversion


Residual multiples are often present in migrated gathers. They may interfere with primaries at mid to far offsets and may bias AVA attributes at near offsets. We offer a high-resolution Radon demultiple which is very effective at removing longer period multiples where there is sufficient moveout difference between the primaries and multiples. The high resolution algorithm avoids damage to primaries, allowing quite tight cuts with effective multiple suppression. For shorter period multiples we use tau-p deconvolution.

Noise Reduction 

Coherent noise reduction is achieved in gathers using a linear high-resolution Radon transform. Dipping noise in the inline-crossline domain is attenuated using f-k-k filtering applied to offset cubes. Random noise is reduced by using edge-preserving spatial filters or anisotropic diffusion filters, applied to gathers or to offset cubes as necessary.

Gather Alignment

Residual velocity errors are corrected by semblance scanning the  gathers. A simultaneous scan for velocity and higher-order moveout (Alkhalifah’s eta parameter) is used and these updated fields are available for QC and further analysis. Non-velocity related problems are addressed using a trim-statics method. There is very tight control on the stability of the time shifts and the algorithm design ensures that type 2 P AVA responses are not damaged by misalignment.

These techniques are applied as necessary. The order in which they are used depends on the nature and severity of the problems in the data, and workflows are designed individually for each individual dataset. Test areas are defined using the health check attribute maps to ensure that all issues are adequately addressed, and the same attribute maps after conditioning are used to QC the work.

PreStack Spectral Balancing

Far offset data typically have lower bandwidth than the near offsets, due to NMO stretch and more attenuation during the longer travel paths. For prestack amplitude analysis, it may be desirable to balance the bandwidth between near and far offsets, producing a more stable wavelet. Two methods are available. The first is based on wavelet transforms, and matches the individual offset transforms to a pilot transform obtained from a near stack. This algorithm is designed to be AVA-friendly and it is stable in the presence of noise. The second approach is to perform deterministic shaping of offset spectra to a desired spectrum.  This method is not data-dependent, but is specific to a target interval.


PreStack AVA Attributes and Inversion

A range of AVA attributes can be calculated from conditioned angle gathers. These include the standard 2 term intercept and gradient, fluid factor, coloured inversion, EEI attributes, and stochastic inversion for lithofacies. This work is highly interactive, making it easy to tune results to specific project objectives and results are optimised by well calibration where possible.  Confidence in the interpretation is established by cross-referencing back to the gathers and the health-check maps.