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Seismic Data Conditioning (AVATAR®) - Seismic - Technology

Most seismic data is processed to optimize image quality for structural interpretation, with little regard to preserving characteristics essential for successful seismic reservoir characterization. No matter how sophisticated the inversion algorithm, use of inadequately processed seismic data will severely impact the quality of the final interpretation.

As a consequence, seismic data can rarely be used “as is” for seismic reservoir characterization without substantial pre-conditioning. RSI uses our AVATAR® toolkit to provide a complete suite of seismic data conditioning steps to optimize the quality of pre and post-stack seismic data prior to use in impedance, AVO and seismic facies inversion applications. Similarly, Geophysical Well Log Analysis GWLA® is used to condition well-logs used for seismic modeling and WellTie™ analysis in iMOSS®.

AVATAR may be applied to pre or post-stack seismic data and is composed of several interlinked steps:

DMULT – Radon Demultiple

High resolution, de-aliased multiple attenuation in the Radon domain relies on residual moveout to discriminate multiples from primaries. Velocities must be picked with sufficient accuracy to distinguish primary energy from slightly slower multiple energy. Standard Radon methods involve transformation of common midpoint gathers into the parabolic Radon domain, where the multiple removal is more efficient and effective. Newer generation algorithms such as the one we utilize, have been written to tackle the de-aliasing and resolution problems associated with poor lateral trace spacing (aliasing) or multiples with large move-out in gathers with large offsets. These high-resolution, de-aliased algorithms are necessary for accurate transformations to and from the Radon domain, and assist in maintaining primary amplitudes while removing the effects of spatial aliasing.

SBALAN – Spectral Balancing

Gabor-Morlet Joint Time-Frequency Analysis (JTFA) is used to separate the frequency spectra of each gather trace into a user-specified number of sub-bands. The sub-bands are calculated using a running Gaussian-shaped window which gives a slowly varying amplitude profile of each sub-band. Then each sub-band spectra is balanced against the corresponding sub-band of a user-specified pilot trace within the gather.
The primary advantage of this approach is two-fold: (1) the bandwidth of the gather at each time sample is determined by the pilot trace, and (2) the total energy contained in each reflector is held constant by computing its energy envelope and requiring that the energy of all sub-bands (after scaling) sum to the original energy envelope amplitude. This ensures that AVO character is not altered in this process.

EPS3D – Edge Preserving Smoothing

This program performs “edge-preserving smoothing” in 3D, which is a methodology to enhance S/N by means of noise reduction. It works in the offset domain, thereby preserving any AVO effects in the gather. It takes into account spatial dip of coherent reflections so that frequency and continuity are preserved. The “edge-preserving” characteristics result from user-defined semblance cutoffs so that summation and filtering do not occur across traces with different reflector characteristics.

ALIGN – Pre-stack Data Alignment

A fundamental assumption made in AVO inversion is that primary reflection events are horizontally aligned (flat) across each CDP gather. The presence of residual move-out, non-hyperbolic move-out, random noise, multiples, tuning effects etc. will violate this assumption and introduce noise (uncertainty) into the AVO results. The ALIGN module within AVATAR corrects for various residual move-out effects; a much-improved AVO signature results.
ALIGN flattens gathers based on a conditional minimization of a reflector’s least-squares fit error by determining a local statics shift on each gather trace. The “condition” that is minimized can be either semblance or AVO fit. Semblance is the most robust but it is only applicable for AVO Class I and III anomalies. AVO Class II phase rotations (especially those that are not full phase reversals) present a special problem that require great care in addressing. For this case, we minimize the fit error to either a 2-term Shuey or 3-term Aki & Richards equation. These least-squares solutions are less stable but are required where AVO Class II anomalies are present or suspected.

QCOMP – Q Compensation (Post-Stack)

Shallow and deep wavelets are compared on a continuous basis to build an adaptive operator designed to match the wavelet characteristics of the deeper section with that of the shallow.