Structural Analysis

Fault Analysis

Exhaustive Search Seismic Fault Analysis

Curvature

Multi-scale Reflector Curvature

Spectral Decomposition

RGB Blending of frequency components

This is the latest fault analysis technique: Exhaustive Search Seismic Fault Analysis. This is an extremely powerful and computationally expensive technique, so is perfectly suited for our high power computing (HPC) cloud system. We believe this is the most effective fault detection system available today. The purpose of the seismic fault processing is to provide fault attribute volumes that aid interpretation in a number of ways including:

  • Providing a rapid structural overview without the need to interpret faults in advance
  • Acting as a guide in manual fault picking or as an input to fault tracking in 3rd party interpretation software, where those tools allow
  • De-risk an existing interpretation by highlighting potential faults which may have been missed or those which unexpectedly break a horizon seal

During the first phase of work we apply a standard processing flow across the entire dataset. We aim to capture 90% or more of the fault response present. This typically favours large faults and works uniformly across the dataset. The result is an initial picture of a wide variety of potential faulting and other edge features.

The second phase of the fault analysis is to examine more closely the faulting and the nature of the faulting and is typically done in conjunction with the client.

The curvature of seismic reflectors is becoming a well-established measure for the characterisation of faults, folds and subsurface deformation.

Although curvature is commonly computed on interpreted horizons as (Roberts, 2011) shows, volume based curvature measures can provide greater insights both because of the independence from the picked surface and the ability to step away from the horizon into stratigraphic units.

We utilise a multi-scale reflector curvature algorithm that describes the local reflector structure in terms of a number of curvature measures:

  • Maximum curvature
  • Mean & Gaussian curvature
  • Most Positive / Negative curvatures
  • Azimuth of maximum curvature

As part of the project we will produce these attribute volumes.

When these attributes are considered in the proper context of area’s deformation history, historical and present-day tectonic setting they can add significant value to the interpretation.  This includes interpretations of density and orientation of sub-seismic fractures relative to the stress field and interpretation of reflector deformation versus seismic scale faulting.

Volumetric Spectral Decomposition is used to isolate seismic signal energy based on local frequency characteristics of the seismic. In doing so, we are able to enhance the visibility of depositional features that respond in a given frequency range.

Seismic frequency response is dependent upon imaging conditions, lithology, density, fluid and thickness variation. However, its use as a qualitative interpretation aid is well established as it can help identify and map the geometries of depositional features.

Spectral Decomposition is particularly powerful when combined with RGB Visualisation. This technique combines the information from 3 adjacent frequency channels to produce a single colour display that shows both the signal energies in each channel and their relative variation.

As part of the study we will apply a Morlet Wavelet Decomposition technique to the dataset over a wide range of frequencies. This technique provides excellent frequency selectivity whilst optimising levels of resolution down the trace for a given frequency band.

Spectral Decomposition can produce many additional datasets, essentially a narrow band amplitude volume for each frequency considered. Due to the volume of data produced we will provide the results of the decomposition over a range of frequencies, in map form based on one  Horizon surface. Both amplitude and RGB maps will be provided.