Seismic Spectral Attributes using Coherence and Semblance Algorithms

Abstract

Fault detection technologies such as coherency algorithms, and derivative methods which evolved recently have proven to be important tools for seismic interpretation. In this paper the results of the application of frequency-based semblance algorithm to imaging subtle geologic features such as micro faults, variation in stratigraphy, etc. in the interpretation of 3Dseismic data from the Niger delta are presented. The semblance images seismic discontinuities by computing the ratio of the total energy of a stack of traces within a time gate to the sum of the energy of the component traces within the same gate. Spectral decomposition uses the discrete Fourier transform to image thickness variability within the window. The aim of this study was to develop a practical technique for mapping subtle stratigraphic units embedded within seismic volume and which are usually masked after normal data interpretation using semblance algorithms and spectral decomposition. The methodology combined conventional semblance techniques and non-conventional technique of the discrete Fourier transform within Matlab software. The geologic features were analyzed to include stratigraphic and near-vertical structural features by using a variable analysis window centered on the top of a conventionally interpreted thin sand interval along an arbitrary line. The line was drawn to connect the entire six wells in the seismic survey within Kingdom suite software. The transform attribute maps of response amplitude, phase and frequency of field Gamma-ray, seismic, and semblance data revealed fault and stratigraphic details and new prospects. The spectral semblance algorithm is valuable in mapping unfavourable geologic environments.

Keywords: Discrete Fourier transform, Coherence, Semblance, Spectral decomposition