Abstract

Geophysical well logging (GWL) data is nonstationary in character.It detects cyclicity, trends, sudden changes in sedimentation and stratigraphy. Wavelet analysis is more effective for analyzing nonstationary signals in which a signal can be represented as the sum of different frequency components with different resolutions. The driving impetus behind wavelet analysis is their property of being localised in time (space) as well as scale (frequency). This provides a time-scale map of a signal, enabling the extraction of features that vary in time. This makes wavelets an ideal tool for analysing signals of a transient or non-stationary nature. Geophysical well-log (bore-hole) data represent the rock physical properties as a function of depth measured in a well. They aid in demarcating the subsurface horizons, identifying abrupt changes in physical properties of rocks and locating cyclicity in stratal succession. Since wavelet transformations can better identify the abrupt changes in cyclicity common in nature, they become important tools for sequence stratigraphy. Currently spectral decomposition methods (Continuous Wavelet Transform, Matching Pursuit Algorithm decomposition, Discrete Wavelet Transform) are used to detect hydrocarbon zones. The wavelet transform is a multi-scale operator and is well known to point out singularities in the analysed signal