Abstract
Abstract. Identification of spatial distribution of lithology as a function of position and scale is a very critical job for lithology modelling in industry. Wavelet transform (WT) is an efficacious and powerful mathematical tool for time (position) and frequency (scale) localization. It has numerous advantages over Fourier transform (FT) to obtain frequency and time information of a signal. Initially continuous wavelet transform (CWT) was applied on gamma ray logs for identification of lithofacies distribution, and later discrete wavelet transform (DWT) was applied on density logs to identify the fracture zones. In this study the data were taken from two different well sites (well 1039 and well 1043) of the Costa Rica convergent margin, Central America. The CWT analysis provides four major sedimentary layers terminated with a concordant igneous intrusion passing through both the wells. In addition, the wavelet-based fractal analysis (WBFA) technique was applied on identified sedimentary successions, and fractal-dimension (FD) values were calculated for every succession to know the presence and distribution of fractures. We found that the second and third successions have a high FD value, whereas the first and fourth successions have a low FD value. These high values may be due to the presence of abundant shale content and low-energy environments in the sedimentary successions.
Highlights
Manual interpretations of well log signals, which have very noisy and highly fluctuations in nature, are quite difficult and require more experience and sophisticated techniques/software
The analysed results prove that the continuous wavelet transform (CWT) is highly suitable in geophysical log signals, whereas the conventional fast Fourier transform (FFT) fails in this case because it considers the whole signal in a stationary form
The CWT is applied on a generated synthetic signal without noise and on signals with 25 % Gaussian white noise
Summary
Manual interpretations of well log signals, which have very noisy and highly fluctuations in nature, are quite difficult and require more experience and sophisticated techniques/software. These difficulties are minimized by a kind of wavelet transform (WT) method. The analysed results prove that the CWT is highly suitable in geophysical log signals, whereas the conventional fast Fourier transform (FFT) fails in this case because it considers the whole signal in a stationary form. The proposed DWT technique acts as a microscope to clearly identify and distinguish the high and low frequency of hidden log signals, and fractal dimension is highly useful for characterizing the fracture density and spatial distribution of fracture zones
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