Wavelet Denoising of Well Logs and its Geological Performance

Abstract

Well logs play a very important role in exploration and even exploitation of energy resources, but they usually contain kinds of noises which affect the results of the geological interpretation of them. It is common knowledge that wavelet transform does better than Fourier transform in noise removal and suppression of such non-stationary signals as logging signals. However, there are variable choices of the parameters such as the wavelet basis (mother wavelet function), the thresholding rule and the decomposition level etc. in denoising with the wavelet transform. In this paper, the wavelet denoising theory and steps are briefly introduced first, and then lots of numerical experiments on real well logs were done by the authors with different combination of the parameters and the denoising effect analyzed by comparison of the differences between the predenoising and post-denoising signals with difference value calculation and frequency spectral analysis. The experiment results show that the wavelet basis 'sym8', the soft threshold rule 'heursure' and 5-level decomposition are outstanding in the wavelet denoising of well logging data. Furthermore, we took the AC (acoustic logs) well logging data of a certain borehole in Jiyang Depression, Shandong province of North China, for a case study to check the combination of the parameters settled above. It is found that the denoised acoustic logging signal outperforms the original one in revealing the geological information of gas bearing layers. So, we believe that the wavelet transform can do an excellent job in the denoising of well logs on condition that the related parameters are set properly. Also, the authors assume that it would be of bright prospect to extract and reveal some more geological information such as stratigraphic sequences, sedimentary facies and reservoir properties etc. with reasonable denoising process of different kinds of logging data at certain scales.

Reconstruction of critical coalbed methane logs with principal component regression model: A case study

“…The signals in wireline logs include high-frequency, medium-frequency, and low-frequency components. The low-frequency and medium-frequency components are mostly useful signals (Chen et al., 2018; Honorio et al., 2012; Yu et al., 2010). In contrast, the high-frequency component is mostly noise.…”

“…At present, many methods have been proposed to improve the reconstruction reliability and accuracy of wireline logs. One method uses filtering or multiscale wavelet analysis (MWA) to denoise wireline logs (Chen et al., 2018; Honorio et al., 2012; Yu et al., 2010). Others use histogram calibration to improve the lateral comparability of logs since logging instruments and logging environments are not often the same (Quartero et al., 2014; Ren et al., 2016).…”

Well Log Denoising and Geological Enhancement Based on Discrete Wavelet Transform and Hybrid Thresholding

“…On the other hand, the energy of the useful signal concentrates on fewer wavelet coefficients with bigger amplitudes (Yu et al, 2010). This disbalance between the energy of the components can be measured by the Shannon entropy (Katul and Vidakovic, 1995).…”

“…They reported that the scheme of image fusion combining WT with the classification of these images is efficient to outline geophysical targets on shallow surfaces. Yu et al (2010) demonstrated that WT is efficient in the process of noise suppression in well logs. In addition, they found that denoised acoustic well logs are superior to the original data when it comes to geological information, such as the identification of layers containing gas.…”

Frequency trend attribute analysis for stratigraphic division and correlation

“…Considering these existing problems and difficulties, the various physical rock parameters provided by logging data can be considered advantageous for studying the formation of multilevel cyclical deposits. Such logging data contain abundant geologic information with relatively high resolution and good continuity (Serra, 1992;Yu et al, 2010). The ultimate goal is to interpret this geologic information with maximum precision and objectivity.…”