Research on De-noising of Downhole Engineering Parameters by Wavelet based on Improved Threshold Function

Abstract

The measurement of downhole engineering parameters is greatly disturbed by the working environment. Effective de-noising methods are required for processing logging-while-drilling (LWD) acquisition signals, in order to obtain downhole engineering parameters accurately and effectively. In this paper, a new de-noising method for measuring downhole engineering parameters was presented, based on a feedback method and a wavelet transform threshold function. Firstly, in view of the mutability and density of downhole engineering data, an improved wavelet threshold function was proposed to de-noise the signal, so as to overcome the shortcomings of data oscillation and deviation caused by the traditional threshold function. Secondly, due to the unknown true value, traditional single denoising effect evaluation cannot meet the requirements of quality evaluation very well. So the root mean square error (RMSE), signal-to-noise ratio (SNR), smoothness (R) and fusion indexs (F) are used as the evaluation parameters of the de-noising effect, which can determine the optimal wavelet decomposition scale and the best wavelet basis. Finally, the proposed method was verified based on the measured downhole data. The experimental results showed that the improved wavelet de-noising method could reduce all kinds of interferences in the LWD signal, providing reliable measurement for analyzing the working status of the drilling bit.

Combined EEMD with a Novel Flexible Wavelet Threshold Function for Weighing Signal Denoising Approach

“…Many scholars have also proposed improved methods related to EMD and WT to adapt to the complexity of signal and the diversity of applications, such as ensemble empirical mode decomposition (EEMD) [21] and variational mode decomposition (VMD) [16,22]. There are many ways to improve wavelet denoising methods, such as improving the threshold function to solve the problem that reconstruction signal may oscillate or distort that caused by traditional threshold functions [23][24][25][26][27][28][29][30][31], setting adaptive threshold [32,33], finding the optimal wavelet basis [34][35][36], and setting self-adaptive wavelet decomposition level [15]; some scholars combine EMD and WT [37].…”

“…Many scholars have proposed that improved threshold functions use different methods. For example, the authors of [23,24] introduced exponential function into threshold function, which makes it between soft and hard threshold function when coefficient is greater than threshold; on this basis, to increase its adaptability, Wang et al [25] divided the area between the soft and hard threshold function into five parts by control α, but when α takes a certain value, the threshold function is not continuous at threshold. The authors of [26] combined this idea with other improvement methods to improve the threshold function.…”