Using wavelet‐domain adaptive filtering to improve signal‐to‐noise ratio of nuclear magnetic resonance log data from tight gas sands

Abstract

A B S T R A C TIn tight gas sands, the signal-to-noise ratio of nuclear magnetic resonance log data is usually low, which limits the application of nuclear magnetic resonance logs in this type of reservoir. This project uses the method of wavelet-domain adaptive filtering to denoise the nuclear magnetic resonance log data from tight gas sands. The principles of the maximum correlation coefficient and the minimum root mean square error are used to decide on the optimal basis function for wavelet transformation. The feasibility and the effectiveness of this method are verified by analysing the numerical simulation results and core experimental data. Compared with the wavelet thresholding denoise method, this adaptive filtering method is more effective in noise filtering, which can improve the signal-to-noise ratio of nuclear magnetic resonance data and the inversion precision of transverse relaxation time T 2 spectrum. The application of this method to nuclear magnetic resonance logs shows that this method not only can improve the accuracy of nuclear magnetic resonance porosity but also can enhance the recognition ability of tight gas sands in nuclear magnetic resonance logs.

A novel denoising method for low SNR NMR logging echo signal based on deep learning

“…Most of the current echo signal denoising algorithms are based on noise estimation. The effectiveness of the algorithms is verified by adding Gaussian white noise during numerical simulation and adjusting the parameters to denoise [7,9]. However, the parameters obtained by the above way will cause the noise characteristics to be mismatched when applied to logging data, making it difficult to get great denoising performance.…”

“…Chen et al identified the singular values in terms of the correlation between the FID and the reference data, and then the radio frequency interference and noise were suppressed by setting the corresponding singular values to zero [8]. Xie et al proposed a method of waveletdomain adaptive filtering to denoise the NMR log data from tight gas sands [9]. Cai and Xiao combined generalized S transform and SVD to filter NMRL signal [10].…”