A machine learning framework for low-field NMR data processing

Abstract

Low-field (nuclear magnetic resonance) NMR has been widely used in petroleum industry, such as well logging and laboratory rock core analysis. However, the signal-to-noise ratio is low due to the low magnetic field strength of NMR tools and the complex petrophysical properties of detected samples. Suppressing the noise and highlighting the available NMR signals is very important for subsequent data processing. Most denoising methods are normally based on fixed mathematical transformation or hand-design feature selectors to suppress noise characteristics, which may not perform well because of their non-adaptive performance to different noisy signals. In this paper, we proposed a “data processing framework” to improve the quality of low field NMR echo data based on dictionary learning. Dictionary learning is a machine learning method based on redundancy and sparse representation theory. Available information in noisy NMR echo data can be adaptively extracted and reconstructed by dictionary learning. The advantages and application effectiveness of the proposed method were verified with a number of numerical simulations, NMR core data analyses, and NMR logging data processing. The results show that dictionary learning can significantly improve the quality of NMR echo data with high noise level and effectively improve the accuracy and reliability of inversion results. • A “data processing framework” is proposed to improve the quality of low-field NMR echo data. • Dictionary learning is employed for adaptive dictionary construction and available signal characteristics extraction for denoising noisy NMR echo data. • Simulations and practical applications have verified the performance of improving quality of NMR echo data and enhancing the accuracy and reliability of inversion results with dictionary learning.