A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning
摘要:Nuclear magnetic resonance (NMR) is a powerful tool for formation evaluation in the oil industry to determine parameters, such as pore structure, fluid saturation, and permeability of porous materials, which are critical to reservoir engineering. The inversion of the measured relaxation data is an ill-posed problem and may lead to deviations of inversion results, which may degrade the accuracy of further data analysis and evaluation. This paper proposes a deep learning method for multi-exponential inversion of NMR relaxation data to improve accuracy. Simulated NMR data are first constructed using a priori knowledge based on the signal parameters and Gaussian distribution. These data are then used to train the neural network designed to consider noise characteristics, signal decay characteristics, signal energy variations, and non-negative features of the T2 spectra. With the validation from simulated data, the models introduced by multi-scale convolutional neural network (CNN) and attention mechanism outperform other approaches in terms of denoising and T2 inversion. Finally, NMR measurements of rock cores are used to compare the effectiveness of the attention multi-scale convolutional neural network (ATT-CNN) model in practical applications. The results demonstrate that the proposed method based on deep learning has better performance than the regularization method. ? 2022
ISSN号:1090-7807
卷、期、页:卷346
发表日期:2023-01-01
影响因子:0.000000
期刊分区(SCI为中科院分区):三区
收录情况:SCI(科学引文索引印刷版),EI(工程索引),SCIE(科学引文索引网络版)
发表期刊名称:Journal of Magnetic Resonance
通讯作者:罗刚
第一作者:肖立志,罗嗣慧,廖广志,邵蓉波
论文类型:期刊论文
论文概要:罗刚,肖立志,罗嗣慧,廖广志,邵蓉波,A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning,Journal of Magnetic Resonance,2023,卷346
论文题目:A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning