基于经验模态分解的核磁共振去噪方法研究

doi:10.11885/j.issn.1674-5086.2019.09.16.32

西南石油大学学报(自然科学版) ›› 2020, Vol. 42 ›› Issue (3): 51-59.DOI: 10.11885/j.issn.1674-5086.2019.09.16.32

基于经验模态分解的核磁共振去噪方法研究

李海涛^1,2, 邓少贵^1,2, 王跃祥³, 何绪全³

1. 中国石油大学(华东)地球科学与技术学院, 山东青岛 266580;
2. 海洋国家实验室海洋矿产资源评价与探测技术功能实验室, 山东青岛 266071;
3. 中国石油西南油气田勘探开发研究院, 四川成都 610093

收稿日期:2019-09-16 出版日期:2020-06-10 发布日期:2020-06-10
通讯作者: 邓少贵,E-mail:dengshg@upc.edu.cn
作者简介:李海涛,1994年生,男,汉族,内蒙古赤峰人,博士研究生,主要从事测井理论、方法与技术研究工作。E-mail:s16010114@s.upc.edu.cn;邓少贵,1970年生,男,汉族,河北遵化人,教授,博士,主要从事测井理论、方法与技术研究工作。E-mail:dengshg@upc.edu.cn;王跃祥,1985年生,男,汉族,四川射洪人,工程师,硕士,主要从事地质勘探与测井评价工作。E-mail:wyuex@petrochina.com.cn;何绪全,1970年生,男,汉族,重庆云阳人,高级工程师,主要从事测井储层评价研究工作。E-mail:hexuquan@petrochina.com.cn

Research on NMR Denoising Method Based on Empirical Mode Decomposition

LI Haitao^1,2, DENG Shaogui^1,2, WANG Yuexiang³, HE Xuquan³

1. School of Geosciences, China University of Petroleum, Qingdao, Shandong 266580, China;
2. Laboralory for Marine Mineral Resources, Qingdao Nalional Laboralory for Marine Science and Technology, Qingdao, Shandong 266071, China;
3. Southwest Oil&Gas Field Research Institute of Petroleum Exploration&Development, PetrChina, Chengdu, Sichuan 610093, China

Received:2019-09-16 Online:2020-06-10 Published:2020-06-10

摘要/Abstract

摘要： 核磁共振（NMR）在孔隙结构评估和流体识别方面具有独特的优势，但NMR信号很容易受到噪声影响。根据NMR噪声的时域和频域特征，提出了基于一种经验模态分解（EMD）的NMR去噪方法。首先，利用EMD将信号由高频到低频分解为一系列的本征模态函数，以此分解噪声和噪声NMR信号，然后，使用曲线趋势法和改进的过零点率曲线确定信号噪声分离准则，将有用信号叠加到剩余项以获得去噪信号。通过岩芯数据和测井数据对比发现，基于EMD的去噪方法可以提高信噪比的同时保留孔隙结构信息，其去噪效果优于小波阈值和EMD小波阈值法，计算得到的孔隙度接近实际孔隙度。

关键词: 经验模态分解(EMD), 核磁噪声特性, 曲线趋势法, 过零点率曲线法, 分离准则

Abstract: NMR has unique advantages in pore structure evaluation and fluid identification, but NMR signals are easy to be influenced by noise. This paper presents a kind of denoising method according to time and frequency domain characteristics of NMR noise based on EMD. Firstly we decompose noise and noisy NMR signals using EMD. Then we determine guidelines of signal-noise separation using curve trend method and improved zero-crossing rate curve. Finally we add useful signals to residual term to obtain pure signals. It is confirmed by core data and logging data experiments that the denosing results are better than wavelet threshold and EMD-wavelet threshold method and porosity calculated is closer to real porosity and the inversion results are consistent with actual pore structure. The denosing method based on EMD can improve signal to noise ratio and reserve pore structure information.

Key words: empirical mode decomposition (EMD), characteristics of NMR noise, curve trend method, improved zero-crossing rate curve, separation criteria

中图分类号:

TE121
P631

李海涛, 邓少贵, 王跃祥, 何绪全. 基于经验模态分解的核磁共振去噪方法研究[J]. 西南石油大学学报(自然科学版), 2020, 42(3): 51-59.

LI Haitao, DENG Shaogui, WANG Yuexiang, HE Xuquan. Research on NMR Denoising Method Based on Empirical Mode Decomposition[J]. 西南石油大学学报(自然科学版), 2020, 42(3): 51-59.

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基于经验模态分解的核磁共振去噪方法研究

Research on NMR Denoising Method Based on Empirical Mode Decomposition

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