测井技术2023，Vol.47Issue(6)：643-652,10.DOI:10.16489/j.issn.1004-1338.2023.06.001
机器学习在核磁共振测井数据处理中的应用进展
Progress and Prospect for Machine Learning Applied in NMR Logging
摘要
Abstract
Low-field nuclear magnetic resonance(NMR)technology has been widely used in petroleum engineering,which plays a critical role in reservoir evaluation and production prediction.However,the extremely weak signal and low signal-to-noise ratio(SNR)of low-field NMR leads to overlapping signals in the NMR relaxation spectra and difficulties in the quantitative evaluation of fluid components.Therefore,it is very important to develop novel and practical NMR data processing methods to improve the application effects of NMR logging technology.With the rapid development of artificial intelligence technology,many scholars have proposed machine learning methods to improve the industry's productivity.Firstly,this paper summarized the application and development of machine learning used in NMR logging.Secondly,the progress of machine learning methods applied in NMR logging data processing are analyzed,which are divided into three aspects including SNR enhancement,spectra resolution improvement,and quantitative fluid identification improvement.Finally,the future development of machine learning applied NMR logging data processing is summarized and recommended.关键词
核磁共振测井/机器学习/深度学习/数据处理/解释与应用Key words
nuclear magnetic resonance logging/machine learning/deep learning/data processing/interpretation and application引用本文复制引用
罗刚,罗嗣慧,肖立志,傅少庆,张家伟,邵蓉波..机器学习在核磁共振测井数据处理中的应用进展[J].测井技术,2023,47(6):643-652,10.基金项目
国家自然科学基金"方位扫描核磁共振探测新方法与实验验证"(42204106) （42204106）
中国石油大学(北京)拔尖人才科研基金"超临界CO2 储层物性变化规律与核磁表征方法研究"(2462023BJRC002) （北京）
