基于拉曼光谱与PLS的钻井液混合物定量分析

2022年 42卷 第No.1期
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Quantitative Analysis of Drilling Fluid Mixtures Based on Raman Spectroscopy and PLS
王国良 韩伟航 李存磊
Guoliang Wang Weihang Han Cunlei Li
辽宁石油化工大学 信息与控制工程学院,辽宁 抚顺 113001 辽宁石油化工大学 石油天然气工程学院,辽宁 抚顺 113001
School of Information and Control Engineering,Liaoning Petrochemical University,Fushun Liaoning 113001,China College of Petroleum Engineering,Liaoning Petrochemical University,Fushun Liaoning 113001,China
随钻测井技术相比传统测井能够获得更真实的地层数据信息,因而更适用于实际应用。但是,在随钻检测的过程中,需要迅速、精确地判断混合物中是否含有原油,即实现混合物的定性分析。激光拉曼光谱分析技术作为当前发展较为完整的分子光谱分析技术,被广泛应用到多种物质分析的领域中。针对原油钻井液混合物的特点,基于激光拉曼光谱分析技术,提出一种以偏最小二乘分析法为基础的定性分析算法,同时对已知获得的拉曼光谱进行平滑去噪、基线校正、归一化等预处理操作,并在此基础上完成以奇异值分解为主要方法的特征提取处理,进而实现对混合物定性分析的目的,并在一定的精度内完成定量计算。
Logging while drilling (LWD) technology can obtain more real formation data information than traditional logging, so it is more suitable for practical applications. What follows is how to quickly and accurately determine whether the mixture contains crude oil in the process of testing while drilling, that is, to achieve qualitative analysis of the mixture. Laser Raman spectroscopy analysis technology, as a relatively complete molecular spectroscopy technology currently developed, has been widely used in the field of many kinds of material analysis. In this paper, according to the characteristics of crude oil drilling fluid mixture, based on laser Raman spectrum analysis technology, a qualitative analysis algorithm based on partial least squares analysis was proposed. At the same time, the known Raman spectra are smoothed and denoised, baseline correction based on the fitting polynomial method, normalization and other pre?processing operations. On this basis, the feature extraction process with singular value decomposition as the main method was completed, thus attained the aim of qualitative analysis of mixture, and completed the quantitative calculation within a certain precision.
拉曼光谱分析技术; 基线校正; 特征提取; 偏最小二乘分析法;
Raman spectroscopy analysis technology; Baseline correction; Feature extraction; Partial least squares analysis;
国家自然科学基金项目(61473140);辽宁省兴辽人才支持计划项目(XLYC1807030);辽宁省高校创新人才计划项目(LR2017029);辽宁省教育厅科研基金项目(L2016024)
10.3969/j.issn.1672-6952.2022.01.014