A Method of Predicting the Viscosity of Crude Oil by Nuclear Magnetic Resonance Spectrum

A technology of nuclear magnetic resonance spectrum and hydrogen nuclear magnetic resonance spectrum, which is applied in the field of crude oil viscosity prediction, can solve the problem that it is difficult to obtain the linear relationship between spectrum and viscosity, and achieve the effect of improving linear correlation and accuracy

Active Publication Date: 2019-02-01
CHINA PETROLEUM & CHEM CORP +1
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Problems solved by technology

Therefore, when using partial least squares to establish a viscosity prediction model, it is difficult to obtain a linear relationship between the spectrum and the viscosity

Method used

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  • A Method of Predicting the Viscosity of Crude Oil by Nuclear Magnetic Resonance Spectrum
  • A Method of Predicting the Viscosity of Crude Oil by Nuclear Magnetic Resonance Spectrum
  • A Method of Predicting the Viscosity of Crude Oil by Nuclear Magnetic Resonance Spectrum

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example 1

[0054] Establish the calibration model according to the method of Comparative Example 1, the difference is that the logarithm of the viscosity of the crude oil sample measured by the standard method is correlated with the peak intensity in the characteristic spectral region, and the correlation between the NMR spectrum and the logarithm of the viscosity (lgμ) is shown in figure 2 . Composition of viscosity logarithmic values ​​into Y matrix-viscosity logarithmic matrix, and then correlating X matrix and Y matrix with partial least squares method to establish a calibration model, the obtained cross-validation results are shown in Table 1, and the correlation between predicted values ​​and measured values ​​is shown in Figure 4 .

[0055] Randomly select 8 unknown crude oil samples to form a verification set, measure their hydrogen nuclear magnetic resonance spectrum, perform first-order differential processing, and take the peak intensities with chemical shifts of 5.5-8.5ppm ...

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Abstract

The invention relates to a method for predicting the crude oil viscosity through nuclear magnetic resonance spectrums. The method comprises: (1) taking different types of crude oil samples, and determining the viscosity value by using a standard method; (2) determining the nuclear magnetic resonance hydrogen spectrums of each crude oil sample, carrying out a first order differentiation treatment on the spectrums, taking the peak intensities in the spectrum zones with chemical shifts of 5.5-8.5 ppm and 1.0-3.7 ppm to associate with the logarithms of the corresponding viscosities determined by using the standard method, and establishing a correcting model through a partial least square method; and (3) determining the nuclear magnetic resonance hydrogen spectrum of the crude oil sample to be determined, carrying out the first order differentiation treatment on the spectrums, taking the peak intensities in the spectrum zones with chemical shifts of 5.5-8.5 ppm and 1.0-3.7 ppm to substitute into the correcting model so as to obtain the viscosity logarithm value of the crude oil sample to be determined, and then converting into the viscosity. With the method, the crude oil viscosity can be predicted through the nuclear magnetic resonance spectrum of the crude oil sample and the correcting model having the liner correlation can be established, and the method has characteristics of rapid analysis, accurate test, and easy operation.

Description

technical field [0001] The invention is a method for predicting the viscosity of crude oil, in particular, a method for predicting the viscosity of crude oil by nuclear magnetic resonance spectrum. Background technique [0002] The commonly used regression methods in chemometrics mainly include linear regression and nonlinear regression, among which linear regression includes partial least squares (PLS), principal component regression (PCR) and multiple linear regression (MLR), and nonlinear includes artificial neural network ( ANN), Radial Basis Neural Network (RBF) and Support Vector Machine Regression (SVR). As the most classic linear statistical modeling method, partial least squares (PLS) plays an important role in mathematical modeling. When the dependent variable has a linear relationship with the independent variable, its predictive ability is quite satisfactory, and its The results are better than principal components regression (PCR). However, for the non-additiv...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N24/08G01N11/00
Inventor 冯云霞褚小立田松柏许育鹏
Owner CHINA PETROLEUM & CHEM CORP
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