Crude oil evaluation method

Abstract

The invention relates to a crude oil evaluation method, which comprises the following steps of: converting an original near infrared spectrum database of known crude oil into a moving window correlation coefficient database, fitting a correlation coefficient spectrum of a crude oil sample to be detected by combining a library fitting algorithm, and obtaining evaluation data of the crude oil sample to be detected on the basis of a fitting coefficient. And the accuracy and the evaluation speed of evaluation data results can be improved.

Description

Technical Field

[0001] The present disclosure relates to a crude oil evaluation method. Background Art

[0002] Crude oil evaluation plays a crucial role in crude oil extraction, trading, and processing. Large petrochemical companies both domestically and internationally are developing rapid crude oil evaluation technologies based on a variety of modern instrumental analytical methods, including chromatography-mass spectrometry, nuclear magnetic resonance, mid-infrared spectroscopy, and near-infrared spectroscopy. Near-infrared spectroscopy has become the preferred method due to its ease of measurement, speed, low cost, and ability to be used on-site. It is now increasingly being applied to fields such as petrochemicals, agriculture, and pharmaceuticals, playing a particularly positive and irreplaceable role in process analysis. However, because crude oil evaluation involves over a hundred analytical items, traditional multivariate calibration methods, such as partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN), are impractical to establish calibration models for each analytical item individually. Therefore, new spectral computational methods are needed to address the simultaneous prediction of over a hundred properties.

[0003] CN102374975A ​​proposes a library spectrum fitting method based on a near-infrared spectral library of oil products and spectral fitting technology. Its basic principle is that samples with similar spectra also have similar properties. The spectrum of an unknown sample to be tested is fitted using one or more spectra in the spectral library. The properties of the sample to be tested are then calculated based on the properties of the oil products involved in the fitted spectra. However, this method is complex, time-consuming, and suffers from inaccurate prediction data. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a crude oil evaluation method to improve the evaluation speed of the crude oil to be tested and the accuracy of the evaluation results.

[0005] To achieve the above objectives, the present disclosure provides a crude oil evaluation method, which comprises the following steps:

[0006] Acquiring a near-infrared spectrum database comprising library spectra of a plurality of known crude oil samples and standard data of the physicochemical properties of each of the known crude oil samples, and collecting a sample spectrum of the crude oil sample to be tested;

[0007] The library spectrum x that is closest to the spectrum of the sample to be tested is obtained from the near-infrared spectrum database by using a distance algorithm. near ;

[0008] Calculate the relationship between each library spectrum in the near infrared spectroscopy database and the library spectrum x near The first moving window correlation coefficient of composes a moving window correlation coefficient database;

[0009] Calculate the spectrum of the sample to be tested and the library spectrum x near The second moving window correlation coefficient is obtained to obtain the correlation coefficient spectrum of the sample to be tested;

[0010] Fitting the correlation coefficient spectrum of the sample to be tested using the moving window correlation coefficient database to obtain fitting coefficients;

[0011] Evaluation data of the physicochemical properties of the crude oil sample to be tested are obtained based on the fitting coefficients and the standard data of the physicochemical properties of the known crude oil sample.

[0012] Optionally, the calculation of the relationship between each library spectrum in the near infrared spectrum database and the library spectrum x near The first moving window correlation coefficient of , constitutes a moving window correlation coefficient database, including:

[0013] The preset width of the band interval is used as the moving window, the sampling point with the lowest wave number is used as the starting point and the moving window is moved toward the direction of high wave number, and the relationship between each library spectrum in the near infrared spectrum database and the library spectrum x is calculated according to formula (1). near The first moving window correlation coefficient is:

[0014]

[0015] In formula (1), R ij is the first moving window correlation coefficient, are the i-th spectrum in the near-infrared spectroscopy database and the library spectrum x near The mean absorbance of the sampling points in the moving window, d is the number of sampling points contained in each moving window, k is the sampling point sequence number, x is the average ... sampling point sequence number, ik is the absorbance of the kth sampling point of the i-th spectrum in the near-infrared spectroscopy database, x jk For the library spectrum x near The absorbance of the kth sampling point.

[0016] Optionally, the calculation of the spectrum of the sample to be tested and the library spectrum x near The second moving window correlation coefficient of the sample to be tested is obtained to obtain the correlation coefficient spectrum, including:

[0017] The preset width of the band interval is used as the moving window, the sampling point with the lowest wave number is used as the starting point and the moving window is moved toward the direction of higher wave number. The spectrum of the sample to be tested and the library spectrum x are calculated according to formula (2). near The second moving window correlation coefficient for each moving window is:

[0018]

[0019] In formula (2), R ij ' is the second moving window correlation coefficient, are the sample spectrum to be tested and the library spectrum x near The mean absorbance of the sampling points in the moving window, d is the number of sampling points contained in each moving window, k is the sampling point sequence number, x is the average ... sampling point sequence number, ik ' is the absorbance of the kth sampling point of the spectrum of the sample to be tested, x jk ' is the library spectrum x near The absorbance of the kth sampling point.

[0020] Optionally, the window width of the moving window is 3 to 25 sampling points, the sampling point interval is 2 to 16 wave numbers, and the distance of each movement of the moving window is 1 to 10 sampling points.

[0021] Optionally, the method further includes:

[0022] The correlation coefficient spectrum of the sample to be tested is normalized according to the following formula (3);

[0023]

[0024] In formula (3), x normalized is the normalized correlation coefficient spectrum of the sample to be tested, x is the original correlation coefficient spectrum of the sample to be tested, is the average moving window correlation coefficient and is calculated according to the following formula (4), m is the number of points for collecting the moving window correlation coefficient spectrum, k is the sampling point number, k = 1, 2, ..., m,

[0025]

[0026] Optionally, fitting the correlation coefficient spectrum of the sample to be tested using the moving window correlation coefficient database to obtain fitting coefficients includes:

[0027] Using all spectra in the moving window correlation coefficient database, the correlation coefficient spectrum of the sample to be tested is fitted according to the following formula (5):

[0028]

[0029] In formula (5), x normalized is the normalized correlation coefficient spectrum of the sample to be tested, v i is the i-th moving window correlation coefficient in the moving window correlation coefficient database, t is the number of moving window correlation coefficients in the moving window correlation coefficient database, a i is the fitting coefficient corresponding to the i-th moving window correlation coefficient in the moving window correlation coefficient database and satisfies the objective function shown in the following formula (6):

[0030]

[0031] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (7):

[0032]

[0033] In formula (7), b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0034] Optionally, the method further includes:

[0035] The fitting degree of the fitting is calculated according to the following formula (8), and the credibility of the evaluation data is determined according to the fitting degree;

[0036]

[0037] In formula (8), s is the fitting degree, x j is the normalized correlation coefficient of the jth moving window of the sample to be tested, is the jth moving window correlation coefficient of the moving window correlation coefficient spectrum after fitting, and m is the number of points in the moving window correlation coefficient spectrum.

[0038] Optionally, the method includes:

[0039] The evaluation data of the physicochemical properties of the crude oil sample to be tested is calculated according to the following formula (9):

[0040]

[0041] In formula (9), is the evaluation data of the physical and chemical properties of the crude oil sample to be tested, q i is the standard data of the physicochemical properties corresponding to the library spectrum involved in the fitting, b i is the normalized fitting coefficient, and n is the number of library spectra involved in the fitting.

[0042] Optionally, the physicochemical properties include one or more of density, acid value, sulfur content, wax content and colloidal asphaltene content.

[0043] Optionally, the density is tested according to standard method SH / T0604-2000;

[0044] The acid value is tested according to the standard method GB / T 7304;

[0045] The sulfur content is tested according to the standard method GB / T17040;

[0046] The wax content is determined according to the standard method SY / T 0537;

[0047] The asphaltene content is determined according to the standard method SY / T 7550.

[0048] Through the above technical solution, the present disclosure converts the original near-infrared spectrum database of known crude oil into a moving window correlation coefficient database, combines the library fitting algorithm to fit the correlation coefficient spectrum of the crude oil sample to be tested, and obtains the evaluation data of the crude oil sample to be tested based on the fitting coefficient, which is conducive to improving the accuracy and evaluation speed of the evaluation data results.

[0049] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0051] Figure 1 It is a schematic flow chart of a crude oil evaluation method provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.

[0053] The present disclosure provides a crude oil evaluation method, such as Figure 1 As shown, the method includes the following steps S101 to S106:

[0054] S101, obtaining a near-infrared spectrum database containing library spectra of a plurality of known crude oil samples and standard data of the physicochemical properties of each of the known crude oil samples, and collecting a spectrum of a crude oil sample to be tested;

[0055] S102, obtaining the library spectrum x that is closest to the spectrum of the sample to be tested from the near-infrared spectrum database by using a distance algorithm near ;

[0056] S103, calculating the difference between each library spectrum in the near infrared spectrum database and the library spectrum x near The first moving window correlation coefficient of composes a moving window correlation coefficient database;

[0057] S104, calculating the spectrum of the sample to be tested and the library spectrum x near The second moving window correlation coefficient is obtained to obtain the correlation coefficient spectrum of the sample to be tested;

[0058] S105, fitting the correlation coefficient spectrum of the sample to be tested using the moving window correlation coefficient database to obtain fitting coefficients;

[0059] S106. Obtain evaluation data of the physicochemical properties of the crude oil sample to be tested based on the fitting coefficient and the standard data of the physicochemical properties of the known crude oil sample.

[0060] This paper converts a database of known crude oil raw near-infrared spectra into a moving window correlation coefficient database. Using a library fitting algorithm, the correlation coefficient spectra of the crude oil samples to be tested are fitted. Based on the fitted coefficients, evaluation data for the crude oil samples to be tested is obtained, effectively improving the accuracy and speed of evaluation data. This method is simple to obtain, does not require library sample modeling, and makes database maintenance simple and convenient, showing promising application prospects.

[0061] In step S101, the known crude oil samples are crude oil samples with known composition and / or properties. They can be crude oil samples from a crude oil database established for near-infrared spectrometers or actual crude oil samples collected from major producing regions around the world. The number of known crude oil samples can preferably be 100 to 200 to further improve the accuracy of the evaluation results.

[0062] The library spectrum of the known crude oil sample and the spectrum of the sample to be tested of the crude oil sample to be tested can be obtained by various near-infrared spectroscopy detection equipment commonly used in the field. In a specific embodiment, the library spectrum and the spectrum of the sample to be tested are respectively detected by Fourier transform near-infrared spectrometer. The conditions for detecting samples using Fourier transform near-infrared spectrometer may be well known to those skilled in the art. The detection of the known crude oil sample and the detection of the crude oil sample to be tested are preferably carried out under the same detection conditions to improve the accuracy of the evaluation results. Specifically, the detection can be a transmission detection, using a cuvette sampling method, and a transmission light path of 0.5 mm; the detection conditions may include: a resolution of 4 to 16 cm -1 , wave number range is 4000cm -1 ~10000cm -1 , scan times 16 to 128 times.

[0063] The library spectrum of the known crude oil sample and the spectrum of the sample to be tested of the crude oil sample to be tested can be calculated by selecting the full spectrum or removing the spectral interval with large noise influence. In addition, the library spectrum of the known crude oil sample and the spectrum of the sample to be tested of the crude oil sample to be tested can each be optionally subjected to spectral preprocessing before the subsequent steps are carried out. The spectral preprocessing performed on the library spectrum and the spectral preprocessing performed on the spectrum of the sample to be tested are preferably the same spectral preprocessing process. For example, the spectral preprocessing can each include but is not limited to differential processing, standardization processing, normalization processing and wavelet transform processing, etc., and can be performed using common methods in the art. Even without spectral preprocessing, the method disclosed herein can also achieve a higher accuracy in predicting the physicochemical properties of crude oil.

[0064] The physicochemical properties refer to the physical and / or chemical properties of a crude oil sample. For example, the physicochemical properties may include one or more of density, acid value, sulfur content, wax content, and colloid and asphaltene content. Furthermore, each physicochemical property is measured using standard methods. Specifically, density can be measured according to the standard method SH / T 0604-2000, Determination of Density of Crude Oil and Petroleum Products (U-Shaped Vibrating Tube Method); acid value can be measured according to the standard method GB / T 7304-2014, Determination of Acid Value of Petroleum Products - Potentiometric Titration; sulfur content can be measured according to the standard method GB / T 17040-2008, Energy Dispersive X-ray Determination of Sulfur Content of Petroleum and Petroleum Products; wax content can be measured according to the standard method SY / T 0537, Determination of Wax Content in Crude Oil; and colloid and asphaltene content can be measured according to the standard method SY / T 7550, Determination of Wax, Resin, and Asphaltene Content in Crude Oil.

[0065] In step S102, the closest library spectrum x near It refers to the library spectrum in the near infrared spectrum database that is close to the spectrum of the sample to be tested in terms of spectral characteristics. The library spectrum x can be obtained by using a distance algorithm well known in the art. near , such as Euclidean distance algorithm, cosine angle algorithm, etc. The present disclosure preferably adopts the cosine angle algorithm, and the specific calculation process is well known to those skilled in the art.

[0066] In steps S103 and S104, the moving window correlation coefficient refers to calculating two correlation coefficients to be compared, i.e., moving correlation coefficients, for each moving window band interval using a correlation coefficient formula for the two spectra to be compared, thereby obtaining a series of moving correlation coefficients for sub-band intervals, where the sub-band interval is the width of a moving window. The specific calculation method may be a conventional moving window coefficient calculation method in the art.

[0067] In one embodiment, the calculation of the relationship between each library spectrum in the near infrared spectrum database and the library spectrum xnear The first moving window correlation coefficient of , constitutes a moving window correlation coefficient database, including:

[0068] The preset width of the band interval is used as the moving window, the sampling point with the lowest wave number is used as the starting point and the moving window is moved toward the direction of high wave number, and the relationship between each library spectrum in the near infrared spectrum database and the library spectrum x is calculated according to formula (1). near The first moving window correlation coefficient is:

[0069]

[0070] In formula (1), R ij is the first moving window correlation coefficient, are the i-th spectrum in the near-infrared spectroscopy database and the library spectrum x near The mean absorbance of the sampling points in the moving window, d is the number of sampling points contained in each moving window, k is the sampling point sequence number, x is the average ... sampling point sequence number, ik is the absorbance of the kth sampling point of the i-th spectrum in the near-infrared spectroscopy database, x jk For the library spectrum x near The absorbance of the kth sampling point.

[0071] Furthermore, the calculation of the spectrum of the sample to be tested and the library spectrum x near The second moving window correlation coefficient of the sample to be tested is obtained to obtain the correlation coefficient spectrum, including:

[0072] The preset width of the band interval is used as the moving window, the sampling point with the lowest wave number is used as the starting point and the moving window is moved toward the direction of higher wave number. The spectrum of the sample to be tested and the library spectrum x are calculated according to formula (2). near The second moving window correlation coefficient for each moving window is:

[0073]

[0074] In formula (2), R ij ' is the second moving window correlation coefficient, are the sample spectrum to be tested and the library spectrum x near The mean absorbance of the sampling points in the moving window, d is the number of sampling points contained in each moving window, k is the sampling point sequence number, x is the average ... sampling point sequence number, ik ' is the absorbance of the kth sampling point of the spectrum of the sample to be tested, x jk ' is the library spectrum x near The absorbance of the kth sampling point.

[0075] The moving window is a spectral window of a preset width of a band interval, starting from the first wave number sampling point of the spectral segment for which the moving correlation coefficient needs to be calculated, and moving one to multiple sampling points each time, which is the moving distance of the window, until the last wave number sampling point. Preferably, the width of a moving window is selected from the sampling point with the lowest wave number, and the correlation coefficient between the absorbance of each spectrum in the second-order differential spectral matrix or the second spectral matrix and the first-order differential mean spectrum in the window is calculated. Then, the moving window is moved in the direction of higher wave numbers by several sampling points, and for the next moving window, the correlation coefficient between the absorbance of each spectrum in the second-order differential spectral matrix or the second spectral matrix and the first-order differential mean spectrum in this moving window is calculated. The moving window is continuously moved according to the above method to calculate the correlation coefficient between the absorbance of each spectrum in the second-order differential spectral matrix or the second spectral matrix and the first-order differential mean spectrum in each moving window. The obtained correlation coefficient value is plotted against the starting position of the corresponding moving window to obtain a moving window correlation coefficient graph. This figure easily illustrates the similarity between two spectra. If the two spectra are identical, the moving correlation coefficient will be 1 across the entire spectral range. If the two spectra differ only in a certain interval, the correlation coefficient for that interval will drop significantly. By calculating the moving window correlation coefficient, we can distinguish between spectra with subtle differences, improving the recognition accuracy of the spectra and facilitating the extraction of implicit information.

[0076] Furthermore, the window width of the moving window should be a fixed value and can be adjusted as needed, for example, it can be 3 to 25 sampling points; the sampling point interval can be determined by the resolution of the spectrometer, for example, it can be 2 to 16 wave numbers; the distance of each movement of the moving window can be 1 to 10 sampling points.

[0077] Through the above calculations, the near-infrared spectrum database is converted into a moving window correlation coefficient database, and the spectrum of the sample to be tested is converted into a correlation coefficient spectrum of the sample to be tested, and then subsequent fitting calculations are performed, which is beneficial to significantly reduce the computational complexity and improve the accuracy of the prediction results.

[0078] In one embodiment, the correlation coefficient spectrum of the sample to be tested is normalized according to the following formula (3), and then the subsequent fitting is performed;

[0079]

[0080] In formula (3), x normalized is the normalized correlation coefficient spectrum of the sample to be tested, x is the original correlation coefficient spectrum of the sample to be tested, is the average moving window correlation coefficient (i.e., the mean of all data points of x) and is calculated according to the following formula (4), m is the number of points for collecting the moving window correlation coefficient spectrum, k is the sampling point number, k = 1, 2, ..., m,

[0081]

[0082] In step S105, the fitting coefficient is preferably a non-zero fitting coefficient after normalization, for example, the fitting coefficient is determined according to a non-negative constrained least squares method.

[0083] In one embodiment, all spectra in the moving window correlation coefficient database are used to fit the correlation coefficient spectrum of the sample to be tested according to the following formula (5):

[0084]

[0085] In formula (5), x normalized is the normalized correlation coefficient spectrum of the sample to be tested, v i is the i-th moving window correlation coefficient in the moving window correlation coefficient database, t is the number of moving window correlation coefficients in the moving window correlation coefficient database, a i is the fitting coefficient corresponding to the i-th moving window correlation coefficient in the moving window correlation coefficient database and satisfies the objective function shown in the following formula (6):

[0086]

[0087] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (7):

[0088]

[0089] In formula (7), b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0090] Furthermore, the method may also include:

[0091] The fitting degree of the fitting is calculated according to the following formula (8), and the credibility of the evaluation data is determined according to the fitting degree;

[0092]

[0093] In formula (8), s is the fitting degree, x j is the normalized correlation coefficient of the jth moving window of the sample to be tested, is the jth moving window correlation coefficient of the moving window correlation coefficient spectrum after fitting, and m is the number of points in the moving window correlation coefficient spectrum.

[0094] The credibility is used to indicate the accuracy of the evaluation data. For example, when the degree of fit is greater than or equal to a preset threshold, it indicates that the evaluation data is credible; when the degree of fit is less than the preset threshold, it indicates that the evaluation data is questionable. The preset threshold can be determined by the repeatability of spectral measurement. The specific method is: select a sample to repeatedly measure the near-infrared spectrum three times, convert the spectrum measured each time into a correlation coefficient spectrum according to the above method, calculate the average correlation coefficient spectrum of the three spectra, and then calculate the difference spectrum between each correlation coefficient spectrum and the average correlation coefficient spectrum, calculate the pseudo-fit (sr) value between the two difference spectra according to the following formula (10), take the largest sr value, and calculate the preset threshold according to the following formula (11),

[0095]

[0096] '"

[0097] In formula (10), sr is the pseudo-fitting degree, △x j and △x j is the value of the jth data point of the difference spectrum between the repeated spectrum and the average spectrum, and m is the number of points in the moving window correlation coefficient spectrum;

[0098] s v =0.75×sr max Formula (11),

[0099] In formula (11), s v is the preset threshold, sr max is the maximum pseudo-fitting value.

[0100] Specifically, the preset threshold may be 10.05.

[0101] In step S106, evaluation data of the physicochemical properties of the crude oil sample to be tested is calculated based on the obtained fitting coefficients and the standard data of the physicochemical properties of the known crude oil samples corresponding to the library spectra involved in the fitting.

[0102] In one embodiment, the evaluation data of the physicochemical properties of the crude oil sample to be tested is calculated according to the following formula (9):

[0103]

[0104] In formula (9), is the evaluation data of the physical and chemical properties of the crude oil sample to be tested, q i is the standard data of the physicochemical properties corresponding to the library spectrum involved in the fitting, b i is the normalized fitting coefficient, and n is the number of library spectra involved in the fitting.

[0105] The disclosed method can be used to evaluate various crude oil samples and has wide applicability.

[0106] The present disclosure is further illustrated below by way of examples, but the present disclosure is not limited thereto.

[0107] The instrument used to collect the near-infrared spectrum of crude oil in the embodiment is a Fourier transform near-infrared spectrometer produced by Thermo Fisher Scientific, model Antaris II, and the spectrum collection conditions are: resolution 8 cm -1 , wave number range 4000~10000cm -1 , cumulative scanning times 64 times, transmission measurement method.

[0108] The density is measured according to the standard method SH / T 0604-2000 Density determination of crude oil and petroleum products (U-shaped vibrating tube method); the acid value is measured according to the standard method GB / T 7304-2014 Determination of the acid value of petroleum products-Potentiometric titration; the sulfur content is measured according to the standard method GB / T17040-2008 Energy dispersive X-ray determination of sulfur content of petroleum and petroleum products; the wax content is measured according to the standard method SY / T0537 Determination of wax content in crude oil; and the colloid and asphaltene content are measured according to the standard method SY / T7550 Determination of wax, colloid and asphaltene content in crude oil.

[0109] Example 1

[0110] (1) 210 crude oil samples from major producing areas around the world were collected, covering almost all major crude oil producing areas. Standard data on crude oil physicochemical properties such as density and acid value were measured using standard methods, and near-infrared spectra of crude oil samples were measured to form a near-infrared spectral database X.

[0111] The spectrum of the crude oil sample t and its corresponding physical property data are collected under the same detection conditions.

[0112] (2) Using the cosine angle algorithm, find the library spectrum x that is closest to the crude oil sample t from the near-infrared spectrum database X. near .

[0113] (III) Calculate the relationship between each library spectrum and library spectrum x in the near-infrared spectral database X according to formula (1) near The first moving window correlation coefficient of composes the moving window correlation coefficient database X*, the window width of the moving window is 11 sampling points, and the distance of each moving window is 1 sampling point.

[0114]

[0115] In formula (1), R ij is the first moving window correlation coefficient, are the i-th spectrum in the near-infrared spectroscopy database and the library spectrum x near The mean absorbance of the sampling points in the moving window, d is the number of sampling points contained in each moving window, k is the sampling point sequence number, x is the average ... sampling point sequence number, ik is the absorbance of the kth sampling point of the i-th spectrum in the near-infrared spectroscopy database, x jk For the library spectrum x near The absorbance of the kth sampling point.

[0116] (IV) Calculate the spectrum of the sample to be tested and the library spectrum x according to formula (2) near The second moving window correlation coefficient is obtained to obtain the correlation coefficient spectrum t* of the sample to be tested. The window width of the moving window is 11 sampling points, and the distance of each moving window is 1 sampling point.

[0117]

[0118] In formula (2), R ij ' is the second moving window correlation coefficient, are the sample spectrum to be tested and the library spectrum x near The mean absorbance of the sampling points in the moving window, d is the number of sampling points contained in each moving window, k is the sampling point sequence number, x is the average ... sampling point sequence number, ik ' is the absorbance of the kth sampling point of the spectrum of the sample to be tested, x jk ' is the library spectrum x near The absorbance of the kth sampling point.

[0119] The correlation coefficient spectrum of the sample to be tested is normalized according to the following formula (3):

[0120]

[0121] In formula (3), x normalized is the normalized correlation coefficient spectrum of the sample to be tested, x is the original correlation coefficient spectrum of the sample to be tested, is the average moving window correlation coefficient and is calculated according to the following formula (4), m is the number of points for collecting the moving window correlation coefficient spectrum, k is the sampling point number, k = 1, 2, ..., m,

[0122]

[0123] (5) Using all spectra in the moving window correlation coefficient database, fitting the correlation coefficient spectrum of the sample to be tested according to the following formula (5):

[0124]

[0125] In formula (5), x normalizedis the normalized correlation coefficient spectrum of the sample to be tested, v i is the i-th moving window correlation coefficient in the moving window correlation coefficient database, t is the number of moving window correlation coefficients in the moving window correlation coefficient database, a i is the fitting coefficient corresponding to the i-th moving window correlation coefficient in the moving window correlation coefficient database and satisfies the objective function shown in the following formula (6):

[0126]

[0127] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (7):

[0128]

[0129] In formula (7), b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0130] The goodness of fit is calculated according to the following formula (8):

[0131]

[0132] In formula (8), s is the fitting degree, x j is the normalized correlation coefficient of the jth moving window of the sample to be tested, is the jth moving window correlation coefficient of the moving window correlation coefficient spectrum after fitting, and m is the number of points in the moving window correlation coefficient spectrum.

[0133] The preset threshold value of the degree of fit is determined by the repeatability of spectral measurement. A sample is selected and the near-infrared spectrum is repeatedly measured three times. The spectrum measured each time is preprocessed and converted into a spectral characteristic index according to the above method. The average spectral index of the three spectra is calculated, and then the difference spectrum between each spectral index and the average spectral index is calculated. The pseudo-fit (SR) value between the two difference spectra is calculated according to the following formula. The largest SR value is taken and the preset threshold is calculated according to the following formula (10):

[0134]

[0135] In formula (10), sr is the pseudo-fitting degree, △x j ' and △x j " is the value of the jth data point of the difference spectrum between the repeated spectrum and the average spectrum, and m is the number of points in the moving window correlation coefficient spectrum;

[0136] s v =0.75×sr max Formula (11),

[0137] In formula (11), sv is the preset threshold, sr max is the maximum pseudo-fitting value.

[0138] The calculated fit s is 20.95, which is higher than the preset threshold of 10.05, indicating that the obtained evaluation data is credible.

[0139] (6) Calculate the evaluation data of the physicochemical properties of the crude oil sample to be tested according to the following formula (9):

[0140]

[0141] In formula (9), is the evaluation data of the physical and chemical properties of the crude oil sample to be tested, q i is the standard data of the physicochemical properties corresponding to the library spectrum involved in the fitting, b i is the normalized fitting coefficient, and n is the number of library spectra involved in the fitting. The final crude oil evaluation results are shown in Table 1.

[0142] Wherein, deviation = evaluation value - measured value.

[0143] Table 1

[0144] project Measured value Rating deviation <![CDATA[Density, g·cm -3 > 0.8704 0.8701 -0.0003 Acid value, mg KOH / g 0.14 0.20 0.06 Sulfur content, weight % 3.02 2.38 -0.64 Wax content, mass% 2.80 3.89 1.09 Gum content, mass% 8.70 9.89 1.19 Asphaltene content, mass% 2.20 2.85 0.65

[0145] Comparative Example 1

[0146] The library spectrum fitting method is used to evaluate the same crude oil sample to be tested. The specific steps are as follows:

[0147] (1) 210 crude oil samples from major producing areas around the world were collected, covering almost all major crude oil producing areas. Standard data on crude oil physicochemical properties such as density and acid value were measured using standard methods, and near-infrared spectra of crude oil samples were measured to form a near-infrared spectral database X.

[0148] The spectrum of the crude oil sample t and its corresponding physical property data are collected under the same detection conditions.

[0149] (2) Following the method of step (5) of Example 1, the spectrum of the sample to be tested was fitted using the near-infrared spectral database X, and evaluation data for the physicochemical properties of the crude oil sample to be tested was calculated according to the method of step (6) of Example 1. The evaluation results are shown in Table 2.

[0150] Table 2

[0151] project Measured value Rating deviation <![CDATA[Density, g·cm -3 > 0.8704 0.8733 0.0029 Acid value, mg KOH / g 0.14 0.21 0.07 Sulfur content, mass% 3.02 2.68 -0.34 Wax content, mass% 2.80 4.13 1.33 Gum content, mass% 8.70 10.09 1.39 Asphaltene content, mass% 2.20 2.32 0.12

[0152] Comparative Example 2

[0153] The PLS modeling method is used to evaluate the same crude oil sample to be tested. The specific steps are as follows:

[0154] (1) 210 crude oil samples from major producing areas around the world were collected, covering almost all major crude oil producing areas. Standard data on crude oil physicochemical properties such as density and acid value were measured using standard methods, and near-infrared spectra of crude oil samples were measured to form a near-infrared spectral database X.

[0155] The spectrum of the crude oil sample t and its corresponding physical property data are collected under the same detection conditions.

[0156] (2) Using the partial least squares (PLS) method, we established evaluation models for each physicochemical property, such as density and acid value. We then used these established evaluation models to evaluate the crude oil sample t. The evaluation results are shown in Table 3.

[0157] Table 3

[0158] project Measured value Predicted value deviation <![CDATA[Density, g·cm -3 > 0.8704 0.8724 0.0020 Acid value, mg KOH / g 0.14 0.00 -0.14 Sulfur content, mass% 3.02 2.29 -0.73 Wax content, mass% 2.80 3.22 0.42 Gum content, mass% 8.70 10.59 1.89 Asphaltene content, mass% 2.20 3.59 1.39

[0159] As can be seen from the comparison between Example 1 and Comparative Examples 1 and 2, the method disclosed herein can significantly reduce the computational complexity, eliminate the need to establish a separate evaluation model for each physicochemical property, and accelerate the evaluation process. This helps improve the evaluation accuracy of the physicochemical property data of the crude oil sample to be tested.

[0160] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0161] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0162] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A crude oil evaluation method, characterized in that: The method comprises the following steps: Acquiring a near-infrared spectrum database comprising library spectra of a plurality of known crude oil samples and standard data of the physicochemical properties of each of the known crude oil samples, and collecting a sample spectrum of the crude oil sample to be tested; The library spectrum x that is closest to the spectrum of the sample to be tested is obtained from the near-infrared spectrum database by using a distance algorithm. near ; Calculate the relationship between each library spectrum in the near infrared spectroscopy database and the library spectrum x near The first moving window correlation coefficient of composes a moving window correlation coefficient database; Calculate the spectrum of the sample to be tested and the library spectrum x near The second moving window correlation coefficient is obtained to obtain the correlation coefficient spectrum of the sample to be tested; Fitting the correlation coefficient spectrum of the sample to be tested using the moving window correlation coefficient database to obtain fitting coefficients; Evaluation data of the physicochemical properties of the crude oil sample to be tested are obtained based on the fitting coefficients and the standard data of the physicochemical properties of the known crude oil sample.

2. The method according to claim 1, wherein The calculation of each library spectrum in the near infrared spectrum database and the library spectrum x near The first moving window correlation coefficient of , constitutes a moving window correlation coefficient database, including: The preset width of the band interval is used as the moving window, the sampling point with the lowest wave number is used as the starting point and the moving window is moved toward the direction of high wave number, and the relationship between each library spectrum in the near infrared spectrum database and the library spectrum x is calculated according to formula (1). near The first moving window correlation coefficient is: In formula (1), R ij is the first moving window correlation coefficient, are the i-th spectrum in the near-infrared spectroscopy database and the library spectrum x near The mean absorbance of the sampling points in the moving window, d is the number of sampling points contained in each moving window, k is the sampling point sequence number, x is the average ... ik is the absorbance of the kth sampling point of the i-th spectrum in the near-infrared spectroscopy database, x jk For the library spectrum x near The absorbance of the kth sampling point.

3. The method according to claim 1, wherein The calculation of the spectrum of the sample to be tested and the library spectrum x near The second moving window correlation coefficient of the sample to be tested is obtained to obtain the correlation coefficient spectrum, including: The preset width of the band interval is used as the moving window, the sampling point with the lowest wave number is used as the starting point and the moving window is moved toward the direction of higher wave number. The spectrum of the sample to be tested and the library spectrum x are calculated according to formula (2). near The second moving window correlation coefficient for each moving window is: In formula (2), R ij ' is the second moving window correlation coefficient, are the sample spectrum to be tested and the library spectrum x near The mean absorbance of the sampling points in the moving window, d is the number of sampling points contained in each moving window, k is the sampling point sequence number, x is the average ... ik ' is the absorbance of the kth sampling point of the spectrum of the sample to be tested, x jk ' is the library spectrum x near The absorbance of the kth sampling point.

4. The method according to claim 2 or 3, wherein: The window width of the moving window is 3 to 25 sampling points, the sampling point interval is 2 to 16 wave numbers, and the distance of each movement of the moving window is 1 to 10 sampling points.

5. The method according to claim 1, wherein The method further includes: The correlation coefficient spectrum of the sample to be tested is normalized according to the following formula (3); In formula (3), x normalized is the normalized correlation coefficient spectrum of the sample to be tested, x is the original correlation coefficient spectrum of the sample to be tested, is the average moving window correlation coefficient and is calculated according to the following formula (4), m is the number of points for collecting the moving window correlation coefficient spectrum, k is the sampling point number, k = 1, 2, ..., m, 6. The method according to claim 5, wherein: The step of fitting the correlation coefficient spectrum of the sample to be tested using the moving window correlation coefficient database to obtain fitting coefficients includes: Using all spectra in the moving window correlation coefficient database, the correlation coefficient spectrum of the sample to be tested is fitted according to the following formula (5): In formula (5), x normalized is the normalized correlation coefficient spectrum of the sample to be tested, v i is the i-th moving window correlation coefficient in the moving window correlation coefficient database, t is the number of moving window correlation coefficients in the moving window correlation coefficient database, a i is the fitting coefficient corresponding to the i-th moving window correlation coefficient in the moving window correlation coefficient database and satisfies the objective function shown in the following formula (6): The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (7): In formula (7), b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

7. The method according to claim 6, wherein: The method further includes: The fitting degree of the fitting is calculated according to the following formula (8), and the credibility of the evaluation data is determined according to the fitting degree; In formula (8), s is the fitting degree, x j is the normalized correlation coefficient of the jth moving window of the correlation coefficient spectrum of the sample to be tested, is the jth moving window correlation coefficient of the moving window correlation coefficient spectrum after fitting, and m is the number of points in the moving window correlation coefficient spectrum.

8. The method according to claim 6, wherein: The method includes: The evaluation data of the physicochemical properties of the crude oil sample to be tested is calculated according to the following formula (9): In formula (9), is the evaluation data of the physical and chemical properties of the crude oil sample to be tested, q i is the standard data of the physicochemical properties corresponding to the library spectrum involved in the fitting, b i is the normalized fitting coefficient, and n is the number of library spectra involved in the fitting.

9. The method according to claim 1, wherein The physicochemical properties include one or more of density, acid value, sulfur content, wax content and colloidal asphaltene content.

10. The method according to claim 9, wherein: The density is tested according to the standard method SH / T0604-2000; The acid value is tested according to the standard method GB / T 7304; The sulfur content is tested according to the standard method GB / T17040; The wax content is determined according to the standard method SY / T 0537; The asphaltene content is determined according to the standard method SY / T 7550.

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