Crude oil evaluation method

Abstract

The invention relates to a crude oil evaluation method, which comprises the following steps: respectively carrying out spectrogram pretreatment on near infrared spectrums of known crude oil samples and crude oil samples to be detected in an established crude oil sample database, and obtaining evaluation data of the crude oil samples to be detected by utilizing a library fitting algorithm and based on multiple times of fitting. And the robustness and the accuracy of an evaluation result are remarkably improved.

Description

Technical Field

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

[0002] Crude oil evaluation plays an important role in crude oil extraction and processing, crude oil trade, etc. At present, crude oil rapid evaluation technology has been developed based on a variety of modern instrumental analysis methods, including chromatography-mass spectrometry, nuclear magnetic resonance, mid-infrared spectroscopy and near-infrared spectroscopy. Among them, near-infrared spectroscopy is favored because of its convenient measurement, fast speed, low cost, and can be used on site. It has been increasingly applied to petrochemical, agricultural and pharmaceutical fields, especially in process analysis. It plays an active and irreplaceable role. However, since crude oil evaluation involves more than 100 analytical items, it is not feasible to use traditional multivariate correction methods, such as partial least squares (PLS), support vector machine (SVM) and artificial neural network (ANN), to establish correction models for each analysis item separately. It is necessary to study and develop new spectral calculation methods to solve the problem of simultaneous prediction of more than 100 properties.

[0003] CN102374975A ​​proposes a library spectrum fitting method, which is based on the near-infrared spectrum library of oil products and spectrum fitting technology. Its basic principle is that samples with similar spectra also have similar properties. The spectrum of the unknown sample to be tested is fitted through one or more spectra in the spectrum library, and then the properties of the sample to be tested are calculated based on the properties of the oil products involved in the fitted spectrum. However, this method still has problems such as unstable and 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 robustness and accuracy of the evaluation results of the crude oil to be tested.

[0005] In order to achieve the above object, the present disclosure provides a crude oil evaluation method, which comprises the following steps:

[0006] Acquire a near infrared spectrum database containing a group of known crude oil samples; wherein the near infrared spectrum database includes a library spectrum obtained by spectral preprocessing of the near infrared spectrum of each known crude oil sample and physical property standard data of each known crude oil sample, and the library spectra of multiple known crude oil samples constitute a spectrum matrix X0;

[0007] Acquiring a near infrared spectrum of the crude oil sample to be tested and performing spectrum preprocessing on the near infrared spectrum of the crude oil sample to be tested to obtain a spectrum to be fitted;

[0008] The spectrum matrix X0 is used to fit the spectrum to be fitted multiple times and the fitting degree is calculated, wherein after each fitting, the library spectrum whose fitting degree is not less than a preset threshold is removed to obtain the spectrum matrix X0 after the i-th fitting. i , and continue to use the spectral matrix X i Perform the next fitting to obtain multiple sets of fitting coefficients;

[0009] According to the plurality of groups of fitting coefficients and the near infrared spectrum database, a plurality of groups of predicted data of physical properties of the crude oil samples to be tested are obtained;

[0010] The physical property evaluation data of the crude oil sample to be tested are determined according to the multiple groups of predicted data of the physical properties of the crude oil sample to be tested.

[0011] Optionally, the spectrum preprocessing includes:

[0012] The absorbance of the characteristic spectral region of the near infrared spectrum is subjected to first-order differential processing and normalized according to the following formula (1):

[0013]

[0014] Among them, x nor is the absorbance vector of the normalized spectrum, x0 is the absorbance vector of the original spectrum, is the average spectral vector and is calculated according to the following formula (2), m is the number of wavelength points for collecting spectral absorbance, k is the sampling point number for collecting spectral absorbance, k = 1, 2, ..., m,

[0015]

[0016] Optionally, the characteristic spectral region is 4000cm -1 ~4700cm -1 and 5300cm -1 ~6000cm -1 .

[0017] Optionally, the method comprises:

[0018] Using a non-negative constrained least square method to determine non-zero fitting coefficients in the fitting coefficients, and normalizing the non-zero fitting coefficients to obtain normalized fitting coefficients;

[0019] According to the normalized fitting coefficient and the near infrared spectrum database, the physical property prediction data of the crude oil sample to be tested is obtained.

[0020] Optionally, the method comprises:

[0021] The fitting is performed according to the following formula (3):

[0022]

[0023] Wherein, p is the spectrum to be fitted, v i is the library spectrum involved in the fitting, t is the number of library spectra involved in the fitting, a i is the fitting coefficient corresponding to the ith library spectrum and satisfies the objective function shown in the following formula (4):

[0024]

[0025] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0026]

[0027] In formula (5), b i is the normalized fitting coefficient, and g is the number of library spectra corresponding to non-zero fitting coefficients.

[0028] Optionally, the method comprises:

[0029] The fitted spectrum is calculated according to the following formula (6) and the fitting degree is calculated according to the following formula (7):

[0030]

[0031]

[0032] Among them, s is the fitting degree, x j is the absorbance of the jth wavelength point of the spectrum to be fitted, is the absorbance of the jth wavelength point of the fitted spectrum, and m is the number of wavelength points for collecting the absorbance of the spectrum.

[0033] Optionally, the method comprises:

[0034] The predicted data of the physical properties of the crude oil sample to be tested are calculated according to the following formula (8):

[0035]

[0036] in, is the predicted data of physical properties of crude oil sample to be tested, q i is the standard data of physical properties corresponding to the library spectrum involved in the fitting.

[0037] Optionally, the method comprises:

[0038] The spectrum matrix X obtained after the nth fitting is used n Performing the n+1th fitting and calculating the fitting degree, stopping the fitting when the fitting degree is lower than the preset threshold, and obtaining n groups of fitting coefficients;

[0039] Obtaining n groups of predicted data of physical properties of the crude oil samples to be tested according to the n groups of fitting coefficients and the near infrared spectrum database;

[0040] According to n groups of predicted data of physical properties of the crude oil samples to be tested, physical property evaluation data of the crude oil samples to be tested are determined.

[0041] Optionally, the preset threshold is 10.05.

[0042] Optionally, the method comprises:

[0043] The predicted data of the physical properties of the plurality of crude oil samples to be tested are averaged to serve as the evaluation data of the physical properties of the crude oil samples to be tested.

[0044] Through the above technical scheme, the present invention performs spectral preprocessing on the near-infrared spectra of known crude oil samples in the established crude oil sample database and the crude oil samples to be tested, and uses the library fitting algorithm to obtain the evaluation data of the crude oil samples to be tested based on multiple fittings, thereby significantly improving the robustness and accuracy of the evaluation results.

[0045] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] 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 specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0047] Figure 1 It is a schematic flow chart of a crude oil evaluation method provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0049] The present disclosure provides a crude oil evaluation method, such as Figure 1 As shown, the method includes the following steps S101 to S105:

[0050] S101, obtaining a near infrared spectrum database containing a group of known crude oil samples; wherein the near infrared spectrum database includes a library spectrum obtained by performing spectral preprocessing on the near infrared spectrum of each known crude oil sample and physical property standard data of each known crude oil sample, and the library spectra of multiple known crude oil samples constitute a spectrum matrix X0;

[0051] S102, obtaining a near infrared spectrum of the crude oil sample to be tested and performing spectrum preprocessing on the near infrared spectrum of the crude oil sample to be tested to obtain a spectrum to be fitted;

[0052] S103, using the spectrum matrix X0 to perform multiple fittings on the spectrum to be fitted and calculate the fitting degree, wherein after each fitting, the library spectrum whose fitting degree is not less than a preset threshold is removed to obtain the spectrum matrix X0 after the i-th fitting. i , and continue to use the spectral matrix X i Perform the next fitting to obtain multiple sets of fitting coefficients;

[0053] S104, obtaining multiple groups of predicted data of physical properties of the crude oil samples to be tested according to the multiple groups of fitting coefficients and the near infrared spectrum database;

[0054] S105, determining physical property evaluation data of the crude oil sample to be tested according to multiple groups of physical property prediction data of the crude oil sample to be tested.

[0055] The present invention performs spectral preprocessing on the near-infrared spectra of known crude oil samples and the crude oil samples to be tested in the established crude oil sample database, and uses the library fitting algorithm to give the prediction data of the crude oil samples to be tested from the existing library samples. Based on multiple fittings, the known crude oils involved in the fitting are removed from the library after each fitting and then the next fitting is performed. The evaluation data of the crude oil samples to be tested is obtained according to the multiple fitting results, which can effectively avoid the problem of unstable prediction data caused by the presence of the same type of crude oil from different ages in the crude oil sample database, and significantly improve the robustness and accuracy of the evaluation results. The evaluation results obtained by this method are simple and easy to obtain, and there is no need to model the library samples. The database maintenance is simple and convenient, and has good application prospects.

[0056] In step S101, the known crude oil sample refers to a crude oil sample with known composition and / or properties, which can be a crude oil sample in a crude oil database established on a near-infrared spectrometer or an actual crude oil sample collected from major production areas around the world. The group of known crude oil samples includes a plurality of known crude oil samples, and the number of the known crude oil samples can preferably be 100 to 200, so as to further improve the robustness and accuracy of the evaluation results.

[0057] The physical property standard data refers to the standard data obtained by testing the physical properties of the known crude oil sample using standard methods. The physical properties involved may include but are not limited to density, acid value, sulfur content, nitrogen content, wax content, carbon residue value, colloid and asphaltene content, actual boiling point distillation data, etc., and the appropriate type and quantity of physical properties can be selected according to needs. Each physical property is measured using a standard method. Specifically, the density can be detected according to the standard method SH / T0604-2000 Density determination method for crude oil and petroleum products (U-shaped vibrating tube method); the acid value can be detected according to the standard method GB / T7304-2014 Determination of acid value of petroleum products-potentiometric titration method; the sulfur content can be detected according to the standard method GB / T17040-2008 Energy dispersive X-ray determination method for sulfur content of petroleum and petroleum products; the carbon residue value can be detected according to the standard method GB / T 17144 Determination of carbon residue in petroleum products; the nitrogen content can be detected according to the standard method GB / T 17674 Determination of nitrogen content in crude oil; the wax content can be detected according to the standard method SY / T 0537 Determination of wax content in crude oil; the colloid and asphaltene content can be detected according to the standard method SY / T 7550 Determination of wax, colloid and asphaltene content in crude oil; the actual boiling point distillation data can be detected according to the standard method GB / T 17280 Standard test method for crude oil distillation.

[0058] In steps S101 and S102, various near-infrared spectroscopy detection devices commonly used in the art can be used to obtain the near-infrared spectrum. In a specific embodiment, a Fourier transform near-infrared spectrometer is used to obtain the near-infrared spectrum. The conditions for detecting samples using a 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 injection method, and the transmission light path is 0.5mm; the detection conditions may include: a resolution of 2 to 16cm -1 , the wave number range is 4000~10000cm -1 , scanning times 32 to 128 times.

[0059] The near infrared spectrum of the known crude oil sample and the near infrared spectrum of the crude oil sample to be tested each need to be subjected to spectral preprocessing before subsequent fitting, and the spectral preprocessing performed on the near infrared spectrum of the known crude oil sample and the spectral preprocessing performed on the near infrared spectrum of the crude oil sample to be tested are preferably the same spectral preprocessing process. For example, the spectral preprocessing may each include, but is not limited to, differential processing, standardization processing, normalization processing, and wavelet transform processing.

[0060] In a specific embodiment, the spectrum preprocessing includes:

[0061] The absorbance of the characteristic spectral region of the near infrared spectrum is subjected to first-order differential processing and normalized according to the following formula (1):

[0062]

[0063] Among them, x nor is the absorbance vector of the normalized spectrum, x0 is the absorbance vector of the original spectrum, is the average spectral vector and is calculated according to the following formula (2), m is the number of wavelength points for collecting spectral absorbance, k is the sampling point number for collecting spectral absorbance, k = 1, 2, ..., m,

[0064]

[0065] The method of first-order differential processing is well known to computer personnel in the field. The characteristic spectrum region is 4000cm -1 ~4700cm -1 and 5300cm -1 ~6000cm -1 The sampling point interval of the characteristic spectrum area can be 2 to 16 wave numbers. By selecting an appropriate characteristic spectrum area range, it is helpful to further improve the accuracy of the evaluation results.

[0066] In step S103, each fitting can obtain a set of initial fitting coefficients, and the sum of this set of initial fitting coefficients is 1. Preferably, the fitting coefficients are non-zero fitting coefficients after normalizing the initial fitting coefficients, for example, the fitting coefficients are determined according to the non-negative constrained least squares method.

[0067] In one embodiment, the method includes: using a non-negative constrained least squares method to determine a non-zero fitting coefficient in the fitting coefficient, and normalizing the non-zero fitting coefficient to obtain a normalized fitting coefficient; and obtaining physical property prediction data of the crude oil sample to be tested based on the normalized fitting coefficient and the near-infrared spectrum database.

[0068] For example, the fitting is performed according to the following formula (3):

[0069]

[0070] Wherein, p is the spectrum to be fitted, v i is the library spectrum involved in the fitting, t is the number of library spectra involved in the fitting, a i is the fitting coefficient corresponding to the ith library spectrum and satisfies the objective function shown in the following formula (4):

[0071]

[0072] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0073]

[0074] In formula (5), b i is the normalized fitting coefficient, and g is the number of library spectra corresponding to non-zero fitting coefficients.

[0075] Further, the method comprises:

[0076] The fitted spectrum is calculated according to the following formula (6) and the fitting degree is calculated according to the following formula (7):

[0077]

[0078]

[0079] Among them, s is the fitting degree, x j is the absorbance of the jth wavelength point of the spectrum to be fitted, is the absorbance of the jth wavelength point of the fitted spectrum, m is the number of wavelength points for collecting the absorbance of the spectrum, and b i is the normalized fitting coefficient, v i is the library spectrum involved in the fitting, and g is the number of library spectra corresponding to the non-zero fitting coefficient.

[0080] The preset threshold value may be determined by the repeatability of spectral measurement. In the present disclosure, the preset threshold value may be 10.05.

[0081] Each fitting is performed according to the above method, and the library spectra involved in the fitting that are successfully fitted (i.e., the fitting degree is not less than the preset threshold) are removed from the spectrum matrix X0 to obtain a new spectrum matrix X i (i=1,2,…,n), continue to use the spectral matrix X i Perform cyclic fitting. The number of fitting can be 2 to 5 times, or stop fitting when fitting is unsuccessful.

[0082] Illustratively, the method comprises:

[0083] The spectrum matrix X obtained after the nth fitting is used n Performing the n+1th fitting and calculating the fitting degree, stopping the fitting when the fitting degree is lower than the preset threshold, and obtaining n groups of fitting coefficients;

[0084] Obtaining n groups of predicted data of physical properties of the crude oil samples to be tested according to the n groups of fitting coefficients and the near infrared spectrum database;

[0085] According to n groups of predicted data of physical properties of the crude oil samples to be tested, physical property evaluation data of the crude oil samples to be tested are determined.

[0086] Using the above method to perform n-time fitting is conducive to maximizing the robustness and accuracy of the evaluation results.

[0087] In step S104, the physical property prediction data of the crude oil sample to be tested is calculated based on the obtained fitting coefficients and the physical property standard data corresponding to the library spectrum involved in the fitting.

[0088] In one embodiment, the method comprises:

[0089] The predicted data of the physical properties of the crude oil sample to be tested are calculated according to the following formula (8):

[0090]

[0091] in, is the predicted data of physical properties of crude oil sample to be tested, q i is the standard data of physical properties corresponding to the library spectrum involved in the fitting.

[0092] In step S105, specifically, the method includes: taking an average value of the physical property prediction data of a plurality of the crude oil samples to be tested as the physical property evaluation data of the crude oil sample to be tested.

[0093] The disclosed method can be used to evaluate various crude oil samples and has wide applicability.

[0094] The present disclosure is further illustrated below by examples, but the present disclosure is not limited thereto.

[0095] The instrument used to collect the near-infrared spectrum of crude oil in this example is a Fourier transform near-infrared spectrometer produced by Thermo, model Antaris II, and the spectrum collection conditions are: resolution 8cm -1 , wave number range 4000~10000cm -1 , cumulative scanning times 64 times, transmission measurement method.

[0096] Density is tested according to standard method SH / T 0604-2000 Density determination of crude oil and petroleum products (U-shaped vibrating tube method); acid value is tested according to standard method GB / T 7304-2014 Determination of acid value of petroleum products - potentiometric titration; sulfur content is tested according to standard method GB / T17040-2008 Energy dispersive X-ray determination of sulfur content of petroleum and petroleum products; carbon residue value is tested according to standard method GB / T17144 Determination of carbon residue in petroleum products; nitrogen content is tested according to standard method GB / T 17674 Determination of nitrogen content in crude oil; wax content is tested according to standard method SY / T 0537 Determination of wax content in crude oil; colloid and asphaltene content are tested according to standard method SY / T 7550 Determination of wax, colloid and asphaltene content in crude oil; true boiling point distillation data is tested according to standard method GB / T 17280 Standard test method for crude oil distillation.

[0097] Example 1

[0098] (I) Construction of a near-infrared spectral database of known crude oil samples

[0099] 210 kinds of crude oil samples from major producing areas around the world were collected, covering the major crude oil producing areas in the world. A known crude oil sample library was formed. Standard data such as crude oil density, acid value, sulfur content, etc. were determined using standard methods, and the near-infrared spectra of crude oil samples were determined. The 4000cm near-infrared spectra of known crude oil samples were -1 ~4700cm -1 and 5300cm -1 ~6000cm -1 The absorbance of the characteristic spectral region is subjected to first-order differential processing and normalized according to the following formula (1):

[0100]

[0101] Among them, x nor is the absorbance vector of the normalized spectrum, x0 is the absorbance vector of the original spectrum, is the average spectral vector and is calculated according to the following formula (2), m is the number of wavelength points for collecting spectral absorbance, k is the sampling point number for collecting spectral absorbance, k = 1, 2, ..., m,

[0102]

[0103] The number of wavenumber sampling points in the characteristic spectrum region is 365, and the library spectra obtained after processing constitute the spectrum matrix X0.

[0104] (ii) collecting the near infrared spectrum of the crude oil sample to be tested and its corresponding physical property data under the same detection conditions, and obtaining the spectrum to be fitted according to the same spectrum preprocessing method as in step (i).

[0105] (III) Using the library spectrum to fit the spectrum to be fitted according to the following formula (3):

[0106]

[0107] Wherein, p is the spectrum to be fitted, v i is the library spectrum involved in the fitting, t is the number of library spectra involved in the fitting, a i is the fitting coefficient corresponding to the ith library spectrum and satisfies the objective function shown in the following formula (4):

[0108]

[0109] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0110]

[0111] In formula (5), b i is the normalized fitting coefficient, and g is the number of library spectra corresponding to non-zero fitting coefficients.

[0112] The fitted spectrum is calculated according to the following formula (6) and the fitting degree is calculated according to the following formula (7):

[0113]

[0114]

[0115] Among them, s is the fit, x j is the absorbance of the jth wavelength point of the spectrum to be fitted, is the absorbance of the jth wavelength point of the fitted spectrum, and m is the number of wavelength points for collecting the absorbance of the spectrum.

[0116] The library spectra involved in the fitting whose fitting degree is not less than the preset threshold value 10.05 are eliminated to obtain the spectrum matrix X1 after the first fitting, and then the above fitting is repeated to obtain the spectrum matrices X2, X3, ... X i , until the fitting degree is lower than the preset threshold, the fitting is stopped. The total number of fitting times n is 3 times, corresponding to 3 sets of normalized fitting coefficients.

[0117] (iv) Calculate the predicted data of the physical properties of the crude oil sample to be tested according to the following formula (8):

[0118]

[0119] in, is the predicted data of physical properties of crude oil sample to be tested, q iis the standard data of physical properties corresponding to the library spectrum involved in the fitting.

[0120] (V) Taking the average value of the predicted data of the physical properties of the crude oil sample to be tested obtained in step (4) as the evaluation data of the physical properties of the crude oil sample to be tested, as shown in Table 1.

[0121] Among them, deviation = predicted value - measured value.

[0122] Table 1

[0123] project Measured value Predicted value deviation <![CDATA[Density, g·cm -3 > 0.8696 0.8681 -0.0015 Acid value, mg KOH / g 0.15 0.13 -0.02 Carbon residue value, weight % 6.17 5.91 -0.26 Sulfur content, weight % 2.40 2.52 0.12 Nitrogen content, weight % 0.12 0.11 -0.01 Wax content, weight % 5.30 4.08 -1.22 Gum content, weight % 9.90 8.68 -1.22 Asphaltene content, weight % 2.80 2.65 -0.15

[0124] Comparative Example 1

[0125] The crude oil sample to be tested was evaluated according to the method of Example 1, except that the library spectrum was used to fit the crude oil spectrum to be tested only once, without multiple fittings. The physical property data of the crude oil sample to be tested was calculated based on the fitting coefficients obtained from the single fitting. The evaluation results are shown in Table 2.

[0126] Table 2

[0127] project Measured value Predicted value deviation <![CDATA[Density, g·cm -3 > 0.8696 0.8716 0.0020 Acid value, mg KOH / g 0.15 0.17 0.02 Carbon residue value, mass % 6.17 5.88 -0.29 Sulfur content, mass% 2.40 2.63 0.23 Nitrogen content, mass% 0.12 0.13 0.01 Wax content, mass % 5.30 4.14 -1.16 Gum content, mass% 9.90 8.62 -1.28 Asphaltene content, mass % 2.80 2.09 -0.71

[0128] From the comparison between Table 1 and Table 2, it can be seen that the method disclosed in the present invention can significantly improve the evaluation accuracy of the physical property data of the crude oil sample to be tested, and the result is more robust.

[0129] 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 in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0130] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0131] In addition, 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: Acquire a near infrared spectrum database containing a group of known crude oil samples; wherein the near infrared spectrum database includes a library spectrum obtained by spectral preprocessing of the near infrared spectrum of each known crude oil sample and physical property standard data of each known crude oil sample, and the library spectra of multiple known crude oil samples constitute a spectrum matrix X0; Acquiring a near infrared spectrum of the crude oil sample to be tested and performing spectrum preprocessing on the near infrared spectrum of the crude oil sample to be tested to obtain a spectrum to be fitted; The spectrum matrix X0 is used to fit the spectrum to be fitted multiple times and the fitting degree is calculated, wherein after each fitting, the library spectrum whose fitting degree is not less than a preset threshold is removed to obtain the spectrum matrix X0 after the i-th fitting. i , and continue to use the spectral matrix X i Perform the next fitting to obtain multiple sets of fitting coefficients; According to the plurality of groups of fitting coefficients and the near infrared spectrum database, a plurality of groups of predicted data of physical properties of the crude oil samples to be tested are obtained; The physical property evaluation data of the crude oil sample to be tested are determined according to the multiple groups of predicted data of the physical properties of the crude oil sample to be tested.

2. The method according to claim 1, wherein: The spectrum preprocessing includes: The absorbance of the characteristic spectral region of the near infrared spectrum is subjected to first-order differential processing and normalized according to the following formula (1): Among them, x nor is the absorbance vector of the normalized spectrum, x0 is the absorbance vector of the original spectrum, is the average spectral vector and is calculated according to the following formula (2), m is the number of wavelength points for collecting spectral absorbance, k is the sampling point number for collecting spectral absorbance, k = 1, 2, ..., m, 3. The method according to claim 2, wherein: The characteristic spectral region is 4000 cm -1 ~4700cm -1 and 5300cm -1 ~6000cm -1 .

4. The method according to claim 1, wherein: The method includes: Using a non-negative constrained least square method to determine non-zero fitting coefficients in the fitting coefficients, and normalizing the non-zero fitting coefficients to obtain normalized fitting coefficients; According to the normalized fitting coefficient and the near infrared spectrum database, the physical property prediction data of the crude oil sample to be tested is obtained.

5. The method according to claim 4, wherein: The method includes: The fitting is performed according to the following formula (3): Wherein, p is the spectrum to be fitted, v i is the library spectrum involved in the fitting, t is the number of library spectra involved in the fitting, a i is the fitting coefficient corresponding to the ith library spectrum and satisfies the objective function shown in the following formula (4): The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5): In formula (5), b i is the normalized fitting coefficient, and g is the number of library spectra corresponding to non-zero fitting coefficients.

6. The method according to claim 5, wherein: The method includes: The fitted spectrum is calculated according to the following formula (6) and the fitting degree is calculated according to the following formula (7): Among them, s is the fit, x j is the absorbance of the jth wavelength point of the spectrum to be fitted, is the absorbance of the jth wavelength point of the fitted spectrum, and m is the number of wavelength points for collecting the absorbance of the spectrum.

7. The method according to claim 6, wherein: The method includes: The predicted data of the physical properties of the crude oil sample to be tested are calculated according to the following formula (8): in, is the predicted data of physical properties of crude oil sample to be tested, q i is the standard data of physical properties corresponding to the library spectrum involved in the fitting.

8. The method according to claim 1, wherein: The method includes: The spectrum matrix X obtained after the nth fitting is used n Performing the n+1th fitting and calculating the fitting degree, stopping the fitting when the fitting degree is lower than the preset threshold, and obtaining n groups of fitting coefficients; Obtaining n groups of predicted data of physical properties of the crude oil samples to be tested according to the n groups of fitting coefficients and the near infrared spectrum database; According to n groups of predicted data of physical properties of the crude oil samples to be tested, physical property evaluation data of the crude oil samples to be tested are determined.

9. The method according to claim 1 or 8, wherein: The preset threshold is 10.

05.

10. The method according to claim 1, wherein: The method includes: The predicted data of the physical properties of the plurality of crude oil samples to be tested are averaged to serve as the evaluation data of the physical properties of the crude oil samples to be tested.

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