Crude oil evaluation method

Abstract

The invention relates to a crude oil evaluation method. The method comprises the following steps: acquiring a spectral index-physicochemical property database of a plurality of known crude oil samples; collecting a to-be-detected sample spectrum of a to-be-detected crude oil sample, and obtaining detection data of a first physicochemical property of the to-be-detected crude oil sample to obtain an initial vector; fitting the initial vector by adopting the spectral index-physicochemical property database to obtain a fitting coefficient; and according to the fitting coefficient and the standard data of the second physicochemical property of the known crude oil sample, obtaining evaluation data of the second physicochemical property of the crude oil sample to be detected. The method disclosed by the invention is beneficial to improving the accuracy of crude oil evaluation.

Description

Technical Field

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

[0002] Crude oil evaluation plays a very important role in crude oil extraction, crude oil trade and crude oil processing. Large petrochemical companies at home and abroad are developing crude oil rapid evaluation technology 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, becoming the preferred technology. At present, it has been increasingly applied to petrochemical, agricultural and pharmaceutical fields, especially playing an active and irreplaceable role in process analysis. 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 low accuracy of predicted data. Summary of the invention

[0004] The purpose of the present disclosure is to provide a crude oil evaluation method to improve the 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] Obtain library spectra of a plurality of known crude oil samples, and obtain standard data of a first physicochemical property and a second physicochemical property of each known crude oil sample; wherein the first physicochemical property and the second physicochemical property respectively include at least one physicochemical property of crude oil;

[0007] Dividing each of the library spectra into a plurality of characteristic bands, and obtaining a plurality of first spectrum indices according to the integrated area of ​​the library spectrum in each of the characteristic bands;

[0008] Merging the first spectral index and the standard data of the first physicochemical property of a plurality of the known crude oil samples to obtain a spectral index-physicochemical property database of the known crude oil samples;

[0009] Collecting a sample spectrum of the crude oil sample to be tested, and obtaining detection data of a first physicochemical property of the crude oil sample to be tested;

[0010] Dividing the spectrum of the sample to be tested into a plurality of characteristic bands, and obtaining a plurality of second spectrum indices according to the integrated area of ​​the spectrum of the sample to be tested in each characteristic band;

[0011] Combining the detection data of the second spectral index and the first physicochemical property of the crude oil sample to be tested to obtain an initial vector;

[0012] Using the spectral index-physical and chemical property database to fit the initial vector to obtain a fitting coefficient;

[0013] The evaluation data of the second physicochemical property of the crude oil sample to be tested is obtained according to the fitting coefficient and the standard data of the second physicochemical property of the known crude oil sample.

[0014] Optionally, n is an integer selected from 10 to 20.

[0015] Optionally, the characteristic band is 3972cm -1 ~4234cm -1 、4238cm -1 ~4470cm -1 、4474cm -1 ~4720cm -1 、4724cm -1 ~5569cm -1 、5573cm -1 ~6070cm -1 、6074cm -1 ~6903cm -1 、6907cm -1 ~7447cm -1 、7451cm -1 ~8084cm -1 8087cm -1 ~8577cm -1 and 8581cm -1 ~10000cm -1 .

[0016] Optionally, the first physicochemical property and the second physicochemical property are each independently selected from one or more of true boiling point distillation cut-off yield, density, acid value, residual carbon value, sulfur content, nitrogen content, wax content and colloid asphaltene content, and the first physicochemical property is different from the second physicochemical property.

[0017] Optionally, the method comprises:

[0018] Combining the first spectral index and the standard data of the first physicochemical property of each known crude oil sample to obtain a spectral index-physicochemical property vector of each known crude oil sample;

[0019] The spectral index-physical and chemical property vector is normalized according to the following formula (1), and the normalized spectral index-physical and chemical property vectors of a plurality of known crude oil samples are merged to obtain the spectral index-physical and chemical property database;

[0020]

[0021] In formula (1), x normalized is the normalized spectral index-physical and chemical property vector, x is the original spectral index-physical and chemical property vector, is the average spectral index-physical and chemical property vector and is calculated according to the following formula (2), m is the number of data points for collecting spectral index-physical and chemical property parameters, k is the sampling point number, k = 1, 2, ..., m,

[0022]

[0023] Optionally, the method comprises:

[0024] 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;

[0025] The evaluation data of the second physicochemical property of the crude oil sample to be tested is obtained according to the normalized fitting coefficient and the standard data of the second physicochemical property of the known crude oil sample.

[0026] Optionally, the method comprises:

[0027] The fitting is performed according to the following formula (3):

[0028]

[0029] In formula (3), p is the initial vector, v i is the spectral index-physical and chemical property vector involved in the fitting, t is the number of spectral index-physical and chemical property vectors involved in the fitting, a iis the fitting coefficient corresponding to the i-th spectral index-physical and chemical property vector and satisfies the objective function shown in the following formula (4):

[0030]

[0031] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0032]

[0033] In formula (5), b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0034] Optionally, the method comprises:

[0035] The evaluation data of the second physicochemical property of the crude oil sample to be tested is calculated according to the following formula (6):

[0036]

[0037] In formula (6), is the evaluation data of the second physical and chemical properties of the crude oil sample to be tested, q i is the standard data of the second physicochemical property corresponding to the library spectrum involved in the fitting, b i is the normalized fitting coefficient.

[0038] Optionally, the library spectrum and the spectrum of the sample to be tested are detected respectively by Fourier transform near-infrared spectrometer.

[0039] Optionally, the detection conditions independently include: a resolution of 2 to 16 cm -1 , wave number range is 4000cm -1 ~10000cm -1 , scanning times 16 to 128 times.

[0040] Through the above technical solution, the present disclosure adopts a library fitting algorithm to fit the spectral index-physical and chemical property database of known crude oil samples to the crude oil samples to be tested, so as to obtain the evaluation data of the crude oil samples to be tested, which is conducive to improving the accuracy of crude oil evaluation.

[0041] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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:

[0043] Figure 1 It is a schematic flow chart of a crude oil evaluation method provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] 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.

[0045] The present disclosure provides a crude oil evaluation method, such as Figure 1 As shown, the method includes the following steps S101 to S108:

[0046] S101, obtaining library spectra of a plurality of known crude oil samples, and obtaining standard data of a first physicochemical property and a second physicochemical property of each known crude oil sample; wherein the first physicochemical property and the second physicochemical property respectively include at least one physicochemical property of crude oil;

[0047] S102, dividing each of the library spectra into a plurality of characteristic bands, and obtaining a plurality of first spectrum indices according to the integrated area of ​​the library spectrum in each of the characteristic bands;

[0048] S103, combining the first spectral index and the standard data of the first physicochemical property of a plurality of the known crude oil samples to obtain a spectral index-physicochemical property database of the known crude oil samples;

[0049] S104, collecting a sample spectrum of the crude oil sample to be tested, and obtaining detection data of a first physicochemical property of the crude oil sample to be tested;

[0050] S105, dividing the spectrum of the sample to be tested into a plurality of characteristic bands, and obtaining a plurality of second spectrum indices according to the integrated area of ​​the spectrum of the sample to be tested in each characteristic band;

[0051] S106, combining the second spectral index and the detection data of the first physicochemical property of the crude oil sample to be tested to obtain an initial vector;

[0052] S107, fitting the initial vector using the spectral index-physical and chemical property database to obtain a fitting coefficient;

[0053] S108, obtaining evaluation data of the second physicochemical property of the crude oil sample to be tested according to the fitting coefficient and the standard data of the second physicochemical property of the known crude oil sample.

[0054] The present invention first processes the original near-infrared spectrum database of known crude oil, establishes a spectral index-physical and chemical property database, and then fits the initial vector of the crude oil sample to be tested with the library fitting algorithm to obtain the evaluation data of the crude oil sample to be tested, which significantly reduces the data processing amount and calculation complexity, and is conducive to improving the accuracy of crude oil evaluation. The evaluation results obtained by this method are simple and easy to obtain, do not need to model the library samples, and the database maintenance is simple and convenient, which has good application prospects.

[0055] In step S101, the known crude oil samples refer to crude oil samples with known composition and / or properties, which can be crude oil samples in the crude oil database established on the near infrared spectrometer or actual crude oil samples collected from major production areas around the world. The number of the known crude oil samples can preferably be 100 to 200 to further improve the accuracy of the evaluation results.

[0056] In the present disclosure, the first physicochemical property and the second physicochemical property refer to the physical and / or chemical properties of the crude oil sample, wherein the second physicochemical property is the physicochemical property that needs to be predicted.

[0057] The number of types of the first physicochemical property and the second physicochemical property can be selected as needed. For example, the first physicochemical property may include 1 to 3 types of crude oil physicochemical properties, and the second physicochemical property includes the physicochemical properties of crude oil excluding the first physicochemical property. Selecting an appropriate number of first physicochemical properties and second physicochemical properties is conducive to reducing the amount of calculation and further improving the accuracy of the evaluation results.

[0058] The types of the first physicochemical property and the second physicochemical property can be selected as needed. For example, the first physicochemical property may include physicochemical properties that are easier to predict and / or secondary physicochemical properties, and the second physicochemical property may include physicochemical properties that are more difficult to predict and / or key physicochemical properties. Specifically, the first physicochemical property and the second physicochemical property are each independently selected from one or more of true boiling point distillation cut yield, density, acid value, residual carbon value, sulfur content, nitrogen content, wax content and colloid asphaltene content, and the first physicochemical property is different from the second physicochemical property.

[0059] In one embodiment, the first physical and chemical property is wax content, resin content and asphaltene content; the second physical and chemical property is selected from density, acid value, residual carbon value, sulfur content, nitrogen content or more. This embodiment is conducive to further improving the accuracy of the evaluation result of the second physical and chemical property.

[0060] Furthermore, various physicochemical properties were determined using standard methods. Specifically, the density can be detected 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 can be detected according to the standard method GB / T 7304-2014 Determination of acid value of petroleum products-potentiometric titration; the sulfur content can be detected according to the standard method GB / T17040-2008 Energy dispersive X-ray determination of sulfur content in 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 / T17280 Standard test method for crude oil distillation.

[0061] In the present disclosure, various near-infrared spectroscopy detection devices commonly used in the art can be used to obtain the library spectrum of known crude oil samples and the spectrum of the sample to be tested of the crude oil sample to be tested. In a specific embodiment, the library spectrum and the spectrum of the sample to be tested are respectively detected by a Fourier transform near-infrared spectrometer. The conditions for detecting the sample 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 , wave number range is 4000cm -1 ~10000cm -1 , scanning times 16 to 128 times.

[0062] In the present disclosure, 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 subjected to spectral preprocessing before subsequent steps, and 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., which can be performed using common methods in the art.

[0063] In step S102, the plurality of characteristic bands may be equally divided or not equally divided, and the whole spectrum may be selected for segmentation, or the spectrum region with large noise influence may be eliminated before segmentation. In one embodiment, the number of the characteristic bands is 10 to 20, that is, the spectrum is divided into 10 to 20 characteristic bands, and 10 to 20 first spectral indices may be obtained correspondingly, thereby significantly reducing the amount of data. The sampling point interval of the characteristic band may be 2 to 16 wave numbers.

[0064] Furthermore, the characteristic band may be 3972cm -1 ~4234cm -1 、4238cm -1 ~4470cm -1 4474cm -1 ~4720cm -1 、4724cm -1 ~5569cm -1 5573cm -1 ~6070cm -1 、6074cm -1 ~6903cm -1 、6907cm -1 ~7447cm -1 、7451cm -1 ~8084cm -1 8087cm -1 ~8577cm -1 and 8581cm -1 ~10000cm -1 Selecting the above characteristic bands is conducive to improving the accuracy of the evaluation results. The spectral line integral area of ​​the library spectrum in each of the above characteristic bands is the first spectral index, and the integral area can be calculated using a common method in the art.

[0065] In step S103, the standard data of the first spectral index and the first physicochemical property of each known crude oil sample are merged to obtain the spectral index-physicochemical property vector of the known crude oil sample, and then the spectral index-physicochemical property vectors of multiple known crude oil samples are merged to obtain the spectral index-physicochemical property database.

[0066] In one embodiment, the first spectral index and the standard data of the first physicochemical property of each known crude oil sample are combined to obtain a spectral index-physicochemical property vector of each known crude oil sample;

[0067] The spectral index-physical and chemical property vector is normalized according to the following formula (1), and the normalized spectral index-physical and chemical property vectors of a plurality of known crude oil samples are merged to obtain the spectral index-physical and chemical property database;

[0068]

[0069] In formula (1), x normalized is the normalized spectral index-physical and chemical property vector, x is the original spectral index-physical and chemical property vector, is the average spectral index-physical and chemical property vector and is calculated according to the following formula (2), m is the number of data points for collecting spectral index-physical and chemical property parameters, k is the sampling point number, k = 1, 2, ..., m,

[0070]

[0071] The specific operations of step S104, step S105 and step S106 may be performed with reference to step S101, step S102 and step S103. The second spectral index of the crude oil sample to be tested is obtained in the same plurality of characteristic bands using the same calculation method.

[0072] In step S107, 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.

[0073] In one embodiment, the method includes: using a non-negative constrained least square 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;

[0074] The evaluation data of the second physicochemical property of the crude oil sample to be tested is obtained according to the normalized fitting coefficient and the standard data of the second physicochemical property of the known crude oil sample.

[0075] For example, the fitting is performed according to the following formula (3):

[0076]

[0077] In formula (3), p is the initial vector, v i is the spectral index-physical and chemical property vector involved in the fitting, t is the number of spectral index-physical and chemical property vectors involved in the fitting, a i is the fitting coefficient corresponding to the i-th spectral index-physical and chemical property vector and satisfies the objective function shown in the following formula (4):

[0078]

[0079] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0080]

[0081] In formula (5), b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0082] In step S108, the evaluation data of the second physicochemical property of the crude oil sample to be tested is calculated according to the obtained fitting coefficient and the standard data of the second physicochemical property corresponding to the library spectrum involved in the fitting.

[0083] In one embodiment, the method includes: calculating the evaluation data of the second physicochemical property of the crude oil sample to be tested according to the following formula (6):

[0084]

[0085] In formula (6), is the evaluation data of the second physical and chemical properties of the crude oil sample to be tested, q i is the standard data of the second physicochemical property corresponding to the library spectrum involved in the fitting, b i is the normalized fitting coefficient.

[0086] Further, the method comprises:

[0087] Calculating the fitting degree of the fitting according to the following formula (7), and determining the credibility of the evaluation data according to the fitting degree;

[0088]

[0089] In formula (7), s is the fitting degree, x j is the jth characteristic index-physical and chemical property parameter of the vector to be fitted, is the jth characteristic index-physical and chemical property parameter of the fitting vector, and u is the number of points of the characteristic index-physical and chemical property parameter.

[0090] 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, the credibility is 1, indicating that the predicted data is credible; when the degree of fit is less than the preset threshold, it indicates that the predicted 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, pre-process the spectrum measured each time according to the above method and convert it into a spectral characteristic index, calculate the average spectral index of the three spectra, and then calculate the difference spectrum between each spectral index and the average spectral index, calculate the pseudo-fit (SR) value between the two difference spectra according to the following formula (8), take the largest SR value, and calculate the preset threshold according to the following formula (9),

[0091]

[0092] '"

[0093] In formula (8), 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 data points of the spectral characteristic index;

[0094] s v =0.75×sr max Formula (9):

[0095] In formula (9), s v is the preset threshold, sr max is the maximum pseudo-fit value.

[0096] Specifically, the preset threshold may be 10.05.

[0097] The disclosed method can be used to evaluate various crude oil samples and has wide applicability. The disclosure is further illustrated by examples below, but the disclosure is not limited thereto.

[0098] 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.

[0099] 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.

[0100] Example 1

[0101] (I) Construction of a database of spectral indexes and physicochemical properties of known crude oil samples

[0102] 210 kinds of crude oil samples were collected from major crude oil producing areas around the world. The crude oil varieties basically covered the major crude oil producing areas in the world, forming a known crude oil sample library. The standard data of crude oil physicochemical properties were determined using standard methods and the near-infrared spectra of crude oil samples were measured. The wax, gum and asphaltene content were selected as the first physicochemical property, and the density, acid value, residual carbon value, sulfur content and nitrogen content were selected as the second physicochemical property.

[0103] After first-order differentiation and vector normalization of the near-infrared spectrum of each known crude oil sample, the 3972 cm -1 ~4234cm -1 、4238cm -1 ~4470cm -1 4474cm -1 ~4720cm -1 、4724cm -1 ~5569cm -1 5573cm -1 ~6070cm -1 、6074cm -1 ~6903cm -1 、6907cm -1 ~7447cm -1 、7451cm -1 ~8084cm -1 8087cm -1 ~8577cm -1 、8581cm -1 ~10000cm-1 The absorbance of a total of 10 characteristic bands constitutes the near-infrared spectrum matrix X of the known crude oil sample library, and the number of wavenumber sampling points of the characteristic bands is 1564.

[0104] The integrated area of ​​the near infrared spectrum of each known crude oil sample in the above 10 characteristic bands is calculated to obtain the first spectral index.

[0105] Combining the first spectral index and the standard data of the first physicochemical property of each crude oil sample to obtain a spectral index-physicochemical property vector of each known crude oil sample;

[0106] The spectral index-physical and chemical property vector is normalized according to the following formula (1);

[0107]

[0108] Among them, x normalized is the normalized spectral index-physical and chemical property vector, x is the original spectral index-physical and chemical property vector, is the average spectral index-physical and chemical property vector and is calculated according to the following formula (2), m is the number of data points for collecting spectral index-physical and chemical property parameters, k is the sampling point number, k = 1, 2, ..., m,

[0109]

[0110] The normalized spectral index-physical and chemical property vectors of multiple known crude oil samples are merged to obtain a spectral index-physical and chemical property database X*, which contains only 13 variables.

[0111] (ii) collecting the spectrum of the crude oil sample to be tested and the detection data of the first physicochemical property under the same detection conditions, obtaining the second spectral index according to the same method as in step (i), and combining the second spectral index of the crude oil sample to be tested and the detection data of the first physicochemical property to obtain an initial vector.

[0112] (III) Using the spectral index-physical and chemical property database X*, the initial vector is fitted according to the following formula (3):

[0113]

[0114] Where p is the initial vector, v i is the spectral index-physical and chemical property vector involved in the fitting, t is the number of spectral index-physical and chemical property vectors involved in the fitting, a i is the fitting coefficient corresponding to the i-th spectral index-physical and chemical property vector and satisfies the objective function shown in the following formula (4):

[0115]

[0116] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0117]

[0118] Among them, b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0119] (iv) Calculate the evaluation data of the second physicochemical property of the crude oil sample to be tested according to the following formula (6):

[0120]

[0121] in, is the evaluation data of the second physicochemical property of the crude oil sample to be tested, qi is the standard data of the second physicochemical property corresponding to the library spectrum involved in the fitting, and b i is the normalized fitting coefficient.

[0122] The goodness of fit is calculated according to the following formula (7):

[0123]

[0124] Among them, s is the fitting degree, x j is the jth characteristic index-physical and chemical property parameter of the vector to be fitted, is the jth characteristic index-physical and chemical property parameter of the fitting vector, and u is the number of points of the characteristic index-physical and chemical property parameter. The calculated fitting degree s is 19.94, which is higher than the preset threshold of 10.05.

[0125] The evaluation results are shown in Table 1. Wherein, deviation = evaluation value - measured value.

[0126] Table 1

[0127] project Measured value Rating deviation <![CDATA[Density, g·cm -3 > 0.8557 0.8569 0.0012 Acid value, mg KOH / g 0.04 0.07 0.03 Carbon residue value, weight % 4.34 4.42 0.08 Sulfur content, weight % 2.04 2.07 0.03 Nitrogen content, weight % 0.09 0.10 0.01

[0128] Comparative Example 1

[0129] The same crude oil sample to be tested is evaluated using the traditional method. The specific steps are as follows:

[0130] (I) Construction of a near-infrared spectral database of known crude oil samples

[0131] 210 kinds of crude oil samples from major producing areas around the world were collected, and the crude oil varieties basically covered the world's major crude oil producing areas. A known crude oil sample library was formed, and standard data on the physical and chemical properties of crude oil were determined using standard methods, and the near-infrared spectra of crude oil samples were measured.

[0132] After first-order differentiation and vector normalization of the near-infrared spectrum of each known crude oil sample, the absorbance of the same 10 characteristic bands as in Example 1 is taken to form a near-infrared spectrum matrix X of the known crude oil sample library, and the number of wavenumber sampling points of the characteristic bands is 1564.

[0133] (ii) collecting the spectrum of the crude oil sample to be tested under the same testing conditions.

[0134] (iii) According to the method of step (iii) of Example 1, the spectrum of the sample to be tested is fitted using the near-infrared spectrum matrix X, and the evaluation data of the physicochemical properties of the crude oil sample to be tested is calculated according to the method of step (iv) of Example 1.

[0135] The evaluation results are shown in Table 2.

[0136] Table 2

[0137] project Measured value Predicted value deviation <![CDATA[Density, g·cm -3 > 0.8557 0.8588 0.0031 Acid value, mg KOH / g 0.04 0.06 0.02 Carbon residue value, mass % 4.34 4.54 0.20 Sulfur content, mass% 2.04 1.98 -0.06 Nitrogen content, mass% 0.09 0.10 0.01

[0138] From the comparison between the embodiment and the comparative example, 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.

[0139] 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.

[0140] 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.

[0141] 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: Obtain library spectra of a plurality of known crude oil samples, and obtain standard data of a first physicochemical property and a second physicochemical property of each known crude oil sample; wherein the first physicochemical property and the second physicochemical property respectively include at least one physicochemical property of crude oil; Dividing each of the library spectra into a plurality of characteristic bands, and obtaining a plurality of first spectrum indices according to the integrated area of ​​the library spectrum in each of the characteristic bands; Merging the first spectral index and the standard data of the first physicochemical property of a plurality of the known crude oil samples to obtain a spectral index-physicochemical property database of the known crude oil samples; Collecting a sample spectrum of the crude oil sample to be tested, and obtaining detection data of a first physicochemical property of the crude oil sample to be tested; Dividing the spectrum of the sample to be tested into a plurality of characteristic bands, and obtaining a plurality of second spectrum indices according to the integrated area of ​​the spectrum of the sample to be tested in each characteristic band; Combining the detection data of the second spectral index and the first physicochemical property of the crude oil sample to be tested to obtain an initial vector; Using the spectral index-physical and chemical property database to fit the initial vector to obtain a fitting coefficient; The evaluation data of the second physicochemical property of the crude oil sample to be tested is obtained according to the fitting coefficient and the standard data of the second physicochemical property of the known crude oil sample.

2. The method according to claim 1, wherein: The number of the characteristic bands is 10 to 20.

3. The method according to claim 1 or 2, wherein: The characteristic band is 3972cm -1 ~4234cm -1 、4238cm -1 ~4470cm -1 4474cm -1 ~4720cm -1 、4724cm -1 ~5569cm -1 、5573cm -1 ~6070cm -1 、6074cm -1 ~6903cm -1 、6907cm -1 ~7447cm -1 、7451cm -1 ~8084cm -1 8087cm -1 ~8577cm -1 and 8581cm -1 ~10000cm -1 .

4. The method according to claim 1, wherein: The first physicochemical property and the second physicochemical property are each independently selected from one or more of true boiling point distillation cut yield, density, acid value, residual carbon value, sulfur content, nitrogen content, wax content and colloid asphaltene content, and the first physicochemical property is different from the second physicochemical property.

5. The method according to claim 1, wherein: The method includes: Combining the first spectral index and the standard data of the first physicochemical property of each known crude oil sample to obtain a spectral index-physicochemical property vector of each known crude oil sample; The spectral index-physical and chemical property vector is normalized according to the following formula (1), and the normalized spectral index-physical and chemical property vectors of a plurality of known crude oil samples are merged to obtain the spectral index-physical and chemical property database; In formula (1), x normalized is the normalized spectral index-physical and chemical property vector, x is the original spectral index-physical and chemical property vector, is the average spectral index-physical and chemical property vector and is calculated according to the following formula (2), m is the number of data points for collecting spectral index-physical and chemical property parameters, k is the sampling point number, k = 1, 2, ..., m, 6. 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; The evaluation data of the second physicochemical property of the crude oil sample to be tested is obtained according to the normalized fitting coefficient and the standard data of the second physicochemical property of the known crude oil sample.

7. The method according to claim 6, wherein: The method includes: The fitting is performed according to the following formula (3): In formula (3), p is the initial vector, v i is the spectral index-physical and chemical property vector involved in the fitting, t is the number of spectral index-physical and chemical property vectors involved in the fitting, a i is the fitting coefficient corresponding to the i-th spectral index-physical and chemical property vector 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 non-zero fitting coefficients.

8. The method according to claim 7, wherein: The method includes: The evaluation data of the second physicochemical property of the crude oil sample to be tested is calculated according to the following formula (6): In formula (6), is the evaluation data of the second physical and chemical properties of the crude oil sample to be tested, q i is the standard data of the second physicochemical property corresponding to the library spectrum involved in the fitting, b i is the normalized fitting coefficient.

9. The method according to claim 1, wherein: The library spectrum and the sample spectrum to be tested are detected by Fourier transform near infrared spectrometer respectively.

10. The method according to claim 9, wherein: The detection conditions independently include: a resolution of 2 to 16 cm -1 , wave number range is 4000cm -1 ~10000cm -1 , scanning times 16 to 128 times.

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