Crude oil property prediction method

Abstract

The invention relates to a crude oil property prediction method, which comprises the following steps of: converting an original near infrared spectrum database of known crude oil into a spectrum characteristic index database, and fitting a crude oil sample to be measured by combining a database fitting algorithm to obtain prediction data of the crude oil sample to be measured, so that the data processing amount and the calculation complexity are obviously reduced; and the accuracy of the prediction result and the prediction speed are effectively improved.

Description

Technical Field

[0001] The present disclosure relates to a method for predicting crude oil properties. Background Art

[0002] Crude oil evaluation plays a very important role in crude oil extraction, crude oil trade and crude oil processing. Rapid detection of crude oil properties is of great significance for optimizing feed, blending and improving efficiency. Existing research on crude oil quick evaluation technology is based on the development of 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 analysis technology has the advantages of rapid and non-destructive analysis, and is used to determine the properties of crude oil. It is a wide and in-depth test project in the petrochemical field. CN102374975A ​​proposes a library spectrum fitting method (Library SpectraFitting Method), which is based on the near-infrared spectrum library of crude oil and spectrum fitting technology, and calculates the properties of the sample to be tested according to the properties of the oil products involved in the fitting spectrum. However, this method has the problems of long time consumption and inaccurate prediction data. Summary of the invention

[0003] The purpose of the present disclosure is to provide a crude oil property prediction method to improve the accuracy and prediction speed of the crude oil property prediction.

[0004] In order to achieve the above object, the present disclosure provides a method for predicting crude oil properties, which comprises the following steps:

[0005] Obtaining library spectra of a plurality of known crude oil samples, and obtaining standard data of the physicochemical properties of each of the known crude oil samples;

[0006] Dividing each of the library spectra into n characteristic spectrum regions, obtaining n first spectrum characteristic indexes according to the integrated area of ​​the library spectrum in each characteristic spectrum region, and combining the first spectrum characteristic indexes of a plurality of the known crude oil samples to obtain a database of spectrum characteristic indexes of the known crude oil samples;

[0007] Collecting a spectrum of the crude oil sample to be tested, dividing the spectrum of the sample to be tested into n characteristic spectrum regions, obtaining n second spectrum characteristic indexes according to the integral area of ​​the spectrum of the sample to be tested in each characteristic spectrum region, and merging the second spectrum characteristic indexes of the crude oil sample to be tested to obtain a vector to be fitted;

[0008] Using the spectral characteristic index database to fit the vector to be fitted to obtain a fitting coefficient;

[0009] According to the fitting coefficient and the standard data of the physicochemical properties of the known crude oil sample, the predicted data of the physicochemical properties of the crude oil sample to be tested are obtained.

[0010] Optionally, n is an integer selected from 10 to 20.

[0011] Optionally, the characteristic spectrum range is 3972-4234 cm -1 4238~4470cm -1 4474~4720cm -1 、4724~5569cm -1 、5573~6070cm -1 、6074~6903cm -1 、6907~7447cm -1 、7451~8084cm -1 、8087~8577cm -1 and 8581~10000cm -1 .

[0012] Optionally, the physicochemical properties are one or more selected from true boiling point distillation cut-off yield, density, acid value, residual carbon value, sulfur content, nitrogen content, wax content and colloid asphaltene content.

[0013] Optionally, the method comprises:

[0014] Combining the first spectral characteristic index of each of the known crude oil samples to obtain a spectral characteristic index vector of each of the known crude oil samples;

[0015] The spectral characteristic index vector is normalized according to the following formula (1), and the normalized spectral characteristic index vectors of the plurality of known crude oil samples are merged to obtain the spectral characteristic index database;

[0016]

[0017] In formula (1), x normalized is the normalized spectral feature index vector, x is the original spectral feature index vector, is the average spectral feature index vector and is calculated according to the following formula (2), m is the number of data points for collecting spectral feature index, k is the sampling point number, k = 1, 2, ..., m,

[0018]

[0019] Optionally, the method comprises:

[0020] 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;

[0021] According to the normalized fitting coefficient and the standard data of the physicochemical properties of the known crude oil sample, the predicted data of the physicochemical properties of the crude oil sample to be tested are obtained.

[0022] Optionally, the method comprises:

[0023] The fitting is performed according to the following formula (3):

[0024]

[0025] In formula (3), p is the vector to be fitted, v i is the spectral feature index vector involved in the fitting, t is the number of spectral feature index vectors involved in the fitting, a i is the fitting coefficient corresponding to the i-th spectral feature index vector and satisfies the objective function shown in the following formula (4):

[0026]

[0027] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0028]

[0029] In formula (5), b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0030] Optionally, the method comprises:

[0031] The fitting degree of the fitting is calculated according to the following formula (6):

[0032]

[0033] In formula (6), s is the fitting degree, x j is the jth spectral characteristic index of the vector to be fitted, is the jth spectral characteristic index of the fitting vector, and m is the number of data points of the spectral characteristic index;

[0034] The spectral repeatability method is used to calculate the preset threshold according to the following formulas (7) and (8):

[0035]

[0036] In formula (7), 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;

[0037] s v =0.75×srmax Formula (8),

[0038] In formula (8), s v is the preset threshold, sr max is the maximum value of pseudo-fit;

[0039] The credibility of the prediction data is determined according to the degree of fit and the preset threshold.

[0040] Optionally, the method comprises:

[0041] The predicted data of the physicochemical properties of the crude oil sample to be tested are calculated according to the following formula (9):

[0042]

[0043] In formula (9), is the predicted 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.

[0044] Optionally, the library spectrum and the sample spectrum to be tested are detected by Fourier transform near infrared spectrometer respectively, and 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.

[0045] Through the above technical scheme, the present invention converts the original near-infrared spectrum database of known crude oil into a spectral characteristic index database, and fits the crude oil sample to be tested with the library fitting algorithm to obtain the predicted data of the crude oil sample to be tested. The data processing amount and calculation complexity are significantly reduced, and the accuracy and prediction speed of the predicted data results are effectively improved.

[0046] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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:

[0048] Figure 1 It is a schematic flow chart of a crude oil property prediction method provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0049] 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.

[0050] The present disclosure provides a method for predicting crude oil properties. Figure 1 As shown, the method includes the following steps S101 to S105:

[0051] S101, obtaining library spectra of a plurality of known crude oil samples, and obtaining standard data of the physicochemical properties of each of the known crude oil samples;

[0052] S102, dividing each of the library spectra into n characteristic spectrum regions, obtaining n first spectrum characteristic indexes according to the integrated area of ​​the library spectrum in each characteristic spectrum region, and merging the first spectrum characteristic indexes of a plurality of the known crude oil samples to obtain a database of spectrum characteristic indexes of the known crude oil samples;

[0053] S103, collecting a spectrum of the crude oil sample to be tested, dividing the spectrum of the sample to be tested into n characteristic spectrum regions, obtaining n second spectrum characteristic indexes according to the integral area of ​​the spectrum of the sample to be tested in each characteristic spectrum region, and merging the second spectrum characteristic indexes of the crude oil sample to be tested to obtain a vector to be fitted;

[0054] S104, fitting the vector to be fitted using the spectral characteristic index database to obtain a fitting coefficient;

[0055] S105, obtaining predicted data of the physicochemical properties of the crude oil sample to be tested according to the fitting coefficient and the standard data of the physicochemical properties of the known crude oil sample.

[0056] The present invention converts the original near-infrared spectrum database of known crude oil into a spectral characteristic index database, and fits the crude oil sample to be tested with the library fitting algorithm to obtain the predicted data of the crude oil sample to be tested, which significantly reduces the data processing amount and calculation complexity, and effectively improves the accuracy and prediction speed of the predicted data results. The prediction 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.

[0057] 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 number of the known crude oil samples can preferably be 100 to 200 to further improve the accuracy of the prediction results.

[0058] In the present disclosure, the physicochemical properties refer to the physical and / or chemical properties of the crude oil sample, and the types and quantities thereof can be selected as needed. Specifically, the physicochemical properties can be one or more selected from true boiling point distillation cut-off yield, density, acid value, residual carbon value, sulfur content, nitrogen content, wax content and colloid asphaltene content.

[0059] 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.

[0060] 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 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 prediction 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.

[0061] 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 are not limited to differential processing, standardization processing, normalization processing and wavelet transform processing, etc., which can be performed using common methods in the art.

[0062] In step S102, the n characteristic spectral regions may be equally divided or not equally divided, and the whole spectrum may be selected for segmentation, or the spectral regions with large noise influence may be eliminated before segmentation. In one embodiment, n is an integer selected from 10 to 20, and the spectrum of the sample to be tested is divided into 10 to 20 characteristic spectral regions, and 10 to 20 first spectral characteristic indices can be obtained accordingly, thereby significantly reducing the amount of data. The sampling point interval of the characteristic spectral region can be 2 to 16 wave numbers.

[0063] Furthermore, the characteristic spectrum range may be 3972-4234 cm -1 4238~4470cm -1 4474~4720cm -1 、4724~5569cm -1 、5573~6070cm -1 、6074~6903cm -1 、6907~7447cm -1 、7451~8084cm -1 、8087~8577cm -1 and 8581~10000cm -1 Selecting the above characteristic spectral region is conducive to improving the accuracy and prediction speed of the prediction result. The spectral line integral area of ​​the library spectrum in each of the above characteristic spectral regions is the first spectral index, and the integral area can be calculated using a common method in the art.

[0064] In one embodiment, the first spectral characteristic index of each of the known crude oil samples is combined to obtain a spectral characteristic index vector of each of the known crude oil samples;

[0065] The spectral characteristic index vector is normalized according to the following formula (1), and the normalized spectral characteristic index vectors of the plurality of known crude oil samples are merged to obtain the spectral characteristic index database;

[0066]

[0067] In formula (1), x normalizedis the normalized spectral feature index vector, x is the original spectral feature index vector, is the average spectral feature index vector and is calculated according to the following formula (2), m is the number of data points for collecting spectral feature index, k is the sampling point number, k = 1, 2, ..., m,

[0068]

[0069] The specific operation of step S103 may be performed with reference to step S101 and step S102. The second spectrum characteristic index of the crude oil sample to be tested is obtained in the same n characteristic spectrum regions by using the same calculation method.

[0070] In step S104, 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.

[0071] 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;

[0072] According to the normalized fitting coefficient and the standard data of the physicochemical properties of the known crude oil sample, the predicted data of the physicochemical properties of the crude oil sample to be tested are obtained.

[0073] For example, the fitting is performed according to the following formula (3):

[0074]

[0075] In formula (3), p is the vector to be fitted, v i is the spectral feature index vector involved in the fitting, t is the number of spectral feature index vectors involved in the fitting, a i is the fitting coefficient corresponding to the i-th spectral feature index vector and satisfies the objective function shown in the following formula (4):

[0076]

[0077] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0078]

[0079] In formula (5), b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0080] Further, the method comprises:

[0081] The fitting degree of the fitting is calculated according to the following formula (6), and the credibility of the predicted data is determined according to the fitting degree;

[0082]

[0083] In formula (6), s is the fitting degree, x j is the jth spectral characteristic index of the vector to be fitted, is the jth spectral characteristic index of the fitting vector, and m is the number of data points of the spectral characteristic index.

[0084] The credibility is used to indicate the accuracy of the predicted 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 (7), take the largest SR value, and calculate the preset threshold according to the following formula (8),

[0085]

[0086] In formula (7), 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;

[0087] s v =0.75×sr max Formula (8),

[0088] In formula (8), s v is the preset threshold, sr max is the maximum pseudo-fit value.

[0089] Specifically, the preset threshold may be 10.05.

[0090] In step S105, the predicted data of the physicochemical properties of the crude oil sample to be tested are calculated based on the obtained fitting coefficients and the standard data of the physicochemical properties corresponding to the library spectrum involved in the fitting.

[0091] In one embodiment, the method comprises: calculating the predicted data of the physicochemical properties of the crude oil sample to be tested according to the following formula (9):

[0092]

[0093] In formula (9), is the predicted 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.

[0094] The disclosed method can be used for prediction of various crude oil samples and has wide applicability. The disclosure is further illustrated by examples below, but the 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 database of spectral characteristic indices of known crude oil samples

[0099] 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. Standard data of physical and chemical properties such as crude oil density were determined using standard methods, and the near-infrared spectra of crude oil samples were measured.

[0100] After first-order differentiation and vector normalization of the near-infrared spectrum of each known crude oil sample, the spectral distribution of 3972-4234 cm -1 4238~4470cm -1 4474~4720cm-1 、4724~5569cm -1 、5573~6070cm -1 、6074~6903cm -1 、6907~7447cm -1 、7451~8084cm -1 、8087~8577cm -1 and 8581~10000cm -1 The absorbance of a total of 10 characteristic spectral regions constitutes the near-infrared spectrum matrix X of the known crude oil sample library, and the number of wavenumber sampling points in the characteristic spectral regions is 1564.

[0101] The integral area of ​​the near infrared spectrum of each known crude oil sample in the above 10 characteristic spectrum regions is calculated to obtain the first spectrum characteristic index.

[0102] Combining the first spectral characteristic index of each crude oil sample to obtain a spectral characteristic index vector of each known crude oil sample;

[0103] The spectral characteristic index vector is normalized according to the following formula (1);

[0104]

[0105] Among them, x normalized is the normalized spectral feature index vector, x is the original spectral feature index vector, is the average spectral feature index vector and is calculated according to the following formula (2), m is the number of data points for collecting spectral feature index, k is the sampling point number, k = 1, 2, ..., m,

[0106]

[0107] The normalized spectral characteristic index vectors of multiple known crude oil samples are merged to obtain a spectral characteristic index database X*, and the spectral index-physical and chemical property database X* contains only 10 variables.

[0108] (ii) collecting a spectrum of the crude oil sample to be tested under the same detection conditions, obtaining a second spectrum characteristic index according to the same method as in step (i), and combining the second spectrum characteristic indexes of the crude oil sample to be tested to obtain a vector to be fitted.

[0109] (III) Use the spectral feature index database X* to fit the vector to be fitted according to the following formula (3):

[0110]

[0111] Where p is the vector to be fitted, v iis the spectral feature index vector involved in the fitting, t is the number of spectral feature index vectors involved in the fitting, ai is the fitting coefficient corresponding to the i-th spectral feature index vector and satisfies the objective function shown in the following formula (4):

[0112]

[0113] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (5):

[0114]

[0115] Among them, b i is the normalized fitting coefficient, and g is the number of non-zero fitting coefficients.

[0116] The goodness of fit is calculated according to the following formula (6):

[0117]

[0118] Among them, s is the fitting degree, x j is the jth spectral characteristic index of the vector to be fitted, is the jth spectral characteristic index of the fitting vector, and m is the number of data points of the spectral characteristic index.

[0119] The preset threshold of the degree of fit is determined by the repeatability of spectral measurement. A sample is selected to repeatedly measure the near-infrared spectrum 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, and the largest SR value is taken. The preset threshold is calculated according to the following formula (8):

[0120]

[0121] In formula (7), 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;

[0122] s v =0.75×sr max Formula (8),

[0123] In formula (8), s v is the preset threshold, sr max is the maximum pseudo-fit value.

[0124] The calculated fit s is 13.82, which is higher than the preset threshold of 10.05, indicating that the obtained prediction data is credible.

[0125] (iv) Calculate the predicted data of the physical and chemical properties of the crude oil sample to be tested according to the following formula (9):

[0126]

[0127] in, is the predicted 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. The results are shown in Table 1.

[0128] Among them, deviation = predicted value - measured value.

[0129] The above calculation process was performed on the same PC, and the total time required to obtain the predicted data was 0.22s.

[0130] Table 1

[0131] project Measured value Predicted value deviation <![CDATA[Density, g·cm -3 > 0.8185 0.8153 -0.0032 Acid value, mg KOH / g 0.02 0.03 0.01 Carbon residue value, weight % 1.42 1.50 0.08 Sulfur content, weight % 0.94 0.84 -0.10 Nitrogen content, weight % 0.04 0.05 0.01 Wax content, mass % 3.40 3.24 -0.16 Gum content, mass% 2.80 3.53 0.73 Asphaltene content, mass % 0.20 0.26 0.06

[0132] Comparative Example 1

[0133] The traditional method is used to predict the same crude oil sample to be tested. The specific steps are as follows:

[0134] (I) Construction of a near-infrared database of known crude oil samples

[0135] 210 kinds of crude oil samples from major producing areas in the world were collected, and the crude oil varieties basically covered the major crude oil producing areas in the world. A known crude oil sample library was formed, and standard data of crude oil physical and chemical properties were determined using standard methods and the near-infrared spectra of crude oil samples were measured.

[0136] 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 spectral regions constitutes the near-infrared spectrum matrix X of the known crude oil sample library, and the number of wavenumber sampling points in the characteristic spectral regions is 1564.

[0137] (ii) collecting the spectrum of the crude oil sample to be tested under the same testing conditions.

[0138] (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 predicted 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.

[0139] The prediction results of this comparative example are shown in Table 2, and the total time required for the prediction data is 0.36s.

[0140] Table 2

[0141] project Measured value Predicted value deviation <![CDATA[Density, g·cm -3 > 0.8185 0.8160 -0.0025 Acid value, mg KOH / g 0.02 0.05 0.03 Carbon residue value, mass % 1.42 1.95 0.53 Sulfur content, mass% 0.94 0.97 0.03 Nitrogen content, mass% 0.04 0.08 0.04 Wax content, mass % 3.40 4.62 1.22 Gum content, mass% 2.80 5.49 2.69 Asphaltene content, mass % 0.20 0.38 0.18

[0142] 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 prediction accuracy and prediction speed of the physical property data of the crude oil sample to be tested.

[0143] 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.

[0144] 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.

[0145] 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 method for predicting crude oil properties, characterized in that: The method comprises the following steps: Obtaining library spectra of a plurality of known crude oil samples, and obtaining standard data of the physicochemical properties of each of the known crude oil samples; Dividing each of the library spectra into n characteristic spectrum regions, obtaining n first spectrum characteristic indexes according to the integrated area of ​​the library spectrum in each characteristic spectrum region, and combining the first spectrum characteristic indexes of a plurality of the known crude oil samples to obtain a database of spectrum characteristic indexes of the known crude oil samples; Collecting a spectrum of the crude oil sample to be tested, dividing the spectrum of the sample to be tested into n characteristic spectrum regions, obtaining n second spectrum characteristic indexes according to the integral area of ​​the spectrum of the sample to be tested in each characteristic spectrum region, and merging the second spectrum characteristic indexes of the crude oil sample to be tested to obtain a vector to be fitted; Using the spectral characteristic index database to fit the vector to be fitted to obtain a fitting coefficient; According to the fitting coefficient and the standard data of the physicochemical properties of the known crude oil sample, the predicted data of the physicochemical properties of the crude oil sample to be tested are obtained.

2. The method according to claim 1, wherein: n is an integer selected from 10-20.

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

4. The method according to claim 1, wherein: The physicochemical properties are one or more selected from true boiling point distillation cut yield, density, acid value, residual carbon value, sulfur content, nitrogen content, wax content and colloid asphaltene content.

5. The method according to claim 1, wherein: The method includes: Combining the first spectral characteristic index of each of the known crude oil samples to obtain a spectral characteristic index vector of each of the known crude oil samples; The spectral characteristic index vector is normalized according to the following formula (1), and the normalized spectral characteristic index vectors of the plurality of known crude oil samples are merged to obtain the spectral characteristic index database; In formula (1), x normalized is the normalized spectral feature index vector, x is the original spectral feature index vector, is the average spectral feature index vector and is calculated according to the following formula (2), m is the number of data points for collecting spectral feature index, 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; According to the normalized fitting coefficient and the standard data of the physicochemical properties of the known crude oil sample, the predicted data of the physicochemical properties of the crude oil sample to be tested are obtained.

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 vector to be fitted, v i is the spectral feature index vector involved in the fitting, t is the number of spectral feature index vectors involved in the fitting, a i is the fitting coefficient corresponding to the i-th spectral feature index 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 fitting degree of the fitting is calculated according to the following formula (6): In formula (6), s is the fitting degree, x j is the jth spectral characteristic index of the vector to be fitted, is the jth spectral characteristic index of the fitting vector, and m is the number of data points of the spectral characteristic index; The spectral repeatability method is used to calculate the preset threshold according to the following formulas (7) and (8): '" In formula (7), 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; s v =0.75×sr max Formula (8), In formula (8), s v is the preset threshold, sr max is the maximum value of pseudo-fit; The credibility of the prediction data is determined according to the degree of fit and the preset threshold.

9. The method according to claim 7, wherein: The method includes: The predicted data of the physicochemical properties of the crude oil sample to be tested are calculated according to the following formula (9): In formula (9), is the predicted 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.

10. 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, and 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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