Crude oil evaluation methods

Abstract

This disclosure relates to a crude oil evaluation method, which includes: acquiring a spectral index-physicochemical property database of multiple known crude oil samples; collecting the spectrum of a crude oil sample to be tested and obtaining detection data of the first physicochemical property of the crude oil sample to be tested to obtain an initial vector; fitting the initial vector using the spectral index-physicochemical property database to obtain fitting coefficients; and obtaining evaluation data of the second physicochemical property of the crude oil sample to be tested based on the fitting coefficients and standard data of the second physicochemical property of the known crude oil samples. This method is beneficial for improving the accuracy of crude oil evaluation.

Description

Technical Field

[0001] This disclosure relates to a crude oil evaluation method. Background Technology

[0002] Crude oil evaluation plays a vital role in crude oil extraction, trading, and processing. Large domestic and international petrochemical companies are developing rapid crude oil evaluation technologies based on various modern instrumental analytical methods, including chromatography-mass spectrometry, nuclear magnetic resonance, mid-infrared spectroscopy, and near-infrared spectroscopy. Among these, near-infrared spectroscopy is highly favored due to its ease of measurement, speed, low cost, and on-site application, making it the preferred technology. It is increasingly being used in petrochemical, agricultural, and pharmaceutical fields, playing a particularly active and irreplaceable role in process analysis. However, since crude oil evaluation involves over a hundred analytical items, traditional multivariate calibration methods, such as partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN), are not feasible for building calibration models for each analytical item individually. Therefore, it is necessary to research and develop new spectral calculation methods to solve the problem of simultaneously predicting over a hundred properties.

[0003] CN102374975A ​​proposes a library spectra fitting method. This method is based on a near-infrared spectral library of oil products and spectral fitting technology. Its basic principle is that samples with similar spectra also have similar properties. The method fits the spectrum of the unknown sample to the library using one or more spectra, and then calculates the properties of the sample based on the properties of the oil products whose spectra were fitted. However, this method still suffers from problems such as low accuracy in predicting data. Summary of the Invention

[0004] The purpose of this 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] To achieve the above objectives, this disclosure provides a crude oil evaluation method, which includes the following steps:

[0006] The library spectra of multiple known crude oil samples are obtained, and standard data of the first and second physicochemical properties of each known crude oil sample are obtained; wherein the first and second physicochemical properties each include at least one crude oil physicochemical property.

[0007] Each of the library spectra is divided into multiple characteristic bands, and multiple first spectral indices are obtained based on the integral area of ​​the library spectrum in each of the characteristic bands.

[0008] The standard data of the first spectral index and the first physicochemical property of multiple known crude oil samples are merged to obtain the spectral index-physicochemical property database of the known crude oil samples;

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

[0010] The spectrum of the sample to be tested is divided into multiple characteristic bands, and multiple second spectral indices are obtained based on the integral area of ​​the spectrum of the sample to be tested in each characteristic band.

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

[0012] The initial vector is fitted using the spectral index-physicochemical property database to obtain fitting coefficients;

[0013] Based on the fitting coefficient and the standard data of the second physicochemical properties of the known crude oil sample, the evaluation data of the second physicochemical properties of the crude oil sample to be tested are obtained.

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

[0015] Optionally, the characteristic band is 3972 cm⁻¹. -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 the following: true boiling point distillation cut yield, density, acid value, residual carbon value, sulfur content, nitrogen content, wax content, and resinous asphaltenes content, and the first physicochemical property is different from the second physicochemical property.

[0017] Optionally, the method includes:

[0018] The standard data of the first spectral index and the first physicochemical property of each known crude oil sample are combined to obtain the spectral index-physicochemical property vector of each known crude oil sample.

[0019] The spectral index-physicochemical property vector is normalized according to the following formula (1), and the normalized spectral index-physicochemical property vectors of multiple known crude oil samples are merged to obtain the spectral index-physicochemical property database.

[0020]

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

[0022]

[0023] Optionally, the method includes:

[0024] The non-zero fitting coefficients in the fitting coefficients are determined by the non-negative constrained least squares method, and the non-zero fitting coefficients are normalized to obtain the normalized fitting coefficients.

[0025] Based on the normalized fitting coefficients and the standard data of the second physicochemical properties of the known crude oil samples, the evaluation data of the second physicochemical properties of the crude oil sample to be tested are obtained.

[0026] Optionally, the method includes:

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

[0028]

[0029] In equation (3), p is the initial vector, v i Let t be the number of spectral index-physicochemical property vectors involved in the fitting, and a be the number of spectral index-physicochemical property vectors involved in the fitting. iLet the fitting coefficients be the spectral index-physicochemical property vectors corresponding to the i-th spectral index and satisfy the objective function shown in equation (4):

[0030]

[0031] Extract the non-zero fitting coefficients from all the obtained fitting coefficients and normalize them according to the following formula (5):

[0032]

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

[0034] Optionally, the method includes:

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

[0036]

[0037] In equation (6), q represents the evaluation data for the second physicochemical property of the crude oil sample to be tested. i b is the standard data for the second physicochemical properties corresponding to the library spectra used in the fitting. i These are the normalized fitting coefficients.

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

[0039] Optionally, the detection conditions each independently include: a resolution of 2–16 cm. -1 The wavenumber range is 4000 cm⁻¹ -1 ~10000cm -1 The number of scans ranges from 16 to 128.

[0040] Through the above technical solution, this disclosure adopts a library fitting algorithm to fit the spectral index-physicochemical property database of known crude oil samples to the crude oil sample to be tested, thereby obtaining the evaluation data of the crude oil sample to be tested, which is beneficial to improving the accuracy of crude oil evaluation.

[0041] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a flowchart illustrating a crude oil evaluation method provided in one embodiment of this disclosure. Detailed Implementation

[0044] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

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

[0046] S101. Obtain the library spectra of multiple known crude oil samples, and obtain standard data for the first and second physicochemical properties of each known crude oil sample; wherein the first and second physicochemical properties each include at least one crude oil physicochemical property;

[0047] S102. Divide each of the library spectra into multiple characteristic bands, and obtain multiple first spectral indices based on the integral area of ​​the library spectra in each of the characteristic bands;

[0048] S103. Merge the standard data of the first spectral index and the first physicochemical property of multiple known crude oil samples to obtain the spectral index-physicochemical property database of the known crude oil samples;

[0049] S104. Collect the spectrum of the crude oil sample to be tested and obtain the detection data of the first physicochemical property of the crude oil sample to be tested;

[0050] S105. Divide the spectrum of the sample to be tested into multiple characteristic bands, and obtain multiple second spectral indices based on the integral area of ​​the spectrum of the sample to be tested in each characteristic band.

[0051] S106. Combine 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;

[0052] S107. The initial vector is fitted using the spectral index-physicochemical property database to obtain the fitting coefficients;

[0053] S108. Based on the fitting coefficient and the standard data of the second physicochemical properties of the known crude oil sample, the evaluation data of the second physicochemical properties of the crude oil sample to be tested are obtained.

[0054] This disclosure first processes the original near-infrared spectral database of known crude oils to establish a spectral index-physicochemical property database. Then, it combines this database fitting algorithm with the initial vector of the crude oil sample to be tested to obtain the evaluation data of the sample. This significantly reduces the amount of data processing and computational complexity, which is beneficial to improving the accuracy of crude oil evaluation. The evaluation results obtained by this method are simple and easy to obtain, do not require modeling of the database samples, and the database is simple and convenient to maintain, showing good application prospects.

[0055] In step S101, the known crude oil sample refers to a crude oil sample with known composition and / or properties. It can be a crude oil sample from an established crude oil database on a near-infrared spectrometer or an actual crude oil sample collected from major global producing regions. Preferably, the number of known crude oil samples can be 100 to 200 to further improve the accuracy of the evaluation results.

[0056] In this 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 and second physicochemical properties can be selected as needed. For example, the first physicochemical property may include 1 to 3 crude oil physicochemical properties, and the second physicochemical property may include crude oil physicochemical properties excluding the first physicochemical property. Selecting an appropriate number of types of the first and second physicochemical properties helps reduce the amount of calculation and further improves 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 easily predictable physicochemical properties and / or secondary physicochemical properties, while the second physicochemical property may include more difficult-to-predict physicochemical properties and / or key physicochemical properties. Specifically, the first physicochemical property and the second physicochemical property are each independently selected from one or more of the following: true boiling point distillation cut yield, density, acid value, carbon residue, sulfur content, nitrogen content, wax content, and asphaltenes content, and the first physicochemical property is different from the second physicochemical property.

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

[0060] Furthermore, each physicochemical property was determined using standard methods. Specifically, density can be tested according to the standard method SH / T 0604-2000 Determination of Density of Crude Oil and Petroleum Products (U-shaped Vibrating Tube Method); acid value can be tested according to the standard method GB / T 7304-2014 Determination of Acid Value of Petroleum Products - Potentiometric Titration Method; sulfur content can be tested according to the standard method GB / T17040-2008 Determination of Sulfur Content of Petroleum and Petroleum Products by Energy Dispersive X-ray Distillation; carbon residue can be tested according to the standard method GB / T 17144 Determination of Carbon Residue of Petroleum Products; nitrogen content can be tested according to the standard method GB / T 17674 Determination of Nitrogen Content in Crude Oil; wax content can be tested according to the standard method SY / T 0537 Determination of Wax Content in Crude Oil; gum and asphaltenes content can be tested according to the standard method SY / T 7550 Determination of Wax, Gum, and Asphaltenes Content in Crude Oil; and true boiling point distillation data can be tested according to the standard method GB / T17280 Standard Test Method for Distillation of Crude Oil.

[0061] In this disclosure, various near-infrared spectroscopy detection devices commonly used in the art can be employed to obtain the library spectrum of a known crude oil sample and the spectrum of the crude oil sample to be tested. In one specific embodiment, the library spectrum and the spectrum of the sample to be tested are detected using a Fourier transform near-infrared spectrometer. The conditions for detecting the samples using a Fourier transform near-infrared spectrometer are well known to those skilled in the art. Preferably, the detection of the known crude oil sample and the detection of the crude oil sample to be tested are performed under the same detection conditions to improve the accuracy of the evaluation results. Specifically, the detection can be a transmission detection method, using a cuvette injection method with a transmission path of 0.5 mm; the detection conditions may include a resolution of 2–16 cm⁻¹. -1 The wavenumber range is 4000 cm⁻¹ -1 ~10000cm -1 The number of scans ranges from 16 to 128.

[0062] In this disclosure, the library spectrum of the known crude oil sample and the test sample spectrum of the crude oil sample to be tested can each undergo spectral preprocessing before subsequent steps. Preferably, the spectral preprocessing performed on the library spectrum and the spectral preprocessing performed on the test sample spectrum are the same spectral preprocessing procedures. For example, the spectral preprocessing can include, but is not limited to, differential processing, standardization processing, normalization processing, and wavelet transform processing, all of which can be performed using methods commonly found in the art.

[0063] In step S102, the multiple characteristic bands may be equally divided or not. The entire spectrum can be divided, or spectral regions with significant noise influence can be removed before division. In one embodiment, the number of characteristic bands is 10 to 20, meaning the spectrum is divided into 10 to 20 characteristic bands, corresponding to 10 to 20 first spectral indices, thus significantly reducing the amount of data. The sampling point interval of the characteristic bands can be 2 to 16 wavenumbers.

[0064] Furthermore, the characteristic band can be 3972 cm⁻¹. -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 aforementioned characteristic bands helps improve the accuracy of the evaluation results. The integral area of ​​the spectral lines in each of the aforementioned characteristic bands is the first spectral index, and the integral area can be calculated using methods commonly found 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. 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 standard data of the first spectral index and the first physicochemical property of each known crude oil sample are combined to obtain the spectral index-physicochemical property vector of each known crude oil sample.

[0067] The spectral index-physicochemical property vector is normalized according to the following formula (1), and the normalized spectral index-physicochemical property vectors of multiple known crude oil samples are merged to obtain the spectral index-physicochemical property database.

[0068]

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

[0070]

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

[0072] In step S107, the fitting coefficients are preferably normalized non-zero fitting coefficients, for example, the fitting coefficients are determined according to the non-negative constraint least squares method.

[0073] In one embodiment, the method includes: determining the non-zero fitting coefficients in the fitting coefficients using the non-negative constrained least squares method, and normalizing the non-zero fitting coefficients to obtain normalized fitting coefficients.

[0074] Based on the normalized fitting coefficients and the standard data of the second physicochemical properties of the known crude oil samples, the evaluation data of the second physicochemical properties of the crude oil sample to be tested are obtained.

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

[0076]

[0077] In equation (3), p is the initial vector, v i Let t be the number of spectral index-physicochemical property vectors involved in the fitting, and a be the number of spectral index-physicochemical property vectors involved in the fitting. i Let the fitting coefficients be the spectral index-physicochemical property vectors corresponding to the i-th spectral index and satisfy the objective function shown in equation (4):

[0078]

[0079] Extract the non-zero fitting coefficients from all the obtained fitting coefficients and normalize them according to the following formula (5):

[0080]

[0081] In equation (5), b i denoted as 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 properties of the crude oil sample to be tested are calculated based on the obtained fitting coefficients and the standard data of the second physicochemical properties corresponding to the library spectra involved in the fitting.

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

[0084]

[0085] In equation (6), q represents the evaluation data for the second physicochemical property of the crude oil sample to be tested. i b is the standard data for the second physicochemical properties corresponding to the library spectra used in the fitting. i These are the normalized fitting coefficients.

[0086] Furthermore, the method includes:

[0087] The goodness of fit of the fit is calculated according to the following formula (7), and the reliability of the evaluation data is determined according to the goodness of fit;

[0088]

[0089] In equation (7), s is the goodness of fit, and x j Let j be the feature index-physical property parameter of the vector to be fitted. Let be the j-th feature index-physical property parameter of the fitted vector, and u be the number of points of the feature index-physical property parameter.

[0090] The confidence level is used to indicate the accuracy of the evaluation data. For example, when the fit is greater than or equal to a preset threshold, the confidence level is 1, indicating that the predicted data is reliable; when the fit is less than the preset threshold, the predicted data is questionable. The preset threshold can be determined by the repeatability of spectral measurements. The specific method is as follows: select a sample and measure the near-infrared spectrum three times. Preprocess the spectrum measured each time according to the above method and convert it into a spectral feature index. Calculate the average spectral index of the three spectra. Then calculate the difference spectrum between each spectral index and the average spectral index. Calculate the pseudofit (sr) value between the pairwise 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 equation (8), sr is the pseudo-fit degree, and Δx j and △x j is the value of the j-th data point in the difference spectrum between the repeating spectrum and the average spectrum, and m is the number of data points for the spectral characteristic index;

[0094] s v =0.75×sr max Equation (9),

[0095] In equation (9), s v As a preset threshold, sr max This represents the maximum value of the pseudofit.

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

[0097] This disclosed method can be used to evaluate various crude oil samples and has broad applicability. Examples are given below to further illustrate this disclosure, but it is not limited thereto.

[0098] In this example, the instrument used to acquire the near-infrared spectrum of crude oil was a Fourier transform near-infrared spectrometer, model Antaris II, manufactured by Thermo Fisher Scientific. The spectral acquisition conditions were: resolution of 8 cm⁻¹. -1 Wavenumber range 4000–10000 cm⁻¹ -1 The cumulative number of scans was 64, using transmission measurement method.

[0099] Density was tested according to standard method SH / T 0604-2000, Determination of Density of Crude Oil and Petroleum Products (U-shaped Vibrating Tube Method); Acid value was tested according to standard method GB / T 7304-2014, Determination of Acid Value of Petroleum Products - Potentiometric Titration Method; Sulfur content was tested according to standard method GB / T17040-2008, Energy Dispersive X-ray Determination of Sulfur Content in Petroleum and Petroleum Products; Carbon Residue was tested according to standard method GB / T17144, Determination of Carbon Residue in Petroleum Products; Nitrogen content was tested according to standard method GB / T 17674, Determination of Nitrogen Content in Crude Oil; Wax content was tested according to standard method SY / T 0537, Determination of Wax Content in Crude Oil; Gum and Asphaltenes content was tested according to standard method SY / T 7550, Determination of Wax, Gum, and Asphaltenes Content in Crude Oil; True Boiling Point Distillation Data was tested according to standard method GB / T 17280, Standard Test Method for Distillation of Crude Oil.

[0100] Example 1

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

[0102] 210 crude oil samples from major producing regions around the world were collected, covering most of the world's major crude oil producing regions, forming a known crude oil sample library. Standard methods were used to determine the standard data of the physicochemical properties of crude oil and to measure the near-infrared spectra of crude oil samples. The contents of wax, gum, and asphaltenes were selected as the first physicochemical properties, and the density, acid value, carbon residue, sulfur content, and nitrogen content were selected as the second physicochemical properties.

[0103] After performing first-order differentiation and vector normalization on the near-infrared spectrum of each known crude oil sample, the value at 3972 cm⁻¹ was taken. -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 spectral matrix X of the known crude oil sample library, and the number of wavenumber sampling points for the characteristic bands is 1564.

[0104] The first spectral index is obtained by calculating the integral area of ​​the near-infrared spectrum of each known crude oil sample in the above 10 characteristic bands.

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

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

[0107]

[0108] Where, x normalized Let be the normalized spectral index-physicochemical property vector, and x be the original spectral index-physicochemical property vector. The average spectral index-physicochemical property vector is calculated according to the following formula (2), where m is the number of data points for collecting the spectral index-physicochemical property parameters, k is the sampling point number, and k = 1, 2, ..., m.

[0109]

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

[0111] (ii) Under the same detection conditions, collect the test sample spectrum and the detection data of the first physicochemical property of the crude oil sample to be tested. According to the same method in step (i), obtain the second spectral index. Combine the second spectral index and the detection data of the first physicochemical property of the crude oil sample to be tested to obtain the initial vector.

[0112] (iii) Fit the initial vector using the spectral index-physicochemical property database X* according to the following formula (3):

[0113]

[0114] Where p is the initial vector, v i Let t be the number of spectral index-physicochemical property vectors involved in the fitting, and a be the number of spectral index-physicochemical property vectors involved in the fitting. i Let the fitting coefficients be the spectral index-physicochemical property vectors corresponding to the i-th spectral index and satisfy the objective function shown in equation (4):

[0115]

[0116] Extract the non-zero fitting coefficients from all the obtained fitting coefficients and normalize them according to the following formula (5):

[0117]

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

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

[0120]

[0121] in, Here, qi represents the evaluation data for the second physicochemical properties of the crude oil sample to be tested, and b represents the standard data for the second physicochemical properties corresponding to the library spectra used in the fitting. i These are the normalized fitting coefficients.

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

[0123]

[0124] Where s is the goodness of fit, x j Let j be the feature index-physical property parameter of the vector to be fitted. Let be the j-th feature index-physical property parameter of the fitted vector, and u be the number of points in the feature index-physical property parameter. The calculated goodness of fit 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 Evaluation value 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, % by weight 2.04 2.07 0.03 Nitrogen content, weight % 0.09 0.10 0.01

[0128] Comparative Example 1

[0129] The traditional method for evaluating the same crude oil sample involves the following steps:

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

[0131] We collected 210 crude oil samples from major producing regions around the world, covering most of the world's major crude oil producing regions, forming a known crude oil sample library. We used standard methods to determine the standard data of the physical and chemical properties of crude oil and measured the near-infrared spectra of crude oil samples.

[0132] After performing first-order differentiation and vector normalization on the near-infrared spectrum of each known crude oil sample, the absorbance of the same 10 characteristic bands as in Example 1 was used to construct the near-infrared spectral matrix X of the known crude oil sample library, with 1564 wavenumber sampling points for the characteristic bands.

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

[0134] (III) Following the method in step (III) of Example 1, the near-infrared spectral matrix X is used to fit the spectrum of the sample to be tested, and the evaluation data of the physicochemical properties of the crude oil sample to be tested are calculated according to the method in 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] As can be seen from the comparison of the embodiments and comparative examples, the method of this disclosure can significantly improve the evaluation accuracy of the physical property data of the crude oil sample to be tested.

[0139] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this 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, this disclosure will not describe the various possible combinations separately.

[0141] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A crude oil evaluation method, characterized in that, The method includes the following steps: The library spectra of multiple known crude oil samples are obtained, and standard data of the first and second physicochemical properties of each known crude oil sample are obtained; wherein the first and second physicochemical properties each include at least one crude oil physicochemical property. Each of the library spectra is divided into multiple characteristic bands, and multiple first spectral indices are obtained based on the integral area of ​​the library spectrum in each of the characteristic bands. The standard data of the first spectral index and the first physicochemical property of multiple known crude oil samples are merged to obtain the spectral index-physicochemical property database of the known crude oil samples; Collect the spectrum of the crude oil sample to be tested, and obtain the detection data of the first physicochemical property of the crude oil sample to be tested; The spectrum of the sample to be tested is divided into multiple characteristic bands, and multiple second spectral indices are obtained based on the integral area of ​​the spectrum of the sample to be tested in each characteristic band. The detection data of the second spectral index and the first physicochemical property of the crude oil sample to be tested are combined to obtain an initial vector; The initial vector is fitted using the spectral index-physicochemical property database to obtain fitting coefficients; Based on the fitting coefficient and the standard data of the second physicochemical properties of the known crude oil sample, the evaluation data of the second physicochemical properties of the crude oil sample to be tested are obtained.

2. The method according to claim 1, wherein, The number of 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 the following: true boiling point distillation cut yield, density, acid value, residual carbon value, sulfur content, nitrogen content, wax content, and resinous asphaltenes content, and the first physicochemical property is different from the second physicochemical property.

5. The method according to claim 1, wherein, The method includes: The standard data of the first spectral index and the first physicochemical property of each known crude oil sample are combined to obtain the spectral index-physicochemical property vector of each known crude oil sample. The spectral index-physicochemical property vector is normalized according to the following formula (1), and the normalized spectral index-physicochemical property vectors of multiple known crude oil samples are merged to obtain the spectral index-physicochemical property database. In equation (1), x normalized Let be the normalized spectral index-physicochemical property vector, and x be the original spectral index-physicochemical property vector. The average spectral index-physicochemical property vector is calculated according to the following formula (2), where m is the number of data points for collecting the spectral index-physicochemical property parameters, k is the sampling point number, and k = 1, 2, ..., m.

6. The method according to claim 1, wherein, The method includes: The non-zero fitting coefficients in the fitting coefficients are determined by the non-negative constrained least squares method, and the non-zero fitting coefficients are normalized to obtain normalized fitting coefficients. Based on the normalized fitting coefficients and the standard data of the second physicochemical properties of the known crude oil samples, the evaluation data of the second 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 equation (3): In equation (3), p is the initial vector, v i Let t be the number of spectral index-physicochemical property vectors involved in the fitting, and a be the number of spectral index-physicochemical property vectors involved in the fitting. i Let the fitting coefficients be the spectral index-physicochemical property vectors corresponding to the i-th spectral index and satisfy the objective function shown in equation (4): Extract the non-zero fitting coefficients from all the obtained fitting coefficients and normalize them according to the following formula (5): In equation (5), b i denoted as 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 are calculated according to the following formula (6): In equation (6), q represents the evaluation data for the second physicochemical property of the crude oil sample to be tested. i b is the standard data for the second physicochemical properties corresponding to the library spectra used in the fitting. i These are the normalized fitting coefficients.

9. The method according to claim 1, wherein, The spectrum of the library and the spectrum of the sample to be tested were detected using a Fourier transform near-infrared spectrometer.

10. The method according to claim 9, wherein, The detection conditions each independently include: a resolution of 2–16 cm. -1 The wavenumber range is 4000 cm⁻¹ -1 ~10000cm -1 The number of scans ranges from 16 to 128.

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