Crude oil virtual cutting method, device and equipment and storage medium

Abstract

The invention discloses a crude oil virtual cutting method, device and equipment and a storage medium, and belongs to the field of crude oil processing.The method comprises the steps that known physical property parameters for crude oil are obtained; obtaining at least one fraction temperature section when the crude oil is subjected to crude oil cutting; on the basis that the known physical property parameters serve as input quantities, predicted physical property parameters, corresponding to the at least one fraction temperature section, of the crude oil are predicted through a crude oil cutting prediction algorithm. In the application, based on the known physical property parameters of the crude oil as the input quantity, the corresponding predicted physical property parameters of the crude oil in each fraction temperature section are predicted through the crude oil cutting prediction algorithm, the obtained predicted physical property parameters are more fit with the known physical property parameters, and the accuracy is higher.

Description

Technical Field

[0001] The present application relates to the field of crude oil processing, and in particular to a crude oil virtual cutting method, device, equipment and storage medium. Background Art

[0002] As the raw material of petroleum is continuously mined, the light and low-sulfur crude oil on the market is decreasing, while the heavy and inferior crude oil is increasing. Crude oil cutting refers to the process of cutting crude oil according to the set cutting temperature to obtain distillation products in each temperature range. Crude oil evaluation refers to the use of specific separation and analysis methods in the laboratory to comprehensively determine and analyze the properties of crude oil and the properties and yield of the finished products (or semi-finished products) that may be obtained. Physical property parameters are parameters used to describe the properties of crude oil. True boiling point distillation is one of the methods of crude oil evaluation.

[0003] However, the physical properties of crude oil will change accordingly depending on the location, time and depth of extraction. In related technologies, a crude oil evaluation database is often established based on the physical property parameters obtained from crude oil evaluation. The crude oil with the highest similarity to the crude oil to be predicted is calculated using a similarity algorithm. The known physical property parameters of the crude oil with the highest similarity are fitted with the known physical property parameters of the crude oil to be predicted to predict the predicted physical property parameters of the crude oil to be predicted.

[0004] However, the method used in the relevant calculation may result in a situation where the known physical property parameters of the crude oil with the highest similarity to the unknown crude oil in the crude oil evaluation database are greatly different from the actual physical property parameters of the crude oil to be predicted. This method cannot accurately obtain the physical property parameters corresponding to the temperature segment of the crude oil fraction after cutting. Summary of the invention

[0005] The embodiment of the present application provides a crude oil virtual cutting method, device, equipment and storage medium. The technical solution is as follows:

[0006] On the one hand, an embodiment of the present application provides a crude oil virtual cutting method, the method is executed by a server, and the method includes:

[0007] Obtaining known physical property parameters for the crude oil;

[0008] obtaining at least one fraction temperature segment when crude oil is cut;

[0009] Based on the known physical property parameters as input, the predicted physical property parameters corresponding to the crude oil in the at least one fraction temperature segment are predicted by a crude oil cutting prediction algorithm.

[0010] On the other hand, an embodiment of the present application provides a crude oil virtual cutting device, the device comprising:

[0011] A first acquisition module, used for acquiring known physical property parameters of the crude oil to be predicted;

[0012] A second acquisition module is used to acquire at least one fraction temperature segment when crude oil cutting is performed on the crude oil to be predicted;

[0013] The prediction module is used to predict the predicted physical property parameters corresponding to the crude oil to be predicted in the at least one fraction temperature segment through a crude oil cutting prediction algorithm based on the known physical property parameters as input.

[0014] On the other hand, an embodiment of the present application provides a computer device, which includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the above-mentioned crude oil virtual cutting method.

[0015] On the other hand, an embodiment of the present application provides a non-temporary computer-readable storage medium having a computer program stored thereon, and the computer program implements the above-mentioned crude oil virtual cutting method when executed by a processor.

[0016] On the other hand, an embodiment of the present application provides a computer program product, which includes a computer program, and the computer program is stored in a computer-readable storage medium; the computer program is read and executed from the computer-readable storage medium by a processor of a computer device, so that the computer device executes the above-mentioned crude oil virtual cutting method.

[0017] The technical solution provided in the embodiments of the present application can bring the following beneficial effects:

[0018] The above technical solution uses the known physical property parameters of crude oil as input, and predicts the predicted physical property parameters of each fraction temperature section of crude oil when crude oil is cut through the crude oil cutting prediction algorithm. When making predictions, the known physical property parameters of the crude oil to be predicted are used for prediction, and the actual physical property parameters of the crude oil to be predicted are more closely matched with the predicted physical property parameters, and the prediction effect is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic diagram of a computer system provided by an exemplary embodiment of the present application is shown;

[0021] Figure 2A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0022] Figure 3 A schematic diagram showing the division of a fraction temperature section and a narrow fraction temperature section of the present application is shown;

[0023] Figure 4 A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0024] Figure 5 A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0025] Figure 6 A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0026] Figure 7 A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0027] Figure 8 A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0028] Fig. 9 A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0029] Fig.10 A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0030] Fig.11 A flowchart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0031] Fig.12 An overall flow chart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown;

[0032] Fig.13 A structural block diagram of a crude oil virtual cutting device provided by an exemplary embodiment of the present application is shown;

[0033] Fig.14 A structural block diagram of a server provided by an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0035] First, the relevant terms involved in this application are introduced:

[0036] Crude oil cutting: refers to the process of cutting crude oil according to the set distillation temperature section during distillation to obtain the distillation products of each temperature section. Among them, the distillation temperature section is also called separation temperature, cutting temperature, distillation range, distillation section, etc. The two endpoints of the distillation temperature section are called the initial distillation point and the final distillation point, respectively. The obtained distillation products are also called oil products. Depending on the cutting temperature, the obtained distillation products usually include gasoline, naphtha, kerosene, diesel, fuel oil, lubricating oil, asphalt, etc.

[0037] Fractionation and distillation: Both fractionation and distillation utilize the different boiling points of components in a liquid mixture. By controlling the temperature and heating, a part of the substance is first vaporized, then condensed and liquefied and then collected. Distillation only performs vaporization and condensation once, and the separated substance is purer; fractionation requires multiple continuous vaporization and condensation, and the separated substance is still a mixture.

[0038] Real boiling point distillation: It is an experimental method used to examine the composition of petroleum fractions. Fractions refer to substances with different boiling point ranges separated according to different boiling points during the petroleum distillation process. Real boiling point distillation is to use a set of intermittent atmospheric and vacuum distillation equipment with high separation accuracy in the laboratory to cut crude oil into many narrow fractions in order from low to high boiling points, and analyze the properties of each narrow fraction. Due to the high fractionation accuracy, the distillation temperature is close to the actual boiling point of the distillate, which can approximately reflect the true situation of the boiling points of each component of crude oil, so it is called real boiling point distillation. During real boiling point distillation, generally one fraction is taken every 3% (mass fraction), or one narrow fraction is cut every 10°C, and the yield and total yield of each fraction are calculated, and the physical properties of each narrow fraction are determined separately.

[0039] Physical properties of crude oil: parameters used to describe the properties of crude oil. Physical properties of crude oil usually include the properties of crude oil, fraction composition, properties of each fraction, potential yield of finished products (or semi-finished products) that may be obtained by cutting crude oil, and their main performance. The properties of crude oil / fractions include the physical and chemical properties of crude oil / fractions. Physical properties include color, density, viscosity, freezing point, etc.; chemical properties include chemical composition, component composition, and impurity content. Fraction composition refers to the weight or volume percentage of the fraction distilled within a certain temperature range (distillation range).

[0040] Figure 1 A schematic diagram of a computer system provided by an exemplary embodiment of the present application is shown. The computer system may include: a server 110 and a client 120 .

[0041] The server 110 may be any one of a plurality of servers, a virtual cloud storage or a cloud computing center. The server 110 is used to obtain known physical property parameters of crude oil. The server 110 is also used to receive at least one distillation temperature segment of crude oil when it is cut from a client. The server 110 is also used to predict the predicted physical property parameters corresponding to the crude oil in at least one distillation temperature segment according to the known physical property parameters of the crude oil by using a crude oil cutting prediction algorithm. The server 110 is also used to send the predicted physical property parameters corresponding to the crude oil to be predicted in at least one distillation temperature segment to the client 120.

[0042] The client 120 is used to obtain at least one fraction temperature segment set by the user when the crude oil is cut, and send it to the server 110. The client 120 is also used to display the predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment received from the server 110 to the user.

[0043] The server 110 and the client 120 are connected via a wired or wireless network.

[0044] It should be noted that, in some embodiments, the server 110 and the client 120 are the same device.

[0045] Figure 2 A schematic diagram of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown. The method is executed by a server, which may be Figure 1 The method comprises:

[0046] Step 210: Obtain known physical property parameters of the crude oil to be predicted;

[0047] Optionally, the known physical property parameters of the crude oil to be predicted are the known physical property parameters of all crude oils, or, the known physical property parameters of the crude oil to be predicted are all known physical property parameters of the crude oil to be predicted, or, the known physical property parameters of the crude oil to be predicted are the known physical property parameters of the crude oil to be predicted that correspond to the predicted physical property parameters.

[0048] Exemplarily, the crude oil to be predicted is crude oil a, and the server obtains the known physical property parameters of all crude oils. For example, the server stores the known physical property parameters of crude oil a, crude oil b, and crude oil c. When predicting the physical property parameters of crude oil a, the known physical property parameters of crude oil a, crude oil b, and crude oil c stored in the server will be obtained.

[0049] Exemplarily, the crude oil to be predicted is crude oil a, and the server obtains the known physical property parameters of all crude oils. For example, the server stores the known physical property parameters of crude oil a, crude oil b, and crude oil c. When predicting the physical property parameters of crude oil a, the known physical property parameters of crude oil a stored in the server will be obtained.

[0050] For example, the crude oil to be predicted is crude oil a, and the server obtains known physical property parameters of all crude oils. For example, the server stores some physical property parameters of crude oil a, crude oil b, and crude oil c, such as density, kinematic viscosity, aniline point, and wax content of crude oil a, density and flash point of crude oil b, and kinematic viscosity and pour point of crude oil C. When predicting the physical property parameters of the density and aniline point of crude oil a, the known physical property parameters corresponding to the density and aniline point of crude oil a stored in the server will be obtained.

[0051] Step 220: obtaining at least one fraction temperature segment when crude oil is cut for the crude oil to be predicted;

[0052] Optionally, at least one fraction temperature segment when crude oil is cut is obtained from a server, or at least one fraction temperature segment when crude oil is cut is obtained from a client.

[0053] Exemplarily, at least one distillation temperature segment during crude oil cutting is at least one preset distillation temperature segment during crude oil cutting saved in the server, or at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during the last crude oil cutting saved in the server.

[0054] Exemplarily, at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during crude oil cutting input by the user in the client, or, at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during crude oil cutting selected by the user in the client, or, at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during crude oil cutting preset and saved in the client, or, at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during the last crude oil cutting saved in the client.

[0055] Exemplarily, obtaining at least one fraction temperature segment when crude oil is cut for the crude oil to be predicted, such as Figure 3 As shown, a total of 3 distillation temperature sections are obtained, among which the value of the distillation temperature section 321 is 260°C to 400°C; the value of the distillation temperature section 322 is 150°C to 280°C; and the value of the distillation temperature section 323 is 0°C to 136°C.

[0056] Step 230: Based on the known physical property parameters as input, the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted are predicted by using a crude oil cutting prediction algorithm.

[0057] Optionally, based on all known physical property parameters of the crude oil to be predicted as input, the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted are predicted using a crude oil cutting prediction algorithm.

[0058] Optionally, based on known physical property parameters corresponding to the predicted physical property parameters of the crude oil to be predicted as input, the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted are predicted using a crude oil cutting prediction algorithm.

[0059] Optionally, based on known physical property parameters of the crude oil to be predicted and similar crude oils to the crude oil to be predicted as input, the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted are predicted using a crude oil cutting prediction algorithm.

[0060] Exemplarily, when the known physical property parameters of the crude oil to be predicted are used as input, and the predicted physical property parameters corresponding to at least one distillation temperature segment of the crude oil to be predicted are predicted by the crude oil cutting prediction algorithm, if the obtained predicted physical property parameters are not accurate enough, the known physical property parameters of similar crude oils can be used as auxiliary input to assist the prediction, so that the predicted physical property parameters corresponding to at least one distillation temperature segment of the crude oil to be predicted are more accurate. Among them, the method of assisting the prediction includes but is not limited to: using part of the known physical property parameters of similar crude oils and the known physical property parameters of the crude oil to be predicted as input for prediction; using the known physical property parameters of the crude oil to be predicted as input, and predicting the predicted physical property parameters corresponding to at least one distillation temperature segment of the crude oil to be predicted by the crude oil cutting prediction algorithm, and then adjusting the predicted physical property parameters according to the known physical property parameters of similar crude oils.

[0061] In some embodiments, the crude oil cutting prediction algorithm includes at least one of the following:

[0062] Spline interpolation algorithm;

[0063] ·Mass yield ratio algorithm;

[0064] Integration algorithm.

[0065] The integration algorithm includes a first integration algorithm and a second integration algorithm; the first integration algorithm and the second integration algorithm are used to predict different predicted physical property parameters through different prediction methods.

[0066] In summary, the method provided in this embodiment uses the known physical property parameters of the crude oil to be predicted to predict the predicted physical property parameters corresponding to at least one distillation temperature segment of the crude oil to be predicted. Since the known physical property parameters of the crude oil to be predicted are obtained by experimental means and are closer to the actual physical property parameters of the crude oil to be predicted, the predicted physical property parameters obtained by prediction by the prediction algorithm are also closer to the actual physical property parameters of the crude oil to be predicted.

[0067] The following will explain the above three crude oil cutting algorithms. The order of explanation does not represent the advantages and disadvantages of the algorithms.

[0068] 1. Spline interpolation algorithm

[0069] Based on Figure 2 In an optional embodiment, if Figure 4 As shown, the following step 231 replaces step 230.

[0070] Step 231: interpolating the known physical property parameters of at least two narrow fraction temperature segments using a spline interpolation algorithm to obtain predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment.

[0071] The spline interpolation algorithm is a method of approximating the original data by fitting a set of piecewise polynomials to obtain a piecewise polynomial that satisfies all known data. Alternatively, the spline interpolation algorithm is an algorithm that approximates a curve based on known points. The curve approximated by the algorithm satisfies the following two conditions:

[0072] Pass through all known data points;

[0073] The curve is smooth, that is, the second-order derivative is continuous at the connection points.

[0074] In some embodiments, the spline interpolation algorithm is an algorithm that calculates predicted physical property parameters corresponding to a narrow fraction temperature range through spline interpolation based on known physical property parameters of the narrow fraction temperature range.

[0075] In some embodiments, the spline interpolation algorithm is used to predict physical property parameters such as yield and viscosity.

[0076] In some embodiments, the spline interpolation algorithm uses a cubic spline interpolation algorithm.

[0077] Optionally, a narrow cut temperature segment is selected every 5°C; or, a narrow cut temperature segment is selected every 10°C; or, a narrow cut temperature segment is selected every 20°C; or, a narrow cut temperature segment is selected every 50°C;

[0078] For example, a narrow fraction temperature section is taken every 10°C, such as Figure 3 As shown, the narrow fraction temperature section 310 is a narrow fraction temperature section divided at intervals of 10°C.

[0079] Optionally, the known physical property parameters of at least two narrow fraction temperature segments are interpolated using a spline interpolation algorithm, and the curve obtained by the spline interpolation algorithm is adjusted using the known physical property parameters of similar crude oils and / or property curves corresponding to the known physical property parameters of similar crude oils to obtain predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted.

[0080] Exemplarily, part of the known physical property parameters of similar crude oils and the known physical property parameters of at least two narrow distillation temperature segments are interpolated by a spline interpolation algorithm to obtain predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment; or, the known physical property parameters of at least two narrow distillation temperature segments are interpolated by a spline interpolation algorithm to obtain property curves corresponding to the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment, and according to the trend and steepness of the property curves corresponding to the known physical property parameters of similar crude oils, the property curves corresponding to the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment are adjusted to obtain the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment.

[0081] In some embodiments, Figure 5 As shown, the following steps 2311 to 2316 replace step 231.

[0082] Step 2311: define n piecewise cubic polynomials, each of the n piecewise cubic polynomials has 4 unknown coefficients, and the n piecewise cubic polynomials have a total of 4n coefficients;

[0083] Exemplarily, n piecewise cubic polynomials are defined, each of the n piecewise cubic polynomials has 4 unknown coefficients, and the n piecewise cubic polynomials have a total of 4n coefficients, as shown in Formula 1:

[0084]

[0085] In the formula, a1, a2, ..., a n represents n unknown coefficients;

[0086] b1, b2, …, b n represents n unknown coefficients;

[0087] c1, c2, …, c n represents n unknown coefficients;

[0088] d1, d2, …, d n represents n unknown coefficients.

[0089] Optional, x1, x2, …, x n+1 represents the narrow fraction temperature range, f(x) represents the known physical property parameters of the narrow fraction temperature range; or, x1, x2, ..., x n+1 represents the known physical property parameters of the narrow fraction temperature range, and f(x) represents the narrow fraction temperature range. n+1 represents a narrow fraction temperature range, and f(x) represents a known physical property parameter of the narrow fraction temperature range for illustration, but is not limited to this.

[0090] Step 2312: Substitute the known physical property parameters of n+1 narrow cut temperature segments into n piecewise cubic polynomials, and each two known physical property parameters of the narrow cut temperature segments correspond to one piecewise cubic polynomial, to obtain 2n first equations;

[0091] Exemplarily, the known physical property parameters of n+1 narrow cut temperature segments and the narrow cut temperature segment are used as x and y, and substituted into n piecewise cubic polynomials. Every two known physical property parameters of the narrow cut temperature segments correspond to one piecewise cubic polynomial, and 2n first equations are obtained, as shown in Formula 2:

[0092]

[0093] In the formula, x1, x2, …, x n+1 Indicates a narrow fraction temperature range;

[0094] y1, y2, …, y n+1 Shown are the known physical property parameters of the i-th narrow fraction temperature range.

[0095] Step 2313: Obtain n first-order derivatives and n second-order derivatives corresponding to n piecewise cubic polynomials;

[0096] The first-order derivative is shown in Equation 3:

[0097]

[0098] Where i represents the i-th piecewise cubic polynomial.

[0099] The second-order derivative is shown in Equation 4:

[0100]

[0101] Where i represents the i-th piecewise cubic polynomial.

[0102] Since the cubic spline interpolation algorithm requires that the second-order derivatives of n piecewise cubic polynomials at the connection points are continuous, the following steps 2314 and 2315 can be obtained.

[0103] Step 2314: Based on the equality of the first-order derivatives of each piecewise polynomial at the connection point, n-1 first intermediate equations are obtained; the known physical property parameters of the second to n narrow fraction temperature segments are substituted into the n-1 first intermediate equations to obtain n-1 second equations;

[0104] Exemplarily, based on the equality of the first-order derivatives of each piecewise polynomial at the connection point, n-1 first intermediate equations are obtained, as shown in Formula 5:

[0105]

[0106] Exemplarily, the known physical property parameters of the second to n narrow fraction temperature segments or the temperatures corresponding to the known physical property parameters of the second to n narrow fraction temperature segments are substituted into n-1 first intermediate equations to obtain n-1 second equations, as shown in Formula 6:

[0107]

[0108] Step 2315: According to the equality of the second-order derivatives of each piecewise polynomial at the connection point, n-1 second intermediate equations are obtained; the known physical property parameters of the second to n narrow fraction temperature segments are substituted into the n-1 second intermediate equations to obtain n-1 third equations;

[0109] Exemplarily, according to the equality of the second-order derivatives of each piecewise polynomial at the connection point, n-1 second intermediate equations are obtained, as shown in Formula 7:

[0110]

[0111] Exemplarily, the known physical property parameters of the second to n narrow fraction temperature segments or the temperatures corresponding to the known physical property parameters of the second to n narrow fraction temperature segments are substituted into n-1 second intermediate equations to obtain n-1 third equations, as shown in Formula 8:

[0112]

[0113] Step 2316: Substitute the known physical property parameters of the first narrow fraction temperature segment and the known physical property parameters of the n+1th narrow fraction temperature segment into the boundary conditions to obtain two fourth equations, where the boundary conditions are equations for determining the properties of the boundary points of the piecewise cubic polynomial;

[0114] Exemplarily, the boundary conditions are natural boundaries, i.e., the two endpoints x1 and x2 of the piecewise cubic polynomial. n+1 The corresponding second-order derivative is 0. Substitute the known physical property parameters of the first narrow cut temperature segment and the known physical property parameters of the n+1th narrow cut temperature segment into the boundary conditions to obtain two fourth equations, which are equations for determining the properties of the boundary points of the piecewise cubic polynomial; or, substitute the temperature corresponding to the known physical property parameters of the first narrow cut temperature segment and the temperature corresponding to the known physical property parameters of the n+1th narrow cut temperature segment into the boundary conditions to obtain two fourth equations, which are equations for determining the properties of the boundary points of the piecewise cubic polynomial, as shown in Formula 9:

[0115] 6a1x1+2b1=0

[0116] 6a n x n+1 +2bn=0 (9)

[0117] Step 2317: According to 2n first equations, n-1 second equations, n-1 third equations and 2 fourth equations, a total of 4n equations, the values ​​of 4n unknown coefficients are obtained;

[0118] For example, according to the above 4n equations, namely, equation 2, equation 6, equation 8, and equation 9, the values ​​of 4n unknown coefficients are obtained.

[0119] Step 2318: According to the values ​​of the 4n unknown coefficients, n piecewise cubic polynomials with the physical property parameters of the target temperature segment as unknowns are obtained, and the target temperature segment includes at least one fraction temperature segment;

[0120] Exemplarily, according to the values ​​of 4n unknown coefficients, n piecewise cubic polynomials with the physical property parameters of the target temperature section as unknowns are obtained, as shown in Formula 1.

[0121] Among them, x and f(x) are unknowns.

[0122] Step 2319: According to at least one distillation temperature segment and n piecewise cubic polynomials with the physical property parameters of the target temperature segment as unknowns, the predicted physical property parameters of the crude oil to be predicted corresponding to at least one distillation temperature segment are obtained.

[0123] Exemplarily, at least one distillation temperature segment is 0°C to 60°C and 100°C to 150°C, 60°C is substituted as the x value into the corresponding piecewise cubic polynomial in the n piecewise cubic polynomials with the physical property parameters of the target temperature segment as unknowns, and the corresponding f(x) value is obtained. The f(x) is the predicted physical property parameter of the crude oil to be predicted at 0°C to 60 degrees Celsius.

[0124] Exemplarily, at least one distillation temperature segment is 100°C to 150°C, and at least one distillation temperature segment is divided into 5 narrow distillation temperature segments a1 to a5. The temperatures corresponding to a1 to a5 are substituted into the corresponding piecewise cubic polynomials in n piecewise cubic polynomials with the physical property parameters of the target temperature segment as unknowns to obtain the corresponding f(x) values ​​f(a1) to f(a5), obtain the mass yields corresponding to the narrow distillation temperature segments a1 to a5, calculate the mixing ratios of the narrow distillation temperature segments a1 to a5, calculate f(a1)*a1, f(a2)*a2, f(a3)*a3, f(a4)*a4, f(a5)*a5 respectively, and add them up to obtain the predicted physical property parameters of the crude oil to be predicted at 100°C to 150°C.

[0125] In summary, the crude oil virtual cutting method provided in this embodiment uses a spline difference algorithm to predict the predicted physical property parameters corresponding to at least one distillation temperature segment of the crude oil to be predicted. According to the characteristics of the spline interpolation algorithm, when predicting the predicted physical property parameters corresponding to at least one distillation temperature segment of the crude oil to be predicted, the obtained n piecewise cubic polynomials with the physical property parameters of the target temperature segment as unknowns must satisfy the known physical property parameters of the narrow distillation temperature segment, and the known physical property parameters of the narrow distillation temperature segment are often the actual physical property parameters actually measured in the laboratory. The predicted physical property parameters obtained by performing the spline interpolation algorithm based on the actual physical property parameters will be more in line with the actual physical property parameters.

[0126] In some embodiments, step 2314 and step 2315 may be exchanged in order or executed simultaneously; step 2312, step 2313 to step 2315, and step 2316 may be exchanged in order or executed simultaneously.

[0127] 2. Mass yield ratio algorithm

[0128] Based on Figure 2 In an optional embodiment, if Figure 6 As shown, the following step 232 replaces step 230.

[0129] Step 232: Perform linear fitting on the known physical property parameters of at least two narrow fraction temperature segments using a mass yield ratio algorithm to obtain predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment.

[0130] The mass yield ratio algorithm is an algorithm that obtains unknown quantities through linear calculation. The mass yield ratio algorithm obtains unknown quantities by fitting the original data with a straight line. The straight line fitting does not require all known data, that is, it does not need to pass through all known points.

[0131] In some embodiments, the mass yield ratio algorithm is a method of fitting known physical property parameters of at least two narrow fraction temperature segments into a straight line using straight line fitting, and calculating the predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment based on the straight line.

[0132] In some embodiments, the mass yield ratio algorithm is used to predict physical property parameters that meet linear conditions, and the linear conditions include the following points:

[0133] Homogeneity, that is, the function that satisfies the ratio is equal to the ratio of the function, such as f(kx) = kf(x);

[0134] Additivity, that is, the function that satisfies the sum is equal to the sum of functions, such as f(x1+x2)=f(x1)+f(x2).

[0135] In some embodiments, the mass yield ratio algorithm is used to calculate physical parameters such as sulfur content (SUL), acid value (NET), carbon residue (CCR), nickel content (NI), vanadium content (V), and nitrogen content (N2).

[0136] The calculation method of the mass ratio yield algorithm is shown in formula 10:

[0137]

[0138] Wherein, M represents the number of narrow fraction temperature sections belonging to the fraction temperature section;

[0139] y i represents the mixing ratio corresponding to the i-th narrow fraction temperature section;

[0140] P i It represents the mapped physical property parameters belonging to the i-th narrow fraction temperature range in the fraction temperature range.

[0141] Optionally, a mass yield ratio algorithm is used to perform straight-line fitting on the known physical property parameters of at least two narrow fraction temperature segments, and the straight line obtained by the mass yield ratio algorithm is adjusted using the known physical property parameters of similar crude oils and / or property curves corresponding to the known physical property parameters of similar crude oils to obtain predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted.

[0142] Exemplarily, linear fitting is performed on some known physical property parameters of similar crude oils and known physical property parameters of at least two narrow distillation temperature segments through a mass yield ratio algorithm to obtain predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment; or, linear fitting is performed on the known physical property parameters of at least two narrow distillation temperature segments through a mass yield ratio algorithm to obtain a property curve corresponding to the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment, and the property curve corresponding to the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment is adjusted according to the trend and steepness of the property curve corresponding to the known physical property parameters of similar crude oils to obtain the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment.

[0143] In some embodiments, Figure 7 As shown, the following steps 2321 to 2324 replace step 232.

[0144] Step 2321: performing linear fitting on known physical property parameters of at least two narrow fraction temperature segments by using a mass yield ratio algorithm to obtain a physical property parameter straight line corresponding to a target temperature segment, wherein the physical property parameter straight line is used to characterize a linear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment, and the target temperature segment includes at least one fraction temperature segment;

[0145] The mapped physical property parameters of the narrow cut temperature segment refer to the mapped physical property parameters of the narrow cut temperature segment corresponding to the narrow cut temperature segment on the physical property parameter straight line.

[0146] Optionally, the abscissa corresponding to the point on the physical property parameter line is the narrow fraction temperature segment, and the ordinate is the mapped physical property parameter of the narrow fraction temperature segment; or, the abscissa corresponding to the point on the physical property parameter line is the mapped physical property parameter of the narrow fraction temperature segment, and the ordinate is the narrow fraction temperature segment. This embodiment of the application is not limited to this.

[0147] Step 2322: for each of the at least one fraction temperature segment, calculating the mixing ratios respectively corresponding to at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is the proportion of the mass yield of the mapped physical property parameter of a single narrow fraction temperature segment in the fraction temperature segment to the mass yield corresponding to the fraction temperature segment;

[0148] In some embodiments, the value of the narrow cut temperature segment, the value of the mapped physical property parameter of the narrow cut temperature segment, and the mass yield of the mapped physical property parameter of the narrow cut temperature segment can be obtained according to the mapped physical property parameter of the narrow cut temperature segment.

[0149] Exemplarily, at least one of the distillation temperature sections includes a distillation temperature section a, and the corresponding temperature section value is 100°C to 150°C, and the mapping physical property parameter of the narrow distillation temperature section is sulfur content. Among them, a narrow distillation temperature section is taken every 10°C, and there are 5 narrow distillation temperature sections a1 to a5. The mass yields of the mapping physical property parameters of the narrow distillation temperature sections a1 to a5 are 0.22, 0.3, 0.53, 0.9, and 1.0, respectively. The mass yield corresponding to the distillation temperature section a is 2.95, and it can be calculated that the mixing ratios corresponding to the narrow distillation temperature sections a1 to a5 are 7.46%, 10.17%, 17.97%, 30.51%, and 33.90%, respectively.

[0150] Step 2323: for each narrow fraction temperature segment belonging to each fraction temperature segment, calculate the product of the mapped physical property parameter of each narrow fraction temperature segment and the mixing ratio corresponding to each narrow fraction temperature segment to obtain the intermediate value corresponding to each narrow fraction temperature segment;

[0151] For example, as shown in step 2322, the mixing ratios of the narrow distillate temperature segments a1 to a5 corresponding to the distillate temperature segment a are 7.46%, 10.17%, 17.97%, 30.51%, and 33.90%, respectively. The mapping physical property parameters of the narrow distillate temperature segments a1 to a5 corresponding to the distillate temperature segment a are 0.001, 0.003, 0.0055, 0.008, and 0.0091, respectively, and the intermediate values ​​corresponding to each narrow distillate temperature segment are 0.0000746, 0.0003051, 0.00098835, 0.0024408, and 0.0030849, respectively.

[0152] Step 2324: Accumulate the intermediate values ​​corresponding to at least two narrow fraction temperature segments in each fraction temperature segment to obtain the predicted physical property parameters corresponding to each fraction temperature segment.

[0153] Exemplarily, as shown in the above step 2323, the intermediate values ​​corresponding to the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a are 0.0000746, 0.0003051, 0.00098835, 0.0024408, and 0.0030849, respectively. By accumulating the intermediate values ​​corresponding to the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a, the sulfur content corresponding to the distillation temperature segment a can be obtained, that is, the distillation temperature segment a corresponds to a predicted physical property parameter of 0.0155689.

[0154] In summary, the crude oil virtual cutting method provided in this embodiment uses the mass yield ratio algorithm to predict the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted, and the predicted physical property parameters meet the linear condition. The mass yield ratio algorithm predicts the predicted physical property parameters by finding the straight line that best meets the actual physical property parameters through straight line fitting, and has a better prediction effect on the predicted physical property parameters that meet the linear condition.

[0155] 3. Integration algorithm

[0156] Based on Figure 2 In an optional embodiment, if Figure 8 As shown, the following step 233 replaces step 230.

[0157] Step 233: curve fitting is performed on the known physical property parameters of at least two narrow fraction temperature segments by using an integral algorithm to obtain predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment.

[0158] The integration algorithm includes a first integration algorithm and a second integration algorithm; the first integration algorithm and the second integration algorithm are used to predict different predicted physical property parameters through different prediction methods.

[0159] The integral algorithm is an algorithm that obtains unknown quantities through nonlinear calculations. The integral algorithm obtains unknown quantities by fitting the original data into a curve. Curve fitting does not require all known data to be satisfied, that is, it does not need to pass through all known points.

[0160] In some embodiments, the integration algorithm is a method of fitting known physical property parameters of at least two narrow fraction temperature segments into a curve using curve fitting, and calculating the predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment based on the curve.

[0161] In some embodiments, the integral algorithm is a method of using a specific index conversion formula for physical property parameters that do not meet linear conditions to obtain a physical property index of the mapped physical property parameters, and by performing curve fitting on the physical property index of the mapped physical property parameters, the predicted physical property parameters corresponding to the crude oil to be predicted in at least one fraction temperature segment are obtained.

[0162] In some embodiments, the first integration algorithm is used to calculate physical parameters such as density and specific gravity.

[0163] In some embodiments, the second integration algorithm is used to calculate physical property parameters such as kinematic viscosity, freezing point, flash point, Redgerstein vapor pressure, and aniline point.

[0164] The calculation method of the first integral algorithm is shown in formula 11:

[0165]

[0166] Wherein, M represents the number of narrow fraction temperature sections belonging to the fraction temperature section;

[0167] y i represents the mixing ratio corresponding to the i-th narrow fraction temperature section;

[0168] P i It represents the mapped physical property parameters belonging to the i-th narrow fraction temperature range in the fraction temperature range.

[0169] The calculation method of the second integral algorithm is shown in formula 12:

[0170]

[0171] Wherein, M represents the number of narrow fraction temperature sections belonging to the fraction temperature section;

[0172] y i represents the mixing ratio corresponding to the i-th narrow fraction temperature section;

[0173] P i represents the mapping physical property parameters belonging to the i-th narrow fraction temperature segment in the fraction temperature segment;

[0174] Index(Pi ) represents the physical property index of the mapped physical property parameters of the i-th narrow fraction temperature segment in the fraction temperature segment.

[0175] Optionally, curve fitting is performed on the known physical property parameters of at least two narrow fraction temperature segments through an integration algorithm, and the curve obtained by the integration algorithm is adjusted using the known physical property parameters of similar crude oils and / or property curves corresponding to the known physical property parameters of similar crude oils to obtain predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted.

[0176] Exemplarily, curve fitting is performed on some known physical property parameters of similar crude oils and known physical property parameters of at least two narrow distillation temperature segments through an integration algorithm to obtain predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment; or, curve fitting is performed on the known physical property parameters of at least two narrow distillation temperature segments through an integration algorithm to obtain a property curve corresponding to the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment, and the property curve corresponding to the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment is adjusted according to the trend and steepness of the property curve corresponding to the known physical property parameters of similar crude oils to obtain the predicted physical property parameters of the crude oil to be predicted in at least one distillation temperature segment.

[0177] 3.1 First integration algorithm

[0178] In some embodiments, Fig. 9 As shown, the following steps 2331 and 2334 replace step 233.

[0179] Step 2331: performing curve fitting on known physical property parameters of at least two narrow fraction temperature segments by using a first integral algorithm to obtain a physical property parameter curve corresponding to a target temperature segment, wherein the physical property parameter curve is used to characterize the nonlinear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment, and the target temperature segment includes at least one fraction temperature segment;

[0180] The mapped physical property parameters of the narrow cut temperature segment refer to the mapped physical property parameters of the narrow cut temperature segment corresponding to the narrow cut temperature segment on the physical property parameter curve.

[0181] Optionally, the abscissa corresponding to the point on the physical property parameter curve is the narrow fraction temperature segment, and the ordinate is the mapped physical property parameter of the narrow fraction temperature segment; or, the abscissa corresponding to the point on the physical property parameter curve is the mapped physical property parameter of the narrow fraction temperature segment, and the ordinate is the narrow fraction temperature segment. This embodiment of the application is not limited to this.

[0182] Step 2332: for each of the at least one fraction temperature segment, calculating the mixing ratios respectively corresponding to at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is the proportion of the mass yield of the mapped physical property parameter of a single narrow fraction temperature segment in the fraction temperature segment to the mass yield corresponding to the fraction temperature segment;

[0183] In some embodiments, the value of the narrow cut temperature segment, the value of the mapped physical property parameter of the narrow cut temperature segment, and the mass yield of the mapped physical property parameter of the narrow cut temperature segment can be obtained according to the mapped physical property parameter of the narrow cut temperature segment.

[0184] Exemplarily, at least one fraction temperature segment includes fraction temperature segment a, and the corresponding temperature segment value is 100°C to 150°C, and the mapping physical property parameter of the narrow fraction temperature segment is density. Among them, a narrow fraction temperature segment is taken every 10°C, and there are 5 narrow fraction temperature segments a1 to a5. The mass yields of the mapping physical property parameters of the narrow fraction temperature segments a1 to a5 are 0.22, 0.3, 0.53, 0.9, and 1.0, respectively. The mass yield corresponding to the fraction temperature segment a is 2.95, and it can be calculated that the mixing ratios corresponding to the narrow fraction temperature segments a1 to a5 are 7.46%, 10.17%, 17.97%, 30.51%, and 33.90%, respectively.

[0185] Step 2333: for each narrow fraction temperature segment belonging to each fraction temperature segment, calculate the ratio of the mixing ratio corresponding to each narrow fraction temperature segment to the mapped physical property parameter of each narrow fraction temperature segment, and obtain the intermediate value corresponding to each narrow fraction temperature segment;

[0186] For example, as shown in step 2332, the mixing ratios of the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a are 7.46%, 10.17%, 17.97%, 30.51%, and 33.90%, respectively. The mapping physical property parameters of the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a are 0.708, 0.7345, 0.7482, 0.7579, and 0.763, respectively, and the intermediate values ​​corresponding to each narrow distillation temperature segment can be obtained as 0.1054, 0.1385, 0.2402, 0.4026, and 0.4443, respectively.

[0187] Step 2334: Accumulate the intermediate values ​​corresponding to at least two narrow fraction temperature segments in each fraction temperature segment, and take the inverse of the accumulated value to obtain the predicted physical property parameter corresponding to each fraction temperature segment.

[0188] For example, as shown in the above step 2333, the intermediate values ​​corresponding to the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a are 0.1054, 0.1385, 0.2402, 0.4026, and 0.4443, respectively. The intermediate values ​​are accumulated to obtain 1.331. The inverse of the accumulated value can be used to obtain the density corresponding to the distillation temperature segment a, that is, the distillation temperature segment a corresponds to a predicted physical property parameter of 0.7513.

[0189] In summary, the crude oil virtual cutting method provided in this embodiment uses an integral algorithm to predict the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted, and the predicted physical property parameters meet the nonlinear condition. The integral algorithm predicts the predicted physical property parameters by finding the curve that best meets the actual physical property parameters through curve fitting, and has a better prediction effect on the predicted physical property parameters that meet the nonlinear condition.

[0190] 3.2 Second integration algorithm

[0191] In some embodiments, Fig.10 As shown, the following steps 2335 and 2339 replace step 233.

[0192] Step 2335: performing curve fitting on known physical property parameters of at least two narrow fraction temperature segments by using a second integral algorithm to obtain a physical property parameter curve corresponding to a target temperature segment, wherein the physical property parameter curve is used to characterize the nonlinear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment, and the target temperature segment includes at least one fraction temperature segment;

[0193] The mapped physical property parameters of the narrow cut temperature segment refer to the mapped physical property parameters of the narrow cut temperature segment corresponding to the narrow cut temperature segment on the physical property parameter curve.

[0194] Optionally, the abscissa corresponding to the point on the physical property parameter curve is the narrow fraction temperature segment, and the ordinate is the mapped physical property parameter of the narrow fraction temperature segment; or, the abscissa corresponding to the point on the physical property parameter curve is the mapped physical property parameter of the narrow fraction temperature segment, and the ordinate is the narrow fraction temperature segment. This embodiment of the application is not limited to this.

[0195] In some embodiments, a curve fitting is performed on the physical property indices of known physical property parameters of at least two narrow fraction temperature segments through a second integral algorithm to obtain a physical property index curve corresponding to a target temperature segment. The physical property index curve is used to characterize the nonlinear relationship between the narrow fraction temperature segment and the physical property indices of the mapped physical property parameters of the narrow fraction temperature segment. The target temperature segment includes at least one fraction temperature segment.

[0196] Optionally, the abscissa corresponding to the point on the physical property index curve is the narrow fraction temperature segment, and the ordinate is the physical property index of the mapped physical property parameter of the narrow fraction temperature segment; or, the abscissa corresponding to the point on the physical property index curve is the physical property index of the mapped physical property parameter of the narrow fraction temperature segment, and the ordinate is the narrow fraction temperature segment. This embodiment of the application is not limited to this.

[0197] In some embodiments, a curve fitting is performed on known physical property parameters of at least two narrow fraction temperature segments through a second integral algorithm to obtain a physical property parameter curve corresponding to a target temperature segment, and the physical property parameter curve is used to characterize the nonlinear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment. The target temperature segment includes at least one fraction temperature segment, wherein the mapped physical property parameters of the narrow fraction temperature segment are physical property indices of the mapped physical property parameters of the narrow fraction temperature segment.

[0198] Step 2336: obtaining the physical property index of the mapped physical property parameter of each narrow fraction temperature segment, wherein the physical property index of the mapped physical property parameter of the narrow fraction temperature segment is calculated by a physical property index algorithm;

[0199] Exemplarily, the physical property parameters required to calculate the physical property index are shown in the table.

[0200] Table 1 Physical property index and physical property parameters

[0201]

[0202]

[0203] Exemplarily, the formula of the physical property index algorithm corresponding to the freezing point is shown in Formula 13:

[0204]

[0205] Where, Index(P) represents the physical property index of the mapped physical property parameters in the narrow fraction temperature range;

[0206] exp represents the exponential with base e;

[0207] P represents the mapped physical property parameter of the narrow fraction temperature range.

[0208] It should be noted that the formulas of the physical property index algorithms corresponding to different physical property parameters are different, and the embodiments of the present application do not limit this.

[0209] Step 2337: for each of the at least one fraction temperature segment, calculating the mixing ratios respectively corresponding to the at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is the proportion of the mass yield of the mapped physical property parameter of the single narrow fraction temperature segment in the fraction temperature segment to the mass yield corresponding to the fraction temperature segment;

[0210] In some embodiments, the value of the narrow cut temperature segment, the value of the mapped physical property parameter of the narrow cut temperature segment, and the mass yield of the mapped physical property parameter of the narrow cut temperature segment can be obtained according to the mapped physical property parameter of the narrow cut temperature segment.

[0211] Exemplarily, at least one fraction temperature segment includes fraction temperature segment a, and the corresponding temperature segment value is 100°C to 150°C, and the mapping physical property parameter of the narrow fraction temperature segment is the aniline point. Among them, a narrow fraction temperature segment is taken every 10°C, and there are 5 narrow fraction temperature segments a1 to a5. The mass yields of the mapping physical property parameters of the narrow fraction temperature segments a1 to a5 are 0.22, 0.3, 0.53, 0.9, and 1.0, respectively. The mass yield corresponding to the fraction temperature segment a is 2.95, and it can be calculated that the mixing ratios corresponding to the narrow fraction temperature segments a1 to a5 are 7.46%, 10.17%, 17.97%, 30.51%, and 33.90%, respectively.

[0212] Step 2338: for each narrow fraction temperature segment belonging to each fraction temperature segment, calculate the product of the physical property index of the mapped physical property parameter of each narrow fraction temperature segment and the mixing ratio corresponding to each narrow fraction temperature segment, and obtain the intermediate value corresponding to each narrow fraction temperature segment;

[0213] For example, as shown in step 2337, the mixing ratios of the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a are 7.46%, 10.17%, 17.97%, 30.51%, and 33.90%, respectively. The mapping physical property parameters of the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a are 50.2, 52.6, 53.1, 53.8, and 54.2, respectively, and the intermediate values ​​corresponding to each narrow distillation temperature segment are 3.745, 5.350, 9.5420, 16.4144, and 18.3738, respectively.

[0214] Step 2339: Accumulate the intermediate values ​​corresponding to at least two narrow fraction temperature segments in each fraction temperature segment to obtain the predicted physical property parameters corresponding to each fraction temperature segment.

[0215] Exemplarily, as shown in the above step 2338, the intermediate values ​​corresponding to the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a are 3.745, 5.350, 9.5420, 16.4144, and 18.3738, respectively. By accumulating the intermediate values ​​corresponding to the narrow distillation temperature segments a1 to a5 corresponding to the distillation temperature segment a, the aniline point corresponding to the distillation temperature segment a can be obtained, that is, the distillation temperature segment a corresponds to a predicted physical property parameter of 53.4252.

[0216] In summary, the crude oil virtual cutting method provided in this embodiment uses an integral algorithm to predict the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted, and the predicted physical property parameters meet the nonlinear condition. The integral algorithm converts the physical property parameters that cannot be directly fitted by curves into physical property indices first, and then fits them, which can effectively predict the predicted physical property parameters that meet the nonlinear condition but cannot be fitted by curves.

[0217] In some embodiments, the crude oil to be predicted in the above-mentioned crude oil virtual cutting method is a single oil type crude oil; or, the crude oil to be predicted in the above-mentioned crude oil virtual cutting method is a mixed oil type crude oil.

[0218] Based on Figures 2 to 10 In an optional embodiment, if Fig.11 As shown, the following steps 234 and 235 replace step 230.

[0219] Step 234: when the crude oil to be predicted is a crude oil of mixed oil types, for each crude oil of the mixed oil types, based on known physical property parameters as input, predict the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil of each oil type by using a crude oil cutting prediction algorithm to obtain the predicted physical property parameters of the crude oil of each oil type, and the mixed oil types of crude oil include at least two oil types;

[0220] Exemplarily, the crude oil to be predicted is a mixed crude oil, including crude oil a and crude oil b. The known physical property parameters of crude oil a and crude oil b are respectively used as input, and the predicted physical property parameters of crude oil a and crude oil b corresponding to at least one fraction temperature segment are predicted by the crude oil cutting prediction algorithm to obtain the predicted physical property parameters of crude oil a and crude oil b.

[0221] Step 235: Taking the predicted physical property parameters of at least two oil types as input, and using a linear addition algorithm, obtain the predicted physical property parameters corresponding to the crude oil of the mixed oil types.

[0222] In some embodiments, the linear summation algorithm is shown in Equation 14:

[0223]

[0224] In the formula, x i The predicted physical property parameters of crude oil of each oil type are shown;

[0225] y i Indicates the blend ratio of each type of crude oil in the blend.

[0226] Optionally, the predicted physical property parameters of at least two oil types in each fraction temperature section are used as input, and the predicted physical property parameters corresponding to the crude oil of the mixed oil types in each fraction temperature section are obtained through a linear addition algorithm.

[0227] Exemplarily, the crude oil to be predicted is a mixed crude oil, including crude oil a and crude oil b, crude oil a accounts for 30% and crude oil b accounts for 70%; at least one distillation temperature segment of the crude oil to be predicted includes distillation temperature segment c1 and distillation temperature segment c2, distillation temperature segment c1 is 100°C to 150°C, and distillation temperature segment c2 is 150°C to 200°C. The predicted physical property parameter of the crude oil to be predicted is density. The predicted physical property parameter of crude oil a in the distillation temperature section c1 is 0.7068 and the predicted physical property parameter of crude oil b in the distillation temperature section c1 is 0.7461 as input. Through the linear addition algorithm, the predicted physical property parameter of the mixed oil in the distillation temperature section c1 is 0.73431; the predicted physical property parameter of crude oil a in the distillation temperature section c2 is 0.7725 and the predicted physical property parameter of crude oil b in the distillation temperature section c2 is 0.7699 as input. Through the linear addition algorithm, the predicted physical property parameter of the mixed oil in the distillation temperature section c1 is 0.77068.

[0228] In summary, the crude oil virtual cutting method provided in this embodiment is not only applicable to crude oil of a single oil type, but also to crude oil of mixed oil types. When calculating the predicted physical property parameters corresponding to the crude oil of the mixed oil types, a linear addition algorithm is used, which can well predict the predicted physical property parameters of the crude oil of the mixed oil types based on the predicted physical property parameters of the crude oil of each oil type in the mixed oil types.

[0229] Fig.12 The overall flow chart of a crude oil virtual cutting method provided by an exemplary embodiment of the present application is shown, and the method comprises:

[0230] Step 410: The server obtains known physical property parameters of the crude oil to be predicted;

[0231] Optionally, the known physical property parameters of the crude oil to be predicted come from a server; or, the known physical property parameters of the crude oil to be predicted come from a crude oil evaluation database; or, the known physical property parameters of the crude oil to be predicted come from a client.

[0232] Exemplarily, the known physical property parameters in the crude oil evaluation database are known physical property parameters purchased from professional manufacturers, or obtained from laboratory analysis, or obtained from publicly available crude oil analysis data, or obtained from the company's own accumulated crude oil historical data.

[0233] Optionally, the known physical property parameters of the crude oil to be predicted are the known physical property parameters of all crude oils, or, the known physical property parameters of the crude oil to be predicted are all known physical property parameters of the crude oil to be predicted, or, the known physical property parameters of the crude oil to be predicted are the known physical property parameters of the crude oil to be predicted that correspond to the predicted physical property parameters.

[0234] Exemplarily, the crude oil to be predicted is crude oil a, and the server obtains the known physical property parameters of all crude oils. For example, the server stores the known physical property parameters of crude oil a, crude oil b, and crude oil c. When predicting the physical property parameters of crude oil a, the known physical property parameters of crude oil a, crude oil b, and crude oil c stored in the server will be obtained.

[0235] Exemplarily, the crude oil to be predicted is crude oil a, and the server obtains the known physical property parameters of all crude oils. For example, the server stores the known physical property parameters of crude oil a, crude oil b, and crude oil c. When predicting the physical property parameters of crude oil a, the known physical property parameters of crude oil a stored in the server will be obtained.

[0236] For example, the crude oil to be predicted is crude oil a, and the server obtains known physical property parameters of all crude oils. For example, the server stores some physical property parameters of crude oil a, crude oil b, and crude oil c, such as density, kinematic viscosity, aniline point, and wax content of crude oil a, density and flash point of crude oil b, and kinematic viscosity and pour point of crude oil C. When predicting the physical property parameters of the density and aniline point of crude oil a, the known physical property parameters corresponding to the density and aniline point of crude oil a stored in the server will be obtained.

[0237] Step 420: The server obtains at least one fraction temperature segment when performing crude oil cutting on the crude oil to be predicted, provided by the client;

[0238] In some embodiments, the crude oil to be predicted provided by the client is a mixed crude oil.

[0239] In some embodiments, the crude oil to be predicted provided by the client is at least one crude oil.

[0240] Exemplarily, the crude oils to be predicted provided by the client are crude oil a1 to crude oil a4, and the server needs to predict crude oil a1 to crude oil a4 respectively.

[0241] Optionally, at least one fraction temperature segment when crude oil is cut is obtained from a server, or at least one fraction temperature segment when crude oil is cut is obtained from a client.

[0242] Exemplarily, at least one distillation temperature segment during crude oil cutting is at least one preset distillation temperature segment during crude oil cutting saved in the server, or at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during the last crude oil cutting saved in the server.

[0243] Exemplarily, at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during crude oil cutting input by the user in the client, or, at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during crude oil cutting selected by the user in the client, or, at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during crude oil cutting preset and saved in the client, or, at least one distillation temperature segment during crude oil cutting is at least one distillation temperature segment during the last crude oil cutting saved in the client.

[0244] Exemplarily, obtaining at least one fraction temperature segment when crude oil is cut for the crude oil to be predicted, such as Figure 3 As shown, a total of 3 distillation temperature sections are obtained, among which the value of the distillation temperature section 321 is 260°C to 400°C; the value of the distillation temperature section 322 is 150°C to 280°C; and the value of the distillation temperature section 323 is 0°C to 136°C.

[0245] Step 430: The server uses the known physical property parameters as input and predicts the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted through a crude oil cutting prediction algorithm.

[0246] Optionally, based on all known physical property parameters of the crude oil to be predicted as input, the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted are predicted using a crude oil cutting prediction algorithm.

[0247] Optionally, based on known physical property parameters corresponding to the predicted physical property parameters of the crude oil to be predicted as input, the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted are predicted using a crude oil cutting prediction algorithm.

[0248] Optionally, based on known physical property parameters of similar crude oils to the crude oil to be predicted as input, predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted are predicted using a crude oil cutting prediction algorithm.

[0249] Optionally, based on known physical property parameters of the crude oil to be predicted and similar crude oils to the crude oil to be predicted as input, the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted are predicted using a crude oil cutting prediction algorithm.

[0250] In some embodiments, the crude oil cutting prediction algorithm includes at least one of the following:

[0251] Spline interpolation algorithm;

[0252] ·Mass yield ratio algorithm;

[0253] Integration algorithm.

[0254] The integration algorithm includes a first integration algorithm and a second integration algorithm; the first integration algorithm and the second integration algorithm are used to predict different predicted physical property parameters through different prediction methods.

[0255] Step 440: The server sends the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted to the client.

[0256] In some embodiments, the predicted physical property parameters of the crude oil to be predicted corresponding to at least one distillation temperature segment obtained by the server are the predicted physical property parameters of the crude oil of mixed oil species corresponding to at least one distillation temperature segment.

[0257] In some embodiments, the predicted physical property parameters of the crude oil to be predicted corresponding to at least one distillation temperature segment obtained by the server are predicted physical property parameters of at least one crude oil corresponding to at least one distillation temperature segment.

[0258] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0259] Please refer to Fig.13 , which shows a structural block diagram of a crude oil virtual cutting device provided by an exemplary embodiment of the present application. The device has the function of implementing the above-mentioned crude oil virtual cutting method example, and the function can be implemented by hardware or by hardware executing corresponding software. Fig.13 As shown, the device 500 may include: a first acquisition module 510 , a second acquisition module 520 and a prediction module 530 .

[0260] The first acquisition module 510 is used to acquire known physical property parameters of the crude oil to be predicted;

[0261] The second acquisition module 520 is used to acquire at least one fraction temperature segment when crude oil is cut for the crude oil to be predicted;

[0262] The prediction module 530 is used to predict the predicted physical property parameters corresponding to at least one fraction temperature segment of the crude oil to be predicted by using the crude oil cutting prediction algorithm based on the known physical property parameters as input.

[0263] In some embodiments, the prediction module 530 includes a first prediction submodule.

[0264] The first prediction submodule is used to interpolate the known physical property parameters of at least two narrow fraction temperature segments by using a spline interpolation algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment.

[0265] In some embodiments, the first prediction submodule includes a definition unit, a first substitution unit, a first acquisition unit, a second substitution unit, a third substitution unit, a fourth substitution unit, a first calculation unit, a fifth substitution unit, and a second calculation unit.

[0266] A definition unit, used for defining n piecewise cubic polynomials, each of the n piecewise cubic polynomials has 4 unknown coefficients, and the n piecewise cubic polynomials have a total of 4n unknown coefficients;

[0267] The first substitution unit is used to substitute the known physical property parameters of n+1 narrow cut temperature sections into n piecewise cubic polynomials, and the known physical property parameters of every two narrow cut temperature sections correspond to one piecewise cubic polynomial, so as to obtain 2n first equations;

[0268] A first acquisition unit is used to acquire n first-order derivatives and n second-order derivatives corresponding to n piecewise cubic polynomials;

[0269] The second substitution unit is used to obtain n-1 first intermediate equations according to the equality of the first-order derivatives of each piecewise polynomial at the connection point; substitute the known physical property parameters of the second to n narrow fraction temperature sections into the n-1 first intermediate equations to obtain n-1 second equations;

[0270] A third substitution unit is used to obtain n-1 second intermediate equations according to the equality of the second-order derivatives of each piecewise polynomial at the connection point, and substitute the known physical property parameters of the second to n narrow fraction temperature sections into the n-1 second intermediate equations to obtain n-1 third equations;

[0271] A fourth substitution unit is used to substitute the known physical property parameters of the first narrow cut temperature section and the known physical property parameters of the n+1th narrow cut temperature section into the boundary conditions to obtain two fourth equations, wherein the boundary conditions are equations for determining the properties of the boundary points of the piecewise cubic polynomial;

[0272] A first calculation unit is used to obtain values ​​of 4n unknown coefficients according to 2n first equations, n-1 second equations, n-1 third equations and 2 fourth equations, a total of 4n equations;

[0273] A fifth substitution unit is used to obtain n piecewise cubic polynomials with physical property parameters of a target temperature segment as unknown numbers according to the values ​​of 4n unknown coefficients, wherein the target temperature segment includes at least one fraction temperature segment;

[0274] The second calculation unit is used to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to at least one distillation temperature segment according to at least one distillation temperature segment and n piecewise cubic polynomials with the physical property parameters of the target temperature segment as unknowns.

[0275] In some embodiments, the prediction module 530 includes a second prediction submodule.

[0276] The second prediction submodule is used to perform linear fitting on the known physical property parameters of at least two narrow fraction temperature segments through a mass yield ratio algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment.

[0277] In some embodiments, the second prediction submodule includes a first fitting unit, a third calculation unit, a fourth calculation unit, and a first accumulation unit.

[0278] A first fitting unit is used to perform straight-line fitting on known physical property parameters of at least two narrow fraction temperature segments by using a mass yield ratio algorithm to obtain a physical property parameter straight line corresponding to a target temperature segment, wherein the physical property parameter straight line is used to characterize a linear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment, and the target temperature segment includes at least one fraction temperature segment;

[0279] A third calculation unit is used to calculate, for each of the at least one fraction temperature segment, a mixing ratio corresponding to at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is a proportion of a mass yield of a mapped physical property parameter of a single narrow fraction temperature segment in the fraction temperature segment to a mass yield corresponding to the fraction temperature segment;

[0280] a fourth calculation unit, for calculating, for each narrow fraction temperature segment belonging to each fraction temperature segment, the product of the mapped physical property parameter of each narrow fraction temperature segment and the mixing ratio corresponding to each narrow fraction temperature segment, to obtain an intermediate value corresponding to each narrow fraction temperature segment;

[0281] The first accumulation unit is used to accumulate the intermediate values ​​corresponding to at least two narrow fraction temperature sections in each fraction temperature section to obtain the predicted physical property parameter corresponding to each fraction temperature section.

[0282] In some embodiments, the prediction module 530 includes a third prediction submodule.

[0283] The third prediction submodule is used to perform curve fitting on the known physical property parameters of at least two narrow fraction temperature segments by using an integral algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to at least one fraction temperature segment.

[0284] In some embodiments, the third prediction submodule includes a second fitting unit, a fifth calculation unit, a sixth calculation unit, and a second accumulation unit.

[0285] a second fitting unit, configured to perform curve fitting on known physical property parameters of at least two narrow fraction temperature segments by using a first integral algorithm, to obtain a physical property parameter curve corresponding to a target temperature segment, wherein the physical property parameter curve is used to characterize a nonlinear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment, wherein the target temperature segment includes at least one fraction temperature segment;

[0286] a fifth calculation unit, for calculating, for each of the at least one fraction temperature segment, a mixing ratio respectively corresponding to at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is a proportion of a mass yield of a mapped physical property parameter of a single narrow fraction temperature segment in the fraction temperature segment to a mass yield corresponding to the fraction temperature segment;

[0287] a sixth calculation unit, for calculating, for each narrow fraction temperature segment belonging to each fraction temperature segment, a ratio of a mixing ratio corresponding to each narrow fraction temperature segment to a mapping physical property parameter of each narrow fraction temperature segment, to obtain an intermediate value corresponding to each narrow fraction temperature segment;

[0288] The second accumulation unit is used to accumulate the intermediate values ​​corresponding to at least two narrow fraction temperature sections in each fraction temperature section, and take the inverse of the accumulated value to obtain the predicted physical property parameter corresponding to each fraction temperature section.

[0289] In some embodiments, the third prediction submodule includes a third fitting unit, a second acquisition unit, a seventh calculation unit, an eighth calculation unit, and a third accumulation unit.

[0290] a third fitting unit, configured to perform curve fitting on known physical property parameters of at least two narrow fraction temperature segments by using a second integral algorithm to obtain a physical property parameter curve corresponding to a target temperature segment, wherein the physical property parameter curve is used to characterize a nonlinear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment, wherein the target temperature segment includes at least one fraction temperature segment;

[0291] A second acquisition unit is used to acquire a physical property index of the mapped physical property parameters of each narrow fraction temperature section, wherein the physical property index of the mapped physical property parameters of the narrow fraction temperature section is calculated by a physical property index algorithm;

[0292] a seventh calculation unit, for calculating, for each of the at least one fraction temperature segment, a mixing ratio respectively corresponding to at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is a proportion of a mass yield of a mapped physical property parameter of a single narrow fraction temperature segment in the fraction temperature segment to a mass yield corresponding to the fraction temperature segment;

[0293] an eighth calculation unit, for calculating, for each narrow fraction temperature segment belonging to each fraction temperature segment, a product of a physical property index of a mapped physical property parameter of each narrow fraction temperature segment and a mixing ratio corresponding to each narrow fraction temperature segment, to obtain an intermediate value corresponding to each narrow fraction temperature segment;

[0294] The third accumulation unit is used to accumulate the intermediate values ​​corresponding to at least two narrow fraction temperature sections in each fraction temperature section to obtain the predicted physical property parameter corresponding to each fraction temperature section.

[0295] In some embodiments, the prediction module 530 includes a fourth prediction submodule and a calculation submodule.

[0296] A fourth prediction submodule is used for, when the crude oil to be predicted is a crude oil of mixed oil species, predicting the predicted physical property parameters corresponding to the crude oil of each oil species in at least one fraction temperature segment by using a crude oil cutting prediction algorithm based on known physical property parameters as input, to obtain the predicted physical property parameters of the crude oil of each oil species in the mixed oil species, wherein the crude oil of the mixed oil species includes at least two oil species;

[0297] The calculation submodule is used to take the predicted physical property parameters of at least two oil types as input, and obtain the predicted physical property parameters corresponding to the crude oil of the mixed oil types through a linear addition algorithm.

[0298] In summary, the device provided in this embodiment uses the known physical property parameters of the crude oil to be predicted to predict the predicted physical property parameters corresponding to at least one distillation temperature segment of the crude oil to be predicted. Since the known physical property parameters of the crude oil to be predicted are obtained by experimental means and are closer to the actual physical property parameters of the crude oil to be predicted, the predicted physical property parameters obtained by prediction by the prediction algorithm are also closer to the actual physical property parameters of the crude oil to be predicted.

[0299] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0300] Please refer to Fig.14 , which shows a structural block diagram of a server provided in one embodiment of the present application. The server can be used to implement the crude oil virtual cutting method provided in the above embodiment. Specifically:

[0301] The server 600 includes a central processing unit (CPU) 601, a system memory 604 including a random access memory (RAM) 602 and a read-only memory (ROM) 603, and a system bus 605 connecting the system memory 604 and the central processing unit 601. The computer device 600 also includes a basic input / output system (I / O system) 606 for facilitating information transmission between various components in the computer device, and a large-capacity storage device 607 for storing an operating system 613, application programs 614, and other program modules 615.

[0302] The basic input / output system 606 includes a display 608 for displaying information and an input device 609 such as a mouse and a keyboard for user inputting information. The display 608 and the input device 609 are connected to the central processing unit 601 through an input / output controller 610 connected to the system bus 605. The basic input / output system 606 may also include an input / output controller 610 for receiving and processing inputs from a plurality of other devices such as a keyboard, a mouse, or an electronic stylus. Similarly, the input / output controller 610 also provides output to a display screen, a printer, or other types of output devices.

[0303] The mass storage device 607 is connected to the central processing unit 601 via a mass storage controller (not shown) connected to the system bus 605. The mass storage device 607 and its associated computer-readable storage medium provide non-volatile storage for the server 600. That is, the mass storage device 607 may include a computer-readable storage medium (not shown) such as a hard disk or a compact disc read-only memory (CD-ROM) drive.

[0304] Without loss of generality, the computer-readable storage medium may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable storage instructions, data structures, program modules or other data. Computer storage media include RAM, ROM, Erasable Programmable Read Only Memory (EPROM), Electronically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other solid-state storage technology, CD-ROM, Digital Versatile Disc (DVD) or other optical storage, cassette, tape, disk storage or other magnetic storage devices. Of course, those skilled in the art will know that the computer storage medium is not limited to the above. The above-mentioned system memory 604 and mass storage device 607 can be collectively referred to as memory.

[0305] The memory stores one or more programs, and the one or more programs are configured to be executed by one or more central processing units 601. The one or more programs contain instructions for implementing the above-mentioned method embodiments. The central processing unit 601 executes the one or more programs to implement the methods provided by the above-mentioned method embodiments.

[0306] According to various embodiments of the present application, the server 600 can also be connected to a remote server on the network through a network such as the Internet. That is, the server 600 can be connected to the network 612 through a network interface unit 611 connected to the system bus 605, or the network interface unit 611 can be used to connect to other types of networks or remote server systems (not shown).

[0307] The memory also includes one or more programs, which are stored in the memory and include steps executed by the server in the method provided in the embodiment of the present application.

[0308] In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned crude oil virtual cutting method is implemented.

[0309] In an exemplary embodiment, a computer program product is also provided. When the computer program product is executed by a processor, it is used to implement the above-mentioned crude oil virtual cutting method.

[0310] It should be understood that the "multiple" mentioned in this article refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. In addition, the step numbers described in this article only illustrate a possible execution sequence between the steps. In some other embodiments, the above steps may not be executed in the order of the numbers, such as two steps with different numbers are executed at the same time, or two steps with different numbers are executed in the opposite order to the diagram. The embodiments of the present application are not limited to this.

[0311] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A crude oil virtual cutting method, characterized in that: The method is executed by a server, and comprises: Obtain known physical property parameters of crude oil to be predicted; Acquire at least one fraction temperature segment when crude oil cutting is performed on the crude oil to be predicted; Based on the known physical property parameters as input, the predicted physical property parameters corresponding to the crude oil to be predicted in the at least one fraction temperature segment are predicted by a crude oil cutting prediction algorithm.

2. The method according to claim 1, characterized in that The crude oil cutting prediction algorithm includes at least one of the following: Spline interpolation algorithm; Mass yield ratio algorithm; Integration algorithm; The integration algorithm includes a first integration algorithm and a second integration algorithm; the first integration algorithm and the second integration algorithm are used to predict different predicted physical property parameters through different prediction methods.

3. The method according to claim 2, characterized in that The known physical property parameters include known physical property parameters of at least two narrow fraction temperature sections, and the division granularity of the narrow fraction temperature section is smaller than the division granularity of the fraction temperature section; The method of predicting the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature segment by using a crude oil cutting prediction algorithm based on the known physical property parameters as input comprises: The known physical property parameters of the at least two narrow fraction temperature segments are interpolated by the spline interpolation algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature segment.

4. The method according to claim 3, characterized in that The at least two narrow fraction temperature sections include n+1 narrow fraction temperature sections, where n is a positive integer; The method of interpolating the known physical property parameters of the at least two narrow fraction temperature segments by the spline interpolation algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature segment includes: Define n piecewise cubic polynomials, each of the n piecewise cubic polynomials has 4 unknown coefficients, and the n piecewise cubic polynomials have a total of 4n unknown coefficients; Substituting the known physical property parameters of the n+1 narrow cut temperature segments into the n piecewise cubic polynomials, the known physical property parameters of every two narrow cut temperature segments correspond to one piecewise cubic polynomial, and 2n first equations are obtained; Obtaining n first-order derivatives and n second-order derivatives corresponding to the n piecewise cubic polynomials; According to the equality of the first-order derivatives of each piecewise polynomial at the connection point, n-1 first intermediate equations are obtained; the known physical property parameters of the second to n narrow fraction temperature sections are substituted into the n-1 first intermediate equations to obtain n-1 second equations; According to the equality of the second-order derivatives of each piecewise polynomial at the connection point, n-1 second intermediate equations are obtained, and the known physical property parameters of the second to n narrow fraction temperature segments are substituted into the n-1 second intermediate equations to obtain n-1 third equations; Substituting the known physical property parameters of the first narrow fraction temperature segment and the known physical property parameters of the n+1th narrow fraction temperature segment into the boundary conditions, two fourth equations are obtained, wherein the boundary conditions are equations for determining the properties of the boundary points of the piecewise cubic polynomial; According to the 2n first equations, the n-1 second equations, the n-1 third equations and the two fourth equations, a total of 4n equations, the values ​​of the 4n unknown coefficients are obtained; According to the values ​​of the 4n unknown coefficients, n piecewise cubic polynomials with the physical property parameters of the target temperature section as unknowns are obtained, wherein the target temperature section includes at least one fraction temperature section; According to the at least one distillation temperature segment and the n piecewise cubic polynomials with the physical property parameters of the target temperature segment as unknowns, the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one distillation temperature segment are obtained.

5. The method according to claim 2, characterized in that: The known physical property parameters include known physical property parameters of at least two narrow fraction temperature sections, and the division granularity of the narrow fraction temperature section is smaller than the division granularity of the fraction temperature section; The method of predicting the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature segment by using a crude oil cutting prediction algorithm based on the known physical property parameters as input comprises: The mass yield ratio algorithm is used to perform linear fitting on the known physical property parameters of the at least two narrow fraction temperature segments to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature segment.

6. The method according to claim 5, characterized in that The method of performing linear fitting on the known physical property parameters of the at least two narrow fraction temperature segments by using the mass yield ratio algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature segment includes: Performing linear fitting on the known physical property parameters of the at least two narrow fraction temperature segments by using the mass yield ratio algorithm to obtain a physical property parameter straight line corresponding to a target temperature segment, wherein the physical property parameter straight line is used to characterize the linear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment, and the target temperature segment includes the at least one fraction temperature segment; For each of the at least one fraction temperature segment, calculating the mixing ratios respectively corresponding to the at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is the proportion of the mass yield of the mapped physical property parameter of a single narrow fraction temperature segment in the fraction temperature segment to the mass yield corresponding to the fraction temperature segment; For each narrow fraction temperature segment belonging to each fraction temperature segment, calculating the product of the mapped physical property parameter of each narrow fraction temperature segment and the mixing ratio corresponding to each narrow fraction temperature segment, to obtain an intermediate value corresponding to each narrow fraction temperature segment; The intermediate values ​​corresponding to at least two narrow fraction temperature sections in each fraction temperature section are accumulated to obtain the predicted physical property parameters corresponding to each fraction temperature section.

7. The method according to claim 2, characterized in that The known physical property parameters include known physical property parameters of at least two narrow fraction temperature sections, and the division granularity of the narrow fraction temperature section is smaller than the division granularity of the fraction temperature section; The method of predicting the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature segment by using a crude oil cutting prediction algorithm based on the known physical property parameters as input comprises: The known physical property parameters of the at least two narrow fraction temperature sections are curve-fitted by the integral algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature section.

8. The method according to claim 7, characterized in that The integration algorithm includes a first integration algorithm; The curve fitting of the known physical property parameters of the at least two narrow fraction temperature sections by the integral algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature section includes: Performing curve fitting on the known physical property parameters of the at least two narrow fraction temperature segments by using the first integral algorithm to obtain a physical property parameter curve corresponding to a target temperature segment, wherein the physical property parameter curve is used to characterize the narrow fraction temperature segment and the nonlinear relationship between the mapped physical property parameters of the narrow fraction temperature segment, and the target temperature segment includes the at least one fraction temperature segment; For each of the at least one fraction temperature segment, calculating the mixing ratios respectively corresponding to the at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is the proportion of the mass yield of the mapped physical property parameter of a single narrow fraction temperature segment in the fraction temperature segment to the mass yield corresponding to the fraction temperature segment; For each narrow fraction temperature segment belonging to each fraction temperature segment, calculating the ratio of the mixing ratio corresponding to each narrow fraction temperature segment to the mapped physical property parameter of each narrow fraction temperature segment, and obtaining an intermediate value corresponding to each narrow fraction temperature segment; The intermediate values ​​corresponding to at least two narrow fraction temperature sections in each fraction temperature section are accumulated, and the reciprocal of the accumulated value is taken to obtain the predicted physical property parameter corresponding to each fraction temperature section.

9. The method according to claim 7, characterized in that: The integration algorithm includes a second integration algorithm; The curve fitting of the known physical property parameters of the at least two narrow fraction temperature sections by the integral algorithm to obtain the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature section includes: Performing curve fitting on the known physical property parameters of the at least two narrow fraction temperature segments by using the second integral algorithm to obtain a physical property parameter curve corresponding to a target temperature segment, wherein the physical property parameter curve is used to characterize the nonlinear relationship between the narrow fraction temperature segment and the mapped physical property parameters of the narrow fraction temperature segment, and the target temperature segment includes the at least one fraction temperature segment; Obtaining a physical property index of a mapped physical property parameter of each narrow fraction temperature segment, wherein the physical property index of the mapped physical property parameter of the narrow fraction temperature segment is calculated by a physical property index algorithm; For each of the at least one fraction temperature segment, calculating the mixing ratios respectively corresponding to the at least two narrow fraction temperature segments in the fraction temperature segment, wherein the mixing ratio is the proportion of the mass yield of the mapped physical property parameter of a single narrow fraction temperature segment in the fraction temperature segment to the mass yield corresponding to the fraction temperature segment; For each narrow fraction temperature segment belonging to each fraction temperature segment, calculating the product of the physical property index of the mapped physical property parameter of each narrow fraction temperature segment and the mixing ratio corresponding to each narrow fraction temperature segment, to obtain an intermediate value corresponding to each narrow fraction temperature segment; The intermediate values ​​corresponding to at least two narrow fraction temperature sections in each fraction temperature section are accumulated to obtain the predicted physical property parameters corresponding to each fraction temperature section.

10. The method according to any one of claims 1 to 9, characterized in that: The method of predicting the predicted physical property parameters of the crude oil to be predicted corresponding to the at least one fraction temperature segment by using a crude oil cutting prediction algorithm based on the known physical property parameters as input comprises: In the case where the crude oil to be predicted is a crude oil of mixed oil types, for each crude oil of the mixed oil types, based on the known physical property parameters as input, the predicted physical property parameters corresponding to the crude oil of each oil type in the at least one fraction temperature segment are predicted by the crude oil cutting prediction algorithm to obtain the predicted physical property parameters of the crude oil of each oil type, and the mixed oil types of crude oil include at least two oil types; The predicted physical property parameters of the at least two oil types are used as input, and the predicted physical property parameters corresponding to the crude oil of the mixed oil types are obtained through a linear addition algorithm.

11. A crude oil virtual cutting device, characterized in that: The device comprises: A first acquisition module, used for acquiring known physical property parameters of the crude oil to be predicted; A second acquisition module is used to acquire at least one fraction temperature segment when crude oil cutting is performed on the crude oil to be predicted; The prediction module is used to predict the predicted physical property parameters corresponding to the crude oil to be predicted in the at least one fraction temperature segment through a crude oil cutting prediction algorithm based on the known physical property parameters as input.

12. A computer device, characterized in that: The computer device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the crude oil virtual cutting method as described in any one of claims 1 to 10.

13. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the crude oil virtual cutting method according to any one of claims 1 to 10 is implemented.

14. A computer program product, characterized in that The computer program product includes a computer program, which is stored in a computer-readable storage medium; the computer program is read and executed from the computer-readable storage medium by a processor of a computer device, so that the computer device executes the crude oil virtual cutting method as described in any one of claims 1 to 10.

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