Method, device and equipment for detecting composition of heavy oil molecules and storage medium

Abstract

The application provides a heavy oil molecular composition detection method, device, equipment and storage medium, and relates to the technical field of oil refining industry. The physical property data and high-resolution mass spectrum data of the heavy oil to be detected are obtained; a predefined molecular library is constructed according to the physical property data and the high-resolution mass spectrum data; the probability density function combination form and the value range of the probability density function parameters are determined through the predefined molecular library; the target value is selected in the value range and is assigned to the corresponding probability density function parameter; the molecular content of the heavy oil to be detected is determined according to the assigned probability density function parameters; the macroscopic physical property similarity is determined according to the molecular content and the physical property data, and the composition similarity is determined according to the molecular content and the high-resolution mass spectrum data; the heavy oil molecular composition of the heavy oil to be detected is determined as the heavy oil molecular composition that meets the similarity requirement of the composition similarity and the macroscopic physical property similarity, so that the accuracy of the molecular content distribution in the heavy oil molecular composition is improved.

Description

Technical Field

[0001] This application relates to the field of oil refining technology, and in particular to a method, apparatus, equipment and storage medium for detecting the molecular composition of heavy oil. Background Technology

[0002] Heavy oil is the residual fraction after crude oil undergoes atmospheric distillation. It has high density, high viscosity, and a boiling point typically above 350°C, making it a difficult component to process in petroleum refining. Currently, traditional lumped kinetic models often struggle to accurately predict the molecular transformation patterns of feedstocks and the properties of products in complex heavy oil systems. Developing molecular-level heavy oil processing models will effectively address these challenges. Accurate petroleum molecular composition models are fundamental to constructing accurate molecular-level heavy oil processing models.

[0003] In related technologies, the average molecular structure model of petroleum (or oil products) is calculated based on nuclear magnetic resonance data using the petroleum average molecular structure calculation method (also known as the BL algorithm), and the petroleum molecular composition is inverted using the macroscopic physical properties of petroleum. However, these methods are difficult to guarantee the accuracy of the molecular content distribution in the petroleum molecular composition model. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and storage medium for detecting the molecular composition of heavy oil, in order to solve the problem of low accuracy in the molecular content distribution of heavy oil molecules.

[0005] In a first aspect, this application provides a method for detecting the molecular composition of heavy oil, comprising:

[0006] Acquire the physical properties and high-resolution mass spectrometry data of the heavy oil to be tested;

[0007] Based on physical property data and high-resolution mass spectrometry data, a predefined molecular library is constructed;

[0008] By using a predefined molecular library, the combination form of probability density functions and the range of values ​​for probability density function parameters are determined;

[0009] Within the range of values, select the target value and assign it to the corresponding probability density function parameter;

[0010] The molecular content of the heavy oil to be tested is determined based on the assigned probability density function parameters.

[0011] Based on molecular content and physical property data, the macroscopic physical property similarity is determined, and based on molecular content and high-resolution mass spectrometry data, the compositional similarity is determined.

[0012] The molecular composition of the heavy oil to be tested is determined to be one that meets the similarity requirements in terms of composition and macroscopic physical properties.

[0013] One possible implementation involves determining the molecular composition of the heavy oil to be tested to be one that meets the similarity requirements in terms of compositional similarity and macroscopic physical property similarity, including:

[0014] The molecular composition of the heavy oil to be tested is optimized according to the following formula:

[0015]

[0016] Stx low ≤x≤x up

[0017] Where Prop Error is the macroscopic property error between the heavy oil molecular composition and physical property data detected by the detection method, Comp Sim is the compositional similarity between the heavy oil molecular composition detected by the detection method and the high-resolution mass spectrometry data, ω is the weighting factor between the two objective functions, and x is the optimization variable. l o w and x up These represent the minimum and maximum values ​​of the optimization variables, determined using physical property data; ∑ denotes the summation sign, N represents the number of physical property data points, and δ... L P represents the weighting factor for the Lth physical property data. exp , L P represents the experimental value of the macroscopic property of the Lth physical property data. pred,L This represents the predicted macroscopic physical properties of the heavy oil molecules in the Lth heavy oil sample detected by the detection method, where k is the number of different types of compounds, n is the number of different homologues, and CS ij This indicates the compositional similarity of homologues of type j in a compound of type i;

[0018] Determine whether the compositional similarity and macroscopic physical property similarity of the optimized heavy oil molecular composition meet the similarity requirements;

[0019] If the similarity requirement is met, the molecular composition of the heavy oil to be tested is determined to be the optimized molecular composition of the heavy oil.

[0020] In one possible implementation, Comp Sim is determined according to the following formula:

[0021]

[0022] Where k is the number of different types of compounds, CS i The compositional similarity of compounds of type i is calculated according to the following formula:

[0023]

[0024] Where n is the number of different homologous species, CSij This represents the compositional similarity of homologues of type j in a compound of type i.

[0025] One possible implementation involves determining whether the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition meet the similarity requirements, including:

[0026] Determine the weighted sum of compositional similarity and macroscopic property similarity;

[0027] Determine whether the weighted sum meets the similarity requirements. The similarity requirements include that the macroscopic property similarity between the heavy oil molecular composition and the physical property data is greater than or equal to the reference macroscopic property similarity, and / or that the compositional similarity between the heavy oil molecular composition and the high-resolution mass spectrometry data is greater than or equal to the reference compositional similarity. The reference macroscopic property similarity is the currently determined optimal macroscopic property similarity, and the reference compositional similarity is the currently determined optimal compositional similarity.

[0028] If the weighted sum meets the similarity requirement, then the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition are determined to meet the similarity requirement.

[0029] If the weighted sum does not meet the similarity requirement, then the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition are determined to be unsatisfactory, and the step of selecting the target value within the range and assigning it to the corresponding probability density function parameter is executed.

[0030] One possible implementation involves constructing a predefined molecular library based on physical property data and high-resolution mass spectrometry data, including:

[0031] Based on the molecular types of physical property data and high-resolution mass spectrometry data, determine the types of compounds included in the predefined molecular library;

[0032] Based on the equivalent double bond number of compound types in high-resolution mass spectrometry data, define the core molecular structure of the compound types contained in the predefined molecular library;

[0033] Based on the carbon number ranges of different types of molecules from high-resolution mass spectrometry data, a predefined molecular library is obtained by expanding the side chain carbon number on the core molecular structure using the SUBEM framework.

[0034] One possible implementation involves selecting a target value within a range and assigning it to the corresponding probability density function parameter. This includes: selecting a target value within a range and assigning it to the corresponding probability density function parameter based on a bi-objective optimization algorithm, whereby the bi-objective optimization algorithm includes a genetic algorithm.

[0035] In one possible implementation, determining the molecular content of the heavy oil to be detected based on the assigned probability density function parameters includes: obtaining the probability value of the probability density function based on the assigned probability density function parameters; and determining the molecular content of the heavy oil based on the probability value.

[0036] Secondly, this application provides a device for detecting the molecular composition of heavy oil, comprising:

[0037] The acquisition module is used to acquire the physical property data and high-resolution mass spectrometry data of the heavy oil to be tested;

[0038] The building module is used to construct predefined molecular libraries based on physical property data and high-resolution mass spectrometry data;

[0039] The first determining module is used to determine the combination form of probability density functions and the range of values ​​for probability density function parameters by using a predefined molecular library;

[0040] The assignment module is used to select a target value within the range of values ​​and assign it to the corresponding probability density function parameter;

[0041] The second determining module is used to determine the molecular content of the heavy oil to be detected based on the assigned probability density function parameters;

[0042] The third determination module is used to determine the macroscopic property similarity based on molecular content and physical property data, and to determine the compositional similarity based on molecular content and high-resolution mass spectrometry data.

[0043] The fourth determination module is used to determine the heavy oil molecular composition of the heavy oil to be tested to be one that meets the similarity requirements in terms of compositional similarity and macroscopic physical property similarity.

[0044] In one possible implementation, the fourth determining module may be specifically used for:

[0045] The molecular composition of the heavy oil to be tested is optimized according to the following formula:

[0046]

[0047] Stx low ≤x≤x up

[0048] Where Prop Error is the macroscopic property error between the heavy oil molecular composition and physical property data detected by the detection method, Comp Sim is the compositional similarity between the heavy oil molecular composition detected by the detection method and the high-resolution mass spectrometry data, ω is the weighting factor between the two objective functions, and x is the optimization variable. l o w and x upThese represent the minimum and maximum values ​​of the optimization variables, determined using physical property data; ∑ denotes the summation sign, N represents the number of physical property data points, and δ... L P represents the weighting factor for the Lth physical property data. exp , L P represents the experimental value of the macroscopic property of the Lth physical property data. pred,L This represents the predicted macroscopic physical properties of the heavy oil molecules in the Lth heavy oil sample detected by the detection method, where k is the number of different types of compounds, n is the number of different homologues, and CS ij This indicates the compositional similarity of homologues of type j in a compound of type i;

[0049] Determine whether the compositional similarity and macroscopic physical property similarity of the optimized heavy oil molecular composition meet the similarity requirements;

[0050] If the similarity requirement is met, the molecular composition of the heavy oil to be tested is determined to be the optimized molecular composition of the heavy oil.

[0051] In one possible implementation, the fourth determining module can also be used to determine Comp Sim according to the following formula:

[0052]

[0053] Where k is the number of different types of compounds, CS i The compositional similarity of compounds of type i is calculated according to the following formula:

[0054]

[0055] Where n is the number of different homologous species, CS ij This represents the compositional similarity of homologues of type j in a compound of type i.

[0056] In one possible implementation, the fourth determining module can also be used to: determine the weighted sum of compositional similarity and macroscopic property similarity; determine whether the weighted sum meets the similarity requirements, the similarity requirements including that the macroscopic property similarity between the heavy oil molecular composition and the property data is greater than or equal to the reference macroscopic property similarity, and / or that the compositional similarity between the heavy oil molecular composition and the high-resolution mass spectrometry data is greater than or equal to the reference compositional similarity, the reference macroscopic property similarity being the currently determined optimal macroscopic property similarity, and the reference compositional similarity being the currently determined optimal compositional similarity; if the weighted sum meets the similarity requirements, then it is determined that the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition meet the similarity requirements; if the weighted sum does not meet the similarity requirements, then it is determined that the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition do not meet the similarity requirements, and the step of selecting a target value within the value range and assigning it to the corresponding probability density function parameter is executed.

[0057] In one possible implementation, the construction module can be specifically used to: determine the types of compounds contained in a predefined molecular library based on the molecular types of physical property data and high-resolution mass spectrometry data; define the core molecular structure of the compound types contained in the predefined molecular library based on the equivalent double bond number of the compound types in the high-resolution mass spectrometry data; and expand the side chain carbon number on the core molecular structure using the SU-BEM framework based on the carbon number range of different types of molecules in the high-resolution mass spectrometry data to obtain the predefined molecular library.

[0058] In one possible implementation, the assignment module can be specifically used to: select a target value within a range based on a bi-objective optimization algorithm and assign it to the corresponding probability density function parameter. The bi-objective optimization algorithm includes a genetic algorithm.

[0059] In one possible implementation, the second determining module can be specifically used to: obtain the probability value of the probability density function based on the assigned probability density function parameters; and determine the molecular content of heavy oil based on the probability value.

[0060] Thirdly, this application provides an electronic device, including a memory and a processor. The memory is used to store program instructions; the processor is used to invoke the program instructions in the memory to execute the method for detecting the composition of heavy oil molecules in the first aspect.

[0061] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, implement the method for detecting the composition of heavy oil molecules in the first aspect.

[0062] Fifthly, this application provides a computer program product comprising a computer program, which, when executed by a processor, is used to implement the method for detecting the composition of heavy oil molecules in the first aspect.

[0063] The method, apparatus, equipment, and storage medium for detecting the molecular composition of heavy oil provided in this application acquire physical property data and high-resolution mass spectrometry (HMS) data of the heavy oil to be detected; construct a predefined molecular library based on the physical property data and HMS data; determine the combination form of probability density functions and the value range of probability density function parameters through the predefined molecular library; select target values ​​within the value range and assign them to the corresponding probability density function parameters; determine the molecular content of the heavy oil to be detected based on the assigned probability density function parameters; determine the macroscopic physical property similarity based on the molecular content and physical property data, and determine the compositional similarity based on the molecular content and HMS data; determine the heavy oil molecular composition of the heavy oil to be detected as a heavy oil molecular composition whose compositional similarity and macroscopic physical property similarity meet the similarity requirements. By combining HMS data with compositional similarity on the basis of macroscopic physical property similarity for detecting the molecular composition of heavy oil, the molecular content distribution of the detected heavy oil molecular composition can be made more consistent with the molecular content distribution of the HMS data, thereby improving the accuracy of the molecular content distribution in the heavy oil molecular composition. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0065] Figure 1 This is a schematic diagram illustrating an application scenario of the method for detecting the molecular composition of heavy oil provided in an embodiment of this application;

[0066] Figure 2 This is a schematic flowchart of a method for detecting the molecular composition of heavy oil provided in an embodiment of this application;

[0067] Figure 3 This is a schematic diagram of a method for constructing a predefined molecular library of heavy oil based on high-resolution mass spectrometry data, provided in an embodiment of this application.

[0068] Figure 4 This is a schematic diagram of the structure of a combination of probability density functions provided in an embodiment of this application;

[0069] Figure 5 This is a schematic flowchart of a method for detecting the molecular composition of heavy oil provided in another embodiment of this application;

[0070] Figure 6 This is a schematic flowchart of a method for detecting the molecular composition of reduced-pressure diesel fuel provided in an embodiment of this application;

[0071] Figure 7 This is a schematic diagram of the detection results of a typical VGO molecular composition detection method provided in an embodiment of this application;

[0072] Figure 8This is a schematic diagram showing the comparison between the molecular content distribution detected by a detection method for typical VGO molecular composition and high-resolution mass spectrometry data provided in an embodiment of this application.

[0073] Figure 9 This is a schematic diagram of the macroscopic property error statistics of a multi-group VGO composition model provided in an embodiment of this application;

[0074] Figure 10 This is a schematic diagram showing the result of comparing the molecular content distribution detected by a detection method using multiple groups of VGO molecules with high-resolution mass spectrometry data according to an embodiment of this application.

[0075] Figure 11 This is a schematic diagram of the detection results of a method for detecting the molecular composition of VGO in different fractions provided in another embodiment of this application;

[0076] Figure 12 This is a schematic diagram of the structure of a heavy oil molecule composition detection device provided in an embodiment of this application;

[0077] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0078] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0079] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0080] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0081] In related technologies, establishing a heavy oil processing model will greatly promote the optimization of heavy oil processing technology and the development of new processes. However, the molecular composition of heavy oil is complex. Regardless of the digital framework of petroleum molecules used to construct the heavy oil molecular composition model, early digital frameworks of petroleum molecules only constrained the composition of petroleum molecules with macroscopic physical properties, making it difficult to guarantee the accuracy of the molecular content distribution in the heavy oil molecular composition model.

[0082] Over time, petroleum analysis techniques have developed rapidly. Researchers can incorporate more molecular-level information during petroleum molecule reconstruction to ensure the accuracy of molecular content distribution in petroleum molecular composition models. For example, gasoline fraction molecular composition models obtained based on gas chromatography-flame ionization detector (GC-FID) technology.

[0083] In addition, significant progress has been made in heavy oil molecular characterization methods, such as the development and application of high-resolution mass spectrometry (HMS) and atomic force microscopy (AFM). HMS can provide qualitative information on the composition and distribution of heavy oil molecules, and the molecular structure can be inferred from the detected precise molecular weight. AFM allows direct observation of the heavy oil molecular structure, verifying the accuracy of heavy oil molecular structures inferred from petrochemical and HMS data. However, heavy oil molecular composition models based on HMS data have not yet been developed and applied.

[0084] To address the aforementioned issues, this application proposes a method for detecting the molecular composition of heavy oil (i.e., constructing a heavy oil molecular composition model, or a heavy oil molecular reconstruction method). This method is based on a petroleum molecular reconstruction method using macroscopic property similarity and compositional similarity, with high-resolution mass spectrometry data as reference data. The reference data is molecular-level characterization data. The compositional similarity between the molecular content of the heavy oil molecular composition and the molecular content of the high-resolution mass spectrometry data is used as the optimization objective. Simultaneously, the macroscopic property similarity between the petroleum properties of the heavy oil molecular composition and the petroleum properties of the physical property data is used as the optimization objective. A dual-objective optimization algorithm is employed to optimize the compositional similarity and macroscopic property similarity, which can make the molecular content distribution of the obtained heavy oil molecular composition conform to the molecular content distribution of the high-resolution mass spectrometry data, thereby improving the accuracy of the molecular content distribution in the heavy oil molecular composition.

[0085] Figure 1 This is a schematic diagram illustrating an application scenario of the method for detecting the molecular composition of heavy oil provided in an embodiment of this application. For example... Figure 1As shown, this application scenario includes a first client 11, a server 12, and a second client 13, wherein the number of both the first client 11 and the second client 13 can be at least one. In practical applications, for example, after researchers and other relevant personnel obtain physical property data and high-resolution mass spectrometry data, they send the physical property data and high-resolution mass spectrometry data to the server 12 for storage through the first client 11; when the server 12 detects an instruction issued by the user through the second client 13 for detecting the heavy oil molecular composition of a certain heavy oil, it executes the heavy oil molecular composition detection method provided in this application based on the stored physical property data and high-resolution mass spectrometry data, obtains the heavy oil molecular composition of the heavy oil, and sends the result of the heavy oil molecular composition to the second client 13, thereby allowing relevant personnel to know the heavy oil molecular composition.

[0086] It should be noted that server 12 can also be replaced by a server cluster or other computing devices with a certain computing power. Both the first and second clients can be mobile phones, computers, laptops, or personal digital assistants (PDAs). Furthermore, the method for detecting the molecular composition of heavy oil provided in this application can also be used to detect the molecular composition of vacuum distillate oil and vacuum diesel oil.

[0087] The following is combined with Figure 1 Application scenarios, refer to Figure 2 This application describes a method for detecting the molecular composition of heavy oil according to exemplary embodiments thereof. It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited to those described herein. Figure 1 The limitations of the application scenarios shown.

[0088] Figure 2 This is a schematic flowchart of a method for detecting the molecular composition of heavy oil provided in an embodiment of this application.

[0089] like Figure 2 As shown, the method for detecting the molecular composition of heavy oil in this application embodiment includes the following steps:

[0090] S201: Obtain the physical property data and high-resolution mass spectrometry data of the heavy oil to be tested.

[0091] In this step, the physical property data and high-resolution mass spectrometry data of the heavy oil to be tested can be obtained by analyzing existing heavy oil samples. The physical property data represents the macroscopic physical properties of petroleum, including its density, elemental content, and distillation point; the high-resolution mass spectrometry data is molecular-level characterization data and can be used as reference data.

[0092] For example, high-resolution mass spectrometry (HMS) data can be obtained through HMS analysis. Currently, even with HMS combined with different ionization sources and chemical derivatization methods, precise quantification of the molecular composition of heavy oil cannot be achieved; the resulting data are semi-quantitative or qualitative molecular characterization data. Therefore, this application proposes a petroleum molecule reconstruction method based on physical property similarity and compositional similarity, combined with HMS data, for heavy oil molecule reconstruction. In other words, it effectively utilizes existing heavy oil molecule characterization data (HMS data) to improve the accuracy of molecular content distribution in the composition of heavy oil molecules.

[0093] S202: Construct a predefined molecular library based on physical property data and high-resolution mass spectrometry data.

[0094] In some embodiments, constructing a predefined molecular library based on physical property data and high-resolution mass spectrometry (HMS) data may include: determining the types of compounds included in the predefined molecular library based on the molecular types in the physical property data and HMS data; defining the core molecular structure of the compound types included in the predefined molecular library based on the equivalent double bond number of the compound types in the HMS data; and expanding the side chain carbon number on the core molecular structure using the SU-BEM framework based on the carbon number range of different types of molecules in the HMS data to obtain the predefined molecular library. The obtained predefined molecular library is a reasonable set of molecular structures defined based on physical property data and HMS data, combined with petrochemical theory, and is a prerequisite for ensuring the accuracy of the molecular content distribution detected by the heavy oil molecular composition detection method.

[0095] Considering that the molecular structure of heavy oil is extremely complex in practice, in some embodiments, the molecular structures included in the predefined molecular library, which encompass all molecular types in heavy oil, can be reasonably simplified.

[0096] Figure 3 This is a schematic diagram of a method for constructing a predefined molecular library of heavy oil based on high-resolution mass spectrometry data, provided in an embodiment of this application. Figure 3 As shown, based on the molecular types detected by high-resolution mass spectrometry, the types of compounds in a predefined molecular library can be determined first. For example, taking high-resolution mass spectrometry data of sulfur-containing compounds with a mass-to-charge ratio (m / z) of 200-600, the included compounds have the molecular formula C 33 H 45 S, C 33 H 51 S and C 33 H 57 S, etc., of which C 33 H 45 The number of equivalent double bonds (DBE) of S is 12, and C 33 H 51S has a DBE of 9, C 33 H 57 The DBE of S is 6. Based on the compound type and DBE range indicated by high-resolution mass spectrometry data, such as thioethers among sulfur-containing compounds, the DBE range for compounds containing one sulfur atom (S1) is 0-17, and the DBE range for compounds containing two sulfur atoms (S2) is 3-15, the core molecular structure of the compound types included in the predefined molecular library can be defined. Then, using the structural unit and bond-electron matrix (SU-BEM) framework, the known compound molecular formula C... 33 H 45 S, C 33 H 51 S and C 33 H 57 S is digitized to obtain the string SU, such as C. 33 H 45 The SU string corresponding to S, DBE=12 after digitization is [A6=1; A4=1; AA=1; N1=1; NS=1;]; C 33 H 51 The SU string corresponding to S and DBE=9 after digitization is [A6=1;AA=1;N1=1;NS=1;];C 33 H 57 After digitizing S and DBE=6, the corresponding SU strings are [A6=1; N3=1; NS=1; IH=-2;], where A6=1, N3=1, etc., are SU strings defined by the SU-BEM framework. Finally, based on the carbon number ranges of different types of molecules indicated by high-resolution mass spectrometry data, such as thioethers in sulfur-containing compounds, the carbon number range (C#) of compound S1 is 7-60, and the carbon number range of compound S2 is 10-60, the side chain carbon numbers are expanded on the core molecular structure to obtain a complete predefined molecular library. Because the characteristics of high-resolution mass spectrometry data were incorporated into the construction of the predefined molecular library, the resulting predefined molecular library is more consistent with reality. For example, for C... 33 H 45 S, C 33 H 51 S and C 33 H 57 After S expands the number of carbon atoms in the side chain, the resulting S1 class compound molecular structure library has an additional number of carbon atoms in the side chain, such as R=1 and R=2.

[0097] Repeat the process described in the above examples to identify all compound types and obtain all molecular types, C#, and DBE ranges contained in the predefined molecular library. For example... Figure 3As shown, the predefined molecular library contains saturated hydrocarbons, aromatics, sulfur-containing compounds, oxygen-containing compounds, basic nitrides, and non-basic nitrides. The DBE range for saturated hydrocarbons is 0-9, and the C# range is 7-60; the DBE range for aromatics is 4-20, and the C# range is 10-60. Sulfur-containing compounds include thioethers and thiophenes. Specifically, the DBE range for S1 compounds in thioethers is 0-17, and the C# range is 7-60; the DBE range for S2 compounds in thioethers is 3-15, and the C# range is 10-60. The DBE range for S1 compounds in thiophenes is 3-18, and the C# range is 7-60; the DBE range for S2 compounds in thiophenes is 8-16, and the C# range is 10-60. Among oxygen-containing compounds, the DBE range for compounds containing one oxygen atom (O1) is 4-19, and the C# range is 7-60; the DBE range for compounds containing two oxygen atoms (O2) is 1-17, and the C# range is 7-60; the DBE range for compounds containing three oxygen atoms (O3) is 5-9, and the C# range is 7-60; and the DBE range for compounds containing four oxygen atoms (O4) is 3-7, and the C# range is 8-60. Among basic nitrides, the DBE range for compounds containing one nitrogen atom (N1) is 4-20, and the C# range is 7-60; the DBE range for compounds containing two nitrogen atoms (N2) is 5-19, and the C# range is 7-60; the DBE range for compounds containing one nitrogen atom and one sulfur atom (N1S1) is 5-20, and the C# range is 7-60; the DBE range for compounds containing one nitrogen atom and one oxygen atom (N1O1) is 4-21, and the C# range is 7-60; and the DBE range for compounds containing two nitrogen atoms and one oxygen atom (N2O1) is 6-18, and the C# range is 8-60. Among non-basic nitrides, the DBE range for compounds containing one nitrogen atom (N1) is 6-20, and the C# range is 8-60; the DBE range for compounds containing two nitrogen atoms (N2) is 8-19, and the C# range is 10-60; the DBE range for compounds containing one nitrogen atom and one sulfur atom (N1S1) is 9-19, and the C# range is 14-60; the DBE range for compounds containing one nitrogen atom and one oxygen atom (N1O1) is 4-18, and the C# range is 7-60; the DBE range for compounds containing one nitrogen atom and two oxygen atoms (N1O2) is 7-16, and the C# range is 9-60; and the DBE range for compounds containing two nitrogen atoms and one oxygen atom (N2O1) is 8-12, and the C# range is 10-60.

[0098] Furthermore, taking vacuum distillate oil as an example, based on the above embodiments, a predefined molecular library containing 12,138 molecules can be obtained through the side chain automatic expansion algorithm.

[0099] S203: Determine the combination form of probability density functions and the range of values ​​for probability density function parameters by using a predefined molecular library.

[0100] Optionally, after constructing a predefined molecular library, the molecular content needs to be optimized to determine the molecular composition of heavy oil. Molecular content optimization is the core of the method for detecting the molecular composition of heavy oil, and its purpose is to ensure that the macroscopic physical properties and molecular content distribution of the heavy oil molecular composition are consistent with the experimental data. The experimental data includes physical property data and high-resolution mass spectrometry data.

[0101] In this step, optimization variables can be defined by selecting appropriate combinations of probability density functions (PDFs) based on the constructed predefined molecular library, thereby indirectly optimizing the molecular content.

[0102] For example, commonly used probability density functions (PDFs) include the gamma probability density function (GammaPDF) and the histogram probability density function (HistogramPDF), as shown in equations (1) and (2), respectively:

[0103]

[0104] f(x) = x i (2)

[0105] In GammaPDF, there are three adjustable parameters: α, β, and γ, used for continuous property constraints, such as carbon number and boiling point. x is the probability density function parameter, i.e., the optimization variable. In HistogramPDF, X... i It refers to the content of a certain type of molecule, such as classifying heavy oil molecules into hydrocarbons, sulfur-containing compounds, nitrogen-containing compounds, and oxygen-containing compounds, X i This corresponds to the content of hydrocarbons, sulfur-containing compounds, nitrogen-containing compounds, and oxygen-containing compounds.

[0106] In addition, the range of values ​​for the probability density function parameters can be determined by combining physical property data, high-resolution mass spectrometry data, and probability density function parameters.

[0107] Figure 4 This is a schematic diagram of the structure of a combination of probability density functions provided in an embodiment of this application. For example... Figure 4As shown, the molecular types of heavy oil molecules include hydrocarbons, nitrides, sulfides, and oxygen-containing compounds. GammaPDF is used for the probability density functions characterizing the boiling point (Tb) and DBE of all heavy oil molecules; HistogramPDF is used for the probability density functions of other compounds such as O1, O2, N1, N1S1, S1, and S2, and the ratio of molecules containing cycloalkane ring structures to those without (AN / NN). It is worth noting that biomarkers exhibit unique distribution patterns among hydrocarbons and sulfur-containing compounds in heavy oil. Therefore, HistogramPDF, which adjusts the content of biomarkers, is included in the PDF combination to ensure the accuracy of their molecular content.

[0108] S204: Select the target value within the range and assign it to the corresponding probability density function parameter.

[0109] In step S203, the range of values ​​for the probability density function parameters has been determined. In this step, a target value can be randomly selected from the determined range as the initial parameter of the probability density function and assigned to the corresponding probability density function parameter.

[0110] S205: Determine the molecular content of the heavy oil to be tested based on the assigned probability density function parameters.

[0111] In this step, the preset molecular library, the combination form of the probability density function, and the assigned probability density function parameters can be obtained from steps S202, S203, and S204, respectively, thereby determining the molecular content of the heavy oil to be detected.

[0112] Furthermore, determining the molecular content of the heavy oil to be detected based on the assigned probability density function parameters can include: obtaining the probability value of the probability density function based on the assigned probability density function parameters; and determining the molecular content of the heavy oil based on the probability value. Specifically, the molecular content of the heavy oil to be detected obtained here is an initial molecular content, which still needs to be optimized using an optimization algorithm.

[0113] S206: Determine the macroscopic property similarity based on molecular content and physical property data, and determine the compositional similarity based on molecular content and high-resolution mass spectrometry data.

[0114] In this step, unlike existing petroleum molecule reconstruction methods based on macroscopic property similarity, this application uses a petroleum molecule reconstruction method based on both macroscopic property similarity and compositional similarity, employing high-resolution mass spectrometry data as reference data for heavy oil molecule reconstruction. Specifically, this method, based on macroscopic property similarity and compositional similarity, uses semi-quantitative molecular characterization data for petroleum molecule reconstruction, achieving maximum similarity between the petroleum molecule composition model and the molecular content distribution in the molecular characterization data.

[0115] For example, based on molecular content and physical property data, the macroscopic physical property similarity is determined, including: determining the macroscopic physical property similarity according to the following formula (3):

[0116] Prop Sim=-Prop Error=-mean(δ|P exp -P pred |) (3)

[0117] Where Prop Sim represents macroscopic property similarity, Prop Error represents macroscopic property error, and P exp P is the experimental value of macroscopic physical properties determined based on physical property data. pred It is a predicted value of macroscopic physical properties determined based on the detected molecular composition of heavy oil. δ is the weighting factor of the physical property data, and mean means the average value.

[0118] For example, compositional similarity is determined based on molecular content and high-resolution mass spectrometry data, including: determining compositional similarity according to the following formula (4):

[0119]

[0120] Among them, CS ij m represents the compositional similarity of homologues of type j in a compound of type i. model,ij m represents the molecular content of type j homologues in type i compounds in the heavy oil molecular composition model. FT-ICR MS,ij This represents the molecular abundance of type j homologues within type i compounds in high-resolution mass spectrometry data, with ∑ indicating the summation symbol. Specifically, CS ij The value is between -1 and 1, and the larger the value, the more similar the two are. S207: Determine the heavy oil molecular composition of the heavy oil to be tested to be a heavy oil molecular composition that meets the similarity requirements in terms of composition similarity and macroscopic physical property similarity.

[0121] In some embodiments, determining that the heavy oil molecular composition of the heavy oil to be tested meets the similarity requirements in terms of compositional similarity and macroscopic physical property similarity may include:

[0122] The molecular composition of the heavy oil to be tested is optimized according to the following formula:

[0123]

[0124] Stx low ≤x≤x up

[0125] Where Prop Error is the macroscopic property error between the heavy oil molecular composition and physical property data detected by the detection method, Comp Sim is the compositional similarity between the heavy oil molecular composition detected by the detection method and the high-resolution mass spectrometry data, ω is the weighting factor between the two objective functions, and x is the optimization variable. low and x up These represent the minimum and maximum values ​​of the optimization variables, which can be determined using physical property data; ∑ represents the summation sign, N represents the number of physical property data points, and δ... L P represents the weighting factor for the Lth physical property data. exp,L P represents the experimental value of the macroscopic property of the Lth physical property data. pred,L This represents the predicted macroscopic physical properties of the heavy oil molecules in the Lth heavy oil sample detected by the detection method, where k is the number of different types of compounds, n is the number of different homologues, and CS ij This indicates the compositional similarity of homologues of type j in a compound of type i;

[0126] Determine whether the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition meet the similarity requirements; if they meet the similarity requirements, then the heavy oil molecular composition of the heavy oil to be tested is determined to be the optimized heavy oil molecular composition.

[0127] In addition, Comp Sim is determined according to the following formula:

[0128]

[0129] Where k is the number of different types of compounds, CS i The compositional similarity of compounds of type i is calculated according to the following formula:

[0130]

[0131] Where n is the number of different homologous species, CS ij This represents the compositional similarity of homologues of type j within compound type i. That is, when calculating CompSim, the compositional similarity of each homologue molecule within the same type of compound should be calculated first, and then the average value should be taken as the compositional similarity of that type of compound to ensure the effective use of high-resolution mass spectrometry data; next, the compositional similarity of different types of compounds should be calculated, and then the average value of the compositional similarity of different types of compounds should be taken as the CompSim value.

[0132] Optionally, the compositional similarity of each homologue molecule in the same type of compound can be expressed by the cosine similarity formula, as shown in formula (4) above.

[0133] The method for detecting the molecular composition of heavy oil provided in this application, based on macroscopic physical property similarity and combined with high-resolution mass spectrometry data, incorporates compositional similarity for the detection of the molecular composition of heavy oil. This makes the molecular content distribution of the detected heavy oil molecular composition more consistent with the molecular content distribution of the high-resolution mass spectrometry data, thereby improving the accuracy of the molecular content distribution in the composition of heavy oil molecules.

[0134] Based on the above embodiments, determining whether the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition meet the similarity requirements may include: determining the weighted sum of the compositional similarity and macroscopic property similarity; determining whether the weighted sum meets the similarity requirements, wherein the similarity requirements include that the macroscopic property similarity between the heavy oil molecular composition and the property data is greater than or equal to the reference macroscopic property similarity, and / or that the compositional similarity between the heavy oil molecular composition and the high-resolution mass spectrometry data is greater than or equal to the reference compositional similarity, wherein the reference macroscopic property similarity is the currently determined optimal macroscopic property similarity, and the reference compositional similarity is the currently determined optimal compositional similarity; if the weighted sum meets the similarity requirements, then it is determined that the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition meet the similarity requirements; if the weighted sum does not meet the similarity requirements, then it is determined that the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition do not meet the similarity requirements, and the step of selecting a target value within the value range and assigning it to the corresponding probability density function parameter is executed.

[0135] Figure 5 This is a schematic flowchart of a method for detecting the molecular composition of heavy oil provided in another embodiment of this application. Figure 5 As shown, the input data includes macroscopic physical property data and high-resolution mass spectrometry data. Based on the input data, the molecular composition of heavy oil can be initialized. Initialization includes a predefined molecular library, a combination of probability density functions, and determining the range of values ​​for the probability density function parameters. The specific implementation process can be found in the above embodiment and will not be repeated here. The predefined molecular library contains the possible molecular structures of the heavy oil to be detected. After the molecular structure is determined, the molecular properties can be determined. Based on the molecular properties and molecular content, the macroscopic physical properties (i.e., petroleum properties) of the heavy oil molecular composition can be determined. Macroscopic physical properties include elemental content and group composition. Then, the macroscopic physical properties of the heavy oil molecular composition are compared with the macroscopic physical properties of the physical property data to determine the macroscopic physical property similarity; the molecular content of the heavy oil molecular composition is compared with the high-resolution mass spectrometry data to determine the compositional similarity. Finally, determine whether the weighted sum of macroscopic property similarity and compositional similarity meets the similarity requirement. If yes, the compositional similarity and macroscopic property similarity of heavy oil molecules meet the similarity requirement, and the composition of heavy oil molecules can be output. If not, the compositional similarity and macroscopic property similarity of heavy oil molecules do not meet the similarity requirement, and it is necessary to continue to execute the step of selecting the target value within the range and assigning it to the corresponding probability density function parameter in order to optimize the composition of heavy oil molecules.

[0136] In some embodiments, selecting a target value within a range and assigning it to the corresponding probability density function parameter can include: selecting a target value within a range and assigning it to the corresponding probability density function parameter based on a bi-objective optimization algorithm, where the bi-objective optimization algorithm includes a genetic algorithm. For example, when optimizing the compositional similarity and macroscopic property similarity of heavy oil molecules by iteratively calculating the optimal compositional similarity and macroscopic property similarity using a bi-objective optimization algorithm, the combination of high-resolution mass spectrometry data in calculating the compositional similarity makes the molecular content distribution of the optimized heavy oil molecule composition closer to the high-resolution mass spectrometry data. This allows for the determination of a unique solution after multiple optimizations, improving optimization efficiency.

[0137] Specifically, the bi-objective optimization algorithm used in this application can be a genetic algorithm (GA).

[0138] In some embodiments, the heavy oil includes vacuum gas oil (VGO), and the detection method for the molecular composition of vacuum gas oil (i.e., the vacuum gas oil molecular composition model) can be constructed using the heavy oil molecular composition detection method provided in this application. Figure 6 This is a schematic flowchart of a method for detecting the molecular composition of reduced-pressure diesel fuel according to an embodiment of this application. (Reference) Figure 6 The method for detecting the molecular composition of depressurized diesel fuel in the embodiments of this application may include the following steps:

[0139] S601: Acquire the physical property data and high-resolution mass spectrometry data of the VGO to be detected.

[0140] The relevant instructions for this step are similar to those for step S201, and will not be repeated here.

[0141] S602: Construct a predefined molecular library of VGO based on physical property data and high-resolution mass spectrometry data.

[0142] The relevant instructions for this step are similar to those for step S202, and will not be repeated here.

[0143] Specifically, the probability density function combination is set as follows: ① The Histogram function is used to classify compounds of different types; ② The Histogram function is used to further classify compounds with different core structures under different types of compound classification; ③ The Gamma function is used to extract the boiling points of all molecules.

[0144] S603: Determine the combination form of probability density functions and the range of values ​​for probability density function parameters by using a predefined molecular library.

[0145] The relevant instructions for this step are similar to those for step S203, and will not be repeated here.

[0146] S604: Select the target value from the range of values ​​for the probability density function parameters and assign it to the corresponding probability density function parameters.

[0147] The relevant instructions for this step are similar to those for step S204, and will not be repeated here.

[0148] S605: Determine the molecular content of VGO molecules based on the assigned probability density function parameters.

[0149] The relevant instructions for this step are similar to those for step S205, and will not be repeated here.

[0150] S606: Determine the macroscopic property similarity based on molecular content and physical property data, and determine the compositional similarity based on molecular content and high-resolution mass spectrometry data.

[0151] The relevant instructions for this step are similar to those for step S206, and will not be repeated here.

[0152] Specifically, a predefined molecular library contains the molecular structures that the VGO to be detected may contain. Once the molecular structure is determined, the molecular properties can be determined. Based on the molecular properties and molecular content, the macroscopic properties of the VGO can be determined, including elemental content and group composition. The macroscopic properties of the VGO molecular composition are compared with the macroscopic properties of the physical property data to determine the similarity of macroscopic properties.

[0153] S607: Determine the molecular composition of the VGO to be detected to be a molecular composition that meets the similarity requirements in terms of compositional similarity and macroscopic physical property similarity.

[0154] The relevant instructions for this step are similar to those for step S207, and will not be repeated here.

[0155] S608: Uses a genetic algorithm to optimize the molecular composition of VGO.

[0156] The relevant instructions for this step are as described in the above embodiments, and will not be repeated here.

[0157] Figure 7 This is a schematic diagram illustrating the detection results of a typical VGO molecular composition detection method provided in an embodiment of this application. For example... Figure 7 As shown, Figure 7 Figure (a) shows the distillation curve (volume fraction (v%) - distillation temperature (K)) of VGO molecular composition detected by the detection method proposed in this application, which is consistent with experimental data. Figure 7 (b) shows that the content of different compounds in the VGO molecular composition detected by the detection method proposed in this application is consistent with the experimental data, that is, the content of different compounds as a mass fraction (wt%) is consistent with the experimental data. Among them, the different compounds include hydrocarbons, thiophenes, thioethers, oxides, non-basic nitrides and basic nitrides.

[0158] Furthermore, Figure 8 This is a schematic diagram showing the comparison between the molecular content distribution detected by a typical VGO molecule composition detection method and high-resolution mass spectrometry data, provided in an embodiment of this application. It is used to compare the consistency between the molecular content distribution of different compounds and the molecular content distribution in high-resolution mass spectrometry data. Figure 8 The image shows a carbon number-DBE plot. Each circle represents a type of molecule, and the size of the circle indicates the molecular abundance. It can be seen that the molecular abundance distributions of hydrocarbons, sulfides, oxides, basic nitrides, and non-basic nitrides are consistent with the high-resolution mass spectrometry data. Figure 8 The figures shown in rows 2, 4, and 6 represent the molecular content distribution detected by the detection method of this application. Figure 8 The figures shown in rows 1, 3, and 5 represent the molecular content distribution of high-resolution mass spectrometry data. These results demonstrate that the method for detecting the molecular composition of heavy oil based on high-resolution mass spectrometry data can effectively construct a method for detecting VGO molecular composition with accurate macroscopic properties and molecular content distribution.

[0159] In addition, to demonstrate the universality of the detection method for heavy oil molecular composition based on high-resolution mass spectrometry data, Figure 9 This is a schematic diagram of the macroscopic property error statistics of a multi-group VGO composition model provided in an embodiment of this application. Figure 9 This method, which involves constructing multiple VGO molecular composition detection methods (i.e., multiple VGO composition models) from different sources with significant compositional differences using the heavy oil molecular composition detection method provided in this application, is obtained. The specific construction method is the same as in the above embodiments and will not be repeated here. Figure 9 As shown in (a), (b), (c), (d), and (e), the predicted values ​​of density, elemental content (including nitrogen, sulfur, and oxygen content), and distillation range (or boiling point) of multiple VGO groups are consistent with the corresponding experimental data, demonstrating the accuracy and universality of the detection method provided in this application in detecting the macroscopic physical properties of multiple VGO molecular compositions.

[0160] Figure 10 This is a schematic diagram showing the comparison between the molecular content distribution detected by a detection method using multiple groups of VGO molecules according to an embodiment of this application and high-resolution mass spectrometry data. It is used to compare the consistency between the molecular content distribution in multiple groups of VGO molecules and the molecular content distribution in high-resolution mass spectrometry data. Figure 10As shown in the figures, (a), (c), and (e) illustrate the relationship between the content-mass fraction (wt%) of different compounds and experimental data, with black representing experimental data values ​​and gray representing predicted values. Figures (b), (d), and (f) show the carbon number-DBE relationship of different compounds, from left to right: aromatics, thiophenes (S1), acidic oxides (O1), and basic nitrides (N1). The predicted values ​​of the contents of different types of compounds detected by the multi-group VGO molecular composition detection method are consistent with the experimental data, reproducing the molecular content distribution pattern of different types of molecules in high-resolution mass spectrometry, and demonstrating the universality of the heavy oil molecular composition detection method based on high-resolution mass spectrometry. Figure 10 In the VGO1 data corresponding to (a) and (b) and the VGO3 data corresponding to (e) and (f), the molecular composition of typical compounds differs significantly, while the VGO2 data corresponding to (c) and (d) are rich in biomarker compounds.

[0161] Furthermore, this application also constructs a method for detecting the molecular composition of VGO in different fractions (i.e., a VGO composition model for different fractions) based on the heavy oil molecular composition detection method provided in this application. Figure 11 This is a schematic diagram illustrating the detection results of a method for detecting the molecular composition of VGO in different fractions provided in another embodiment of this application. Figure 11 The diagram illustrates a method for detecting the molecular composition of VGO in different fractions of vacuum distillate extracted from different side streams of a vacuum distillation tower. (a) shows the vacuum distillation tower, from which fractions 1, 2, 3, and 4 are obtained, with fraction 1 being the lightest and fraction 4 the heaviest. (b) and (f) are comparison charts of the detection results for fraction 1; (c) and (g) are comparison charts of the detection results for fraction 2; (d) and (h) are comparison charts of the detection results for fraction 3; and (e) and (i) are comparison charts of the detection results for fraction 4. In... Figure 11 In the figures, (b), (c), (d), and (e) show the relationship between the volume fraction (v%) of VGO molecular composition in different fractions, the distillation temperature (K), and experimental data. It can be seen that as the fraction becomes heavier, the distillation temperature gradually increases, which is consistent with the experimental pattern. (f), (g), (h), and (i) show the carbon number-DBE relationship of different compounds. From left to right, they are aromatics, thiophenes (S1), acidic oxides (O1), basic nitrides (N1), and non-basic nitrides (N1). It can be seen that the molecular content distribution in VGO molecular composition in different fractions gradually shifts towards the higher carbon number and higher DBE end as the fraction becomes heavier. The detection method provided in this application reproduces the above experimental pattern very well. Moreover, the molecular content distribution of different types of compounds in VGO molecular composition in different fractions is consistent with the molecular content distribution of high-resolution mass spectrometry data, which further proves the universality of the detection method of heavy oil molecular composition based on high-resolution mass spectrometry.

[0162] In summary, this application has at least the following advantages:

[0163] 1. Based on macroscopic physical property similarity, and combined with high-resolution mass spectrometry data, compositional similarity is added for the detection of heavy oil molecular composition. This makes the molecular content distribution of the detected heavy oil molecular composition more consistent with the molecular content distribution of the high-resolution mass spectrometry data, thereby improving the accuracy of the molecular content distribution in the heavy oil molecular composition.

[0164] 2. The construction of the predefined molecular library incorporates the characteristics of high-resolution mass spectrometry data, making the resulting predefined molecular library more consistent with the actual situation and ensuring the accuracy of the molecular content distribution in the heavy oil molecular composition.

[0165] 3. It effectively utilizes high-resolution mass spectrometry data, enabling the detection method for heavy oil molecular composition based on high-resolution mass spectrometry data to have good accuracy, repeatability and universality.

[0166] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0167] Figure 12 This is a schematic diagram of a heavy oil molecular composition detection device provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. Figure 12 As shown, the heavy oil molecule composition detection device 120 includes: an acquisition module 1201, a construction module 1202, a first determination module 1203, an assignment module 1204, a second determination module 1205, a third determination module 1206, and a fourth determination module 1207. Wherein:

[0168] The acquisition module 1201 is used to acquire the physical property data and high-resolution mass spectrometry data of the heavy oil to be tested;

[0169] Module 1202 is used to construct a predefined molecular library based on physical property data and high-resolution mass spectrometry data;

[0170] The first determining module 1203 is used to determine the combination form of probability density functions and the range of values ​​for probability density function parameters by using a predefined molecular library;

[0171] The assignment module 1204 is used to select a target value within the range of values ​​and assign it to the corresponding probability density function parameter;

[0172] The second determining module 1205 is used to determine the molecular content of the heavy oil to be detected based on the assigned probability density function parameters.

[0173] The third determination module 1206 is used to determine the macroscopic property similarity based on molecular content and physical property data, and to determine the compositional similarity based on molecular content and high-resolution mass spectrometry data.

[0174] The fourth determination module 1207 is used to determine the heavy oil molecular composition of the heavy oil to be tested to be a heavy oil molecular composition that meets the similarity requirements in terms of composition similarity and macroscopic physical property similarity.

[0175] In one possible implementation, the fourth determining module 1207 may be specifically used for:

[0176] The molecular composition of the heavy oil to be tested is optimized according to the following formula:

[0177]

[0178] Stx low ≤x≤x up

[0179] Where Prop Error is the macroscopic property error between the heavy oil molecular composition and physical property data detected by the detection method, Comp Sim is the compositional similarity between the heavy oil molecular composition detected by the detection method and the high-resolution mass spectrometry data, ω is the weighting factor between the two objective functions, and x is the optimization variable. low and x up These represent the minimum and maximum values ​​of the optimization variables, determined using physical property data; ∑ denotes the summation sign, N represents the number of physical property data points, and δ... L P represents the weighting factor for the Lth physical property data. exp,L P represents the experimental value of the macroscopic property of the Lth physical property data. pred,L This represents the predicted macroscopic physical properties of the heavy oil molecules in the Lth heavy oil sample detected by the detection method, where k is the number of different types of compounds, n is the number of different homologues, and CS ij This indicates the compositional similarity of homologues of type j in a compound of type i;

[0180] Determine whether the compositional similarity and macroscopic physical property similarity of the optimized heavy oil molecular composition meet the similarity requirements;

[0181] If the similarity requirement is met, the molecular composition of the heavy oil to be tested is determined to be the optimized molecular composition of the heavy oil.

[0182] In one possible implementation, the fourth determining module 1207 can also be used to determine CompSim according to the following formula:

[0183]

[0184] Where k is the number of different types of compounds, CS i The compositional similarity of compounds of type i is calculated according to the following formula:

[0185]

[0186] Where n is the number of different homologous species, CS ij This represents the compositional similarity of homologues of type j in a compound of type i.

[0187] In one possible implementation, the fourth determining module 1207 can also be used to: determine the weighted sum of compositional similarity and macroscopic property similarity; determine whether the weighted sum meets the similarity requirements, the similarity requirements including that the macroscopic property similarity between the heavy oil molecular composition and the property data is greater than or equal to the reference macroscopic property similarity, and / or that the compositional similarity between the heavy oil molecular composition and the high-resolution mass spectrometry data is greater than or equal to the reference compositional similarity, the reference macroscopic property similarity being the currently determined optimal macroscopic property similarity, and the reference compositional similarity being the currently determined optimal compositional similarity; if the weighted sum meets the similarity requirements, then it is determined that the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition meet the similarity requirements; if the weighted sum does not meet the similarity requirements, then it is determined that the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition do not meet the similarity requirements, and the step of selecting a target value within the value range and assigning it to the corresponding probability density function parameter is executed.

[0188] In one possible implementation, the construction module 1202 can be specifically used to: determine the types of compounds contained in the predefined molecular library based on the molecular types of physical property data and high-resolution mass spectrometry data; define the core molecular structure of the compound types contained in the predefined molecular library based on the equivalent double bond number of the compound types in the high-resolution mass spectrometry data; and expand the side chain carbon number on the core molecular structure using the SU-BEM framework based on the carbon number range of different types of molecules in the high-resolution mass spectrometry data to obtain the predefined molecular library.

[0189] In one possible implementation, the assignment module 1204 can be specifically used to: select a target value within a range based on a bi-objective optimization algorithm and assign it to the corresponding probability density function parameter. The bi-objective optimization algorithm includes a genetic algorithm.

[0190] In one possible implementation, the second determining module 1205 may be specifically used to: obtain the probability value of the probability density function based on the assigned probability density function parameters; and determine the molecular content of heavy oil based on the probability value.

[0191] The heavy oil molecule composition detection device provided in this application embodiment has a similar implementation principle and technical effect to the above embodiments. For details, please refer to the above embodiments, which will not be repeated here.

[0192] Figure 13 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. For example, the electronic device can be provided as a server. Figure 13 As shown, the electronic device 130 includes:

[0193] Processor 1301 further includes one or more processors and memory resources represented by memory 1302 for storing instructions executable by processor 1301, such as application programs. The application programs stored in memory 1302 may include one or more modules, each corresponding to a set of processing instructions. Furthermore, processor 1301 is configured to execute instructions to perform the aforementioned method for detecting the composition of heavy oil molecules.

[0194] Electronic device 130 may also include a power supply component 1303 configured to perform power management of electronic device 130, a wired or wireless network interface 1304 configured to connect electronic device 130 to a network, and an input / output interface 1305. Electronic device 130 can operate on an operating system stored in memory 1302, such as x86, Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0195] Figure 13 The processor 1301 mentioned can be a general-purpose processor, including a central processing unit, a network processor (NP), etc.; a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0196] The memory 1302 may include random access memory (RAM), and may also include static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk, such as at least one disk storage device.

[0197] Those skilled in the art will understand that Figure 13 The electronic devices shown do not constitute a limitation on electronic devices and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0198] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed, the above-mentioned method for detecting the composition of heavy oil molecules is implemented.

[0199] This application also provides a computer program product, including a computer program that, when executed, implements the above-mentioned method for detecting the composition of heavy oil molecules.

[0200] This application also provides a chip for executing instructions, which is used to perform the heavy oil molecule composition detection method as described in any of the above method embodiments.

[0201] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0202] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0203] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for detecting the molecular composition of heavy oil, characterized in that, include: Acquire the physical properties and high-resolution mass spectrometry data of the heavy oil to be tested; Based on the physical property data and the high-resolution mass spectrometry data, a predefined molecular library is constructed; The combination form of the probability density function and the range of values ​​for the probability density function parameters are determined using the predefined molecular library. Within the range of values, select the target value and assign it to the corresponding probability density function parameter; The molecular content of the heavy oil to be detected is determined based on the assigned probability density function parameters. Based on the molecular content and the physical property data, the macroscopic physical property similarity is determined, and based on the molecular content and the high-resolution mass spectrometry data, the compositional similarity is determined. The heavy oil molecular composition of the heavy oil to be tested is determined to be a heavy oil molecular composition that meets the similarity requirements of composition similarity and macroscopic physical property similarity; The determination of the heavy oil molecular composition of the heavy oil to be tested as a heavy oil molecular composition that meets the similarity requirements of composition similarity and macroscopic physical property similarity includes: The heavy oil molecular composition of the heavy oil to be tested is optimized according to the following formula: Wherein, Prop Error is the macroscopic property error between the heavy oil molecular composition and physical property data of the heavy oil to be detected by the detection method, Comp Sim is the compositional similarity between the heavy oil molecular composition and high-resolution mass spectrometry data of the heavy oil to be detected by the detection method, ω is the weighting factor between the two objective functions; x is the optimization variable, x low and x up These represent the minimum and maximum values ​​of the optimization variables, determined using physical property data; ∑ denotes the summation sign, N represents the number of physical property data points, and δ... L P represents the weighting factor for the Lth physical property data. exp,L P represents the experimental value of the macroscopic property of the Lth physical property data. pred,L This represents the predicted macroscopic physical property of the heavy oil molecules of the Lth heavy oil sample detected by the aforementioned detection method, where k is the number of different types of compounds, n is the number of different homologues, and CS ij This indicates the compositional similarity of homologues of type j in a compound of type i; Determine whether the compositional similarity of the optimized heavy oil molecular composition and the macroscopic physical property similarity meet the similarity requirements; If the similarity requirement is met, the heavy oil molecular composition of the heavy oil to be detected is determined to be the optimized heavy oil molecular composition. The Comp Sim is determined according to the following formula: Where k is the number of different types of compounds, CS i The compositional similarity of compounds of type i is calculated according to the following formula: Where n is the number of different homologous species, CS ij This represents the compositional similarity of homologues of type j in a compound of type i.

2. The detection method according to claim 1, characterized in that, Whether the compositional similarity of the optimized heavy oil molecular composition and the macroscopic physical property similarity meet the similarity requirements includes: Determine the weighted sum of compositional similarity and macroscopic property similarity; Determine whether the weighted sum meets the similarity requirements, wherein the similarity requirements include that the macroscopic property similarity between the heavy oil molecular composition and the physical property data is greater than or equal to the reference macroscopic property similarity, and / or that the compositional similarity between the heavy oil molecular composition and the high-resolution mass spectrometry data is greater than or equal to the reference compositional similarity, wherein the reference macroscopic property similarity is the currently determined optimal macroscopic property similarity, and the reference compositional similarity is the currently determined optimal compositional similarity; If the weighted sum meets the similarity requirement, then the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition are determined to meet the similarity requirement. If the weighted sum does not meet the similarity requirement, then the compositional similarity and macroscopic property similarity of the optimized heavy oil molecular composition do not meet the similarity requirement, and the step of selecting the target value within the range and assigning it to the corresponding probability density function parameter is executed.

3. The detection method according to any one of claims 1 to 2, characterized in that, The step of constructing a predefined molecular library based on the physical property data and the high-resolution mass spectrometry data includes: Based on the molecular types of the physical property data and the high-resolution mass spectrometry data, determine the types of compounds contained in the predefined molecular library; Based on the equivalent number of double bonds of the compound types in the high-resolution mass spectrometry data, the core molecular structure of the compound types contained in the predefined molecular library is defined. Based on the carbon number ranges of different types of molecules in the high-resolution mass spectrometry data, the side chain carbon numbers are expanded on the core molecular structure through the structural unit and bond electron matrix SUBEM framework to obtain a predefined molecular library.

4. The detection method according to any one of claims 1 to 2, characterized in that, The step of selecting a target value within the range and assigning it to the corresponding probability density function parameter includes: Based on a bi-objective optimization algorithm, an objective value is selected within the range of values ​​and assigned to the corresponding probability density function parameter. The bi-objective optimization algorithm includes a genetic algorithm.

5. The detection method according to any one of claims 1 to 2, characterized in that, The step of determining the molecular content of the heavy oil to be detected based on the assigned probability density function parameters includes: Based on the assigned probability density function parameters, the probability value of the probability density function is obtained; The molecular content of the heavy oil is determined based on the probability value.

6. A device for detecting the composition of heavy oil molecules, characterized in that, include: The acquisition module is used to acquire the physical property data and high-resolution mass spectrometry data of the heavy oil to be tested; The construction module is used to construct a predefined molecular library based on the physical property data and the high-resolution mass spectrometry data; The first determining module is used to determine the combination form of the probability density function and the range of values ​​for the probability density function parameters through the predefined molecular library; The assignment module is used to select a target value within the range of values ​​and assign it to the corresponding probability density function parameter; The second determining module is used to determine the molecular content of the heavy oil to be detected based on the assigned probability density function parameters; The third determining module is used to determine the macroscopic property similarity based on the molecular content and the physical property data, and to determine the compositional similarity based on the molecular content and the high-resolution mass spectrometry data. The fourth determining module is used to determine that the heavy oil molecular composition of the heavy oil to be detected is a heavy oil molecular composition that meets the similarity requirements of composition similarity and macroscopic physical property similarity. The fourth determining module is specifically used to optimize the heavy oil molecular composition of the heavy oil to be detected according to the following formula: Wherein, Prop Error is the macroscopic property error between the heavy oil molecular composition and physical property data of the heavy oil to be detected by the detection device, Comp Sim is the compositional similarity between the heavy oil molecular composition detected by the detection device and the high-resolution mass spectrometry data, ω is the weighting factor between the two objective functions; x is the optimization variable, x low and x up These represent the minimum and maximum values ​​of the optimization variables, determined using physical property data; ∑ denotes the summation sign, N represents the number of physical property data points, and δ... L P represents the weighting factor for the Lth physical property data. exp,L P represents the experimental value of the macroscopic property of the Lth physical property data. pred,L This represents the predicted macroscopic physical properties of the heavy oil molecules in the Lth heavy oil sample detected by the detection device, where k is the number of different types of compounds, n is the number of different homologues, and CS ij This indicates the compositional similarity of homologues of type j in a compound of type i; Determine whether the compositional similarity of the optimized heavy oil molecular composition and the macroscopic physical property similarity meet the similarity requirements; If the similarity requirement is met, the heavy oil molecular composition of the heavy oil to be detected is determined to be the optimized heavy oil molecular composition. The Comp Sim is determined according to the following formula: Where k is the number of different types of compounds, CS i The compositional similarity of compounds of type i is calculated according to the following formula: Where n is the number of different homologous species, CS ij This represents the compositional similarity of homologues of type j in a compound of type i.

7. An electronic device, characterized in that, include: Memory and processor; The memory is used to store program instructions; The processor is used to call program instructions in the memory to execute the method for detecting the composition of heavy oil molecules as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, implement the method for detecting the composition of heavy oil molecules as described in any one of claims 1 to 5.

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