A method for identifying micro-association structure in dense oil molecular simulation trajectories

Abstract

The application provides a method for identifying micro-aggregate structure in a heavy oil molecule simulation trajectory, which comprises the following steps: analyzing a molecular dynamics trajectory, identifying aromatic ring structures in heavy oil molecular components and calculating geometric centers of the aromatic ring structures, combining periodic boundary conditions, and applying an improved DBSCAN clustering algorithm to realize automatic identification and clustering of the aggregates; the clustering results are post-processed and optimized, the final aggregate set is generated after merging and sharing clusters, and the number, size distribution, morphological characteristics and dynamic evolution information of the aggregates are analyzed based on the structural characteristics of the aggregates. The method can accurately capture the pi-pi stacking behavior of heavy oil molecules, and further reveal the morphological characteristics, dynamic evolution law of heavy oil micro-aggregates and the influence of the micro-aggregates on the apparent viscosity of heavy oil, thereby providing theoretical support for the analysis of the viscosity-causing mechanism of heavy oil and the design of viscosity reducers, and complementing experimental research, and promoting the efficient development and green utilization of complex heavy oil systems.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of molecular simulation, and in particular to a method for identifying micro-aggregate structures in a heavy oil molecular simulation trajectory. BACKGROUND

[0002] Heavy oil is a kind of heavy oil with complex molecular composition and various structures, which has poor flowability and seriously restricts the efficiency of oilfield exploitation and transportation. A large number of studies have shown that the high viscosity characteristics of heavy oil are not only related to the macromolecular components such as resin and asphaltene in the molecules, but also closely related to the micro-aggregate structure dominated by π-π stacking interaction between molecules. The micro-aggregate structure (π-π interaction induced) is considered to be one of the important reasons for the increase in viscosity of heavy oil.

[0003] At present, the research on the molecular association behavior of heavy oil mainly relies on experimental methods and molecular dynamics simulation. Although the experimental method can reveal the viscosity characteristics of heavy oil at the macroscopic scale, it is difficult to directly observe the micro-aggregate structure and analyze the mechanism of viscosity of heavy oil. Molecular dynamics simulation can provide trajectory information at the molecular level, providing an important means for exploring the formation mechanism and evolution law of the aggregate. However, in processing molecular dynamics trajectory, the conventional clustering analysis is usually based on the overall clustering of all atoms or molecular centers, but the π-π association structure of heavy oil is often mainly participated by aromatic structure. If this feature is ignored, not only a large amount of irrelevant information will be introduced, but also the recognition efficiency of the aggregate will be low, the degree of automation will be insufficient, and it will be difficult to deal with large-scale data.

[0004] In addition, the existing clustering analysis method (such as the conventional DBSCAN algorithm) often fails to fully consider the influence of periodic boundary conditions when applied to molecular simulation data, which easily leads to misjudgment or omission of heavy oil aggregates, ignores aggregates across the periodic boundary, and is difficult to obtain accurate and reliable aggregate structure characteristics. The visualization of the recognition results and further structure-activity relationship analysis lack complete automation process support, which limits its application value in the analysis of viscosity mechanism of heavy oil and the design of viscosity reducer.

[0005] Therefore, an integrated method capable of automatically recognizing aromatic ring structure based on molecular dynamics trajectory, improving clustering analysis, and outputting results after processing and visualization is urgently needed to efficiently and accurately represent the micro-aggregate structure of heavy oil, reveal the internal relationship between the micro-aggregate structure and the apparent viscosity of heavy oil, and provide a new technical means for molecular mechanism research and engineering application of heavy oil. SUMMARY

[0006] Therefore, the present application aims to provide a method for identifying micro-aggregate structures in heavy oil molecular simulation trajectories, which is characterized by the centroid of aromatic structures, and realizes accurate identification, dynamic tracking and statistical analysis of aggregate structures by combining improved density-based clustering and post-processing of results.

[0007] To achieve the above object, the technical scheme of the present application is as follows:

[0008] A method for identifying micro-aggregate structures in heavy oil molecular simulation trajectories, comprising the following steps:

[0009] S1, analyzing the molecular dynamics trajectory of the constructed heavy oil model, identifying the aromatic ring structure in the heavy oil molecules and extracting its geometric features;

[0010] S2, using an improved clustering algorithm to cluster the identified aromatic ring structure to obtain preliminary aggregate structures;

[0011] S3, post-processing the clustering results to form an optimized aggregate molecule set;

[0012] S4, generating aggregate structure data and visualization results;

[0013] S5, based on the aggregate structure data, the structure of the aggregate is characterized, analyzed, and the relationship between the micro-aggregated structure and the apparent viscosity of the heavy oil is obtained.

[0014] Further, the aromatic ring structure identification method is as follows:

[0015] For six-membered ring structure, if all the atoms on the ring are carbon atoms and the number of atoms connected to the carbon atom is 3, then the ring structure is an aromatic ring structure; if the ring has both carbon atoms and oxygen atoms or nitrogen atoms or sulfur atoms, and the number of atoms connected to the carbon atom is 3, and the number of atoms connected to the oxygen atom, nitrogen atom or sulfur atom is 2, then the ring structure is an aromatic ring structure;

[0016] For five-membered ring structure, if the ring has both carbon atoms and nitrogen atoms, and the number of atoms connected to the carbon atom and the nitrogen atom is 3, then the ring structure is an aromatic ring structure; if the ring has both carbon atoms and oxygen atoms or sulfur atoms, and the number of atoms connected to the carbon atom is 3, and the number of atoms connected to the oxygen atom or sulfur atom is 2, then the ring structure is an aromatic ring structure.

[0017] Further, the specific steps of S1 are as follows:

[0018] S11, verifying the structure file, trajectory file and related parameters of the heavy oil model to ensure the consistency of the molecular composition of the structure file and the trajectory file;

[0019] S12, reading the structure file and trajectory file information, and obtaining atomic coordinates, simulation box vectors and molecular topological relationships frame by frame;

[0020] S13, identify the aromatic ring structure in the molecule, and return the index of the atom in each aromatic ring and the topological relationship of the molecule to facilitate subsequent extraction of atomic coordinates or reverse mapping; extract the geometric features of the aromatic ring structure, i.e. calculate the geometric center coordinates of each aromatic ring structure to form an aromatic ring center coordinate dataset for each frame as the clustering input.

[0021] Further, in S2, the improved clustering algorithm is a DBSCAN algorithm based on considering the periodic boundary conditions, which considers the periodic boundary conditions of the simulation system when performing clustering, and modifies the distance calculation by using the nearest mirror principle to handle the nearest mirror atoms.

[0022] Further, the improved clustering algorithm uses a neighborhood radius parameter ε and a minimum sample number parameter minPts as clustering conditions, and sets the parameters based on the standard of π-π stacking interaction. When minPts aromatic ring geometric centers are within the threshold range ε, they can be clustered into an associated body, and the output result is the clustering label of each aromatic ring center in each frame.

[0023] Further, in S3, the post-processing includes reverse mapping the aromatic ring clustering results to the corresponding molecules, removing noise points, removing isolated molecule sets, and merging associated bodies that share molecules; wherein the noise points are molecules containing a single aromatic ring and not participating in the formation of associated bodies, and the isolated molecule set is a molecule containing multiple aromatic rings and not participating in the formation of associated bodies; the merging of associated bodies that share molecules uses an intersection and union determination algorithm to merge them into a consistent associated body set, thereby avoiding repeated or fragmented counting of the same associated body.

[0024] Further, in S4, the associated body structure data includes the molecular composition, number, size, shape and dynamic evolution characteristics of the associated body.

[0025] Further, in the visualization result, each associated body is assigned a different color to distinguish it. If the same associated body appears on different boundaries due to the periodic boundary condition, resulting in a visual split of the associated body, any one molecule is taken as a reference object, and the other molecules are translated to make all the molecules closest, thereby ensuring that the same associated body molecules are displayed in the nearest neighbor mode.

[0026] Further, in S5, the structure characterization includes one or more of the number of associated bodies, the proportion of molecular components, the total number of molecules participating in the associated body, the size distribution and evolution of the associated body, the radius of gyration, the length of the principal axis, the shape anisotropy factor, and the cluster density.

[0027] Compared with the prior art, the method for identifying micro-association structure in heavy oil molecular simulation trajectory has the following advantages:

[0028] (1) The method for identifying the structure of micro-aggregates in the molecular simulation trajectory of heavy oil according to the present application can systematically and comprehensively characterize the structural features of molecular aggregates in the heavy oil system. Through quantitative analysis of multi-dimensional indicators such as the number, size distribution, component participation, density and shape diversity of the aggregates, the micro-aggregation rules and intrinsic property differences of the heavy oil system can be revealed, which reflects the efficiency and applicability of the method in analyzing the molecular aggregation behavior and key molecular action of heavy oil, thereby highlighting the application value of the present application in the characterization and property analysis of heavy oil.

[0029] (2) The method for identifying the structure of micro-aggregates in the molecular simulation trajectory of heavy oil according to the present application can realize the whole-process calculation from molecular dynamics simulation trajectory to aggregate identification, visualization and statistical analysis, solving the problems of low efficiency of micro-aggregate identification, insufficient consideration of periodic boundary conditions, imperfect treatment of shared molecular clusters and difficulty in establishing quantitative correlation with rheological properties in the existing analysis of heavy oil molecular simulation trajectory. The method overcomes the low efficiency and poor precision of traditional manual and clustering algorithm identification methods. The method can be widely applied to the molecular action mechanism analysis, rheological property prediction and molecular design and optimization process of heavy oil and other complex heavy oil systems, and provides an efficient and automated calculation tool for studying the viscosity-causing mechanism and molecular control strategy of heavy oil. BRIEF DESCRIPTION OF DRAWINGS

[0030] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application, illustrate the preferred embodiments of the application and assist in the explanation of the application. The drawings included are:

[0031] Figure 1 is the basic scenario and logic diagram of the present application;

[0032] Figure 2 is the construction of the heavy oil system and the display of different asphaltenes;

[0033] Figure 3 is the molecular structure of saturated components used in the heavy oil system;

[0034] Figure 4 is the molecular structure of aromatic and resin components used in the heavy oil system;

[0035] Figure 5 is the microstructure of aggregates obtained by analyzing two equilibrium heavy oils based on the method of the present application;

[0036] Figure 6 is the change of the number of aggregates in two heavy oil systems over time;

[0037] Figure 7 is the probability distribution of the size of aggregates in two heavy oil systems;

[0038] Figure 8 The curves show the evolution of the size of the associative compounds in two heavy oil systems over time.

[0039] Figure 9 The probability density distribution curves of the associative density of the two heavy oil systems are shown.

[0040] Figure 10 The percentages and participation rates of the four components in the two heavy oil system associations are shown; SAT represents saturated components, ARO represents aromatic components, RES represents resins, and ASP represents asphaltenes.

[0041] Figure 11 The probability density distribution of the anisotropy factor for the shape of the associative aggregates in the two heavy oil systems;

[0042] Figure 12 The radial distribution function of the centroid of the two heavy oil system associations;

[0043] Figure 13 The probability density distribution of the closest distance between two heavy oil system associations is shown. Detailed Implementation

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] This invention proposes a method for identifying the microscopic associative structure in simulated trajectories of heavy oil molecules, the method comprising the following steps:

[0047] S1. Analyze the molecular dynamics trajectory of the constructed heavy oil model, identify the aromatic ring structure in the heavy oil molecule and extract its geometric features;

[0048] S11. Parameter parsing and verification:

[0049] This method supports input of single-frame molecular structure files and multi-frame molecular dynamics trajectory files. It allows selection of specific molecular sets for analysis and the setting of specific time periods and intervals for selective parsing. The method improves robustness and versatility by performing integrity and validity checks on the input structure files, trajectory files, and related parameters, including: 1) file format verification (accepting only structure and trajectory files); 2) topology and coordinate matching verification (verifying the consistency of molecular composition between the structure and trajectory files, i.e., verifying whether the molecular structures and numbers in the structure file match those in the trajectory file); and 3) time interval validity checks (including the rationality of the start frame, time interval, and end frame). For example, the start frame must be greater than or equal to 0, the end frame must be less than or equal to the total number of frames in the trajectory file, and the time interval must be less than the difference between the end frame and the start frame.

[0050] S12. Trajectory Analysis and Preprocessing:

[0051] The system reads the structure and trajectory information output from molecular dynamics simulations and uses the molecular dynamics trajectory analysis tool MDAnalysis to obtain atomic coordinates, simulation box vectors, and molecular topological relationships frame by frame. Based on user settings, preprocessing operations such as specific system screening (specific component sets), time period or time interval downsampling can be performed to reduce data redundancy and improve computational efficiency.

[0052] Among them, molecular topological relationships include the types of molecules in the system, the number of atoms in each molecule, whether each atom is in a ring structure, and the number of bonds each atom forms with other atoms, in order to help identify which atoms in the molecule are aromatic ring atoms.

[0053] S13. Identify aromatic ring structures in molecules. The identification results return the index of the atoms in each aromatic ring and their topological relationship in the molecule, which is convenient for subsequent extraction of atomic coordinates or reverse mapping. Extract the geometric features of aromatic ring structures, that is, calculate the geometric center coordinates of each aromatic ring structure to form an aromatic ring center coordinate dataset for each frame, which is used as clustering input.

[0054] The method for identifying aromatic ring structures is as follows:

[0055] For a six-membered ring structure, if the ring consists entirely of carbon atoms and the number of atoms bonded to each carbon atom is 3, then the ring structure is an aromatic ring structure; if the ring contains carbon atoms, oxygen atoms, nitrogen atoms, or sulfur atoms, and the number of atoms bonded to each carbon atom is 3, while the number of atoms bonded to each oxygen atom, nitrogen atom, or sulfur atom is 2, then the ring structure is an aromatic ring structure.

[0056] For a five-membered ring structure, if the ring contains both carbon and nitrogen atoms, and both carbon and nitrogen atoms are connected to 3 atoms, then the ring structure is an aromatic ring structure; if the ring contains both carbon and oxygen or sulfur atoms, and the carbon atom is connected to 3 atoms, and the oxygen or sulfur atom is connected to 2 atoms, then the ring structure is an aromatic ring structure.

[0057] S2. An improved clustering algorithm is used to cluster the identified aromatic ring structures to obtain preliminary association structures;

[0058] Among them, the improved clustering algorithm is the DBSCAN algorithm based on considering periodic boundary conditions. This algorithm considers the periodic boundary conditions of the simulated system when performing clustering, and corrects the distance calculation by adopting the nearest mirror principle to handle the nearest mirror atoms.

[0059] Under the definition of simulated box vectors, the shortest mirror distance corresponding to the coordinates of the aromatic ring center is calculated to generate a corrected distance matrix, thus avoiding molecules that cross the boundary from being incorrectly classified as being far away, thereby ensuring the accuracy and physical rationality of clustering.

[0060] Specific implementation method: When calculating the shortest mirror image distance of the aromatic ring's centroid, first calculate the coordinate difference between the two centroids, and then check whether the difference in each direction exceeds half the length of the box; if it does, subtract or add the box length to map it back to the nearest mirror image position. After this processing, calculate the distance between the two points, which is their shortest mirror image distance.

[0061] Specifically, the improved clustering algorithm uses the geometric centers of aromatic rings as input and employs the DBSCAN algorithm for density clustering. The clustering criterion considers the geometric characteristics of the π–π packing of aromatic rings: the nearest neighbor relationship is restricted by setting the neighborhood radius parameter ε=0.45nm, and the core points are determined by the minimum number of points minPts=2 (this can be modified according to the requirements of the association degree; the higher the value, the higher the association degree of the output associative group). When the geometric centers of two aromatic rings are within the threshold range, they can be clustered into one associative group.

[0062] S3. Post-process the clustering results to form an optimized set of associated molecules;

[0063] Aromatic ring centers are mapped back to molecule sets to obtain molecular-level association information, identifying the molecular components contained in each association. Noise points (molecules containing a single aromatic ring that did not participate in association formation) and isolated molecule sets (molecules containing multiple aromatic rings that did not participate in association formation) are removed. For cases where multiple associations share molecules, intersection and union algorithms are used to merge them into a consistent association set, thereby avoiding duplicate or fragmented counting of the same association.

[0064] S4. Generate association structure data and visualization results; association structure data includes the molecular composition, quantity, size, shape and dynamic evolution characteristics of the association.

[0065] Based on clustered molecular sets, an association structure file and a visualization script for 3D molecular display are generated, including display optimization instructions such as association coloring and transparency. The structure of all molecules involved in association formation is extracted; each association is assigned a different color in the visualization script for differentiation; molecules within the same association may appear on different boundaries due to periodic boundary conditions, leading to a visual splitting phenomenon in the association. Using any one molecule as a reference, other molecules are translated to ensure that all molecules within the same association are displayed in a nearest-neighbor manner.

[0066] S5. Based on the structural data of the associative compounds, the structure of the associative compounds is characterized and analyzed, and the relationship between the micro-aggregate structure and the apparent viscosity of heavy oil is obtained.

[0067] Structural characterization includes one or more of the following: number of associates, proportion of molecular components, percentage of total number of molecules involved in the associates, size distribution and evolution of the associates, radius of gyration, principal axis length, shape anisotropy factor, and cluster density.

[0068] By extracting the coordinates of all atoms in each aggregate and substituting them into existing formulas to obtain the corresponding data, the calculation is performed on all aggregates in each frame and the average value is taken.

[0069] The following will further illustrate the specific implementation process and application effects of this invention with actual experimental examples.

[0070] During the operation of this method, users only need to provide molecular structure files, molecular dynamics trajectory files and relevant analysis parameters, and the program can automatically execute the entire association identification and analysis process on the computer to obtain the microscopic aggregation structure and statistical characteristics of the heavy oil molecular system.

[0071] Example 1: Constructing a Heavy Oil Model

[0072] Two high-viscosity heavy oil systems with different viscosity-inducing mechanisms were constructed based on two different asphaltenes molecular systems. One group of asphaltenes exhibited relatively weak π-π interactions and contained island-type asphaltenes (as01-as04), while the other group of asphaltenes showed significant π-π interactions (as05). Figure 2 As shown. The remaining heavy oil components remain consistent, as... Figure 3 and Figure 4 As shown in Table 1.

[0073] Table 1 Composition of Heavy Oil System

[0074]

[0075] An initial heavy oil model was randomly constructed using Packmol modeling software. Two-step energy minimization processes, employing the steepest descent method and the conjugate gradient method, were performed to reduce interatomic energy and forces. Subsequently, a 10 ns pre-equilibrium molecular dynamics simulation was conducted under an isothermal-isobaric (NPT) ensemble (423 K, 1 MPa), followed by simulated annealing. During simulated annealing, the temperature was increased from 423 K to 723 K at a rate of 10 K / ns, held at 723 K for 30 ns, and then decreased to 323 K at a rate of 10 K / ns. A second 20 ns pre-equilibrium simulation was then performed under the NPT ensemble (323 K, 1 MPa). Finally, a 50 ns production simulation under the NPT ensemble (323 K, 1 MPa) was conducted, and the trajectory of the last 20 ns was analyzed.

[0076] These simulations were performed using the GROMACS 2025.1 software package. Bonding interaction and Lennard-Jones potential parameters were derived from the CHARMM36 force field. Charge information was obtained using the GNN-RESP charge scheme from the AutoFF website. V-rescale temperature coupling and C-rescale pressure coupling schemes were employed for temperature and pressure regulation, respectively. The time step was set to 1 fs, and periodic boundary conditions were applied in the x, y, and z directions. The nearest neighbor Verlet list was updated every 20 steps. The cutoff radius for van der Waals interactions was set to 1.2 nm, while electrostatic interactions were handled using the smoothed particle mesh Ewald (PME) method.

[0077] Example 2: Microscopic Association Structure Analysis

[0078] For the two fully balanced heavy oil systems, the method of this invention was used for analysis, and the steps are as follows:

[0079] First, verify the structure file, trajectory file, and related parameters (such as molecular structure, number of molecules, start frame, time interval, and end frame) of the heavy oil model to ensure the consistency of molecular composition between the structure file and the trajectory file.

[0080] To improve efficiency, for a 50ns (1000 frames) simulation, the last 20ns are used for analysis. Therefore, the starting frame is set to 600, no ending frame is set (the default is the last frame), and the interval is 10, that is, the analysis is performed once every 10 frames, and finally the average data of 40 frames is statistically analyzed.

[0081] 2. For the above 40 frames of data, read the structure and trajectory information output by the molecular dynamics simulation, and use the molecular dynamics trajectory analysis tool MDAnalysis to obtain the atomic coordinates, simulation box vectors (obtain the side length of the box) and molecular topological relationships (including the molecular types in the system, the number of atoms contained in each molecule, whether each atom is in a ring structure, and the number of bonds between each atom and other atoms) frame by frame.

[0082] Third, the aromatic ring structure in the molecule is identified based on the aromatic ring structure recognition method, and the geometric features of the aromatic ring structure are extracted. That is, the geometric center coordinates of each aromatic ring structure are calculated to form the aromatic ring center coordinate dataset for each frame.

[0083] IV. Handling of Periodic Boundary Conditions: For the problem of molecular movement across the box under periodic boundary conditions (PBC), the nearest mirror principle is used to correct the distance calculation.

[0084] Specific implementation method: When calculating the shortest mirror image distance of the aromatic ring's centroid, first calculate the coordinate difference between the two centroids, and then check whether the difference in each direction exceeds half the length of the box; if it does, subtract or add the box length to map it back to the nearest mirror image position. After this processing, calculate the distance between the two points, which is their shortest mirror image distance.

[0085] V. Density clustering based on improved DBSCAN, the clustering criterion considers the geometric characteristics of aromatic ring π–π packing: the nearest neighbor relationship is restricted by the neighborhood radius parameter ε=0.45nm, and the core point is determined by the minimum number of points minPts=2. When the geometric centers of two aromatic rings are within the threshold range, they can be clustered into an association, such as... Figure 1 A schematic diagram of the associative compound is shown in the figure.

[0086] 6. Post-processing of clustering results: Based on the information of the molecules to which each aromatic ring belongs stored in step 3, the clustering labels of the aromatic ring centers are mapped to the molecular level to obtain the clustering labels to which each molecule belongs, thus obtaining the molecules contained in each associative compound.

[0087] VII. Visualization and Result Output: Based on the clustered molecular set, generate the association structure file and assign different colors to each association to distinguish them.

[0088] All molecules in the heavy oil system were selected, and the trajectory data of the last 20 ns were analyzed. The association structure in the heavy oil system was analyzed every 1 ns to obtain the microscopic association structures of the two heavy oil systems, such as... Figure 4 As shown.

[0089] This figure is a structural file generated using the method of this invention, displayed using VMD visualization software, and the display method was optimized using a visualization optimization script generated by the method of this invention. It is clear from the figure that the association degree of heavy oil system 2 is significantly stronger than that of heavy oil system 1. In heavy oil system 1, asphaltenes tend to form a network structure, with heavy oil molecules extending and entangled within the system. This network structure becomes a key factor in the viscosity of heavy oil, and the overall microstructure exhibits gel-like characteristics. In heavy oil system 2, asphaltenes tend to form a tight π–π associated structure, such as… Figure 5 The structure shown in red circle in the heavy oil system 2 is as follows: the asphaltene assemblage is dispersed in the heavy oil system, and other aromatic ring-containing components are attached to the assemblage to form larger clusters, and the whole exhibits a colloidal state.

[0090] VIII. Characterization and analysis of association structure.

[0091] Based on the clustering results, the obtained associative aggregates are systematically and quantitatively characterized, including quantitative and shape indicators such as the number of associative aggregates, the proportion of molecular components, the percentage of the total number of molecules participating in the associative aggregates, the size and evolution of associative aggregates, associative aggregate density, and distribution characteristics. This method can generate distribution histograms, probability distribution curves, and dynamic evolution charts, thereby comprehensively revealing the microscopic aggregation laws of molecular associative aggregates, such as... Figures 6 to 13 As shown.

[0092] Figure 6 The changes in the number of associates in the two heavy oil systems are shown. It can be seen that although the average number of associates in heavy oil system 1 is slightly higher than that in heavy oil system 2, it fluctuates significantly over time, reflecting the instability of the association behavior in heavy oil system 1.

[0093] Figure 7 The probability distribution of association size (i.e., the number of heavy oil molecules contained in each association) was statistically analyzed. Figure 8 The curves show the evolution of the size of the associative compounds in the two heavy oil systems over time. Figure 7 and Figure 8 The two figures show that the aggregate size in heavy oil system 2 is significantly larger than that in system 1, and that a large number of large-sized aggregates not present in system 1 are present. These results confirm the intrinsic properties of the two heavy oil systems: heavy oil system 2 tends to form compact and larger aggregates, while the aggregates in system 1 are smaller and unstable. This conclusion is also supported by... Figure 9 This is supported by statistical data on the density of the associative compounds.

[0094] Figure 10The study presented the proportions and participation rates of four components in heavy oil aggregates. It was found that, except for asphaltenes (ASP), the proportions and participation rates of saturated arsenic (SAT), aromatic arsenic (ARO), and resins (RES) in the aggregates were essentially consistent. The only significant difference lay in asphaltenes: almost 100% of the asphaltenes in heavy oil system 2 participated in aggregate formation, while only about 60% participated in system 1. This indicates that asphaltenes are a key molecular factor influencing the behavior of heavy oil aggregates and a major determinant of the intrinsic properties of heavy oil systems.

[0095] Figure 11 The probability density distribution of the shape anisotropy factor of heavy oil aggregates was calculated. Shape anisotropy factor k 2 k is typically used to describe the degree of non-spherical symmetry of the shape of a molecule or aggregate in space. It measures the difference in size of an object in different directions; a larger value indicates a more elongated or flattened shape, while a smaller value indicates a shape closer to a sphere. For asphaltene aggregates, k... 2 The larger the size, the more elongated and irregular the aggregate; the smaller the size, the closer it is to a spherical cluster. Figure 11 This indicates that asphaltene has no significant effect on the spatial shape of heavy oil aggregates; rather, the number of aggregates and their density are the key factors affecting the intrinsic properties of heavy oil.

[0096] Figure 12 and Figure 13 The distribution characteristics of associative compounds in the heavy oil system are shown. Figure 12 The radial distribution function (RDF) represents the centroid of the aggregates. Because the asphaltenes in heavy oil system 1 have a relatively loose molecular structure, their aggregates are more dispersed in space, thus the overall radial distribution does not show a significant peak. In contrast, the asphaltenes in heavy oil system 2 have a denser core, and the radial distribution curve of its aggregate centroid shows a significant peak at approximately 1.3 nm. This characteristic is observed in the closest distribution of the aggregates. Figure 13 This is also reflected in the T-shaped stacking structure of heavy oil aggregates.

[0097] In summary, this method can systematically and comprehensively characterize the structural features of molecular associations in heavy oil systems. Through quantitative analysis of multi-dimensional indicators such as the number of associations, size distribution, component participation, density, and shape anisotropy, it can reveal the microscopic aggregation law and intrinsic property differences of heavy oil systems. This demonstrates the efficiency and applicability of this method in analyzing the molecular aggregation behavior and key molecular effects of heavy oil, thus highlighting the application value of this invention in the structural characterization and property analysis of heavy oil.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying microscopic associative structures in simulated trajectories of heavy oil molecules, characterized in that, The method includes the following steps: S1. Analyze the molecular dynamics trajectory of the constructed heavy oil model, identify the aromatic ring structure in the heavy oil molecule and extract its geometric features; S2. An improved clustering algorithm is used to cluster the identified aromatic ring structures to obtain preliminary association structures; The improved clustering algorithm is based on the DBSCAN algorithm that considers periodic boundary conditions. This algorithm takes into account the periodic boundary conditions of the simulated system when performing clustering, and corrects the distance calculation by adopting the nearest mirror principle to handle the nearest mirror atoms. The improved clustering algorithm uses the neighborhood radius parameter ε and the minimum number of samples parameter minPts as clustering conditions, and sets the parameters based on the π-π stacking action standard. When the geometric centers of the minPts aromatic rings are within the threshold range ε, they can be clustered into an association. The output result is the cluster label to which each aromatic ring center belongs in each frame. S3. Post-process the clustering results to form an optimized set of associated molecules; S4. Generate association structure data and visualization results; S5. Based on the structural data of the associative compounds, the structure of the associative compounds is characterized and analyzed, and the relationship between the micro-aggregate structure and the apparent viscosity of heavy oil is obtained.

2. The method for identifying microscopic association structures in simulated trajectories of heavy oil molecules according to claim 1, characterized in that, The method for identifying aromatic ring structures is as follows: For a six-membered ring structure, if the ring consists entirely of carbon atoms and the number of atoms bonded to each carbon atom is 3, then the ring structure is an aromatic ring structure; if the ring contains carbon atoms, oxygen atoms, nitrogen atoms, or sulfur atoms, and the number of atoms bonded to each carbon atom is 3, while the number of atoms bonded to each oxygen atom, nitrogen atom, or sulfur atom is 2, then the ring structure is an aromatic ring structure. For a five-membered ring structure, if the ring contains both carbon and nitrogen atoms, and both carbon and nitrogen atoms are connected to 3 atoms, then the ring structure is an aromatic ring structure; if the ring contains both carbon and oxygen or sulfur atoms, and the carbon atom is connected to 3 atoms, and the oxygen or sulfur atom is connected to 2 atoms, then the ring structure is an aromatic ring structure.

3. The method for identifying microscopic association structures in simulated trajectories of heavy oil molecules according to any one of claims 1-2, characterized in that, The specific steps of S1 are as follows: S11. Verify the structure file, trajectory file, and related parameters of the heavy oil model to ensure the consistency of molecular composition between the structure file and the trajectory file; S12. Read the structure file and trajectory file information, and obtain the atomic coordinates, simulated box vectors and molecular topological relationships frame by frame; S13. Identify aromatic ring structures in molecules. The identification results return the index of the atoms in each aromatic ring and their topological relationship in the molecule, which is convenient for subsequent extraction of atomic coordinates or reverse mapping. Extract the geometric features of aromatic ring structures, that is, calculate the geometric center coordinates of each aromatic ring structure to form an aromatic ring center coordinate dataset for each frame, which is used as clustering input.

4. The method for identifying microscopic association structures in simulated trajectories of heavy oil molecules according to claim 1, characterized in that, In S3, post-processing includes back-mapping the aromatic ring clustering results to the corresponding molecules, removing noise points, removing isolated molecule sets, and merging associations with shared molecules. Noise points are molecules containing a single aromatic ring that do not participate in association formation, and isolated molecule sets are molecules containing multiple aromatic rings that do not participate in association formation. Merging associations with shared molecules uses an intersection and union determination algorithm to merge them into a consistent set of associations, thereby avoiding duplicate or fragmented counting of the same association.

5. The method for identifying microscopic association structures in simulated trajectories of heavy oil molecules according to claim 1, characterized in that, In S4, the association structure data includes the molecular composition, number, size, shape, and dynamic evolution characteristics of the associations.

6. The method for identifying microscopic association structures in simulated trajectories of heavy oil molecules according to claim 1, characterized in that, In the visualization results, each association is assigned a different color to distinguish them. If molecules in the same association appear on different boundaries due to periodic boundary conditions, causing the association to appear to split visually, any molecule is used as a reference object, and other molecules are translated to make all molecules the closest to each other, thereby ensuring that molecules in the same association are displayed in a nearest neighbor manner.

7. The method for identifying microscopic association structures in simulated trajectories of heavy oil molecules according to claim 1, characterized in that, In S5, structural characterization includes one or more of the following: number of associates, proportion of molecular components, percentage of the total number of molecules participating in the associates, size distribution and evolution of the associates, radius of gyration, principal axis length, shape anisotropy factor, and cluster density.

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