Oil pipeline simulation analysis system

Abstract

The invention discloses an oil pipeline simulation analysis system, relates to the technical field of oil pipelines, and provides accurate data support for subsequent dynamic monitoring and pipeline cleaning decision by constructing a three-dimensional visual model and identifying wax precipitation temperature and deposition risk in time. Through the risk identification module, the system can analyze and calculate the wax precipitation temperature based on the oil product attribute data obtained in real time, and timely judge whether the deposition risk exists in the oil pipeline or not. After receiving a dynamic tracking instruction, the dynamic analysis module can obtain the deposition thickness of each position in a pipeline in real time and analyze the influence of oil temperature and flow velocity changes on deposition distribution, the analysis provides important input data for the pipe cleaning module, the deposition thickness and a heat loss coefficient are fitted through a deep learning technology, and the deposition thickness and the heat loss coefficient are matched with each other. The system may automatically calculate a delivery stability score. When the conveying stability score exceeds a set threshold value, the system can automatically start a pigging measure execution mechanism.

Description

Technical Field

[0001] The invention relates to the technical field of oil pipelines, and in particular to an oil pipeline simulation analysis system. Background Art

[0002] The simulation analysis of oil pipelines belongs to the broad field of energy transportation and pipeline engineering. This field involves the simulation and analysis of the flow process of liquids, gases or solid particles in pipelines, aiming to optimize transportation efficiency, ensure safety and reduce operating costs. In the simulation process of oil pipelines, the key is to simulate the flow behavior of oil products and evaluate the changes in parameters such as pressure, flow rate, temperature, etc. inside the pipeline, especially the thermodynamic characteristics and rheological properties of oil products during transportation. Specifically in the field of oil pipeline simulation, the stability of oil transportation is a core issue, especially in complex pipeline networks. Temperature fluctuations, flow rate changes and sediment formation of oil products may lead to unstable flow, affecting transportation efficiency and even equipment safety.

[0003] Although modern oil pipeline systems have attempted to monitor and optimize oil transportation status under various environmental conditions by deploying sensors, computer control systems and data analysis methods on a large scale, the existing oil pipeline simulation system still has some obvious deficiencies and limitations. First, traditional oil pipeline simulations often only focus on macro parameters such as flow rate and pressure, and lack dynamic analysis of wax precipitation temperature and deposition distribution of oil products under different environmental conditions, which makes it difficult to accurately predict risks during long-term transportation. In addition, existing risk identification and cleaning measures are mostly empirical judgments, lacking sufficient real-time data support and automated response mechanisms. The impact of changes in oil temperature and flow rate on sediments in the pipeline is difficult to fully evaluate under dynamic conditions, resulting in sediment accumulation and increased oil viscosity in some pipe sections, thereby affecting transportation efficiency and safety. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an oil pipeline simulation analysis system, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an oil pipeline simulation analysis system, including a data module, a simulation module, a risk identification module, a dynamic analysis module and a pigging module; The data module is used to obtain the historical data of the oil pipeline in advance, obtain the interface type after data preprocessing, select the network interface according to the interface type, and input the preprocessed historical data of the oil pipeline into the local database through the network interface; The simulation module is used to simulate the flow state of oil in the oil pipeline according to the historical data of the oil pipeline in the database in combination with the fluid dynamics simulation software to build a three-dimensional visualization model. Based on the three-dimensional visualization model, the relationship between the oil in the oil pipeline being precipitated from the liquid phase and transformed into the solid phase at different temperatures is detected to generate relevant oil property data; The risk identification module is used to analyze and calculate the wax precipitation temperature Lxw based on the relevant oil property data, and to determine whether there is a deposition risk on the inner wall of the current oil pipeline based on the wax precipitation temperature Lxw. If so, a dynamic tracking instruction is triggered; The dynamic analysis module is used to monitor the environmental change data of the oil product in the transmission process in real time after receiving the dynamic tracking instruction, obtain the heat loss coefficient Rsxs after feature extraction, and analyze the influence of the oil product temperature change and flow rate change on the deposition distribution in the oil pipeline to evaluate the deposition thickness H at each position in the oil pipeline; The cleaning module is used to use deep learning technology to fit the deposition thickness H and heat loss coefficient Rsxs at various locations in the oil pipeline to obtain the transportation stability score Swpz. If the transportation stability score Swpz exceeds a preset evaluation threshold Y, the cleaning measure execution mechanism is initiated.

[0006] Preferably, the data module includes a data collection unit and a processing unit; The data collection unit is used to query and extract historical data of the oil pipeline based on the data stored in the enterprise server through SQL, wherein the historical data of the oil pipeline includes relevant dimension information of the oil pipeline, relevant oil product transmission information and relevant temperature change information, wherein the relevant dimension information of the oil pipeline includes the pipe diameter at each position in the oil pipeline, the pipe curvature and the transmission distance Gsc at the corresponding position in the oil pipeline; the relevant oil product transmission information includes the flow velocity and deposition state of the oil product at each position in the oil pipeline; the relevant temperature change information includes the temperature state of the oil product at each position in the oil pipeline and the temperature state of each position on the inner wall of the oil pipeline; The processing unit is used to convert the format of the historical data of the oil pipeline, and preprocess the historical data of the oil pipeline after the format conversion to perform noise removal, missing value filling and data smoothing operations, and then input the preprocessed historical data of the oil pipeline into the database through the network interface.

[0007] Preferably, the simulation module includes a modeling unit and a test data acquisition unit; The modeling unit is used to import the historical data of the oil pipeline in the database into the fluid dynamics simulation software, so as to reproduce the geometric state of the oil pipeline and the transmission state of the oil product in the oil pipeline in the fluid dynamics simulation software, and construct a three-dimensional visualization model, wherein the fluid dynamics simulation software includes ANSYS Fluent, OpenFOAM and COMSOL Multiphysics; The test data acquisition unit is used to detect the relationship between the oil product in the oil pipeline being precipitated from the liquid phase and transformed into the solid phase at different temperatures based on the three-dimensional visualization model, so as to generate relevant oil product attribute data, wherein the relevant oil product attribute data includes the concentration Yf of the wax component in the liquid phase of the oil product, the concentration Gf of the wax component in the solid phase of the oil product, and the melting enthalpy of the wax component under different temperatures. and the melting point Rdz of the wax component.

[0008] Preferably, the risk identification module includes a first identification unit and a second identification unit; The first identification unit is used to analyze the distribution behavior of the wax component in the oil product in the oil pipeline between the liquid phase and the solid phase according to the relevant oil product attribute data obtained by the test data acquisition unit, and calculate the wax precipitation temperature Lxw. Specifically, the wax precipitation temperature Lxw is obtained by the following formula: ; In the formula, It indicates the concentration of wax components in the liquid phase of oil products at temperature i; It indicates the concentration of wax components in the solid phase of oil products at temperature i; is the melting enthalpy of the wax component; R is the gas constant; Rdz is the melting point of the wax component; i is the number of the corresponding temperature value; ln is the logarithmic function with the constant e as the base, and e is the Euler number.

[0009] Preferably, the state of the oil in the current oil pipeline is compared according to the oil temperature Yw simulated in the current three-dimensional visualization model and the wax precipitation temperature Lxw to determine whether there is a risk of deposition on the inner wall of the current oil pipeline. The specific determination content is as follows: If the oil temperature Yw ≤ wax precipitation temperature Lxw, it is judged that there is a risk of deposition on the inner wall of the current oil pipeline, and the dynamic tracking instruction will be triggered for further analysis; If the oil temperature Yw>wax precipitation temperature Lxw, it is judged that there is no risk of deposition on the inner wall of the current oil pipeline, and the dynamic tracking instruction will not be triggered at this time.

[0010] Preferably, the second identification unit is used to determine the pour point of the oil product by monitoring the critical temperature at which the oil product loses fluidity using dynamic cooling conditions. The specific determination content is as follows: insert a rotating measuring device into the oil product in advance, gradually cool the temperature, and each cooling is 3 degrees, while measuring the flow state of the oil product. When the measured value shows that the oil product stops flowing, record the temperature range when the oil product stops flowing to determine the temperature at which the pour point of the oil product is located, and record it as the pour point limit range Q, and stop the temperature cooling operation at the same time; wherein the rotating measuring device includes a rotating viscometer; Based on the pour point limit range Q, the constraint conditions for triggering the emergency maintenance mechanism are set, and the specific settings are as follows: by comparing the pour point limit range Q with the oil temperature Yw simulated in the current three-dimensional visualization model, if the oil temperature Yw>the pour point limit range Q, the emergency maintenance mechanism will not be triggered temporarily; if the oil temperature Yw≤the pour point limit range Q, the emergency maintenance mechanism will be triggered.

[0011] Preferably, the kinetic analysis module includes a heat loss analysis unit and a presentation state analysis unit; The heat loss analysis unit is used to monitor the environmental change data of the oil product during the transmission process in real time after receiving the dynamic tracking instruction, wherein the environmental change data includes the fluid flow velocity Lsz, the change value Ncb of the internal environment difference, the initial position in the oil pipeline, and the The deposition thickness at , the oil temperature Yw at each position, the difference between the oil inflow temperature and the temperature at position x , the temperature difference of oil transition from liquid to sediment state , the reference flow velocity Ccs affected by deposition and the sensitivity coefficient of flow velocity to deposition ; By extracting the transport distance Gsc, the fluid flow velocity Lsz and the change value Ncb of the internal environment difference at the corresponding position in the oil pipeline from the environmental change data, and combining the relevant size information of the oil pipeline, the pipeline transport distance Gsc, the change value Ncb of the internal environment difference and the fluid flow velocity Lsz are associated, and after dimensionless processing, the heat loss coefficient Rsxs is obtained. The heat loss coefficient Rsxs is obtained by the following formula: ; In the formula, represents the heat loss coefficient at position x in the oil pipeline; It represents the distance transported at position x in the oil pipeline; Indicates the change in the internal environmental difference at position x in the oil pipeline; represents the fluid flow velocity at position x in the oil pipeline; , and are weight values, where 0 < <1,0< <1,0< <1, , and The specific value is set by the user according to the situation.

[0012] Preferably, the presentation state analysis unit is used to analyze the influence of oil temperature change and flow rate change on the deposition distribution in the oil pipeline according to the environmental change data, so as to evaluate the deposition thickness H at each position in the oil pipeline, and specifically obtain the deposition thickness H at each position in the oil pipeline according to the following formula: ; In the formula, represents the deposition thickness at position x in the oil pipeline; Indicates the initial position in the oil pipeline The thickness of the sediment at It represents the difference between the oil inflow temperature and the temperature at position x; Indicates the temperature difference at which the oil product transitions from liquid to sedimentary state; represents the fluid flow velocity at position x in the oil pipeline; Ccs represents the reference flow velocity affected by deposition; It represents the sensitivity coefficient of flow velocity to sedimentation.

[0013] Preferably, the cleaning module includes a comprehensive analysis unit and a treatment unit; The comprehensive analysis unit is used to construct a transportation evaluation model using deep learning technology, and input the deposition thickness H and heat loss coefficient Rsxs at various positions in the oil pipeline into the trained transportation evaluation model. After dimensionless processing, the transportation stability score Swpz is obtained by fitting. The transportation stability score Swpz is obtained by the following formula: ; Where n represents the number of monitoring locations, x=0, 1, 2, ..., n, represents the heat loss coefficient at position x in the oil pipeline, represents the average heat loss coefficient, represents the deposition thickness at position x in the oil pipeline, represents the average deposition thickness, Bjs represents the number of reducers set, , and are weight values, where 0 < <1,0< <1,0< <1, , and The specific value is set by the user according to the situation.

[0014] Preferably, the governance unit is used for a preset evaluation threshold value Y, and the evaluation threshold value Y is compared and analyzed with the transportation stability score Swpz to determine the stability state of the oil in the current oil pipeline during transportation. The specific comparison content is as follows: If the transportation stability score Swpz exceeds the evaluation threshold value Y, it is determined that the oil in the current oil pipeline is not in a stable state during transportation. At this time, the heating system will be started in the oil pipeline, and the working state of the pump will be adjusted to increase the flow rate to 1.2 times the original flow rate. At the same time, according to the deposition thickness H at each position obtained in the presentation state analysis unit, the pigging equipment will be started to remove the sediment in the oil pipeline through the pig; If the transportation stability score Swpz does not exceed the evaluation threshold Y, it is judged that the oil in the current oil pipeline is in a stable state during transportation. At this time, the state of the oil in the pipeline will be continuously monitored, and after the oil transportation operation is completed, the sediment in the oil pipeline will be cleared by a pipe cleaner.

[0015] The present invention provides an oil pipeline simulation analysis system, which has the following beneficial effects: (1) Through the combination of data module and simulation module, the system can accurately simulate the flow state of oil in the oil pipeline based on historical data and fluid dynamics simulation software, and build a three-dimensional visualization model. This process enables the system to detect the liquid-solid equilibrium behavior of oil at different temperatures in real time, timely identify the wax precipitation temperature Lxw and deposition risk, and provide accurate data support for subsequent dynamic monitoring and cleaning decisions. Automated risk identification and dynamic tracking: Through the risk identification module, the system can analyze and calculate the wax precipitation temperature Lxw based on the real-time acquired oil property data, and timely determine whether there is a deposition risk in the oil pipeline. If there is a risk, the system will trigger the dynamic tracking instruction and start the real-time monitoring program. This risk identification and dynamic tracking mechanism enables the oil pipeline to respond to changes in factors such as temperature and flow rate in real time during the transportation of oil products, ensuring that potential deposition problems are discovered and avoided in a timely manner. Efficient deposition assessment and cleaning mechanism: After receiving the dynamic tracking instruction, the dynamic analysis module can obtain the deposition thickness H at each position in the pipeline in real time, and analyze the impact of oil temperature and flow rate changes on deposition distribution. This analysis provides important input data for the cleaning module. The system can automatically calculate the transportation stability score Swpz by fitting the deposition thickness and heat loss coefficient through deep learning technology. When the transportation stability score exceeds the set threshold Y, the system can automatically start the cleaning measure execution mechanism to promptly remove the sediment in the pipeline to prevent the accumulation of sediment from having an adverse effect on pipeline operation. In short, the system further improves the operating efficiency of the oil pipeline through precise simulation and dynamic monitoring, automated risk identification, real-time deposition assessment, and intelligent cleaning mechanism. The system can evaluate the deposition risk and stability in real time during the transportation of oil products, thereby effectively avoiding pipeline blockage, reducing energy consumption, improving transportation efficiency, reducing the risk of pipeline damage or accidents caused by sediments, and enhancing the long-term stability of the pipeline; (2) The No. 1 identification unit calculates the wax precipitation temperature Lxw by comprehensively utilizing the concentration data of the wax components in the liquid and solid phases of the oil, the melting enthalpy of the wax components, the gas constant and the melting point and other thermodynamic parameters. This process can accurately quantify the thermodynamic process of the wax components precipitating from the liquid phase to the solid phase, and accurately describe the temperature change of the wax precipitation based on the liquid-solid equilibrium relationship. This calculation mechanism can timely predict the wax precipitation temperature according to the wax precipitation characteristics of the oil under different working conditions, and help analyze and judge the deposition risk of the wax components in the oil pipeline. Real-time monitoring and temperature comparison judgment: By comparing the wax precipitation temperature Lxw with the actual temperature Yw of the oil in the current oil pipeline, the system can evaluate in real time whether the oil is in a state that may cause deposition. If the oil temperature is lower than the wax precipitation temperature, it indicates that the wax components may precipitate and form deposition in the pipeline. At this time, the system will automatically trigger the dynamic tracking instruction for further analysis and risk identification. This function can effectively prevent pipeline blockage or reduced operating efficiency caused by oil deposition. Accurate deposition risk prediction and early warning: Through accurate wax precipitation temperature calculation and real-time oil temperature monitoring, the system can timely identify potential risks and conduct dynamic tracking through early warning mechanism before wax precipitation risk occurs. This dynamic monitoring method can achieve real-time tracking of temperature changes in the oil pipeline, provide early warning for pipeline operation management, and reduce operation interruption, maintenance costs and production losses caused by sediment accumulation; (3) Determination of oil pour point: The No. 2 identification unit measures the flow state of the oil in real time by inserting a rotating measuring device and gradually cooling the oil. When the oil stops flowing, the temperature range when the flow stops is recorded, so as to accurately determine the pour point limit range Q of the oil. This measurement process can accurately reflect the fluidity of the oil in a low temperature environment and provide a scientific basis for subsequent oil stability assessment. Real-time temperature monitoring based on the pour point limit range: According to the pour point limit range Q of the oil, the system can monitor the temperature Yw of the oil in real time, and determine whether it is necessary to trigger the emergency maintenance mechanism based on the comparison results. When the oil temperature is lower than or equal to the pour point limit range, the system will automatically trigger the emergency maintenance mechanism to identify and prevent potential risks caused by loss of oil fluidity in advance. Through this monitoring mechanism, the system can avoid blockage of oil pipelines or damage to equipment caused by insufficient oil fluidity; (4) The heat loss analysis unit monitors the environmental change data at each location in the oil pipeline in real time. Combined with the size information and dimensionless processing of the oil pipeline, the heat loss coefficient Rsxs can be accurately calculated. This calculation is based on the correlation between the pipeline transportation distance, the change of the internal environmental difference and the fluid flow velocity, ensuring that the impact of environmental factors on the heat loss during oil transportation can be fully quantified. Through this analysis, the heat loss hotspots that may be generated during oil transportation can be identified, providing strong data support for optimizing pipeline design and operating conditions. Real-time monitoring of deposition risk: The combination of the heat loss analysis unit and the presentation state analysis unit helps to monitor the deposition risk of oil during transportation in real time. By accurately evaluating the impact of flow velocity and temperature changes on deposition distribution, the working state of the oil pipeline can be dynamically adjusted and the deposition thickness H at different locations can be predicted. This comprehensive analysis based on temperature, flow velocity and deposition sensitivity can timely discover potential deposition risks and provide important decision support for subsequent cleaning measures. Dynamic prediction of deposition thickness: The presentation state analysis unit can dynamically evaluate and predict the deposition thickness at each location in the oil pipeline based on the oil inflow temperature, flow velocity changes and deposition influence coefficient. Through this analysis, the distribution of sediments in the pipeline can be discovered, and the locations where accumulation and sedimentation may occur can be identified, thereby providing an accurate basis for the maintenance and cleaning of the oil pipeline. This prediction mechanism helps prevent the loss of oil fluidity and avoid pipeline blockage or reduced transmission efficiency due to sediment accumulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a schematic diagram of the flow chart of an oil pipeline simulation analysis system. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 ,The present invention provides an oil pipeline simulation analysis system, including a data module, a simulation module, a risk identification module, a dynamic analysis module and a pigging module; The data module is used to obtain the historical data of the oil pipeline in advance, obtain the interface type after data preprocessing, select the network interface according to the interface type, and input the preprocessed historical data of the oil pipeline into the local database through the network interface; The simulation module is used to simulate the flow state of oil in the oil pipeline according to the historical data of the oil pipeline in the database in combination with the CFD simulation software to build a three-dimensional visualization model. Based on the three-dimensional visualization model, the relationship between the oil in the oil pipeline being precipitated from the liquid phase and transformed into the solid phase at different temperatures is detected to generate relevant oil property data; The risk identification module is used to analyze and calculate the wax precipitation temperature Lxw based on the relevant oil property data, and to determine whether there is a deposition risk on the inner wall of the current oil pipeline based on the wax precipitation temperature Lxw. If so, a dynamic tracking instruction is triggered; The dynamic analysis module is used to monitor the environmental change data of the oil product in the transmission process in real time after receiving the dynamic tracking instruction, obtain the heat loss coefficient Rsxs after feature extraction, and analyze the influence of the oil product temperature change and flow rate change on the deposition distribution in the oil pipeline to evaluate the deposition thickness H at each position in the oil pipeline; The cleaning module is used to use deep learning technology to fit the deposition thickness H and heat loss coefficient Rsxs at various locations in the oil pipeline to obtain the transportation stability score Swpz. If the transportation stability score Swpz exceeds a preset evaluation threshold Y, the cleaning measure execution mechanism is initiated.

[0019] During the operation of this system, accurate data analysis and real-time monitoring: the data module obtains the historical data of the oil pipeline in advance and inputs it into the database after data preprocessing, ensuring that the system can conduct comprehensive analysis based on rich historical data, providing a solid data foundation for subsequent simulation and risk identification. Through the operation of this module, the system can obtain various key data in real time, providing accurate input for subsequent simulation analysis. Visualization and optimization of oil flow state: the simulation module combines the fluid dynamics (CFD) simulation software to accurately simulate the flow state of oil in the oil pipeline and build a three-dimensional visualization model. This visualization result allows the process of oil precipitation from liquid phase to solid phase at different temperatures to be clearly displayed, effectively helping users to intuitively understand the oil flow and wax precipitation risks, and providing a scientific basis for optimizing pipeline operation. Efficient deposition risk identification and dynamic response: the risk identification module uses oil attribute data to analyze and calculate the wax precipitation temperature Lxw and determine the deposition risk of the inner wall of the current oil pipeline. If the system identifies a deposition risk, it immediately triggers a dynamic tracking instruction to ensure that the risk can be discovered and responded to in a timely manner at an early stage, reducing the potential threat of oil deposition to pipeline operation. Accurate sediment thickness assessment and environmental change monitoring: The dynamic analysis module monitors the environmental change data of oil products in the transmission process in real time, and after extracting features, accurately calculates the heat loss coefficient Rsxs. By analyzing the impact of oil temperature and flow rate changes on the deposition distribution in the pipeline, the system can accurately assess the deposition thickness H of different pipe sections, provide a reliable basis for the operation and maintenance of the pipeline, and identify possible deposition problems in the pipeline in advance. Intelligent pigging and stability assurance: The pigging module uses deep learning technology, combined with the deposition thickness H and the heat loss coefficient Rsxs, to accurately fit the transportation stability score Swpz. When the transportation stability score exceeds the preset evaluation threshold Y, the system will automatically start pigging measures to ensure the stable transportation of oil products and the long-term safe operation of the pipeline. This mechanism can dynamically adjust the pigging strategy according to the actual operation data, reduce manual intervention and excessive pigging, and improve the pigging efficiency and effect. In short, the system further improves the operation stability of the oil pipeline, reduces the risk of deposition, optimizes the oil transportation efficiency, and reduces the risk of failure through intelligent pigging measures, ensuring the long-term safe and efficient operation of the oil pipeline system.

[0020] Example 2: Please refer to Figure 1 ,Specifically: the data module includes a data collection unit and a processing unit; The data collection unit is used to query and extract historical data of the oil pipeline based on the data stored in the enterprise server through SQL, wherein the historical data of the oil pipeline includes relevant dimension information of the oil pipeline, relevant oil product transmission information and relevant temperature change information, wherein the relevant dimension information of the oil pipeline includes the pipe diameter at each position in the oil pipeline, the pipe curvature and the transmission distance Gsc at the corresponding position in the oil pipeline; the relevant oil product transmission information includes the flow velocity and deposition state of the oil product at each position in the oil pipeline; the relevant temperature change information includes the temperature state of the oil product at each position in the oil pipeline and the temperature state of each position on the inner wall of the oil pipeline; The processing unit is used to convert the format of the historical data of the oil pipeline, with the purpose of converting the original data into the format required by the processing system (such as from CSV to JSON), and preprocessing the historical data of the oil pipeline after the format conversion to perform noise removal, missing value filling and data smoothing operations, wherein the missing value filling methods include mean filling, median filling, interpolation filling and regression filling; and then the preprocessed historical data of the oil pipeline is input into the database through the network interface.

[0021] The simulation module includes a modeling unit and a test data acquisition unit; The modeling unit is used to import the historical data of the oil pipeline in the database into the fluid dynamics (CFD) simulation software, so as to reproduce the geometric state of the oil pipeline and the transmission state of the oil product in the oil pipeline in the fluid dynamics (CFD) simulation software, and build a three-dimensional visualization model, wherein the fluid dynamics (CFD) simulation software includes ANSYS Fluent, OpenFOAM and COMSOL Multiphysics; The test data acquisition unit is used to detect the relationship between the oil product in the oil pipeline being precipitated from the liquid phase and transformed into the solid phase at different temperatures based on the three-dimensional visualization model, so as to generate relevant oil product attribute data, wherein the relevant oil product attribute data includes the concentration Yf of the wax component in the liquid phase of the oil product, the concentration Gf of the wax component in the solid phase of the oil product, and the melting enthalpy of the wax component under different temperatures. and the melting point Rdz of the wax component.

[0022] In this embodiment, accurate historical data collection and processing: the data collection unit in the data module extracts the historical data of the oil pipeline from the enterprise server through SQL query technology, covering the size information, oil product transmission information and temperature change information of the oil pipeline. This process ensures the comprehensiveness and accuracy of the data source and can reflect the various operating parameters of the oil pipeline under different working conditions. The data processing unit performs format conversion, noise removal, missing value filling and data smoothing operations on the collected raw data to ensure the quality and consistency of the data, which provides high-quality input data for subsequent simulation analysis. Build a three-dimensional model based on fluid dynamics simulation: the modeling unit in the simulation module imports historical data into the fluid dynamics simulation software, and accurately reproduces the geometric state of the oil pipeline and the transmission state of the oil product through advanced simulation software such as ANSYS Fluent, OpenFOAM and COMSOL Multiphysics. This process can not only build an accurate three-dimensional visualization model, but also provide comprehensive dynamic simulation support for the temperature change, flow rate change and deposition behavior of the oil product during the transportation process. Through the efficient calculation of the simulation software, the system can fully understand the transportation of oil products and monitor possible abnormal conditions in real time. Accurate detection of oil precipitation and solid phase transition: The test data acquisition unit uses a three-dimensional visualization model to accurately detect the process of oil precipitation from liquid phase to solid phase under different temperature conditions. The system generates detailed oil property data by simulating the changes in the wax composition of oil at different temperatures, including important data such as the concentration of wax components in the liquid phase, the concentration of wax components in the solid phase, the melting enthalpy of wax components, and the melting point. This data can help analyze and predict the wax precipitation temperature and solid phase transition risks of oil products, thereby providing accurate early warning for wax deposition problems that may occur during pipeline transportation.

[0023] Example 3: Please refer to Figure 1 ,Specifically: the risk identification module includes a first identification unit and a second identification unit; The first identification unit is used to analyze the distribution behavior of the wax component in the oil product in the oil pipeline between the liquid phase and the solid phase according to the relevant oil product attribute data obtained by the test data acquisition unit, and calculate the wax precipitation temperature Lxw. Specifically, the wax precipitation temperature Lxw is obtained by the following formula: ; In the formula, It indicates the concentration of wax components in the liquid phase of the oil product when the temperature is i. When the temperature drops, the wax concentration in the liquid phase will gradually decrease. It represents the concentration of wax components in the solid phase of oil products at temperature i. Usually, in the precipitated wax crystals, the concentration of wax components in the solid phase of oil products can be regarded as approximately 1 (i.e., the precipitates are mainly wax crystals); is the melting enthalpy of the wax component, which indicates the energy absorbed when the wax component changes from solid to liquid, and is used to quantify the thermodynamic properties of liquid-solid phase transition; R represents the gas constant, which indicates the energy involved in each Kelvin temperature change per mole of substance in the thermodynamic process, and its value is 8.314 J / (mol*K). In thermodynamic formulas, the gas constant R is used to connect energy (such as free energy, heat, etc.) with temperature and the microscopic behavior of molecules; Rdz represents the melting point of the wax component, which reflects the temperature when the wax component is completely melted from the solid phase to the liquid phase; i represents the number of the corresponding temperature value; ln represents the logarithmic function with the constant e as the base, and e represents the Euler number, which is about 2.71828; This formula describes the equilibrium relationship between the wax component being dissolved in the oil and being precipitated in the form of crystals under thermodynamic equilibrium conditions, which follows the liquid-solid equilibrium; When the temperature is close to the melting point of the wax component, most of the wax component is in the liquid phase. When the temperature is lower than the melting point of the wax component and continues to decrease, the solid phase gradually increases, the solubility of the wax in the liquid phase decreases, and solid crystals precipitate. It describes the change in the free energy of the system when the temperature is reduced, which drives the wax component to precipitate from the liquid phase to the solid phase; It represents the ratio of liquid wax concentration to solid wax concentration. The effect of temperature reduction on the equilibrium is quantified.

[0024] Among them, the wax precipitation temperature Lxw refers to the temperature at which the wax component in the oil begins to precipitate from the liquid phase; The wax concentration Yf in the liquid phase of oil products and the wax concentration Gf in the solid phase of oil products at different temperatures can be measured using optical sensors (such as ultraviolet spectrometers, infrared spectrometers) or laser scattering sensors. These sensors estimate the wax concentration by analyzing the absorption or scattering characteristics of oil products to light. Melting enthalpy of wax components It needs to be measured by differential scanning calorimetry (DSC); The melting point Rdz of the wax component can be determined by measuring the melting temperature of the oil using a melting point meter or by differential scanning calorimetry (DSC).

[0025] According to the oil temperature Yw simulated in the current three-dimensional visualization model, combined with the wax precipitation temperature Lxw, the state of the oil in the current oil pipeline is compared to determine whether there is a risk of deposition on the inner wall of the current oil pipeline. The specific determination content is as follows: If the oil temperature Yw ≤ wax precipitation temperature Lxw, it is judged that there is a risk of deposition on the inner wall of the current oil pipeline, and the dynamic tracking instruction will be triggered for further analysis; If the oil temperature Yw>wax precipitation temperature Lxw, it is judged that there is no risk of deposition on the inner wall of the current oil pipeline, and the dynamic tracking instruction will not be triggered at this time.

[0026] In this embodiment, accurate wax precipitation temperature prediction: the wax precipitation temperature Lxw is accurately calculated by the first identification unit according to the relevant attribute data of the oil (such as the concentration of the wax component, melting enthalpy, melting point, etc.), combined with the thermodynamic formula. This temperature reflects the key temperature at which the wax component in the oil is precipitated from the liquid phase. Thermodynamic parameters in the formula, such as melting enthalpy and gas constant, make the calculation of the wax precipitation temperature more scientific and accurate, thereby providing an accurate basis for subsequent deposition risk assessment. Dynamic deposition risk identification and early warning mechanism: By comparing the real-time monitored oil temperature Yw with the wax precipitation temperature Lxw, the system can timely determine whether there is a deposition risk on the inner wall of the current oil pipeline. When the oil temperature is close to or lower than the wax precipitation temperature, the system will immediately trigger a dynamic tracking instruction for further analysis and monitoring. This real-time monitoring and risk judgment mechanism can warn of possible deposition problems in advance, thereby avoiding pipeline blockage or poor oil flow caused by wax precipitation of oil products. Improve the safety and stability of oil pipelines: Based on the comparison between wax precipitation temperature and oil temperature, the system can determine whether the pipeline is in a high-risk state, and trigger tracking instructions based on real-time data analysis. This mechanism can not only effectively prevent the occurrence of wax precipitation problems, but also ensure the stability of oil products during transportation, avoid the accumulation of sediments that affect oil transportation efficiency, and improve the long-term operation safety and economy of the pipeline.

[0027] Example 4: Please refer to Figure 1 Specifically: the second identification unit is used to determine the pour point of the oil product by using dynamic cooling conditions and monitoring the critical temperature at which the oil product loses fluidity. The specific determination content is as follows: insert a rotating measuring device into the oil product in advance, gradually cool the temperature, and each cooling is 3 degrees, and measure the flow state of the oil product at the same time. When the measured value shows that the oil product stops flowing, record the temperature range when the oil product stops flowing to determine the temperature at which the pour point of the oil product is located, and record it as the pour point limit range Q, and stop the temperature cooling operation at the same time; wherein, the rotating measuring device includes a rotating viscometer; Based on the pour point limit range Q, the constraint conditions for triggering the emergency maintenance mechanism are set, and the specific settings are as follows: by comparing the pour point limit range Q with the oil temperature Yw simulated in the current three-dimensional visualization model, if the oil temperature Yw>the pour point limit range Q, the emergency maintenance mechanism will not be triggered temporarily; if the oil temperature Yw≤the pour point limit range Q, the emergency maintenance mechanism will be triggered.

[0028] In this embodiment, accurate determination of oil pour point and fluidity monitoring: the second identification unit uses a rotating measuring device (such as a rotating viscometer) to gradually cool the oil and monitor the oil flow state in real time, accurately determining the pour point temperature range Q of the oil. This process ensures that the fluidity changes of the oil at different temperatures can be accurately measured, especially when the oil is close to losing fluidity, the pour point of the oil can be accurately determined, thereby avoiding pipeline blockage or reduced transportation efficiency due to loss of oil fluidity. Real-time temperature comparison and emergency maintenance triggering mechanism: Based on the determined pour point limit range Q, the system can compare it with the oil temperature Yw in the three-dimensional visualization model in real time. When the oil temperature exceeds the pour point limit range Q, the system determines that the oil still maintains good fluidity, so the emergency maintenance mechanism is not triggered; when the oil temperature is lower than or equal to the pour point limit range Q, it indicates that the oil has lost its fluidity, and the system immediately triggers the emergency maintenance mechanism, and handles and intervenes in time to avoid pipeline blockage or equipment damage due to oil solidification or deposition. Improve pipeline stability and anti-blocking capabilities: Through precise pour point determination and oil temperature monitoring, the system can detect and warn the critical temperature of oil fluidity loss in real time, effectively avoiding pipeline blockage and transportation interruption caused by loss of fluidity during oil transportation. This timely maintenance mechanism can greatly reduce sudden problems in pipeline operation and improve the stability and reliability of oil pipelines. Optimize maintenance response and cost control: Through intelligent temperature comparison and dynamic maintenance trigger mechanism, the system can accurately identify the critical point of oil fluidity loss, intervene in advance and take emergency maintenance measures, avoiding unnecessary excessive maintenance or misjudgment. At the same time, the mechanism can optimize maintenance response time and resource allocation, reduce pipeline shutdown and maintenance costs, and improve the operational efficiency of the entire oil pipeline.

[0029] Example 5: Please refer to Figure 1 ,Specifically: the kinetic analysis module includes a heat loss analysis unit and a presentation state analysis unit; The heat loss analysis unit is used to monitor the environmental change data of the oil product during the transmission process in real time after receiving the dynamic tracking instruction, wherein the environmental change data includes the fluid flow velocity Lsz, the change value Ncb of the internal environment difference, the initial position in the oil pipeline, and the The deposition thickness at , the oil temperature Yw at each position, the difference between the oil inflow temperature and the temperature at position x , the temperature difference of oil transition from liquid to sediment state , the reference flow velocity Ccs affected by deposition and the sensitivity coefficient of flow velocity to deposition ; By extracting the transport distance Gsc, the fluid flow velocity Lsz and the change value Ncb of the internal environment difference at the corresponding position in the oil pipeline from the environmental change data, and combining the relevant size information of the oil pipeline, the pipeline transport distance Gsc, the change value Ncb of the internal environment difference and the fluid flow velocity Lsz are associated, and after dimensionless processing, the heat loss coefficient Rsxs is obtained. The heat loss coefficient Rsxs is obtained by the following formula: ; In the formula, represents the heat loss coefficient at position x in the oil pipeline; It represents the distance transported at position x in the oil pipeline; Indicates the change in the internal environmental difference at position x in the oil pipeline; represents the fluid flow velocity at position x in the oil pipeline; , and are weight values, where 0 < <1,0< <1,0< <1, , and The specific value is set by the user according to the situation.

[0030] The presentation state analysis unit is used to analyze the influence of oil temperature change and flow rate change on the deposition distribution in the oil pipeline according to the environmental change data, so as to evaluate the deposition thickness H at each position in the oil pipeline. Specifically, the deposition thickness H at each position in the oil pipeline is obtained according to the following formula: ; In the formula, represents the deposition thickness at position x in the oil pipeline; Indicates the initial position in the oil pipeline The thickness of the sediment at It represents the difference between the oil inflow temperature and the temperature at position x; Indicates the temperature difference at which the oil product transitions from liquid to sedimentary state; It represents the fluid flow velocity at position x in the oil pipeline, reflecting the change of flow velocity with distance; Ccs represents the reference flow velocity affected by deposition; It represents the sensitivity coefficient of flow velocity to sedimentation, and describes the contribution weight of flow velocity to sedimentation thickness; Reflecting the decreasing characteristics of the exponential function, the exponential function As Z increases, it decreases, which conforms to the physical change law of deposition thickness: when the temperature difference increases (temperature decreases) or the flow rate decreases, the deposition thickness increases; when the temperature difference decreases (temperature increases) or the flow rate increases, the deposition thickness decreases. or ; represents the temperature attenuation term, reflecting the contribution of temperature change to the deposition thickness; It represents the velocity attenuation term, reflecting the contribution of velocity change to sediment thickness.

[0031] The distance Gsc transported at the corresponding position in the oil pipeline can be measured by a distance sensor or a laser rangefinder. In addition, a displacement sensor can also be used to track the flow path of the oil in the pipeline.

[0032] The fluid flow velocity Lsz can be monitored and obtained by a flow velocity sensor; The change value Ncb of the internal environment difference at the corresponding position in the oil pipeline refers to the difference between the oil product temperature at a certain position and the temperature of the inner wall of the pipeline, relative to the change of the difference between the oil product temperature and the temperature of the inner wall of the pipeline at the previous position, which can be monitored and obtained by the temperature sensor; Initial position in the oil pipeline The deposition thickness at It can be monitored and obtained by ultrasonic thickness gauge; The oil temperature Yw at each position, the difference between the oil inflow temperature and the temperature at each position Lsz, and the oil inflow temperature and the temperature difference of the oil transition from liquid to deposited state All of them can be monitored and obtained through temperature sensors; Sensitivity of flow velocity to sedimentation The simulation is performed through the three-dimensional visualization model. The specific acquisition steps are as follows: Set different flow rate conditions and select a certain time step to ensure that the deposition process gradually converges. The simulation runs to obtain the distribution of sedimentation thickness under different flow rate conditions to obtain simulation data. The simulation data includes the distribution of sedimentation thickness under different flow rate conditions. Two groups of flow rate conditions and the sedimentation thickness under the corresponding conditions are randomly extracted from the simulation data to obtain the sensitivity coefficient of flow rate to deposition. : ; in, and Represent two different flow rate conditions, and They represent the sedimentation thickness corresponding to two different flow rate conditions respectively; The purpose of the reference flow rate Ccs affected by deposition is to find the point where the flow rate and the deposition thickness growth rate change significantly. Specifically, the reference flow rate Ccs affected by deposition is obtained by the following equation:

[0033] in, Indicates the deposition thickness growth rate under the flow rate Lsz condition (unit: m / s or mm / s); represents the initial growth rate (the maximum value of the deposition rate at low flow rate); finally, according to the above equation, by analyzing the deposition rate growth rate The curve changes and the location of the significant change point is determined; In this embodiment, real-time heat loss monitoring and analysis: the heat loss analysis unit monitors the environmental change data during the oil product transmission process in real time, accurately extracts the key data such as the transportation distance, fluid flow velocity, and internal environment difference change at each position in the oil pipeline, and calculates the heat loss coefficient Rsxs in combination with the relevant size information of the pipeline. This process can reflect the heat loss caused by temperature changes and flow rate changes during the transportation of oil products, thereby providing accurate environmental basic data for subsequent deposition evaluation. Accurately evaluate the deposition thickness H: the presentation state analysis unit uses the environmental change data provided by the heat loss analysis unit, combined with the temperature change and flow rate change of the oil product, to accurately calculate the deposition thickness H at each position in the oil pipeline. By dynamically evaluating the thickness of the deposition layer according to the changes in flow rate and temperature difference, a quantitative basis is provided for the judgment and prevention of deposition risks in the oil pipeline. The exponential function in the formula reflects the influence of flow rate and temperature difference on deposition thickness, so that the calculation of deposition thickness is more in line with the physical change law and actual operation conditions. Optimized correlation between heat loss coefficient and deposition thickness: Through the dimensionless heat loss coefficient Rsxs, combined with the reference flow rate affected by deposition and the sensitivity coefficient of flow rate to deposition, the maintenance and control measures of the oil pipeline can be further optimized. Strengthen the health monitoring and management of oil pipelines: This technology can accurately evaluate the combined impact of temperature difference, flow rate and heat loss on deposition thickness during the oil transportation process in the oil pipeline, and timely adjust the operation strategy and maintenance measures of the pipeline according to the deposition conditions under different conditions. Through this technology, the accumulation process of pipeline deposition can be identified in time to avoid pipeline blockage or equipment damage due to excessive deposition, thereby improving the overall operation efficiency and safety of the pipeline system.

[0034] Example 6: Please refer to Figure 1 ,Specifically: the cleaning module includes a comprehensive analysis unit and a governance unit; The comprehensive analysis unit is used to construct a transportation evaluation model using deep learning technology, and input the deposition thickness H and heat loss coefficient Rsxs at various positions in the oil pipeline into the trained transportation evaluation model. After dimensionless processing, the transportation stability score Swpz is obtained by fitting. The transportation stability score Swpz is obtained by the following formula: ; Where n represents the number of monitoring locations, x=0, 1, 2, ..., n, represents the heat loss coefficient at position x in the oil pipeline, represents the average heat loss coefficient, represents the deposition thickness at position x in the oil pipeline, represents the average deposition thickness, Bjs represents the number of reducers set, , and are weight values, where 0 < <1,0< <1,0< <1, , and The specific value is set by the user according to the situation.

[0035] The above-mentioned set number of reducers Bjs refers to the number of reduced diameter pipes and expanded diameter pipes, which can be identified and obtained through a geometric dimension sensor.

[0036] The governance unit is used to pre-set the evaluation threshold value Y, and compares and analyzes the evaluation threshold value Y with the transportation stability score Swpz to determine the stability state of the oil in the current oil pipeline during transportation. The specific comparison content is as follows: If the transportation stability score Swpz exceeds the evaluation threshold value Y, it is judged that the oil in the current oil pipeline is not in a stable state during transportation, indicating that the flow may be unstable or wax precipitation, blockage and other problems may occur due to factors such as sediment accumulation, excessive heat loss and uneven flow rate. At this time, the heating system will be started in the oil pipeline to further avoid wax precipitation and deposition caused by too low oil temperature, and the working state of the pump will be adjusted to increase the flow rate to 1.2 times the original flow rate to increase the shear force in the oil pipeline, reduce the formation of sediment, and keep the oil in the pipeline at a high fluidity. Increasing the flow rate helps to reduce the risk of oil sedimentation and solid sedimentation. At the same time, according to the sediment thickness H at each position obtained in the presentation state analysis unit, the cleaning equipment is started to remove the sediment in the oil pipeline through the cleaning device to prevent further accumulation of sediment and ensure smooth flow in the pipeline; If the transportation stability score Swpz does not exceed the evaluation threshold value Y, it is judged that the oil in the current oil pipeline is in a stable state during transportation, indicating that there is no major sedimentation or wax precipitation problem. At this time, the state of the oil in the pipeline will be continuously monitored to ensure that the stability score will not change suddenly. After the oil transportation operation is completed, the sediment in the oil pipeline will be cleared by a pipe cleaner to reduce the fluidity of the oil in the subsequent oil transportation operation. Therefore, even if the current oil state is stable, regular pipe cleaning operations are necessary to prevent the accumulation of oil sedimentation and wax precipitation.

[0037] In this embodiment, accurate transportation stability score: the comprehensive analysis unit constructs a transportation evaluation model through deep learning technology, inputs the deposition thickness H and heat loss coefficient Rsxs at each position in the oil pipeline into the model, and after dimensionless processing, fits the transportation stability score Swpz, which comprehensively reflects the comprehensive influence of factors such as deposition, heat loss and flow rate in the oil pipeline, and can accurately evaluate the stability of the oil product during transportation. Through this score, potential problems in the oil transportation process can be monitored in real time, providing data support for subsequent governance measures. Identify and respond to unstable states in advance: When the transportation stability score Swpz exceeds the set evaluation threshold Y, the system can identify in advance the flow instability, wax precipitation or blockage problems that may occur in the oil product during transportation. At this time, the governance unit can automatically start the heating system to avoid wax precipitation and sediment accumulation caused by too low temperature, and at the same time adjust the working state of the pump to increase the flow rate to 1.2 times the original flow rate. By increasing the flow rate, enhancing the shear force, reducing the formation of deposits, and ensuring the smooth flow of oil in the pipeline, this operation not only effectively solves the problems of deposition and wax precipitation, but also greatly improves the stability and safety of the oil transportation system. Dynamic pipe cleaning mechanism: By cooperating with the presentation state analysis unit, the system can start the pipe cleaning equipment when the oil state is unstable based on the real-time monitoring of the deposition thickness H. The pipe cleaning device can effectively remove the sediment in the pipeline and prevent further accumulation of sediments, thereby ensuring the fluidity of the oil in the pipeline and reducing the risk of blockage. This dynamic pipe cleaning mechanism ensures the long-term operation stability of the oil pipeline and reduces maintenance costs and accident risks. Intelligent automatic adjustment and optimization: The present invention can evaluate the transportation stability of oil products in real time and intelligently, and automatically adjust the transportation conditions in the pipeline when unstable factors are detected. By automatically adjusting the working status of the heating system and the pump, combined with the pipe cleaning measures, the optimal control of the fluidity of the oil products is achieved. This automatic adjustment greatly reduces the need for manual intervention and improves the efficiency and safety of the entire oil transportation process.

[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An oil pipeline simulation analysis system, characterized by: It includes data module, simulation module, risk identification module, dynamic analysis module and pigging module; The data module is used to obtain the historical data of the oil pipeline in advance, obtain the interface type after data preprocessing, select the network interface according to the interface type, and input the preprocessed historical data of the oil pipeline into the local database through the network interface; The simulation module is used to simulate the flow state of oil in the oil pipeline according to the historical data of the oil pipeline in the database in combination with the fluid dynamics simulation software to build a three-dimensional visualization model. Based on the three-dimensional visualization model, the relationship between the oil in the oil pipeline being precipitated from the liquid phase and transformed into the solid phase at different temperatures is detected to generate relevant oil property data; The risk identification module is used to analyze and calculate the wax precipitation temperature Lxw based on the relevant oil property data, and to determine whether there is a deposition risk on the inner wall of the current oil pipeline based on the wax precipitation temperature Lxw. If so, a dynamic tracking instruction is triggered; The dynamic analysis module is used to monitor the environmental change data of the oil product in the transmission process in real time after receiving the dynamic tracking instruction, obtain the heat loss coefficient Rsxs after feature extraction, and analyze the influence of the oil product temperature change and flow rate change on the deposition distribution in the oil pipeline to evaluate the deposition thickness H at each position in the oil pipeline; The cleaning module is used to use deep learning technology to fit the deposition thickness H and heat loss coefficient Rsxs at various locations in the oil pipeline to obtain the transportation stability score Swpz. If the transportation stability score Swpz exceeds a preset evaluation threshold Y, the cleaning measure execution mechanism is initiated.

2. The oil pipeline simulation analysis system according to claim 1, characterized in that: The data module includes a data collection unit and a processing unit; The data collection unit is used to query and extract historical data of the oil pipeline based on the data stored in the enterprise server through SQL, wherein the historical data of the oil pipeline includes relevant dimension information of the oil pipeline, relevant oil product transmission information and relevant temperature change information, wherein the relevant dimension information of the oil pipeline includes the pipe diameter at each position in the oil pipeline, the pipe curvature and the transmission distance Gsc at the corresponding position in the oil pipeline; the relevant oil product transmission information includes the flow velocity and deposition state of the oil product at each position in the oil pipeline; the relevant temperature change information includes the temperature state of the oil product at each position in the oil pipeline and the temperature state of each position on the inner wall of the oil pipeline; The processing unit is used to convert the format of the historical data of the oil pipeline, and preprocess the historical data of the oil pipeline after the format conversion to perform noise removal, missing value filling and data smoothing operations, and then input the preprocessed historical data of the oil pipeline into the database through the network interface.

3. The oil pipeline simulation analysis system according to claim 2, characterized in that: The simulation module includes a modeling unit and a test data acquisition unit; The modeling unit is used to import the historical data of the oil pipeline in the database into the fluid dynamics simulation software, so as to reproduce the geometric state of the oil pipeline and the transmission state of the oil in the oil pipeline in the fluid dynamics simulation software, and construct a three-dimensional visualization model, wherein the fluid dynamics simulation software includes ANSYS Fluent, OpenFOAM and COMSOL Multiphysics; The test data acquisition unit is used to detect the relationship between the oil product in the oil pipeline being precipitated from the liquid phase and transformed into the solid phase at different temperatures based on the three-dimensional visualization model, so as to generate relevant oil product attribute data, wherein the relevant oil product attribute data includes the concentration Yf of the wax component in the liquid phase of the oil product, the concentration Gf of the wax component in the solid phase of the oil product, and the melting enthalpy of the wax component under different temperatures. and the melting point Rdz of the wax component.

4. The oil pipeline simulation analysis system according to claim 3 is characterized in that: The risk identification module includes a first identification unit and a second identification unit; The first identification unit is used to analyze the distribution behavior of the wax component in the oil product in the oil pipeline between the liquid phase and the solid phase according to the relevant oil product attribute data obtained by the test data acquisition unit, and calculate the wax precipitation temperature Lxw. Specifically, the wax precipitation temperature Lxw is obtained by the following formula: ； In the formula, It indicates the concentration of wax components in the liquid phase of oil products at temperature i; It indicates the concentration of wax components in the solid phase of oil products at temperature i; is the melting enthalpy of the wax component; R is the gas constant; Rdz is the melting point of the wax component; i is the number of the corresponding temperature value; ln is the logarithmic function with the constant e as the base, and e is the Euler number.

5. The oil pipeline simulation analysis system according to claim 4, characterized in that: According to the oil temperature Yw simulated in the current three-dimensional visualization model, combined with the wax precipitation temperature Lxw, the state of the oil in the current oil pipeline is compared to determine whether there is a risk of deposition on the inner wall of the current oil pipeline. The specific determination content is as follows: If the oil temperature Yw ≤ wax precipitation temperature Lxw, it is judged that there is a risk of deposition on the inner wall of the current oil pipeline, and the dynamic tracking instruction will be triggered for further analysis; If the oil temperature Yw>wax precipitation temperature Lxw, it is judged that there is no risk of deposition on the inner wall of the current oil pipeline, and the dynamic tracking instruction will not be triggered at this time.

6. The oil pipeline simulation analysis system according to claim 4, characterized in that: The second identification unit is used to determine the pour point of the oil product by monitoring the critical temperature at which the oil product loses fluidity under dynamic cooling conditions. The specific determination content is as follows: insert a rotating measuring device into the oil product in advance, gradually cool the temperature, and each cooling is 3 degrees, while measuring the flow state of the oil product. When the measured value shows that the oil product stops flowing, record the temperature range when the oil product stops flowing to determine the temperature at which the pour point of the oil product is located, and record it as the pour point limit range Q, and stop the temperature cooling operation at the same time; wherein the rotating measuring device includes a rotating viscometer; Based on the pour point limit range Q, the constraint conditions for triggering the emergency maintenance mechanism are set, and the specific settings are as follows: by comparing the pour point limit range Q with the oil temperature Yw simulated in the current three-dimensional visualization model, if the oil temperature Yw>the pour point limit range Q, the emergency maintenance mechanism will not be triggered temporarily; if the oil temperature Yw≤the pour point limit range Q, the emergency maintenance mechanism will be triggered.

7. The oil pipeline simulation analysis system according to claim 2, characterized in that: The dynamic analysis module includes a heat loss analysis unit and a presentation state analysis unit; The heat loss analysis unit is used to monitor the environmental change data of the oil product during the transmission process in real time after receiving the dynamic tracking instruction, wherein the environmental change data includes the fluid flow velocity Lsz, the change value Ncb of the internal environment difference, the initial position in the oil pipeline, and the The deposition thickness at , the oil temperature Yw at each position, the difference between the oil inflow temperature and the temperature at position x , the temperature difference of oil transition from liquid to sediment state , the reference flow velocity Ccs affected by deposition and the sensitivity coefficient of flow velocity to deposition ; By extracting the transport distance Gsc, the fluid flow velocity Lsz and the change value Ncb of the internal environment difference at the corresponding position in the oil pipeline from the environmental change data, and combining the relevant size information of the oil pipeline, the pipeline transport distance Gsc, the change value Ncb of the internal environment difference and the fluid flow velocity Lsz are associated, and after dimensionless processing, the heat loss coefficient Rsxs is obtained. The heat loss coefficient Rsxs is obtained by the following formula: ； In the formula, represents the heat loss coefficient at position x in the oil pipeline; It represents the distance transported at position x in the oil pipeline; Indicates the change in the internal environmental difference at position x in the oil pipeline; represents the fluid flow velocity at position x in the oil pipeline; , and are weight values, where 0 < <1,0< <1,0< <1, , and The specific value is set by the user according to the situation.

8. The oil pipeline simulation analysis system according to claim 7, characterized in that: The presentation state analysis unit is used to analyze the influence of oil temperature change and flow rate change on the deposition distribution in the oil pipeline according to the environmental change data, so as to evaluate the deposition thickness H at each position in the oil pipeline. Specifically, the deposition thickness H at each position in the oil pipeline is obtained according to the following formula: ； In the formula, represents the deposition thickness at position x in the oil pipeline; Indicates the initial position in the oil pipeline The thickness of the sediment at It represents the difference between the oil inflow temperature and the temperature at position x; Indicates the temperature difference at which the oil product transitions from liquid to sedimentary state; represents the fluid flow velocity at position x in the oil pipeline; Ccs represents the reference flow velocity affected by deposition; It represents the sensitivity coefficient of flow velocity to sedimentation.

9. The oil pipeline simulation analysis system according to claim 8, characterized in that: The pigging module includes a comprehensive analysis unit and a governance unit; The comprehensive analysis unit is used to construct a transportation evaluation model using deep learning technology, and input the deposition thickness H and heat loss coefficient Rsxs at various positions in the oil pipeline into the trained transportation evaluation model. After dimensionless processing, the transportation stability score Swpz is obtained by fitting. The transportation stability score Swpz is obtained by the following formula: ； Where n represents the number of monitoring locations, x=0, 1, 2, ..., n, represents the heat loss coefficient at position x in the oil pipeline, represents the average heat loss coefficient, represents the deposition thickness at position x in the oil pipeline, represents the average deposition thickness, Bjs represents the number of reducers set, , and are weight values, where 0 < <1,0< <1,0< <1, , and The specific value is set by the user according to the situation.

10. The oil pipeline simulation analysis system according to claim 9, characterized in that: The governance unit is used to pre-set the evaluation threshold value Y, and compares and analyzes the evaluation threshold value Y with the transportation stability score Swpz to determine the stability state of the oil in the current oil pipeline during transportation. The specific comparison content is as follows: If the transportation stability score Swpz exceeds the evaluation threshold value Y, it is determined that the oil in the current oil pipeline is not in a stable state during transportation. At this time, the heating system will be started in the oil pipeline, and the working state of the pump will be adjusted to increase the flow rate to 1.2 times the original flow rate. At the same time, according to the deposition thickness H at each position obtained in the presentation state analysis unit, the pigging equipment will be started to remove the sediment in the oil pipeline through the pig; If the transportation stability score Swpz does not exceed the evaluation threshold Y, it is judged that the oil in the current oil pipeline is in a stable state during transportation. At this time, the state of the oil in the pipeline will be continuously monitored, and after the oil transportation operation is completed, the sediment in the oil pipeline will be cleared by a pipe cleaner.

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