An oil pipeline simulation analysis system

Abstract

The present invention discloses an oil pipeline simulation analysis system, which relates to the technical field of oil pipelines. By constructing a three-dimensional visualization model, it can timely identify the wax precipitation temperature and deposition risk, providing accurate data support for subsequent dynamic monitoring and pigging decision-making. Through the risk identification module, the system can analyze and calculate the wax precipitation temperature based on the oil product property data obtained in real time, and timely judge whether there is a deposition risk in the oil pipeline. After receiving the dynamic tracking instruction, the kinetic analysis module can obtain the deposition thickness at each position in the pipeline in real time, and analyze the influence of the changes in oil product temperature and flow rate on the deposition distribution. This analysis provides important input data for the pigging module. By fitting the deposition thickness and heat loss coefficient through deep learning technology, the system can automatically calculate the transportation stability score. When the transportation stability score exceeds the set threshold, the system can automatically start the pigging measure execution mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of oil pipelines, and particularly 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, which involves the simulation and analysis of the flow process of liquids, gases or solid particles in pipelines, aiming to optimize the 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, evaluate the changes of parameters such as pressure, flow rate, temperature, etc. inside the pipeline, especially the thermodynamic properties and rheological properties of oil products during transportation. Specifically in the field of oil pipeline simulation, the stability of oil product transportation is a core issue. Especially in complex pipeline networks, temperature fluctuations, flow rate changes and sediment formation of oil products may all lead to unstable flow, affecting transportation efficiency and even the safety of equipment.

[0003] Although modern oil pipeline systems have tried to monitor and optimize the oil product transportation status under various environmental conditions through large-scale deployment of sensors, computer control systems and data analysis means, the existing oil pipeline simulation systems still have some obvious deficiencies and limitations. First of all, traditional oil pipeline simulations often only focus on macroscopic parameters such as flow rate and pressure, lacking dynamic analysis of the wax precipitation temperature and deposition distribution of oil products under different environmental conditions, resulting in difficulty in accurately predicting risks during long-term transportation. In addition, existing risk identification and pigging measures are mostly empirical judgments, lacking sufficient real-time data support and automated response mechanisms. The impact of temperature and flow rate changes of oil products on the sediments inside the pipeline is difficult to comprehensively evaluate under dynamic conditions, resulting in phenomena such as sediment accumulation and increased oil product viscosity in some pipeline sections, thus affecting transportation efficiency and safety. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an oil pipeline simulation analysis system, which solves the problems in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: an oil pipeline simulation analysis system, including a data module, a simulation module, a risk identification module, a kinetic analysis module and a pigging module;

[0006] The data module is used to pre-acquire the historical data of the oil pipeline, and after preprocessing the historical data of the oil pipeline, obtain the interface type, select a 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;

[0007] The simulation module is used to simulate the oil flow state in the oil pipeline based on the historical data of the oil pipeline in the database and in combination with the fluid dynamics simulation software, so as to construct a three-dimensional visualization model. Based on the three-dimensional visualization model, the relationship between the oil in the oil pipeline precipitating from the liquid phase and transforming into the solid phase at different temperatures is detected to generate relevant oil property data;

[0008] The risk identification module is used to analyze and calculate the wax precipitation temperature Lxw based on the relevant oil property data. Based on the wax precipitation temperature Lxw, it is judged whether there is a deposition risk on the inner wall of the current oil pipeline. If so, a dynamic tracking instruction is triggered;

[0009] The kinetic analysis module is used to, after receiving the dynamic tracking instruction, monitor the environmental change data of the oil during the transmission process in real time. After feature extraction, the heat loss coefficient Rsxs is obtained, and by analyzing the influence of the oil temperature change and the flow rate change on the deposition distribution in the oil pipeline, the deposition thickness H at each position in the oil pipeline is evaluated;

[0010] The pigging module is used to utilize deep learning technology to fit and obtain the transportation stability score Swpz through the deposition thickness H and the heat loss coefficient Rsxs at each position in the oil pipeline. If the transportation stability score Swpz exceeds the pre-set evaluation threshold Y, the pigging measure execution mechanism is started.

[0011] Preferably, the data module includes a data collection unit and a processing unit;

[0012] The data collection unit is used to query and extract the historical data of the oil pipeline according to the storage in the enterprise server through SQL. The historical data of the oil pipeline includes the relevant dimension information of the oil pipeline, the relevant oil transmission information, and the relevant temperature change information. Among them, the relevant dimension information of the oil pipeline includes the pipe diameter, pipe bend degree at each position in the oil pipeline, and the distance Gsc transported at the corresponding position in the oil pipeline; the relevant oil transmission information includes the flow rate and deposition state of the oil at each position in the oil pipeline; the relevant temperature change information includes the temperature state of the oil at each position in the oil pipeline and the temperature state of each position on the inner wall of the oil pipeline;

[0013] 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 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.

[0014] Preferably, the simulation module includes a modeling unit and a test data acquisition unit;

[0015] The modeling unit is used to import the historical data of the oil pipeline in the database into the hydrodynamic 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 hydrodynamic simulation software, and construct a three-dimensional visualization model. Among them, the hydrodynamic simulation software includes ANSYS Fluent, OpenFOAM and COMSOL Multiphysics;

[0016] The test data acquisition unit is used to detect the relationship between the oil product in the oil pipeline precipitating from the liquid phase and turning into the solid phase at different temperatures based on the three-dimensional visualization model, so as to generate relevant oil product attribute data. Among them, 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, the melting enthalpy of the wax component and the melting point Rdz of the wax component.

[0017] Preferably, the risk identification module includes a first identification unit and a second identification unit;

[0018] 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 in the test data acquisition unit, and calculate the wax precipitation temperature Lxw. Specifically, the wax precipitation temperature Lxw is obtained through the following formula:

[0019] ;

[0020] In the formula, represents the concentration of the wax component in the liquid phase of the oil product at the temperature value of i; represents the concentration of the wax component in the solid phase of the oil product at the temperature value of i; is the melting enthalpy of the wax component; R represents the gas constant; Rdz represents the melting point of the wax component; 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.

[0021] Preferably, according to the oil product temperature Yw simulated in the current three-dimensional visualization model and in combination with the wax precipitation temperature Lxw, the state of the oil product in the current oil pipeline is compared to judge whether there is a deposition risk on the inner wall of the current oil pipeline. The specific judgment content is as follows:

[0022] If the oil product temperature Yw ≤ the wax precipitation temperature Lxw, it is judged that there is a deposition risk on the inner wall of the current oil pipeline. At this time, a dynamic tracking instruction will be triggered for further analysis;

[0023] If the oil product temperature Yw > the wax precipitation temperature Lxw, it is judged that there is no deposition risk on the inner wall of the current oil pipeline for the time being. At this time, the dynamic tracking instruction will not be triggered temporarily.

[0024] Preferably, the second identification unit is used to determine the pour point of the oil product by monitoring the critical temperature at which the fluidity of the oil product is lost under dynamic cooling conditions. The specific determination content is as follows: A rotational measurement device is inserted into the oil product in advance, the temperature is gradually cooled, and each cooling is 3 degrees. At the same time, the flow state of the oil product is measured. When the measurement 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 denote it as the pour point limit range Q. At the same time, stop the temperature cooling operation; among them, the rotational measurement device includes a rotational viscometer.

[0025] Based on the pour point limit range Q, set the constraint conditions for triggering the emergency maintenance mechanism. The specific setting content is as follows: By comparing the pour point limit range Q with the oil product temperature Yw simulated in the current three-dimensional visualization model, if the oil product temperature Yw > the pour point limit range Q, the emergency maintenance mechanism is not triggered temporarily; if the oil product temperature Yw ≤ the pour point limit range Q, the emergency maintenance mechanism is triggered.

[0026] Preferably, the kinetic analysis module includes a heat loss analysis unit and a presentation state analysis unit;

[0027] The heat loss analysis unit is used to monitor the environmental change data of the oil product during transmission in real time after receiving the dynamic tracking instruction. Among them, 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 the deposition thickness at the oil product temperature Yw at each position, the difference between the inflow temperature of the oil product and the temperature at position x the temperature difference between the oil product transitioning from the liquid state to the deposited state the reference flow velocity Ccs affected by deposition, and the sensitivity coefficient of the flow velocity to deposition ; By extracting the transported 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 dimension information of the oil pipeline, by correlating the pipeline transport distance Gsc, the change value Ncb of the internal environment difference, and the fluid flow velocity Lsz, and after dimensionless processing, the heat loss coefficient Rsxs is obtained. The heat loss coefficient Rsxs is obtained through the following formula:

[0028] ;

[0029] In the formula, represents the heat loss coefficient at position x in the oil pipeline; represents the transported distance at position x in the oil pipeline; represents the change value of the internal environment difference at position x in the oil pipeline; represents the fluid flow velocity at position x in the oil pipeline; 、 and are all weight values, where 0 < < 1, 0 < < 1, 0 < < 1, 、 and The specific values are set by the user according to the situation.

[0030] Preferably, the presentation state analysis unit is used to analyze the influence of oil temperature change and flow velocity 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:

[0031] ;

[0032] In the formula, represents the deposition thickness at position x in the oil pipeline; represents the deposition thickness at the initial position in the oil pipeline; represents the difference between the inflow temperature of the oil product and the temperature at position x; represents the temperature difference for the transition of the oil product from the liquid state to the deposition state; represents the fluid flow velocity at position x in the oil pipeline; Ccs represents the reference flow velocity for deposition influence; represents the sensitivity coefficient of the flow velocity to deposition.

[0033] Preferably, the pigging module includes a comprehensive analysis unit and a treatment unit;

[0034] The comprehensive analysis unit is used to construct a transportation evaluation model by using deep learning technology, and input the deposition thickness H and the heat loss coefficient Rsxs at each position 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:

[0035] ;

[0036] In the formula, n represents the number of monitoring positions, 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 set number of reducer pipes, , and are all weight values, where 0 < < 1, 0 < < 1, 0 < < 1, and and The specific values are set by the user according to the situation.

[0037] Preferably, the governance unit is used for a preset evaluation threshold Y. By comparing and analyzing the evaluation threshold Y with the transportation stability score Swpz, to judge the stable state of the oil product in the current oil pipeline during transportation. The specific comparison content is as follows:

[0038] If the transportation stability score Swpz exceeds the evaluation threshold Y, it is judged that the oil product in the current oil pipeline is not in a stable state during transportation. At this time, a 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 from the presentation state analysis unit, a pigging device will be started to remove the sediment in the oil pipeline through a pig;

[0039] If the transportation stability score Swpz does not exceed the evaluation threshold Y, it is judged that the oil product in the current oil pipeline is in a stable state during transportation. At this time, the state of the oil product in the pipeline will be continuously monitored, and after the oil transportation operation is completed, the sediment in the oil pipeline will be removed through a pig.

[0040] The present invention provides an oil pipeline simulation analysis system, which has the following beneficial effects:

[0041] (1) Through the combination of the data module and the simulation module, the system can accurately simulate the oil flow state in the oil pipeline based on historical data and hydrodynamic simulation software, and construct a three-dimensional visualization model. This process enables the system to detect the liquid-solid equilibrium behavior of the oil at different temperatures in real time, identify the wax precipitation temperature Lxw and deposition risk in a timely manner, and provide accurate data support for subsequent dynamic monitoring and pigging decision-making. 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 oil property data obtained in real time, and timely judge whether there is a deposition risk in the oil pipeline. If there is a risk, the system will trigger a dynamic tracking instruction and start a 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 oil transportation process, ensuring the timely discovery and avoidance of potential deposition problems. Efficient deposition evaluation and pigging mechanism: After receiving the dynamic tracking instruction, the kinetic analysis module can obtain the deposition thickness H at each position in the pipeline in real time, and analyze the influence of changes in oil temperature and flow rate on the deposition distribution. This analysis provides important input data for the pigging module. By fitting the deposition thickness and heat loss coefficient through deep learning technology, the system can automatically calculate the transportation stability score Swpz. When the transportation stability score exceeds the set threshold Y, the system can automatically start the pigging measure execution mechanism to timely remove the sediments in the pipeline and prevent the adverse impact of deposition accumulation on the pipeline operation. In short, through precise simulation and dynamic monitoring, automated risk identification, real-time deposition evaluation, and intelligent pigging mechanism, the system further improves the operation efficiency of the oil pipeline. The system can evaluate the deposition risk and stability in real time during the oil transportation process, thus 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;

[0042] (2) The first identification unit calculates the wax precipitation temperature Lxw by comprehensively utilizing the concentration data of wax components in the liquid and solid phases of the oil product, the melting enthalpy of wax components, thermodynamic parameters such as the gas constant and melting point. This process can accurately quantify the thermodynamic process of wax components precipitating from the liquid phase to the solid phase, and accurately describe the temperature change of wax precipitation according to the liquid-solid equilibrium relationship. This calculation mechanism can predict the wax precipitation temperature in a timely manner according to the wax precipitation characteristics of the oil product under different working conditions, helping to analyze and judge the deposition risk of 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 product in the current oil pipeline, the system can evaluate in real time whether the oil product is in a state that may cause deposition. If the oil product temperature is lower than the wax precipitation temperature, it indicates that wax components may precipitate and form deposits in the pipeline. At this time, the system will automatically trigger a dynamic tracking instruction for further analysis and risk identification. This function can effectively prevent pipeline blockage or reduced operation efficiency caused by oil product deposition. Precise deposition risk prediction and early warning: Through precise calculation of wax precipitation temperature and real-time monitoring of oil product temperature, the system can identify potential risks in a timely manner through an early warning mechanism and conduct dynamic tracking before the 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 interruptions, maintenance costs, and production losses caused by sediment accumulation;

[0043] (3) Determination of the pour point of the oil product: The second identification unit inserts a rotating measurement device and gradually cools the oil product to measure the flow state of the oil product in real time. When the oil product stops flowing, record the temperature range at the time of stopping flow, so as to accurately determine the pour point limit range Q of the oil product. This determination process can accurately reflect the fluidity of the oil product in a low-temperature environment and provide a scientific basis for subsequent evaluation of the stability of the oil product. Real-time temperature monitoring based on the pour point limit range: According to the pour point limit range Q of the oil product, the system can monitor the temperature Yw of the oil product in real time and judge whether it is necessary to trigger an emergency maintenance mechanism according to the comparison result. When the oil product temperature is lower than or equal to the pour point limit range, the system will automatically trigger an emergency maintenance mechanism to identify and prevent potential risks caused by the loss of fluidity of the oil product in advance. Through this monitoring mechanism, the system can avoid pipeline blockage or equipment damage caused by insufficient fluidity of the oil product;

[0044] (4) The heat loss analysis unit monitors the environmental change data at various positions inside the oil pipeline in real time. By combining the size information of the oil pipeline and dimensionless processing, the heat loss coefficient Rsxs can be accurately calculated. This calculation is based on the correlation between the pipeline transportation distance, the change in the internal environment difference, and the fluid flow velocity, ensuring that the impact of environmental factors on heat loss during the oil product transmission process can be comprehensively quantified. Through this analysis, the heat loss hotspots that may occur during the oil product transmission process 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 presented state analysis unit helps to monitor the deposition risk of the oil product during transportation in real time. By accurately evaluating the impact of flow velocity and temperature change on the deposition distribution, the working state of the oil pipeline can be dynamically adjusted, and the deposition thickness H at different positions can be predicted. This comprehensive analysis based on temperature, flow velocity, and deposition sensitivity can timely detect potential deposition risks and provide important decision-making support for subsequent pigging measures. Dynamic prediction of deposition thickness: The presented state analysis unit can dynamically evaluate and predict the deposition thickness at various positions inside the oil pipeline based on the oil product inflow temperature, flow velocity change, and deposition influence coefficient. Through this analysis, the distribution of sediments in the pipeline can be found, and the positions where accumulation and precipitation may occur can be identified, providing accurate basis for the maintenance and pigging work of the oil pipeline. This prediction mechanism helps to prevent the loss of oil product fluidity and avoid pipeline blockage or decreased transmission efficiency caused by sediment accumulation. Description of the Drawings

[0045] Figure 1 It is a schematic diagram of the block diagram process of a simulation analysis system for an oil pipeline according to the present invention. Detailed Embodiments

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0047] Embodiment 1: Please refer to Figure 1 , the present invention provides a simulation analysis system for an oil pipeline, including a data module, a simulation module, a risk identification module, a kinetic analysis module, and a pigging module;

[0048] The data module is used to pre-obtain the historical data of the oil pipeline, and after preprocessing the historical data of the oil pipeline, obtain the interface type, 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;

[0049] The simulation module is used to simulate the oil flow state in the oil pipeline based on the historical data of the oil pipeline in the database and in combination with computational fluid dynamics (CFD) simulation software, so as to construct a three-dimensional visualization model. Based on the three-dimensional visualization model, the relationship between the oil product in the oil pipeline precipitating from the liquid phase and transforming into the solid phase at different temperatures is detected to generate relevant oil product attribute data;

[0050] The risk identification module is used to analyze and calculate the wax precipitation temperature Lxw based on the relevant oil product attribute data. Based on the wax precipitation temperature Lxw, it is judged whether there is a deposition risk on the inner wall of the current oil pipeline. If so, a dynamic tracking instruction is triggered;

[0051] The kinetic analysis module is used to, after receiving the dynamic tracking instruction, monitor the environmental change data of the oil product during the transmission process in real time. After feature extraction, the heat loss coefficient Rsxs is obtained, and the influence of the oil product temperature change and flow velocity change on the deposition distribution in the oil pipeline is analyzed to evaluate the deposition thickness H at each position in the oil pipeline;

[0052] The pigging module is used to utilize deep learning technology to fit and obtain the transportation stability score Swpz through the deposition thickness H and the heat loss coefficient Rsxs at each position in the oil pipeline. If the transportation stability score Swpz exceeds the preset evaluation threshold Y, the pigging measure execution mechanism is started.

[0053] During the operation of this system, there is precise data analysis and real-time monitoring: The data module pre-obtains the historical data of the oil pipeline 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 inputs for subsequent simulation analysis. Visualization and optimization of the oil flow state: The simulation module combines computational fluid dynamics (CFD) simulation software to accurately simulate the flow state of the oil in the pipeline and construct a three-dimensional visualization model. This visualization result clearly shows the process of the oil precipitating from the liquid phase and turning into the solid phase at different temperatures, effectively helping users 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 property data to analyze and calculate the wax precipitation temperature Lxw and judge the deposition risk on 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 detected early and responded to in a timely manner, reducing the potential threat of oil deposition to pipeline operation. Precise deposition thickness evaluation and environmental change monitoring: The kinetic analysis module monitors the environmental change data of the oil during transmission in real time. After extracting features, it accurately calculates the heat loss coefficient Rsxs. By analyzing the influence of oil temperature and flow rate changes on the deposition distribution in the pipeline, the system can accurately evaluate the deposition thickness H of different pipe segments, providing a reliable basis for pipeline operation and maintenance and identifying potential deposition problems in the pipeline in advance. Intelligent pigging and stability guarantee: The pigging module uses deep learning technology to accurately fit the transportation stability score Swpz by combining the deposition thickness H and the heat loss coefficient Rsxs. When the transportation stability score exceeds the preset evaluation threshold Y, the system automatically initiates pigging measures to ensure the stable transportation of oil and the long-term safe operation of the pipeline. This mechanism can dynamically adjust the pigging strategy according to actual operation data, reducing manual intervention and over-pigging, and improving the pigging efficiency and effect. In summary, through real-time and precise monitoring and dynamic response, this system further improves the operation stability of the oil pipeline, reduces deposition risks, optimizes the oil transportation efficiency, and reduces failure risks through intelligent pigging measures, ensuring the long-term safe and efficient operation of the oil pipeline system.

[0054] Example 2: Please refer to Figure 1 , specifically: The data module includes a data collection unit and a processing unit;

[0055] The data collection unit is used to query and extract the historical data of the oil pipeline according to the data stored in the enterprise server through SQL. 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. Among them, the relevant dimension information of the oil pipeline includes the pipe diameter, pipe curvature at each position in the oil pipeline, and the distance Gsc transported at the corresponding position in the oil pipeline; the relevant oil product transmission information includes the flow rate 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 the inner wall of the oil pipeline at each position.

[0056] The processing unit is used to convert the format of the historical data of the oil pipeline, aiming to convert the original data into the format required by the processing system (such as converting from CSV to JSON), and preprocess the historical data of the oil pipeline after format conversion to perform noise removal, missing value filling, and data smoothing operations. Among them, the methods for filling missing values include mean filling, median filling, interpolation filling, and regression filling; then the preprocessed historical data of the oil pipeline is input into the database through the network interface.

[0057] The simulation module includes a modeling unit and a test data collection unit;

[0058] The modeling unit is used to import the historical data of the oil pipeline in the database into the computational fluid dynamics (CFD) simulation software to reproduce the geometric state of the oil pipeline and the transmission state of the oil product in the oil pipeline in the computational fluid dynamics (CFD) simulation software, and construct a three-dimensional visualization model. Among them, the computational fluid dynamics (CFD) simulation software includes ANSYS Fluent, OpenFOAM, and COMSOL Multiphysics;

[0059] The test data collection unit is used to detect the relationship between the oil product in the oil pipeline precipitating from the liquid phase and transforming into the solid phase at different temperatures based on the three-dimensional visualization model to generate relevant oil product attribute data. Among them, 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, the melting enthalpy of the wax component and the melting point Rdz of the wax component.

[0060] In this embodiment, precise 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 various operating parameters of the oil pipeline under different working conditions. The data processing unit then 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. Building a 3D model based on fluid dynamics simulation: The modeling unit in the simulation module imports the historical data into fluid dynamics simulation software. Through advanced simulation software such as ANSYS Fluent, OpenFOAM, and COMSOL Multiphysics, it accurately reproduces the geometric state of the oil pipeline and the transmission state of the oil product. This process can not only build an accurate 3D visualization model but also provide comprehensive dynamic simulation support for the temperature change, flow rate change, and deposition behavior of the oil product during transportation. Through the efficient calculation of the simulation software, the system can comprehensively understand the transportation situation of the oil product and monitor possible abnormal states in real time. Precise detection of oil product precipitation and solid-phase transformation: The test data collection unit can accurately detect the process of the oil product precipitating from the liquid phase and transforming into the solid phase under different temperature conditions by using the 3D visualization model. The system generates detailed oil product attribute data by simulating the wax composition change of the oil product at different temperatures, including the concentration of wax components in the liquid phase, the concentration of wax components in the solid phase, the melting enthalpy of the wax components, and important data such as the melting point. This data can help analyze and predict the wax precipitation temperature of the oil product and the risk of solid-phase transformation, thus providing an accurate early warning for possible wax deposition problems during pipeline transportation.

[0061] Example 3: Please refer to Figure 1 , specifically: The risk identification module includes a first identification unit and a second identification unit;

[0062] The first identification unit is used to analyze the distribution behavior of wax components 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 in the test data collection unit, and calculate the wax precipitation temperature Lxw. Specifically, the wax precipitation temperature Lxw is obtained through the following formula:

[0063] ;

[0064] In the formula, represents the concentration of wax components in the liquid phase of the oil product at a temperature value of 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;

[0065] 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;

[0066] 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.

[0067] 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;

[0068] It represents the ratio of liquid wax concentration to solid wax concentration. The effect of temperature reduction on the equilibrium is quantified.

[0069] 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;

[0070] 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.

[0071] Melting enthalpy of wax components It needs to be measured by differential scanning calorimetry (DSC);

[0072] 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).

[0073] Based on the oil temperature Yw simulated in the current 3D visualization model and combined with the wax precipitation temperature Lxw, compare the state of the oil in the current oil pipeline to determine whether there is a deposition risk on the inner wall of the current oil pipeline. The specific judgment content is as follows:

[0074] If the oil temperature Yw ≤ the wax precipitation temperature Lxw, it is determined that there is a deposition risk on the inner wall of the current oil pipeline. At this time, a dynamic tracking instruction will be triggered for further analysis;

[0075] If the oil temperature Yw > the wax precipitation temperature Lxw, it is determined that there is no deposition risk on the inner wall of the current oil pipeline for the time being. At this time, the dynamic tracking instruction will not be triggered temporarily.

[0076] In this embodiment, the accurate prediction of the wax precipitation temperature: The first identification unit accurately calculates the wax precipitation temperature Lxw according to the relevant attribute data of the oil (such as the concentration of wax components, melting enthalpy, melting point, etc.) in combination with the thermodynamic formula. This temperature reflects the critical temperature at which the wax components in the oil precipitate from the liquid phase. The thermodynamic parameters in the formula, such as melting enthalpy and gas constant, make the calculation of the wax precipitation temperature more scientific and accurate, thus providing an accurate basis for the 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 early warn of possible deposition problems, thereby avoiding pipeline blockage or poor oil flow caused by wax precipitation in the oil. Improve the safety and stability of the oil pipeline: The system can judge whether the pipeline is in a high-risk state according to the comparison between the wax precipitation temperature and the oil temperature, and trigger a tracking instruction 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 the oil during transportation, avoid affecting the oil transportation efficiency due to the accumulation of sediments, and improve the long-term operation safety and economy of the pipeline.

[0077] Example 4: Please refer to Figure 1 , specifically: The second identification unit is used to determine the pour point of the oil by using dynamic cooling conditions and monitoring the critical temperature at which the fluidity of the oil is lost. The specific determination content is as follows: A rotary measuring device is inserted into the oil in advance, the temperature is gradually cooled, and each cooling is 3 degrees. At the same time, the flow state of the oil is measured. When the measured value shows that the oil stops flowing, record the temperature range when the oil stops flowing to determine the temperature at which the pour point of the oil is located, and record it as the pour point limit range Q, and at the same time stop the temperature cooling operation; Among them, the rotary measuring device includes a rotational viscometer;

[0078] Based on the pour point limit range Q, 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 is not triggered temporarily; if the oil temperature Yw ≤ the pour point limit range Q, the emergency maintenance mechanism is triggered.

[0079] In this embodiment, accurate determination of the pour point of the oil product and monitoring of fluidity: The second identification unit uses a rotational measurement device (such as a rotational viscometer) to gradually cool the oil product and real-time monitor the flow state of the oil product, accurately determining the pour point temperature range Q of the oil product. This process ensures that the changes in the fluidity of the oil product at different temperatures can be accurately measured. Especially when the oil product is approaching the loss of fluidity, the pour point of the oil product can be accurately judged, thereby avoiding pipeline blockage or reduced transportation efficiency caused by the loss of fluidity of the oil product. Real-time temperature comparison and emergency maintenance trigger 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 product 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 fluidity of the oil product has been lost, and the system immediately triggers the emergency maintenance mechanism to perform timely processing and intervention to avoid pipeline blockage or equipment damage caused by the solidification or deposition of the oil product. Improving pipeline stability and anti-blocking ability: Through accurate determination of the pour point and monitoring of the oil temperature, the system can real-time detect and warn of the critical temperature at which the fluidity of the oil product is lost, effectively avoiding pipeline blockage and transportation interruption caused by the loss of fluidity of the oil product during transportation. This timely maintenance mechanism can greatly reduce sudden problems during pipeline operation and improve the stability and reliability of the oil pipeline. Optimizing maintenance response and cost control: Through intelligent temperature comparison and dynamic maintenance trigger mechanism, the system can accurately identify the critical point of the loss of fluidity of the oil product, intervene in advance and take emergency maintenance measures, avoiding unnecessary over-maintenance or misjudgment. At the same time, this mechanism can optimize the maintenance response time and resource allocation, reduce pipeline shutdown and maintenance costs, and improve the operation efficiency of the entire oil pipeline.

[0080] Example 5: Please refer to Figure 1 , specifically: The kinetic analysis module includes a heat loss analysis unit and a presentation state analysis unit;

[0081] The heat loss analysis unit is used to, after receiving the dynamic tracking instruction, real-time monitor the environmental change data of the oil product during transmission, where the environmental change data includes the fluid flow velocity Lsz, the change value Ncb of the internal environmental difference, and the initial position in the oil pipeline the deposition thickness at , the oil temperature Yw at each position, the difference between the inflow temperature of the oil and the temperature at position x , the temperature difference for the transition of the oil from the liquid state to the deposition state , the reference flow velocity Ccs affected by deposition and the sensitivity coefficient of flow velocity to deposition ; By extracting the transported 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 dimension information of the oil pipeline, by correlating the pipeline transportation distance Gsc, the change value Ncb of the internal environment difference, and the fluid flow velocity Lsz, and after dimensionless processing, to obtain the heat loss coefficient Rsxs, the heat loss coefficient Rsxs is obtained through the following formula:

[0082] ;

[0083] In the formula, represents the heat loss coefficient at position x in the oil pipeline; represents the transported distance at position x in the oil pipeline; represents the change value of the internal environment difference at position x in the oil pipeline; represents the fluid flow velocity at position x in the oil pipeline; , and are all weight values, where, 0 < < 1, 0 < < 1, 0 < < 1, , and The specific values are set by the user according to the situation.

[0084] The presented state analysis unit is used to analyze the influence of the oil temperature change and the flow velocity 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:

[0085] ;

[0086] In the formula, represents the deposition thickness at position x in the oil pipeline; represents the initial position in the oil pipeline at the deposition thickness; represents the difference between the inflow temperature of the oil and the temperature at position x; represents the temperature difference for the transition of the oil from the liquid state to the deposition state; It represents the fluid flow velocity at position x in the oil pipeline, reflecting the variation of the flow velocity with distance; Ccs represents the reference flow velocity affected by deposition; It represents the sensitivity coefficient of the flow velocity to deposition, describing the contribution weight of the flow velocity to the deposition thickness; It reflects the decreasing characteristic of the exponential function, and the exponential function decreases as Z increases, which conforms to the physical variation law of the deposition thickness: when the temperature difference increases (temperature decreases) or the flow velocity decreases, the deposition thickness increases; when the temperature difference decreases (temperature increases) or the flow velocity increases, the deposition thickness decreases. Here, Z refers to or ;

[0087] It represents the temperature attenuation term, reflecting the contribution of temperature change to the deposition thickness;

[0088] It represents the flow velocity attenuation term, reflecting the contribution of flow velocity change to the deposition thickness.

[0089] The conveyed distance Gsc at the corresponding position in the oil pipeline can measure the distances at different positions in the oil pipeline through a distance sensor or a laser rangefinder. In addition, a displacement sensor can also be used to track the flow path of the oil product in the pipeline.

[0090] The fluid flow velocity Lsz can be monitored and obtained through a flow velocity sensor;

[0091] 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 and the pipeline inner wall temperature at a certain position, relative to the change situation of the difference between the oil product temperature and the pipeline inner wall temperature at the previous position, and it can be monitored and obtained through a temperature sensor;

[0092] The initial position in the oil pipeline of the deposition thickness can be monitored and obtained through an ultrasonic thickness gauge;

[0093] The oil product temperature Yw at each position, the difference Lsz between the inflow temperature of the oil product and the temperature at each position, and the temperature difference between the inflow temperature of the oil product and the transition from the liquid state to the deposition state can all be monitored and obtained through a temperature sensor;

[0094] The sensitivity coefficient of the flow velocity to deposition is obtained through simulation of a three-dimensional visualization model. The specific obtaining steps are as follows:

[0095] Set different flow velocity conditions and select a certain time step to ensure that the deposition process gradually converges. The simulation runs to obtain the sedimentation thickness distribution under different flow velocity conditions to acquire simulation data. The simulation data includes the sedimentation thickness distribution under different flow velocity conditions, and two sets of flow velocity conditions and the corresponding sedimentation thicknesses are randomly extracted from the simulation data to obtain the sensitivity coefficient of the flow velocity to deposition. :

[0096] ;

[0097] Among them, and respectively represent two different flow velocity conditions. and respectively represent the sedimentation thicknesses corresponding to the two different flow velocity conditions.

[0098] The purpose of the reference flow velocity Ccs for sedimentation influence is to find the point where the flow velocity and the growth rate of the sedimentation thickness change significantly. The reference flow velocity Ccs for sedimentation influence is obtained specifically through the following equation:

[0099]

[0100] Among them, represents the growth rate of the sedimentation thickness under the flow velocity Lsz condition (unit: m / s or mm / s); represents the initial growth rate (the maximum value of the sedimentation rate at low flow velocities); finally, according to the above equation, by analyzing the curve change of the sedimentation rate growth rate the position of the significant change point is determined.

[0101] In this embodiment, real-time heat loss monitoring and analysis: The heat loss analysis unit accurately extracts key data such as the conveying distance, fluid flow velocity, and change in the internal environment difference at each position in the oil pipeline by monitoring the environmental change data during the oil product transmission in real time, and combines the relevant dimension information of the pipeline to calculate the heat loss coefficient Rsxs. This process can reflect the heat loss generated by the oil product during the transmission due to temperature change and flow velocity change, thereby providing accurate environmental basic data for subsequent deposition evaluation. Accurately evaluate the deposition thickness H: The state presentation analysis unit uses the environmental change data provided by the heat loss analysis unit, combines the oil product temperature change and flow velocity change, and accurately calculates the deposition thickness H at each position in the oil pipeline. By dynamically evaluating the thickness of the deposition layer according to the flow velocity and temperature difference changes, a quantitative basis is provided for the deposition risk judgment and prevention and control of the oil pipeline. The exponential function in the formula reflects the influence law of the flow velocity and temperature difference on the deposition thickness, making the calculation of the deposition thickness more in line with the physical change law and actual operation situation. Optimization correlation between the heat loss coefficient and the deposition thickness: Through the dimensionless heat loss coefficient Rsxs, combined with the reference flow velocity affecting deposition and the sensitivity coefficient of the flow velocity to deposition, the maintenance and control measures of the oil pipeline can be further optimized. Strengthen the health monitoring and management of the oil pipeline: This technology can accurately evaluate the comprehensive influence of temperature difference, flow velocity, and heat loss on the deposition thickness during the oil product transmission in the oil pipeline, and timely adjust the operation strategy and maintenance measures of the pipeline according to the deposition situation under different conditions. Through this technology, the accumulation process of pipeline deposition can be timely identified, avoiding pipeline blockage or equipment damage caused by excessive deposition, and improving the overall operation efficiency and safety of the pipeline system.

[0102] Example 6: Please refer to Figure 1 , specifically: The pigging module includes an integrated analysis unit and a treatment unit;

[0103] The integrated analysis unit is used to construct a conveying evaluation model using deep learning technology, and input the deposition thickness H and heat loss coefficient Rsxs at each position in the oil pipeline into the trained conveying evaluation model. After dimensionless processing, the conveying stability score Swpz is obtained by fitting. The conveying stability score Swpz is obtained through the following formula:

[0104] ;

[0105] In the formula, n represents the number of monitoring positions, 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, and Bjs represents the set number of reducer pipes. , and are all weight values, where 0 < < 1, 0 < < 1, 0 < < 1, , and The specific values are set by the user according to the situation.

[0106] The set number Bjs of the above-mentioned reduced-diameter pipe refers to the number of reduced-diameter pipes and enlarged-diameter pipes, which can be identified and obtained through a geometric dimension sensor.

[0107] The governance unit is used for a preset evaluation threshold Y. By comparing and analyzing the evaluation threshold Y with the transportation stability score Swpz, to judge the stable state of the oil product in the current oil pipeline during transportation. The specific comparison content is as follows:

[0108] If the transportation stability score Swpz exceeds the evaluation threshold Y, it is judged that the oil product in the current oil pipeline is not in a stable state during transportation, indicating that problems such as flow instability or wax precipitation and blockage may occur due to factors such as sediment accumulation, excessive heat loss, and uneven flow velocity. At this time, a heating system will be started in the oil pipeline to further prevent wax precipitation and deposition caused by too low oil temperature, and adjust the working state of the pump to increase the flow velocity to 1.2 times the original flow velocity, so as to increase the shear force in the oil pipeline, reduce sediment formation, keep the oil product highly fluid in the pipeline, and increasing the flow velocity helps to reduce the risk of oil product deposition and solid deposition. At the same time, according to the deposition thickness H at each position obtained in the presented state analysis unit, a pigging device is started, and the sediment in the oil pipeline is removed by a pig to prevent further accumulation of sediment and ensure smooth flow in the pipeline;

[0109] If the transportation stability score Swpz does not exceed the evaluation threshold Y, it is judged that the oil product in the current oil pipeline is in a stable state during transportation, indicating that there are no major deposition or wax precipitation problems. At this time, the state of the oil product in the pipeline will be continuously monitored to ensure that the stability score does not change suddenly. After the oil transportation operation is completed, the sediment in the oil pipeline is removed by a pig to reduce the fluidity of the oil product during subsequent oil transportation operations. Therefore, even if the current oil product state is stable, regular pigging operations are necessary to prevent the accumulation of oil product deposition and wax precipitation.

[0110] In this embodiment, the accurate transportation stability score: The comprehensive analysis unit constructs a transportation evaluation model through deep learning technology. The deposition thickness H and the heat loss coefficient Rsxs at each position in the oil pipeline are input into the model. After dimensionless processing, the transportation stability score Swpz is fitted. This score comprehensively reflects the combined effects of factors such as deposition, heat loss, and flow velocity in the oil pipeline and can accurately evaluate the stability during the oil transportation process. Through this score, potential problems during the transportation process can be monitored in real time, providing data support for subsequent treatment measures. Identifying and addressing unstable states in advance: When the transportation stability score Swpz exceeds the set evaluation threshold Y, the system can identify in advance possible problems such as unstable flow, wax precipitation, or blockage during the oil transportation. At this time, the treatment unit can automatically start the heating system to avoid wax precipitation and sediment accumulation caused by too low temperature. At the same time, it adjusts the working state of the pump to increase the flow velocity to 1.2 times the original flow velocity. By increasing the flow velocity, the shear force is enhanced, the formation of sediment is reduced, and the smooth flow of oil in the pipeline is ensured. This operation not only effectively solves the problems of sediment and wax precipitation but also greatly improves the stability and safety of the oil transportation system. Dynamic pigging mechanism: By cooperating with the presented state analysis unit, the system can start the pigging equipment based on the real-time monitoring of the deposition thickness H when the oil state is unstable. The pigging device can effectively remove the sediment in the pipeline, prevent the further accumulation of sediment, thereby ensuring the fluidity of the oil in the pipeline and reducing the risk of blockage. This dynamic pigging mechanism ensures the long-term operation stability of the oil pipeline, reducing the maintenance cost and accident risk. Intelligent automatic adjustment and optimization: The present invention can evaluate the transportation stability of oil in real time and intelligently, and automatically adjust the transportation conditions in the pipeline when unstable factors are detected. By automatically adjusting the working states of the heating system and the pump and combining pigging measures, the optimal control of the fluidity of the oil is achieved. This automatic adjustment greatly reduces the need for manual intervention and improves the efficiency and safety of the entire oil transportation process.

[0111] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made in these embodiments without departing from the principles and spirit of the present invention. 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 risk identification module includes a first identification unit and a second identification unit; 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, a constraint condition for triggering an emergency maintenance mechanism is set, and the specific setting content is as follows: by comparing the pour point limit range Q with the oil product temperature Yw simulated in the current three-dimensional visualization model, if the oil product temperature Yw> the pour point limit range Q, the emergency maintenance mechanism is not triggered temporarily; if the oil product temperature Yw≤the pour point limit range Q, the emergency maintenance mechanism 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 transportation stability score Swpz through the deposition thickness H and the heat loss coefficient Rsxs at various locations in the oil pipeline. If the transportation stability score Swpz exceeds a preset evaluation threshold Y, the cleaning measure execution mechanism is initiated; 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, Indicates 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.

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 the 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 the relevant dimension information of the oil pipeline, the relevant oil product transmission information and the 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 distance transported at the corresponding position in the oil pipeline ; The relevant oil product transmission information includes the flow rate and deposition state of the oil product at various positions in the oil pipeline; the relevant temperature change information includes the temperature state of the oil product at various positions in the oil pipeline and the temperature state of various positions 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 separating from the liquid phase and transforming 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 of wax components in the liquid phase of the oil product at different temperatures. , the concentration of wax components in the solid phase of oil products , melting enthalpy of wax components and the melting point of the wax component .

4. The oil pipeline simulation analysis system according to claim 3 is characterized in that: 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; represents the gas constant; It represents the melting point of the wax component; 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.

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 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 rate , the change value of the internal environment difference , Initial position in the oil pipeline The deposition thickness at , Oil temperature at each location , the difference between the oil inflow temperature and the temperature at position x , the temperature difference of oil transition from liquid to sediment state , Reference velocity for sedimentation effect And the sensitivity coefficient of flow velocity to sedimentation ; By extracting the distance transported at the corresponding position in the oil pipeline from the environmental change data , fluid flow velocity and the change value of internal environment difference , and combined with the relevant size information of the oil pipeline, the pipeline transportation distance Gsc, the change value of the internal environment difference Ncb and the fluid flow velocity Lsz are correlated, 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.

7. The oil pipeline simulation analysis system according to claim 6, 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; Reference flow velocity representing the effect of sedimentation; It represents the sensitivity coefficient of flow velocity to sedimentation.

8. The oil pipeline simulation analysis system according to claim 7, 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.

Images