A dynamic risk assessment method and system for a gas gathering station

Abstract

The application discloses a kind of dynamic risk assessment method and system of gas gathering station, can realize for the dynamic of gas gathering station risk assessment, rich risk quantification rating, high safety and reliability, improve the work efficiency of accident danger solution, provide guidance for accident prevention.It includes collecting gas gathering station data information, and node division is carried out to gas gathering station according to process flow;Analysis of the process parameter deviation generated by each node, according to data information and process parameter deviation, the risk scenario of each node of gas gathering station is identified;Process steady-state model of gas gathering station is constructed, and the process simulation of identified risk scenario is carried out;Change the process parameter of process simulation to obtain the simulation running result of different deviation degree deviation, determine the dangerous peak value of process parameter;According to the interval division of process parameter deviation when process parameter reaches dangerous peak value, according to the number of dangerous factors in interval, risk level is re-determined for the risk scenario of each node, to realize the dynamic risk assessment of gas gathering station.

Description

Technical Field

[0001] This invention relates to the field of risk assessment technology for gas gathering stations, specifically a dynamic risk assessment method and system for gas gathering stations. Background Technology

[0002] Safety instruments (such as sensors and transmitters) are selected based on the normal process flow of the gas gathering station. Their optimal operating range is generally between 1 / 3 and 2 / 3 of their range. This is because when the operating range is greater than 1 / 3, the instrument's linearity, repeatability, relative error, and accuracy are the best. However, if used for a long time at more than 2 / 3 of the range, the measurement function of its internal components will be reduced, causing zero drift and range changes, accelerating component aging, and reducing service life.

[0003] When a blowout occurs at a high-temperature well cluster gasification station, emergency process changes are sometimes necessary to prevent the blowout gas from entering the environment. This involves connecting the blowout well's natural gas to an adjacent gathering and transportation system for collection and processing, utilizing adjacent pipelines and equipment. However, this can lead to situations where the feed gas temperature at the station exceeds the original design operating temperature range (e.g., the original design temperature range is 10–45°C, but the temporary process change causes the process instruments to suddenly experience abnormally high temperatures approaching 75°C). This causes the instruments to operate outside their designed operating range. Engineering case studies show that the longer the instruments operate outside their design temperature range, the lower their accuracy and stability; the higher the temperature exceeds the design operating range, the greater the decrease in accuracy and stability. Ultimately, this results in instrument indication deviations, fault alarms, or false alarms, leading to delayed emergency response and significant uncertainty regarding the continued production risk of the gasification station.

[0004] Existing literature and case studies reveal that current domestic research on instrument reliability primarily focuses on the impact of ambient temperature on measurement uncertainty, lacking research on the impact of medium overheating on instrument measurement accuracy. Furthermore, current risk assessment methods generally involve using existing risk assessment techniques to determine the probability and severity of accidents, thereby establishing a risk assessment model to obtain the assessment results. For gas gathering stations, risk assessment mainly employs the traditional HAZOP method to identify potential process deviations, and then uses a risk matrix to determine the risk level of the risk scenario. However, this type of method suffers from certain subjectivity, bias ambiguity, and static nature, and the analysis results lack a clear distinction between primary and secondary factors. Currently, there is no sufficiently accurate and reliable method for instrument reliability analysis and dynamic risk assessment of gas gathering stations under sudden high-temperature fluctuations. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a dynamic risk assessment method and system for gas gathering stations, which can realize dynamic risk assessment of gas gathering stations, enrich risk quantification rating, have high safety and reliability, improve the efficiency of resolving accident hazards, and provide guidance for accident prevention.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A dynamic risk assessment method for gas gathering stations includes the following steps:

[0008] Collect data from gas gathering stations and divide the gas gathering stations into nodes according to the process flow;

[0009] Analyze the process parameter deviations generated at each node, and identify the risk scenarios at each node of the gas gathering station based on the data and process parameter deviations;

[0010] Construct a process steady-state model for the gas gathering station and simulate the process for the identified risk scenarios;

[0011] By changing the process parameters in the process simulation, simulation results with different degrees of deviation are obtained, and the dangerous peak values ​​of the process parameters are determined.

[0012] The process parameter deviation is divided into intervals based on the occurrence of dangerous peak values. The risk level of each node is re-determined based on the number of dangerous factors within the interval, thereby realizing dynamic risk assessment of the gas gathering station. The number of dangerous factors refers to the number of times the process parameter reaches dangerous peak values ​​during operation under each condition.

[0013] Preferably, the data information of the gas gathering station includes the gas gathering station's material information, process operation condition information, equipment size information, and location information.

[0014] Preferably, the analysis of process parameter deviations generated at each node, and the identification of risk scenarios for each node of the gas gathering station based on data information and process parameter deviations, includes:

[0015] Based on the data, the HAZOP method was used to analyze the process parameter deviations at each node of the gas gathering station. The process parameter deviations included the causes and consequences of the deviations.

[0016] The severity of the consequences of deviations is classified using the risk matrix method.

[0017] Based on the risk classification, the risk scenarios of each node of the gas gathering station are identified, and the initial risk level is determined.

[0018] Preferably, after analyzing the process parameter deviations generated at each node and identifying the risk scenarios of each node in the gas gathering station based on the data information and process parameter deviations, the method further includes performing reliability analysis on the safety instruments in the identified high-temperature operating risk scenarios of the gas gathering station, specifically including the following steps:

[0019] Obtain the maximum applicable temperature T0 of the safety instrument and the temperature of the overheated medium being measured, and establish a mathematical model for the instrument's reliability.

[0020] The instrument reliability mathematical model is obtained by inputting multiple sets of measurement data and solving to obtain the maximum error Δ of the instrument reading under high temperature conditions. max ;

[0021] Maximum error Δ of comparator reading max and instrument permissible error Δ x Determine the reliability of safety instruments:

[0022] like If the safety instrument's reliability meets the requirements, it can continue to be used, and the readings of the safety instrument should be corrected according to the error value.

[0023] Otherwise, if the safety instrument's reliability does not meet the requirements, the safety instrument should be replaced.

[0024] Preferably, the mathematical model for the reliability of the instrument is expressed as follows:

[0025]

[0026] In the formula, Δ represents the temperature of the medium measured by the instrument exceeding [a certain value]. The indicated error after I; g This is the reading from the high-temperature instrument; I s δ represents the standard indicated value; δ represents the zero drift and time drift of the high-temperature instrument; c represents the temperature influence coefficient; and T represents the temperature value of any over-temperature medium.

[0027] Preferably, the step of changing the process parameters in the process simulation to obtain simulation results with different degrees of deviation, and determining the dangerous peak values ​​of the process parameters, includes:

[0028] Based on the steady-state operation of the process, flow control controllers and level control controllers are added. By changing the process parameters of the process simulation, simulation results with different degrees of deviation are obtained. The steady-state process model is transformed into a dynamic process model. The changes of each process parameter are recorded, the boundary conditions of each process parameter are determined, and the dangerous peak values ​​of each process parameter are obtained.

[0029] Preferably, the process parameters include temperature, pressure, and liquid level.

[0030] Preferably, after re-determining the risk level of each node's risk scenario based on the number of risk factors within the interval, the method further includes determining the risk transmission path, with the specific steps as follows:

[0031] In the Aspen HYSYS simulation software, set the process simulation time, collect process parameters at the set time intervals, and summarize the data collection results into a table.

[0032] Based on the table analysis, determine the time required for each process parameter to reach the critical peak, and compare the time values;

[0033] The order in which each process parameter reaches the dangerous peak value is arranged according to the comparison results serves as the risk transmission path under this risk scenario.

[0034] Preferably, the step of dividing the process parameter deviation into intervals based on the condition that the process parameters reach the dangerous peak value also includes dividing the process parameter deviation into intervals based on the initial alarm value of the safety instruments in the gas gathering station.

[0035] A dynamic risk assessment system for a gas gathering station, comprising:

[0036] The data acquisition and processing unit is used to collect data information from the gas gathering station and divide the gas gathering station into nodes according to the process flow.

[0037] The risk level assessment unit is used to analyze the process parameter deviations generated at each node and identify the risk scenarios at each node of the gas gathering station based on data information and process parameter deviations.

[0038] The process simulation unit is used to construct a steady-state process model of the gas gathering station using Aspen HYSYS simulation software, and to simulate the process of identified risk scenarios.

[0039] The hazardous peak acquisition unit is used to change the process parameters of the process simulation to obtain simulation results with different degrees of deviation, and to determine the hazardous peak value of the process parameters;

[0040] The secondary risk level determination unit is used to divide the process parameter deviation into intervals based on the condition that the process parameters reach the dangerous peak value, and to re-determine the risk level of each node's risk scenario based on the number of dangerous factors within the interval, thereby realizing dynamic risk assessment of the gas gathering station. The number of dangerous factors represents the number of times the process parameters reach the dangerous peak value during each operating condition.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] This invention provides a dynamic risk assessment method for gas gathering stations. Taking gas gathering stations experiencing sudden high-temperature fluctuations as the target, it identifies risk scenarios under such conditions using the traditional HAZOP method and establishes a mathematical model using AspenHYSYS simulation software to preliminarily determine the risk level of these high-temperature risk scenarios. Building upon traditional risk assessment methods, this invention utilizes AspenHYSYS to quantify the degree of deviation of process parameters in each risk scenario. Furthermore, it introduces the concept of hazard factors to re-determine the risk level of hazardous scenarios with different degrees of deviation for the same deviation. This enriches the quantitative risk rating in HAZOP analysis, achieves dynamic risk assessment, improves the efficiency of resolving hazardous events, and offers high safety and reliability, providing guidance for accident prevention. Attached Figure Description

[0043] Figure 1 This is a flowchart of the steps of the dynamic risk assessment method of the present invention;

[0044] Figure 2 This is a flowchart illustrating the specific implementation process of the present invention;

[0045] Figure 3 This is a graph showing the temperature change trend of the medium measured by the safety instrument under abnormal high-temperature conditions in one embodiment of the present invention. Detailed Implementation

[0046] The principles and features of the present invention will be further described in detail below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention. It should be noted that the accompanying drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0047] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or it can be in a centered component. When a component is said to be "connected to" another component, it can be directly connected to the other component or it may also be in a centered component. When a component is said to be "set to" another component, it can be directly set on the other component or it may also be in a centered component.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0049] This invention provides a dynamic risk assessment method for gas gathering stations, such as... Figure 1 As shown, it includes the following steps:

[0050] Collect data from gas gathering stations and divide the gas gathering stations into nodes according to the process flow;

[0051] Based on the HAZOP method, the process parameter deviations generated at each node are analyzed, and the risk scenarios of each node of the gas gathering station are identified based on the data information and process parameter deviations.

[0052] A steady-state process model of the gas gathering station was constructed using Aspen HYSYS simulation software to simulate the identified risk scenarios.

[0053] By changing the process parameters in the process simulation, simulation results with different degrees of deviation are obtained, and the dangerous peak values ​​of the process parameters are determined.

[0054] The process parameter deviation is divided into intervals based on the occurrence of dangerous peak values. The risk level of each node is re-determined based on the number of dangerous factors within the interval, thereby realizing dynamic risk assessment of the gas gathering station. The number of dangerous factors refers to the number of times the process parameter reaches dangerous peak values ​​during operation under each condition.

[0055] This invention designs a dynamic risk assessment method for gas gathering stations. Taking gas gathering stations experiencing sudden high-temperature fluctuations as the object, it identifies the risk scenarios existing in the gas gathering station under such conditions using the traditional HAZOP method. A mathematical model is then established using AspenHYSYS simulation software to preliminarily determine the risk level of the high-temperature risk scenarios at the gas gathering station. Building upon traditional risk assessment methods for gas gathering stations, AspenHYSYS is used to quantify the degree of deviation of process parameters in each risk scenario. Furthermore, the concept of hazard factors is introduced to re-determine the risk level of hazardous scenarios with different degrees of deviation for the same deviation. This enriches the quantitative risk rating in HAZOP analysis, achieves dynamic risk assessment, improves the efficiency of resolving hazardous events, and offers high safety and reliability, providing guidance for accident prevention.

[0056] This invention also provides a dynamic risk assessment system for gas gathering stations, used to implement the dynamic risk assessment method described in this invention, comprising:

[0057] The data acquisition and processing unit is used to collect data information from the gas gathering station and divide the gas gathering station into nodes according to the process flow.

[0058] The risk level determination unit is used to analyze the process parameter deviations generated at each node based on the HAZOP method, and to identify the risk scenarios of each node of the gas gathering station based on the data information and process parameter deviations.

[0059] The process simulation unit is used to construct a steady-state process model of the gas gathering station using Aspen HYSYS simulation software, and to simulate the process of identified risk scenarios.

[0060] The hazardous peak acquisition unit is used to change the process parameters of the process simulation to obtain simulation results with different degrees of deviation, and to determine the hazardous peak value of the process parameters;

[0061] The secondary risk level determination unit is used to divide the process parameter deviation into intervals based on the condition that the process parameters reach the dangerous peak value, and to re-determine the risk level of each node's risk scenario based on the number of dangerous factors within the interval, thereby realizing dynamic risk assessment of the gas gathering station. The number of dangerous factors represents the number of times the process parameters reach the dangerous peak value during each operating condition.

[0062] The dynamic risk assessment method for gas gathering stations described in this invention, such as... Figure 2 As shown, the specific implementation steps are as follows:

[0063] (1) Data collection:

[0064] Data collection includes process flow diagrams (PFD), piping and instrumentation (P&ID) diagrams, etc. To achieve dynamic simulation of the process system, all relevant process parameters need to be obtained, including material information, process operating conditions, equipment dimensions, etc. To perform instrument system response analysis under high-temperature conditions, it is necessary to collect data such as the gas gathering station control system specifications and monitoring instrument manufacturer data.

[0065] (2) Hazard analysis under high-temperature conditions:

[0066] 2.1 Node Division: Based on comprehensive information on materials, equipment, facilities, processes, operations, and locations of the gas gathering station, determine the abnormal high-temperature operating conditions of the gas gathering station and divide the nodes according to the process flow;

[0067] 2.2 High-Temperature Operating Condition Hazard Analysis: The HAZOP method is used to analyze the abnormal high-temperature operating conditions of the gas gathering station, analyze all possible deviations at each node, the causes of the deviations and their consequences, and then use the traditional risk matrix method to classify the severity of the consequences and identify risk scenarios.

[0068] (3) Aspen HYSYS dynamic simulation

[0069] 3.1 Steady-state modeling with Aspen HYSYS: A steady-state simulation system for the process was established and debugged in the Aspen HYSYS simulation software. The process was brought into normal operation, and its stability and accuracy were verified by comparing it with known relevant parameters.

[0070] 3.2 Aspen HYSYS Deviation Dynamic Simulation: Based on steady-state operation, flow and level control controllers were added to determine the boundary conditions for each parameter. The process model was transformed into a dynamic model, and process simulations were performed on identified risk scenarios. By continuously changing the process conditions in the simulation, the impact of different degrees of deviation from the HAZOP analysis on the process was realized. The simulation verified the implementation of the safety measures in the HAZOP analysis report in the dynamic simulation, recording parameter changes and determining the dangerous peak values ​​for each process parameter.

[0071] (4) Safety Instrument Reliability Analysis

[0072] Identify the instrument systems affected in high-temperature operating risk scenarios, and determine the reliability of the instruments based on the high temperature they withstand and the duration of high-temperature operation.

[0073] The trend of the measured medium temperature under abnormal high-temperature conditions is as follows: Figure 3 As shown, based on this, the following mathematical model is established to address the impact of over-temperature operation on instrument reliability:

[0074]

[0075] In the formula, Δ represents the temperature of the medium measured by the instrument exceeding [a certain value]. The indicated error is expressed in the same way as the permissible error, as a percentage (%). g This is the reading from the high-temperature instrument; I S The standard indicated value is δ; the zero drift and time drift of the high-temperature instrument are δ and c is the temperature influence coefficient; T is the temperature value of any over-temperature medium; T0 is the maximum applicable temperature of the instrument, in °C; T1 is the highest temperature reached by the medium measured by the instrument, in °C; then the average temperature of the over-temperature medium measured by the instrument is... The duration of high-temperature operation is Δt = t2 - t1.

[0076] The values ​​of δ and c can be obtained by using multiple sets of measurement data, and finally the maximum error Δ of the instrument reading under high temperature conditions can be obtained. max and compare it with the allowable error Δ of the instrument. x Compare and judge the reliability of the instruments.

[0077] like If the instrument's reliability meets the requirements, it can continue to be used, and the safety instrument reading can be corrected according to the error value to ensure the safe operation of the gas gathering station;

[0078] Otherwise, if the instrument's reliability does not meet the requirements, it should be replaced promptly.

[0079] Regarding temperature process parameters, here's an example:

[0080] Provided that the safety instruments meet the reliability requirements, the risk level of high-temperature operating conditions is determined based on the over-temperature temperature and the duration of the high-temperature condition:

[0081] Using Aspen HYSYS to simulate abnormal high-temperature operating conditions, the average temperature was obtained to determine the time when each process parameter reached its critical peak. Based on conservative analysis, the longest time T for each process parameter to reach its critical peak was selected. S The hazard level of the working condition is determined by comparing it with the duration Δt of the high-temperature working condition. The specific criteria for determining the hazard level are shown in Table 1.

[0082] Table 1: Risk Level Determination for High-Temperature Operating Conditions

[0083]

[0084] (5) Risk assessment and classification

[0085] For deviations in process parameters other than temperature, the process parameter variables involved in each node, namely temperature, pressure, and liquid level, can be defined as factors for assessing the risk level. The number of hazard factors corresponding to each operating condition is defined as S, which means the number of times the system parameters reach the process hazard peak during operation under each operating condition.

[0086] Based on the Aspen HYSYS simulation of deviations of different degrees, the data is analyzed, and the deviations are divided into intervals according to the state of the process parameters reaching the dangerous peak. Then, the risk level of the dangerous conditions of different degrees of deviation of the same deviation is determined a second time according to the number of dangerous factors in the deviation interval. The risk level determination criteria are shown in Table 2. This realizes the dynamic risk assessment of the process and provides a reference for the next step of the proposed measures.

[0087] Table 2: Risk Level Determination

[0088]

[0089]

[0090] For example, suppose the node being analyzed contains n devices, and each device involves three process parameters: temperature, pressure, and liquid level. Then the node has a total of 3n hazard factors.

[0091] If the simulated abnormal high-temperature operating condition includes two pieces of equipment, and the number of hazard factors in different valve opening ranges is as follows, then its risk level can be determined a second time according to Table 3 below:

[0092] Table 3: Risk Level Determination Based on Hazard Factors

[0093]

[0094] (6) Determining the risk transmission path:

[0095] Set the Aspen HYSYS process simulation time and collect process parameters at regular time intervals. For ease of analysis, the data collection results can be summarized into a table. Analyze the simulation data based on the table to determine the time required for each parameter to reach the critical peak, compare the magnitudes of these time values, and summarize the order in which the parameters reach the critical peak. This will reveal the risk transmission path under this hazardous scenario.

[0096] (7) Emergency Response

[0097] By using Aspen HYSYS dynamic risk assessment, the risk transmission path of a specific risk scenario and the time when process parameters reach dangerous peak values ​​under abnormal operating conditions can be identified, thus optimizing emergency response procedures. Furthermore, the causes of hazardous accident scenarios can be deduced from the risk transmission path, providing a reference for accident prevention.

[0098] This invention utilizes the traditional HAZOP method to systematically identify risk scenarios at gas gathering stations under sudden high-temperature fluctuations, identifies the instrumentation systems present in these scenarios, analyzes the impact of high-temperature conditions on instrumentation, assesses the reliability of safety instruments, and combines Aspen HYSYS simulation software to determine the risk level of high-temperature risk scenarios at gas gathering stations. Furthermore, for deviations other than temperature, the invention simulates different degrees of deviation, divides the deviations into intervals, determines the peak values ​​(or initial alarm values) of process parameters within each deviation interval, and re-determines the risk level. Finally, it determines the risk transmission path based on the time it takes for each parameter to reach its peak value, providing a reference for the safe operation of gas gathering stations under sudden high-temperature fluctuations.

[0099] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A dynamic risk assessment method for gas gathering stations, characterized in that, Includes the following steps: Data information from gas gathering stations is collected, and the gas gathering stations are divided into nodes according to the process flow. The data information of the gas gathering stations includes material information, process operation condition information, equipment size information, and location information of the gas gathering stations. This analysis examines the process parameter deviations at each node, identifying risk scenarios for each node in the gas gathering station based on data and these deviations. This includes: using the HAZOP method to analyze process parameter deviations at each node, including their causes and consequences; classifying the severity of consequences using a risk matrix; identifying risk scenarios for each node based on these classifications, and initially determining the risk level; and performing reliability analysis on safety instruments in the identified high-temperature operating risk scenarios at the gas gathering station. Specifically, this involves: obtaining the maximum applicable temperature T0 and the measured over-temperature medium temperature of the safety instruments, and establishing a mathematical model for instrument reliability; inputting multiple sets of measurement data into the mathematical model to solve for the maximum error of the instrument readings under high-temperature conditions. Maximum error of comparator reading and instrument permissible error Determine the reliability of safety instruments: like If the safety instrument's reliability meets the requirements, it can continue to be used, and the safety instrument's reading should be corrected according to the error value. Otherwise, if the reliability of the safety instrument does not meet the requirements, the safety instrument should be replaced. The mathematical model for the reliability of the instrument is expressed as follows: In the formula, For instruments measuring medium temperatures exceeding The indicated value error after that; This is the reading from the high-temperature instrument. This is the standard indicated value; represents the zero drift and time drift of the high-temperature instrument; c is the temperature influence coefficient; T is the temperature value of any over-temperature medium. Construct a process steady-state model for the gas gathering station and simulate the process for the identified risk scenarios; The process parameters of the process simulation are changed to obtain simulation results with different degrees of deviation, and the dangerous peak values ​​of the process parameters are determined. This includes: adding a flow control controller and a level control controller on the basis of steady-state process operation, obtaining simulation results with different degrees of deviation by changing the process parameter conditions of the process simulation, transforming the steady-state process model into a dynamic process model, recording the changes of each process parameter, determining the boundary conditions of each process parameter, and obtaining the dangerous peak values ​​of each process parameter. The process parameter deviations are divided into intervals based on the occurrence of dangerous peak values. The risk level of each node is then reassessed based on the number of hazardous factors within each interval, enabling dynamic risk assessment of the gas gathering station. The number of hazardous factors represents the number of times the process parameters reach dangerous peak values ​​during each operating condition. The assessment also includes determining the risk transmission path, with the specific steps as follows: In the Aspen HYSYS simulation software, set the process simulation time, collect process parameters at the set time intervals, and summarize the data collection results into a table. Based on the table analysis, determine the time required for each process parameter to reach the critical peak, and compare the time values; The order in which each process parameter reaches the dangerous peak value is arranged according to the comparison results serves as the risk transmission path under this risk scenario.

2. The dynamic risk assessment method for a gas gathering station according to claim 1, characterized in that, The process parameters include temperature, pressure, and liquid level.

3. The dynamic risk assessment method for a gas gathering station according to claim 1, characterized in that, The method of dividing the process parameter deviation into intervals based on the condition that the process parameters reach the dangerous peak also includes dividing the process parameter deviation into intervals based on the initial alarm value of the safety instrument in the gas gathering station.

4. A dynamic risk assessment system for a gas gathering station, used to implement the dynamic risk assessment method for a gas gathering station as described in claim 1, characterized in that, include: The data acquisition and processing unit is used to collect data information from the gas gathering station and divide the gas gathering station into nodes according to the process flow. The risk level assessment unit is used to analyze the process parameter deviations generated at each node and identify the risk scenarios at each node of the gas gathering station based on data information and process parameter deviations. The process simulation unit is used to construct a steady-state process model of the gas gathering station using Aspen HYSYS simulation software, and to simulate the process of identified risk scenarios. The hazardous peak acquisition unit is used to change the process parameters of the process simulation to obtain simulation results with different degrees of deviation, and to determine the hazardous peak value of the process parameters; The secondary risk level determination unit is used to divide the process parameter deviation into intervals based on the condition that the process parameters reach the dangerous peak value, and to re-determine the risk level of each node's risk scenario based on the number of dangerous factors within the interval, thereby realizing dynamic risk assessment of the gas gathering station. The number of dangerous factors represents the number of times the process parameters reach the dangerous peak value during each operating condition.

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