Risk analysis method for shale oil gathering and transportation high-temperature key device under in-situ conversion technology

Abstract

The invention discloses a risk analysis method for a shale oil gathering and transportation high-temperature key device under an in-situ conversion technology. The risk analysis method comprises the following steps: S1, identifying and determining a high-temperature section and a high-temperature section key device in a shale oil gathering and transportation process; s2, identifying the type of an accident caused by a high-temperature section key device, and determining a key event causing the accident; s3, taking the key events as connections, combining accident tree analysis and event tree analysis, and establishing a high-temperature section key device accident risk Bow-Tie model; s4, converting a Bow-Tie model into a Bayesian network based on the accident risk Bow-Tie model of the key device in the high-temperature section; and S5, converting the Bayesian network into a dynamic Bayesian network, and completing risk analysis of the shale oil gathering and transportation high-temperature key device. According to the method, the high-temperature section key device is identified, an accident risk Bow-Tie model is completed for the high-temperature section key device, the Bow-Tie model is converted into a Bayesian network and further converted into a dynamic Bayesian network, and accurate and comprehensive risk analysis can be accurately carried out on the high-temperature key device of the shale oil gathering and transportation process.

Description

Technical Field

[0001] The present invention relates to the technical field of shale oil in-situ conversion, and in particular to a risk analysis method for key high-temperature equipment of shale oil gathering and transportation under in-situ conversion technology. Background Art

[0002] The reserves of shale oil in my country are considerable, and most of the shale oil in my country belongs to medium-low maturity continental shale oil. For medium-low maturity continental shale oil, in-situ conversion technology is needed in the exploitation. This technology converts the unsaturated organic matter in the rock mass into saturated hydrocarbons by injecting heat into the reservoir, and then produces the required oil and gas through the production well. The oil and gas produced by the in-situ conversion technology are characterized by high temperature and high sulfur. The temperature of the produced material from the production well is as high as 300°C or above. The produced material is transported to the cooling and separation system through pipelines, and is subjected to layer-by-layer cooling, separation and desulfurization to finally obtain finished oil.

[0003] Shale oil produced by in-situ conversion is different from conventional oil and gas. In actual production, the process flow of high-temperature produced materials in oil and gas gathering and transportation is complex. The high-temperature equipment in the shale oil gathering and transportation process is very dangerous. Compared with the low-temperature transportation and separation equipment, the probability of leakage in the high-temperature equipment is much higher than that in the low-temperature equipment. Therefore, a risk analysis method is urgently needed. According to the physicochemical characteristics of shale oil produced by in-situ conversion technology, a scientific and flexible analysis method is used to conduct risk analysis on the high-temperature section, explore the important factors causing leakage of key equipment in the high-temperature section, so as to strengthen safety protection and safety management and prevent accidents, which is of great significance to the safe transportation and separation of shale oil. Summary of the invention

[0004] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a risk analysis method for key high-temperature equipment of shale oil gathering and transportation under in-situ conversion technology, and the specific technical scheme is as follows.

[0005] A risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology includes the following steps:

[0006] S1, identify and determine the high temperature section and key devices in the shale oil gathering and transportation process;

[0007] S2, identify the types of accidents caused by key devices in the high-temperature section and determine the key events that caused the accidents;

[0008] S3, using key events as the connection, combined the accident tree analysis and event tree analysis to establish the Bow-Tie model of accident risk of key devices in the high-temperature section;

[0009] S4, based on the Bow-Tie model of accident risk of key devices in the high-temperature section, the Bow-Tie model is converted into a Bayesian network;

[0010] S5, transform the Bayesian network into a dynamic Bayesian network to complete the risk analysis of key high-temperature equipment for shale oil gathering and transportation.

[0011] Furthermore, in step S1, the system with an operating temperature ≧154°C is set as the high-temperature section, including a high-temperature pipeline transportation system and a primary cooling system; the key devices of the high-temperature section are set as: high-temperature pipelines and pipeline valves of the high-temperature pipeline transportation system, a primary water-cooled heat exchanger and a primary oil-gas separator of the primary cooling system; in step S2, the accident types include personnel scalding accidents and leakage accidents, and the key event is leakage of key devices in the high-temperature section.

[0012] Furthermore, in step S3, the causes of key events are analyzed by accident tree analysis method to obtain basic events and event relationships that lead to key events; the analysis factors of the accident tree analysis method include human factors, environmental factors, material factors, substance factors, and component factors.

[0013] Furthermore, the personnel factors refer to: leakage of key devices in the high-temperature section caused by the staff's lack of operating experience or failure to operate according to operating standards; the environmental factors refer to: physical damage to the key devices in the high-temperature section caused by the service environment; the material factors refer to: damage to the sealing of the key devices in the high-temperature section caused by performance defects of the main materials of the key devices in the high-temperature section; the material factors refer to: damage to the sealing of the key devices in the high-temperature section caused by the contents transported or reacted inside the key devices in the high-temperature section; the component factors refer to: failure of the sealing of the key devices in the high-temperature section caused by their parts.

[0014] Furthermore, in step S3, the consequence accidents caused by the key events are analyzed by the event tree analysis method, and the link events and evolution paths leading to the occurrence of each consequence accident are obtained; the consequence accidents include pool fires, explosions, environmental pollution, and human poisoning; the analysis factors of the evolution path include inducing events and safety barriers.

[0015] Furthermore, the triggering event refers to: the conditions that lead to the occurrence of an accident with specific consequences; the safety barrier includes safety measures and safety devices, the safety measures refer to: safety behaviors and safety designs made by humans in actual production to prevent accidents, and the safety devices refer to: alarm devices, emergency shut-off devices and / or fire extinguishing devices existing inside the system.

[0016] Furthermore, in step S4, converting the Bow-Tie model into a Bayesian network includes the following steps:

[0017] S401, setting the parent node of the Bayesian network to the basic event of the accident tree and the link event of the event tree, and setting the child nodes of the Bayesian network to the remaining events in the Bow-Tie model;

[0018] S402, setting the conditional probability between the parent node and the child node of the Bayesian network based on the relationship between events and the event evolution shown by the Bow-Tie model;

[0019] S403, setting the prior probability of the parent node of the Bayesian network to: the occurrence probability and failure probability of the basic event and the link event, calculating the probability of each node in the Bayesian network, and obtaining the occurrence probability of the key event and the occurrence probability of each consequence accident;

[0020] S404, using the characteristics of the Bayesian network to calculate the posterior probability of each node under the condition of leakage of the key device in the high-temperature section; taking the key event as the target node, performing sensitivity analysis on each node of the Bayesian network.

[0021] Furthermore, the node relationships in the Bayesian network are all represented by directed arrows, and the arrows point from the control event to the affected event.

[0022] Furthermore, setting the conditional probability between the parent node and the child node of the Bayesian network includes the following:

[0023] a. According to the event relationship in the accident tree in the Bow-Tie model, determine the conditional probability table of the child node. The determination method follows the mapping principle of the accident tree to the Bayesian network;

[0024] b. For the OR gate relationship in the accident tree, in order to fully and comprehensively determine all the influencing factors of an event during the analysis process, the Nosiy-Max model is used for further optimization, and the Leak probability is set to 0.0005;

[0025] c. For the AND gate relationship in the fault tree, the influence of different parent nodes on child nodes is distinguished by setting the conditional probability of child nodes in setting the conditional probability of the Bayesian network; the influence of the parent node is calculated according to the following formula:

[0026]

[0027] In the formula, E is the degree of influence, %; x i Assign a value to the dominance of a parent node;

[0028] The degree of influence is divided into five levels: the conditional probability of the parent node on the child node is 0.9995, which is defined as complete influence, the conditional probability of 0.85 is defined as a large degree of influence, the conditional probability of 0.65 is defined as a medium degree of influence, the conditional probability of 0.5 is defined as a small degree of influence, and the conditional probability of 0.3 is defined as a small degree of influence but there is influence.

[0029] Furthermore, the occurrence probability and failure probability of basic events and safety barrier events are obtained through expert scoring method and reference to data, among which the fuzzy triangular number algorithm is used for mathematical transformation of the expert scoring language results.

[0030] Furthermore, converting the Bayesian network into a dynamic Bayesian network includes the following specific steps: a. determining the impact of basic events based on Bayesian network analysis, and determining basic events whose probabilities increase over time, and using their nodes as dynamic parent nodes; b. setting the number of calculation time periods for the dynamic Bayesian network, setting the transfer speed based on the speed of change of the probability of the dynamic parent node, setting the transfer probability of the dynamic parent node based on expert experience, performing dynamic Bayesian network calculations, obtaining the time-varying trends of each node, converting the Bayesian network into a dynamic Bayesian network, and obtaining the time-varying trends of key events.

[0031] Furthermore, the number of calculation time periods of the Bayesian network is a common multiple of the dynamic parent node probability transfer speed; the dynamic parent node probability transfer speed is expressed in the number of time periods and is set to 4 change speeds: for nodes with a very fast probability growth rate, the probability is set to change once every 1 time period, the probability transfer speed of nodes with a fast growth rate is set to change once every 2 nodes, the transfer probability of nodes with a slow growth rate is set to change once every 3 nodes, and the transfer probability of nodes with a very slow growth rate is set to change once every 5 nodes.

[0032] Based on the above technical solution, the present invention has the following beneficial effects:

[0033] 1. Define the system with working temperature ≥154℃ as high-temperature system. This temperature setting can effectively cover the highly dangerous systems in the shale oil gathering and transportation process. By identifying and determining the key devices in the high-temperature section, the dangerous equipment in the shale oil gathering and transportation process can be accurately identified to avoid comprehensive analysis of a large number of equipment within the process and improve the efficiency of risk avoidance operations.

[0034] 2. By setting the analysis factors as human factors, environmental factors, material factors, substance factors, and component factors, objective and accurate causes can be obtained efficiently and comprehensively, making the accident tree analysis more scientific and effective; by taking inducing events, safety measures, and safety devices as analysis factors, objective and accurate consequence accidents can be obtained efficiently and comprehensively, making the event tree analysis more scientific and effective.

[0035] 3. For the OR gate relationship in the accident tree, the Nosiy-Max model is used for further optimization, and the Leak probability is set to 0.0005, which can fully and comprehensively determine all the influencing factors of an event during the analysis process.

[0036] 4. For the AND gate relationship in the accident tree, the conditional probability of the child node is set to distinguish the degree of influence of different parent nodes on the child node in setting the conditional probability of the Bayesian network, and the degree of influence is divided into 5 levels. When the influence is complete, the conditional probability of the parent node on the child node is 0.9995, when the influence is large, the corresponding conditional probability is 0.85, when the influence is medium, the corresponding conditional probability is 0.65, when the influence is small, the corresponding conditional probability is 0.5, and when the influence is small but there is an influence, the corresponding conditional probability is 0.3, which can make up for the shortcoming that the accident tree cannot distinguish the influence of basic events.

[0037] 5. The occurrence probability and failure probability of basic events and safety barrier events are obtained through expert scoring method and reference to data, which can make the calculated probability of each node more realistic.

[0038] 6. The transfer speed obtained by converting the probability of the dynamic parent node into the number of time periods is expressed and set to 4 change speeds. For nodes with a very fast probability growth rate, the probability can be set to change once every 1 time period, the probability transfer speed of nodes with a fast growth rate is set to change once every 2 nodes, the transfer probability of nodes with a slow growth rate is set to change once every 3 nodes, and the transfer probability of nodes with a very slow growth rate is set to change once every 5 nodes. The number of calculation time periods of the Bayesian network is a common multiple of the dynamic parent node probability transfer speed, which can make the calculation results more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 : Schematic diagram of the shale oil gathering and transportation process for in-situ conversion in an exemplary embodiment;

[0040] Figure 2 : Schematic diagram of the process flow of the method in an exemplary embodiment. DETAILED DESCRIPTION

[0041] It should be noted that:

[0042] 1. Certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in components' functions as the criterion for distinction.

[0043] 2. Unless otherwise defined, technical or scientific terms used in the present disclosure should have the common meanings understood by persons having ordinary skills in the field to which the present disclosure belongs.

[0044] The following is combined with Figure 1 -Attached Figure 2 , the present invention is described in detail by way of embodiments.

[0045] This embodiment describes a risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology, including the following steps:

[0046] S1, identify and determine the high-temperature section and key devices in the high-temperature section of shale oil gathering and transportation process.

[0047] As attached Figure 1 As shown in Figure 1, shale oil gathering and transportation processes can be divided into six systems, namely:

[0048] High-temperature pipeline transportation system, the system working temperature is ≧300℃; primary cooling system, the system working temperature range is 154℃-300℃; secondary cooling system, the system working temperature range is 80℃-154℃; tertiary cooling system, the system working temperature range is 5℃-80℃; amine desulfurization treatment system, the system working temperature range is 45℃-80℃; direct oxidation treatment system, the system working temperature range is 150℃-300℃.

[0049] There are also a lot of corrosive substances such as carbon dioxide, hydrogen sulfide and hydrogen in the shale oil well production. At high temperatures, these corrosive substances will accelerate the corrosion of equipment. At the same time, due to the influence of thermal stress, the devices in the high-temperature system are subjected to long-term thermal stress, and the material is unevenly stressed, resulting in cracks and creep, which can easily destroy the sealing of the device.

[0050] When the system operating temperature of the shale oil gathering and transportation process is ≧154°C, the corrosive effect of the corrosive substances in the oil and gas in this temperature range becomes more and more obvious, which will reduce the service life of the devices and components in the system, and cause damage such as corrosion perforation. The cracks generated by the device materials under the action of thermal stress will accelerate the formation of corrosion perforation, resulting in the leakage of oil and gas in the device. The main components of the high-temperature system are gaseous hydrocarbons and toxic gases, which are volatile, flammable, explosive and toxic. Once a leak occurs, it will have immeasurable serious consequences, such as explosion accidents and poisoning accidents. To avoid the above risks, this embodiment defines the system with an operating temperature of ≧154°C as a high-temperature system. The setting of this temperature can effectively cover the system with high risks in the shale oil gathering and transportation process. The systems with an operating temperature exceeding 154°C include high-temperature pipeline transportation systems, primary cooling systems and direct oxidation treatment systems. The sulfide contained in the direct oxidation treatment system is mainly ammonium sulfate, which is not corrosive. Therefore, although the direct oxidation treatment system is a high-temperature system, there is no risk of leakage.

[0051] Therefore, the high-temperature system in the shale oil gathering and transportation process under the in-situ conversion technology is divided into a high-temperature pipeline transportation system and a primary cooling system.

[0052] Since the shale oil and its conversion products in the high-temperature system are in gaseous and / or liquid state, and the main part of pipeline transportation is pipeline and pipeline valve, and the main part of the cooling system is water-cooled heat exchanger and oil-gas separator, the key devices of the high-temperature section are respectively: high-temperature pipeline and pipeline valve of the high-temperature pipeline transportation system, and the first-level water-cooled heat exchanger and first-level oil-gas separator of the first-level cooling system.

[0053] This step can accurately identify the dangerous equipment in the shale oil gathering and transportation process by identifying and determining the key devices in the high-temperature section, so as to avoid comprehensive analysis of a large number of equipment within the process and improve the efficiency of risk avoidance operations.

[0054] S2, identify the types of accidents caused by key devices in the high-temperature section and determine the key events that cause the accidents.

[0055] The substances transported or loaded inside the key devices in the high-temperature section may induce high-temperature wellhead production, including but not limited to flammable organic matter and toxic gases, leading to scalding accidents and leakage accidents.

[0056] Leakage accidents have risks such as wide impact and difficult to eliminate accident consequences. Compared with personnel scalding accidents, leakage accidents have a higher risk level. Therefore, this embodiment conducts a risk analysis on leakage accidents of key devices in the high-temperature section and defines leakage of key devices in the high-temperature section as a key event.

[0057] S3, using key events as the connection, combines accident tree analysis and event tree analysis to establish the Bow-Tie model of accident risk for key devices in the high-temperature section.

[0058] S301, analyzing the causes of key events through the accident tree analysis method, and obtaining the basic events and event relationships that lead to the key events.

[0059] In order to efficiently and comprehensively obtain objective and accurate causes and make the accident tree analysis more scientific and effective, this embodiment provides the following analysis factors and establishes an accident tree model based on the following analysis factors.

[0060] The analysis factors include but are not limited to: human factors, environmental factors, material factors, substance factors, and component factors. Among them:

[0061] The human factors refer to the leakage of key devices in the high-temperature section caused by the lack of operating experience or failure to operate according to the operating standards of the staff. According to the type of operation, the impact of human factors is set as "operation error" and "maintenance error";

[0062] The environmental factors are: the service environment of key devices can have a significant negative impact on the devices, common harsh working environments such as extremely cold weather, long-term sunshine, etc., and physical damage to key devices in high-temperature sections caused by natural disasters;

[0063] The material factors refer to: the material properties of the main body of the high-temperature section key device, especially in the later service process of the high-temperature section key device, the defects of the material properties will destroy the sealing of the high-temperature section key device;

[0064] The material factors refer to: the contents transported or reacted inside the key devices of the high temperature section, the physical and chemical properties of the contents themselves will affect the sealing of the key devices of the high temperature section;

[0065] The component factors refer to: parts that constitute the key devices in the high-temperature section, the failure of which will directly lead to the failure of the sealing of the key devices in the high-temperature section.

[0066] S302, analyzing the consequence accidents caused by the key events through an event tree analysis method, and obtaining the link events and evolution paths that lead to the occurrence of each consequence accident.

[0067] The consequence accident refers to the type of accident determined according to the dangerousness of the leakage of substances from the key equipment in the high-temperature section. According to the physical and chemical properties of shale oil under the in-situ conversion technology, the leakage of substances from the key equipment in the high-temperature section recorded in this embodiment is divided into flammable liquids, flammable gases and toxic gases.

[0068] When combustibles leak from a device, due to the pressure inside the device, once they encounter an ignition source, a jet fire may occur; when combustible liquids leak and accumulate and encounter an ignition source, a pool fire may occur; when combustible gas leaks and spreads to a certain concentration and encounters an ignition source, an explosion may occur; when toxic gases spread, environmental pollution may occur, and when people inhale toxic gases, poisoning accidents may occur. Therefore, the consequence accidents recorded in this embodiment include, but are not limited to, pool fires, explosions, environmental pollution, and human poisoning.

[0069] In order to efficiently and comprehensively obtain objective and accurate consequence accidents and make the event tree analysis more scientific and effective, this embodiment provides the following analysis factors of the evolution path, including but not limited to inducing events and safety barriers.

[0070] The triggering event means that a specific consequence accident requires certain conditions to occur, and the event that meets the conditions is the triggering event. For example, a fire or explosion accident requires the existence of an ignition source, so the triggering event is "ignition of dangerous substances";

[0071] The safety barrier refers to: existing safety measures and safety devices that can terminate or prevent the occurrence of consequential accidents; among which,

[0072] The safety measures refer to: safety behaviors and safety designs made by humans in actual production, which can prevent accidents from happening, such as closing valves in time to prevent leakage and workers wearing gas masks.

[0073] The safety device refers to: an alarm device, emergency shut-off device or fire extinguishing device inside the system, which can automatically or assist staff to respond quickly to avoid accidents when abnormal situations occur; or a device that can quickly prevent the accident damage from worsening and expanding in the early stages of an accident.

[0074] S4, based on the Bow-Tie model of accident risk of key devices in the high-temperature section, the Bow-Tie model is converted into a Bayesian network through the following steps.

[0075] S401, setting the parent node of the Bayesian network to the basic events of the accident tree and the link events of the event tree, and setting the child nodes of the Bayesian network to the remaining events in the Bow-Tie model.

[0076] In order to represent the relationship between basic events in the accident tree and the evolution of consequence accidents in the event tree, the node relationships in the Bayesian network are set to be represented by directed arrows, which start from the control event and point to the affected event.

[0077] S402, based on the relationship between events and event evolution shown by the Bow-Tie model, the conditional probability between the parent node and the child node of the Bayesian network is set, specifically:

[0078] a. According to the event relationship in the accident tree in the Bow-Tie model, determine the conditional probability table of the child node. The determination method follows the mapping principle of the accident tree to the Bayesian network;

[0079] b. For the OR gate relationship in the accident tree, in order to fully and comprehensively determine all the influencing factors of an event in the analysis process, the Nosiy-Max model is used for further optimization, and the Leak probability is set to 0.0005.

[0080] The Leak probability means that when the parent node does not occur, there is a 0.0005 probability that the node will occur, that is, there is a 0.0005 probability that the node occurs not because of the parent node but because of other factors that are not considered.

[0081] c. Regarding the AND gate relationship in the accident tree, in order to make up for the shortcoming that the accident tree cannot distinguish the influence of basic events, the influence of different parent nodes on child nodes can be distinguished by setting the conditional probability of child nodes when setting the conditional probability of the Bayesian network.

[0082] According to the dominance of the parent node over the child node, each parent node is scored. The scoring interval is (0,5], where the weaker the dominance of the parent node, the lower the score. The influence of the parent node is calculated according to the following formula:

[0083]

[0084] In the formula,

[0085] E is the degree of influence, %;

[0086] x i Assign a value to the dominance of a parent node.

[0087] In this way, we can calculate the influence of different parent nodes on a child node. The influence is divided into 5 levels, and the corresponding conditional probabilities are shown in the following table.

[0088]

[0089] S403, setting the prior probability of the Bayesian network parent node to: the occurrence probability and failure probability of the basic event and the link event.

[0090] The probability of each node in the Bayesian network is calculated to obtain the probability of occurrence of key events and the probability of occurrence of each consequence accident.

[0091] In order to make the calculated probability of each node more realistic, the occurrence probability and failure probability of basic events and safety barrier events are obtained through expert scoring method and reference to data. Among them, the fuzzy triangular number algorithm is used for mathematical transformation of the expert scoring language results, and the safety barrier failure probability can be consulted from existing statistical data.

[0092] S404, using the characteristics of the Bayesian network to calculate the posterior probability of each node under the condition of leakage of the key device in the high-temperature section; taking the key event as the target node, performing sensitivity analysis on each node of the Bayesian network.

[0093] S5, transform the Bayesian network into a dynamic Bayesian network, specifically:

[0094] S501, determine the impact of basic events based on Bayesian network analysis, and determine basic events whose probabilities increase over time, and use their nodes as dynamic parent nodes.

[0095] The dynamic parent node refers to a node with high sensitivity obtained through static Bayesian sensitivity analysis, and the probability of the event represented by the node will change significantly during the service of key equipment in the high-temperature section of shale oil.

[0096] S502, set the number of calculation time periods of the dynamic Bayesian network, set the transfer speed according to the speed of change of the probability of the dynamic parent node; set the transfer probability of the dynamic parent node according to expert experience; perform dynamic Bayesian network calculations to obtain the time-varying trend of each node, convert the Bayesian network into a dynamic Bayesian network, and obtain the time-varying trend of key events.

[0097] The number of calculation time periods of the Bayesian network is a common multiple of the dynamic parent node probability transfer speed, and the larger the multiple, the more accurate the calculation result.

[0098] The transfer speed of the dynamic parent node probability is expressed in time periods, which can be set to 4 change speeds. For nodes with very fast probability growth, the probability can be set to change once every 1 time period, the probability transfer speed of nodes with fast growth is set to change once every 2 nodes, the transfer probability of nodes with slow growth is set to change once every 3 nodes, and the transfer probability of nodes with very slow growth is set to change once every 5 nodes. This representation is only applicable to the description of the dynamic parent node transfer speed in dynamic Bayesian analysis. The specific speed setting is artificially specified and can be adjusted according to the analysis needs.

[0099] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology, characterized in that: The following steps are involved: S1, identify and determine the high temperature section and key devices in the shale oil gathering and transportation process; S2, identify the types of accidents caused by key devices in the high-temperature section and determine the key events that caused the accidents; S3, using key events as the connection, combined the accident tree analysis and event tree analysis to establish the Bow-Tie model of accident risk of key devices in the high-temperature section; S4, based on the Bow-Tie model of accident risk of key devices in the high-temperature section, the Bow-Tie model is converted into a Bayesian network; S5, transform the Bayesian network into a dynamic Bayesian network to complete the risk analysis of key high-temperature equipment for shale oil gathering and transportation.

2. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 1 is characterized in that: In step S1, the system with an operating temperature of ≧154°C in the shale oil gathering and transportation process is set as the high-temperature section, including a high-temperature pipeline transportation system and a primary cooling system; the key devices of the high-temperature section are set as: a high-temperature pipeline and pipeline valve of the high-temperature pipeline transportation system, a primary water-cooled heat exchanger and a primary oil-gas separator of the primary cooling system; In step S2, the accident types include personnel scalding accidents and leakage accidents, and the key event is leakage of key equipment in the high-temperature section.

3. The risk analysis method for key high-temperature equipment of shale oil gathering and transportation under in-situ conversion technology according to claim 2 is characterized in that: In step S3, the causes of key events are analyzed by using an accident tree analysis method to obtain basic events and event relationships that lead to key events. The analysis factors of the accident tree analysis method include human factors, environmental factors, material factors, substance factors, and component factors.

4. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 3 is characterized in that: The personnel factors refer to: leakage of key high-temperature section equipment due to lack of operating experience or failure to follow operating standards by staff; the environmental factors refer to: physical damage to the key high-temperature section equipment caused by its service environment; the material factors refer to: damage to the sealing of the key high-temperature section equipment caused by performance defects of the main materials of the key high-temperature section equipment; the material factors refer to: damage to the sealing of the key high-temperature section equipment caused by the contents transported or reacted inside the equipment; the component factors refer to: failure of the sealing of the key high-temperature section equipment caused by its parts.

5. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 2 is characterized in that: In step S3, the consequence accidents caused by the key events are analyzed by the event tree analysis method, and the link events and evolution paths leading to the occurrence of each consequence accident are obtained; the consequence accidents include pool fires, explosions, environmental pollution, and personnel poisoning; the analysis factors of the evolution path include inducing events and safety barriers.

6. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 5 is characterized in that: The triggering event is a condition that leads to an accident with specific consequences; the safety barrier includes safety measures and safety devices, the safety measures are safety behaviors and safety designs artificially made in actual production that can prevent accidents from occurring, and the safety devices are alarm devices, emergency shut-off devices and / or fire extinguishing devices existing inside the system.

7. A risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to any one of claims 3 to 6, characterized in that: In step S4, converting the Bow-Tie model into a Bayesian network includes the following steps: S401, setting the parent node of the Bayesian network to the basic event of the accident tree and the link event of the event tree, and setting the child nodes of the Bayesian network to the remaining events in the Bow-Tie model; S402, setting the conditional probability between the parent node and the child node of the Bayesian network based on the relationship between events and the event evolution shown by the Bow-Tie model; S403, setting the prior probability of the parent node of the Bayesian network to: the occurrence probability and failure probability of the basic event and the link event, calculating the probability of each node in the Bayesian network, and obtaining the occurrence probability of the key event and the occurrence probability of each consequence accident; S404, using the characteristics of the Bayesian network to calculate the posterior probability of each node under the condition of leakage of the key device in the high-temperature section; taking the key event as the target node, performing sensitivity analysis on each node of the Bayesian network.

8. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 7 is characterized in that: The node relationships in the Bayesian network are represented by directed arrows, which start from the control event and point to the affected event.

9. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 7 is characterized in that: Setting the conditional probabilities between parent and child nodes in a Bayesian network involves the following: a. According to the event relationship in the accident tree in the Bow-Tie model, determine the conditional probability table of the child node. The determination method follows the mapping principle of the accident tree to the Bayesian network; b. For the OR gate relationship in the accident tree, in order to fully and comprehensively determine all the influencing factors of an event during the analysis process, the Nosiy-Max model is used for further optimization, and the Leak probability is set to 0.0005; c. For the AND gate relationship in the fault tree, the influence of different parent nodes on child nodes is distinguished by setting the conditional probability of child nodes in setting the conditional probability of the Bayesian network; the influence of the parent node is calculated according to the following formula: In the formula, E is the degree of influence, %; x i Assign a value to the dominance of a parent node; The degree of influence is divided into five levels: the conditional probability of the parent node on the child node is 0.9995, which is defined as complete influence, the conditional probability of 0.85 is defined as a large degree of influence, the conditional probability of 0.65 is defined as a medium degree of influence, the conditional probability of 0.5 is defined as a small degree of influence, and the conditional probability of 0.3 is defined as a small degree of influence but there is influence.

10. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 7 is characterized in that: The occurrence probability and failure probability of basic events and safety barrier events are obtained through expert scoring method and reference to data. Among them, the fuzzy triangular number algorithm is used for mathematical transformation of the expert scoring language results.

11. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 7 is characterized in that: Converting a Bayesian network into a dynamic Bayesian network includes the following specific steps: a. Determine the impact of basic events based on Bayesian network analysis, and determine basic events whose probabilities increase over time, and use their nodes as dynamic parent nodes; b. Set the number of calculation time periods for the dynamic Bayesian network, set the transfer speed based on the speed of change of the probability of the dynamic parent node, set the transfer probability of the dynamic parent node based on expert experience, perform dynamic Bayesian network calculations, obtain the time-varying trends of each node, convert the Bayesian network into a dynamic Bayesian network, and obtain the time-varying trends of key events.

12. The risk analysis method for key high-temperature equipment for shale oil gathering and transportation under in-situ conversion technology according to claim 11 is characterized in that: The number of calculation time periods of the Bayesian network is a common multiple of the dynamic parent node probability transfer speed; the dynamic parent node probability transfer speed is expressed in the number of time periods and is set to 4 change speeds: for nodes with a very fast probability growth rate, the probability is set to change once every 1 time period, the probability transfer speed of nodes with a fast growth rate is set to change once every 2 nodes, the transfer probability of nodes with a slow growth rate is set to change once every 3 nodes, and the transfer probability of nodes with a very slow growth rate is set to change once every 5 nodes.

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