Method and apparatus for dynamic risk assessment of underground gas storage facilities

Abstract

The application provides a dynamic evaluation method and equipment for underground facilities of gas storage. The method comprises the following steps: dividing the underground facilities of the gas storage and the geological structure into units; establishing a failure fault tree of the underground facilities based on a logical relationship between events; converting the failure fault tree into a fault Bayesian network; for each branch, using an LR fuzzy number to integrate a set weight distribution into a qualitative language of node failure possibility, and converting the qualitative language into a node fuzzy failure probability through a fuzzy set theory; for each branch, introducing a time variable on the basis of the fault Bayesian network and the node fuzzy failure probability, constructing a dynamic Bayesian network, and obtaining a failure probability in continuous time; and obtaining a failure probability of the top event in continuous time. According to the application, dynamic analysis and evaluation of risks of the underground facilities of the gas storage can be realized, and the evaluation result has high reliability.

Description

Technical Field

[0001] This invention relates to the field of risk assessment for gas storage facilities, and more specifically, to a method and equipment for dynamic risk assessment of underground facilities of gas storage facilities. Background Technology

[0002] The underground facilities of gas storage facilities are generally located in complex environments with numerous risk factors. Furthermore, geological conditions, formation pressures, well depths, well locations, drilling methods, and underground environments vary between different storage areas, making quantitative risk assessment extremely difficult. Underground facilities cannot be directly observed or measured, making it very challenging to obtain fault data. To conduct risk assessments of gas storage facilities, qualitative risk assessment methods are often used. Although considerable quantitative research has been conducted, the level of research is insufficient, with a predominance of qualitative components; risk studies mostly rely on single-factor analyses and lack systematic research.

[0003] Currently, commonly used safety assessment methods are divided into qualitative and quantitative assessments. In the risk assessment of underground gas storage facilities, the fault tree-analysis method is frequently used, primarily employing qualitative and semi-quantitative analysis. Furthermore, the basic construction conditions of each gas storage facility vary significantly, requiring different research methods, and many technical methods cannot be directly applied. Summary of the Invention

[0004] In view of this, this application proposes a highly reliable dynamic analysis and evaluation method for the risks of underground gas storage facilities. This application also proposes corresponding electronic equipment and computer-readable storage media.

[0005] According to one aspect of this application, a dynamic risk assessment method for underground gas storage facilities is proposed, the method comprising:

[0006] Step 1: Based on risk factor identification, divide the underground facilities and geological structure of the gas storage facility into units;

[0007] Step 2: Taking the divided units as objects, analyze the failure influencing factors and establish a failure tree for underground facilities based on the logical relationship between events. The top event of the failure tree includes gas storage explosion and gas storage leakage, and each branch of the top event corresponds to one of the units.

[0008] Step 3: Convert the failure fault tree into a failure Bayesian network, map the events in the failure fault tree to nodes in the failure Bayesian network, and connect the nodes according to the logical relationship between the events. The top event is mapped to the root node of the failure Bayesian network, and each branch of the top event is mapped to a branch of the root node.

[0009] Step 4: For each branch, use LR fuzzy numbers to synthesize the set weight distribution into a qualitative language of the node failure probability, and then transform it into the node fuzzy failure probability through fuzzy set theory.

[0010] Step 5: For each branch, based on the fault Bayesian network and the fuzzy failure probability of the node, introduce a time variable to construct a dynamic Bayesian network and obtain the failure probability in continuous time.

[0011] Step 6: Combine the failure probabilities of each branch over continuous time to obtain the failure probability of the top event over continuous time.

[0012] In one embodiment of this aspect, in step 1, the underground facilities and geological structure of the gas storage facility are divided into the following four units: the gas production tree and wellhead device unit, the casing string unit, the tubing unit, and the geological structure unit.

[0013] In one embodiment of this aspect, the second-highest event of the failure tree includes: failure of the gas production tree and wellhead equipment, failure of the casing string, failure of the tubing, and failure caused by geological structure.

[0014] In one embodiment of this aspect, in step 3, when the failure fault tree is converted into the failure Bayesian network, recurring events are mapped to the same node.

[0015] In one embodiment of this aspect, step 4 specifically includes:

[0016] For each branch, based on multiple sets of weights, the possible failure values ​​of the node are aggregated into a fuzzy number that can reflect the possibility of node failure.

[0017] After obtaining the fuzzy numbers of all nodes, the LR fuzzy number sorting method is used to convert the fuzzy numbers into the node fuzzy failure probability.

[0018] In one embodiment of this aspect, the LR fuzzy number sorting method is used to convert fuzzy numbers into node fuzzy failure probabilities, including:

[0019] Determine the maximum fuzzy number set and calculate the possible left and right fuzzy values ​​of the LR fuzzy number;

[0020] By combining the possible values ​​of the left and right fuzzy numbers, the comprehensive possible value of the fuzzy number is obtained;

[0021] Using the probability transformation formula, the comprehensive possible value of the fuzzy number is converted into the node fuzzy failure probability under the current state.

[0022] In one embodiment of this aspect, step 5 specifically includes:

[0023] Using the node fuzzy failure probability as the initial prior probability, the failure probability of the faulty Bayesian network is inferred from top to bottom to obtain the posterior probability.

[0024] By introducing a time variable and modifying the faulty Bayesian network, the posterior probability of the previous time node is used as the prior probability of the next time node to construct the dynamic Bayesian network, thus obtaining the failure probability over continuous time.

[0025] In one embodiment of this aspect, the method further includes:

[0026] For each branch, based on the change, sensitivity, and influence of the posterior probability relative to the prior probability, the path most likely to cause the branch to fail is analyzed. By combining the sensitivity of each node on the path, key risk nodes are identified.

[0027] According to another aspect of this application, an electronic device is also provided, the electronic device comprising:

[0028] Memory, which stores executable instructions;

[0029] A processor that executes the executable instructions in the memory to implement the method as described above.

[0030] According to another aspect of this application, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the method described above.

[0031] The technical solution proposed in this application combines safety evaluation methods and mathematical theories such as fault tree analysis, triangular fuzzy numbers, and Bayesian networks. It collects operational and fault data, integrates the data, identifies risk factors, and establishes a fault tree. The fault tree is then mapped to a fault Bayesian network. The failure probability of each unit is derived from the fault Bayesian network from top to bottom. A time variable is introduced to modify the Bayesian network, enabling dynamic analysis and evaluation of the risks of underground gas storage facilities. In practical engineering, the dynamic evaluation results obtained from this application have high accuracy and are of great reference value. Attached Figure Description

[0032] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0033] Figure 1 A flowchart illustrating a dynamic risk assessment method for underground gas storage facilities according to an embodiment of this application is shown.

[0034] Figures 2(a), 2(b), 2(c), and 2(d) illustrate the logical relationship of a failure fault tree according to an embodiment of this application and the probability diagram of the corresponding Bayesian network.

[0035] Figure 3 An exemplary schematic diagram is shown, illustrating a method for mapping a branch of a fault tree to a faulty Bayesian network according to one embodiment of this application. Detailed Implementation

[0036] Preferred embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0037] Please see Figure 1 . Figure 1 A flowchart illustrating a dynamic risk assessment method for underground gas storage facilities according to an embodiment of this application is shown. As shown, the method includes steps 1 to 6.

[0038] Step 1: Based on risk factor identification, divide the underground facilities and geological structure of the gas storage facility into units.

[0039] Gas storage facilities present diverse and complex risks. Risk factors in underground facilities involve multiple aspects, including surface equipment (primarily the wellhead and production tree, and pipelines connecting to the injection and production facilities), the wellbore of the injection and production facilities, the casing of the injection and production facilities, auxiliary facilities, and geological conditions. According to this embodiment, the underground injection and production facilities and geological structure of the gas storage facility are divided into units, and these units are analyzed based on fault types and influencing factors.

[0040] In one example, based on years of engineering experience and a deep understanding of risk factors, the inventors can divide the underground facilities and geological structure of the gas storage facility into the following four units: the gas production tree and wellhead equipment unit, the casing string unit, the tubing unit, and the geological structure unit.

[0041] Step 2: Taking the divided units as objects, analyze the failure influencing factors and establish a failure tree for underground facilities based on the logical relationship between events. The top event of the failure tree includes gas storage explosion and gas storage leakage, and each branch of the top event corresponds to one of the units.

[0042] In one example, the second-highest events in the failure tree include: gas tree and wellhead equipment failure, casing string failure, tubing failure, and geological structure-induced failure. These second-highest events are also called direct cause events.

[0043] Step 3: Convert the failure fault tree into a failure Bayesian network, map the events in the failure fault tree to nodes in the failure Bayesian network, and connect the nodes according to the logical relationship between the events. The top event is mapped to the root node of the failure Bayesian network, and each branch of the top event is mapped to a branch of the root node.

[0044] In one example, when converting the failure fault tree into the failure Bayesian network, repeating events can be mapped to the same node. For instance, if event A causes both intermediate event B and intermediate event C, then repeating event A is considered the same node.

[0045] Please see Figure 2(a) , 2(b) Figures 2(a), 2(b), 2(c), and 2(d) illustrate the logical relationships of a fault tree and the probability diagrams of the corresponding fault Bayesian network according to an embodiment of this application. Specifically, Figure 2(a) shows the logical OR gate of the fault tree; Figure 2(b) shows the connection relationships and probabilities of the fault Bayesian network corresponding to the logical OR gate of the fault tree; Figure 2(c) shows the logical AND gate of the fault tree; and Figure 2(d) shows the connection relationships and probabilities of the fault Bayesian network corresponding to the logical AND gate of the fault tree.

[0046] Figure 3 An exemplary schematic diagram illustrating the mapping of a branch of a failure fault tree to a failure Bayesian network according to one embodiment of this application is shown. Table 1 shows... Figure 3 The events in the failure tree shown are briefly explained.

[0047] Table 1 Fault Tree Event Description

[0048]

[0049]

[0050] Back Figure 1 Step 4: For each branch, use LR fuzzy numbers to synthesize the set weight distribution into a qualitative language of node failure probability, and then transform it into node fuzzy failure probability through fuzzy set theory.

[0051] In one example, step 4 specifically includes: for each branch, aggregating the node's potential failure values ​​into a fuzzy number that reflects the node's failure probability based on multiple sets of weights; after obtaining the fuzzy numbers for all nodes, using the LR fuzzy number sorting method, converting the fuzzy numbers into node fuzzy failure probabilities. The multiple sets of weights can be given by multiple experts based on experience.

[0052] The LR fuzzy number sorting method, which transforms fuzzy numbers into node fuzzy failure probabilities, can include: determining the largest set of fuzzy numbers and calculating the left and right possible fuzzy values ​​of the LR fuzzy numbers; combining the left and right possible fuzzy values ​​to obtain the comprehensive possible value of the fuzzy numbers; and using a probability transformation formula to convert the comprehensive possible value of the fuzzy numbers into the node fuzzy failure probability under the current state.

[0053] In step 4, LR fuzzy numbers are used to synthesize the expert capability weight distribution into a qualitative language of the event failure probability, which is then transformed into the event fuzzy failure rate through fuzzy set theory.

[0054] Because expert opinions are subjective and the probability scores of events are fuzzy, fuzzy probability numbers from fuzzy mathematics are used to reduce subjectivity. Based on the weight values ​​of different experts, the failure probability values ​​of an event are aggregated into fuzzy numbers that reflect the likelihood of failure. After obtaining the fuzzy numbers of failures for all events, the LR fuzzy number sorting method is used to transform the fuzzy numbers into fuzzy probabilities, i.e., the node fuzzy failure probability, which is the fuzzy failure probability of the corresponding event. This node fuzzy failure probability can be used as the prior probability of the faulty Bayesian network obtained in step 3.

[0055] Step 5: For each branch, based on the fault Bayesian network and the fuzzy failure probability of the node, introduce a time variable to construct a dynamic Bayesian network and obtain the failure probability in continuous time.

[0056] In one example, step 5 specifically includes: using the node's fuzzy failure probability as the initial prior probability, performing top-down failure probability inference on the fault Bayesian network to obtain the posterior probability; introducing a time variable to modify the fault Bayesian network, using the posterior probability of the previous time node as the prior probability of the next time node, constructing the dynamic Bayesian network to obtain the failure probability over continuous time. Because it is calculated on a branch-by-branch basis, each branch corresponds to a unit obtained in step 1, which can also be considered as the unit failure probability.

[0057] In one example, the method according to this embodiment further includes: for each branch, analyzing the path most likely to cause the branch to fail based on the change, sensitivity, and influence of the posterior probability relative to the prior probability, and identifying key risk nodes by combining the sensitivity of each node on the path.

[0058] As described above, step 3 maps the traditional fault tree model to a fault Bayesian network, and step 4 yields the prior probabilities of this Bayesian network. Based on these two factors, in step 5, the fault Bayesian network can be inferred from top to bottom using the units divided in step 1 as units. Each unit corresponds to a branch of the root node. As shown in Figure 2(b), P(M) = P(X1) + P(X2); as shown in Figure 2(a), P(M) = P(X1) * P(X2).

[0059] To more intuitively reflect the change in posterior probability relative to prior probability, the rate of change is used to describe the impact of an event on the probability of system failure. A higher value indicates a greater impact of the event on failure, making it a key risk factor. Furthermore, to ensure the reliability of key risk factor identification, sensitivity and influence analysis can be used as auxiliary analyses. A comprehensive decision-making process is then made by referencing the results of these three analyses to determine the most likely path to unit failure. Combining this with high-sensitivity events along the path, key risk factors are identified.

[0060] By introducing a time variable, the failure probability of some nodes is corrected to a failure probability distribution. The posterior probability of this time node is used as the prior probability of the next time node to construct a dynamic Bayesian network and infer the failure probability of each unit in continuous time.

[0061] Suppose there are three nodes, A, B, and C, which together form a simple static Bayesian network. If we want to extend it to the time dimension, we need to extend nodes A and B from the Bayesian network at time t1 to the Bayesian network at time t2, and so on until the Bayesian network at time tn.

[0062] Step 6: Combine the failure probabilities of each branch to obtain the failure probability of the top event over continuous time.

[0063] The technical solution proposed in this application combines safety evaluation methods and mathematical theories such as fault tree analysis, triangular fuzzy numbers, and Bayesian networks. It collects operational and fault data, integrates the data, identifies risk factors, and establishes a fault tree. The fault tree is then mapped to a fault Bayesian network. The failure probability of each unit is derived from the fault Bayesian network from top to bottom. A time variable is introduced to modify the Bayesian network, enabling dynamic analysis and evaluation of the risks of underground gas storage facilities. In practical engineering, the dynamic evaluation results obtained from this application have high accuracy and are of great reference value.

[0064] The electronic device according to embodiments of this application includes a memory and a processor.

[0065] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0066] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this application, the processor is used to execute computer-readable instructions stored in the memory.

[0067] Those skilled in the art should understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this application.

[0068] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0069] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the dynamic risk assessment method for underground gas storage facilities.

[0070] A computer-readable storage medium according to embodiments of this application stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of this application are performed.

[0071] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0072] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for dynamic risk assessment of underground facilities in a gas storage facility, characterized in that, The method includes: Step 1: Based on risk factor identification, the underground facilities and geological structure of the gas storage facility are divided into units. The underground facilities and geological structure of the gas storage facility are divided into the following four units: gas production tree and wellhead equipment unit, casing string unit, tubing unit, and geological structure unit. Step 2: Taking the divided units as objects, analyze the failure influencing factors and establish a fault tree for underground facilities based on the logical relationships between events. The top events of the fault tree include gas storage explosion and gas storage leakage, and each branch of the top event corresponds to one of the units. The second-to-top events of the fault tree include: failure of the gas production tree and wellhead equipment, failure of the casing string, failure of the tubing, and failure caused by geological structure. Step 3: Convert the failure fault tree into a failure Bayesian network, map the events in the failure fault tree to nodes in the failure Bayesian network, and connect the nodes according to the logical relationship between the events. The top event is mapped to the root node of the failure Bayesian network, and each branch of the top event is mapped to a branch of the root node. Step 4: For each branch, use LR fuzzy numbers to synthesize the set weight distribution into a qualitative language of the node failure probability, and then transform it into the node fuzzy failure probability through fuzzy set theory. Step 5: For each branch, based on the fault Bayesian network and the fuzzy failure probability of the node, introduce a time variable to construct a dynamic Bayesian network and obtain the failure probability in continuous time. Step 6: Combine the failure probabilities of each branch over continuous time to obtain the failure probability of the top event over continuous time; Step 5 specifically includes: Using the node fuzzy failure probability as the initial prior probability, the failure probability of the faulty Bayesian network is inferred from top to bottom to obtain the posterior probability. By introducing a time variable, the faulty Bayesian network is modified, and the posterior probability of the previous time node is used as the prior probability of the next time node to construct the dynamic Bayesian network, thereby obtaining the failure probability in continuous time. For each branch, based on the change, sensitivity, and influence of the posterior probability relative to the prior probability, the path most likely to cause the branch to fail is analyzed. By combining the sensitivity of each node on the path, key risk nodes are identified.

2. The method according to claim 1, characterized in that, In step 3, when the failure fault tree is converted into the failure Bayesian network, repeated events are mapped to the same node.

3. The method according to claim 1, characterized in that, Step 4 specifically includes: For each branch, based on multiple sets of weights, the possible failure values ​​of the node are aggregated into a fuzzy number that can reflect the possibility of node failure. After obtaining the fuzzy numbers of all nodes, the LR fuzzy number sorting method is used to convert the fuzzy numbers into the node fuzzy failure probability.

4. The method according to claim 3, characterized in that, The LR fuzzy number sorting method is used to convert fuzzy numbers into node fuzzy failure probabilities, including: Determine the maximum fuzzy number set and calculate the possible left and right fuzzy values ​​of the LR fuzzy number; By combining the possible values ​​of the left and right fuzzy numbers, the comprehensive possible value of the fuzzy number is obtained; Using the probability transformation formula, the comprehensive possible value of the fuzzy number is converted into the node fuzzy failure probability under the current state.

5. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method of any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-4.

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