Oil and gas pipeline network risk intelligent perception early warning system and method

Abstract

The present application relates to oil and gas pipeline transportation safety technical field, especially to an oil and gas pipeline network risk intelligent perception early warning system and method, the system includes a sensing subsystem and a cognitive subsystem;The sensing subsystem is used for: obtaining the risk threat data and the running state data of the oil and gas pipeline network, and sends to the cognitive subsystem;The cognitive subsystem is used for: based on the pipeline basic data, the pipeline normal operating state data, the risk occurrence state data, the risk threat data and the running state data of the oil and gas pipeline network, utilizes data analysis model, carries out risk identification to the oil and gas pipeline network, according to the risk identification result, determines whether to carry out early warning.The risk of the oil and gas pipeline network is perceived and intelligently warned in real time on line, to ensure the safe operation of the oil and gas pipeline.

Description

Technical Field

[0001] This invention relates to the field of oil and gas pipeline transportation safety technology, and in particular to an intelligent risk perception and early warning system and method for oil and gas pipeline networks. Background Technology

[0002] Oil and gas pipelines are a crucial component of the energy supply system, and their safe and efficient operation is vital to national energy security. Currently, various human activities, geological disasters, and their resulting secondary disasters seriously threaten the safe operation of pipelines. Once an oil and gas pipeline fails and leaks, it will cause severe personal injury, property damage, and environmental pollution, significantly impacting public safety. Currently, monitoring and early warning of oil and gas pipeline safety risks mainly rely on conventional methods such as pipeline deformation monitoring, drone inspections, and satellite remote sensing. However, given the increasingly large scale of pipeline networks, more complex operating environments, and more stringent inherent safety requirements, existing monitoring technologies cannot meet the need for comprehensive, real-time perception of major pipeline safety risks.

[0003] Therefore, given the characteristics of gas pipeline safety risks such as "spatiotemporal randomness, wide distribution, concealed occurrence, and catastrophic consequences," developing intelligent perception methods and systems for major risks that are applicable around the clock, across all regions, and involving all elements is of paramount practical significance for ensuring the safe operation of gas pipelines and thereby enhancing national energy transmission security. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent perception and early warning system and method for oil and gas pipeline network risks, which addresses the shortcomings of the existing technology.

[0005] The technical solution of the intelligent sensing and early warning system for oil and gas pipeline network risks of the present invention is as follows:

[0006] It includes a perception subsystem and a cognitive subsystem;

[0007] The sensing subsystem is used to: acquire risk and threat data and operational status data of the oil and gas pipeline network, and send them to the cognitive subsystem;

[0008] The cognitive subsystem is used to: identify risks in the oil and gas pipeline network based on pipeline basic data, pipeline normal operation status data, risk occurrence status data, risk threat data, and operation status data, using a data analysis model, and determine whether to issue an early warning based on the risk identification results.

[0009] The technical solution of the intelligent sensing and early warning method for oil and gas pipeline network risks of the present invention is as follows:

[0010] The perception subsystem acquires risk and threat data and operational status data of the oil and gas pipeline network and sends them to the cognitive subsystem;

[0011] The cognitive subsystem uses a data analysis model to identify risks in the oil and gas pipeline network based on pipeline basic data, pipeline normal operation status data, risk occurrence status data, risk threat data, and operation status data. Based on the risk identification results, it determines whether to issue an early warning.

[0012] The beneficial effects of this invention are as follows:

[0013] This enables real-time online sensing and intelligent early warning of risks in oil and gas pipeline networks, ensuring the safe operation of these pipelines. Attached Figure Description

[0014] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0015] Figure 1 This is one of the structural schematic diagrams of an intelligent sensing and early warning system for oil and gas pipeline network risks according to an embodiment of the present invention;

[0016] Figure 2 This is a second schematic diagram of the structure of an intelligent sensing and early warning system for oil and gas pipeline network risks according to an embodiment of the present invention;

[0017] Figure 3 This is a flowchart illustrating an intelligent risk perception and early warning method for oil and gas pipeline networks according to an embodiment of the present invention. Detailed Implementation

[0018] like Figure 1 and Figure 2 As shown, an intelligent sensing and early warning system for oil and gas pipeline network risks according to an embodiment of the present invention includes a sensing subsystem and a cognitive subsystem;

[0019] The perception subsystem is used to acquire risk and threat data and operational status data of oil and gas pipeline networks and send them to the cognition subsystem. The risk and threat data includes data on human construction threats, geological disaster threats, and deliberate sabotage threats, while the operational status data includes operational pressure data, temperature data, and stress data.

[0020] The sensing subsystem consists of sensors and data acquisition devices deployed on the pipeline body, pipeline route, and pipeline stations, used to acquire real-time data such as pipeline stress and strain, pipeline temperature, pipeline vibration, and pipeline equipment status.

[0021] The sensing subsystem uses sensors and data acquisition devices to acquire real-time data on major risks and threats to oil and gas pipeline networks and the status of the pipeline itself.

[0022] The cognitive subsystem is used to: identify risks in the oil and gas pipeline network based on pipeline basic data, pipeline normal operation status data, risk occurrence status data, risk threat data, and operation status data, and to determine whether to issue an early warning based on the risk identification results.

[0023] The data analysis models include at least one of the following: pipeline leakage multi-source fusion analysis model, pipeline third-party damage multi-source fusion analysis model, pipeline geological disaster threat multi-source analysis model, pipeline body deformation multi-source fusion analysis model, pipeline equipment operation status multi-source diagnostic model, pipeline medium simulation model, and pipeline safety status comprehensive analysis model.

[0024] The cognitive subsystem receives data from the sensing subsystem and inputs it into the established data analysis model. The data analysis model includes data from the sensing subsystem, pipeline basic data, and pipeline normal operation status data. Based on the analysis results, it determines whether to issue an early warning.

[0025] The cognitive subsystem consists of a multi-source fusion analysis model for pipeline leakage, a multi-source fusion analysis model for pipeline third-party damage, a multi-source analysis model for pipeline geological disaster threats, a multi-source fusion analysis model for pipeline deformation, a multi-source diagnostic model for pipeline equipment operation status, a pipeline medium simulation model, and a comprehensive analysis model for pipeline safety status. It is used to analyze and understand the status of the pipeline body, environment, and medium, as well as to recognize and warn of major risks such as pipeline leakage and pipeline damage.

[0026] The cognitive subsystem acquires basic pipeline data through the oil and gas pipeline network asset integrity platform, obtains knowledge of the normal operation status and major risk occurrence status of the pipeline through the comprehensive scenario test platform, and uses data analysis models to identify and warn of major risks.

[0027] The following is a description of the oil and gas pipeline network asset integrity platform and the comprehensive scenario test platform:

[0028] 1) The oil and gas pipeline asset integrity platform is used for the management of basic equipment and facilities of the oil and gas pipeline network, providing a basis for the status analysis and emergency management of the oil and gas pipeline network; the oil and gas pipeline network dispatch and control platform is used for the control of the pipeline network operation status, providing a basis for the real-time control of the oil and gas pipeline network status.

[0029] 2) The comprehensive scenario test platform consists of test loops, pipeline stress test platforms, pipeline inspection test platforms, pipeline intelligent sensing test platforms, pipeline geological disaster test platforms, pipeline third-party damage test platforms, pipeline leakage test platforms, pipeline integrated network test platforms, pipeline sensor test platforms, and pipeline corrosion test platforms. It is used to simulate and test major risks that pipelines may face, and to provide scientific means to understand the occurrence patterns of major risks and the development patterns of pipeline operation status.

[0030] Optionally, the above technical solution also includes an application subsystem;

[0031] When it is determined that an early warning should be issued, the cognitive subsystem generates an early warning message based on the risk identification results and sends it to the application subsystem.

[0032] After the application subsystem performs decision analysis on the early warning information, it sends the decision analysis results to the oil and gas pipeline network dispatch and control platform, which then controls the operation status of the oil and gas pipeline network based on the decision analysis results.

[0033] The application subsystem consists of a pipeline equipment maintenance module, a pipeline emergency decision-making module, a pipeline equipment control module, and a pipeline risk inspection module, which are used to manage and control pipeline operation status, equipment maintenance, and emergency response to sudden events.

[0034] The application subsystem receives early warning information about major risks from the cognitive subsystem, performs decision analysis on the information, and then sends the information to the oil and gas pipeline network dispatch and control platform to control the operating pipeline compressors, oil pumps, valves and other equipment, thereby controlling the pipeline's operating status.

[0035] Optionally, the above technical solution also includes a transmission subsystem, which is used to: receive and forward the risk threat data and operation status data of the oil and gas pipeline network sent by the perception subsystem to the cognition subsystem.

[0036] The transmission subsystem is composed of a pipeline-specific data transmission optical cable, satellite communication network, mobile wireless communication network, industrial Internet of Things, Internet and other transmission networks. Data used for status control of the operating pipeline is transmitted via the pipeline-specific data transmission optical cable. Data sensed by the sensing subsystem is transmitted via satellite communication network, mobile wireless communication network, industrial Internet of Things and Internet, depending on communication conditions and data type.

[0037] Optionally, in the above technical solution, the pipeline basic data includes data such as pipeline material, pipeline size, pipeline medium, pipeline geographic information, and pipeline equipment and facilities information.

[0038] In another embodiment, the intelligent perception and early warning system for oil and gas pipeline network risks of the present invention is an intelligent perception and early warning system for major risks in complex oil and gas pipeline networks. It can perceive data on the pipeline environment, pipeline body, and pipeline medium in an all-round way. By using multi-scenario comprehensive test, multi-source data fusion, knowledge graph and other methods, it can realize real-time online perception and intelligent early warning of major risks, and ensure the safe operation of oil and gas pipelines.

[0039] Specifically, the complex oil and gas pipeline network is divided into three major sensing objects: the oil and gas pipeline network environment, the oil and gas pipeline network itself, and the pipeline network medium. For these three objects, a "sensing, transmission, knowledge, and application" system architecture is constructed, including a sensing subsystem, a transmission subsystem, a knowledge subsystem, and an application subsystem. It also includes a comprehensive scenario test platform for verifying the technology and effectiveness of each subsystem, a digital platform for data management and interaction, an oil and gas pipeline network scheduling and control platform for pipeline operation status control, an oil and gas pipeline network asset integrity platform for pipeline infrastructure management, and actual operating pipelines. The sensing subsystem collects comprehensive data on the pipeline network itself, its environment, and the media, and performs preliminary processing and analysis. The transmission subsystem transmits the data collected by the sensing subsystem to the integrated scenario testing platform, digital platform, oil and gas pipeline scheduling and control platform, and oil and gas pipeline asset integrity platform, according to data requirements and types. The integrated scenario testing platform verifies the sensing data and discovers patterns of change in pipeline objects through scenario design. The cognition subsystem utilizes real-time sensing data, basic pipeline data, and patterns of pipeline changes to form knowledge about pipeline objects and their corresponding scenarios, enabling an understanding of the oil and gas pipeline network's status and major risks. The application subsystem provides targeted application services based on the different data and knowledge requirements of various pipeline scenarios, enabling intelligent control of the pipeline network when major risks occur. The integrated scenario testing platform can simulate actual operating scenarios and major risks in the oil and gas pipeline network, providing a scientific basis for the cognition subsystem to form knowledge about the pipeline network's operating status. The digital platform is responsible for managing oil and gas pipeline network data and the management of the pipeline network's digital twin, providing a foundation for the development and prediction of the oil and gas pipeline network's status.

[0040] In another embodiment, the complex oil and gas pipeline network is divided into three major sensing objects: the oil and gas pipeline network environment, the oil and gas pipeline network itself, and the pipeline network medium. For these three objects, a "sensing, transmission, knowledge, and application" system architecture is constructed, including a sensing subsystem, a transmission subsystem, a knowledge subsystem, and an application subsystem. It also includes a comprehensive scenario test platform for verifying the technology and effectiveness of each subsystem, a digital platform for data management and interaction, an oil and gas pipeline network scheduling and control platform for pipeline operation status control, an oil and gas pipeline network asset integrity platform for pipeline infrastructure management, and the actual operating pipelines.

[0041] This embodiment takes the major risk of third-party damage as an example. When a major risk of third-party damage occurs, the sensing subsystem collects comprehensive data on the pipeline body, soil vibration along the pipeline, engineering machinery, personnel activities, and pipeline media, and performs preliminary processing and analysis on the data. The transmission subsystem transmits the data collected by the sensing subsystem to the integrated scenario test platform, digital platform, oil and gas pipeline dispatch and control platform, and oil and gas pipeline asset integrity platform according to data requirements and types. The integrated scenario test platform verifies the sensing data and discovers the patterns of pipeline object changes through scenario design. The cognition subsystem uses real-time sensing data and pipeline basic data to achieve early identification and warning of third-party damage through a multi-source fusion analysis model, and sends the warning information to the application subsystem. The application subsystem receives the warning information from the cognition subsystem, analyzes and confirms the warning information in conjunction with the pipeline basic data, and simultaneously sends the information to pipeline management, patrol personnel, and the oil and gas pipeline dispatch and control platform. After receiving the information, management and patrol personnel promptly control the third-party damage personnel, and the oil and gas pipeline dispatch and control platform promptly controls the pipeline operation status and stops the pipeline operation in time when pipeline damage occurs.

[0042] The beneficial effects of this invention are as follows:

[0043] 1) For the first time, a method and system for intelligent perception and early warning of major risks covering the entire business process, time period, and network of oil and gas pipelines has been created. It can monitor the operation status of oil and gas pipelines in real time and provide early warning of major risks, effectively ensuring the safe operation of our oil and gas pipeline network and having significant economic and social benefits.

[0044] 2) For the first time, intelligent perception and early warning of the operational status and major risks of oil and gas pipeline networks have been achieved. Based on an intelligent technology architecture, the system is designed with four subsystems: perception, transmission, cognition, and application. It makes full use of the existing unified system functions and data of the oil and gas pipeline network, and adopts intelligent computing and analysis models to achieve intelligent perception and early warning of operational status and major risks, fully demonstrating the system's level of intelligence.

[0045] 3) This system is the first to design a comprehensive scenario testing platform and integrate it as a component of the overall system. Compared to conventional operating systems and testing platforms, this system incorporates the comprehensive scenario testing platform as part of the overall system, achieving an organic combination of actual operation and comprehensive testing. This allows for the analysis of real-time data through testing scenarios and the reception of actual operating status data to optimize testing scenarios. It represents an innovative application of the integration of testing platforms and sensing systems.

[0046] like Figure 3 As shown in the figure, an intelligent sensing and early warning method for oil and gas pipeline network risks according to an embodiment of the present invention includes the following steps:

[0047] S1. The perception subsystem acquires risk and threat data and operational status data of the oil and gas pipeline network and sends them to the cognitive subsystem.

[0048] S2. The cognitive subsystem uses data analysis models to identify risks in the oil and gas pipeline network based on pipeline basic data, pipeline normal operation status data, risk occurrence status data, risk threat data, and operation status data. Based on the risk identification results, it determines whether to issue an early warning.

[0049] Optionally, the above technical solution also includes:

[0050] S3. When it is determined that an early warning should be issued, the cognitive subsystem generates early warning information based on the risk identification results and sends it to the application subsystem.

[0051] S4. After the application subsystem performs decision analysis on the early warning information, it sends the decision analysis results to the oil and gas pipeline network dispatch and control platform, which then controls the operation status of the oil and gas pipeline network based on the decision analysis results.

[0052] Optionally, in the above technical solution, the process by which the sensing subsystem sends risk threat data and operational status data of the oil and gas pipeline network to the cognitive subsystem includes:

[0053] S10. The perception subsystem transmits risk and threat data and operational status data of the oil and gas pipeline network to the cognition subsystem through the transmission subsystem.

[0054] Optionally, in the above technical solution, the pipeline basic data includes pipeline material, pipeline size, pipeline medium, pipeline geographic information, and pipeline equipment and facility information.

[0055] Optionally, in the above technical solution, the data analysis model includes at least one of the following: pipeline leakage multi-source fusion analysis model, pipeline third-party damage multi-source fusion analysis model, pipeline geological disaster threat multi-source analysis model, pipeline body deformation multi-source fusion analysis model, pipeline equipment operation status multi-source diagnostic model, pipeline medium simulation model, and pipeline safety status comprehensive analysis model.

[0056] In another embodiment, the following steps are included:

[0057] S100, the perception subsystem uses sensors and collectors to acquire real-time data on geological disaster status of oil and gas pipelines and pipeline status, and transmits it to the cognition subsystem through the transmission subsystem;

[0058] S101. The cognitive subsystem obtains basic pipeline data through the oil and gas pipeline network asset integrity platform, acquires knowledge of the normal operation status of pipelines and the occurrence status of geological disasters through the comprehensive scenario test platform, and uses data analysis models to identify and warn of major geological disaster risks.

[0059] S102. The application subsystem receives early warning information about geological disasters from the cognitive subsystem, performs decision analysis on the information, and then sends the information to the oil and gas pipeline network dispatch and control platform to control the operating pipeline compressors, oil pumps, valves and other equipment, thereby controlling the pipeline's operating status.

[0060] The pipeline basic data includes data on pipeline materials, pipeline dimensions, pipeline media, pipeline geographic information, and pipeline equipment and facilities.

[0061] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given in this application. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of this invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0062] The implementation of each step in the above-described intelligent perception and early warning method for oil and gas pipeline network risks of the present invention can be referred to the content of the embodiment of the intelligent perception and early warning system for oil and gas pipeline network risks described above, and will not be repeated here.

[0063] Those skilled in the art will know that this invention can be implemented as a system, method, or computer program product.

[0064] Therefore, this disclosure can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product in one or more computer-readable media, the computer-readable medium containing computer-readable program code.

[0065] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0066] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An intelligent risk perception and early warning system for oil and gas pipeline networks, characterized in that, It includes a perception subsystem and a cognitive subsystem; The sensing subsystem is used to: acquire risk and threat data and operational status data of the oil and gas pipeline network, and send them to the cognitive subsystem; The cognitive subsystem is used to: identify risks in the oil and gas pipeline network based on basic pipeline data and pipeline normal operation status data and risk occurrence status data obtained through the comprehensive scenario test platform, combined with risk threat data and operation status data sent by the perception subsystem, and use a data analysis model to determine whether to issue an early warning based on the risk identification results; wherein, the comprehensive scenario test platform is used to simulate major risk scenarios in the oil and gas pipeline network, providing a knowledge base for the data analysis model of risk occurrence patterns and operation status development patterns, and the comprehensive scenario test platform includes at least one of the following: test loop, pipeline stress test platform, pipeline inspection test platform, pipeline intelligent perception test platform, pipeline geological disaster test platform, pipeline third-party damage test platform, pipeline leakage test platform, pipeline integrated network test platform, pipeline sensor test platform, and pipeline corrosion test platform; It also includes application subsystems; When it is determined that an early warning should be issued, the cognitive subsystem generates early warning information based on the risk identification result and sends it to the application subsystem; After performing decision analysis on the early warning information, the application subsystem sends the decision analysis results to the oil and gas pipeline network scheduling and control platform, which then controls the operation status of the oil and gas pipeline network based on the decision analysis results. It also includes a transmission subsystem; The transmission subsystem is used to: receive and forward the risk and threat data and operational status data of the oil and gas pipeline network sent by the sensing subsystem to the cognitive subsystem; The pipeline basic data includes pipeline materials, pipeline dimensions, pipeline medium, pipeline geographic information, and pipeline equipment and facility information; The data analysis model includes at least one of the following: pipeline leakage multi-source fusion analysis model, pipeline third-party damage multi-source fusion analysis model, pipeline geological hazard threat multi-source analysis model, pipeline body deformation multi-source fusion analysis model, pipeline equipment operation status multi-source diagnostic model, pipeline medium simulation model, and pipeline safety status comprehensive analysis model.

2. A method for intelligent perception and early warning of risks in oil and gas pipeline networks, characterized in that, include: The perception subsystem acquires risk and threat data and operational status data of the oil and gas pipeline network and sends them to the cognitive subsystem; The cognitive subsystem, based on the basic pipeline data of the oil and gas pipeline network, and the normal operation status data and risk occurrence status data of the pipeline obtained through the comprehensive scenario test platform, combined with the risk threat data and operation status data sent by the perception subsystem, uses a data analysis model to identify risks in the oil and gas pipeline network and determine whether to issue an early warning based on the risk identification results. The comprehensive scenario test platform is used to simulate major risk scenarios in the oil and gas pipeline network, providing a knowledge base for the data analysis model regarding the patterns of risk occurrence and the development of operation status. The comprehensive scenario test platform includes at least one of the following: a test loop, a pipeline stress test platform, a pipeline inspection test platform, a pipeline intelligent perception test platform, a pipeline geological disaster test platform, a pipeline third-party damage test platform, a pipeline leakage test platform, a pipeline integrated network test platform, a pipeline sensor test platform, and a pipeline corrosion test platform. Also includes: When it is determined that an early warning should be issued, the cognitive subsystem generates early warning information based on the risk identification result and sends it to the application subsystem; After performing decision analysis on the early warning information, the application subsystem sends the decision analysis results to the oil and gas pipeline network scheduling and control platform, which then controls the operation status of the oil and gas pipeline network based on the decision analysis results. The perception subsystem transmits the risk and threat data and operational status data of the oil and gas pipeline network to the cognition subsystem through the transmission subsystem; The pipeline basic data includes pipeline materials, pipeline dimensions, pipeline medium, pipeline geographic information, and pipeline equipment and facility information; The data analysis model includes at least one of the following: pipeline leakage multi-source fusion analysis model, pipeline third-party damage multi-source fusion analysis model, pipeline geological hazard threat multi-source analysis model, pipeline body deformation multi-source fusion analysis model, pipeline equipment operation status multi-source diagnostic model, pipeline medium simulation model, and pipeline safety status comprehensive analysis model.

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