Oil and gas field production management system integrating online simulation and accident early warning

Abstract

The invention relates to an oil and gas field production management system integrating online simulation and accident early warning, which comprises a central control operation station, a monitoring module, an image acquisition module, an AI image recognition processing module, a real-time alarm module, an alarm recording module and a display module, and is characterized in that a video stream of oil and gas field production is obtained through the central control operation station; dividing a video stream into two parts, wherein one part of the video stream enters a monitoring module; the other part of the video stream is displayed in the display module, and the video stream displayed in the display module is acquired through the image acquisition module; the video stream is input into the AI image recognition processing module for image feature extraction, when an abnormal condition is detected, information needing to be alarmed is output, the information needing to be alarmed is input into the real-time alarm module for alarming, and an alarm signal and monitoring data of the monitoring module are sent to a user. According to the method, the neural network and edge calculation are adopted, so that the method can stably work in a severe marine environment, and the real-time performance and accuracy of data are ensured.

Description

Technical Field

[0001] The invention relates to an oil and gas field production management system integrating online simulation and accident early warning, belonging to the technical field of oil and gas field production platforms. Background Art

[0002] At present, FPSO (Floating Production Storage and Offloading) production and operation, marginal oil field development and production and operation, oil and gas field production and maintenance, LNG (liquefied natural gas) transportation and other businesses are gradually becoming intelligent, and the equipment and facility operation data on them is particularly important for the development of offshore oil and gas production and transportation. In actual on-site scenarios, there is a lot of data uploaded by the data collection end, but only a small part of the data is monitored and managed by intelligent equipment, most of the data is not included in the intelligent monitoring points, and many data are not processed intuitive data, and it is necessary to analyze the event judgment basis based on multiple data results. Summary of the invention

[0003] In response to the above problems, the purpose of the present invention is to provide an oil and gas field production management system that integrates online simulation and accident warning, which adopts advanced neural network technology and edge computing technology, can work stably in harsh marine environments, and ensure the real-time and accuracy of data.

[0004] To achieve the above-mentioned purpose, the present invention proposes the following technical solutions: an oil and gas field production management system integrating online simulation and accident warning, comprising: a central control operation station, a display module, an image acquisition module, an AI image recognition processing module, a real-time alarm module, an alarm recording module and a monitoring module, wherein a video stream of oil and gas field production is obtained through the central control operation station; the video stream is divided into two parts, one part of the video stream enters the monitoring module, and the other part of the video stream is displayed in the display module, and the video stream displayed in the display module is obtained through the image acquisition module; the video stream is input into the AI ​​image recognition processing module for image feature extraction, and when an abnormal situation is detected, the information requiring alarm is output, the information requiring alarm is input into the real-time alarm module, an alarm is issued, and the alarm information and the monitoring data of the monitoring module are sent to the data user device through the data interface.

[0005] Furthermore, the image acquisition module receives the central control operation station, restores the streaming media data of the video stream into an image, preprocesses the image, performs feature extraction on the preprocessed image, and extracts representative features.

[0006] Furthermore, the AI ​​image recognition processing module includes an artificial intelligence model, historical monitoring data is input into the initial artificial intelligence model, transfer learning is performed on the artificial intelligence model, the weight of the artificial intelligence model is optimized, and an optimal artificial intelligence model is obtained. The preprocessed image in the image acquisition module is input into the optimal artificial intelligence model to generate a result of whether the scene in the image is abnormal.

[0007] Furthermore, the artificial intelligence model is a convolutional neural network model VGG.

[0008] Furthermore, the image acquisition module adopts edge computing methods to perform image preprocessing at local edge nodes, and uploads the preprocessed images to the AI ​​image recognition processing module in the cloud for in-depth analysis.

[0009] Furthermore, the edge node includes a multi-core processor, a high-speed random access memory RAM, a solid-state drive SSD and an uninterruptible power supply system UPS; the central control operation station transmits video signals through HDMI and transmits images to the cloud and other systems through an Ethernet port and a wireless network.

[0010] Furthermore, the uninterruptible power supply system UPS adopts anti-corrosion materials.

[0011] Furthermore, the oil and gas field production management system also includes an alarm recording module, which sends an alarm signal when an abnormal situation is detected. The alarm recording module is used to make a decision to determine whether the alarm signal is real alarm information and save the alarm information.

[0012] Furthermore, the monitoring module, image acquisition module, AI image recognition processing module, real-time alarm module and display module are distributedly structured based on the microservice underlying Spring Cloud.

[0013] Furthermore, the oil and gas field production management system performs data transmission through a data interface layer, and the data interface layer includes a network interface module, a data formatting module and an API interface module. The network interface module is used to interact with an external system; the data formatting module is used to convert the result into a required format; and the API interface module is used to provide a standard application programming interface.

[0014] The technical solution of the present invention has at least the following technical effects or advantages: 1. The present invention integrates advanced neural network technology and edge computing technology to achieve efficient data analysis and processing, and can work stably in harsh marine environments to ensure the real-time and accuracy of data. The oil and gas field production management system uses neural network technology, especially convolutional neural networks, and the intelligent monitoring system can more accurately identify and classify objects in images while maintaining a high recall rate to ensure that no abnormalities are missed.

[0015] 2. The present invention realizes the comprehensive management of offshore oil and gas production and transportation equipment and facilities, ensuring that when one set of equipment fails, the other set can still operate normally.

[0016] 3. The use of edge computing technology enables the oil and gas field production management system to respond to events quickly without waiting for data to be transmitted to the remote server and returned, which can reduce the demand for computing, storage and network bandwidth in the cloud center. By preprocessing and removing redundant image information, all image analysis can be completed at the edge. With integrated edge computing technology, the system can ensure high data availability even in harsh marine environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of an oil and gas field production management system integrating online simulation and accident warning in one embodiment of the present invention; Figure 2 It is a functional schematic diagram of an oil and gas field production management system integrating online simulation and accident warning in one embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail through specific embodiments. However, it should be understood that the provision of specific embodiments is only for a better understanding of the present invention, and they should not be understood as limitations of the present invention. In the description of the present invention, it should be understood that the terms used are only for the purpose of description and cannot be understood as indicating or implying relative importance.

[0019] In order to solve the problem that only a small part of the data in the prior art is monitored and managed by intelligent equipment, most of the data is not included in the intelligent monitoring points, and a lot of data is not intuitive data after processing, the present invention proposes an oil and gas field production management system that integrates online simulation and accident warning, and the integrated module is secondary developed according to the distributed architecture of the microservice bottom SpringCloud, supporting multiple operating systems. By accessing the oil and gas field production management system and using the AI ​​image recognition processing function, AE (A refers to alarm, E refers to event) information is obtained from the monitoring screen to realize the analysis, monitoring and alarm of AE data and non-monitoring point alarm, solving the problem that traditional monitoring points need to install a large number of equipment and software authorization points, and using the original equipment and facility operation data to analyze the existing alarm information or hidden danger events; the system uses independent hardware equipment and power supply, and only needs to connect the video signal of the equipment and facility operation data to be applied, realizing the decoupling from the existing intelligent equipment. The scheme of the present invention is described in detail below through embodiments in conjunction with the accompanying drawings.

[0020] This example discloses an oil and gas field production management system that integrates online simulation and accident warning. Figure 1 and 2 As shown, it includes: a central control operation station, a display module, an image acquisition module, an AI image recognition processing module, a real-time alarm module, an alarm recording module and a monitoring module. The video stream of oil and gas field production output by the central control operation station is divided into two parts, one of which is directly transmitted to the monitoring module through the OPC AE protocol; the other part of the video stream is displayed in the display module, and the video stream displayed in the display module is obtained through the image acquisition module; the image acquisition module receives the central control operation station, restores the streaming media data of the video stream to an image, pre-processes the image, extracts features from the pre-processed image, and extracts representative features. The video stream is input into the AI ​​image recognition processing module for image feature extraction. When an abnormal situation is detected, the information that needs to be alarmed is output, and the information that needs to be alarmed is input into the real-time alarm module to trigger the corresponding alarm, and the alarm signal and the monitoring data of the monitoring module are sent to the user. The alarm recording module is used to make a decision to determine whether the alarm signal is a real alarm information. The alarm recording module will record the alarm information in detail and save the alarm information for access and use by other data user devices.

[0021] The scheme in this embodiment realizes efficient data analysis and processing, can work stably in harsh marine environments, and ensures the real-time and accuracy of data. The oil and gas field production management system uses neural network technology, especially convolutional neural network, and the intelligent monitoring system can more accurately identify and classify objects in images while maintaining a high recall rate to ensure that no abnormalities are missed.

[0022] The preprocessing method of the image acquisition module in this embodiment includes operations such as noise reduction, frame rate adjustment and resolution scaling to optimize analysis efficiency, and the alarm information finally generated is sent to the user's mobile terminal, such as a mobile phone, tablet, laptop, etc., to promptly remind the operation management personnel to handle it. In this embodiment, the alarm module can send an alarm notification to the management personnel by means of email, text message or instant messaging software, or the alarm method in the alarm module can also be realized by means of buzzer alarm, sound and light alarm, warning light flashing different colors of light, etc.

[0023] like Figure 2 As shown in the figure, the AI ​​image recognition processing module includes an AI model, inputs historical monitoring data into the initial AI model, performs transfer learning on the AI ​​model, optimizes the weight of the AI ​​model, and mainly adjusts the learning rate and batch size of the model. In the early stage of training, a slightly larger learning rate (0.01) is used to speed up the convergence speed. In the later stage, the learning rate is multiplied by 0.1 after every 5 training rounds, and the learning rate is finally stabilized at 0.001 through learning rate decay. At the same time, a larger batch size can make the gradient estimation more stable, but it will cause the model to converge to a narrower local optimal solution. A smaller batch size can introduce more randomness, which helps the model to jump out of the local optimal solution, but it will cause the variance of the gradient estimation to be larger. After multiple debugging, the batch size is finally stabilized at 32, and the gradient stability and exploration ability are balanced, and the optimal AI model is obtained. The pre-processed image in the image acquisition module is input into the optimal AI model to generate the result of whether the scene in the image is abnormal, that is, to identify the data that needs to be alarmed and the ordinary event data, so as to identify the working status and potential risks of various offshore facilities, realize real-time monitoring and alarm of AE data, and then display it in the display module, where the display module can be a display or display screen, etc. In this embodiment, the artificial intelligence model is preferably a convolutional neural network model VGG, which uses a convolutional neural algorithm to ensure that key tasks are executed first, reduce delays, and achieve rapid response.

[0024] The image acquisition module uses edge computing methods to preprocess images at local edge nodes, and uploads the frequently preprocessed images to the AI ​​image recognition processing module in the cloud for in-depth analysis. The application of edge computing technology in the gas field production management system means that the oil and gas field production management system can respond to events quickly without waiting for data to be transmitted to the remote server and returned. In addition, the demand for computing, storage, and network bandwidth in the cloud center can be reduced, and redundant image information can be removed through preprocessing, so that all image analysis can be completed at the edge. With integrated edge computing technology, the system can ensure high data availability even in harsh marine environments.

[0025] The edge nodes include high-performance multi-core processors, high-speed random access memory RAM, solid-state drives SSDs, and uninterruptible power supply systems UPS; the central control operation station transmits video signals via HDMI, and transmits images to the cloud and other systems via Ethernet ports and wireless networks. The uninterruptible power supply system UPS uses anti-corrosion materials to adapt to the marine environment and ensure continuous power supply.

[0026] The monitoring module integrates the accident assessment function and instrument detection function. It extracts key instrument data through image recognition, cleans and preprocesses the noise or error information contained in the data, extracts features related to the accident, and establishes an accident pattern library containing various known accident patterns of offshore oil and gas facilities. Through the probabilistic risk assessment (PRA) technology, the probability of an accident is calculated, and the risk value is obtained by combining the consequence analysis. At the same time, according to the results of the accident risk assessment, the corresponding measures are matched from the pre-established response strategy library to generate possible accident patterns, risk levels, recommended response strategies, etc., to achieve comprehensive management of offshore oil and gas production and transportation equipment and facilities.

[0027] The monitoring module, image acquisition module, AI image recognition and processing module, real-time alarm module, and display module are distributed based on the Spring Cloud microservice framework, realizing comprehensive management of offshore oil and gas production and transportation equipment and facilities, ensuring the high modularity and flexibility of the system.

[0028] like Figure 1 As shown, data is transmitted through the data interface layer in the oil and gas field production management system. The data interface layer includes a network interface module, a data formatting module and an API interface module. The network interface module is used to interact with the external system; the data formatting module is used to convert the results into the required format; and the API interface module is used to provide a standard application programming interface.

[0029] In addition, in order to ensure high data availability in harsh marine environments, it is necessary to set up two AI image recognition processing modules for the collected video stream data to ensure absolute decoupling. Once an uncontrollable event occurs at sea that causes the failure of one of the AI ​​image recognition processing modules, the other AI image recognition processing module can still maintain normal operation.

[0030] In this embodiment, the oil and gas field production management system supports multiple operating systems, such as Windows, Linux, macOS and other systems, which can run stably, improving the applicability and convenience of the system. The oil and gas field production management system regularly checks and updates the plan, obtains the latest model and software version through cloud services or physical media, and collects user feedback and actual alarm cases for continuous optimization and improvement of the model.

[0031] The system monitoring module also integrates a variety of data analysis algorithms such as statistical analysis, trend prediction and pattern recognition. Based on the statistical analysis algorithm, by collecting the basic parameters of offshore oil and gas facilities, accident records, and marine environmental data at the time of the accident, the frequency of accidents is counted by time, the correlation between accidents and facility parameters is analyzed, and the accident risk is evaluated through the risk matrix method; based on the trend prediction algorithm, by analyzing various factors affecting oil and gas equipment failures, such as equipment service life, maintenance records, environmental conditions, etc., a decision tree model is constructed to predict the future failure probability of the equipment; based on the pattern recognition algorithm, by collecting the operating parameters of offshore oil and gas facilities and equipment (such as compressors, pumps, etc.), such as temperature, pressure, vibration frequency, etc. as data samples. These data are divided into different clusters by the K-means clustering algorithm. Data under normal operating conditions are usually clustered in one or several clusters, while fault data will deviate from normal clusters due to abnormal changes in their operating parameters. For example, when fault detection is performed on an offshore platform oil pump, if the vibration frequency and temperature of the oil pump suddenly increase, these abnormal data points will not overlap with the clusters under normal operation, so that the fault can be identified. Through fault identification, fault analysis and fault prediction, the facility operation status is comprehensively evaluated, and warning levels and response measures are formulated based on the analysis results to provide decision-making references for operation managers.

[0032] The present invention provides an efficient and accurate offshore oil and gas production and transportation equipment and facility operation data oil and gas field production management system, which can operate stably in complex marine environments without the need for additional monitoring point equipment. It can intelligently and comprehensively analyze multiple data results and is decoupled from existing intelligent devices to ensure independent operation.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not deviate from the spirit and scope of the present invention should be included in the protection scope of the claims of the present invention. The above content is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An oil and gas field production management system integrating online simulation and accident warning, characterized in that: include: Central control operation station, monitoring module, image acquisition module, AI image recognition processing module, real-time alarm module, alarm recording module and display module, The video stream of oil and gas field production is obtained through the central control operation station; the video stream is divided into two parts, one part of which enters the monitoring module; the other part of the video stream is displayed in the display module, and the video stream displayed in the display module is obtained through the image acquisition module; the video stream is input into the AI ​​image recognition processing module for image feature extraction, and when an abnormal situation is detected, the information that needs to be alarmed is output, and the information that needs to be alarmed is input into the real-time alarm module to alarm, and the alarm signal and the monitoring data of the monitoring module are sent to the user.

2. The oil and gas field production management system integrating online simulation and accident warning as claimed in claim 1, characterized in that: The image acquisition module receives the central control operation station, restores the streaming media data of the video stream into an image, pre-processes the image, performs feature extraction on the pre-processed image, and extracts representative features.

3. The oil and gas field production management system integrating online simulation and accident warning as claimed in claim 1, characterized in that: The AI ​​image recognition processing module includes an artificial intelligence model. Historical monitoring data is input into the initial artificial intelligence model, transfer learning is performed on the artificial intelligence model, the weight of the artificial intelligence model is optimized, and the optimal artificial intelligence model is obtained. The preprocessed image in the image acquisition module is input into the optimal artificial intelligence model to generate a result of whether the scene in the image is abnormal.

4. The oil and gas field production management system integrating online simulation and accident warning as claimed in claim 3, characterized in that: The artificial intelligence model is a convolutional neural network model VGG.

5. The oil and gas field production management system integrating online simulation and accident warning as claimed in claim 1, characterized in that: The image acquisition module adopts edge computing method to perform image preprocessing at the local edge node, and uploads the preprocessed images to the AI ​​image recognition processing module in the cloud for in-depth analysis.

6. The oil and gas field production management system integrating online simulation and accident warning as claimed in claim 5, characterized in that: The edge node includes a multi-core processor, a high-speed random access memory RAM, a solid-state drive SSD and an uninterruptible power supply system UPS; the central control operation station transmits video signals via HDMI and transmits images to the cloud and other systems via an Ethernet port and a wireless network.

7. The oil and gas field production management system integrating online simulation and accident warning as claimed in claim 6, characterized in that: The uninterruptible power supply system UPS adopts anti-corrosion materials.

8. The oil and gas field production management system integrating online simulation and accident warning as claimed in claim 1, characterized in that: The oil and gas field production management system also includes an alarm recording module, which sends an alarm signal when an abnormal situation is detected. The alarm recording module is used to make a decision to determine whether the alarm signal is real alarm information and save the alarm information.

9. The oil and gas field production management system integrating online simulation and accident warning as claimed in claim 1, characterized in that: The monitoring module, image acquisition module, AI image recognition processing module, real-time alarm module, alarm recording module and display module are distributedly structured based on the Spring Cloud microservice framework.

10. The oil and gas field production management system integrating online simulation and accident warning according to claim 1, characterized in that: The oil and gas field production management system performs data transmission through a data interface layer, and the data interface layer includes a network interface module, a data formatting module and an API interface module. The network interface module is used to interact with an external system; the data formatting module is used to convert the result into a required format; and the API interface module is used to provide a standard application programming interface.

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