An oil and gas pipeline monitoring and prediction system based on deep learning

Abstract

The present invention discloses an oil and gas pipeline monitoring and prediction system based on deep learning, which belongs to the field of oil and gas pipeline detection technology. The present invention collects various data from sensors and equipment deployed in the oil and gas pipeline through a sensor detection module, and performs preprocessing, deploys the trained deep learning model into the monitoring system of the oil and gas pipeline, and automatically analyzes the real-time data, thereby realizing the monitoring and prediction of abnormal points and sections of the oil and gas pipeline in the real-time monitoring and anomaly detection module and the fault prediction and early warning module, and presents the results and related information to the user in a graphical manner to help the user better understand and use the system. On the basis of the sensor monitoring of the physical state of oil and gas in the pipeline, the present invention uses a deep learning model to predict the abnormal points and times of potential accidents in real time, significantly improving the efficiency of monitoring and prediction work, thereby ensuring the safety of the oil and gas pipeline.

Description

Technical Field

[0001] The present invention relates to the field of real-time monitoring of oil and gas pipelines, and in particular to an oil and gas pipeline monitoring and prediction system based on deep learning. Background Art

[0002] Oil and gas pipelines are pipeline systems used to transport oil and natural gas. These systems are typically buried underground or on the seafloor. Pumping stations and compressor stations maintain the flow of oil and gas, transporting them from production to consumption. Oil and gas pipeline transportation offers advantages such as high throughput, high continuity, low cost, safety, reliability, and minimal environmental impact, making it a vital component of the modern oil and gas industry. Furthermore, the application of monitoring and prediction systems for oil and gas pipelines is crucial. By monitoring pipeline operating conditions, environmental parameters, and other data in real time, anomalies can be detected and early warning issued, thereby preventing or mitigating accidents such as oil and gas leaks.

[0003] However, the current field of real-time monitoring of oil and gas pipelines faces the following problems: Oil and gas pipelines often traverse complex terrain and environments, and are vulnerable to threats such as natural disasters, third-party construction, and oil and gas theft, which may lead to safety accidents such as pipeline damage and leakage, seriously affecting the safety of human life and property and the environment along the pipeline; Oil and gas pipeline monitoring and prediction involves a large amount of data processing, model building, and predictive analysis. Problems such as accurately obtaining real-time pipeline data, how to effectively process and analyze this data, and how to establish reliable predictive models still need to be solved.

[0004] Deep learning is a new research direction in the field of machine learning. It primarily aims to enable machines to acquire human-like analytical and learning capabilities by learning the inherent patterns and representational hierarchies of sample data. The ultimate goal of deep learning is to enable machines to recognize and interpret various data, such as text, images, and sounds, thereby achieving artificial intelligence.

[0005] Digital Twin makes full use of data such as physical models, sensor updates, and operation history, integrates multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes, and completes mapping in virtual space to reflect the entire life cycle of the corresponding physical equipment.

[0006] Therefore, in order to solve the above problems, deep learning technology and digital twin technology are combined to realize oil and gas pipeline monitoring and prediction to improve its accuracy and real-time performance. Summary of the Invention

[0007] The purpose of this invention is to solve the defects in the existing technology and propose an oil and gas pipeline monitoring and prediction system based on deep learning.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A deep learning-based oil and gas pipeline monitoring and prediction system includes a sensor detection module, a digital twin model, a real-time monitoring and anomaly detection module, a deep learning model, a fault prediction and warning module, and a visual user interaction module;

[0010] The sensor detection module collects data from various sensors and equipment in the oil and gas pipeline, including pressure data, temperature data, flow data, vibration data, chemical composition data, position and posture data, and infrared image data;

[0011] The infrared image data needs to be preprocessed to improve the prediction quality of the deep learning model;

[0012] The real-time monitoring and anomaly detection module performs real-time analysis on the data collected in real time to detect whether there are any anomalies;

[0013] The deep learning models described above are the core of the system and include three types of deep learning networks: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs). These models learn the normal operation patterns of pipelines and the characteristics of potential problems by training on large amounts of historical data.

[0014] The fault prediction and warning module uses an empirical deep learning model with rich historical data learning experience to predict the type and time of possible pipeline failures;

[0015] The visualization user interaction module includes three-dimensional visualization and statistical visualization modules. Through three-dimensional visualization and statistical visualization technology, it provides users with an intuitive and easy-to-understand data display method, thereby helping users better understand and utilize the monitoring and prediction results of the system.

[0016] Furthermore, the specific steps of the pre-processing are as follows:

[0017] a. First, acquire sensor data and perform data cleaning, including removing duplicate data, processing missing values, and removing noise. Specifically, a two-dimensional Gaussian filter is used to perform convolution operations on infrared image data to eliminate image brightness noise caused by the confined space within the oil and gas pipeline:

[0018]

[0019] Where x is the horizontal coordinate of the pixel, y is the vertical coordinate of the pixel, G(x,y) is the new pixel value, and σ is the standard deviation of the image;

[0020] b. Perform z-score standardization on the data to convert it to the same scale to eliminate the dimensional differences between different features:

[0021]

[0022] Where Z is the standard score, X is the raw data obtained by the sensor, E[X] is the average value of X, and σ(X) is the standard deviation of X;

[0023] c. Based on the needs of the specific task, extract features related to the oil and gas pipeline status from the raw data, including statistical features, time series features, and features based on signal processing after Laplace transform:

[0024]

[0025] Among them, F(s) is a complex function that transforms the function in the time domain into the complex frequency domain, e -st is the convergence factor, s = σ + jω is the complex frequency, where the real part σ is always positive and the imaginary part jω can be positive, negative or zero;

[0026] d. For image or time series data, perform data augmentation through rotation, translation, scaling, cropping, etc. to increase the diversity of the dataset and the generalization ability of the model;

[0027] e. For outliers that significantly deviate from the normal range, you can choose to treat them as noise and remove them, or treat them as special cases and perform special processing.

[0028] Furthermore, the digital twin model includes a geometric model, a physical model and a data exchange platform;

[0029] The geometric model is combined with the oil and gas pipeline construction drawings and scanned point cloud data, and BIM technology is used to accurately model the geometric shape of the oil and gas pipeline in three dimensions;

[0030] The physical model stores various physical parameters acquired by the sensor, including pressure, temperature, flow, vibration information, chemical composition, position and posture, and infrared images;

[0031] The data exchange platform synchronizes, interacts, and updates the geometric model and the physical model based on the data acquired by the sensor detection module, ensuring that the status and information between the two can be kept consistent in real time:

[0032] M {DT} =(VM,PM,DEP,DD,CN)

[0033] Among them, M{DT} represents the overall framework of the digital twin model with five key dimensions, VM is the geometric model, PM is the physical model, DEP is the data exchange platform, DD is the sensor twin data, and CN is the connection between the various parts.

[0034] Furthermore, the specific steps of the fault prediction and early warning module are as follows:

[0035] a. Collect sensor data stored in geometric and physical models after preprocessing in the digital twin model, and use deep learning models to automatically extract useful features from the raw data;

[0036] b. Input the real-time collected data into a trained deep learning model. The deep learning model will predict future conditions based on the learned patterns, identify potential failure risks, and generate a failure warning signal based on the prediction results.

[0037] c. According to business needs and data distribution, set appropriate warning thresholds based on model performance. When the model's prediction results exceed the preset thresholds, the warning mechanism is triggered:

[0038]

[0039] Where P is the precision, R is the recall, TP is the true positive, FP is the false positive, FN is the false negative, and F1 is the warning threshold, which is the balance point in the precision-recall curve.

[0040] d. Once the early warning mechanism is triggered, the system generates early warning information and promptly notifies relevant personnel so that appropriate intervention measures can be taken to avoid the occurrence of the fault or mitigate the impact of the fault;

[0041] e. Regularly evaluate the model's prediction performance based on accuracy, recall, and F1 score. Based on the evaluation results, adjust the parameters of the deep learning model to improve the accuracy and reliability of fault prediction and early warning.

[0042] f. Continuously deploy the optimized model in the production environment to provide real-time monitoring and early warning of oil and gas pipelines. Regularly maintain and update the model to adapt to changes in pipeline operating status and new failure modes.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention proposes a deep learning-based oil and gas pipeline monitoring and prediction system. The system collects various data from sensors and equipment in the oil and gas pipeline through a sensor detection module, pre-processes the collected data, and deploys the trained deep learning model into the oil and gas pipeline monitoring system to automatically analyze real-time data. This enables monitoring and prediction of abnormal points and sections of the oil and gas pipeline in the real-time monitoring and anomaly detection module and the fault prediction and early warning module. The system's operating results and related information are graphically displayed to the user to help the user better understand and use the system. By improving accuracy, achieving real-time monitoring, enhancing prediction capabilities, demonstrating adaptability, reducing costs, improving scalability, and optimizing visual interaction, the present invention provides a comprehensive and efficient solution for the safe operation and maintenance of oil and gas pipelines, significantly improving the effectiveness and efficiency of monitoring and prediction work. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0046] Figure 1 This is a schematic diagram of the functional modules of an oil and gas pipeline monitoring and prediction system based on deep learning proposed in the present invention;

[0047] Figure 2 This is a schematic diagram of the preprocessing process of an oil and gas pipeline monitoring and prediction system based on deep learning proposed by the present invention in an embodiment;

[0048] Figure 3 This is a process diagram of a fault prediction and early warning module in an embodiment of an oil and gas pipeline monitoring and prediction system based on deep learning proposed by the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to illustrate the present invention and are not intended to limit the present invention.

[0050] In one embodiment, Figure 1 As shown, a deep learning-based oil and gas pipeline monitoring and prediction system is provided, including a sensor detection module, a digital twin model, a real-time monitoring and anomaly detection module, a deep learning model, a fault prediction and warning module, and a visual user interaction module.

[0051] The sensor detection module collects data from various sensors and equipment in the oil and gas pipeline, including pressure data, temperature data, flow data, vibration data, chemical composition data, position and posture data, and infrared image data;

[0052] The digital twin model includes a geometric model, a physical model and a data exchange platform;

[0053] The real-time monitoring and anomaly detection module performs real-time analysis on the data collected in real time to detect whether there are any anomalies;

[0054] The deep learning models described above are the core of the system and include three types of deep learning networks: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs). These models learn the normal operation patterns of pipelines and the characteristics of potential problems by training on large amounts of historical data.

[0055] The fault prediction and warning module uses an empirical deep learning model with rich historical data learning experience to predict the type and time of possible pipeline failures;

[0056] The visualization user interaction module includes three-dimensional visualization and statistical visualization modules. Through three-dimensional visualization and statistical visualization technology, it provides users with an intuitive and easy-to-understand data display method, thereby helping users better understand and utilize the monitoring and prediction results of the system.

[0057] In this embodiment, the system dynamically collects pipeline operation data through sensors in real time and feeds it into the digital twin model. A deep learning model is then used to extract features and identify patterns from this data. The model, trained with a large amount of historical data, can automatically learn and identify normal and abnormal operating modes. When the model detects an abnormal pattern or predicts a potential failure, an early warning mechanism is triggered. The triggering of the early warning is usually based on a preset threshold. When the prediction result exceeds this threshold, the system generates early warning information, including the type of fault, predicted time of occurrence, etc., and promptly notifies relevant personnel to handle it.

[0058] In this way, real-time monitoring and fault warning of oil and gas pipelines can be achieved, thereby improving the safety and operation efficiency of the pipelines.

[0059] In one embodiment, this embodiment is based on the above example, such as Figure 2 As shown in the figure, a preprocessing process of the acquired sensor data is proposed, and the specific steps are as follows:

[0060] 1) First, obtain sensor data and perform data cleaning, including removing duplicate data, processing missing values, and removing noise:

[0061]

[0062] Where x is the horizontal coordinate of the pixel, y is the vertical coordinate of the pixel, G(x,y) is the new pixel value, and σ is the standard deviation of the image;

[0063] 2) Perform z-score standardization on the data to convert it to the same range or scale to eliminate the dimensional differences between different features:

[0064]

[0065] Where Z is the standard score, X is the raw data obtained by the sensor, E[X] is the average value of X, and σ(X) is the standard deviation of X;

[0066] 3) Based on the needs of specific tasks, extract features related to the status of the oil and gas pipeline from the raw data, including statistical features, time series features, and features based on signal processing after Fourier transform:

[0067]

[0068] Among them, F(s) is a complex function that transforms the function in the time domain into the complex frequency domain, e -st is the convergence factor, s = σ + jω is the complex frequency, where the real part σ is always positive and the imaginary part jω can be positive, negative or zero;

[0069] 4) For image or time series data, data augmentation can be performed through rotation, translation, scaling, cropping, etc. to increase the diversity of the dataset and the generalization ability of the model;

[0070] 5) For outliers that deviate significantly from the normal range, you can choose to treat them as noise and remove them, or treat them as special cases and give them special treatment.

[0071] After these steps, the sensor data is cleaned to ensure accuracy and consistency. The data is normalized and standardized to meet the input requirements of the deep learning model, thereby achieving higher model prediction accuracy.

[0072] In one embodiment, this embodiment is based on the above example, such as Figure 3 As shown in the figure, the digital twin model includes a geometric model, a physical model, and a data exchange platform. The deep learning model includes a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory network (LSTM). The specific steps for implementing fault prediction and early warning are as follows:

[0073] 1) Collect sensor data stored in geometric and physical models after preprocessing in the digital twin model, and use deep learning models to automatically extract useful features from the raw data;

[0074] 2) Input the real-time collected data into a trained deep learning model. The deep learning model will predict future states based on the learned patterns, identify potential failure risks, and generate failure warning signals based on the predicted results.

[0075] 3) According to business needs and data distribution, set appropriate warning thresholds based on model performance. When the model's prediction results exceed the preset thresholds, the warning mechanism is triggered:

[0076]

[0077] Where P is the precision, R is the recall, TP is the true positive, FP is the false positive, FN is the false negative, and F1 is the warning threshold, which is the balance point in the precision-recall curve.

[0078] 4) Once the early warning mechanism is triggered, the system generates early warning information and promptly notifies relevant personnel so that appropriate measures can be taken to intervene and avoid the occurrence of the fault or mitigate the impact of the fault;

[0079] 5) Regularly evaluate the model's prediction performance based on accuracy, recall, and F1 score. Based on the evaluation results, adjust the parameters of the deep learning model to improve the accuracy and reliability of fault prediction and early warning.

[0080] 6) The optimized model is continuously deployed in the production environment to provide real-time monitoring and early warning of oil and gas pipelines, and the model is regularly maintained and updated to adapt to changes in pipeline operating status and new failure modes.

[0081] The foregoing description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be readily conceived by a person skilled in the art within the scope of the technical invention disclosed herein are intended to be encompassed within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the scope of protection of the claims.

Claims

1. A deep learning-based oil and gas pipeline monitoring and prediction system, characterized by: It includes sensor detection module, digital twin model, real-time monitoring and anomaly detection module, deep learning model, fault prediction and warning module, and visual user interaction module; The sensor detection module collects data from sensors deployed in oil and gas pipelines, including pressure data, temperature data, flow data, vibration data, chemical composition data, position and posture data, and infrared image data; The infrared image data needs to be preprocessed to improve the prediction quality of the deep learning model; The real-time monitoring and anomaly detection module performs real-time analysis on the data collected in real time to detect whether there are any anomalies; The deep learning model includes three deep learning networks: convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory network (LSTM). By training on a large amount of historical data, it learns the normal operation patterns of the pipeline and the characteristics of potential problems. The fault prediction and warning module is equipped with an empirical deep learning model with historical data learning to predict the type and time of possible pipeline faults; The visualization user interaction module includes a three-dimensional visualization and statistical visualization module, which provides users with an intuitive and easy-to-understand data display method through three-dimensional visualization and statistical visualization technology; The specific steps of the pretreatment are as follows: a. First, obtain sensor data and perform data cleaning, including removing duplicate data, processing missing values, and removing noise. For infrared image data, a two-dimensional Gaussian filter is used for convolution to eliminate image brightness noise caused by the confined space within the oil and gas pipeline: Where x is the horizontal coordinate of the pixel, y is the vertical coordinate of the pixel, G(x,y) is the new pixel value, and σ is the standard deviation of the image; b. Perform z-score standardization on the data to convert it to the same scale to eliminate the dimensional differences between different features: Where Z is the standard score, X is the raw data obtained by the sensor, E[X] is the average value of X, and σ(X) is the standard deviation of X; c. Based on the needs of the specific task, extract features related to the oil and gas pipeline status from the raw data, including statistical features, time series features, and features based on signal processing after Laplace transform: Among them, F(s) is a complex function that transforms the function in the time domain into the complex frequency domain, e -st is the convergence factor, s=σ+jω is the complex frequency, where the real part σ is always positive and the imaginary part jω can be positive, negative or zero; d. For image or time series data, perform data augmentation through rotation, translation, scaling, and cropping to increase the diversity of the dataset and the generalization ability of the model; e. For outliers that deviate significantly from the normal range, they are considered as noise and removed.

2. The oil and gas pipeline monitoring and prediction system based on deep learning according to claim 1 is characterized in that: The digital twin model includes a geometric model, a physical model and a data exchange platform; The geometric model is combined with the oil and gas pipeline construction drawings and scanned point cloud data, and BIM technology is used to accurately model the geometric shape of the oil and gas pipeline in three dimensions; The physical model stores various physical parameters acquired by the sensor, including pressure, temperature, flow, vibration information, chemical composition, position and posture, and infrared images; The data exchange platform synchronizes, interacts, and updates the geometric model and the physical model based on the data acquired by the sensor detection module, ensuring that the status and information between the two can be kept consistent in real time: Among them, M{DT} represents the overall framework of the digital twin model with five key dimensions, VM is the geometric model, PM is the physical model, DEP is the data exchange platform, DD is the sensor twin data, and CN is the connection between the various parts.

3. The oil and gas pipeline monitoring and prediction system based on deep learning according to claim 1 is characterized in that: The specific steps of the fault prediction and early warning module are as follows: a. Collect sensor data from the digital twin model that has been pre-processed and stored in the geometric and physical models, and use deep learning models to automatically extract features from the raw data; b. Input the real-time collected data into a trained deep learning model. The deep learning model will predict future conditions based on the learned patterns, identify potential failure risks, and generate a failure warning signal based on the predicted results. c. According to business needs and data distribution, set appropriate warning thresholds based on model performance. When the model's prediction results exceed the preset thresholds, the warning mechanism is triggered: Where P is the precision, R is the recall, TP is the true positive, FP is the false positive, FN is the false negative, and F1 is the warning threshold, which is the balance point in the precision-recall curve. d. Once the early warning mechanism is triggered, the system generates early warning information and promptly notifies relevant personnel so that appropriate intervention measures can be taken to avoid the occurrence of the fault or mitigate the impact of the fault; e. Regularly evaluate the model's prediction performance based on accuracy, recall, and F1 score. Based on the evaluation results, adjust the parameters of the deep learning model to improve the accuracy and reliability of fault prediction and early warning. f. Continuously deploy the optimized model in the production environment to provide real-time monitoring and early warning of oil and gas pipelines. Regularly maintain and update the model to adapt to changes in pipeline operating status and new failure modes.

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