Emergency parking intelligent regulation and control technology

Abstract

The invention relates to the field of oil and gas field production, in particular to an emergency shutdown intelligent regulation and control technology. The technical problems that in the prior art, an oil and gas field production emergency shutdown regulation and control technology is difficult to obtain and process production data in real time, the timeliness and accuracy of emergency shutdown are affected, faults which do not occur are difficult to predict, part of data under part of complex working conditions cannot be accurately recognized and processed, and the reliability of emergency shutdown is affected are solved. And false alarm or missing alarm of the system is caused. According to the technical scheme, a data acquisition module, a data preprocessing module, a model construction module, a data analysis module and a control module are included in the emergency parking intelligent regulation and control technology; according to the method, the original data is projected to the low-dimensional space through orthogonal transformation, the main features in the data are extracted, and in fault detection, the distribution difference of normal data and fault data in the low-dimensional space can be identified through PCA, so that the fault is detected and early warned in time, and the timeliness and accuracy of the emergency stop regulation and control technology are improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field production, and in particular to intelligent control technology for emergency shutdown. Background Technology

[0002] Emergency shutdown control technology for oil and gas field production is an important safety protection measure, designed to quickly shut down production processes in emergencies to protect production equipment, personnel, and the environment from damage. This technology integrates a Safety Instrumented System (SIS) and an Emergency Shutdown System (ESD) to achieve real-time monitoring and rapid response of the production process. When the system detects abnormalities such as overpressure, overtemperature, leakage, or fire, the emergency shutdown system will automatically or manually trigger, executing preset shutdown logic to cut off the power, gas, or material supply to the relevant equipment or process, thereby preventing the accident from escalating. However, existing emergency shutdown control technologies for oil and gas field production suffer from data transmission delays and difficulties in real-time acquisition and processing of production data due to equipment failure, affecting the timeliness and accuracy of emergency shutdowns. Furthermore, there are limitations in the breadth and depth of data analysis, making it difficult to predict failures that have not yet occurred. Data from some complex operating conditions may not be accurately identified and processed, leading to false alarms or missed alarms. Summary of the Invention

[0003] To overcome the limitations of existing emergency shutdown control technologies in oil and gas field production, such as data transmission delays and difficulties in real-time acquisition and processing of production data due to equipment failures, which affect the timeliness and accuracy of emergency shutdowns, and the limitations in the breadth and depth of data analysis, making it difficult to predict failures that have not yet occurred, and the inability to accurately identify and process data under some complex operating conditions, leading to false alarms or missed alarms in the system.

[0004] The technical solution of this invention is: intelligent emergency braking control technology, comprising: The data acquisition module is used to collect various parameter data in the oil and gas field production process in real time using multiple sets of sensing devices; The data preprocessing module is used to preprocess the data collected by the data acquisition module. It uses data cleaning, noise reduction and normalization to preprocess the data and standardize the data. The model building module is used to perform dimensionality reduction on preprocessed data using the PCA algorithm, build a PCA model, extract the main feature variables from the data, and calculate the statistics of these variables. The data analysis module is used to project real-time collected data into a low-dimensional space constructed by the PCA model, calculate the statistics of the projected data, and compare them with the statistics of normal data to determine whether the current data belongs to faulty data. The control module is used to trigger an emergency stop command when fault data or potential fault data is detected. This command cuts off the power or gas supply to the relevant equipment via automated control devices to prevent the fault from escalating and accidents from occurring.

[0005] Preferably, the data acquisition module uses multiple sets of sensors to collect various parameter data in real time during the oil and gas field production process; the data preprocessing module preprocesses the data collected by the data acquisition module by performing data cleaning, noise reduction, and normalization, and standardizing the data; the model building module uses the PCA algorithm to reduce the dimensionality of the preprocessed data, builds a PCA model, extracts the main feature variables in the data, and calculates the statistics of these variables; the data analysis module projects the real-time collected data into the low-dimensional space constructed by the PCA model, calculates the statistics of the projected data, and compares them with the statistics of normal data to determine whether the current data belongs to fault data; when the control module detects fault data or detects the possible existence of fault data, it triggers an emergency shutdown command, and cuts off the power or gas supply of relevant equipment through automated control equipment to prevent the fault from escalating and accidents from occurring.

[0006] Preferably, the data acquisition module includes sensor devices and a data transmission unit. The sensor devices are installed at key locations in the oil and gas field to monitor parameters that need to be monitored, and the data transmission unit is used to transmit the parameter data collected by the sensor devices to the data preprocessing module using IoT Zigbee technology.

[0007] Preferably, the data preprocessing module identifies and deletes or corrects erroneous data, such as values ​​that deviate significantly from the normal range. It removes or corrects errors, outliers, and duplicate records in the data through data cleaning to ensure the accuracy and consistency of the data. It reduces random errors and noise in the data through data denoising to improve the signal-to-noise ratio of the data. It transforms the data to the same scale through normalization to eliminate the influence of different units on the analysis results.

[0008] Preferably, the data preprocessing module includes the following steps during operation: S101: Check data integrity, identify and handle missing values, and fill in missing values ​​using methods such as mean, median, mode or interpolation; S102: Identify and delete or correct erroneous data, set thresholds, and delete or correct data that deviates significantly from the normal range; S103: Use unique identifiers and data content comparison to identify and delete duplicate records; S104: Select a time series denoising technique and use a time series decomposition method to remove noise; S106: The Z-score standardization method is selected for normalization, so that the mean of each feature variable is 0 and the variance is 1; the standardization formula is as follows: ; Where X is the original data, μ is the mean, and σ is the standard deviation.

[0009] As a preferred approach, the model building module performs dimensionality reduction on the preprocessed data, calculates the covariance matrix of the standardized data to reflect the correlation between variables, performs eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors, constructs a PCA model, extracts the main feature variables in the data, and calculates the statistics of these variables.

[0010] Preferably, the model building module includes the following steps when it is working: S201: Given m feature variables and n samples, organize the data into an n×m matrix X; S202: Calculate the covariance matrix Σ according to the formula; the calculation formula is as follows: ;in It is the transpose of X, and the covariance matrix is ​​an m×m symmetric matrix that reflects the correlation between the feature variables. S203: Perform eigenvalue decomposition on the covariance matrix Σ to solve for its eigenvalues ​​and corresponding eigenvectors; the eigenvalues ​​represent the importance of each principal component, i.e., the variance contribution rate, and the eigenvectors are the directions of each principal component; S204: Select the top k principal components based on the magnitude of the eigenvalues ​​using the cumulative contribution rate method, that is, select the top k principal components so that their cumulative variance contribution rate reaches a certain threshold. S205: Arrange the selected k eigenvectors by column to form the principal component matrix P; each column of the principal component matrix is ​​the direction vector of a principal component.

[0011] As a preferred approach, the data analysis module projects the real-time collected data into a low-dimensional space constructed by the PCA model. The main features of the data are preserved, while noise and redundant information are effectively removed. The projected data is then used to calculate statistics such as mean, variance, and covariance. These statistics reflect the distribution characteristics and trends of the data and are compared with the statistics of normal data to determine whether the current data belongs to faulty data.

[0012] Preferably, the data analysis module includes the following steps when it is working: S301: Project the original data X onto the principal component matrix P to obtain the dimension-reduced data T=XP; S302: Calculate statistics using the dimension-reduced data T; S303: Calculate the T² statistic of the dimensionality-reduced data T, which reflects the overall change of the data in the principal component space; the calculation formula is T² = T'T / (n-1), where n is the sample size; S304: Calculate the SPE statistic, which reflects the variation of the data in the residual space, i.e., the part not explained by the principal components; the formula is SPE = e'e, where e is the residual vector, e = X - PT; S305: Based on historical normal data, calculate the control limits, i.e., thresholds, for the T² statistic and SPE statistic respectively; the control limits are determined by setting a certain confidence level. S306: Calculate the T² and SPE statistics of the current data in real time and compare them with the control limits; if either statistic exceeds its control limit, the system is considered to be in danger of failure.

[0013] Preferably, the production data is dimensionality reduced by PCA method to extract the main feature variables, and combined with iterative PCA method to monitor and diagnose multiple faults in the production process in real time.

[0014] As a preferred embodiment, after detecting a fault, the control module confirms the authenticity of the fault signal, eliminates possible false alarms, analyzes the fault characteristics, uses methods such as contribution plots to find the variables or principal components that contribute the most to the fault, and combines the results of the fault characteristics and contribution plot analysis to determine the type of fault and possible causes; after confirming the fault, it immediately initiates the emergency shutdown procedure to shut down or isolate the faulty equipment to prevent the fault from escalating further.

[0015] Preferably, the control module includes the following steps during operation: S401: Receives fault detection signals; S402: Verify the authenticity of the fault signal by comparing historical data and system logs to eliminate possible false alarms; S403: If the fault signal is confirmed to be genuine, proceed with the fault identification and diagnosis process; if it is a false alarm, record it and notify the relevant personnel. S404: Collect data before and after the fault occurred, including real-time data, historical data, and possible system logs; S405: Analyze the time series characteristics of fault data, such as trends, periodicity, and abrupt changes; S406: Convert the fault data to the frequency domain, analyze the distribution and changes of each frequency component, and identify the characteristic frequency of the fault. S407: Extract fault features from the analysis results, such as amplitude changes at specific frequencies and abnormal patterns in time series. S408: Based on the PCA model, calculate the contribution of each variable or principal component to the T² statistic or SPE statistic; S409: Display the contribution in a graphical form, using bar charts or pie charts to represent the contribution ratio of each variable or principal component; S410: Based on the contribution plot, identify the variable or principal component that contributes the most to the fault; S411: Match the extracted fault features with a known fault mode library to identify the type of fault; S412: Combining fault type, contribution graph analysis, and system knowledge, infer the possible causes of the fault; S413: Compile the fault type, cause, and analysis process into a diagnostic report; S414: Confirm the fault and diagnose its potential hazard, and immediately issue an emergency stop command for the relevant equipment or system; S415: As instructed, shut down or isolate the faulty equipment, cut off the relevant power supply, and ensure that the fault does not escalate further; S416: Real-time monitoring of the parking process to ensure the safe stopping of all equipment and to record key parameters and statuses during the parking process.

[0016] Preferably, an automatic response mechanism can automatically trigger an emergency stop command or take other emergency measures when a fault is detected.

[0017] The beneficial effects of this invention are: 1. Compared to existing emergency shutdown control technologies for oil and gas field production, which suffer from data transmission delays and difficulties in real-time acquisition and processing of production data due to equipment failures, affecting the timeliness and accuracy of emergency shutdowns, this invention addresses these limitations. It also lags behind in the breadth and depth of data analysis, making it difficult to predict failures that have not yet occurred. Furthermore, data from some complex operating conditions may not be accurately identified and processed, leading to false alarms or missed alarms. This invention projects the original data into a low-dimensional space through orthogonal transformation, extracting the main features of the data. In fault detection, PCA can identify the distribution differences between normal and fault data in the low-dimensional space, thereby enabling timely fault detection and early warning, and improving the timeliness and accuracy of emergency shutdown control technology. 2. Given m feature variables and n samples, organize the data into an n×m matrix X; calculate the covariance matrix Σ; perform eigenvalue decomposition on the covariance matrix Σ to solve for its eigenvalues ​​and corresponding eigenvectors; the eigenvalues ​​represent the importance of each principal component, i.e., the variance contribution rate, and the eigenvectors are the directions of each principal component; select the top k principal components based on the magnitude of the eigenvalues ​​using the cumulative contribution rate method, i.e., select the top k principal components such that their cumulative variance contribution rate reaches a certain threshold; arrange the selected k eigenvectors column-wise to form the principal component matrix P; each column of the principal component matrix is ​​the direction vector of a principal component; project the original data X onto the principal component matrix P to obtain the dimension-reduced data T=XP; calculate statistics using the dimension-reduced data T; calculate the T² statistic of the dimension-reduced data T, which reflects the overall change of the data in the principal component space; the calculation formula is T² = T'T / (n-1), where n is the sample size; calculate the SPE statistic, which reflects the variation of the data in the residual space, i.e., the part not explained by the principal components; the calculation formula is SPE = e'e, where e is the residual vector, e = X - PT; calculate the control limits (thresholds) of the T² statistic and SPE statistic based on historical normal data; the control limits are determined by setting a certain confidence level; calculate the T² statistic and SPE statistic of the current data in real time and compare them with the control limits; if either statistic exceeds its control limit, the system is considered to have a possible fault; thus, timely fault detection and early warning of possible faults are achieved. 3. Verify the authenticity of the fault signal by comparing historical data and system logs to eliminate possible false alarms; if the fault signal is confirmed to be genuine, proceed with the fault identification and diagnosis process; if it is a false alarm, record and notify relevant personnel; collect data before and after the fault occurs, including real-time data, historical data, and possible system logs; analyze the time series characteristics of the fault data, such as trends, periodicity, and abrupt changes; convert the fault data to the frequency domain and analyze the distribution and changes of each frequency component to identify the characteristic frequencies of the fault; extract fault features from the analysis results, such as amplitude changes at specific frequencies and abnormal patterns in the time series; calculate the contribution of each variable or principal component to the T² statistic or SPE statistic based on the PCA model; display the contribution graphically, using bar charts or pie charts to represent each variable or principal component. The system analyzes the contribution ratio of principal components; identifies the variables or principal components that contribute the most to the fault based on the contribution plot; matches the extracted fault features with a known fault mode library to identify the fault type; combines fault type, contribution plot analysis, and system knowledge to infer possible causes of the fault; compiles the fault type, cause, and analysis process into a diagnostic report; confirms the fault and diagnoses its potential hazards, and immediately issues an emergency shutdown command to the relevant equipment or system; according to the command, shuts down or isolates the faulty equipment, cuts off the relevant energy supply, and ensures that the fault does not escalate further; monitors the shutdown process in real time to ensure that all equipment stops safely, and records key parameters and statuses during the shutdown process; thus, it achieves timely response to emergency shutdown control during fault handling, reduces false alarms, and improves the timeliness and accuracy of emergency shutdown response. Attached Figure Description

[0018] Figure 1 The diagram shown illustrates the workflow of the intelligent emergency parking control technology system of the present invention. Figure 2 The diagram shows the workflow of the data analysis module for the intelligent emergency parking control technology of this invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Please see Figure 1-2 The present invention provides an embodiment of an intelligent emergency parking control technology, comprising: The data acquisition module is used to collect various parameter data in the oil and gas field production process in real time using multiple sets of sensing devices; The data preprocessing module is used to preprocess the data collected by the data acquisition module. It uses data cleaning, noise reduction and normalization to preprocess the data and standardize the data. The model building module is used to perform dimensionality reduction on preprocessed data using the PCA algorithm, build a PCA model, extract the main feature variables from the data, and calculate the statistics of these variables. The data analysis module is used to project real-time collected data into a low-dimensional space constructed by the PCA model, calculate the statistics of the projected data, and compare them with the statistics of normal data to determine whether the current data belongs to faulty data. The control module is used to trigger an emergency stop command when fault data or potential fault data is detected. This command cuts off the power or gas supply to the relevant equipment via automated control devices to prevent the fault from escalating and accidents from occurring.

[0021] Preferably, the data acquisition module uses multiple sets of sensors to collect various parameter data in real time during the oil and gas field production process; the data preprocessing module preprocesses the data collected by the data acquisition module by performing data cleaning, noise reduction, and normalization, and standardizing the data; the model building module uses the PCA algorithm to reduce the dimensionality of the preprocessed data, builds a PCA model, extracts the main feature variables in the data, and calculates the statistics of these variables; the data analysis module projects the real-time collected data into the low-dimensional space constructed by the PCA model, calculates the statistics of the projected data, and compares them with the statistics of normal data to determine whether the current data belongs to fault data; when the control module detects fault data or detects the possible existence of fault data, it triggers an emergency shutdown command, and cuts off the power or gas supply of relevant equipment through automated control equipment to prevent the fault from escalating and accidents from occurring.

[0022] Preferably, the data acquisition module includes sensor devices and a data transmission unit. The sensor devices are installed at key locations in the oil and gas field to monitor parameters that need to be monitored, and the data transmission unit is used to transmit the parameter data collected by the sensor devices to the data preprocessing module using IoT Zigbee technology.

[0023] Preferably, the data preprocessing module identifies and deletes or corrects erroneous data, such as values ​​that deviate significantly from the normal range. It removes or corrects errors, outliers, and duplicate records in the data through data cleaning to ensure the accuracy and consistency of the data. It reduces random errors and noise in the data through data denoising to improve the signal-to-noise ratio of the data. It transforms the data to the same scale through normalization to eliminate the influence of different units on the analysis results.

[0024] Preferably, the data preprocessing module includes the following steps during operation: S101: Check data integrity, identify and handle missing values, and fill in missing values ​​using methods such as mean, median, mode or interpolation; S102: Identify and delete or correct erroneous data, set thresholds, and delete or correct data that deviates significantly from the normal range; S103: Use unique identifiers and data content comparison to identify and delete duplicate records; S104: Select a time series denoising technique and use a time series decomposition method to remove noise; S106: The Z-score standardization method is selected for normalization, so that the mean of each feature variable is 0 and the variance is 1; the standardization formula is as follows: ; Where X is the original data, μ is the mean, and σ is the standard deviation.

[0025] As a preferred approach, the model building module performs dimensionality reduction on the preprocessed data, calculates the covariance matrix of the standardized data to reflect the correlation between variables, performs eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors, constructs a PCA model, extracts the main feature variables in the data, and calculates the statistics of these variables.

[0026] Preferably, the model building module includes the following steps when it is working: S201: Given m feature variables and n samples, organize the data into an n×m matrix X; S202: Calculate the covariance matrix Σ according to the formula; the calculation formula is as follows: ;in It is the transpose of X, and the covariance matrix is ​​an m×m symmetric matrix that reflects the correlation between the feature variables. S203: Perform eigenvalue decomposition on the covariance matrix Σ to solve for its eigenvalues ​​and corresponding eigenvectors; the eigenvalues ​​represent the importance of each principal component, i.e., the variance contribution rate, and the eigenvectors are the directions of each principal component; S204: Select the top k principal components based on the magnitude of the eigenvalues ​​using the cumulative contribution rate method, that is, select the top k principal components so that their cumulative variance contribution rate reaches a certain threshold. S205: Arrange the selected k eigenvectors by column to form the principal component matrix P; each column of the principal component matrix is ​​the direction vector of a principal component.

[0027] As a preferred approach, the data analysis module projects the real-time collected data into a low-dimensional space constructed by the PCA model. The main features of the data are preserved, while noise and redundant information are effectively removed. The projected data is then used to calculate statistics such as mean, variance, and covariance. These statistics reflect the distribution characteristics and trends of the data and are compared with the statistics of normal data to determine whether the current data belongs to faulty data.

[0028] Preferably, the data analysis module includes the following steps when it is working: S301: Project the original data X onto the principal component matrix P to obtain the dimension-reduced data T=XP; S302: Calculate statistics using the dimension-reduced data T; S303: Calculate the T² statistic of the dimensionality-reduced data T, which reflects the overall change of the data in the principal component space; the calculation formula is T² = T'T / (n-1), where n is the sample size; S304: Calculate the SPE statistic, which reflects the variation of the data in the residual space, i.e., the part not explained by the principal components; the formula is SPE = e'e, where e is the residual vector, e = X - PT; S305: Based on historical normal data, calculate the control limits, i.e., thresholds, for the T² statistic and SPE statistic respectively; the control limits are determined by setting a certain confidence level. S306: Calculate the T² and SPE statistics of the current data in real time and compare them with the control limits; if either statistic exceeds its control limit, the system is considered to be in danger of failure.

[0029] Preferably, the production data is dimensionality reduced by PCA method to extract the main feature variables, and combined with iterative PCA method to monitor and diagnose multiple faults in the production process in real time.

[0030] As a preferred embodiment, after detecting a fault, the control module confirms the authenticity of the fault signal, eliminates possible false alarms, analyzes the fault characteristics, uses methods such as contribution plots to find the variables or principal components that contribute the most to the fault, and combines the results of the fault characteristics and contribution plot analysis to determine the type of fault and possible causes; after confirming the fault, it immediately initiates the emergency shutdown procedure to shut down or isolate the faulty equipment to prevent the fault from escalating further.

[0031] Preferably, the control module includes the following steps during operation: S401: Receives fault detection signals; S402: Verify the authenticity of the fault signal by comparing historical data and system logs to eliminate possible false alarms; S403: If the fault signal is confirmed to be genuine, proceed with the fault identification and diagnosis process; if it is a false alarm, record it and notify the relevant personnel. S404: Collect data before and after the fault occurred, including real-time data, historical data, and possible system logs; S405: Analyze the time series characteristics of fault data, such as trends, periodicity, and abrupt changes; S406: Convert the fault data to the frequency domain, analyze the distribution and changes of each frequency component, and identify the characteristic frequency of the fault. S407: Extract fault features from the analysis results, such as amplitude changes at specific frequencies and abnormal patterns in time series. S408: Based on the PCA model, calculate the contribution of each variable or principal component to the T² statistic or SPE statistic; S409: Display the contribution in a graphical form, using bar charts or pie charts to represent the contribution ratio of each variable or principal component; S410: Based on the contribution plot, identify the variable or principal component that contributes the most to the fault; S411: Match the extracted fault features with a known fault mode library to identify the type of fault; S412: Combining fault type, contribution graph analysis, and system knowledge, infer the possible causes of the fault; S413: Compile the fault type, cause, and analysis process into a diagnostic report; S414: Confirm the fault and diagnose its potential hazard, and immediately issue an emergency stop command for the relevant equipment or system; S415: As instructed, shut down or isolate the faulty equipment, cut off the relevant power supply, and ensure that the fault does not escalate further; S416: Real-time monitoring of the parking process to ensure the safe stopping of all equipment and to record key parameters and statuses during the parking process.

[0032] Preferably, an automatic response mechanism can automatically trigger an emergency stop command or take other emergency measures when a fault is detected.

[0033] Example 1 Optionally, during data preprocessing and model building, real-time data of multiple characteristic variables, including well pressure (MPa), wellhead temperature (°C), crude oil flow rate (m³ / h), and gas component concentration (e.g., methane %vol, carbon dioxide %vol), are collected from various monitoring points in the oil and gas field. The collected data is cleaned to remove outliers and missing values ​​caused by sensor malfunctions or transmission errors. The cleaned data is then standardized to ensure uniformity in the data range of each characteristic variable, facilitating subsequent analysis. Assuming data is collected at n time points, each containing observations of m characteristic variables, an n×m data matrix X is formed. The covariance matrix Σ of the data matrix X is calculated, reflecting the linear correlation between the characteristic variables. Eigenvalue decomposition is performed on the covariance matrix Σ to obtain eigenvalues ​​and corresponding eigenvectors. The magnitude of the eigenvalues ​​represents the explanatory power of each principal component for the variation in the original data. The top k principal components with a cumulative contribution rate reaching a certain threshold (e.g., 85%) are selected to form the principal component matrix P. These principal components represent the main directions of variation in the data and are mutually independent.

[0034] Example 2 Optionally, when projecting data, a PCA model is constructed based on historical data to obtain the principal component matrix P. The preprocessed real-time data X is then projected onto the principal component matrix P to obtain the dimensionality-reduced data T. For example, for the current data of oil well A, after PCA projection, the dimensionality-reduced data T is obtained, which contains the main feature information of the data. Example 3 Optionally, when performing statistical analysis, the T² statistic is calculated to reflect the overall change of the data in the principal component space; the SPE statistic is calculated to reflect the change of the data in the residual space; control limits for the T² and SPE statistics are set based on historical normal data, and the T² and SPE statistics of the current data are compared with the control limits to obtain the following data:

[0035] In the above example, the SPE statistic exceeded its control limit (1.0), while the T² statistic remained within the normal range. This indicates that there are abnormal changes in the residual space of the current data, which may be caused by some unknown factors, such as equipment failure, improper operation, or changes in the external environment.

[0036] Example 4 Optionally, during fault detection, identification, and emergency shutdown control in oil and gas field production, the control module receives a fault detection signal from the data analysis module, indicating that well B may have a fault. By comparing the historical data and system log of well B, the control module finds that the current data anomaly pattern is similar to the previous fault records, and there are no records of related maintenance activities or external interference in the system log. Therefore, the fault signal is verified to be true. After confirming that the fault signal is true, the control module enters the fault identification and diagnosis process. The control module collects data before and after the well B failure, including real-time data such as wellhead pressure, temperature, flow rate, and gas composition, as well as historical data and system logs over a period of time. Analyzing the time-series characteristics of the failure data reveals that the wellhead pressure gradually decreased before the failure, accompanied by temperature fluctuations, indicating a possible leak or blockage. Converting the failure data to the frequency domain for analysis reveals a significant increase in amplitude at specific frequency ranges, which may be related to mechanical vibration or abnormal fluid flow. Failure characteristics are extracted from the analysis results, such as the rate of wellhead pressure decrease and amplitude changes at specific frequencies. Based on the PCA model, the contribution of each variable (such as wellhead pressure, temperature, flow rate, etc.) to the T² statistic or SPE statistic is calculated; the contribution is displayed in the form of a bar chart, and it is found that the contribution of wellhead pressure is the highest, indicating that its change has the greatest impact on the fault; based on the contribution chart, wellhead pressure is identified as the variable that contributes the most to the fault. The extracted fault features were matched with a known fault mode library and found to be highly similar to the fault mode of "oil well leakage". Combining fault type, contribution graph analysis and system knowledge, it was inferred that oil well B may have leaked, possibly due to seal failure or pipeline corrosion. The control module compiles the fault type (oil well leakage), cause (seal failure or pipeline corrosion), and analysis process into a diagnostic report for operator reference; confirms the fault and diagnoses its potential hazards (such as environmental pollution or safety risks), and immediately issues an emergency shutdown command to oil well B and its related systems; according to the command, it closes the oil production valves of oil well B, isolates the faulty equipment, and cuts off the relevant energy supply (such as electricity and hydraulic power) to ensure that the fault does not escalate further; it monitors the shutdown process in real time to ensure that all equipment stops safely, and records key parameters (such as pressure, temperature, flow rate, etc.) and status changes during the shutdown process to provide data support for subsequent fault handling.

[0037] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. Emergency braking intelligent control technology; characterized by: Including: The data acquisition module is used to collect various parameter data in the oil and gas field production process in real time using multiple sets of sensing devices; The data preprocessing module is used to preprocess the data collected by the data acquisition module. It uses data cleaning, noise reduction and normalization to preprocess the data and standardize the data. The model building module is used to perform dimensionality reduction on preprocessed data using the PCA algorithm, build a PCA model, extract the main feature variables from the data, and calculate the statistics of these variables. The data analysis module is used to project real-time collected data into a low-dimensional space constructed by the PCA model, calculate the statistics of the projected data, and compare them with the statistics of normal data to determine whether the current data belongs to faulty data. The control module is used to trigger an emergency stop command when fault data or potential fault data is detected. This command cuts off the power or gas supply to the relevant equipment via automated control devices to prevent the fault from escalating and accidents from occurring.

2. The emergency parking intelligent control technology according to claim 1, characterized in that: The data acquisition module includes sensor devices and a data transmission unit. The sensor devices are installed at key locations in the oil and gas field to monitor the parameters that need to be monitored. The data transmission unit is used to transmit the parameter data collected by the sensor devices to the data preprocessing module using IoT Zigbee technology.

3. The emergency parking intelligent control technology according to claim 2, characterized in that: The data preprocessing module identifies and deletes or corrects erroneous data, such as values ​​that deviate significantly from the normal range. It removes or corrects errors, outliers, and duplicate records in the data through data cleaning to ensure the accuracy and consistency of the data. It reduces random errors and noise in the data through data denoising to improve the signal-to-noise ratio of the data. It transforms the data to the same scale through normalization to eliminate the influence of different units on the analysis results.

4. The emergency parking intelligent control technology according to claim 3, characterized in that: The data preprocessing module includes the following steps during operation: S101: Check data integrity, identify and handle missing values, and fill in missing values ​​using methods such as mean, median, mode or interpolation; S102: Identify and delete or correct erroneous data, set thresholds, and delete or correct data that deviates significantly from the normal range; S103: Use unique identifiers and data content comparison to identify and delete duplicate records; S104: Select a time series denoising technique and use a time series decomposition method to remove noise; S106: The Z-score standardization method is selected for normalization, so that the mean of each feature variable is 0 and the variance is 1; the standardization formula is as follows: ； Where X is the original data, μ is the mean, and σ is the standard deviation.

5. The emergency parking intelligent control technology according to claim 4, characterized in that: The model building module performs dimensionality reduction on the preprocessed data, calculates the covariance matrix of the standardized data to reflect the correlation between variables, performs eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors, constructs a PCA model, extracts the main feature variables in the data, and calculates the statistics of these variables.

6. The emergency parking intelligent control technology according to claim 5, characterized in that: When the model building module is working, it includes the following steps: S201: Given m feature variables and n samples, organize the data into an n×m matrix X; S202: Calculate the covariance matrix Σ according to the formula; the calculation formula is as follows: ;in It is the transpose of X, and the covariance matrix is ​​an m×m symmetric matrix that reflects the correlation between the feature variables. S203: Perform eigenvalue decomposition on the covariance matrix Σ to solve for its eigenvalues ​​and corresponding eigenvectors; the eigenvalues ​​represent the importance of each principal component, i.e., the variance contribution rate, and the eigenvectors are the directions of each principal component; S204: Select the top k principal components based on the magnitude of the eigenvalues ​​using the cumulative contribution rate method, that is, select the top k principal components so that their cumulative variance contribution rate reaches a certain threshold. S205: Arrange the selected k eigenvectors by column to form the principal component matrix P; each column of the principal component matrix is ​​the direction vector of a principal component.

7. The emergency parking intelligent control technology according to claim 6, characterized in that: The data analysis module projects the real-time collected data into a low-dimensional space constructed by the PCA model. The main features of the data are preserved, while noise and redundant information are effectively removed. The projected data is then used to calculate statistics such as mean, variance, and covariance. These statistics reflect the distribution characteristics and trends of the data and are compared with the statistics of normal data to determine whether the current data belongs to faulty data.

8. The emergency parking intelligent control technology according to claim 7, characterized in that: When the data analysis module is working, it includes the following steps: S301: Project the original data X onto the principal component matrix P to obtain the dimension-reduced data T=XP; S302: Calculate statistics using the dimension-reduced data T; S303: Calculate the T² statistic of the dimensionality-reduced data T, which reflects the overall change of the data in the principal component space; the calculation formula is T² = T'T / (n-1), where n is the sample size; S304: Calculate the SPE statistic, which reflects the variation of the data in the residual space, i.e., the part not explained by the principal components; the formula is SPE = e'e, where e is the residual vector, e = X - PT; S305: Based on historical normal data, calculate the control limits, i.e., thresholds, for the T² statistic and SPE statistic respectively; the control limits are determined by setting a certain confidence level. S306: Calculate the T² and SPE statistics of the current data in real time and compare them with the control limits; if either statistic exceeds its control limit, the system is considered to be in danger of failure.

9. The emergency parking intelligent control technology according to claim 8, characterized in that: After detecting a fault, the control module confirms the authenticity of the fault signal, eliminates possible false alarms, analyzes the fault characteristics, uses methods such as contribution plots to find the variables or principal components that contribute the most to the fault, and combines the results of the fault characteristics and contribution plot analysis to determine the type of fault and possible causes. Once the fault is confirmed, immediately initiate the emergency shutdown procedure, shut down or isolate the faulty equipment, and prevent the fault from escalating further.

10. The emergency parking intelligent control technology according to claim 9, characterized in that: When the control module is working, it includes the following steps: S401: Receives fault detection signals; S402: Verify the authenticity of the fault signal by comparing historical data and system logs to eliminate possible false alarms; S403: If the fault signal is confirmed to be genuine, proceed with the fault identification and diagnosis process; if it is a false alarm, record it and notify the relevant personnel. S404: Collect data before and after the fault occurred, including real-time data, historical data, and possible system logs; S405: Analyze the time series characteristics of fault data, such as trends, periodicity, and abrupt changes; S406: Convert the fault data to the frequency domain, analyze the distribution and changes of each frequency component, and identify the characteristic frequency of the fault. S407: Extract fault features from the analysis results, such as amplitude changes at specific frequencies and abnormal patterns in time series. S408: Based on the PCA model, calculate the contribution of each variable or principal component to the T² statistic or SPE statistic; S409: Display the contribution in a graphical form, using bar charts or pie charts to represent the contribution ratio of each variable or principal component; S410: Based on the contribution plot, identify the variable or principal component that contributes the most to the fault; S411: Match the extracted fault features with a known fault mode library to identify the type of fault; S412: Combining fault type, contribution graph analysis, and system knowledge, infer the possible causes of the fault; S413: Compile the fault type, cause, and analysis process into a diagnostic report; S414: Confirm the fault and diagnose its potential hazard, and immediately issue an emergency stop command for the relevant equipment or system; S415: As instructed, shut down or isolate the faulty equipment, cut off the relevant power supply, and ensure that the fault does not escalate further; S416: Real-time monitoring of the parking process to ensure the safe stopping of all equipment and to record key parameters and statuses during the parking process.

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