A Smart Fault Diagnosis Method for Offshore Oil and Gas Fields Based on Multiple Validation Analysis

Abstract

This invention discloses a multi-validation analysis-based intelligent fault diagnosis method for offshore oil and gas fields. This method mainly includes six steps: data acquisition and preprocessing, matrix causal graph analysis, knowledge graph analysis, wavelet analysis, multi-validation analysis, and processing scheme generation. The multi-validation analysis-based intelligent fault diagnosis method for offshore oil and gas fields provided by this invention comprehensively utilizes multiple analysis techniques. Through multi-validation analysis, it conducts a comprehensive and in-depth analysis and judgment of the causes of faults, avoiding the limitations of single analysis methods and greatly improving the accuracy of fault diagnosis. Matrix causal graph analysis and knowledge graph analysis can quickly narrow down the scope of fault investigation, wavelet analysis can accurately detect the time and location of fault occurrence, and multi-validation analysis can quickly determine the final cause of the fault, thereby greatly shortening the fault investigation and processing time, improving production efficiency, and exhibiting strong adaptability.

Description

Technical Field

[0001] This invention belongs to the field of intelligent production technology for offshore oil and gas fields, and particularly relates to an intelligent fault diagnosis method for offshore oil and gas fields using multiple verification analyses. Background Technology

[0002] Offshore oil and gas production systems are large and complex systems, and their stable operation is crucial to ensuring oil and gas production. However, offshore oil and gas fields currently face many challenges in the production process.

[0003] On the one hand, the existing troubleshooting process is cumbersome, like walking through a maze, requiring one to check for possible problems one by one, which is time-consuming and labor-intensive. Moreover, sometimes after the cause of the fault is found, the lack of spare parts will lead to a further extension of the downtime, which will seriously affect the overall output of the platform.

[0004] On the other hand, while domestic and international manufacturers currently possess some specialized software for fault diagnosis and health management, most of these are geared towards a single professional system and lack comprehensive fault diagnosis and health management for the entire offshore oil and gas production process. Furthermore, there is a lack of specialized analysis software specifically for offshore oil and gas production systems, failing to meet the actual needs of intelligent production in offshore oil and gas fields.

[0005] Therefore, there is an urgent need to design a smart fault diagnosis method for offshore oil and gas fields based on multiple verification analyses to solve the problems mentioned above. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses. This method has the advantages of comprehensive and in-depth analysis and diagnosis of faults in offshore oil and gas field production systems, and rapid identification of fault causes. It solves the problems of cumbersome fault diagnosis processes in the prior art, which cannot meet the actual needs of intelligent production in offshore oil and gas fields.

[0007] To achieve the above objectives, the specific technical solution of the intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses of the present invention is as follows: A smart fault diagnosis method for offshore oil and gas fields using multiple verification analysis mainly includes the following steps: S1. Data Acquisition and Preprocessing: Data is acquired from multiple data sources in the offshore oil and gas field production system, including process control systems, emergency shutdown systems, fire and gas detection systems, electrical equipment management systems, mechanical equipment health management systems, and intelligent inspection systems. The types of data collected include sensor data, equipment status data, and alarm data; The collected raw data is preprocessed, including data cleaning, noise filtering, and normalization, to eliminate noise and outliers in the data and improve the quality and usability of the data. S2. Matrix Cause-and-Effect Graph Analysis: Construct a matrix cause-and-effect graph model of the offshore oil and gas field production system, taking each device and subsystem in the system as nodes and the causal relationships between devices as edges, forming a complex network structure. Determine the strength of the causal relationship between each node and construct a causal relationship matrix; When a system failure occurs, the causal relationship between nodes is analyzed based on real-time collected data, and possible fault source nodes are identified through matrix operations to narrow down the scope of fault investigation. S3. Knowledge Graph Analysis: Construct a knowledge graph for the offshore oil and gas field production system, representing equipment information, fault phenomena, fault causes, and handling methods in the form of graphs to form a structured knowledge base; When the system malfunctions, the real-time collected fault phenomena are matched with nodes in the knowledge graph, and graph reasoning algorithms are used to find possible causes of the fault and corresponding solutions. By combining the results of matrix cause-effect graph analysis, the causes of failures obtained from knowledge graph reasoning are verified and supplemented, thereby improving the accuracy of failure diagnosis. S4. Wavelet analysis: Perform wavelet transform on the collected sensor data to decompose the signal into different frequency scales and extract the time-frequency features of the signal. By analyzing wavelet coefficients at different frequency scales, abnormal features in signals, namely abrupt changes and oscillations, are detected and correlated with system and equipment failures. Based on the results of wavelet analysis, the time and location of the fault can be determined, providing more accurate information for fault diagnosis. S5. Multiple Validation Analysis: Multiple validation analysis is performed based on the results of matrix cause-effect graph analysis, knowledge graph analysis, and wavelet analysis. By setting different weights and thresholds, the causes of failures obtained by various analysis methods are comprehensively evaluated and judged. If multiple analysis methods yield the same cause of failure, then that cause of failure is determined as the final diagnostic result. If the causes of failure obtained by different analysis methods differ, further analysis of the reasons for the differences should be conducted until the final cause of failure is determined. S6. Processing solution generation: Based on the finally determined cause of the failure, extract the corresponding processing methods from the knowledge graph, and generate specific processing solutions and suggestions in combination with the actual situation; The solution includes troubleshooting steps, required spare parts, and safety precautions, providing detailed technical guidance to on-site operators.

[0008] Furthermore, in S1, the data cleaning algorithm is used to remove duplicate values, erroneous values, and unreasonable values ​​from the data; Normalization methods employ linear function normalization or Z-score standardization to unify data of different dimensions to the same scale.

[0009] Furthermore, in S2, when constructing the causal relationship matrix, the strength value of the causal relationship between each node is determined, and the strength value range is set to [0, 1], where 0 represents no causal relationship and 1 represents a strong causal relationship; Matrix operations are performed using Bayesian network inference algorithms or Markov chain Monte Carlo methods to calculate the failure probability of each node.

[0010] Furthermore, in S3, natural language processing technology is used to extract equipment information, fault phenomena, fault causes and handling methods from equipment manuals, operating procedures and historical fault records, and store them in the knowledge graph in the form of triples, namely subject-predicate-object. Graph reasoning algorithms can employ rule-based reasoning algorithms or deep learning-based graph neural network reasoning algorithms.

[0011] Furthermore, in S4, a suitable wavelet basis function is selected to perform wavelet transform on the acquired sensor data; By setting a threshold for wavelet coefficients, abnormal features in the signal can be detected. When the wavelet coefficients exceed the set threshold, it is determined that the signal has abnormal features.

[0012] Furthermore, in S5, the weights of matrix cause-effect graph analysis, knowledge graph analysis, and wavelet analysis are set, and the weight values ​​are allocated according to the accuracy and reliability of each analysis method in historical fault diagnosis. The threshold is set as a critical value for the comprehensive evaluation score. When the comprehensive evaluation score exceeds the threshold, the cause of the failure is determined. The overall evaluation score is calculated by weighting and summing the fault causes obtained from each analysis method.

[0013] Furthermore, in S6, the processing methods extracted from the knowledge graph are adjusted and optimized based on the actual situation on site, including the operating environment of the system and equipment, the current production tasks, and the inventory status of spare parts, to generate specific processing solutions and suggestions.

[0014] The intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses of the present invention has the following advantages: (1) This invention comprehensively utilizes a variety of analysis techniques and uses multiple verification analysis methods to conduct a comprehensive and in-depth analysis and judgment of the cause of the fault, avoiding the limitations of a single analysis method and greatly improving the accuracy of fault diagnosis.

[0015] (2) The present invention utilizes matrix causal graph analysis and knowledge graph analysis to quickly narrow down the scope of fault investigation, wavelet analysis to accurately detect the time and location of fault occurrence, and multiple verification analysis to quickly determine the final cause of fault, thereby greatly shortening the fault investigation and processing time and improving production efficiency.

[0016] (3) Based on knowledge graphs and expert models, this invention can automatically learn and accumulate fault diagnosis experience, continuously optimize the diagnosis algorithm, and improve the level of intelligence. At the same time, it can analyze and predict based on real-time data and historical data to achieve early warning and prevention of faults.

[0017] (4) This invention is applicable to various equipment and subsystems in offshore oil and gas field production systems, and can cope with complex and ever-changing working conditions and numerous interference factors, and has strong adaptability and versatility. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0021] The following is a reference to the appendix. Figure 1 This invention describes a smart fault diagnosis method for offshore oil and gas fields based on multiple verification analyses.

[0022] like Figure 1 As shown, the intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analysis in this invention mainly includes the following steps: S1. Data Acquisition and Preprocessing: Data is collected from multiple data sources in the offshore oil and gas field production system. These data sources include, but are not limited to, PCS (Process Control System), ESD (Emergency Shutdown System), FGS (Fire and Gas Detection System), Electrical Equipment Management System, Mechanical Equipment Health Management System, and Intelligent Inspection System. The types of data collected include sensor data, equipment status data, and alarm data.

[0023] The process involves preprocessing the collected raw data, including data cleaning, noise filtering, and normalization, to eliminate noise and outliers and improve data quality and usability.

[0024] In data acquisition and preprocessing, data cleaning algorithms are used to remove duplicate, erroneous, and unreasonable values ​​from the data; normalization methods, such as linear function normalization or Z-score standardization, unify data of different dimensions to the same scale.

[0025] Preferably, sensors and data acquisition devices are installed in various key equipment and subsystems of the offshore oil and gas field production system to collect real-time operational data and status information. The collected data is transmitted to a data processing center via wired or wireless communication networks.

[0026] In the data processing center, data cleaning algorithms are used to remove noise and outliers from the data, and normalization methods are used to unify data of different dimensions to the same scale for subsequent analysis and processing.

[0027] S2. Matrix Cause-Effect Graph Analysis: Construct a matrix cause-effect graph model of the offshore oil and gas field production system, treating each device and subsystem in the system as nodes and the causal relationships between devices as edges, forming a complex network structure. Specifically, based on historical data and expert experience, the strength of the causal relationship between each node is determined, and a causal relationship matrix is ​​constructed. When a system failure occurs, the changes in the causal relationship between each node are analyzed based on real-time collected data, and possible fault source nodes are identified through matrix operations, thus narrowing down the scope of fault investigation.

[0028] In matrix causal graph analysis, when constructing the causal relationship matrix, the strength value of the causal relationship between each node is determined. The strength value range is set to [0, 1], where 0 represents no causal relationship and 1 represents a strong causal relationship. The matrix operation uses Bayesian network inference algorithm or Markov chain Monte Carlo method to calculate the failure probability of each node.

[0029] Preferably, a matrix causal graph model is constructed based on the technological process and equipment connection relationships of the offshore oil and gas field production system. By inviting domain experts to evaluate and score the causal relationships between equipment, the element values ​​in the causal relationship matrix are determined.

[0030] When a system failure occurs, device data is collected in real time and the causal relationship matrix is ​​updated. The failure probability of each node is calculated through matrix operations, and the node with the higher failure probability is identified as a possible source of failure.

[0031] S3. Knowledge Graph Analysis: Construct a knowledge graph for the offshore oil and gas field production system, representing knowledge such as equipment information, fault phenomena, fault causes, and handling methods in the form of graphs to form a structured knowledge base; When a system malfunctions, the real-time collected fault phenomena are matched with nodes in the knowledge graph. The graph reasoning algorithm is used to find possible causes of the fault and corresponding solutions. The results of matrix causal graph analysis are combined to verify and supplement the fault causes obtained from the knowledge graph reasoning, thereby improving the accuracy of fault diagnosis.

[0032] In knowledge graph analysis, natural language processing technology is used to extract equipment information, fault phenomena, fault causes and handling methods from equipment manuals, operating procedures and historical fault records. These are stored in the knowledge graph in the form of triples, namely subject-predicate-object. The graph reasoning algorithm adopts rule-based reasoning algorithm or deep learning-based graph neural network reasoning algorithm.

[0033] Preferably, a knowledge graph of the offshore oil and gas field production system is constructed using natural language processing and graph database technologies. Equipment information, fault phenomena, causes, and handling methods are extracted from documents such as equipment manuals, operating procedures, and historical fault records, and stored in the knowledge graph in the form of triples.

[0034] When the system malfunctions, the real-time collected fault phenomena are matched with nodes in the knowledge graph, and graph reasoning algorithms are used to find possible causes of the fault and corresponding solutions.

[0035] S4. Wavelet Analysis: Perform wavelet transform on the collected sensor data to decompose the signal into different frequency scales and extract the time-frequency features of the signal; analyze the wavelet coefficients at different frequency scales to detect abnormal features in the signal, such as abrupt changes and oscillations, which are related to system and equipment failures. Based on the results of wavelet analysis, the time and location of the fault can be determined, providing more accurate information for fault diagnosis.

[0036] In wavelet analysis, a suitable wavelet basis function is selected to perform wavelet transform on the collected sensor data; by setting a threshold for the wavelet coefficients, abnormal features in the signal are detected. When the wavelet coefficients exceed the set threshold, it is determined that the signal has abnormal features.

[0037] Preferably, a suitable wavelet basis function is selected to perform wavelet transform on the acquired sensor data, decomposing the signal into different frequency scales. The wavelet coefficients at each frequency scale are analyzed to detect abnormal features in the signal. Based on the time and location of the abnormal features, the time and possible location of the fault are determined.

[0038] S5. Multiple Validation Analysis: The results of matrix cause-effect graph analysis, knowledge graph analysis, and wavelet analysis are combined to perform multiple validation analysis; by setting different weights and thresholds, the causes of failure obtained by each analysis method are comprehensively evaluated and judged. If multiple analysis methods yield the same cause of failure, then that cause of failure is determined as the final diagnostic result. If the causes of failure obtained by different analysis methods differ, further analysis of the reasons for the differences should be conducted, and in-depth investigation should be carried out in combination with expert experience and real-time data until the final cause of failure is determined.

[0039] In the multiple verification analysis, the weights of matrix causal graph analysis, knowledge graph analysis and wavelet analysis are set. The weight values ​​are allocated according to the accuracy and reliability of each analysis method in historical fault diagnosis. The threshold is set as a critical value of the comprehensive evaluation score. When the comprehensive evaluation score exceeds the threshold, the cause of the fault is determined. The comprehensive evaluation score is calculated by weighted summation based on the weights of the fault causes obtained by each analysis method.

[0040] Preferably, weights and thresholds are set for matrix causal graph analysis, knowledge graph analysis, and wavelet analysis. The causes of failure obtained by each analysis method are comprehensively evaluated according to their weights. If the comprehensive evaluation result exceeds the set threshold, the cause of failure is determined as the final diagnosis result; if the comprehensive evaluation result does not exceed the threshold, further analysis of the reasons for the differences is conducted, and in-depth investigation is carried out in combination with expert experience and real-time data until the final cause of failure is determined.

[0041] S6. Processing solution generation: Based on the finally determined cause of the failure, extract the corresponding processing methods from the knowledge graph, and generate specific processing solutions and suggestions in combination with the actual situation; The solution includes troubleshooting steps, required spare parts, and safety precautions, providing detailed technical guidance for on-site operators.

[0042] In generating processing solutions, the processing methods extracted from the knowledge graph are adjusted and optimized based on the actual on-site conditions, including the operating environment of the system and equipment, the current production tasks, and the inventory of spare parts, to generate specific processing solutions and suggestions.

[0043] Preferably, based on the finally determined cause of the fault, corresponding processing methods are extracted from the knowledge graph, and specific processing solutions and suggestions are generated in combination with the actual situation on site. The processing solutions are displayed to the on-site operators through a human-machine interface, and the operators carry out fault repair and processing according to the processing solutions.

[0044] This invention proposes an intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analysis. By comprehensively utilizing matrix causal graph technology, knowledge graph technology, wavelet analysis technology, and multiple verification analysis technology, it conducts a comprehensive and in-depth analysis and diagnosis of faults in offshore oil and gas field production systems. This enables rapid identification of fault causes and provides solutions and suggestions for addressing shutdowns that have already occurred, thereby improving the accuracy and efficiency of fault diagnosis and reducing downtime.

[0045] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent fault diagnosis in offshore oil and gas fields based on multiple verification analyses, characterized in that, The main steps include: S1. Data Acquisition and Preprocessing: Data is acquired from multiple data sources in the offshore oil and gas field production system, including process control systems, emergency shutdown systems, fire and gas detection systems, electrical equipment management systems, mechanical equipment health management systems, and intelligent inspection systems. The types of data collected include sensor data, equipment status data, and alarm data; The collected raw data is preprocessed, including data cleaning, noise filtering, and normalization, to eliminate noise and outliers in the data and improve the quality and usability of the data. S2. Matrix Cause-and-Effect Graph Analysis: Construct a matrix cause-and-effect graph model of the offshore oil and gas field production system, taking each device and subsystem in the system as nodes and the causal relationships between devices as edges, forming a complex network structure. Determine the strength of the causal relationship between each node and construct a causal relationship matrix; When a system failure occurs, the causal relationship between nodes is analyzed based on real-time collected data, and possible fault source nodes are identified through matrix operations to narrow down the scope of fault investigation. S3. Knowledge Graph Analysis: Construct a knowledge graph for the offshore oil and gas field production system, representing equipment information, fault phenomena, fault causes, and handling methods in the form of graphs to form a structured knowledge base; When the system malfunctions, the real-time collected fault phenomena are matched with nodes in the knowledge graph, and graph reasoning algorithms are used to find possible causes of the fault and corresponding solutions. By combining the results of matrix cause-effect graph analysis, the causes of failures obtained from knowledge graph reasoning are verified and supplemented, thereby improving the accuracy of failure diagnosis. S4. Wavelet analysis: Perform wavelet transform on the collected sensor data to decompose the signal into different frequency scales and extract the time-frequency features of the signal. By analyzing wavelet coefficients at different frequency scales, abnormal features in signals, namely abrupt changes and oscillations, are detected and correlated with system and equipment failures. Based on the results of wavelet analysis, the time and location of the fault can be determined, providing more accurate information for fault diagnosis. S5. Multiple Validation Analysis: Multiple validation analysis is performed based on the results of matrix cause-effect graph analysis, knowledge graph analysis, and wavelet analysis. By setting different weights and thresholds, the causes of failures obtained by various analysis methods are comprehensively evaluated and judged. If multiple analysis methods yield the same cause of failure, then that cause of failure is determined as the final diagnostic result. If the causes of failure obtained by different analysis methods differ, further analysis of the reasons for the differences should be conducted until the final cause of failure is determined. S6. Processing solution generation: Based on the finally determined cause of the failure, extract the corresponding processing methods from the knowledge graph, and generate specific processing solutions and suggestions in combination with the actual situation; The solution includes troubleshooting steps, required spare parts, and safety precautions, providing detailed technical guidance to on-site operators.

2. The intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses according to claim 1, characterized in that, In S1, the data cleaning algorithm is used to remove duplicate, erroneous, and unreasonable values ​​from the data; Normalization methods employ linear function normalization or Z-score standardization to unify data of different dimensions to the same scale.

3. The intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses according to claim 1, characterized in that, In S2, when constructing the causal relationship matrix, the strength value of the causal relationship between each node is determined. The strength value range is set to [0, 1], where 0 represents no causal relationship and 1 represents a strong causal relationship. Matrix operations are performed using Bayesian network inference algorithms or Markov chain Monte Carlo methods to calculate the failure probability of each node.

4. The intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses according to claim 1, characterized in that, In S3, natural language processing technology is used to extract equipment information, fault phenomena, fault causes and handling methods from equipment manuals, operating procedures and historical fault records, and store them in the knowledge graph in the form of triples, namely subject-predicate-object. Graph reasoning algorithms can employ rule-based reasoning algorithms or deep learning-based graph neural network reasoning algorithms.

5. The intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses according to claim 1, characterized in that, In S4, a suitable wavelet basis function is selected to perform wavelet transform on the acquired sensor data; By setting a threshold for wavelet coefficients, abnormal features in the signal can be detected. When the wavelet coefficients exceed the set threshold, it is determined that the signal has abnormal features.

6. The intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analysis according to claim 1, characterized in that, In S5, the weights of matrix cause-effect graph analysis, knowledge graph analysis, and wavelet analysis are set, and the weight values ​​are allocated according to the accuracy and reliability of each analysis method in historical fault diagnosis. The threshold is set as a critical value for the comprehensive evaluation score. When the comprehensive evaluation score exceeds the threshold, the cause of the failure is determined. The overall evaluation score is calculated by weighting and summing the fault causes obtained from each analysis method.

7. The intelligent fault diagnosis method for offshore oil and gas fields based on multiple verification analyses according to claim 1, characterized in that, In S6, the processing methods extracted from the knowledge graph are adjusted and optimized based on the actual situation on site, including the operating environment of the system and equipment, the current production tasks, and the inventory status of spare parts, so as to generate specific processing solutions and suggestions.

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