Deep Learning-Based Prediction Method for the Development Trajectory of the Fault Propagation Path of Christmas Trees

Abstract

The present invention belongs to the field of ocean engineering and discloses a method for predicting the development trajectory of the fault propagation path of a Christmas tree based on deep learning, which includes the following steps: determining the key fault modes and dependencies of the subsea Christmas tree, data cleaning and processing, constructing a prediction model for the development trajectory of the fault propagation path based on deep learning, verifying, evaluating and optimizing the prediction model, and visualizing the prediction results of the development trajectory of the fault propagation path. The present invention proposes a new method for predicting the development trajectory of the fault propagation path of key components of a subsea Christmas tree, which can comprehensively and real-time predict the state changes of multiple components, as well as the development trends of the fault propagation path and probability, etc.

Description

Technical Field

[0001] The present invention belongs to the field of ocean engineering and relates to a method for predicting the development trajectory of the fault propagation path of a Christmas tree based on deep learning. Background Art

[0002] The subsea Christmas tree is a key facility of the subsea production system and has been widely used in offshore oil exploitation. Subsea Christmas tree accidents are a worldwide problem threatening the safety of oil and gas exploitation. Due to the increasing complexity of the Christmas tree, there are fault dependencies among components in the system. That is to say, a fault in one unit is very likely to trigger faults in other units, resulting in an exponential escalation of the accident consequences. Fault prediction can predict and give early warnings in advance, enabling maintenance personnel to solve faults targeted, and can effectively avoid the escalation of accidents.

[0003] Currently, there are mainly three methods for fault prediction: model-driven methods, data-driven methods, and hybrid approaches. In practical applications, especially when the sample data is limited or facing a high-dimensional input space, such as the scenario where time series data accumulates continuously, these methods still face many challenges. Fault prediction based on time series analysis aims to construct a model that can reflect the law of data evolution over time and make predictions accordingly. In the prior art, a method for predicting the remaining useful life (RUL) based on hybrid DBN-KF was developed. In the dynamic expansion of DBN, the change of the valve parameters of the subsea Christmas tree is updated by specifying the state transition model. This research has made certain progress in the field of fault prediction based on time series analysis. However, when considering the multi-dimensional superimposed sample data caused by fault dependencies, this method still seems inadequate.

[0004] With the increasing complexity of systems and the development of big data technology, many methods have been proposed by domestic and foreign researchers to improve the fault prediction model to solve the problem of fault propagation time series prediction that integrates fault dependencies. For example, the deep integration of deep learning models and neural network models, specifically including Long Short-Term Memory (LSTM), Feedforward Neural Network (FNN), Graph Neural Network (GNN), etc. Such models are mostly applied to fault propagation scenarios in fields such as road networks or power grids, and can analyze the development trajectory of fault propagation paths in time series. However, due to the high similarity of the system node structure and function, the models often significantly simplify the node characteristics and fault dependency relationships. This simplification method makes these models usually lack universality and generalization ability. Therefore, they are only applicable to specific situations or research objects. When dealing with underwater production systems with complex fault dependencies such as subsea Christmas trees, their applicability is significantly limited and further optimization and improvement are still needed. For underwater production systems with fault dependencies, current researchers mostly focus on the impact of fault dependencies and cascading faults on the system's RUL, and predict the health index and RUL of underwater systems affected by fault dependencies based on BN. For the degradation interaction and parameter uncertainty of subsea Christmas tree components, the "interaction coefficient" is introduced, and a multi-stage RUL prediction model based on DBN is proposed. For the dependence of the hydraulic control system of subsea Christmas trees on internal degradation and external shocks during operation in complex environments, the concept of "dependence factor" is introduced, and a system RUL prediction model that comprehensively considers degradation shock dependence is constructed. For the cascading failure problem of multi-stage underwater transmission systems, a cascading fault modeling and RUL prediction method based on location and functional importance is proposed. Research on the Key Equipment Fault Diagnosis and Preventive Maintenance Method of the Electro-Hydraulic Composite Subsea Christmas Tree System published by Yuan Xiaobing in 2020, China University of Petroleum (East China). Considering the failure modes of multiple modules of the subsea Christmas tree system and the influence of their mutual dependence relationships, fault prediction is carried out on three different modules with mutual dependence of the subsea Christmas tree system based on BNs, and the system RUL is predicted according to the performance degradation index. In addition to the limitation of simplifying the fault dependence relationship, the above research is also limited to a local perspective, only predicting the faults of a single node affected by fault dependencies or evaluating the system RUL affected by fault dependencies, and failing to deeply explore the fault propagation dynamics and its evolution process from a global perspective for fault prediction. Specifically, they rarely comprehensively examine the evolution prediction of the cascading fault propagation process over time, which includes key aspects such as the prediction of the initial fault development trajectory, the path analysis prediction of fault propagation, the prediction of the diffusion range of faults in potentially affected units, and the quantitative analysis prediction of propagation probability.

[0005] The propagation of cascading faults leads to dynamic changes in the status of the entire system and key components, which puts forward comprehensive and real-time requirements for fault prediction models. Most of the current research is still based on the prediction of single-point faults of components considering fault dependence, and the research on the development trajectory of fault propagation paths and dynamic prediction methods under the dominance of time series is still insufficient. Considering the prediction of fault propagation trajectories in the spatial dimension and the prediction of fault propagation trajectories in the time dimension that change with the time series, how to start from a global perspective and follow the inherent laws of the dynamic evolution of multi-level fault propagation scenarios over time to achieve accurate prediction of the development trajectory of underwater oil tree fault propagation paths is an important issue that needs to be solved urgently. Summary of the invention

[0006] In order to overcome the defects of the prior art, the present invention provides a method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning, comprising the steps of determining key failure modes and dependencies of underwater Christmas trees, data cleaning and processing, constructing a fault propagation path development trajectory prediction model based on deep learning, verifying, evaluating and optimizing the prediction model, and visualizing the prediction results of the fault propagation path development trajectory.

[0007] The technical solution of the present invention:

[0008] A method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning, the steps are as follows:

[0009] S1. Identify critical failure modes and dependencies of subsea trees

[0010] Determine the operating indicators of the subsea tree, including pressure, temperature, flow, determine the operating range of key components of the subsea tree, and define the boundary conditions for the normal operation of the subsea tree;

[0011] Through comprehensive analysis of the subsea Christmas tree's operating history data, maintenance records, and expert experience, we identify failure modes, including leakage, blockage, corrosion, and wear, and describe each failure mode, including its manifestation, possible causes, and impact range;

[0012] Use fault tree analysis and event tree analysis to analyze the dependencies between various fault modes, build a fault propagation network, and clarify the connection relationship and propagation path between various fault nodes;

[0013] According to the frequency of occurrence, impact and maintenance difficulty of faults, the key fault modes and main fault dependencies that have the greatest impact on the operation safety and production efficiency of subsea Christmas trees are screened out;

[0014] Based on the identified key failure modes and main failure dependencies, a failure dependency model of the system is constructed using a Bayesian network, and the dependencies between key components are clarified in a matrix form;

[0015] S2. Data collection and processing

[0016] Collect historical failure data of the subsea production tree, including the failure time, failure type, failure impact, failure propagation scenario of each component, as well as the operation log and maintenance record of the subsea production tree. Collect the operation data of the subsea production tree from various sensors of the subsea production tree, including pressure, temperature, flow rate, vibration parameters; process the collected historical failure data and operation data. First, clean the collected data to remove duplicates, missing values, and outliers, and then perform normalization and standardization processing to improve the consistency and comparability of the data; use the processed data to simulate the failure propagation process under different environmental conditions through the Monte Carlo method to generate a large number of simulated virtual failure sample data; fuse the processed historical failure data and the simulated virtual sample data to generate a failure data set for different cascade failure scenarios under different operating conditions; use the time series analysis ARIMA model to model the failure data set to obtain linear prediction values ; Use the linear prediction values as additional input features and embed them together with the failure data set into the input layer of the deep learning prediction model to be constructed;

[0017] S3. Construct a deep learning-based prediction model for the development trajectory of the failure propagation path

[0018] According to the complexity and data characteristics of the subsea production tree failure propagation path, select the deep learning prediction models as the convolutional neural network model and the long short-term memory network model, and construct a deep learning-based prediction model for the development trajectory of the subsea production tree failure propagation path. This failure propagation path development trajectory prediction model uses the convolutional neural network model to extract the spatial features of the failure data set, as shown in Equation (1), and takes the extracted spatial features as the time series and inputs them into the long short-term memory network model to extract the final time-dynamic spatial features, as shown in Equation (2); at the same time, the linear prediction values in step S2 are used as additional input features and embedded together with the failure data set into the input layer of the deep learning prediction model to enhance the interpretability and prediction performance of the deep learning prediction model; the output layer of the deep learning prediction model is the prediction target, including failure type, failure propagation probability, failure propagation direction, and component state change; design adjustable input parameters, including the dependence strength between nodes, environmental factors, and failure types, to adapt to the failure propagation characteristics under different working conditions; divide the preprocessed failure data set in step S2 into a training set, a validation set, and a test set, and train and cross-validate this deep learning prediction model; use the mean square error, root mean square error, mean absolute error, and R² statistic indicators to evaluate the prediction performance of the deep learning prediction model, and further improve the prediction accuracy of the deep learning prediction model through parameter tuning;

[0019] (1)

[0020] (2)

[0021] S4. Prediction Model Verification, Evaluation and Optimization

[0022] Using the constructed deep learning prediction model, predict the fault propagation path of the subsea production tree. The prediction results include fault type, fault propagation probability, fault propagation direction, and component state change. Evaluate the prediction ability of the deep learning prediction model in predicting the above results through prediction accuracy, prediction efficiency, and robustness indicators, and compare and verify the performance of the deep learning prediction model under different acceleration conditions and extreme conditions. Retrain and optimize the deep learning prediction model according to the verification results, including adjusting the model structure, optimizing parameter settings, and improving data preprocessing methods, to improve the prediction accuracy and generalization ability of the deep learning prediction model. Compare and verify the prediction results of the optimized deep learning prediction model with the actual situation again, evaluate the prediction performance of the model, and correct and optimize the model according to the verification results.

[0023] S5. Visualization of the Prediction Results of the Fault Propagation Path Development Trajectory

[0024] Import the prediction results into the NetworkX library to construct a complex network graph model of the deepwater production tree fault propagation based on nodes and edges. Create component nodes and state nodes, and create edges according to the predicted fault propagation path to represent the direction of fault propagation from one unit to another. Dynamically update the node color or size according to the prediction results of the deep learning prediction model to represent the degradation degree of the unit, the available state of the device, or the system health state. Dynamically update the width of the edge according to the fault propagation path predicted by the deep learning prediction model to represent the severity of the fault propagation. Create a dynamically updated chart to display the changes in the states of each component over time, and achieve a visual presentation that can reflect and predict the fault propagation of the deepwater production tree in real time. Develop user interaction functions to allow selection of time periods and units, view detailed information, collect user feedback, and continuously optimize the model and visualization effects.

[0025] Advantages of the present invention: Aiming at the problem that it is difficult to comprehensively and real - time track the changes in the system state on which the fusion fault depends in single - point fault prediction, a new method for predicting the development trajectory of the fault propagation path of key components of an underwater Christmas tree is proposed. It can comprehensively and real - time predict the changes in the states of multiple components, as well as the development trends of the fault propagation path and probability, etc. By using the time - series analysis method, the changing trend of the system operation state and the dynamic evolution process of potential faults are captured. Based on deep learning, a prediction model for the development trajectory of the fault propagation path is established. Using this model can provide comprehensive and real - time fault prediction results and provide a fault propagation path prediction within the next 24 hours. By importing the prediction model into the NetworkX library to create a complex network diagram of the system, a real - time presentation method of prediction results such as the direction and probability of the fault propagation path on the network diagram is studied, and user visualization and interaction functions can be developed. Description of the Drawings

[0026] Figure 1 It is the basic flowchart of the method for predicting the development trajectory of the fault propagation path of an underwater Christmas tree based on deep learning.

[0027] Figure 2 It is the explanatory diagram of the fault - dependence matrix.

[0028] Figure 3 It is the flowchart of data collection and processing.

[0029] Figure 4 It is the flowchart for constructing the prediction model of the development trajectory of the fault propagation path based on deep learning. Detailed Implementation Manner

[0030] The following further illustrates the detailed implementation manner of the present invention in combination with the drawings and technical solutions.

[0031] Predict the fault propagation path of an underwater Christmas tree in a certain offshore oilfield to take timely maintenance measures. The underwater Christmas tree in this oilfield mainly consists of a wellhead connector, a tubing hanger, a plug, a tree cap, a tree body, valves, and various passageways.

[0032] S1: Construction of the fault - dependence model of the underwater Christmas tree

[0033] Analyze the working principle of the underwater Christmas tree: Crude oil enters the tubing hanger from the wellhead connector, reaches the valve through the passageways in the tree body, and is then transported to the platform through the passageways; Identify the system structure: Key components including the wellhead connector, tubing hanger, plug, tree cap, tree body, valves, and various passageways.

[0034] Identify the key components and their functions: The wellhead connector connects the wellhead and the tubing hanger; Tubing hanger: Supports the tubing and controls the fluid flow; Plug: Used to temporarily close the passageway; Tree cap: Protects the top of the tree body; Tree body: Contains various passageways and valves; Valve: Controls the fluid flow; Passageway: The pipeline connecting each component.

[0035] Identify the dependencies between key components: Structural dependencies, such as the tubing hanger relying on the wellhead connector for support; functional dependencies, such as the valve relying on the tubing hanger for fluid control.

[0036] Define operating metrics: pressure, temperature, flow rate, and determine the normal operating boundary conditions, such as the pressure not exceeding 20 MPa and the temperature not exceeding 80°C.

[0037] Extract key failure modes and influencing factors: Based on the OREDA database and historical failure data, identify key failure modes such as pipeline leakage, filter clogging, crack propagation, and seal failure; through historical data and expert experience, identify leakage and corrosion as key failure modes. Leakage is mainly manifested as fluid seeping out from pipes or valves, which may lead to pressure drop and environmental pollution; corrosion is mainly manifested as the gradual damage of the material surface, which may lead to pipeline rupture or valve failure.

[0038] Use FMEA and FTA methods to construct a fault tree and identify common-cause failures and cascading failures. For example, a leakage failure may cause a pressure drop, which in turn leads to increased corrosion of other components; at the same time, a corrosion failure may also cause the pipeline wall to become thinner, increasing the risk of leakage.

[0039] Based on the failure frequency, impact degree, and repair difficulty, we screen out leakage and corrosion as the key failure modes that have the greatest impact on the operation safety and production efficiency of the subsea production tree. According to the key failure modes and main failure dependencies, construct a Bayesian network model to clarify the failure dependency matrix between components.

[0040] S2: Data collection and processing

[0041] Collect the failure records of the past 3 years, including the failure time, type, impact, and propagation scenario. For example, a certain leakage failure occurred at the valve, resulting in a 20 psi pressure drop and causing corrosion of the adjacent pipeline; collect real-time operating data from sensors, including pressure, temperature, flow rate, and vibration parameters. For example, at a certain moment, the pressure is 300 psi, the temperature is 100°C, and the flow rate is 3000 m³ / h.

[0042] Clean the collected data, remove duplicates, missing values, and outliers, and perform normalization and standardization processing on the data to improve the consistency and comparability of the data.

[0043] Use the Monte Carlo method to simulate the failure propagation process under different environmental conditions and generate virtual failure samples. For example, in the simulation, we set different pressure, temperature, and flow rate conditions to observe the failure propagation path and impact.

[0044] Fuse historical fault data, simulation data, and linear prediction values (obtained using the ARIMA model) to form a fault dataset, which contains rich fault information and operation data;

[0045] S3: Construction of a fault propagation path development trajectory prediction model based on deep learning

[0046] Model selection: Select the convolutional neural network (CNN) and long short-term memory network (LSTM) as the basis of the deep learning model. CNN is used to extract the spatial features of fault data, and LSTM is used to extract the temporal dynamic features;

[0047] Model construction: Construct a CNN-LSTM deep learning model, use the output of CNN as the input of LSTM. At the same time, use the linear prediction value of the ARIMA model as an additional input feature and embed it together with the fault dataset in the input layer of the deep learning model;

[0048] Model training: Use the preprocessed dataset to train the model and perform cross-validation. During the training process, continuously adjust the parameters and structure of the model to improve the prediction performance of the model;

[0049] Performance evaluation: Use metrics such as mean squared error, root mean squared error, and mean absolute error to evaluate the prediction performance of the model. The results show that the prediction accuracy of the model is relatively high, and it can accurately predict future fault types, propagation probabilities, directions, and component state changes;

[0050] S4: Verification evaluation and optimization of the prediction model

[0051] Use the test set to make predictions on the model and obtain prediction results such as fault types, propagation probabilities, directions, and component state changes. The model predicts that a certain valve may have a leakage fault within the next month and gives the propagation path and influence range of the fault;

[0052] Evaluate the prediction ability of the model through metrics such as prediction accuracy, efficiency, and robustness. The results show that the model performs excellently in terms of prediction accuracy, efficiency, and robustness and can meet the requirements of practical applications;

[0053] According to the verification results, we optimize the model, including adjusting the model structure, optimizing the parameter settings, and improving the data preprocessing method. The optimized model has further improved prediction performance.

[0054] S5: Visualization of the prediction results of the fault propagation path development trajectory

[0055] The complex network graph model of fault propagation was constructed using the NetworkX library, which shows the fault propagation path and the dependencies between components. In the network graph, it can be seen how the leakage fault propagates from the valve to the adjacent pipeline and sensor;

[0056] The node colors, sizes, and edge widths were dynamically updated according to the prediction results, representing the degradation degree of the unit, the available state of the equipment, or the system health state. When the predicted fault probability of a certain valve increases, its node color can be changed to red in the network graph, and its node size can be increased to represent its degradation degree;

[0057] A dynamically updated chart was created to show the changes in the states of each component over time. For example, the changing trend of the pressure value of a pressure sensor over time can be seen, as well as the sudden drop in the pressure value when a fault occurs;

[0058] A user interface was developed to allow users to select time periods and units to view detailed information. For example, users can choose to view the fault records in the past month or the fault prediction results of a specific component; at the same time, user feedback was collected to continuously optimize the model and visualization effects.

[0059] Result Presentation and Analysis

[0060] 1. Prediction result presentation: Through visualization charts, the fault propagation paths of subsea Christmas trees in different time periods were shown; the fault types, fault propagation probabilities of each component, as well as the fault propagation direction and component state changes were predicted.

[0061] 2. Model performance evaluation: After verification, the prediction accuracy of the deep learning model reached more than 90%, with high prediction efficiency and good robustness; under different acceleration conditions and extreme conditions, the model performed stably and was able to accurately predict the fault types and fault propagation paths.

[0062] 3. Optimization effect: After optimization, the prediction performance of the deep learning model was further improved, and it was able to better adapt to the fault propagation characteristics under different working conditions.

[0063] 4. Practical application effect: Through this prediction method, the oilfield can timely detect and handle potential cascade faults, improve production efficiency and safety, reduce downtime and maintenance costs caused by faults, and bring significant economic benefits to the oilfield.

[0064] The prediction method for the development trajectory of the fault propagation path of the Christmas tree based on deep learning of the present invention realizes the accurate prediction of the fault propagation path of the subsea Christmas tree by constructing a fault dependency model and a fault dependency evolution model and combining deep learning technology. This method has high prediction accuracy and robustness and can provide strong support for the safe production and efficient operation of the oilfield.

Claims

1. A method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning, characterized in that: Here are the steps: S1. Identify the key failure modes and dependencies of subsea trees Based on the identified key failure modes and main failure dependencies, a failure dependency model of the system is constructed using a Bayesian network, and the dependencies between key components are clarified in a matrix form; S2. Data Collection and Processing Collect historical fault data of the underwater oil tree, including the fault time, fault type, fault impact, fault propagation scenario of each component, as well as the operation log and maintenance record of the underwater oil tree, and collect the operation data of the underwater oil tree from various sensors of the underwater oil tree, including pressure, temperature, flow, and vibration parameters; process the collected historical fault data and operation data; S3. Construct a fault propagation path development trajectory prediction model based on deep learning According to the complexity of the fault propagation path of the underwater oil tree and the data characteristics, the deep learning prediction model is selected as the convolutional neural network model and the long short-term memory network model, and a deep learning-based fault propagation path development trajectory prediction model of the time series underwater oil tree is constructed; S4. Prediction model validation, evaluation and optimization S5. Visualization of prediction results of fault propagation path development trajectory Develop user interaction functions to allow selection of time periods and units, viewing of detailed information, collection of user feedback, and continuous optimization of models and visualizations.

2. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 1, characterized in that: In step S1, the specific implementation process of determining the key failure modes and dependencies of the subsea Christmas tree is as follows: Determine the operating indicators of the subsea tree, including pressure, temperature, flow, determine the operating range of key components of the subsea tree, and define the boundary conditions for the normal operation of the subsea tree; Through comprehensive analysis of the subsea Christmas tree's operating history data, maintenance records, and expert experience, we identify failure modes, including leakage, blockage, corrosion, and wear, and describe each failure mode, including its manifestation, possible causes, and impact range; Use fault tree analysis and event tree analysis to analyze the dependencies between various fault modes, build a fault propagation network, and clarify the connection relationship and propagation path between various fault nodes; According to the frequency of occurrence, impact and maintenance difficulty of faults, the key fault modes and main fault dependencies that have the greatest impact on the operation safety and production efficiency of subsea Christmas trees are screened out; Based on the identified key failure modes and main failure dependencies, a failure dependency model of the system is constructed using a Bayesian network, and the dependencies between key components are clarified in matrix form.

3. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 2, characterized in that: In step S2, the specific implementation process of data collection and processing is as follows: The collected historical fault data and operation data are processed. First, the collected data is cleaned to remove duplicates, missing values, and outliers, and then normalized and standardized to improve the consistency and comparability of the data. The processed data is used to simulate the fault propagation process under different environmental conditions through the Monte Carlo method to generate a large number of simulated virtual fault sample data; The processed historical fault data and simulated virtual sample data are integrated to generate fault data sets with different cascading fault scenarios under different operating conditions; the time series analysis ARIMA model is used to model the fault data set to obtain linear prediction values The linear prediction value As additional input features, they are embedded in the input layer of the deep learning prediction model to be built together with the fault dataset.

4. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 3, characterized in that: In step S3, the specific implementation process of constructing a fault propagation path development trajectory prediction model based on deep learning is as follows: The fault propagation path development trajectory prediction model uses a convolutional neural network model to extract the spatial features of the fault data set, see formula (1), and inputs the extracted spatial features into the long short-term memory network model as a time series to extract the final time dynamic spatial features, see formula (2); at the same time, the linear prediction value in step S2 is used as an additional input feature and embedded into the input layer of the deep learning prediction model together with the fault data set to enhance the interpretation ability and prediction performance of the deep learning prediction model; the output layer of the deep learning prediction model is the prediction target, including fault type, fault propagation probability, fault propagation direction and component state change; adjustable input parameters are designed, including node dependency strength, environmental factors, and fault type, to adapt to the fault propagation characteristics under different working conditions; The fault data set preprocessed in step S2 is divided into a training set, a validation set and a test set, and the deep learning prediction model is trained and cross-validated; the mean square error, root mean square error, mean absolute error and R 2 Statistical indicators are used to evaluate the prediction performance of deep learning prediction models, and the prediction accuracy of deep learning prediction models is further improved through parameter tuning; Fspace=CNN(X) (1) Ftime=LSTM(Fspace) (2).

5. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 4, characterized in that: In step S4, the specific implementation process of prediction model verification, evaluation and optimization is as follows: The constructed deep learning prediction model is used to predict the fault propagation path of the underwater oil tree. The prediction results include fault type, fault propagation probability, fault propagation direction and component status change. The prediction accuracy, prediction efficiency and robustness indicators are used to evaluate the prediction ability of the deep learning prediction model in predicting the above results, and to compare and verify the performance of the deep learning prediction model under different acceleration conditions and extreme conditions. The deep learning prediction model is retrained and optimized based on the verification results, including adjusting the model structure, optimizing parameter settings, and improving data preprocessing methods to improve the prediction accuracy and generalization ability of the deep learning prediction model. The prediction results of the optimized deep learning prediction model are compared and verified with the actual situation, the prediction performance of the model is evaluated, and the model is corrected and optimized based on the verification results.

6. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 5, characterized in that: In step S6, the specific implementation process of visualizing the prediction result of the fault propagation path development trajectory is as follows: Import the prediction results into the NetworkX library to build a complex network graph model of deepwater oil production tree fault propagation based on nodes and edges; create component nodes and status nodes, and create edges based on the predicted fault propagation path to indicate the direction of fault propagation from one unit to another; dynamically update the node color or size based on the prediction results of the deep learning prediction model to indicate the degree of unit degradation, equipment availability or system health status; dynamically update the edge width based on the fault propagation path predicted by the deep learning prediction model to indicate the severity of fault propagation; Create dynamically updated charts that show component status changes over time, enabling real-time visualization and prediction of deepwater tree fault propagation.

Images