Wellbore integrity real-time monitoring and risk prediction method

Abstract

The invention relates to a shaft integrity real-time monitoring and risk prediction method. The method comprises the following steps: a data acquisition step: acquiring time sequence parameter data of various types of parameters through a plurality of sensors at a set position of a shaft; a preprocessing step: preprocessing the multiple types of time sequence parameter data to obtain feature data of various types of parameters; a risk prediction step: inputting the feature data of the various types of parameters into a pre-constructed artificial intelligence model, performing quantitative evaluation on the integrity of the current time period of the various types of parameters, and performing risk prediction on the future time period of the preset duration of the various types of parameters; and an interface reporting step: displaying results of integrity quantitative evaluation and risk prediction on a user interface.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas production equipment, and in particular to a method for real-time monitoring and risk prediction of wellbore integrity. Background Art

[0002] In the oil and gas industry, the monitoring and assessment of wellbore integrity is a key link to ensure production safety, environmental protection and effective use of resources. Wellbore integrity involves the stability of the wellbore structure and the integrity of its functions, which is directly related to production efficiency and accident risk management.

[0003] Traditional wellbore integrity assessment technology mainly relies on the experience of experts or workers, and has problems such as poor real-time performance, poor risk prediction effect and incomplete data analysis.

[0004] For example, workers rely on regular physical inspections (including acoustic, electromagnetic or radioactive tools) to measure the wellbore. Although this method can provide direct physical measurement data, it is based on periodic manual work, has high labor costs, and cannot monitor the operating status of the wellbore in real time, so its reference value for risk prediction in the future is low.

[0005] Analysis methods based on expert systems, such as risk prediction for future periods based on statistical methods, are usually based on a large amount of historical data. In order to reduce algorithm complexity and calculation amount, calculations are generally based on a small number of parameters. However, the evaluation of wellbores based on a small number of parameters may result in incomplete data and low accuracy. Summary of the invention

[0006] The present invention provides a real-time monitoring and risk prediction method for wellbore integrity. Compared with the prior art, the method can perform real-time monitoring of the wellbore and improve the accuracy of risk prediction.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present application provides a method for real-time monitoring and risk prediction of wellbore integrity, comprising:

[0009] Data acquisition step: collecting time series parameter data of multiple types of parameters through multiple sensors at set positions in the wellbore;

[0010] Preprocessing step: preprocess various types of time series parameter data to obtain characteristic data of various types of parameters;

[0011] Risk prediction step: input the characteristic data of various parameters into the pre-built artificial intelligence model to quantitatively evaluate the integrity of the current time period of various parameters, and predict the risks of the future time periods of the preset duration of various parameters;

[0012] Interface reporting step: Display the results of integrity quantitative evaluation and risk prediction on the user interface.

[0013] In one implementation, the multiple types of parameters include: pressure, temperature, flow rate, and chemical composition.

[0014] In one implementation, the preprocessing step includes: data cleaning and denoising, data outlier processing, and feature extraction.

[0015] In one implementation, the feature extraction in the preprocessing step extracts feature data of the time series parameter data based on a data window and a fast Fourier transform.

[0016] In one implementation, the artificial intelligence model is a CNN-LSTM-Attention model based on deep learning, including:

[0017] The CNN layer is used to further extract the spatial distribution characteristics of the feature data based on the feature data of the time series parameter data;

[0018] LSTM layer, extracts the time correlation features of each parameter feature data;

[0019] The Attention layer integrates the attention mechanism based on the spatial distribution characteristics and temporal correlation characteristics of each parameter feature data to obtain the final fusion features.

[0020] In one implementation, the process of building a CNN-LSTM-Attention model based on deep learning includes:

[0021] Determine the network structure and parameters of the model based on genetic algorithm;

[0022] Collecting historical data sets, the historical data sets include values ​​and annotation results of various types of parameters in preset time periods, the annotation results represent the integrity evaluation results of the current time period, and the risk types and risk ratings of future preset time periods;

[0023] Based on the historical data set, the initial model of the network structure and parameters determined by the genetic algorithm is trained to obtain a trained model.

[0024] In one implementation, the genetic algorithm determines the number of network layers, the number of neurons and the activation function of the model based on a natural selection mechanism.

[0025] In one implementation, in the real-interface reporting step, a user graphical interface is provided to intuitively display real-time data flow, prediction results and risk ratings.

[0026] The technical solution of the present invention significantly improves the accuracy and real-time performance of data analysis through integrated deep learning technology and intelligent algorithms, thereby effectively improving the monitoring efficiency and prediction accuracy of wellbore integrity. First, the real-time data processing capability of the system enables it to instantly analyze various types of data collected by wellbore sensors, such as pressure, temperature, and chemical composition. This instant analysis is achieved through the advanced CNN-LSTM-Attention model, which combines the spatial feature extraction capability of convolutional neural networks and the time series processing advantages of long short-term memory networks, as well as the focus of attention mechanisms, to achieve efficient recognition and analysis of complex patterns in wellbore data. In addition, the application of genetic algorithms in the model training process optimizes the network structure and parameter configuration, ensuring that the model can maintain optimal performance under different operating environments. In this way, the present invention can not only monitor the wellbore status in real time, but also predict potential risks in the future, so that operators can respond in time to prevent possible accidents. In summary, the technical solution of the present invention effectively solves the deficiencies of existing technologies in real-time monitoring, data analysis, accuracy, and prediction capabilities by combining advanced data processing and learning technologies, and provides an efficient and comprehensive wellbore integrity assessment tool for the oil and gas industry, significantly improving the safety and operating efficiency of oil wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic diagram of a method for real-time monitoring and risk prediction of wellbore integrity in one embodiment of the present invention;

[0028] Figure 2 This is an example diagram of the network structure of an artificial intelligence model in one embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0030] In view of the defects and problems of existing technologies, such as Figure 1 The present application provides a method for real-time monitoring and risk prediction of wellbore integrity, comprising:

[0031] (1) Data acquisition step: collecting time series parameter data of various parameters through multiple sensors at set positions in the wellbore;

[0032] Specifically, a series of high-precision sensors are deployed in key parts of the wellbore, such as the wellhead, production layer, and near the packer, to monitor and collect various data about the wellbore in real time. These sensors can detect key indicators such as pressure, temperature, flow rate, and chemical composition (such as pH value and gas content) to ensure that the data obtained is comprehensive and accurate.

[0033] Furthermore, the data is sent to the central data processing center in real time via an encrypted network connection. Using advanced communication technologies, such as LTE or 5G networks, the speed and security of data transmission are guaranteed, and data loss or delay during transmission is reduced.

[0034] (2) Preprocessing step: preprocessing multiple types of time series parameter data to obtain characteristic data of various types of parameters;

[0035] Specifically, data preprocessing is a very critical step. In order to ensure the accuracy, completeness and availability of the data, we propose a series of preprocessing steps to provide high-quality basic data for subsequent visualization and analysis.

[0036] Data cleaning and denoising:

[0037] Wellbore data obtained from external data sources (such as databases, sensors, or other devices) often contain noise, outliers, or erroneous data. For example, the data collected by the sensor may be disturbed by the environment, resulting in abnormal values. First, these outliers are removed through data cleaning algorithms, and statistical analysis methods (such as median filtering or sliding window method) are used to remove noise. For data points with large errors, they can be eliminated through anomaly detection algorithms (such as Z-Score or IQR method).

[0038] Missing data filling and inference:

[0039] Wellbore data often have missing values, especially when collected by sensors, some data may be lost due to failures, occlusions, etc. When processing missing data, the present invention uses the following techniques:

[0040] Interpolation method: Use the adjacent valid data for linear interpolation or polynomial interpolation to fill the missing segments. This method is suitable for situations where there is less missing data and can ensure a smooth transition of data.

[0041] Data inference: Use machine learning models (such as KNN, regression models, etc.) to predict missing data points. For example, infer missing wellbore dimensions or steel grades through the relationship between existing data points.

[0042] Interval estimation: For wellbore data with obvious hierarchical structure, interval estimation is used to fill in the missing data through context information and data intervals to ensure that the filled data is reasonable and contextually consistent.

[0043] The data preprocessing in this step also includes feature extraction.

[0044] Data windowing techniques and signal processing algorithms (such as fast Fourier transform) are used to extract key features from time series data. This includes identifying periodic patterns, long-term and short-term trends, and abnormal behaviors from complex data streams to prepare data for input into deep learning models.

[0045] (3) Risk prediction step: input the characteristic data of various parameters into the pre-built artificial intelligence model to quantitatively evaluate the integrity of the current time period of various parameters, and to predict the risks of the future time periods of the preset time periods of various parameters;

[0046] Specifically, the deep learning model used in this step is: CNN-LSTM-Attention model.

[0047] Figure 2 As shown, the network architecture of the model includes:

[0048] CNN Layers: Use convolutional neural network layers to process and identify spatial features in time series data, such as fluctuation patterns and frequency distributions, which helps the model understand complex structures in the data.

[0049] LSTM layer: Long short-term memory networks handle temporal dependencies and effectively capture time series associations in the data, such as periodic behaviors and long-term dependency patterns, which are key to understanding the dynamic changes in wellbore integrity.

[0050] Attention layer: Through the attention mechanism, the model can focus on the most critical part of the time series, optimize the focus and accuracy of the prediction, and thus provide more accurate risk prediction at critical moments.

[0051] This step, when building the model, includes:

[0052] The network structure and parameters of the model are determined based on genetic algorithms; the structure and parameter configuration of the neural network are automatically optimized using genetic algorithms, including selecting the most suitable number of layers, number of neurons, and activation functions. This process simulates the mechanism of natural selection and finds the optimal solution through iterative selection, crossover, and mutation operations.

[0053] Collecting historical data sets, the historical data sets include values ​​and annotation results of various types of parameters in preset time periods, the annotation results represent the integrity evaluation results of the current time period, and the risk types and risk ratings of future preset time periods;

[0054] Based on historical data sets, the initial model of the network structure and parameters determined by the genetic algorithm is trained to obtain a trained model; the model is trained on a large number of historical data sets, and cross-validation and regularization techniques are applied to avoid overfitting and ensure that the model has good generalization ability. During the model training process, the parameters are continuously adjusted and optimized to achieve the best prediction effect.

[0055] (4) Interface reporting step: Display the results of the integrity quantitative evaluation and risk prediction on the user interface.

[0056] This step is based on visualization tools and provides a user-friendly graphical interface to intuitively display real-time data flow, prediction results and risk ratings. The interface design supports multi-platform access, including desktop and mobile devices, to ensure that users can obtain the latest information at any time. Detailed risk assessment reports are automatically generated based on real-time analysis results, including risk level, potential cause analysis and recommended prevention or response measures. Reports can be automatically sent to relevant decision makers and technical teams to accelerate the decision-making process.

[0057] In summary, an innovative solution is provided for the problems of lagging monitoring technology and inaccurate predictions faced by the current oil and gas industry. The system significantly improves the accuracy and real-time performance of data analysis through integrated deep learning technology and intelligent algorithms, thereby effectively improving the monitoring efficiency and prediction accuracy of wellbore integrity. First, the system's real-time data processing capability enables it to instantly analyze various types of data collected by wellbore sensors, such as pressure, temperature, and chemical composition. This instant analysis is achieved through the advanced CNN-LSTM-Attention model, which combines the spatial feature extraction capability of convolutional neural networks and the time series processing advantages of long and short-term memory networks, as well as the focus of the attention mechanism, to achieve efficient recognition and analysis of complex patterns in wellbore data. In addition, the application of genetic algorithms in the model training process optimizes the network structure and parameter configuration, ensuring that the model can maintain optimal performance under different operating environments. In this way, the present invention can not only monitor the wellbore status in real time, but also predict potential risks in the future, so that operators can respond in time to prevent possible accidents. In summary, the technical solution of the present invention effectively solves the shortcomings of the existing technology in real-time monitoring, data analysis, accuracy and prediction capabilities by combining advanced data processing and learning technologies, providing the oil and gas industry with an efficient and comprehensive wellbore integrity assessment tool, which significantly improves the safety and operational efficiency of oil wells.

[0058] In the several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0059] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time monitoring and risk prediction method for wellbore integrity, characterized in that: include: Data acquisition step: collecting time series parameter data of multiple types of parameters through multiple sensors at set positions in the wellbore; Preprocessing step: preprocess multiple types of time series parameter data to obtain characteristic data of various types of parameters; Risk prediction step: input the characteristic data of various parameters into the pre-built artificial intelligence model to quantitatively evaluate the integrity of the current time period of various parameters, and predict the risks of the future time periods of the preset duration of various parameters; Interface reporting step: Display the results of integrity quantitative evaluation and risk prediction on the user interface.

2. The method for real-time monitoring and risk prediction of wellbore integrity according to claim 1, characterized in that: The various parameters include pressure, temperature, flow rate and chemical composition.

3. The method for real-time monitoring and risk prediction of wellbore integrity according to claim 1, characterized in that: The preprocessing steps include: data cleaning and denoising, data outlier processing and feature extraction.

4. The method for real-time monitoring and risk prediction of wellbore integrity according to claim 3, characterized in that: The feature extraction in the preprocessing step extracts feature data of the time series parameter data based on the data window and the fast Fourier transform.

5. The method for real-time monitoring and risk prediction of wellbore integrity according to claim 1, characterized in that: The artificial intelligence model is a CNN-LSTM-Attention model based on deep learning, including: The CNN layer is used to further extract the spatial distribution characteristics of the feature data based on the feature data of the time series parameter data; LSTM layer, extracts the time correlation features of each parameter feature data; The Attention layer integrates the attention mechanism based on the spatial distribution characteristics and temporal correlation characteristics of each parameter feature data to obtain the final fusion features.

6. The method for real-time monitoring and risk prediction of wellbore integrity according to claim 5, characterized in that: The CNN-LSTM-Attention model based on deep learning includes the following steps: Determine the network structure and parameters of the model based on genetic algorithm; Collecting historical data sets, the historical data sets include values ​​and annotation results of various types of parameters in preset time periods, the annotation results represent the integrity evaluation results of the current time period, and the risk types and risk ratings of future preset time periods; Based on the historical data set, the initial model of the network structure and parameters determined by the genetic algorithm is trained to obtain a trained model.

7. The method for real-time monitoring and risk prediction of wellbore integrity according to claim 6, characterized in that: The genetic algorithm determines the number of network layers, the number of neurons and the activation function of the model based on a natural selection mechanism.

8. The method for real-time monitoring and risk prediction of wellbore integrity according to claim 1, characterized in that: In the real-interface reporting step, a user graphical interface is provided to intuitively display real-time data flow, prediction results and risk ratings.

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