Downhole packer coupling integrity diagnostic system and digital twin life prediction method

Abstract

The application discloses a downhole packer coupling integrity diagnosis system and a digital twin life prediction method. The system comprises a data acquisition layer, an edge computing layer, a cloud analysis layer with a built-in L4 level digital twin system and an application layer connected in sequence. The method collects vibration, corrosion and other parameters through sensors, pre-processes them through the edge layer, calls a three-field coupling dynamic model in the cloud, calculates multi-field parameters, failure probability and residual life by combining LSTM and Bayesian fusion algorithm, and outputs diagnosis results and maintenance suggestions in the application layer to form a closed loop. The application solves the problems of traditional dependence on experience, difficulty in quantifying multi-field coupling and low level of digital twin, reduces maintenance cost and risk of well control accidents, and provides key technical support for predictive maintenance of oil and gas wells.

Description

Technical Field

[0001] This invention relates to the oil and gas industry in the energy sector, specifically to the fields of oil and gas field development engineering, downhole completion technology, production well maintenance and management, and downhole tool reliability research. In particular, it relates to a downhole packer coupled integrity diagnostic system and a digital twin lifetime prediction method, which is applicable to intelligent development of oil and gas wells and predictive maintenance scenarios for downhole operations. Background Technology

[0002] In the process of oil and gas extraction, downhole packers are critical downhole tools, and their reliability directly affects the production efficiency and operational safety of oil and gas wells. Traditional downhole packer maintenance mainly relies on engineers' experience-based judgment, lacking real-time monitoring methods. This makes it difficult to cope with complex faults caused by the coupling effects of multiple physical fields such as vibration, corrosion, and seal failure. This leads to frequent passive maintenance and high costs associated with unplanned production shutdowns.

[0003] Currently, the industry has gradually begun to explore the application of multiphysics coupling analysis and digital twin technology.

[0004] In multiphysics coupling analysis, vibration analysis has achieved real-time monitoring and typical pattern recognition. However, the complex downhole environment results in significant noise in vibration signals, insufficient feature extraction accuracy, and a lack of a three-field coupling mechanism model for vibration, corrosion, and sealing. Although downhole monitoring sensors and environmental prediction models have been developed for corrosion analysis, most are indirect measurements. In complex fluid environments, the accuracy of predictions is limited due to insufficient long-term data accumulation. Sealing analysis achieves condition monitoring through pressure-temperature joint monitoring, but single-factor evaluation models are difficult to adapt to the performance degradation patterns under multi-field coupling.

[0005] In terms of digital twin technology, most existing systems are at Level 3 (static model + real-time data), while the implementation of Level 4 (full lifecycle twin + dynamic simulation + prediction algorithm) still has obvious shortcomings: lack of three-field coupled virtual model, poor synchronization between real-time data and simulation model, insufficient accuracy of lifecycle prediction algorithm, and system response delay affecting the timeliness of decision-making; at the same time, weak multi-source heterogeneous data fusion capability, low edge-cloud collaboration efficiency, and lack of unified standards lead to limited system scalability and compatibility.

[0006] In summary, existing technologies cannot meet the needs of the oil and gas industry's transformation towards "intelligent downhole operations" and "predictive maintenance," and there is an urgent need for an integrated technical solution that can achieve three-field coupled dynamic analysis and L4-level digital twin closed-loop prediction. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a downhole packer coupled integrity diagnostic system and a digital twin lifetime prediction method, realizing a paradigm shift from "experience-driven" to "data-driven" and solving problems such as difficulty in multi-physics coupling quantification, low digital twin level, and insufficient predictive maintenance capabilities.

[0008] To achieve the above objectives, this invention first discloses a downhole packer coupled integrity diagnostic system, the key features of which are: a data acquisition layer, an edge computing layer, a cloud analysis layer, and an application layer connected in sequence; the data acquisition layer is used to collect and transmit vibration, corrosion, sealing, and operating parameters of the downhole packer; the edge computing layer is used to preprocess the collected parameters; the cloud analysis layer has a built-in L4 level digital twin system for constructing a dynamic model of vibration-corrosion-sealing three-field coupling, and based on this model, packer integrity diagnosis and life prediction are carried out; the application layer is used to output diagnostic results, early warning information, and maintenance suggestions.

[0009] Furthermore, the data acquisition layer includes a downhole sensor network and a data transmission module. The downhole sensor network consists of a vibration accelerometer, a pH probe, strain gauges, and temperature and pressure sensors. The vibration accelerometer is used to monitor the mechanical vibration spectrum, with a monitoring range of 0-10kHz and a sampling rate ≥1MHz. The pH probe is used to monitor the acidity and alkalinity of the corrosive environment, with a pressure resistance ≥100MPa. The strain gauge is used to measure the stress distribution of the sealing element, with an accuracy of ±0.1%FS. The temperature and pressure sensors are used to collect downhole operating parameters, with a temperature monitoring range of -20-200℃. The data transmission module uses high-voltage resistant optical fiber or electromagnetic wave relay to achieve data transmission.

[0010] Furthermore, the edge computing layer uses the Kalman filter algorithm to eliminate signal noise, uses wavelet transform to extract vibration features, and achieves data synchronization with a synchronization error of <1ms through a time synchronization protocol; at the same time, it uses a lossless algorithm to compress the preprocessed data.

[0011] Furthermore, the L4-level digital twin system of the cloud-based analysis layer includes a dynamic simulation engine and digital threads. The dynamic simulation engine supports multi-rate simulation, employs explicit solutions for rapid vibration processes at the millisecond level, and implicit solutions for slow corrosion processes at the day level. It also features a hardware-in-the-loop testing interface with a response latency of <50ms. The digital threads enable seamless data flow between design, manufacturing, and operation and maintenance, with a data link breakage rate of <0.1%. The three-field coupled dynamic model adopts a multi-scale modeling framework. At the macroscale, a structural dynamics model is constructed using ANSYS finite element analysis software, while at the microscale, the H2S corrosion process is simulated using LAMMPS molecular dynamics tools.

[0012] Furthermore, the application layer includes a visual dashboard, an early warning push module, and a maintenance work order generation module; the early warning push module pushes early warning information based on a threshold triggering mechanism, and the maintenance work order generation module automatically generates maintenance work orders based on lifespan prediction results.

[0013] Based on the foregoing description, the present invention also provides a digital twin lifetime prediction method based on the downhole packer coupled integrity diagnostic system, the key of which includes the following steps: S1: The vibration, corrosion, sealing and operating parameters of the packer are collected through the downhole sensor network of the data acquisition layer and transmitted to the edge computing layer via the data transmission module; S2: The edge computing layer performs noise filtering, feature extraction, data synchronization and compression on the parameters before transmitting them to the cloud analysis layer; S3: The cloud-based analysis layer calls the three-field coupled dynamic model in the L4-level digital twin system for simulation analysis, and combines the life prediction algorithm to calculate the packer failure probability and remaining life. S4: The cloud-based analytics layer transmits diagnostic results, failure probabilities, and remaining lifespan data to the application layer. The application layer displays the data through a visual dashboard, triggers alerts, and generates maintenance work orders. Simultaneously, it transmits on-site feedback data back to the cloud-based analytics layer to update the L4-level digital twin system model, forming a closed loop.

[0014] Furthermore, the calculation of vibration field parameters in step S3 includes: calculation of time-varying mechanical stress, using the formula: ,in For time-varying mechanical stress, The vibration acceleration data are measured by a vibration acceleration sensor. The elastic modulus of the packer material; the vibration-corrosion coupling coefficient is calculated using the following formula: ,in This is the root mean square value of the vibration stress. For integration time, For corrosion rate, This represents the change in corrosion rate. This represents the change in the root mean square value of the vibration stress.

[0015] Furthermore, the calculation of corrosion field parameters in step S3 includes: dynamic corrosion rate calculation, the formula is as follows: ,in For dynamic corrosion rate, Let be the material corrosion constant. The concentration of H2S in the downhole gas medium. The activation energy of the corrosion reaction. The gas constant is... This is the real-time temperature measured by the temperature sensor. The vibration-corrosion coupling coefficient is... The root mean square value of vibration stress; the remaining wall thickness is predicted using the following formula: ,in, For the remaining wall thickness, This is the initial wall thickness of the packer. For integration variables, for Corrosion rate at any given time.

[0016] Furthermore, the calculation of sealing field parameters in step S3 includes: calculation of sealing stress relaxation, the formula is as follows: ,in for Sealing stress at any time, For the initial sealing stress, For time, Let be the relaxation time constant of the rubber seal. This is the contact area compensation coefficient. The annular pressure difference is used to calculate the blockage risk index, using the following formula: ,in To block the risk index, The viscosity of the downhole fluid. This refers to the fluctuation in the sealing contact area caused by vibration. Initial sealing contact area, This represents the remaining wall thickness.

[0017] Furthermore, step S3, the three-field coupling integrated evaluation and lifetime prediction calculation, includes: failure probability calculation, the formula is as follows: ,in for The probability of failure at time 1. For integration variables, for Time-varying mechanical stress at time t, For the material's yield strength, for Corrosion rate over time As the baseline corrosion rate, for The congestion risk index at any time. This is the critical congestion index. The fitting parameters are used; the remaining lifetime is calculated using the following formula: ,when hour For remaining lifespan, This is the critical time when the failure probability reaches 0.95. The current time is used; the lifetime prediction algorithm is a Bayesian fusion algorithm of LSTM time series prediction algorithm and physical model simulation, and is equipped with a failure mode library containing 12 typical failure scenarios. This failure mode library is built based on 200+ field failure cases and is updated with ≥5 cases per month.

[0018] Compared with the prior art, the significant advantages of the present invention are: (1) Achieving quantitative analysis of multi-physics coupling: Traditional techniques are difficult to quantify the coupling effect of vibration, corrosion, and sealing. However, this invention constructs a dynamic model of vibration-corrosion-sealing coupling. Through precise parameter calculation and model simulation, the impact of multi-physics coupling on downhole packers can be accurately quantified. For example, through a series of formulas such as time-varying mechanical stress calculation, vibration-corrosion coupling coefficient calculation, and dynamic corrosion rate calculation, the interaction and variation law between various physical fields are clearly presented, providing a solid data foundation for the integrity diagnosis and life prediction of packers.

[0019] (2) Achieving Level 4 Digital Twin Closed-Loop Prediction: Most existing digital twin systems are at Level 3, while the Level 4 digital twin system of this invention has significant advantages. The dynamic simulation engine supports multi-rate simulation, and can adopt different solution methods for the fast-changing process of vibration and the slow-changing process of corrosion, with extremely short response delay, ensuring the efficient operation of the system. The digital thread realizes the connection between design-manufacturing-operation and maintenance data, reducing the data chain breakage rate. At the same time, the model is updated by combining the feedback data of the application layer to form a closed loop, which greatly improves the accuracy and real-time performance of the digital twin, and can more accurately predict the failure probability and remaining life of the packer.

[0020] (3) Improve the accuracy of lifetime prediction algorithm: This invention uses the LSTM time series prediction algorithm and the Bayesian fusion algorithm based on physical model simulation for lifetime prediction, and is equipped with a failure mode library built based on 200+ field failure cases and updated monthly. This combination fully utilizes the advantages of data-driven and physical models, can more comprehensively consider various possible failure scenarios, effectively improve the accuracy of lifetime prediction, and provide a reliable basis for predictive maintenance of oil and gas wells.

[0021] (4) Enhanced System Scalability and Compatibility: Addressing the issues of weak multi-source heterogeneous data fusion capabilities, low edge-cloud collaboration efficiency, and lack of unified standards in existing systems, this invention features a rationally designed data transmission and processing flow between the data acquisition layer, edge computing layer, and cloud analysis layer. It employs effective data synchronization and compression algorithms, improving the fusion capability of multi-source heterogeneous data and the collaboration efficiency between the edge and cloud. Furthermore, the overall system architecture adheres to certain standards, exhibiting good scalability and compatibility, and is able to adapt to the ever-evolving needs of the oil and gas industry.

[0022] (5) Real-time monitoring and early warning: The data acquisition layer of this invention can collect vibration, corrosion, sealing and operating parameters of the downhole packer in real time. Through preprocessing of the edge computing layer and analysis of the cloud analysis layer, the application layer can output diagnostic results, early warning information and maintenance suggestions in a timely manner. The early warning push module pushes early warning information based on the threshold triggering mechanism, and the maintenance work order generation module automatically generates maintenance work orders based on the life prediction results, so that maintenance personnel can understand the status of the packer in a timely manner, take corresponding maintenance measures, and reduce the cost of unplanned downtime. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a diagram illustrating the core technical architecture of the downhole packer coupling integrity diagnostic system in Example 1. Figure 2 This is a flowchart of the downhole packer coupling integrity diagnostic system in Example 1; Figure 3 This is a data transmission architecture diagram of the downhole packer coupling integrity diagnostic system in Example 1; Figure 4 This is a workflow timing diagram of the digital twin lifetime prediction method in Example 1; Figure 5 This is a schematic diagram of the calculation process of the digital twin lifetime prediction method in Example 1. Detailed Implementation

[0025] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0026] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] Figures 1 to 3 The first embodiment of the present invention is shown: a downhole packer coupled integrity diagnostic system, comprising a data acquisition layer, an edge computing layer, a cloud analysis layer, and an application layer connected in sequence; the data acquisition layer is used to collect and transmit vibration, corrosion, sealing, and operating parameters of the downhole packer; the edge computing layer is used to preprocess the collected parameters; the cloud analysis layer has a built-in L4 level digital twin system for constructing a dynamic model of vibration-corrosion-sealing three-field coupling, and based on this model, performing packer integrity diagnosis and life prediction; the application layer is used to output diagnostic results, early warning information, and maintenance suggestions.

[0028] At the data acquisition layer, high-precision sensors can capture key parameters of the downhole packer in real time and accurately, providing a solid data foundation for subsequent analysis. The edge computing layer preprocesses the acquired parameters, effectively reducing the computational burden on the cloud and improving the system's response speed and processing efficiency. The core highlight of this invention is the L4-level digital twin system built into the cloud analysis layer. Its vibration-corrosion-sealing three-field coupled dynamic model can comprehensively and deeply simulate the actual operating conditions of the packer in the complex downhole environment. Packer integrity diagnosis based on this model can accurately identify potential problems, such as minor seal leaks and early signs of corrosion. Simultaneously, packer life prediction is more scientific and reliable. Combining historical data and real-time monitoring information, and utilizing advanced algorithms and models, the lifespan of the packer can be predicted in advance, providing strong support for maintenance decisions. The diagnostic results, early warning information, and maintenance suggestions output by the application layer are highly targeted and practical. The diagnostic results are presented in an intuitive and easy-to-understand manner, allowing operators to quickly understand the current status of the packer; early warning information can be issued in a timely manner when the packer malfunctions, reminding relevant personnel to take measures to avoid accidents; maintenance recommendations provide specific operational guidance to maintenance personnel based on the diagnostic results and life prediction, such as the timing of seal replacement and methods of corrosion prevention.

[0029] In specific implementation, the data acquisition layer includes a downhole sensor network and a data transmission module. The downhole sensor network consists of a vibration accelerometer, a pH probe, strain gauges, and temperature and pressure sensors. The vibration accelerometer is used to monitor the mechanical vibration spectrum, with a monitoring range of 0-10kHz and a sampling rate ≥1MHz. The pH probe is used to monitor the acidity and alkalinity of the corrosive environment, with a pressure resistance ≥100MPa. The strain gauge is used to measure the stress distribution of the sealing element, with an accuracy of ±0.1%FS. The temperature and pressure sensors are used to collect downhole operating parameters, with a temperature monitoring range of -20-200℃. The data transmission module uses high-voltage resistant optical fiber or electromagnetic wave relay to achieve data transmission.

[0030] The data acquisition layer is designed with extremely high precision and reliability. The components of the downhole sensor network have clearly defined functions and advanced performance parameters, enabling comprehensive and accurate acquisition of key data about the packer in the complex downhole environment. Vibration accelerometers monitor mechanical vibration spectra across a wide frequency range with a high sampling rate, sensitively capturing subtle vibration changes during packer operation and providing crucial information for analyzing its operational status. pH probes operate stably under high pressure, accurately monitoring the acidity and alkalinity of corrosive environments, helping to promptly identify factors that may cause packer corrosion. Strain gauges measure the stress distribution of sealing elements with high precision, effectively identifying potential sealing problems. Temperature and pressure sensors can acquire downhole operating parameters over a wide temperature range, ensuring comprehensive monitoring of the packer's operating environment. The data transmission module uses high-pressure resistant optical fiber or electromagnetic wave relay for data transmission. This not only adapts to the high-pressure environment downhole, ensuring the stability and accuracy of data during transmission, but also effectively avoids signal interference. This allows the collected key data to be transmitted to the subsequent analysis layer in a timely and reliable manner, providing a solid data foundation for building a dynamic model of vibration-corrosion-sealing three fields in the L4 level digital twin system. This, in turn, ensures the scientific validity and reliability of packer integrity diagnosis and life prediction.

[0031] Specifically, the edge computing layer uses the Kalman filter algorithm to eliminate signal noise, uses wavelet transform to extract vibration features, and uses a time synchronization protocol to achieve data synchronization with a synchronization error of <1ms; at the same time, it uses a lossless algorithm to compress the preprocessed data.

[0032] These processing methods significantly improve data quality and usability. The Kalman filter algorithm effectively filters out noise interference in the signal, making the signals used for subsequent analysis purer and more accurate, providing a solid foundation for accurately determining the packer's operating status. Wavelet transform's precise extraction of vibration features allows for in-depth analysis of key information contained in the vibration signal, helping to promptly detect potential abnormal vibrations in the packer. The time synchronization protocol ensures high consistency of data from all sensors in time, with a synchronization error of less than 1ms. This is crucial for building accurate dynamic models and avoids analysis errors caused by time asynchrony. Furthermore, lossless algorithms compress the preprocessed data, reducing data transmission volume, lowering transmission costs and time, while ensuring data integrity. This allows a large amount of effective data to be efficiently transmitted to subsequent analysis layers, further improving the overall system's operating efficiency and analytical accuracy, providing stronger support for packer integrity diagnosis and life prediction.

[0033] In this embodiment, the L4-level digital twin system of the cloud analysis layer includes a dynamic simulation engine and a digital thread. The dynamic simulation engine supports multi-rate simulation, uses explicit solutions for fast-changing vibration processes at the millisecond level, and implicit solutions for slow-changing corrosion processes at the day level. It also has a hardware-in-the-loop testing interface with a response latency of <50ms. The digital thread enables data integration between design, manufacturing, and operation and maintenance, with a data link breakage rate of <0.1%. The three-field coupled dynamic model adopts a multi-scale modeling framework. At the macroscale, a structural dynamic model is constructed using ANSYS finite element analysis software, and at the microscale, the H2S corrosion process is simulated using LAMMPS molecular dynamics tools.

[0034] In fault diagnosis, the L4-level digital twin system, relying on a dynamic simulation engine and digital threads, can quickly and accurately locate potential packer faults by comparing real-time acquired data with the simulation model. For millisecond-level rapid vibration processes, the explicit solution of the dynamic simulation engine can promptly capture abnormal vibration signals, and combined with data from the digital threads, determine whether it is due to loosening of the packer's internal structure or other mechanical faults. For day-level slow corrosion processes, the implicit solution can continuously track the packer's corrosion status, predict the impact of corrosion on the packer's integrity, and issue early corrosion warnings. In lifetime prediction, the three-field coupled dynamic model plays a crucial role. The macro-scale structural dynamics model, using ANSYS finite element analysis software, can simulate the packer's stress conditions in complex downhole environments, analyzing the stress distribution and deformation of the packer under different operating conditions. At the micro-scale, the LAMMPS molecular dynamics tool simulates the H2S corrosion process, providing a deeper understanding of the damage mechanism of corrosion on the molecular structure of the packer material. By combining simulation results at both macro and micro scales, a more accurate packer life prediction model is constructed, providing scientifically sound timing recommendations for packer replacement and maintenance. This helps prevent downhole accidents caused by packer failure, reduces production costs, and improves production efficiency. Simultaneously, the system's data transmission and processing capabilities significantly enhance the real-time performance and accuracy of diagnosis and prediction. Lossless algorithm-compressed data is efficiently transmitted to the cloud analysis layer, and the extremely low data link breakage rate of the digital thread ensures data continuity and integrity, enabling the L4-level digital twin system to promptly acquire the latest data for analysis. The dynamic simulation engine's extremely short response latency ensures the system can quickly react to new data, promptly updating diagnostic and prediction results, providing timely and effective decision-making support for packer operation and maintenance management.

[0035] In practical applications, the application layer includes a visual dashboard, an early warning push module, and a maintenance work order generation module; the early warning push module pushes early warning information based on a threshold triggering mechanism, and the maintenance work order generation module automatically generates maintenance work orders based on lifespan prediction results.

[0036] The visual dashboard, through intuitive charts and data displays, helps staff quickly understand the real-time status and performance indicators of the downhole packer, such as pressure, temperature, and corrosion levels. The threshold triggering mechanism of the early warning push module is precisely set; once relevant indicators exceed safe limits, the system immediately pushes early warning information to relevant personnel via various methods (such as SMS, email, and system pop-ups), ensuring timely attention to the problem. The maintenance work order generation module automatically generates detailed maintenance work orders based on life prediction results and according to preset rules and processes. The work orders include specific information about the packer, maintenance tasks, estimated maintenance time, and required resources, greatly improving the planning and efficiency of maintenance work. Simultaneously, real-time data sharing and interaction are achieved between the modules in the application layer, forming an organic whole. When the early warning push module issues an alert, the maintenance work order generation module can quickly generate a corresponding maintenance work order based on the current situation, and the visual dashboard will also update and display relevant information in real time, providing comprehensive decision support for staff. Moreover, this application layer has good scalability and compatibility, and can be integrated with other existing management systems of the enterprise to further improve the overall management level of the enterprise.

[0037] Please see Figure 4 and Figure 5 Based on the foregoing description, this embodiment also provides a digital twin lifetime prediction method based on the downhole packer coupled integrity diagnostic system, including the following steps: S1: The vibration, corrosion, sealing and operating parameters of the packer are collected through the downhole sensor network of the data acquisition layer and transmitted to the edge computing layer via the data transmission module; S2: The edge computing layer performs noise filtering, feature extraction, data synchronization and compression on the parameters and then transmits them to the cloud analysis layer; S3: The cloud-based analysis layer calls the three-field coupled dynamic model in the L4-level digital twin system for simulation analysis, and combines the life prediction algorithm to calculate the packer failure probability and remaining life. S4: The cloud-based analytics layer transmits diagnostic results, failure probabilities, and remaining lifespan data to the application layer. The application layer displays the data through a visual dashboard, triggers alerts, and generates maintenance work orders. Simultaneously, it transmits on-site feedback data back to the cloud-based analytics layer to update the L4-level digital twin system model, forming a closed loop.

[0038] This digital twin lifetime prediction method offers numerous advantages. In the data acquisition phase, the downhole sensor network can acquire key packer parameters comprehensively and in real-time, ensuring data accuracy and timeliness. Vibration parameters reflect the stability of the packer's downhole operation and the presence of abnormal vibrations, potentially indicating loosening or damage to certain components. Corrosion parameters allow for timely understanding of the packer's corrosion level, enabling proactive protective measures to prevent seal failures caused by corrosion. The acquisition of sealing and operating parameters provides strong support for a comprehensive evaluation of packer performance. Edge computing layer processing is also crucial. Noise filtering removes interference from the acquired data, resulting in cleaner data for subsequent analysis. Feature extraction extracts the most representative features from massive datasets, reducing data volume while improving analysis efficiency. Data synchronization and compression ensure rapid and stable data transmission to the cloud analysis layer, preventing data transmission issues from affecting analysis results. The simulation analysis and lifetime prediction algorithms in the cloud analysis layer are the core of the entire method. The three-field coupled dynamic model comprehensively considers the interactions of various physical fields of the packer downhole, simulating the actual operation of the packer under different working conditions. Combined with a life prediction algorithm, it can accurately calculate the failure probability and remaining life of the packer, providing a scientific basis for packer maintenance and management. The application-layer visualization dashboard displays data, allowing staff to intuitively understand the packer's status. Triggering an early warning function can promptly notify relevant personnel when abnormalities occur in the packer, enabling appropriate measures to be taken. Generating maintenance work orders makes maintenance work more standardized and efficient. Field feedback data is transmitted back to the cloud analysis layer to update the model, forming a closed loop that continuously improves the accuracy and reliability of the digital twin system, better adapting to the complex and ever-changing working conditions downhole. This digital twin life prediction method can effectively improve the management level of downhole packers, reduce maintenance costs, and minimize safety risks and economic losses caused by packer failure, providing strong protection for safe production in industries such as oil extraction.

[0039] In specific implementation, the calculation of vibration field parameters in step S3 includes: calculation of time-varying mechanical stress, the formula of which is: ,in For time-varying mechanical stress, The vibration acceleration data are measured by a vibration acceleration sensor. The elastic modulus of the packer material; the vibration-corrosion coupling coefficient is calculated using the following formula: ,in , This is the root mean square value of the vibration stress. For integration time, For corrosion rate, This represents the change in corrosion rate. This represents the change in the root mean square value of the vibration stress.

[0040] Time-varying mechanical stress calculation accurately reflects the stress changes experienced by packers under vibration environments, providing crucial data for assessing their mechanical performance. Using this formula, combined with measured data from vibration acceleration sensors and the elastic modulus of the packer material, time-varying mechanical stress at different times can be calculated in real time, helping to detect potential mechanical damage risks early. The vibration-corrosion coupling coefficient calculation considers the interaction between vibration and corrosion. In complex downhole environments, packers are affected not only by vibration but also by corrosion; the coupling between these two factors accelerates packer damage. This coefficient quantifies the degree of this coupling effect. By analyzing the relationship between changes in corrosion rate and the root mean square value of vibration stress, it provides important data for predicting the lifespan of packers under vibration-corrosion environments. In practical applications, using these calculation results, operators can rationally adjust the operating parameters of the packer based on the magnitude and trend of time-varying mechanical stress, avoiding prolonged high-stress conditions. Simultaneously, based on the vibration-corrosion coupling coefficient, targeted anti-corrosion measures can be developed, such as using corrosion-resistant materials and strengthening protective coatings, thereby effectively extending the service life of the packer and reducing replacement and maintenance costs. Furthermore, these calculation results can provide more accurate data input for digital twin models, further improving the model's prediction accuracy and reliability, enabling digital twin systems to better simulate the actual operation of packers, and providing stronger guarantees for safe production in industries such as oil extraction.

[0041] In this embodiment, the calculation of corrosion field parameters in step S3 includes: dynamic corrosion rate calculation, the formula is as follows: ,in For dynamic corrosion rate, Let be the material corrosion constant. The concentration of H2S in the downhole gas medium. The activation energy of the corrosion reaction. The gas constant is... This is the real-time temperature measured by the temperature sensor. The vibration-corrosion coupling coefficient is... The root mean square value of vibration stress; the remaining wall thickness is predicted using the following formula: ,in For the remaining wall thickness, This is the initial wall thickness of the packer. For integration variables, for Corrosion rate at any given time.

[0042] The aforementioned corrosion field parameter calculation methods can more accurately reflect the corrosion status of downhole packers in complex environments. The dynamic corrosion rate calculation comprehensively considers multiple factors such as H2S concentration in the downhole gas medium, real-time temperature, and vibration stress. Compared to traditional corrosion rate calculation methods that only consider a single factor, it can more realistically simulate the corrosion process of the packer under actual operating conditions. The remaining wall thickness prediction formula is based on the dynamic corrosion rate, continuously updating the remaining wall thickness of the packer over time, providing crucial data for packer integrity assessment. This precise corrosion field parameter calculation helps to detect potential corrosion problems in the packer in advance, allowing for timely maintenance or replacement measures. For example, when the calculated remaining wall thickness approaches the safety threshold, maintenance plans can be arranged in advance to avoid downhole accidents caused by packer corrosion damage, improving the safety and reliability of downhole operations. Simultaneously, these calculation results can also provide a basis for packer design optimization. In subsequent designs, material selection or structural design can be adjusted based on actual corrosion conditions to extend the packer's service life and reduce production costs. Furthermore, by combining these corrosion field parameters with digital twin technology, a virtual model of the packer can be constructed, reflecting its actual condition downhole in real time. Through analysis and prediction of the virtual model, the use and maintenance strategies of the packer can be further optimized, enabling effective diagnosis of the coupling integrity of the downhole packer and accurate prediction of its lifespan, thus promoting the development of intelligent and efficient downhole operations.

[0043] Specifically, the calculation of sealing field parameters in step S3 includes: calculation of sealing stress relaxation, the formula is as follows: ,in for Sealing stress at any time, For the initial sealing stress, For time, Let be the relaxation time constant of the rubber seal. This is the contact area compensation coefficient. The annular pressure difference is used to calculate the blockage risk index, using the following formula: ,in To block the risk index, The viscosity of the downhole fluid. This refers to the fluctuation in the sealing contact area caused by vibration. This represents the initial sealing contact area. This represents the remaining wall thickness.

[0044] These two key calculations allow for the evaluation of the packer's sealing performance from different perspectives. The sealing stress relaxation calculation clearly reflects the changes in sealing stress over time. Since rubber seals experience stress relaxation during long-term use, this directly affects the packer's sealing effect. This formula can predict the sealing stress at different time points, allowing for early assessment of whether the sealing performance will decline to the point of affecting the packer's normal operation. The clogging risk index calculation considers multiple factors, including downhole fluid viscosity, fluctuations in the sealing contact area, the initial sealing contact area, and the remaining wall thickness. Downhole fluid viscosity affects the fluid flow characteristics around the packer; fluctuations in the sealing contact area may be due to vibrations caused by complex downhole conditions; and the remaining wall thickness reflects the packer's corrosion level. The clogging risk index, calculated by combining these factors, accurately assesses the likelihood of packer clogging. Once the specific values ​​of the sealing stress relaxation and clogging risk index are obtained, they can be compared with preset safety thresholds. If the calculated value of the sealing stress relaxation is close to or below the safety threshold, the seal needs to be inspected or replaced promptly. If the blockage risk index exceeds the safe range, the packer needs to be cleaned or measures taken to reduce the viscosity of the downhole fluid. This assessment method based on precise calculations greatly improves the accuracy and reliability of packer condition monitoring, providing strong support for the stable operation of downhole work. Simultaneously, these calculation results can be fed back into the digital twin model to further optimize the virtual model, making it more accurately reflect the actual condition of the packer, thereby enabling more precise management and maintenance of downhole packers.

[0045] In practical applications, step S3, the three-field coupling integrated evaluation and lifetime prediction calculation, includes: failure probability calculation, the formula is as follows: ,in for The probability of failure at time 1. For integration variables, for Time-varying mechanical stress at time t, For the material's yield strength, for Corrosion rate over time As the baseline corrosion rate, for The congestion risk index at any time. This is the critical congestion index. The fitting parameters are used; the remaining lifetime is calculated using the following formula: ,when hour For remaining lifespan, This is the critical time when the failure probability reaches 0.95. The current time is used; the lifetime prediction algorithm is a Bayesian fusion algorithm of LSTM time series prediction algorithm and physical model simulation, and is equipped with a failure mode library containing 12 typical failure scenarios. This failure mode library is built based on 200+ field failure cases and is updated with ≥5 cases per month.

[0046] This three-field coupled comprehensive evaluation and lifetime prediction calculation method has several advantages. In the failure probability calculation, by considering multiple key factors such as time-varying mechanical stress, corrosion rate, and blockage risk index, it can comprehensively and accurately reflect the failure probability faced by the downhole packer at different times. Time-varying mechanical stress changes continuously with the downhole environment, the corrosion rate reflects the degree of corrosion on the packer, and the blockage risk index reflects the potential risk of blockage. Integrating these factors into the failure probability formula makes the calculation results more consistent with actual working conditions. The remaining lifetime calculation method determines the remaining lifetime based on the difference between the critical time when the failure probability reaches 0.95 and the current time, providing a clear reference for field personnel. Once the remaining lifetime is calculated, they can prepare for equipment maintenance and replacement in advance, avoiding downhole operation failures due to sudden packer failure and ensuring the continuity and stability of production. The use of a fusion algorithm of LSTM time-series prediction algorithm and Bayesian algorithm based on physical model simulation as the lifetime prediction algorithm fully leverages the advantages of both algorithms. The LSTM time series prediction algorithm can process time series data, capture long-term dependencies in the data, and accurately predict the lifespan trend of packers. Physical model simulation, based on actual physical principles, simulates the working process of the packer. A Bayesian fusion algorithm combines these two methods, making the prediction results more reliable. The failure mode library is built upon a large number of field failure cases and is continuously updated monthly, ensuring its timeliness and accuracy. By analyzing these failure cases, the causes and modes of packer failure under different operating conditions can be summarized, providing rich knowledge support for lifespan prediction and fault diagnosis. Field personnel can use the information in the failure mode library to identify potential problems before failures occur and take corresponding preventative measures, improving the performance and reliability of downhole packers. Simultaneously, as new failure cases are continuously added, the failure mode library can be continuously improved, further enhancing the accuracy of lifespan prediction and fault diagnosis.

[0047] The following detailed description of the specific implementation method of this embodiment one is based on the field application case of Moxi 022-X13 well.

[0048] I. System Deployment Data acquisition layer deployment: A vibration accelerometer (monitoring range 0-10kHz, sampling rate 1MHz) is installed on the packer body, a pH probe (pressure resistant 100MPa) is deployed in the annular fluid, strain gauges (accuracy ±0.1%FS) are attached to the surface of the sealing element, and temperature and pressure sensors (temperature range -20-200℃) are installed at key downhole locations; high-pressure resistant optical fiber is used to transmit sensor data to the ground relay station.

[0049] Edge computing layer configuration: Deploy edge computing nodes, run Kalman filtering algorithm to eliminate vibration signal noise, extract vibration time-frequency features through wavelet transform, use time synchronization protocol to achieve data synchronization (synchronization error 0.8ms), and transmit the data to the cloud analysis platform after processing the data through lossless compression algorithm.

[0050] Cloud-based analysis layer construction: L4 level digital twin system was built, and a macroscopic structural dynamic model of the packer was constructed based on ANSYS. The microscopic process of H2S corrosion was simulated through LAMMPS. The dynamic simulation engine was set with a vibration simulation time step of 1ms and a corrosion simulation time step of 1 day. The hardware-in-the-loop test interface response delay was 45ms. The digital thread integrated the well's design, manufacturing and historical operation and maintenance data, with a data link breakage rate of 0.08%.

[0051] Application layer configuration: The visual dashboard displays parameters such as vibration spectrum, corrosion rate, and sealing stress in real time. The early warning threshold is set to failure probability PF=0.8. The maintenance work order module is associated with the existing operation and maintenance management system of the oilfield.

[0052] II. Life Prediction Process Data Acquisition and Preprocessing: From January to June 2024, the well casing pressure (0.01-33.56 MPa), oil pressure (9.53-42.07 MPa), wellhead temperature (9.6-54℃), and daily gas production (0.19-17.69×10⁻⁶) were collected using sensors. 4 m³ / d), H2S concentration (18.59 ppm), Parameters such as content (126.221ppm); the edge computing layer filters, extracts features and compresses the data before transmitting it to the cloud.

[0053] Three-field coupling simulation analysis: Vibration field analysis: Calculation of time-varying mechanical stress Root mean square value of vibration stress The vibration-corrosion coupling coefficient is obtained. (Acidic environment calibration value).

[0054] Corrosion field analysis: Substituting into the dynamic corrosion rate formula The calculated average corrosion rate was 0.35 mm / year; through The remaining wall thickness is projected to be 6.8 mm by the end of 2024.

[0055] Sealing field analysis: Calculation of sealing stress relaxation The stress relaxation rate is 12% / year; the blockage risk index is... Calculated (critical value) ).

[0056] Overall assessment: Passed Calculate the current PF = 0.72; when hour, As of June 10, 2024, the remaining lifetime RUL is 238 days.

[0057] Failure mode matching: Based on the failure mode library, the primary failure risk is the combined failure of rubber swelling and metal hydrogen embrittlement (probability 68%), and the secondary risk is thread loosening caused by vibration (probability 22%).

[0058] III. Application Effects Early warning and maintenance: On June 12, 2024, the application layer pushed out an early warning of sealing stress decay, and generated short-term measures (intensified casing pressure monitoring and injection of 200ppm corrosion inhibitor within 30 days), medium-term intervention (planned well repair and replacement of sealing components and installation of vibration dampers within 90-120 days) and system optimization suggestions.

[0059] Performance verification: The on-site acoustic emission detection vibration identification accuracy rate was 92.1% (0.4% error compared to the predicted value of 91.7%), the ultrasonic thickness measurement corrosion rate was 0.33 mm / year (0.02 mm error compared to the predicted value of 0.35 mm / year), and the packer showed signs of sealing failure on October 22, 2024 (error of +7 days compared to the predicted RUL=238 days), verifying the accuracy and reliability of the invention.

[0060] In summary, the downhole packer coupled integrity diagnostic system and digital twin lifetime prediction method proposed in this invention have demonstrated significant advantages and good results in the field application of the Moxi 022-X13 well: Significantly improved technical performance: vibration mode recognition accuracy reaches 91.7%, corrosion rate prediction error is controlled within ±0.2mm / year, seal failure warning can be given 14 days in advance, and life prediction deviation is ±7 days (95% confidence level). Compared with traditional technology, the accuracy is significantly improved.

[0061] The economic benefits are significant: by reducing unplanned downtime (maintenance costs are reduced by more than 30%) and extending packer life (usage time is increased by 40%), the operation and maintenance costs of oil and gas extraction are greatly reduced.

[0062] HSE levels have been optimized: effectively preventing well control accidents caused by seal failure (risk reduced by 90%), reducing the frequency of high-risk downhole operations, and reducing downhole exposure time for maintenance personnel by 75%.

[0063] It strongly supports the industry's transformation: It has achieved deep integration of petroleum engineering with new-generation information technologies such as digital twins and multiphysics simulation, providing a key technology paradigm for the construction of digital oil and gas fields and promoting the industry's transformation from "post-failure maintenance" to "pre-failure early warning".

[0064] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A digital twin lifetime prediction method for a downhole packer coupled integrity diagnostic system, characterized in that: The downhole packer coupled integrity diagnostic system comprises a data acquisition layer, an edge computing layer, a cloud analysis layer, and an application layer connected in sequence. The data acquisition layer is used to collect and transmit vibration, corrosion, sealing, and operating parameters of the downhole packer. The edge computing layer is used to preprocess the collected parameters. The cloud analysis layer has a built-in L4-level digital twin system for constructing a dynamic model of the vibration-corrosion-sealing three-field coupling, and based on this model, packer integrity diagnostics and life prediction are carried out. The application layer is used to output diagnostic results, early warning information, and maintenance suggestions. The digital twin lifetime prediction method includes the following steps: S1: The vibration, corrosion, sealing and operating parameters of the packer are collected through the downhole sensor network of the data acquisition layer and transmitted to the edge computing layer via the data transmission module; S2: The edge computing layer performs noise filtering, feature extraction, data synchronization and compression on the parameters before transmitting them to the cloud analysis layer; S3: The cloud-based analysis layer calls the three-field coupled dynamic model in the L4-level digital twin system for simulation analysis, and combines the life prediction algorithm to calculate the packer failure probability and remaining life. S4: The cloud-based analytics layer transmits diagnostic results, failure probabilities, and remaining lifespan data to the application layer. The application layer displays the data through a visual dashboard, triggers alerts, and generates maintenance work orders. At the same time, it transmits field feedback data back to the cloud-based analytics layer to update the L4-level digital twin system model, forming a closed loop. The calculation of vibration field parameters in step S3 includes: calculation of time-varying mechanical stress, using the formula: ,in For time-varying mechanical stress, for Time-varying strain of the packer material The vibration acceleration data are measured by a vibration acceleration sensor. The elastic modulus of the packer material; the vibration-corrosion coupling coefficient is calculated using the following formula: ,in , This is the root mean square value of the vibration stress. For integration time, For corrosion rate, This represents the change in corrosion rate. This represents the change in the root mean square value of the vibration stress. Step S3 involves calculating the corrosion field parameters, including: dynamic corrosion rate calculation, using the formula: ,in For dynamic corrosion rate, Let be the material corrosion constant. The concentration of H2S in the downhole gas medium. The activation energy of the corrosion reaction. The gas constant is... This is the real-time temperature measured by the temperature sensor. The vibration-corrosion coupling coefficient is... The root mean square value of vibration stress; the remaining wall thickness is predicted using the following formula: ,in For the remaining wall thickness, This is the initial wall thickness of the packer. For integration variables, for Corrosion rate at any given time; Step S3 involves calculating the sealing field parameters, including: calculating the sealing stress relaxation, using the following formula: ,in for Sealing stress at any time, The initial sealing stress, For time, Let be the relaxation time constant of the rubber seal. This is the contact area compensation coefficient. The annular pressure difference is used to calculate the blockage risk index, using the following formula: ,in To block the risk index, The viscosity of the downhole fluid. This refers to the fluctuation in the sealing contact area caused by vibration. δ(t) represents the initial sealing contact area, and δ(t) represents the remaining wall thickness.

2. The digital twin lifetime prediction method for the downhole packer coupled integrity diagnostic system according to claim 1, characterized in that: The data acquisition layer includes a downhole sensor network and a data transmission module. The downhole sensor network consists of a vibration accelerometer, a pH probe, strain gauges, and temperature and pressure sensors. The vibration accelerometer is used to monitor the mechanical vibration spectrum, with a monitoring range of 0-10kHz and a sampling rate of ≥1MHz. The pH probe is used to monitor the acidity and alkalinity of the corrosive environment, with a pressure resistance of ≥100MPa. The strain gauge is used to measure the stress distribution of the sealing element, with an accuracy of ±0.1%FS. The temperature and pressure sensors are used to collect downhole operating parameters, with a temperature monitoring range of -20-200℃. The data transmission module uses high-voltage resistant optical fiber or electromagnetic wave relay to achieve data transmission.

3. The digital twin lifetime prediction method for the downhole packer coupled integrity diagnostic system according to claim 1, characterized in that: The edge computing layer uses Kalman filtering to eliminate signal noise, wavelet transform to extract vibration features, and time synchronization protocol to achieve data synchronization with a synchronization error of <1ms; at the same time, lossless algorithm is used to compress the preprocessed data.

4. The digital twin lifetime prediction method for the downhole packer coupled integrity diagnostic system according to claim 1, characterized in that: The L4-level digital twin system of the cloud-based analysis layer includes a dynamic simulation engine and digital threads. The dynamic simulation engine supports multi-rate simulation, employs explicit solutions for rapid vibration processes at the millisecond level, and implicit solutions for slow corrosion processes at the day level. It also has a hardware-in-the-loop testing interface with a response latency of <50ms. The digital threads enable seamless data flow between design, manufacturing, and operation and maintenance, with a data link breakage rate of <0.1%. The three-field coupled dynamic model adopts a multi-scale modeling framework. At the macroscale, a structural dynamics model is constructed using ANSYS finite element analysis software, while at the microscale, the H2S corrosion process is simulated using LAMMPS molecular dynamics tools.

5. The digital twin lifetime prediction method for the downhole packer coupled integrity diagnostic system according to any one of claims 1-4, characterized in that: The application layer includes a visual dashboard, an early warning push module, and a maintenance work order generation module; the early warning push module pushes early warning information based on a threshold triggering mechanism, and the maintenance work order generation module automatically generates maintenance work orders based on lifespan prediction results.

6. The digital twin lifetime prediction method for the downhole packer coupled integrity diagnostic system according to claim 1, characterized in that: Step S3, the three-field coupling integrated assessment and lifetime prediction calculation, includes: failure probability calculation, the formula is as follows: ,in Let be the failure probability at time t, and τ be the integration variable. for Time-varying mechanical stress at time t, For the material's yield strength, for Corrosion rate over time As a baseline corrosion rate, for The congestion risk index at any time This is the critical congestion index. , , The fitting parameters are used; the remaining lifetime is calculated using the following formula: ,when hour, For remaining lifespan, This is the critical time when the failure probability reaches 0.

95. The current time is used; the lifetime prediction algorithm is a Bayesian fusion algorithm of LSTM time series prediction algorithm and physical model simulation.

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