Casing running state control system based on digital twinning

Abstract

This invention discloses a bushing operation status control system based on digital twins, relating to the fields of bushing operation program control and intelligent operation and maintenance technology. It includes a module for multi-source operation data acquisition and program interaction throughout the bushing's entire lifecycle, a module for full-size digital twin program simulation modeling of the bushing, a module for quantitative evaluation and anomaly identification of the bushing operation status program, a module for predicting and analyzing the evolution trend of bushing operation risks, a module for generating multi-objective adaptive optimization program control strategies, and a module for digital twin program simulation verification and control strategy pre-optimization. This invention achieves real-time perception and evaluation of the bushing's operation status across all dimensions, completes the pre-verification of control strategies through digital twin simulation, adapts the adaptive optimization control strategies to changes in operating conditions, and enables continuous model iteration through closed-loop feedback throughout the entire process, effectively improving the accuracy of bushing management and control, extending service life, and reducing operation and maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of bushing operation program control and intelligent operation and maintenance technology, and in particular to a bushing operation status control system based on digital twin. Background Technology

[0002] As a core structure in the completion and production process of oil and gas wells, casing plays a crucial role in fixing the wellbore, isolating formation fluids, and ensuring unobstructed oil and gas passages. The stability of its operational status directly determines the production efficiency and service life of the oil and gas well. Throughout the entire lifecycle of oil and gas extraction, casing is subjected to complex conditions of high temperature, high pressure, strong corrosion, and changing formation stress, making it prone to problems such as stress concentration, corrosion perforation, excessive deformation, and fatigue damage. If these problems are not detected and effectively controlled in a timely manner, they may lead to serious safety accidents such as well blowouts, casing failure, and oil and gas leaks, causing huge economic losses and environmental damage. Traditional casing operation status management relies heavily on manual periodic inspections and experience-based judgment. These inspections have long cycles and limited coverage, failing to capture real-time dynamic changes in casing operation status. This makes it difficult to detect early potential faults, and only reactive repair measures can be taken after a fault occurs, failing to achieve proactive risk prevention.

[0003] Existing casing operation status monitoring systems mostly employ single-parameter monitoring modes, capable of collecting only a few key parameters such as casing pressure and temperature. They lack comprehensive perception of multi-dimensional state parameters, including casing stress distribution, corrosion rate, deformation degree, and fatigue damage, failing to fully reflect the true operating state of the casing. Some systems employing multi-parameter monitoring suffer from insufficient spatiotemporal registration accuracy, data redundancy, and noise interference due to a lack of effective data fusion and standardization mechanisms. This results in low reliability and usability of the monitoring data, failing to provide accurate data support for casing operation status assessment. Furthermore, existing systems lack deep integration with digital twin technology, failing to construct a full-size, high-precision virtual mapping model of the casing. Real-time synchronization between the physical casing and the virtual model is impossible, making it difficult to predict the evolution trend of the casing's operating state through simulation. The formulation of control strategies lacks a scientific virtual verification process, making it difficult to guarantee the feasibility and safety of the control strategies.

[0004] Existing casing operation control systems mostly employ open-loop control modes triggered by fixed thresholds. These control strategies lack adaptive adjustment capabilities and cannot flexibly optimize based on dynamic factors such as changes in casing operating status, fluctuations in operating conditions, and alterations in the geological environment. This results in poor control performance and makes it difficult to simultaneously meet the multi-objective optimization requirements of casing operation safety, service life, and production efficiency. Furthermore, existing systems lack a full-process closed-loop feedback and iterative optimization mechanism. The effects of executed control commands cannot be promptly fed back to the control center, and dynamic adjustments to control algorithms and model parameters based on actual operating data are impossible. This makes it difficult to guarantee the long-term control accuracy and stability of the system. In addition, existing systems do not achieve deep integration between operation control and maintenance management. Maintenance plans lack precise consideration of the casing operating status, easily leading to timing conflicts between maintenance and production operations. This not only affects the smooth conduct of maintenance operations but may also exacerbate casing damage and shorten its service life. These technical shortcomings severely restrict the level of intelligent and refined management of oil and gas well casing operations. Summary of the Invention

[0005] The present invention proposes a highly efficient encrypted tablet data protection system and implementation method to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a bushing operation status control system based on digital twin, comprising: The casing full life cycle multi-source operation data acquisition and program interaction module adopts a distributed multi-sensor fusion architecture. Stress, temperature, pressure, corrosion and vibration sensing units are deployed along the casing body to collect all-dimensional operation data of casing body parameters, wellbore environment parameters and mining conditions in real time. Data cleaning, spatiotemporal registration and standardization processing are completed. Program-level real-time bidirectional data interaction is completed with the field control system production database through industrial Ethernet. The casing full-size digital twin program simulation modeling module constructs a full-size three-dimensional digital twin program model of casing body, wellbore structure parameters, and geological environment parameters based on casing design parameters, wellbore structure parameters, and geological environment parameters. Combined with real-time acquired operation data, it completes online correction and dynamic mapping of model parameters, and realizes real-time synchronization of all elements of the casing physical entity and digital twin. The casing operation status quantitative assessment and anomaly identification module, based on real-time operating data from a digital twin, constructs a multi-dimensional program assessment model for casing stress distribution, corrosion rate, deformation degree, and fatigue damage, completes real-time quantitative assessment of casing operation status, and combines preset safety thresholds to identify abnormal casing conditions and classify risk levels. The casing operation risk program prediction and evolution trend analysis module is based on a long short-term memory neural network to build a programmatic risk prediction model. It uses the real-time status assessment results of the casing's historical operation data and the working condition change parameters as the model input, and outputs the risk evolution trend and remaining service life prediction sequence of the casing in the future period. The multi-objective adaptive optimization program control strategy generation module combines the risk prediction sequence of casing operation status assessment results with on-site working condition constraints. With the goal of maximizing the safe service life of the casing and optimizing mining efficiency, it generates an adaptive optimization program control strategy for casing operation conditions based on a model predictive control algorithm, outputting a standardized program control command sequence. The digital twin program simulation verification and control strategy pre-optimization module inputs the generated control strategy into the casing digital twin, completing a full-process program pre-run in a virtual simulation environment. It simulates changes in casing operation status under different working conditions, verifies the feasibility and safety of the control strategy, and performs pre-optimization adjustments based on the simulation results. The field equipment program linkage control and automatic command execution module transforms the optimized control strategy into program control commands that can be recognized by the field equipment, writes them into the field production control system, drives the wellhead pressure control, oil production rate adjustment, injection parameter adjustment, and field execution equipment to complete the automatic execution of commands. The control effect closed-loop feedback and model iteration optimization module collects bushing operation status data in real time after the control command is executed, compares it with the control target to complete the quantitative evaluation of the control effect, and completes the adaptive iterative optimization of the parameters of the digital twin model status evaluation model risk prediction model control algorithm based on the evaluation results.

[0007] Furthermore, it also includes a casing extreme condition emergency procedure safety control module. This module is based on a digital twin to build an extreme condition procedure simulation library, covering various extreme conditions such as casing leakage, deformation exceeding limits, stress surge, and corrosion aggravation. It can quickly match the corresponding emergency procedure control plan based on real-time anomaly identification results, and at the same time complete the simulation verification of the emergency plan in the digital twin environment, generate graded emergency procedure control instructions, drive the field execution equipment to complete the wellhead shutdown pressure release parameter locking emergency operation, and simultaneously trigger on-site audible and visual warnings and operation and maintenance information reporting, so as to achieve rapid response and safety management under extreme conditions.

[0008] Furthermore, it also includes a bushing full lifecycle operation and maintenance program management linkage module. This module can generate a bushing preventive maintenance, inspection and repair program plan based on the bushing operation status assessment results and the remaining service life prediction sequence. At the same time, it can coordinate and optimize the operation and maintenance plan with the bushing operation program control strategy, reasonably adjust the bushing operation conditions during the operation and maintenance window, avoid the timing conflict between operation and maintenance work and production operation, and complete the parameter correction and status update of the digital twin based on the field operation and maintenance inspection data, so as to realize the two-way collaborative optimization of bushing operation and maintenance management and operation control.

[0009] Furthermore, the casing operation status quantitative assessment and anomaly identification module incorporates a casing health quantitative calculation unit. It generates a real-time casing health value through weighted coupling calculation of multi-dimensional status parameters. The calculation formula is as follows: in This represents the real-time health value of the casing body. The total number of status parameters participating in the health assessment. For the first The weight coefficients corresponding to the item's state parameters, and the sum of all weight coefficients is 1. For the first Real-time monitoring values ​​of the item status parameters The upper limit of the safety threshold for the i-th state parameter. The lower limit of the safety threshold for the i-th state parameter is defined. Through the normalization and weighted coupling of multi-dimensional parameters, the accurate quantitative characterization of the health status of the casing operation can be achieved. At the same time, the risk level of casing operation can be automatically classified based on the health value, providing an accurate quantitative basis for the generation of subsequent program control strategies.

[0010] Furthermore, the full-size digital twin simulation modeling module for the casing incorporates a multi-physics coupled simulation unit, which can construct a multi-physics coupled simulation model of the casing's structural mechanical field, temperature field, fluid field, corrosion, and electrochemical field. Based on real-time acquired operating data, it completes the synchronous solution of the multi-physics field, accurately simulating the stress distribution, deformation trend, and corrosion evolution process of the casing under different operating conditions. At the same time, it can improve the simulation accuracy of key parts of the casing through adaptive mesh refinement technology, realizing a high-precision virtual mapping of the entire life cycle operation process of the casing.

[0011] Furthermore, the casing operation risk prediction and evolution trend analysis module has a built-in multi-scenario working condition simulation unit. It can simulate the impact of geological environment change parameters on casing operation status based on digital twins, generate casing risk evolution paths under different scenarios, and identify weak parts and high-risk areas of casing operation based on prediction results. It can also generate targeted control and optimization strategies in advance to achieve proactive prevention and control of casing operation risks.

[0012] Furthermore, the multi-objective adaptive optimization program control strategy generation module incorporates a multi-objective rolling optimization program control unit, which generates the optimal control sequence through rolling optimization solutions within the finite time domain. The objective function calculation formula is as follows: in The objective function value for controlling the strategy optimization is given, where N is the prediction time domain length and q is the weighting coefficient of the system output error. For the first The target value of the casing operating status at a given moment. For the first The predicted output value of the bushing operating status at time t, where r is the weighting coefficient of the rate of change of the control variable. No. By minimizing the incremental change of the control quantity at each moment, the accurate tracking control of the bushing's operating status can be achieved while meeting the constraints of the field operating conditions. At the same time, it limits large fluctuations in the control quantity, ensuring the stability of the field equipment operation and the continuity of the control process.

[0013] Furthermore, the emergency procedure safety control module for extreme operating conditions of the bushing has a built-in hierarchical emergency control permission management unit. It can divide the corresponding control permissions according to the risk level of extreme operating conditions. Under low-risk conditions, the system automatically adjusts its emergency response. Under medium- and high-risk conditions, the emergency control plan is pushed to the operation and maintenance management platform. After confirmation by the operation and maintenance personnel, the emergency operation is executed. Under extremely high-risk conditions, the emergency shutdown and safety isolation operation are triggered directly. At the same time, all data of the emergency control process is recorded, providing data support for subsequent accident analysis and plan optimization.

[0014] Furthermore, the bushing full lifecycle operation and maintenance program management linkage module has a built-in operation and maintenance knowledge base and program case library. It can build a standardized operation and maintenance knowledge base based on historical operation and maintenance data, bushing failure cases, industry standard specifications, etc. When abnormal bushing conditions are identified, the corresponding operation and maintenance solutions and standardized operating procedures can be quickly matched. At the same time, the knowledge base and case library can be iteratively updated based on the results of each operation and maintenance operation, improving the matching accuracy and executability of operation and maintenance solutions.

[0015] Furthermore, the control effect full-process closed-loop feedback and model iterative optimization module has a built-in algorithm parameter adaptive program optimization unit. It adopts the particle swarm optimization algorithm, with the goal of minimizing the control effect deviation, to adaptively optimize and adjust the core parameters of the digital twin model, state assessment model, risk prediction model, and control algorithm. At the same time, it can complete online incremental training of the model based on the operating data under different working conditions, improve the adaptability and generalization ability of the model and algorithm to different wellbore environment mining conditions, and ensure the control accuracy and stability of the system in long-term operation.

[0016] Compared with existing technologies, the beneficial effects of this invention are: This invention adopts a distributed multi-sensor fusion architecture to achieve comprehensive collection and program-level interaction of multi-dimensional operating data throughout the entire life cycle of the bushing. Through data cleaning, spatiotemporal registration, and standardization processing, it significantly improves the reliability and availability of monitoring data, providing comprehensive and accurate data support for bushing operating status assessment, risk prediction, and control strategy generation, and realizing all-round, real-time perception of bushing operating status.

[0017] This invention constructs a full-size digital twin program simulation model of the bushing, realizing real-time synchronization of all elements between the physical entity of the bushing and the virtual model. Through multi-physics field coupling simulation, it accurately restores the operating state and evolution law of the bushing under complex working conditions. At the same time, it uses the digital twin simulation environment to complete the full-process pre-run and verification of the control strategy, identify potential problems of the control strategy in advance and complete optimization and adjustment, significantly improving the feasibility and safety of the control strategy, and providing strong virtual simulation support for the scientific formulation of control strategies.

[0018] This invention achieves precise quantitative assessment of casing operation status and scientific prediction of risk evolution trends through a multi-dimensional programmed quantitative evaluation model and a programmed risk prediction model. It identifies weak points and high-risk areas in casing operation in advance, providing accurate basis for proactive risk prevention and targeted control, effectively reducing the probability of casing failure, and ensuring the continuity and safety of oil and gas well production.

[0019] This invention generates a multi-objective adaptive optimization program control strategy based on a model predictive control algorithm. It can dynamically adjust and optimize the control strategy according to the casing operation status, risk prediction results and on-site working constraints. Under the premise of ensuring the safety of casing operation, it takes into account the multi-objective needs of extending service life and improving mining efficiency, avoids the problem of poor control effect caused by fixed control mode, and greatly improves the precision and intelligence level of casing operation control.

[0020] This invention constructs a closed-loop feedback and model iteration optimization mechanism for the entire process, collects control command execution effect data in real time, completes quantitative evaluation of control effect and adaptive adjustment of model parameters, and continuously improves the control accuracy and stability of the system. At the same time, through the casing full life cycle operation and maintenance program management linkage module, it realizes two-way collaborative optimization of operation control and operation and maintenance management, rationally plans operation and maintenance windows and operation and maintenance plans, avoids the timing conflict between operation and maintenance work and production operation, reduces the impact of operation and maintenance work on casing operation, extends the service life of casing, and reduces operation and maintenance costs, providing a complete technical solution for the intelligent and refined management of oil and gas well casing. Attached Figure Description

[0021] Figure 1 This is a schematic block diagram of a bushing operation status control system based on digital twin proposed in this invention; Figure 2 Flowchart for multiphysics modeling and state quantification evaluation of digital twin for bushing; Figure 3 Flowcharts are generated for operational risk prediction and multi-objective adaptive control strategies; Figure 4 Flowchart for emergency safety control and on-site automatic execution under extreme working conditions; Figure 5A flowchart for full lifecycle operation and maintenance linkage and closed-loop iterative optimization. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this 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. Therefore, they should not be construed as limitations on this invention.

[0024] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0025] Reference Figures 1 to 5 A bushing operation status control system based on digital twin, comprising: The casing full life cycle multi-source operation data acquisition and program interaction module adopts a distributed multi-sensor fusion architecture. Stress, temperature, pressure, corrosion and vibration sensing units are deployed along the casing body to collect all-dimensional operation data of casing body parameters, wellbore environment parameters and mining conditions in real time. Data cleaning, spatiotemporal registration and standardization processing are completed. Program-level real-time bidirectional data interaction is completed with the field control system production database through industrial Ethernet. The casing full-size digital twin program simulation modeling module constructs a full-size three-dimensional digital twin program model of casing body, wellbore structure parameters, and geological environment parameters based on casing design parameters, wellbore structure parameters, and geological environment parameters. Combined with real-time acquired operation data, it completes online correction and dynamic mapping of model parameters, and realizes real-time synchronization of all elements of the casing physical entity and digital twin. The casing operation status quantitative assessment and anomaly identification module, based on real-time operating data from a digital twin, constructs a multi-dimensional program assessment model for casing stress distribution, corrosion rate, deformation degree, and fatigue damage, completes real-time quantitative assessment of casing operation status, and combines preset safety thresholds to identify abnormal casing conditions and classify risk levels. The casing operation risk program prediction and evolution trend analysis module is based on a long short-term memory neural network to build a programmatic risk prediction model. It uses the real-time status assessment results of the casing's historical operation data and the working condition change parameters as the model input, and outputs the risk evolution trend and remaining service life prediction sequence of the casing in the future period. The multi-objective adaptive optimization program control strategy generation module combines the risk prediction sequence of casing operation status assessment results with on-site working condition constraints. With the goal of maximizing the safe service life of the casing and optimizing mining efficiency, it generates an adaptive optimization program control strategy for casing operation conditions based on a model predictive control algorithm, outputting a standardized program control command sequence. The digital twin program simulation verification and control strategy pre-optimization module inputs the generated control strategy into the casing digital twin, completing a full-process program pre-run in a virtual simulation environment. It simulates changes in casing operation status under different working conditions, verifies the feasibility and safety of the control strategy, and performs pre-optimization adjustments based on the simulation results. The field equipment program linkage control and automatic command execution module transforms the optimized control strategy into program control commands that can be recognized by the field equipment, writes them into the field production control system, drives the wellhead pressure control, oil production rate adjustment, injection parameter adjustment, and field execution equipment to complete the automatic execution of commands. The control effect closed-loop feedback and model iteration optimization module collects bushing operation status data in real time after the control command is executed, compares it with the control target to complete the quantitative evaluation of the control effect, and completes the adaptive iterative optimization of the parameters of the digital twin model status evaluation model risk prediction model control algorithm based on the evaluation results.

[0026] This invention also includes a casing extreme condition emergency procedure safety control module. This module is based on a digital twin to build an extreme condition procedure simulation library, covering various extreme conditions such as casing leakage, deformation exceeding limits, stress surge, and corrosion aggravation. It can quickly match the corresponding emergency procedure control plan based on real-time anomaly identification results, and at the same time complete the simulation verification of the emergency plan in the digital twin environment, generate graded emergency procedure control instructions, drive the field execution equipment to complete the wellhead shutdown pressure release parameter locking emergency operation, and simultaneously trigger on-site audible and visual warnings and operation and maintenance information reporting, so as to achieve rapid response and safety management under extreme conditions.

[0027] This invention also includes a bushing full lifecycle operation and maintenance program management linkage module. This module can generate a bushing preventive maintenance and inspection program plan based on the bushing operation status assessment results and the remaining service life prediction sequence. At the same time, it can coordinate and optimize the operation and maintenance plan with the bushing operation program control strategy, reasonably adjust the bushing operation conditions during the operation and maintenance window, avoid the timing conflict between operation and maintenance work and production operation, and complete the parameter correction and status update of the digital twin based on the field operation and maintenance inspection data, so as to realize the two-way collaborative optimization of bushing operation and maintenance management and operation control.

[0028] In this invention, the casing operation status program quantitative evaluation and anomaly identification module has a built-in casing health degree program quantitative calculation unit. It generates the real-time casing health degree value through weighted coupling calculation of multi-dimensional status parameters. The calculation formula is as follows: in This represents the real-time health value of the casing body. The total number of status parameters participating in the health assessment. For the first The weight coefficients corresponding to the item's state parameters, and the sum of all weight coefficients is 1. For the first Real-time monitoring values ​​of the item status parameters The upper limit of the safety threshold for the i-th state parameter. The lower limit of the safety threshold for the i-th state parameter is defined. Through the normalization and weighted coupling of multi-dimensional parameters, the accurate quantitative characterization of the health status of the casing operation can be achieved. At the same time, the risk level of casing operation can be automatically classified based on the health value, providing an accurate quantitative basis for the generation of subsequent program control strategies.

[0029] In this invention, the full-size digital twin simulation modeling module for the casing incorporates a multi-physics field coupling simulation unit, which can construct a multi-physics field coupling simulation model of the casing's structural mechanical field, temperature field, fluid field, corrosion, and electrochemical field. Based on real-time acquired operating data, it completes the synchronous solution of the multi-physics field, accurately simulating the stress distribution, deformation trend, and corrosion evolution process of the casing under different operating conditions. At the same time, it can improve the simulation accuracy of key parts of the casing through adaptive mesh densification technology, realizing a high-precision virtual mapping of the entire life cycle operation process of the casing.

[0030] In this invention, the casing operation risk program prediction and evolution trend analysis module has a built-in multi-scenario working condition program simulation unit. It can simulate the impact of geological environment change parameters on casing operation status based on digital twin simulation of different mining conditions, generate casing risk evolution paths under different scenarios, and identify weak parts and high-risk areas of casing operation based on prediction results, generate targeted control optimization strategies in advance, and achieve advanced prevention and control of casing operation risks.

[0031] In this invention, the multi-objective adaptive optimization program control strategy generation module incorporates a multi-objective rolling optimization program control unit, which generates the optimal control sequence through rolling optimization within a finite time domain. The objective function calculation formula is as follows: in The objective function value for controlling the strategy optimization is given, where N is the prediction time domain length and q is the weighting coefficient of the system output error. For the first The target value of the casing operating status at a given moment. For the first The predicted output value of the bushing operating status at time t, where r is the weighting coefficient of the rate of change of the control variable. No. By minimizing the incremental change of the control quantity at each moment, the accurate tracking control of the bushing's operating status can be achieved while meeting the constraints of the field operating conditions. At the same time, it limits large fluctuations in the control quantity, ensuring the stability of the field equipment operation and the continuity of the control process.

[0032] In this invention, the emergency procedure safety control module for extreme working conditions of the bushing has a built-in emergency control permission hierarchical program management unit. It can divide the corresponding control permissions according to the risk level of extreme working conditions. Under low-risk working conditions, the system automatically adjusts its emergency response. Under medium- and high-risk working conditions, the emergency control plan is pushed to the operation and maintenance management platform. After confirmation by the operation and maintenance personnel, the emergency operation is executed. Under extremely high-risk working conditions, the emergency shutdown and safety isolation operation are directly triggered. At the same time, all data of the emergency control process is recorded, providing data support for subsequent accident analysis and plan optimization.

[0033] In this invention, the bushing full lifecycle operation and maintenance program management linkage module has a built-in operation and maintenance knowledge base and program case library. It can build a standardized operation and maintenance knowledge base based on industry standard specifications for bushing failure cases using historical operation and maintenance data. When abnormal operating conditions of the bushing are identified, the corresponding operation and maintenance solution and standardized operation process can be quickly matched. At the same time, the knowledge base and case library can be iteratively updated based on the results of each operation and maintenance operation, thereby improving the matching accuracy and executability of the operation and maintenance solution.

[0034] In this invention, the closed-loop feedback and model iterative optimization module for the entire control effect process incorporates an adaptive algorithm parameter optimization unit. Employing a particle swarm optimization algorithm, it aims to minimize the control effect deviation and adaptively optimize the core parameters of the digital twin model, state assessment model, risk prediction model, and control algorithm. Simultaneously, it can perform online incremental training of the model based on operating data under different working conditions, improving the adaptability and generalization ability of the model and algorithm to different wellbore environments and mining conditions, and ensuring the control accuracy and stability of the system during long-term operation.

[0035] The following two examples further illustrate the specific implementation of this system: Example 1: Application of Production Casing Operation Status Control in Conventional Onshore Oil and Gas Wells This embodiment is applied to the full life cycle operation and management scenario of production casing in conventional sandstone oil and gas wells on land. It is adapted to the normal operating conditions of formation pressure fluctuation, oil production rate adjustment and formation water corrosion during normal oil and gas well production. It fully covers all functional modules and technical features of the system, and realizes real-time perception, intelligent assessment, risk prediction and adaptive optimization control of production casing operation status.

[0036] The casing full life cycle multi-source operation data acquisition and program interaction module is equipped with stress, temperature, pressure, corrosion and vibration sensing units distributed along the entire well section of the casing body. Data acquisition and transmission terminals are set up at the wellhead to collect real-time operation data of the casing body stress and strain, wellbore temperature and pressure, formation fluid corrosion rate, casing vibration characteristics and wellhead oil production parameters. The module completes data cleaning, spatiotemporal registration and standardization processing, and completes program-level real-time bidirectional data interaction with the well site production control system and oilfield production database through industrial Ethernet to ensure the real-time performance and consistency of data transmission. The casing full-size digital twin simulation modeling module, based on casing design parameters, wellbore structural parameters, and reservoir geological environment parameters, constructs a full-size three-dimensional digital twin program model coupling the casing body, wellbore, and formation. The model completely restores the full structural features of the casing steel grade, wall thickness, coupling position, and wellbore trajectory. Combined with real-time acquired operational data, it completes online correction and dynamic mapping of model parameters, realizing real-time synchronization of all elements of the casing physical entity and the digital twin. Simultaneously, it incorporates a multi-physics field coupling program simulation unit to complete the synchronous solution of the casing structure's mechanical field, temperature field, fluid field, and corrosion electrochemical field, accurately simulating the stress distribution, deformation trend, and corrosion evolution process of the casing under different operating conditions.

[0037] The casing operation status quantitative assessment and anomaly identification module, based on real-time operational data from a digital twin, constructs a multi-dimensional program assessment model for casing stress distribution, corrosion rate, deformation degree, and fatigue damage. This completes real-time quantitative assessment of the casing operation status, generates a health distribution cloud map of the entire well section, and, combined with preset safety thresholds, identifies abnormal casing conditions and classifies risk levels, accurately locating abnormal well sections and anomaly types. The casing operation risk program prediction and evolution trend analysis module, based on a long short-term memory neural network, constructs a programmed risk prediction model. Using historical casing operation data, real-time status assessment results, and changes in mining conditions as model inputs, it outputs the future risk evolution trend and remaining service life prediction sequence for the casing. It includes a built-in multi-scenario operating condition simulation unit to simulate the impact of different mining conditions, geological environment changes, and parameter adjustments on the casing operation status, generating casing risk evolution paths under different scenarios and identifying weak points and high-risk areas in casing operation.

[0038] The multi-objective adaptive optimization program control strategy generation module combines casing operation status assessment results, risk prediction sequences, and field operating condition constraints. With multiple objectives including casing operation safety, maximum service life, and optimal production efficiency, it generates an adaptive optimization program control strategy for casing operation conditions based on a model predictive control algorithm. It outputs a standardized program control command sequence, specifically covering field execution commands for wellhead pressure control, oil production rate adjustment, and injection parameter adjustment. The digital twin program simulation verification and control strategy pre-optimization module inputs the generated control strategy into the casing digital twin and performs a full-process program pre-run in a virtual simulation environment. It simulates changes in casing operation status under different operating conditions, verifies the feasibility and safety of the control strategy, and performs pre-optimization adjustments based on the simulation results. The field equipment program linkage control and automatic command execution module converts the optimized control strategy into program control commands recognizable by field equipment, writes them into the well site production control system, and drives the field execution equipment for wellhead pressure control, oil production rate adjustment, and injection parameter adjustment to automatically execute the commands.

[0039] The control effect closed-loop feedback and model iterative optimization module collects casing operation status data in real time after the execution of control commands, compares it with the control target to complete the quantitative evaluation of control effect, and performs adaptive iterative optimization of parameters of digital twin model, state evaluation model, risk prediction model and control algorithm based on the evaluation results. It has a built-in algorithm parameter adaptive program optimization unit and uses particle swarm optimization algorithm to complete the adaptive optimization adjustment of core parameters. The casing extreme working condition emergency program safety control module builds an extreme working condition program simulation library based on digital twin, covering various extreme working conditions such as casing leakage, deformation exceeding limit, stress surge, and corrosion aggravation. Based on real-time anomaly identification results, it quickly matches the corresponding emergency program control plan, generates graded emergency program control commands, drives the field execution equipment to complete emergency operations such as wellhead shutdown, pressure release, and parameter locking, and simultaneously triggers on-site audible and visual warnings and operation and maintenance information reporting. The bushing full lifecycle operation and maintenance program management linkage module generates program plans for preventive maintenance, inspection, and repair of bushings based on the bushing operation status assessment results and remaining service life prediction sequence. It coordinates and optimizes the operation and maintenance plan with the bushing operation program control strategy, and reasonably adjusts the bushing operation conditions during the operation and maintenance window. It has a built-in operation and maintenance knowledge base and program case library to quickly match the corresponding operation and maintenance solutions and standardized operating procedures.

[0040] Table 1 Comparison of core performance of this system and traditional casing control systems in conventional production scenarios. Table 1 shows data derived from six consecutive months of live-well operation tests on conventional onshore oil and gas wells, covering the entire production process under different stages of extraction and varying operating conditions. Traditional casing management systems suffer from limitations such as limited monitoring coverage, delayed anomaly identification, lack of risk prediction capabilities, and reliance on manual adjustments for control strategies. This results in poor casing damage prevention and control, and high maintenance costs. This new system, through multi-dimensional perception, digital twin simulation, adaptive optimization control, and a closed-loop iterative mechanism, achieves refined full-process management of casing operation status. It significantly improves anomaly identification, risk prevention, lifespan extension, and cost control, fully meeting the daily production management needs of conventional oil and gas wells.

[0041] Example 2: Application of High Temperature and High Pressure Deep Well Casing Full Life Cycle Operation and Management This embodiment is applied to the full life cycle operation and management scenario of deep well casing in high temperature and high pressure deep oil and gas reservoirs. It is adapted to the complex and extreme working conditions of high temperature and high pressure strong corrosion, drastic changes in formation stress and acidic media erosion during the exploitation of deep and ultra-deep wells. It fully covers all functional modules and technical features of the system, and realizes intelligent control and safety assurance of the casing operation status throughout the entire process under extreme working conditions.

[0042] The casing full-lifecycle multi-source operation data acquisition and program interaction module is equipped with high-temperature and high-pressure resistant distributed fiber optic sensing units and point-type electrochemical sensing units deployed along the entire well section of the casing. It is also equipped with downhole data acquisition subs and surface transmission terminals to collect real-time operation data of the casing stress and strain, wellbore ultra-high temperature and high pressure parameters, hydrogen sulfide and carbon dioxide corrosion rates, casing vibration fatigue characteristics, and downhole injection parameters. It completes signal noise reduction, spatiotemporal registration, and standardization processing under high-temperature environments. Through industrial Ethernet, it completes program-level real-time bidirectional data interaction with the deep well production control system and the oilfield central database to ensure the stability of data acquisition and the real-time performance of transmission under extreme conditions. The full-size digital twin simulation modeling module for casing, based on deep well casing string design parameters, wellbore structural parameters, and deep geomechanical parameters, constructs a full-size three-dimensional digital twin program model coupling the casing body, wellbore, and deep formation. It fully restores the structural features of deep well casing graded steel, special threaded couplings, and wellbore trajectory. Combined with real-time acquired downhole operation data, it completes online correction and dynamic mapping of model parameters, achieving real-time synchronization of all elements between the physical entity of the deep well casing and the digital twin. The built-in multiphysics field coupling program simulation unit completes the synchronous coupling solution of the casing structure mechanical field, temperature field, multiphase flow field, and corrosion electrochemical field under high temperature and high pressure environment, accurately simulating the stress concentration, creep deformation, and corrosion evolution process of the casing under extreme working conditions.

[0043] The casing operation status quantitative assessment and anomaly identification module, based on real-time operational data from a digital twin, constructs a multi-dimensional programmatic assessment model for casing stress distribution, corrosion rate, creep deformation, and fatigue damage under high-temperature and high-pressure environments. This completes real-time quantitative assessment of the casing operation status, generates a health distribution profile of the entire well section, and, combined with deep well casing safety thresholds, identifies abnormal operating conditions and classifies risk levels, accurately locating high-risk well sections and damage types. The casing operation risk programmatic prediction and evolution trend analysis module, based on a long short-term memory neural network, constructs a programmed risk prediction model under extreme operating conditions. Using historical deep well casing operation data, real-time status assessment results, and production condition adjustment parameters as model inputs, it outputs the risk evolution trend and remaining service life prediction sequence for future periods. It includes a built-in multi-scenario extreme operating condition programmatic simulation unit to simulate the impact of different production parameter adjustments, formation stress changes, and temperature and pressure fluctuations on the casing operation status, generating casing risk evolution paths under different extreme scenarios and identifying weak points and failure risks in advance.

[0044] The multi-objective adaptive optimization program control strategy generation module combines deep well casing operation status assessment results, risk prediction sequences, and extreme operating condition constraints. With multiple objectives including casing operation safety, maximum service life, and optimal production efficiency, it generates an adaptive optimization program control strategy for deep well casing operation conditions based on a model predictive control algorithm. It outputs a standardized program control command sequence, specifically covering field execution commands for downhole injection parameter adjustment, wellhead pressure control, and gas / oil production rate optimization. The digital twin program simulation verification and control strategy pre-optimization module inputs the generated control strategy into the deep well casing digital twin, performing a full-process program pre-run in a virtual simulation environment. It simulates changes in casing operation status under extreme conditions, verifies the feasibility and safety of the control strategy, and performs pre-optimization adjustments based on the simulation results. The field equipment program linkage control and automatic command execution module converts the optimized control strategy into program control commands recognizable by field equipment, writes them into the deep well production control system, and drives the wellhead pressure control equipment, downhole injection regulation equipment, and production rate control equipment to automatically execute commands.

[0045] The control effect closed-loop feedback and model iterative optimization module collects real-time deep well casing operation status data after control command execution, compares it with control targets to complete a quantitative evaluation of control effect, and performs adaptive iterative optimization of parameters for the digital twin model, state assessment model, risk prediction model, and control algorithm based on the evaluation results. It includes a built-in algorithm parameter adaptive optimization unit, employing particle swarm optimization to adaptively adjust core parameters, improving the adaptability of the model and algorithm under extreme conditions. The casing extreme condition emergency procedure safety control module constructs a deep well extreme condition program simulation library based on the digital twin, covering various extreme conditions such as casing leakage, excessive deformation, sudden stress increase, accelerated corrosion, and abnormal wellhead pressure. Based on real-time anomaly identification results, it quickly matches corresponding emergency procedure control plans, generates tiered emergency procedure control commands, and drives field equipment to complete emergency operations such as wellhead shutdown, pressure release, and parameter locking. It also includes a built-in emergency control permission tiered procedure management unit, classifying corresponding control permissions according to risk level to achieve rapid response and safety management under extreme conditions. The casing full lifecycle operation and maintenance program management linkage module generates program plans for deep well casing-specific inspections, preventive maintenance, and well workover operations based on casing operation status assessment results and remaining service life prediction sequences. It coordinates and optimizes the operation and maintenance plan with the casing operation program control strategy, and reasonably adjusts the casing operation conditions during the operation window. It has a built-in deep well operation and maintenance knowledge base and program case library to quickly match the corresponding operation and maintenance solutions and standardized operation processes.

[0046] Table 2 Performance Comparison of This System and Traditional Control System under Extreme Operating Conditions Table 2 data comes from 12 consecutive months of field industrial testing in high-temperature, high-pressure deep wells, covering various extreme operating conditions including temperature and pressure fluctuations, acidic medium erosion, and formation stress changes. Traditional casing management systems suffer from problems such as delayed anomaly identification, slow emergency response, and poor adaptability of control strategies under extreme conditions, which can easily lead to casing failure accidents and fail to guarantee long-term continuous and stable production in deep wells. This system, through high-temperature resistant full-dimensional perception, digital twin simulation of extreme conditions, hierarchical emergency management and adaptive optimization control, can achieve precise management and proactive risk prevention of casing operation under extreme conditions, significantly reducing the incidence of casing failure accidents, extending the continuous production cycle of deep wells, and fully adapting to the complex mining management needs of high-temperature, high-pressure deep wells.

[0047] refer to Figure 1This demonstration showcases the overall business process and core architecture of a bushing operation status control system based on digital twins. The system begins with multi-source operational data acquisition, constructing a real-time mapping between the physical world and the digital twin. The core processes sequentially involve full-scale twin modeling, state quantification and anomaly identification, and risk prediction and evolution analysis. Subsequently, the system generates multi-objective adaptive control strategies and pre-tests and verifies them in the twin environment. Finally, automatic execution of commands is achieved through the linkage of field equipment, and the model is continuously iterated and optimized using closed-loop feedback throughout the entire process. Furthermore, the system also runs emergency control for extreme operating conditions and full lifecycle operation and maintenance management in parallel, comprehensively ensuring the safe and efficient operation of the bushing.

[0048] Reference Figure 2 This paper details the construction of the casing's digital twin model and the precise quantitative assessment of its operational status. The system first collects comprehensive data across multiple dimensions, including stress, temperature, and pressure. Combining this with design parameters and the geological environment, it constructs a high-precision three-dimensional mapping model using multi-physics coupled simulation units (covering mechanics, fluid dynamics, and electrochemistry). Based on the synchronous operation of the model, the status assessment module uses weighted coupling and normalization of multi-dimensional status parameters to calculate the casing's real-time health status. By comparing this health status with a preset safety threshold, the system automatically identifies abnormal operating conditions and classifies risk levels, providing a scientific basis for subsequent management and control.

[0049] Reference Figure 3 This diagram focuses on the mechanism for proactively predicting casing operation risks and generating optimal control strategies. The system uses historical operating data, real-time status assessments, and changes in operating conditions as inputs, and constructs a risk evolution and remaining life prediction model based on a long short-term memory neural network. For predicted high-risk areas, the system combines multi-scenario operating condition simulations and employs a multi-objective adaptive optimization strategy generation module (comprehensively considering service life and mining efficiency) to output the optimal control sequence through rolling optimization calculations within a finite time domain. After this sequence is generated, it first undergoes full-process pre-simulation and safety verification in a digital twin virtual environment, and is only deployed for execution after adjustments based on the simulation results.

[0050] Reference Figure 4 This diagram illustrates the system's rapid response mechanism in the face of extremely dangerous operating conditions and the on-site execution path of daily commands. When the system detects anomalies such as leaks or excessive deformation, it quickly invokes the emergency control module. This module has a built-in hierarchical access control mechanism: based on the risk level, it takes actions such as automatic system adjustment, manual confirmation, or direct triggering of emergency shutdown and pressure relief. For conventional or optimized control strategies, the system translates them into low-level commands that the equipment can recognize and writes them into the on-site control system, driving equipment such as wellhead valves and water injection pumps to operate automatically, while simultaneously completing audible and visual warnings, data reporting, and recording of the entire operation process.

[0051] refer to Figure 5 This diagram illustrates the system's long-term operation and maintenance plan and the self-learning closed loop of the algorithm model. The full lifecycle operation and maintenance module automatically generates preventative maintenance plans based on lifespan prediction results and a standardized operation and maintenance knowledge base, and coordinates with operation control strategies during the operation and maintenance window to avoid production conflicts. After control commands are executed, the system collects feedback data in real time for quantitative evaluation of the effects. Based on the evaluation deviation, the system initiates an adaptive optimization unit, using swarm intelligence optimization algorithms to optimize parameters in core algorithm layers such as the digital twin model, state assessment, and risk prediction, and continuously improves the model's generalization ability and long-term control accuracy through online incremental training.

[0052] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A bushing operation status control system based on digital twin, characterized in that, include: The casing full life cycle multi-source operation data acquisition and program interaction module adopts a distributed multi-sensor fusion architecture. Stress, temperature, pressure, corrosion and vibration sensing units are deployed along the casing body to collect all-dimensional operation data of casing body parameters, wellbore environment parameters and mining conditions in real time. Data cleaning, spatiotemporal registration and standardization processing are completed. Program-level real-time bidirectional data interaction is completed with the field control system production database through industrial Ethernet. The casing full-size digital twin program simulation modeling module constructs a full-size three-dimensional digital twin program model of casing body, wellbore structure parameters, and geological environment parameters based on casing design parameters, wellbore structure parameters, and geological environment parameters. Combined with real-time acquired operation data, it completes online correction and dynamic mapping of model parameters, and realizes real-time synchronization of all elements of the casing physical entity and digital twin. The casing operation status quantitative assessment and anomaly identification module, based on real-time operating data from a digital twin, constructs a multi-dimensional program assessment model for casing stress distribution, corrosion rate, deformation degree, and fatigue damage, completes real-time quantitative assessment of casing operation status, and combines preset safety thresholds to identify abnormal casing conditions and classify risk levels. The casing operation risk program prediction and evolution trend analysis module is based on a long short-term memory neural network to build a programmatic risk prediction model. It uses the real-time status assessment results of the casing's historical operation data and the working condition change parameters as the model input, and outputs the risk evolution trend and remaining service life prediction sequence of the casing in the future period. The multi-objective adaptive optimization program control strategy generation module combines the risk prediction sequence of casing operation status assessment results with on-site working condition constraints. With the goal of maximizing the safe service life of the casing and optimizing mining efficiency, it generates an adaptive optimization program control strategy for casing operation conditions based on a model predictive control algorithm, outputting a standardized program control command sequence. The digital twin program simulation verification and control strategy pre-optimization module inputs the generated control strategy into the casing digital twin, completing a full-process program pre-run in a virtual simulation environment. It simulates changes in casing operation status under different working conditions, verifies the feasibility and safety of the control strategy, and performs pre-optimization adjustments based on the simulation results. The field equipment program linkage control and automatic command execution module transforms the optimized control strategy into program control commands that can be recognized by the field equipment, writes them into the field production control system, drives the wellhead pressure control, oil production rate adjustment, injection parameter adjustment, and field execution equipment to complete the automatic execution of commands. The control effect closed-loop feedback and model iteration optimization module collects bushing operation status data in real time after the control command is executed, compares it with the control target to complete the quantitative evaluation of the control effect, and completes the adaptive iterative optimization of the parameters of the digital twin model status evaluation model risk prediction model control algorithm based on the evaluation results.

2. The bushing operation status control system based on digital twin according to claim 1, characterized in that, It also includes a casing extreme condition emergency procedure safety control module. This module is based on a digital twin to build an extreme condition procedure simulation library, covering various extreme conditions such as casing leakage, deformation exceeding limits, stress surge, and corrosion aggravation. It can quickly match the corresponding emergency procedure control plan based on real-time anomaly identification results, and at the same time complete the simulation verification of the emergency plan in the digital twin environment, generate graded emergency procedure control instructions, drive the field execution equipment to complete the wellhead shutdown pressure release parameter locking emergency operation, and simultaneously trigger on-site audible and visual warnings and operation and maintenance information reporting, so as to achieve rapid response and safety management under extreme conditions.

3. The bushing operation status control system based on digital twin according to claim 1, characterized in that, It also includes a bushing full lifecycle operation and maintenance program management linkage module. This module can generate a bushing preventive maintenance, inspection and repair program plan based on the bushing operation status assessment results and the remaining service life prediction sequence. At the same time, it can coordinate and optimize the operation and maintenance plan with the bushing operation program control strategy, reasonably adjust the bushing operation conditions during the operation and maintenance window, avoid the timing conflict between operation and maintenance work and production operation, and complete the parameter correction and status update of the digital twin based on the field operation and maintenance inspection data, so as to realize the two-way collaborative optimization of bushing operation and maintenance management and operation control.

4. The bushing operation status control system based on digital twin according to claim 1, characterized in that, The casing operation status quantitative assessment and anomaly identification module has a built-in casing health quantitative calculation unit. It generates a real-time casing health value through weighted coupling calculation of multi-dimensional status parameters. The calculation formula is as follows: in This represents the real-time health value of the casing body. The total number of status parameters participating in the health assessment. For the first The weight coefficients corresponding to the item's state parameters, and the sum of all weight coefficients is 1. For the first Real-time monitoring values ​​of the item status parameters The upper limit of the safety threshold for the i-th state parameter. The lower limit of the safety threshold for the i-th state parameter is defined. Through the normalization and weighted coupling of multi-dimensional parameters, the accurate quantitative characterization of the health status of the casing operation can be achieved. At the same time, the risk level of casing operation can be automatically classified based on the health value, providing an accurate quantitative basis for the generation of subsequent program control strategies.

5. A bushing operation status control system based on digital twin according to claim 1, characterized in that, The full-size digital twin simulation modeling module for the casing has a built-in multi-physics coupled simulation unit, which can construct a multi-physics coupled simulation model of the casing's structural mechanical field, temperature field, fluid field, corrosion and electrochemical field. Based on real-time acquired operating data, it completes the synchronous solution of the multi-physics field, accurately simulates the stress distribution, deformation trend and corrosion evolution process of the casing under different working conditions, and improves the simulation accuracy of key parts of the casing through adaptive mesh refinement technology, realizing high-precision virtual mapping of the entire life cycle operation process of the casing.

6. A bushing operation status control system based on digital twin according to claim 1, characterized in that, The casing operation risk prediction and evolution trend analysis module has a built-in multi-scenario working condition simulation unit. It can simulate the impact of geological environment change parameters on casing operation status based on digital twins, generate casing risk evolution paths under different scenarios, and identify weak parts and high-risk areas of casing operation based on prediction results. It can also generate targeted control and optimization strategies in advance to achieve proactive prevention and control of casing operation risks.

7. A bushing operation status control system based on digital twin according to claim 1, characterized in that, The multi-objective adaptive optimization program control strategy generation module has a built-in multi-objective rolling optimization program control unit. It generates the optimal control sequence through rolling optimization in the finite time domain. The objective function calculation formula is as follows: in The objective function value for controlling the strategy optimization is given, where N is the prediction time domain length and q is the weighting coefficient of the system output error. For the first The target value of the casing operating status at a given moment. For the first The predicted output value of the bushing operating status at time t, where r is the weighting coefficient of the rate of change of the control variable. For the first By minimizing the incremental change of the control quantity at each moment, the accurate tracking control of the bushing's operating status can be achieved while meeting the constraints of the field operating conditions. At the same time, it limits large fluctuations in the control quantity, ensuring the stability of the field equipment operation and the continuity of the control process.

8. A bushing operation status control system based on digital twin according to claim 2, characterized in that, The emergency procedure safety control module for extreme operating conditions of the bushing has a built-in hierarchical emergency control permission management unit. It can divide the corresponding control permissions according to the risk level of extreme operating conditions. Under low-risk conditions, the system automatically adjusts its emergency response. Under medium- and high-risk conditions, the emergency control plan is pushed to the operation and maintenance management platform. After confirmation by the operation and maintenance personnel, the emergency operation is executed. Under extremely high-risk conditions, the emergency shutdown and safety isolation operation are triggered directly. At the same time, all data of the emergency control process is recorded, providing data support for subsequent accident analysis and plan optimization.

9. A bushing operation status control system based on digital twin according to claim 3, characterized in that, The bushing full lifecycle operation and maintenance program management linkage module has a built-in operation and maintenance knowledge base and program case library. It can build a standardized operation and maintenance knowledge base based on historical operation and maintenance data, bushing failure cases, industry standard specifications, etc. When abnormal bushing conditions are identified, the corresponding operation and maintenance solutions and standardized operating procedures can be quickly matched. At the same time, the knowledge base and case library can be iteratively updated based on the results of each operation and maintenance operation, improving the matching accuracy and executability of operation and maintenance solutions.

10. A bushing operation status control system based on digital twin according to claim 1, characterized in that, The control effect closed-loop feedback and model iterative optimization module has a built-in adaptive algorithm parameter optimization unit. It adopts the particle swarm optimization algorithm, with the goal of minimizing the control effect deviation. It adaptively optimizes and adjusts the core parameters of the digital twin model, state assessment model, risk prediction model, and control algorithm. At the same time, it can complete online incremental training of the model based on the operating data under different working conditions, improve the adaptability and generalization ability of the model and algorithm to different wellbore environments and mining conditions, and ensure the control accuracy and stability of the system in long-term operation.

Images