Gas lift intelligent flow distribution device

Abstract

The invention discloses a gas lift intelligent flow distribution device which comprises a gas injection unit used for injecting high-pressure gas into a shaft; the plurality of sensor units are deployed in a shaft, a wellhead and a surface pipeline and are used for monitoring well condition parameters in real time; the power supply management unit supplies power to the gas injection unit, the sensor unit and the intelligent control unit; the intelligent control unit is in communication connection with the gas injection unit and the sensor unit; multi-dimensional data such as bottom hole pressure, temperature and flow can be continuously collected and processed, optimal gas injection parameters are calculated on line and executed in real time by means of a shaft model and an optimization algorithm which are continuously self-updated, the flow distribution device can actively respond to dynamic conditions such as stratum liquid supply capacity changes and fluid property changes, and the flow distribution efficiency is improved. Therefore, the fault risks of gas lift efficiency reduction, gas channeling, blockage and the like caused by response delay are greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of air lift distribution technology, and more particularly to an intelligent air lift distribution device. Background Technology

[0002] Gas lift technology is a technique that improves fluid production efficiency by injecting high-pressure gas into the wellbore. The distribution device is responsible for controlling each step of the gas injection process, which directly affects the stability and economy of the production system. As oil and gas field development extends to more complex geological conditions, traditional distribution devices are no longer sufficient to cope with the challenges.

[0003] Existing gas lift distribution devices mostly adopt control strategies based on fixed parameters or simple feedback, lacking the ability to respond in real time to dynamic changes in the wellbore. For example, in scenarios such as sudden changes in bottom hole pressure, changes in fluid composition, or equipment wear, traditional devices often cannot adjust gas injection parameters in time, leading to a decrease in lifting efficiency or increased energy waste, and even causing faults such as gas channeling or wellbore blockage. Furthermore, existing systems usually operate in isolation, making it difficult to integrate production management data and multi-source sensor information. These problems will accumulate into significant efficiency losses and increased operation and maintenance costs in the long run.

[0004] Therefore, in response to the problems mentioned above, the present invention proposes an air-lift intelligent distribution device. Summary of the Invention

[0005] To overcome the problems of insufficient self-adaptability and low optimization efficiency of existing gas lift distribution devices, this invention proposes an intelligent gas lift distribution device. By integrating real-time data acquisition, intelligent algorithms, and collaborative control methods, it achieves dynamic optimization and adaptive adjustment of gas injection parameters, thereby improving lifting accuracy and energy efficiency.

[0006] The technical solution of the present invention is: an air-lift intelligent distribution device, comprising: A gas injection unit is used to inject high-pressure gas into the wellbore. This unit includes a gas compressor, a pressure regulating valve, and a flow controller. The gas injection unit adjusts the injection pressure, injection rate, and injection time according to control signals. The gas compressor is preferably a variable frequency controlled screw compressor, whose output pressure range is continuously adjustable between 5 MPa and 25 MPa to adapt to different well depths and formation pressures. The pressure regulating valve has a response time of less than 100 milliseconds and an accuracy better than ±0.5% of its full range. The flow controller has a range ratio of 1:50, ensuring precise flow control over a wide range from low-yield to high-yield wells. Multiple sensor units are deployed in the wellbore, wellhead, and surface pipelines to monitor well condition parameters in real time, including bottom hole pressure, wellhead pressure, temperature, gas flow rate, liquid flow rate, and fluid composition. Each sensor unit includes a pressure sensor, temperature sensor, flow meter, and component analyzer. The sensor units are connected to a data acquisition module to continuously acquire and transmit monitoring data. The power management unit provides power to the gas injection unit, sensor unit, and intelligent control unit, and supports backup power switching. The intelligent control unit, which is communicatively connected to the gas injection unit and sensor unit, includes a processor, a memory, and a communication interface. The memory stores a computer program, a predefined wellbore model, and a historical operation database. When the computer program is executed by the processor, the following steps are performed: A1 receives real-time monitoring data from the sensor unit through the communication interface and performs preprocessing on the real-time monitoring data, including data filtering, outlier detection and data normalization. The data filtering uses the Kalman filter algorithm to effectively suppress measurement noise and improve signal quality. The outlier detection uses the isolated forest algorithm to quickly identify abnormal situations such as sensor drift or failure. A2, based on preprocessed real-time monitoring data and wellbore model, calculates the optimal gas injection parameters through an optimization algorithm, which includes a genetic algorithm to minimize energy consumption and maximize fluid production. The optimal gas injection parameters include target injection pressure, target injection rate, and target injection time. A3 generates a control signal and sends it to the gas injection unit via the communication interface to drive the pressure regulating valve and flow controller to achieve optimal gas injection parameters. The intelligent control unit updates the wellbore model regularly based on historical operating data and machine learning algorithms.

[0007] Preferably, the intelligent control unit communicates with the cloud server, uploads real-time monitoring data, operation logs and model update requests through the communication interface, and downloads the optimized wellbore model from the cloud server.

[0008] Preferably, the intelligent control unit identifies abnormal states of the gas injection unit or sensor unit by analyzing the deviation between real-time monitoring data and preset thresholds or pattern libraries, and triggers local alarms or sends fault reports to maintenance personnel through communication interfaces. The pattern library stores characteristic waveforms of common faults, such as the frequency characteristics of pressure fluctuations caused by compressor valve plate wear, and the slow upward trend of bottom hole pressure caused by tubing wax buildup. The diagnostic results are accompanied by a confidence assessment. When the confidence level is higher than 95%, the system can automatically perform predetermined safety operations, such as reducing the injection pressure or starting backup equipment.

[0009] Preferably, the gas injection unit includes multiple injection valves, each corresponding to a different depth or branch of the wellbore. The intelligent control unit independently controls the opening, closing, and opening degree of each injection valve. Based on the wellbore model, individualized injection parameters are calculated for each injection valve. The injection valve is a hydraulically or electrically controlled needle valve with an opening degree control accuracy of 0.1°, enabling precise gas volume distribution. The wellbore model divides the wellbore into several segments, each with independent pressure, temperature, and fluid property parameters. The optimization algorithm calculates the most suitable gas injection volume for each segment, thereby effectively suppressing premature gas breakthrough and conical entry.

[0010] Preferably, the intelligent control unit is based on a wellbore model and uses a multi-objective optimization algorithm to calculate individualized injection parameters for each injection valve. This multi-objective optimization algorithm simultaneously considers pressure balance, flow stability, and sand control requirements, and adjusts the operating sequence of the injection valves to cope with changes in well conditions. The multi-objective optimization algorithm is a non-dominated sorting genetic algorithm, and its three optimization objectives are: (1) The pressure gradient in each section of the wellbore is uniform; (2) Minimize fluctuations in wellhead production flow rate; (3) The fluid velocity inside the wellbore is higher than the critical sand-carrying velocity but lower than the safe velocity that will cause erosion.

[0011] The algorithm outputs a set of Pareto optimal solutions, and the operator or the upper-level system selects an optimal solution to execute based on the current production strategy.

[0012] Preferably, the intelligent control unit is also connected to the production management system, receiving production plans, output targets, and equipment status information through a communication interface, and adjusting gas injection parameters based on this information to match real-time production needs and resource allocation strategies. When the production management system issues a "peak shaving and production increase" command, the intelligent control unit will temporarily increase the gas injection rate within the energy consumption constraints to maximize short-term output; conversely, during peak electricity consumption periods or equipment maintenance periods, an "economic mode" will be adopted to optimize injection parameters with the primary goal of reducing energy consumption.

[0013] Preferably, the sensor unit includes a distributed fiber optic sensor deployed along the wellbore for real-time monitoring of temperature and pressure distribution along the wellbore. The sensor transmits spatial analysis data to the intelligent control unit via a data acquisition module for wellbore model calibration and abnormal area location. The distributed fiber optic sensor has a temperature measurement accuracy of ±0.1℃ and a pressure measurement accuracy of ±0.01%FS. By analyzing abnormal "cold spots" or "hot spots" in the temperature profile, the working status of the gas lift valve can be accurately determined, and gas leak points or liquid outlet locations can be identified.

[0014] Preferably, the intelligent control unit uses a reinforcement learning algorithm to optimize the gas injection strategy by continuously interacting with the wellbore environment. This reinforcement learning algorithm uses production volume, energy consumption, and equipment lifespan as reward functions to iteratively update the strategy network, achieving long-term adaptive optimization. Preferably, this reinforcement learning algorithm is a near-end strategy optimization algorithm, whose state space includes real-time monitoring data and its historical trends, whose action space contains the adjustment amounts of the gas injection parameters, and whose reward function R can be quantified as: ; in , , and Weighting coefficients are set based on economic and safety requirements.

[0015] Preferably, the device also includes a user interface connected to the intelligent control unit for displaying real-time monitoring data, gas injection parameters, alarm information and system status, and receiving user input, including parameter settings, model adjustments and manual control commands.

[0016] Preferably, the intelligent control unit also has the function of predicting the risk of wax deposition, blockage or corrosion in the wellbore. By analyzing real-time monitoring data trends and historical event databases, it uses a time series prediction model to adjust gas injection parameters in advance, including increasing injection pressure or pulse injection, to mitigate risks and extend equipment life.

[0017] The beneficial effects of this invention are: 1. This invention can continuously collect and process multi-dimensional data such as bottom hole pressure, temperature and flow rate. Utilizing a continuously self-updating wellbore model and optimization algorithm, it calculates and executes the optimal gas injection parameters online and in real time. This enables the distribution device to actively respond to dynamic conditions such as changes in formation fluid supply capacity and fluid properties, thereby significantly reducing the risk of gas lift efficiency decline and gas channeling, blockage and other failures caused by response lag, and ensuring the continuous, stable and efficient lifting process.

[0018] 2. This invention not only enables real-time control based on local data, but also receives and integrates global information such as production plans and equipment status. It utilizes the powerful computing power of the cloud for model training and strategy optimization, thereby ensuring efficient production of a single well while matching the gas injection strategy with the oilfield's production rhythm, energy consumption indicators, and maintenance plans. This achieves a globally optimal balance among multiple objectives such as lift efficiency, energy consumption control, and asset lifespan, significantly improving the overall economic benefits and resource utilization efficiency of gas lift extraction. Attached Figure Description

[0019] Figure 1 The diagram shown is a schematic representation of the system framework of this invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but 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.

[0021] Please see Figure 1 The present invention provides an embodiment of an air-lift intelligent distribution device: In this embodiment, the gas injection unit will be described as follows: The gas compressor in this unit is a variable frequency controlled screw compressor. The output pressure range of this compressor is 5-25MPa. The motor speed is controlled by the frequency converter to control the exhaust pressure. Compared with the traditional fixed frequency compressor, it has better energy saving effect and can save more than 25% under partial load conditions.

[0022] The pressure regulating valve adopts a pilot-operated structure and is equipped with a high-precision stepper motor to drive the valve core. The response time is less than 100 milliseconds, and the control accuracy reaches ±0.5% of the full range. The valve material is made of corrosion-resistant alloy, which is suitable for harsh well conditions containing hydrogen sulfide. The valve opening degree and flow characteristics have been precisely calibrated to ensure good linear regulation characteristics in the entire operating range.

[0023] The flow controller uses a Coriolis mass flow meter with a range ratio of 1:50 and an accuracy of ±0.1% of the reading. The flow meter is directly installed on the injection pipeline to detect the gas mass flow rate in real time and feed the signal back to the flow controller. The controller uses a PID algorithm to control the flow rate by adjusting the compressor speed and the opening of the pressure regulating valve.

[0024] In this embodiment, the sensor unit is described as follows: The downhole sensors employ three types of sensors: first, a quartz capacitive pressure sensor with a measurement range of 0-40 MPa, an accuracy of ±0.01%FS, and a temperature compensation range of 0-150℃; second, a PT100 temperature sensor with a measurement range of -20-200℃ and an accuracy of ±0.1℃; and third, a capacitive fluid composition analyzer that analyzes the oil, gas, and water ratio in real time by measuring the dielectric constant.

[0025] Fiber optic sensors are deployed along the outer wall of the tubing to monitor the temperature and pressure distribution throughout the wellbore. The spatial resolution is 1 meter, the temperature measurement accuracy is ±0.1℃, the pressure measurement accuracy is ±0.01%FS, and the maximum monitoring depth is 5000 meters. The fiber optic data collected by the sensors is processed by a dedicated demodulator to generate a wellbore temperature and pressure profile, thereby providing spatial distribution data.

[0026] In this embodiment, the intelligent control unit is described as follows: The wellbore model for this device adopts a multiphase flow transient model, which is based on the laws of conservation of mass, momentum, and energy. It considers the slippage effect between the gas and liquid phases, the variation of fluid PVT properties with pressure, and wellbore friction loss. The model is solved using the finite difference method, discretizing the wellbore into several grids, with independent governing equations established for each grid. Specifically: The entire wellbore (from bottom to top) is divided into N continuous, tiny grids along the flow direction (Z-axis). The number of grids N is dynamically adjusted according to the well depth and accuracy requirements, and each grid is usually between 5 and 20 meters long.

[0027] Each grid cell is assigned initial values ​​for pressure, temperature, gas phase velocity, liquid phase velocity, and gas-phase ratio (gas holdup). These initial values ​​can be derived from steady-state model calculations or initial well test data. Formation volume coefficients, dissolved gas-oil ratios, viscosity, and density of oil, gas, and water are input, and their correlations with pressure and temperature are established. Data such as tubing inner diameter and roughness are input; if multiple injection points exist, the model will precisely mark the grid position corresponding to each injection valve.

[0028] A transient solution loop is triggered within each control cycle (e.g., per second), receiving real-time data from the sensor unit as boundary conditions and performing transient solutions. The model is solved using the fully implicit finite difference method, and the following control equations are established on each grid: Continuity equation: ; Momentum equation: ; in For density, denoted as liquid holdup, v as velocity, subscripts l and g represent liquid and gas phases respectively, m represents mixed phase, f is friction coefficient, and D is pipe diameter.

[0029] The model automatically determines the current flow pattern (such as bubbly flow, slug flow, annular flow, etc.) based on parameters such as real-time gas-liquid velocity or gas holdup in each grid. For example, in annular flow, the gas phase is the core, and the friction calculation method is significantly different from that in slug flow.

[0030] The aforementioned nonlinear partial differential equations are discretized across the entire grid, forming a large nonlinear equation system. The Newton-Raphson iterative method is used for solving this system. The solver continuously adjusts variables such as pressure and gas holdup in each grid until the residuals (imbalance values) of all equations are less than a preset convergence criterion. After solving the current time step, the time is advanced by one step. The model employs an adaptive time step strategy, automatically reducing the step size when iterative convergence becomes difficult to ensure computational stability.

[0031] The key parameters such as wellhead pressure, bottom hole pressure, and temperature distribution obtained from the solution are compared with the real-time monitoring data transmitted back by the sensor unit. The error between the model's predicted value and the actual measured value is calculated. At this time, the Long Short-Term Memory (LSTM) network is activated. The model's prediction error, historical injection parameters, and real-time monitoring data are used as inputs to the LSTM. The LSTM network analyzes these time series data and outputs the correction amount for key empirical parameters in the model (such as friction coefficient multipliers and tuning parameters in the slip relation). This correction process is performed periodically (e.g., once per hour), enabling the mathematical model to "learn" and adapt to factors that cannot be directly described by physical equations, such as wellbore waxing or changes in equipment performance.

[0032] After the solution is completed, the model will output a complete panoramic view of the wellbore status, including the pressure distribution profile, temperature distribution profile, gas holdup and flow pattern distribution, bottom hole flowing pressure (a crucial control parameter), and predicted production and gas production at the wellhead. These results are used by the machine learning algorithm, which repeatedly "trials and errors" in the virtual space to calculate a set of optimal gas injection parameters (target injection pressure, target injection rate, etc.) that maximize production and minimize energy consumption. Finally, these optimal parameters are converted into control signals and sent to the gas injection unit for execution, thereby completing a complete intelligent flow distribution cycle.

[0033] For the case of multiple injection valves, the NSGA-II algorithm is used for multi-objective optimization. The optimization objectives include maximizing the liquid production rate, minimizing the gas injection rate, homogenizing the pressure distribution, and controlling the flow rate within a safe range. The fitness function of the algorithm is: ; in For oil production, This refers to the gas injection volume. For pressure fluctuation, T is the equipment operating time, and w is the weighting coefficient.

[0034] The intelligent control unit communicates with the cloud server, uploading real-time monitoring data, operation logs, and model update requests through the communication interface, and downloading the optimized wellbore model from the cloud server.

[0035] In this embodiment, it can also be connected to the production management system to receive production plans, output targets and equipment status information through a communication interface, and adjust gas injection parameters based on this information to match real-time production needs and resource allocation strategies. The device also includes a user interface connected to the intelligent control unit to display real-time monitoring data, gas injection parameters, alarm information and system status, and to receive user input, including parameter settings, model adjustments and manual control commands.

[0036] In this embodiment, the system supports two modes: online learning and batch learning. In online learning mode, the model parameters are updated every hour to ensure timely tracking of well condition changes. In batch learning mode, deep training is performed every morning to update the network weights.

[0037] This invention provides Embodiment 1: This embodiment is applied to a conventional vertical well with a depth of 2500 meters. The target oil layer is located at a depth of 2300 meters in the middle of the wellbore. Production data shows that the casing pressure of this well is 3.5 MPa, the tubing pressure is 2.8 MPa, and the initial daily fluid production before implementing this system was approximately 45 cubic meters, with a gas-liquid ratio of 85. / Overall, it exhibits typical characteristics of medium-deep wells and medium production, providing a suitable test scenario for verifying the universality of this system.

[0038] In view of the actual situation of the well, the gas injection unit of the present invention adopts a variable frequency screw compressor with an output pressure range of 8-12MPa, and an injection valve that can be independently controlled by the intelligent control unit is installed at three key depths of 1800 meters, 2000 meters and 2200 meters in the well. At the same time, the downhole sensor unit is deployed at a density of one measuring point every 200 meters, and fiber optic sensors covering the entire well are deployed.

[0039] index Traditional devices This invention Improvement range <![CDATA[Liquid production rate (m 3 )]]> 42.5 45.8 +7.8% <![CDATA[Gas injection volume (m 3 / d)]]> 12500 10800 -13.6% <![CDATA[Gas lift efficiency (m 3 / thousand m 3 )]]> 3.40 4.24 +24.7% Pressure fluctuation (MPa) 0.35 0.12 -65.7% Number of failures 3 0 -100% As shown in the table above, after 30 days of continuous operation, the intelligent distribution device of this invention has significantly improved in all key indicators compared to the traditional air lift distribution device.

[0040] This invention provides Embodiment 2: This embodiment is applied to a horizontal well with an 800-meter-long horizontal section. The horizontal section of the well is divided into 5 independent production sections. Geological assessment shows that the permeability of each section varies greatly, ranging from 3 to 15 mD, and the formation pressure gradient is not uniformly distributed, varying between 0.8 and 1.2 MPa / 100m.

[0041] The significant differences in physical properties across different sections of a horizontal well mean that traditional gas lift technology cannot achieve a balanced lift. This can easily lead to premature and excessive intake of injected gas in high-permeability sections, causing gas channeling and thus inhibiting liquid lift in lower-permeability sections. Ultimately, this results in severely uneven reservoir utilization and a decline in overall recovery rate.

[0042] This invention independently sets up injection valves and corresponding monitoring points in each horizontal section, and establishes a multi-segment transient flow model of horizontal wells that can describe the flow coupling relationship between each segment. At the same time, it uses a multi-objective collaborative optimization algorithm based on NSGA-II to calculate and allocate appropriate gas injection volume for each segment, thereby achieving global optimal lift.

[0043] Evaluation indicators Traditional devices This invention Uniformity of product output in each section 0.45 0.82 <![CDATA[Total liquid production (m 3 / d)]]> 68.5 85.2 Time to see the Qi (days) 25 62 Energy consumption index 1.00 0.76 As shown in the table above, after 60 days of continuous operation, the uniformity of liquid production in each section increased significantly from 0.45 in the traditional unit to 0.82, indicating a substantial improvement in the liquid production profile. Based on this, the total liquid production also increased significantly, while the gas emergence time was delayed from 25 days to 62 days, effectively slowing down gas channeling. The comprehensive energy consumption index also decreased to 0.76 in the traditional unit, achieving the dual goals of increasing production and reducing energy consumption.

[0044] Comparative Example 1 provided by the present invention: This experiment simulates a real oilfield data environment with a simulation duration of 180 days, covering the production scenarios of 12 wells (including 8 vertical wells and 4 horizontal wells). Noise is added to the actual production data to simulate the uncertainties in real-world measurements.

[0045] Four comparative schemes were set up in the experiment: Comparative Example 1 was a fixed parameter air lift device, Comparative Example 2 was an air lift device using simple PID control, Comparative Example 3 was an air lift device based on preset rule control, and the experimental example was the present invention.

[0046] plan <![CDATA[Average daily output (m 3 )]]> Production fluctuation coefficient Production efficiency Comparative Example 1 45.2 0.35 0.78 Comparative Example 2 47.8 0.28 0.82 Comparative Example 3 49.2 0.24 0.85 Experimental Example 53.6 0.15 0.92 As shown in the table above, the present invention achieves an average daily output of 53.6 cubic meters, with the lowest output fluctuation coefficient (0.15) and the highest production efficiency index (0.92).

[0047] plan <![CDATA[Gas injection volume (10,000 m 3 / d)]]> <![CDATA[Unit consumption (m 3 / m 3 )]]> Energy Efficiency Index Comparative Example 1 15.2 336 1.00 Comparative Example 2 14.5 303 1.11 Comparative Example 3 13.8 280 1.20 Experimental Example 12.6 235 1.43 As shown in the table above, in terms of energy consumption, the daily gas injection volume of this invention is reduced to 126,000 cubic meters, and the unit energy consumption is only 235. / Its energy efficiency index reaches 1.43, which is much higher than other options.

[0048] Stability Indicators Comparative Example 1 Comparative Example 2 Comparative Example 3 This invention Number of times pressure exceeded limit 23 15 9 2 Number of traffic anomalies 18 12 7 1 System adjustment times 0 46 28 135 Downtime due to fault (h) 45 28 16 5 As shown in the table above, the number of times the pressure exceeded the limit and the flow was abnormal was controlled to single digits (2 times and 1 time respectively) in this invention, and the downtime due to failure was shortened to only 5 hours through 135 active adaptive adjustments.

[0049] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An air-lift intelligent distribution device, characterized in that, Including: A gas injection unit is used to inject high-pressure gas into the wellbore. The gas injection unit includes a gas compressor, a pressure regulating valve, and a flow controller. The gas injection unit is used to adjust the injection pressure, injection rate, and injection time according to control signals. Multiple sensor units are deployed in the wellbore, wellhead, and surface pipelines to monitor well condition parameters in real time, including bottom hole pressure, wellhead pressure, temperature, gas flow rate, liquid flow rate, and fluid composition. Each sensor unit includes a pressure sensor, temperature sensor, flow meter, and component analyzer. The sensor units are connected to a data acquisition module to continuously acquire and transmit monitoring data. The power management unit provides power to the gas injection unit, sensor unit, and intelligent control unit, and supports backup power switching. The intelligent control unit, which is communicatively connected to the gas injection unit and sensor unit, includes a processor, a memory, and a communication interface. The memory stores a computer program, a predefined wellbore model, and a historical operation database. When the computer program is executed by the processor, the following steps are performed: A1 receives real-time monitoring data from the sensor unit through the communication interface and preprocesses the real-time monitoring data, including data filtering, outlier detection and data normalization. A2, based on preprocessed real-time monitoring data and wellbore model, calculates the optimal gas injection parameters through an optimization algorithm, which includes a genetic algorithm to minimize energy consumption and maximize fluid production. The optimal gas injection parameters include target injection pressure, target injection rate, and target injection time. A3 generates a control signal and sends it to the gas injection unit via the communication interface to drive the pressure regulating valve and flow controller to achieve optimal gas injection parameters. The intelligent control unit updates the wellbore model regularly based on historical operating data and machine learning algorithms.

2. The air-lift intelligent distribution device according to claim 1, characterized in that: The intelligent control unit communicates with the cloud server, uploads real-time monitoring data, operation logs and model update requests through the communication interface, and downloads the optimized wellbore model from the cloud server.

3. The air-lift intelligent distribution device according to claim 1, characterized in that: The intelligent control unit identifies abnormal states of the gas injection unit or sensor unit by analyzing the deviation between real-time monitoring data and preset thresholds or mode libraries, and triggers local alarms or sends fault reports to maintenance personnel through the communication interface.

4. The air-lift intelligent distribution device according to claim 1, characterized in that: The gas injection unit includes multiple injection valves, each corresponding to a different depth or branch of the wellbore. The intelligent control unit independently controls the opening, closing, and opening degree of each injection valve, and calculates individualized injection parameters for each injection valve based on the wellbore model.

5. The air-lift intelligent distribution device according to claim 4, characterized in that: The intelligent control unit is based on the wellbore model and uses a multi-objective optimization algorithm to calculate individualized injection parameters for each injection valve. The multi-objective optimization algorithm simultaneously considers pressure balance, flow stability and sand control requirements, and adjusts the operation sequence of the injection valve to cope with changes in well conditions.

6. The air-lift intelligent distribution device according to claim 1, characterized in that: The intelligent control unit is also connected to the production management system, receiving production plans, output targets and equipment status information through a communication interface, and adjusting gas injection parameters based on this information to match real-time production needs and resource allocation strategies.

7. The air-lift intelligent distribution device according to claim 1, characterized in that: The sensor unit includes distributed fiber optic sensors deployed along the wellbore to monitor the temperature and pressure distribution along the wellbore in real time. The spatial resolution data is then sent to the intelligent control unit via a data acquisition module for wellbore model calibration and abnormal area location.

8. The air-lift intelligent distribution device according to claim 1, characterized in that: The intelligent control unit uses a reinforcement learning algorithm to optimize the gas injection strategy by continuously interacting with the wellbore environment. The reinforcement learning algorithm uses production volume, energy consumption, and equipment lifespan as reward functions to iteratively update the strategy network, thereby achieving long-term adaptive optimization.

9. The air-lift intelligent distribution device according to claim 1, characterized in that: The device also includes a user interface connected to an intelligent control unit, used to display real-time monitoring data, gas injection parameters, alarm information and system status, and to receive user input, including parameter settings, model adjustments and manual control commands.

10. The air-lift intelligent distribution device according to claim 1, characterized in that: The intelligent control unit also has the function of predicting the risk of wax buildup, blockage or corrosion in the wellbore. By analyzing real-time monitoring data trends and historical event databases, it uses time series prediction models to adjust gas injection parameters in advance, including increasing injection pressure or pulse injection, to mitigate risks and extend equipment life.

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