Dynamic blockage management system and method for underground water recharge well

Abstract

The invention discloses a dynamic blockage management system and method for an underground water recharge well. The system comprises a monitoring and acquisition module, a data processing and risk assessment module, a flow rate adjustment optimization and dynamic feedback module and a storage and remote management module. The method comprises the following steps: monitoring key parameters such as water flow rate, suspended particulate matter concentration, fluid density and temperature and the like in a well injection process in real time, and carrying out data preprocessing; deriving a comprehensive blockage risk index calculation formula based on the key parameters, and comparing with a set threshold to judge whether flow velocity adjustment is triggered or not; by analyzing the relationship among the blockage risk, the pressure loss and the injection amount in real time, an objective function is constructed to seek an optimal solution; the well injection flow velocity is dynamically adjusted based on the solved optimal flow velocity and is dynamically fed back; and carrying out data storage and remote management. The device can dynamically adjust the injection flow rate, remarkably improve the injection efficiency, reduce the maintenance cost, and effectively manage and reduce the physical blockage problem in the underground water recharge process.

Description

Technical Field

[0001] The present invention belongs to the technical field of water resource management, and relates to a dynamic plugging management system and method for a groundwater recharge well. Based on real-time monitoring and intelligent flow velocity optimization, the system realizes dynamic prevention and management of physical plugging of the recharge well by controlling the deposition of particulate matter and flow velocity regulation in the recharge well, effectively improving the stability and efficiency of the groundwater recharge process. Background Art

[0002] The global water resource shortage problem is becoming increasingly severe, and groundwater recharge has become an important means to maintain water resource balance, especially in arid and semi-arid regions with significant application value. Recharge well injection is a key method for groundwater recharge, which realizes the replenishment of groundwater reserves by injecting water sources into underground aquifers. However, during the recharge well injection process, suspended particulate matter in the water is prone to deposit around the wellbore and aquifer, resulting in a decrease in permeability and forming physical plugging. This phenomenon not only reduces the injection efficiency but also causes the gradual loss of the well's function, ultimately requiring high-cost cleaning or maintenance.

[0003] Existing plugging management measures mostly rely on mechanical cleaning, chemical injection, or pretreatment of injected water. However, these methods are easily restricted by the characteristics of suspended particles and the geological conditions around the well, and it is difficult to maintain their effects in the long term. With the dynamic changes in injection water quality and geological conditions, traditional methods cannot achieve real-time monitoring and dynamic optimization, and frequent maintenance also increases the operating cost. Summary of the Invention

[0004] Aiming at the deficiencies existing in the prior art, the purpose of the present invention is to provide a dynamic plugging management system and method for a groundwater recharge well. By real-time monitoring the water flow rate and suspended particulate matter concentration during the well injection process and dynamically adjusting the injection flow rate, it can effectively prevent and manage physical plugging problems, significantly improve the injection efficiency, and reduce the maintenance cost.

[0005] To solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] A dynamic plugging management system for a groundwater recharge well, comprising:

[0007] A monitoring and acquisition module: used for real-time acquisition of key parameters of the recharge well: the flow rate of the fluid in the well, the concentration of suspended particulate matter, the fluid density, and the temperature; including an ultrasonic flow velocity sensor, a suspended particulate matter concentration sensor, a density sensor, and a temperature sensor;

[0008] A data processing and risk assessment module: used for preprocessing the collected parameter data and calculating the plugging risk index based on the fluid dynamics model to comprehensively evaluate the plugging risk;

[0009] Flow rate adjustment optimization and dynamic feedback module: used to compare the calculated blockage risk index with the set threshold. When the blockage risk reaches the set threshold, the flow rate adjustment process is triggered and the optimal flow rate is determined through the genetic optimization algorithm. During the reinjection process, the module also continuously and dynamically monitors key parameters and automatically fine-tunes the flow rate based on feedback.

[0010] Storage and remote management module: used to store system operation data and optimization records, capable of real-time monitoring, historical analysis and abnormal alarm; managers can view key indicators and adjust parameters through the remote monitoring platform to achieve intelligent management and long-term performance optimization.

[0011] A method for dynamic blockage management of groundwater recharge wells, based on the groundwater recharge well dynamic blockage management system, comprises the following steps:

[0012] Step 1: Real-time monitoring and data preprocessing:

[0013] Data collection: measuring the flow rate of the fluid in the well, the concentration of suspended particles, the diameter of suspended particles, the density and temperature of the fluid, the density of suspended particles, the effective length and diameter of the reinjection well;

[0014] Real-time data transmission: All real-time collected data are transmitted to the data processing and risk assessment module in real time;

[0015] Data preprocessing and exception handling: Filter the real-time collected data to remove noise and abnormal values ​​to ensure the stability and accuracy of the measured data;

[0016] Step 2: Blockage risk assessment:

[0017] Based on the fluid dynamics model, Stoke's law and Darcy-Weisbach equation are used as the basis to calculate the settling velocity of particles and the pressure loss in the well;

[0018] The comprehensive clogging risk index (CRI) is calculated based on the suspended particle concentration, particle settling velocity and pressure loss in the well;

[0019] The CRI value calculated in real time is compared with the preset risk threshold. If the CRI exceeds the threshold, the flow rate adjustment process is triggered; if the CRI is lower than the threshold, no flow rate adjustment is required;

[0020] Step 3: Calculate the optimal flow rate for flow rate adjustment optimization:

[0021] After the flow rate adjustment process is triggered, the relationship between the clogging risk, pressure loss and injection volume is analyzed in real time to construct the objective function;

[0022] The genetic algorithm is used to solve the objective function to obtain the optimal flow rate, so as to realize the comprehensive optimization of the blockage risk, pressure loss and injection volume;

[0023] Step Four, Flow Rate Adjustment and Dynamic Feedback:

[0024] Apply the solved optimal flow rate to the well injection process and conduct continuous online monitoring; collect data in real time, and combine with the set trigger conditions to judge whether it is necessary to re-optimize the flow rate;

[0025] Step Five, Data Storage and Remote Management:

[0026] After completing the real-time monitoring and triggering of flow rate optimization, store the key data in the local or cloud database. The remote monitoring platform realizes the real-time management and intelligent decision-making support for the well injection process, and provides the visualization and real-time alarm functions of key indicators.

[0027] The present invention further includes the following technical features:

[0028] Specifically, in the above-mentioned Step One, the data collection includes:

[0029] Flow Rate Measurement: Initialize the ultrasonic flow rate sensor, and set the sampling frequency to 10 times per second for continuously monitoring the flow rate v of the fluid in the well;

[0030] Suspended Particle Concentration Measurement: Calibrate the suspended particle concentration sensor, and set the sampling frequency to once per second to collect the concentration Cs of the suspended particles in the water;

[0031] Suspended Particle Diameter Measurement: Collect the suspended particles in the water and confirm the average diameter d of the particles;

[0032] Fluid Density and Temperature Monitoring: Use a density sensor to measure the density ρ of the fluid in real time f ; and indirectly calculate the viscosity μ of the fluid by monitoring the water temperature through a temperature sensor;

[0033] Well Structure Parameter Confirmation: Determine the effective length L and well diameter D of the well;

[0034] Suspended Particle Density Measurement: Collect the suspended particles in the water, and measure the particle density ρ according to the source and composition of the particles p .

[0035] Specifically, in the above-mentioned Step One, the data collected in real time is filtered by using the Kalman filter or the exponentially weighted moving average method.

[0036] Specifically, in the above-mentioned Step Two, the calculation formula for the sedimentation velocity Vs of the particles is as follows:

[0037]

[0038] Among them, ρ p is the density of particulate matter, ρ f is the fluid density, g is the acceleration due to gravity, d is the diameter of the particulate matter, and μ is the viscosity of the fluid.

[0039] Specifically, in the second step, the formula for calculating the pressure loss in the well is:

[0040]

[0041] Among them: f is the friction coefficient, L is the effective length of the well, D is the well diameter, ρ f is the fluid density, and v is the flow velocity.

[0042] Specifically, in the second step, the formula for calculating the comprehensive blockage risk index CRI is:

[0043]

[0044] Among them, Cs is the concentration of suspended particulate matter monitored in real time, Vs is the sedimentation velocity of particulate matter calculated in real time, and ΔP is the pressure loss in the well calculated in real time; Cs avg , Vs avg , ΔP avg are the average reference values of the concentration of suspended particulate matter, sedimentation velocity, and pressure loss under historical data or steady state, respectively.

[0045] Specifically, the objective function in the third step is:

[0046]

[0047] Among them, J(v) is the total optimization objective function, ω 1 , ω 2 and ω 3 are adjustment coefficients, Q desired is the target injection volume, CRI(v) is the comprehensive blockage risk index at the current flow velocity v, ΔP(v) is the pressure loss at the current flow velocity v, and Q(v) is the actual injection volume at the current flow velocity v.

[0048] Specifically, in the third step, using the genetic algorithm to solve the objective function to obtain the optimal flow velocity includes:

[0049] Define the initial parameters and constraints: Set the initial parameters and constraints for the optimization solution, including the flow velocity range, pressure loss limit, and injection volume balance requirements. At the same time, define the initial adjustment coefficient; if the CRI index is higher than the preset threshold, then increase ω 1 to preferentially reduce the blockage risk; if the pressure loss is close to the upper limit value ΔPmax, then increase the weight of ω 2 to control the flow velocity; if the deviation of the injection volume is large, then increase ω3 To ensure that the injection volume meets the target value; Constraint conditions: flow rate range v min ≤v≤v max , clogging risk index CRI ≤ set risk threshold, maximum allowable pressure loss ΔP ≤ ΔP max , and injection volume balance requirements;

[0050] Iteratively solve the optimal flow rate using the genetic algorithm: Iteratively solve the objective function through the genetic algorithm to find the optimal flow rate v opt , realizing the comprehensive optimization of clogging risk, pressure loss, and injection volume.

[0051] Specifically, in step four, apply the solved optimal flow rate to the well injection process and conduct continuous online monitoring; collect data in real time and determine whether it is necessary to re-optimize the flow rate in combination with the set trigger conditions;

[0052] The key indicators for continuous online monitoring include clogging risk index CRI, pressure loss ΔP, and injection volume Q; when CRI exceeds the preset threshold, further check whether any one of ΔP and Q exceeds the reasonable range: Definition of the reasonable range: The reasonable range of ΔP means that the pressure loss shall not exceed the maximum allowable value ΔPmax, that is, ΔP ≤ ΔPmax; The reasonable range of Q means that the injection volume deviation does not exceed the set allowable error range to ensure meeting the injection requirements;

[0053] If any one of ΔP or Q is out of the reasonable range, immediately trigger the flow rate optimization process, recalculate and apply the new optimal flow rate; if only CRI is abnormal and both ΔP and Q are within the reasonable range, delay the optimization operation to avoid frequent adjustment of the flow rate and reduce the impact on the well injection stability during the adjustment process.

[0054] Compared with the prior art, the present invention has the following technical effects:

[0055] The system of the present invention realizes automatic control and flow rate adjustment through the combination of real-time monitoring, clogging risk assessment, and optimization algorithm, ensuring the efficiency and stability of the well injection process.

[0056] The system of the present invention reduces the maintenance cost and extends the service life of the well by reducing the frequency of clogging.

[0057] The system of the present invention is flexibly designed, can adapt to different types of well injection facilities and geological conditions, and has broad application potential. Brief Description of the Drawings

[0058] Figure 1 is the flow chart of the method of the present invention. Detailed Embodiments

[0059] The present invention provides a dynamic clogging management system and method for groundwater recharge wells. The core design is based on an advanced sensor network, data processing algorithms, and flow rate control mechanisms. Combining geological characteristics and water quality conditions, it provides an efficient and intelligent clogging management solution. By real-time monitoring and dynamically adjusting the flow rate during the well injection process, it effectively manages and reduces physical clogging problems that occur during groundwater recharge.

[0060] The following are specific embodiments of the present invention. It should be noted that the present invention is not limited to the following specific embodiments, and all equivalent transformations made on the basis of the technical solutions of this application fall within the protection scope of the present invention.

[0061] Embodiment:

[0062] This embodiment provides a dynamic clogging management system for groundwater recharge wells, including:

[0063] Monitoring and acquisition module: It includes an ultrasonic flow rate sensor, a suspended particulate matter concentration sensor, a density sensor, and a temperature sensor, which are used to collect key physical parameters of the recharge well in real time, such as the flow rate of the fluid in the well, the concentration of suspended particulate matter, the density of the fluid, and the temperature. Each sensor provides a comprehensive data source for the system, ensuring the accuracy and continuity of subsequent analysis. Specifically, in this embodiment, the ultrasonic flow rate sensor is installed in the middle or at an appropriate depth in the recharge well to monitor the real-time flow rate of the fluid in the well. The suspended particulate matter concentration sensor is installed near the wellhead (inlet area) in the recharge well to more accurately monitor the real-time change of the particulate matter concentration in the injected water and effectively evaluate the deposition trend of the particulate matter on the well wall. The density sensor is installed in the middle or near the bottom area of the recharge well and avoids the wellhead turbulence or the upper particulate matter concentration area. The temperature sensor is installed in the middle of the recharge well to monitor the water temperature in the well.

[0064] Data processing and risk assessment module: It integrates data preprocessing and clogging risk assessment functions. Specifically, this module is used to: clean the data through the Kalman filter or the exponentially weighted moving average method, and calculate the clogging risk index CRI based on the hydrodynamic model. Specifically, by calculating parameters such as the settling velocity and pressure loss, it comprehensively evaluates the clogging risk and provides a scientific basis for flow rate optimization.

[0065] Flow rate optimization and dynamic feedback module: When the clogging risk reaches the set threshold, this module triggers the flow rate adjustment process and determines the optimal flow rate through the genetic optimization algorithm. During the recharge process, this module also continuously monitors the key parameters dynamically and automatically fine-tunes the flow rate according to the feedback to ensure the efficient and stable operation of the system;

[0066] Storage and Remote Management Module: It is used to store the data during system operation and optimization records, and supports real-time monitoring, historical analysis, and anomaly alarm; managers can view key indicators and adjust parameters through the remote monitoring platform to achieve intelligent management and long-term performance optimization.

[0067] This embodiment also provides a method for dynamic plugging management of groundwater recharge wells, which is implemented based on the above-mentioned dynamic plugging management system of groundwater recharge wells, as Figure 1 shown, and includes the following steps:

[0068] Step 1, Real-time Monitoring and Data Preprocessing

[0069] Before starting the plugging management process, initialize all sensors, set the real-time data acquisition and transmission methods, and perform data preprocessing and anomaly detection; this step ensures the accuracy and continuity of system data and provides a reliable data basis for subsequent analysis and decision-making.

[0070] Step 1.1, Data Acquisition: Measure the flow rate of the fluid in the well, the concentration of suspended particulate matter, the diameter of suspended particulate matter, the density and temperature of the fluid, the density of suspended particulate matter, the effective length and well diameter of the recharge well;

[0071] Initialize the sensors and set the real-time acquisition frequency for each item of data to ensure continuous and accurate monitoring of each variable:

[0072] Flow Rate Measurement: Initialize the ultrasonic flow rate sensor, set the sampling frequency to 10 times per second, and use it to continuously monitor the flow rate v of the fluid in the well, unit: m / s.

[0073] Concentration Measurement of Suspended Particulate Matter: Calibrate the laser scattering method suspended particulate matter concentration sensor, set the sampling frequency to once per second, and collect the concentration Cs of suspended particulate matter in water, unit: mg / L. This data directly reflects the distribution of particulate matter in water.

[0074] Diameter Measurement of Suspended Particulate Matter: Collect suspended particulate matter in water, confirm the average diameter d of the particulate matter, unit: m. This value is determined by experiments and remains as a fixed reference value for analyzing the potential risks of particle sedimentation and plugging.

[0075] Fluid Density and Temperature Monitoring: Use a density sensor to measure the density ρ of the fluid in real time f , unit: kg / m 3 ; Monitor the water temperature in real time through a temperature sensor to indirectly calculate the viscosity μ of the fluid, unit: Pa·s, to ensure a comprehensive understanding of the physical properties of water.

[0076] Confirmation of Well Structure Parameters: Determine the effective length L of the well, unit: m and the well diameter D, unit: m. These data will remain unchanged throughout the monitoring period and are the basic parameters of the system.

[0077] Acceleration due to gravity: The preset acceleration due to gravity constant g = 9.81 m / s in the system 2 , which is used for all subsequent gravity-based related calculations.

[0078] Measurement of suspended particulate matter density: Collect suspended particulate matter in water and measure the density ρ of the particulate matter according to the source and composition of the particulate matter p , unit: kg / m 3 , and this density remains a fixed value during actual use.

[0079] Step 1.2, Real-time data transmission: Transmit all the real-time collected data in Step 1.1, including flow rate, suspended particulate matter concentration, fluid density, and temperature data, to the data processing and risk assessment module in real time; ensure the integrity and real-time nature of the data during the transmission process, avoid delays or data loss, and automatically record abnormal situations during data transmission to provide high-quality continuous data for system analysis.

[0080] Step 1.3, Data preprocessing and anomaly processing: Filtering and cleaning: Perform filtering on the real-time collected flow rate, particulate matter concentration, and environmental parameter data, specifically using the Kalman filter or the exponentially weighted moving average method, to remove noise and outliers and ensure the stability and accuracy of the measurement data. Anomaly value marking and removal: Automatically detect and mark abnormal data to ensure the use of high-precision and reliable data during data processing and analysis.

[0081] Step Two, Blockage risk assessment

[0082] During the groundwater recharge process, the application of the hydrodynamic model is crucial, especially in predicting the sedimentation behavior of particulate matter and pressure loss. This model provides data support for the system to calculate the blockage risk in real time and lays a foundation for the optimization of the injection flow rate of the well.

[0083] Based on the hydrodynamic model, specifically using Stokes' law and the Darcy-Weisbach equation as the basis, they are respectively used to calculate the sedimentation velocity of particulate matter in the fluid and the pressure loss in the well.

[0084] Step 2.1, According to Stokes' law, the calculation formula for the sedimentation velocity Vs of particulate matter is as follows:

[0085]

[0086] Among them,

[0087] ρ p is the density of particulate matter (kg / m 3 );

[0088] ρ f is the fluid density (kg / m3 ), the temperature, composition and other information of the fluid can be measured and then calculated indirectly, or directly monitored through a density sensor;

[0089] g is the acceleration due to gravity (9.81 m / s 2 );

[0090] d is the particle diameter (m), which is determined by experiment and averaged;

[0091] μ is the viscosity of the fluid (Pa·s), which can be obtained by referring to standard engineering manuals and fluid property tables at a known temperature.

[0092] Through this formula, the system can calculate the settling velocity of particulate matter at different flow rates in real time, and judge the deposition possibility and blockage trend of particulate matter on the wellbore wall.

[0093] Step 2.2, during the well injection process, pressure loss is a key factor affecting fluid transportation efficiency. Calculate the pressure loss of the fluid in the well through the Darcy-Weisbach equation to predict possible blockage risk areas. The pressure loss calculation formula:

[0094]

[0095] where: f is the friction coefficient, determined by looking up the Moody diagram according to the Reynolds number and pipe roughness ε; the Reynolds number Re calculation formula is: μ is the viscosity of the fluid (Pa·s); L is the effective length of the well (m); D is the well diameter (m); ρ f is the fluid density (kg / m 3 ), obtained through a density sensor or indirect measurement; v is the flow velocity (m / s), measured by an ultrasonic flow velocity sensor; the pipe roughness ε is determined according to the pipe material and processing technology, referring to the standard roughness table, or detected by a surface profilometer.

[0096] Step 2.3, based on the comprehensive blockage risk index CRI calculation method of suspended particulate matter concentration, particulate matter settling velocity and well pressure loss, through normalizing and adaptively balancing the key variables monitored in real time, comprehensively reflecting various factors in the well injection process, so as to achieve accurate quantification of the blockage risk. This design has a self-balancing mechanism, which can effectively balance the influence of each variable, reduce the subjectivity of setting fixed weights, and each variable can be obtained through existing sensors or calculation methods, which is convenient for real-time implementation. This CRI calculation method provides a reliable basis for subsequent flow velocity optimization and injection volume control. The specific formula is as follows:

[0097]

[0098] where:

[0099] Cs is the concentration of suspended particulate matter for real-time monitoring, with the unit of mg / L;

[0100] Vs is the sedimentation velocity of particulate matter obtained by real-time calculation, with the unit of m / s, calculated based on Stokes' law;

[0101] ΔP is the pressure loss in the well obtained by real-time calculation, with the unit of Pa, calculated based on the Darcy-Weisbach equation;

[0102] Cs avg 、Vs avg 、ΔP avg are the average reference values of the concentration of suspended particulate matter, sedimentation velocity, and pressure loss obtained by the system through historical data or under steady-state conditions respectively. Through the CRI calculated by this formula, the system can quantify the current clogging risk level in real time, providing a basis for the next judgment.

[0103] Step 2.4, to ensure that the system adjusts the flow rate under an appropriate clogging risk level, the present invention introduces a risk threshold judgment mechanism. Compare the CRI value obtained by real-time calculation with a preset risk threshold (such as 0.7). If the CRI exceeds this threshold, it indicates that the clogging risk reaches or exceeds the safety upper limit set by the system, triggering the flow rate adjustment process.

[0104] If the CRI is lower than the threshold, the system considers that the clogging risk is within an acceptable range and no flow rate adjustment is required.

[0105] The setting of this risk threshold is mainly based on: by analyzing the historical CRI data under different sedimentation velocities, particulate matter concentrations, and pressure losses, identifying the critical value range of CRI with a higher clogging frequency, and then through experimental debugging, observing the clogging situation of the system during the process of gradually increasing the CRI, further determining the safety upper limit value, and finally selecting a reasonable threshold that can effectively warn of the clogging risk and avoid frequent adjustments. Through this risk threshold judgment mechanism, the system only triggers adjustments when the clogging risk significantly increases, thereby reducing the energy consumption and operating costs of the system and improving the overall operating efficiency.

[0106] Step three, calculate the optimal flow rate for flow rate adjustment and optimization

[0107] After completing the calculation of the clogging risk index (CRI) and triggering the flow rate adjustment signal, the present invention enters the flow rate adjustment and optimization stage. By analyzing the relationship between the clogging risk, pressure loss, and injection volume in real time, the well injection flow rate is dynamically adjusted to achieve efficient control of the system. This step is based on the triggering signal of the previous step and the current monitoring data, and balances the clogging risk and injection efficiency through the solution of the optimal flow rate.

[0108] Specifically, the flow rate adjustment and optimization include the following sub-steps:

[0109] Step 3.1, construct the objective function: After triggering the flow rate adjustment, based on the blockage risk (blockage risk index CRI), pressure loss, and injection volume, construct the objective function to achieve the comprehensive optimization of blockage risk, pressure loss, and injection volume;

[0110] The specific objective function is:

[0111]

[0112] Where:

[0113] J(v) is the total optimization objective function, used to evaluate the system performance at the flow rate v;

[0114] ω1 and ω2 are adjustment coefficients, respectively measuring the influence of the blockage risk index (CRI) and pressure loss (ΔP) on the objective function;

[0115] ω3 is an adjustment coefficient, used to balance the stability of the injection volume to ensure that the system meets the established injection volume requirements during the flow rate adjustment process;

[0116] Q desired is the target injection volume, such as 1000m 3 / day, and Q(v) is the actual injection volume at the current flow rate v.

[0117] Step 3.2, optimize the solution process: After the system triggers the flow rate adjustment, the system enters the optimization solution stage, and uses the genetic algorithm to solve the objective function to find the optimal flow rate v opt to achieve the comprehensive optimization of blockage risk, pressure loss, and injection volume. The following is the specific process:

[0118] Step 3.2.1, define the initial parameters and constraints

[0119] The system sets the initial parameters and constraints for the optimization solution, including the flow rate range, pressure loss limit, and injection volume balance requirements. At the same time, define the initial adjustment coefficients ω1, ω2, ω3 to measure the influence of different optimization objectives on the flow rate.

[0120] Set the initial value v 0 according to historical data and system debugging experience. During the optimization process, the adjustment coefficients will be dynamically adjusted based on real-time feedback. The following is the adjustment logic:

[0121] If the CRI index is higher than the preset threshold, the system increases ω1 to preferentially reduce the blockage risk;

[0122] If the pressure loss is close to the upper limit value ΔPmax, increase the weight of ω2 to control the flow rate;

[0123] If the injection volume deviation is large, increase ω3 to ensure that the injection volume meets the target value.

[0124] Constraints: including the flow rate range v min ≤ v ≤ v max , the blockage risk index CRI ≤ the set risk threshold, the maximum allowable pressure loss ΔP ≤ ΔP max , and the injection volume balance requirement to ensure that the injection volume meets the demand during long-term operation.

[0125] Step 3.2.2, Iteratively solve the optimal flow rate by genetic algorithm

[0126] The system iteratively solves the objective function J(v) by genetic algorithm to find the optimal flow rate v opt , realizing the comprehensive optimization of blockage risk, pressure loss and injection volume. During the optimization process, the system dynamically adjusts the weight coefficients ω1, ω2 and ω3 automatically to achieve adaptive priority control of different optimization objectives. The specific process includes:

[0127] ① Initialize the population: Generate a set of initial candidate flow rates (i.e., the initial population) within the defined range of flow rates. Each candidate flow rate v is regarded as an "individual". The size of the initial population is usually set to 20 - 50 individuals to ensure sufficient diversity in the solution space;

[0128] ② Calculate the fitness function: For each individual in the population, calculate its corresponding objective function value J(v), which includes the CRI, pressure loss ΔP and injection volume deviation at the current flow rate. Calculate the fitness value by combining the adjusted ω1, ω2 and ω3 in real time. The smaller the objective function value, the higher the fitness, and the system preferentially retains the individuals with higher fitness. During the fitness calculation, the system dynamically adjusts the weight coefficients ω1, ω2 and ω3 according to real-time feedback: when the CRI index is higher than the set threshold, the system automatically increases ω1, making the objective function pay more attention to the blockage risk and preferentially reducing the blockage risk; when the pressure loss ΔP approaches the upper limit, the system increases ω2 to ensure that the pressure loss does not exceed the limit; if the injection volume deviation is large, increase ω3 to preferentially adjust the flow rate to meet the injection volume demand;

[0129] ③ Selection: The system selects the individuals with higher fitness to enter the next generation according to the fitness value, preferentially retaining the flow rate values with higher fitness, while retaining a certain degree of randomness to avoid falling into local optima;

[0130] ④ Crossover: Among the selected individuals, randomly select pairs and generate new "offspring" individuals through crossover operations. The crossover rate is usually set to 0.8, that is, 80% of the individuals will perform crossover operations;

[0131] ⑤ Mutation: To maintain population diversity and avoid being trapped in local optimal solutions, mutation operations are performed on some newly generated individuals. The mutation rate is generally set to 0.1, that is, 10% of the individuals will mutate to further explore the solution space;

[0132] ⑥ Update population: Replace the low-fitness individuals in the original population with the newly generated individuals to generate a new generation of population. Each generation of the population contains a certain number of new individuals, enabling the algorithm to gradually approach the optimal solution.

[0133] ⑦ Iteration and termination conditions: Repeat the steps of fitness calculation, selection, crossover, and mutation, and perform multiple iterations until the termination conditions are met. The termination conditions can be set to reach a preset number of generations (e.g., 100 generations), or the objective function value converges to a certain minimum range, indicating that the optimal flow rate v opt has been basically stable.

[0134] Step Four, Flow Rate Adjustment and Dynamic Feedback

[0135] After obtaining the optimal flow rate v opt the system directly applies this flow rate to the well injection process and conducts continuous online monitoring. The key monitoring indicators include the clogging risk index (CRI), pressure loss (ΔP), and injection volume (Q). The system collects these data in real time and combines the set trigger conditions to determine whether the flow rate needs to be re-optimized.

[0136] Specifically, when the CRI exceeds the preset threshold, the system will further check whether any one of ΔP and Q exceeds the reasonable range:

[0137] Definition of the reasonable range: The reasonable range of ΔP means that the pressure loss shall not exceed the maximum allowable value ΔPmax, that is, ΔP ≤ ΔPmax; the reasonable range of Q means that the deviation of the injection volume shall not exceed the set allowable error range (±10%) to ensure that the injection requirements are met.

[0138] If any one of ΔP or Q is abnormal (exceeds the above range), the system immediately triggers the flow rate optimization process, recalculates and applies the new optimal flow rate v opt '. If only the CRI is abnormal while ΔP and Q are both within the reasonable range, the system will delay the optimization operation to avoid frequent flow rate adjustments and reduce the impact on the stability of well injection during the adjustment process. Through this mechanism, the system can effectively avoid unnecessary flow rate adjustments, ensure that the flow rate during the injection process not only meets the stability requirements but also can respond promptly to changes in the actual operating conditions, thereby improving the operating efficiency and reliability of the recharge well.

[0139] Step Five, Data Storage and Remote Management

[0140] After the system completes real-time monitoring and flow rate optimization triggering, it stores key data (flow rate v, clogging risk index CRI, pressure loss ΔP, actual injection volume Q, and optimization triggering conditions) in a local or cloud database. These data are used to record monitoring and adjustment information in real time, support the verification and traceability of the operating state, and provide support for performance improvement and trend prediction through historical data analysis. In addition, these data provide a reliable basis for subsequent algorithm optimization and model calibration, further improving the accuracy of optimization calculations.

[0141] The remote monitoring platform realizes real-time management and intelligent decision-making support for the well injection process, providing visualization of key indicators and real-time alarm functions. When a clogging risk or other parameters (ΔP, Q) are detected outside the set range, the platform issues an alarm in a timely manner and recommends adjustment strategies to assist managers in responding quickly. The platform also supports historical data backtracking and analysis to help identify potential problems and guide optimization decisions. Managers can remotely control the flow rate or adjust threshold parameters through the platform to meet actual operating requirements and ensure the long-term stability and intelligent operation of the system.

Claims

1. A groundwater recharge well dynamic blockage management system, characterized in that: include: Monitoring and acquisition module: used to collect key parameters of the reinjection well in real time: flow rate of the fluid in the well, concentration of suspended particles, fluid density and temperature; Including ultrasonic flow rate sensor, suspended particle concentration sensor, density sensor, temperature sensor; Data processing and risk assessment module: used to pre-process the collected parameter data, calculate the blockage risk index based on the fluid dynamics model, and comprehensively assess the blockage risk; Flow rate adjustment optimization and dynamic feedback module: used to compare the calculated blockage risk index with the set threshold. When the blockage risk reaches the set threshold, the flow rate adjustment process is triggered and the optimal flow rate is determined through the genetic optimization algorithm. During the reinjection process, the module also continuously and dynamically monitors key parameters and automatically fine-tunes the flow rate based on feedback. Storage and remote management module: used to store system operation data and optimization records, capable of real-time monitoring, historical analysis and abnormal alarm; managers can view key indicators and adjust parameters through the remote monitoring platform to achieve intelligent management and long-term performance optimization.

2. A method for dynamic blockage management of groundwater recharge wells, characterized in that: The method is based on the groundwater recharge well dynamic blockage management system according to claim 1, and comprises the following steps: Step 1: Real-time monitoring and data preprocessing: Data collection: measuring the flow rate of the fluid in the well, the concentration of suspended particles, the diameter of suspended particles, the density and temperature of the fluid, the density of suspended particles, the effective length and diameter of the reinjection well; Real-time data transmission: All real-time collected data are transmitted to the data processing and risk assessment module in real time; Data preprocessing and exception handling: Filter the real-time collected data to remove noise and abnormal values ​​to ensure the stability and accuracy of the measured data; Step 2: Blockage risk assessment: Based on the fluid dynamics model, Stoke's law and Darcy-Weisbach equation are used as the basis to calculate the settling velocity of particles and the pressure loss in the well; The comprehensive clogging risk index (CRI) is calculated based on the suspended particle concentration, particle settling velocity and pressure loss in the well; The CRI value calculated in real time is compared with the preset risk threshold. If the CRI exceeds the threshold, the flow rate adjustment process is triggered; if the CRI is lower than the threshold, no flow rate adjustment is required; Step 3: Calculate the optimal flow rate for flow rate adjustment optimization: After the flow rate adjustment process is triggered, the relationship between the clogging risk, pressure loss and injection volume is analyzed in real time to construct the objective function; Genetic algorithm is used to solve the objective function to obtain the optimal flow rate, so as to achieve comprehensive optimization of clogging risk, pressure loss and injection volume; Step 4: Flow rate adjustment and dynamic feedback: Apply the optimal flow rate to the well injection process and conduct continuous online monitoring; collect data in real time and determine whether the flow rate needs to be re-optimized based on the set trigger conditions; Step 5: Data storage and remote management: After completing real-time monitoring and flow rate optimization triggering, key data is stored in a local or cloud database. The remote monitoring platform realizes real-time management of the well injection process and intelligent decision support, providing visualization and real-time alarm functions for key indicators.

3. The method for dynamic blockage management of groundwater recharge wells according to claim 2, characterized in that: In the step 1, data collection includes: Flow rate measurement: Initialize the ultrasonic flow rate sensor and set the sampling frequency to 10 times / second to continuously monitor the flow rate v of the fluid in the well; Suspended particle concentration measurement: calibrate the suspended particle concentration sensor, set the sampling frequency to once per second, and collect the concentration Cs of suspended particles in the water; Suspended particle diameter measurement: Collect suspended particles in water and determine the average particle diameter d; Fluid density and temperature monitoring: Use density sensor to measure fluid density ρ in real time f ; Use the temperature sensor to monitor the water temperature in real time to indirectly calculate the fluid viscosity μ; Confirmation of well structure parameters: determine the effective length L and diameter D of the well; Suspended particle density measurement: Collect suspended particles in water and determine the particle density ρ based on the source and composition of the particles. p .

4. The method for dynamic blockage management of groundwater recharge wells according to claim 2, characterized in that: In the step 1, Kalman filtering or exponentially weighted moving average method is used to filter the data collected in real time.

5. The method for dynamic blockage management of groundwater recharge wells according to claim 3, characterized in that: In step 2, the settling velocity Vs of the particles is calculated as follows: Among them, ρ p is the particle density, ρ f is the fluid density, g is the gravitational acceleration, d is the particle diameter, and μ is the viscosity of the fluid.

6. The method for dynamic blockage management of groundwater recharge wells according to claim 5, characterized in that: In step 2, the pressure loss calculation formula in the well is: Where: f is the friction coefficient, L is the effective length of the well, D is the well diameter, ρ f is the fluid density and v is the flow velocity.

7. The method for dynamic blockage management of groundwater recharge wells according to claim 6, characterized in that: In step 2, the calculation formula of the comprehensive congestion risk index CRI is: Where Cs is the concentration of suspended particles monitored in real time, Vs is the particle settling velocity calculated in real time, and ΔP is the well pressure loss calculated in real time; Cs avg , Vs avg , ΔP avg They are respectively the average reference values ​​of suspended particle concentration, settling velocity and pressure loss under historical data or steady state.

8. The method for dynamic blockage management of groundwater recharge wells according to claim 7, characterized in that: The objective function in step 3 is: Among them, J(v) is the overall optimization objective function, ω1, ω2 and ω3 are adjustment coefficients, Q desired is the target injection volume, CRI(v) is the comprehensive clogging risk index at the current flow rate v, ΔP(v) is the pressure loss at the current flow rate v, and Q(v) is the actual injection volume at the current flow rate v.

9. The method for dynamic blockage management of groundwater recharge wells according to claim 8, characterized in that: In step 3, a genetic algorithm is used to solve the objective function to obtain the optimal flow rate, including: Define initial parameters and constraints: Set the initial parameters and constraints for the optimization solution, including flow rate range, pressure loss limit and injection volume balance requirements. At the same time, define the initial adjustment coefficient; if the CRI index is higher than the preset threshold, increase ω1 to prioritize reducing the risk of blockage; if the pressure loss is close to the upper limit ΔPmax, increase the weight of ω2 to control the flow rate; if the injection volume deviation is large, increase ω3 to ensure that the injection volume meets the target value; Constraints: flow rate range v min ≤v≤v max , Blockage risk index CRI ≤ set risk threshold, Maximum allowable pressure loss ΔP ≤ ΔP max , and injection volume balance requirements; Genetic algorithm iterative solution to the optimal flow rate: The objective function is iteratively solved by genetic algorithm to find the optimal flow rate v opt , achieving comprehensive optimization of clogging risk, pressure loss and injection volume.

10. The method for dynamic blockage management of groundwater recharge wells according to claim 8, characterized in that: In the step 4, the optimal flow rate is applied to the well injection process and continuously monitored online; data is collected in real time, and it is determined whether the flow rate needs to be re-optimized in combination with the set trigger conditions; The key indicators for continuous online monitoring include the blockage risk index CRI, pressure loss ΔP and injection volume Q. When CRI exceeds the preset threshold, further check whether any of ΔP and Q exceeds the reasonable range: Definition of reasonable range: The reasonable range of ΔP means that the pressure loss shall not exceed the maximum allowable value ΔPmax, that is, ΔP≤ΔPmax; The reasonable range of Q means that the injection volume deviation does not exceed the set allowable error range to ensure that the injection demand is met; If either ΔP or Q is out of the reasonable range, the flow rate optimization process will be triggered immediately, and the new optimal flow rate will be recalculated and applied; if only the CRI is abnormal and both ΔP and Q are within the reasonable range, the optimization operation will be delayed to avoid frequent adjustment of the flow rate and reduce the impact on the well injection stability during the adjustment process.

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