Intelligent Drainage Optimization Method for Coalbed Methane Wells Based on Digital Twin and Deep Learning

Abstract

This invention discloses an intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning, belonging to the field of oil and gas field development technology. The method includes data preprocessing, multi-source data fusion and feature engineering calculation, CNN-GRU-Attention hybrid model pre-training, reservoir permeability dynamic evolution mechanism model calculation, and multi-objective optimization and dynamic control steps. This invention constructs a closed-loop control system for the entire coalbed methane well drainage process, realizing real-time perception of the drainage process, accurate production capacity prediction, and intelligent decision-making on drainage parameters. It connects the technical links from data acquisition to control execution, achieving real-time, fully automated closed-loop control of the drainage process, significantly improving production capacity prediction accuracy and reducing the incidence of coal powder pump jamming.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, specifically relating to an intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning. Background Technology

[0002] Coalbed methane, an important unconventional natural gas resource, is extracted through drainage and depressurization, which desorbs methane adsorbed on the coal matrix. Deep coalbed methane well drainage faces the following technical challenges: (1) Complex dynamic response of reservoirs: Coal reservoirs are simultaneously affected by the effective stress effect and the matrix shrinkage effect during production. In the early stage of drainage, as the formation pressure decreases, the effective stress increases, leading to fracture closure and a decrease in permeability; as gas desorption occurs, the matrix shrinkage effect gradually appears, causing fractures to open and permeability to rebound. The coupling effect of these two effects makes the reservoir permeability exhibit a highly nonlinear change.

[0003] (2) Difficulty in controlling drainage: Traditional drainage systems often adopt fixed patterns such as "seven-stage" or "five-stage", including preparation stage, stabilization and depressurization stage, and continuous production stage. However, these fixed patterns cannot adapt to the dynamic response differences of reservoirs under different geological conditions. Especially in deep coal seams, excessively rapid depressurization can easily lead to the production of large amounts of coal powder, which can block seepage channels and cause irreversible reservoir damage.

[0004] (3) Strong system coupling: The drainage system involves multiple subsystems such as geological conditions, wellbore technology, equipment parameters and production system, and there are complex coupling relationships between these systems. Traditional methods are difficult to accurately describe the impact of these coupling relationships on the final production capacity.

[0005] Existing coalbed methane drainage optimization technologies have the following main shortcomings: (1) Limitations of numerical simulation methods: Although traditional numerical simulation methods (such as commercial software such as Eclipse and CMG) can consider geomechanical effects, the modeling process is complex, requires a large number of detailed reservoir parameters, has high computational costs, and is difficult to optimize in real time. In addition, the accuracy of the model depends heavily on the accuracy of geological modeling, while the geological parameters of deep coal seams are difficult to obtain and have great uncertainty.

[0006] (2) Shortcomings of traditional machine learning methods: Although the jet pump drainage control method disclosed in patent CN117266804A introduces a deep learning model, the model structure is relatively simple, using only a basic neural network, and its ability to capture the complex spatiotemporal relationships between drainage parameters is limited. In addition, this method does not consider the dynamic changes in reservoir properties, resulting in insufficient long-term prediction accuracy.

[0007] (3) Static nature of control strategy: Although the quantitative drainage method based on the fluid material balance theory has achieved progress from qualitative to quantitative, the adjustment of the drainage system still relies on manual experience and lacks an automated closed-loop control system. When reservoir conditions change, it is difficult to adjust the drainage parameters in a timely manner.

[0008] (4) Limitations of single-objective optimization: Existing optimization methods focus on a single objective (such as maximum daily gas production) and ignore the balance of multiple objectives such as stable production period, final recovery rate and economic benefits, which may lead to the unsustainability of the drainage system in practical applications.

[0009] Therefore, it is essential to develop an intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning. Summary of the Invention

[0010] To address the problems of long drainage cycles, frequent coal powder pump jamming, and large production fluctuations in existing technologies, this invention aims to provide an intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning. This method is particularly suitable for optimizing drainage regimes in deep coalbed methane wells (depth greater than 1500 meters). It enables real-time perception, accurate prediction, and intelligent decision-making during the coalbed methane well drainage process, establishing a complete closed-loop system from data acquisition to control execution. It can simultaneously capture spatial features and temporal dependencies using a hybrid deep learning model, significantly improving production prediction accuracy. It constructs a reservoir dynamic characterization method that deeply integrates data-driven and mechanistic models, enhancing the physical rationality and generalization ability of the model. It establishes a multi-objective optimization framework to balance multiple objectives such as production, stable production period, and coal powder control, achieving scientific optimization of the drainage regime. Finally, it forms differentiated drainage strategy templates applicable to coalbed methane wells of different production levels, improving the method's practicality.

[0011] The objective of this invention is achieved by including the following steps: S1. Data Preprocessing: Collect production data for more than 2 years from the target coalbed methane well and at least 3 adjacent wells, and simultaneously collect static geological parameters and engineering parameters of the target well; perform outlier removal, missing value imputation (using linear interpolation + random forest imputation), and normalization on the collected data, and unify the data time frequency (daily frequency recommended) to form a standardized multi-source database. S2. Multi-source data fusion and feature engineering calculation: Based on multi-source databases, core feature parameters reflecting drainage dynamics and reservoir status are extracted and used as input features for the CNN-GRU-Attention hybrid model. S3. Pre-training of the CNN-GRU-Attention Hybrid Model: This invention designs a CNN-GRU-Attention hybrid deep learning model, which extracts spatial features of data through multi-scale CNN, captures temporal dependencies through bidirectional GRU, and highlights the contribution of key time points through multi-head self-attention mechanism, thereby achieving high-precision prediction of production capacity. The specific process is as follows: S301. Input Layer Design: Input data is a multi-dimensional time series matrix. ,in The time step is typically 180-365 days. The number of features (including static and dynamic features) includes static features (such as coal seam thickness, burial depth, etc.) and dynamic features (such as daily gas production, flowing pressure, etc.); static features are encoded through a fully connected network and then concatenated with dynamic features in the time dimension. S302, CNN Feature Extraction: Multi-scale convolutional kernels are used to extract spatial features from time series data, capturing local information at different time scales to form multi-scale feature maps, which serve as input for GRU time series modeling; a one-dimensional convolutional neural network is used to capture the spatial correlation between multiple parameters; let the input sequence be... ,in Convolution operation is represented as:

[0012] In the formula, The kernel size; For the first The weight matrix of each convolutional kernel; For bias terms; The ReLU activation function is used; this invention employs a multi-scale convolution kernel ( =3, 5, 7) Extract features at different time scales in parallel, and finally stitch them together to form a multi-scale feature map; S303, GRU Temporal Modeling: The multi-dimensional spatial features extracted by CNN are input into a bidirectional GRU network to capture temporal dependencies. The model extracts long-term dependencies from historical data, enhancing predictive ability. GRU calculation is as follows: Update Gate: Reset Door: Candidate hidden state: Final hidden state: In the formula, Output features for the CNN feature extraction step. Represents the Hadamard product; The hidden states of the forward GRU and the reverse GRU are concatenated along the feature dimension to obtain the final GRU temporal features; S304. Attention Mechanism Design: A multi-head self-attention mechanism is introduced (four attention heads are set up in engineering) to capture temporal features in different feature subspaces, highlighting the contribution of key time points (such as reservoir damage, production mutation) to the current prediction. The formula is as follows:

[0013] In the formula, , , These are obtained from the hidden state sequences through linear transformation; this invention employs four attention heads, capturing temporal features in different subspaces. S305, Output Layer Design: The output layer is based on a dual-branch design, including a regression branch and a classification branch. The attention features obtained in step S304 are input into the fully connected network. The regression branch is used for production capacity prediction (such as core production capacity parameters such as daily gas production, daily water production, and bottom hole flowing pressure in the next 30 days), and the classification branch is used to identify the current production stage of the gas well (preparation, pressure reduction, stable production, etc.) and the reservoir damage risk level (low, medium, high). The output layer realizes integrated output of "prediction and identification", providing comprehensive support for production capacity decision-making. S4. Calculation of dynamic evolution mechanism model of reservoir permeability: The permeability change trend is identified by the drainage indicator curve, and the permeability is quantitatively predicted by the improved Palmer-Mansoori model. At the same time, the results of this model are used as physical constraints to correct the prediction results of the CNN-GRU-Attention hybrid model to ensure that it conforms to the physical evolution law of coal reservoir. S5. Multi-objective optimization and dynamic control: Construct a production capacity differentiated drainage strategy and determine the production capacity level of the target well. By setting objective functions, decision variables and constraints, construct a multi-objective optimization system. Use the improved NSGA-II to perform population initialization, fitness calculation, genetic operation and constraint processing on the multi-objective optimization system. Finally, solve for the optimal drainage parameter sequence and use model predictive control (MPC) to dynamically regulate the drainage parameters in real time.

[0014] Preferably, the production data mentioned in step S1 includes bottom hole flowing pressure, wellhead casing pressure, oil pressure, daily gas production, daily water production, cumulative gas production, cumulative water production, and pulverized coal concentration; The static geological parameters include coal seam thickness, burial depth, high-rank coal, gas content, original permeability, porosity, original reservoir pressure, critical desorption pressure, Langmuir volume, Langmuir pressure, Young's modulus, and Poisson's ratio. The engineering parameters include well depth, casing program, completion method, fracture half-length, flow capacity, jet pump model, pump depth, and motor power.

[0015] Preferably, the core characteristic parameters of step S2 include daily production increase efficiency, daily stable production pressure reduction, pulverized coal output index, and permeability change trend indicators, wherein: Daily production efficiency = Daily production increase / Bottom flow pressure drop, which characterizes the increase in gas production per unit pressure drop and quantitatively evaluates the actual effectiveness of drainage and pressure reduction measures. Daily stable production pressure drop = pressure drop during stable production period / stable production time, which represents the rate of pressure drop required to maintain stable production of gas wells and reflects the reservoir's energy replenishment capacity and stable production potential; The coal powder production index = water production × coal powder concentration in water / gas production. It comprehensively considers the coupling relationship between water production, gas production and coal powder concentration to quantitatively evaluate the degree of coal powder production. The permeability change trend index = (daily gas production / current flowing pressure) / (initial gas production / initial flowing pressure) characterizes the relative change of reservoir permeability, achieving an indirect quantitative characterization of dynamic changes in permeability.

[0016] Preferably, the specific process of step S4 is as follows: S401. Permeability Trend Identification Based on Drainage Indication Curve: By analyzing the curve shape of the relationship between cumulative water production and bottom hole flowing pressure, the dynamic trend of reservoir permeability is qualitatively identified. Three typical curves and their corresponding characteristics are as follows: Horizontal curve: This indicates that the reservoir permeability remains stable, reflecting a basic balance between the effective stress effect and the matrix shrinkage effect. Convex curve: This indicates that the reservoir permeability is gradually improving, reflecting that the matrix shrinkage effect is dominant; Concave curve: This indicates that the reservoir permeability continues to deteriorate, reflecting that the effective stress effect is dominant. S402. Improved Palmer-Mansoori Quantitative Permeability Prediction Model: Based on the Palmer-Mansoori model, incorporating coal and petrological geomechanical characteristics and matrix shrinkage effects, a dynamic prediction formula for reservoir permeability considering multi-factor coupling is established to complete the quantitative calculation of permeability. The formula is as follows:

[0017] In the formula, This refers to relative penetration rate; , These are the elastic parameters of coal and rock. Initial porosity; , For current and initial pressure; The matrix shrinkage coefficient; For maximum strain; The Langmuir pressure is used; the output of this model serves as a physical constraint, which is then fused with the prediction results of the CNN-GRU-Attention hybrid model to ensure that the prediction results conform to physical laws.

[0018] Preferably, in step S5, the objective function is to balance production capacity, stable production period, coal dust control, and output stability, i.e., to construct a four-objective optimization system: Maximize cumulative gas production: Maximize stable production period: Minimize the risk of coal dust damage: Minimize output fluctuations: Decision variables: Select the core adjustable parameters during the sampling process. , where ΔP is the stage pressure drop amplitude; For the rate of voltage reduction; This is the period of stabilization. To control water production; Constraints: Based on engineering practice, equipment performance, and reservoir characteristics, four types of hard constraints are defined: Engineering constraints: Equipment constraints: Operational constraints: (To prevent excessively rapid pressure reduction from causing pulverized coal production) Geological constraints: (To ensure that methane desorption continues).

[0019] Preferably, the NSGA-II improvement in step S5 includes adaptive crossover and mutation probability, feasibility rule constraint processing, and GPU parallel computing acceleration. Among them, adaptive crossover and mutation probability dynamically adjusts the crossover and mutation probabilities according to the population diversity to avoid premature convergence of the algorithm; feasibility rule constraint processing penalizes solutions that violate the constraints to improve the engineering feasibility of the solutions; GPU parallel computing acceleration uses GPU parallel computing to evaluate the population fitness, significantly shortens the algorithm's computation time, and meets the needs of real-time optimization.

[0020] Preferably, the S5 step's differentiated production drainage strategy categorizes production capacity into three levels based on the gas well's unobstructed flow rate: high-yield, medium-yield, and low-yield. This achieves standardized and adaptable control of drainage parameters. The specific templates are as follows: Unobstructed flow rate > 5 × 10 4 m 3 / d is the template for high-yield wells. High-yield well templates: rapid step-by-step pressure reduction, with an initial pressure reduction rate of 0.3MPa / d to 0.5MPa / d; stable production pressure is controlled at 70% to 80% of the critical desorption pressure; pressure reduction is slowed down when the coal powder concentration exceeds 500mg / L; Unobstructed flow rate is 2×10 4 m 3 / d~5×10 4 m 3 / d is the template for medium-yield wells. For medium-yield well templates: gradually reduce pressure in stages, with a pressure reduction rate of 0.1MPa / d to 0.3MPa / d; control the stable production flow pressure at 60% to 70% of the critical desorption pressure; adjust the working system when the coal powder concentration exceeds 300mg / L. Unobstructed flow rate < 2 × 10 4 m 3 / d is a low-production well template. Low-production well template: linear and slow pressure reduction, pressure reduction rate 0.05MPa / d~0.1MPa / d; stable production flow pressure is controlled at 50%~60% of the critical desorption pressure; strict control of pressure reduction rate to prevent coal powder production from the source.

[0021] Preferably, the S5 step model predictive control (MPC) dynamically adjusts the drainage parameters in real time. By constructing a four-level real-time dynamic control system (sensing-network-control-execution), and employing an MPC strategy of rolling optimization + feedback correction, adaptive adjustment of the drainage parameters is achieved. The specific process is as follows: (1) Data acquisition (sensing layer): Set up a downhole permanent pressure thermometer, electromagnetic flowmeter and water quality online monitoring instrument to collect measured data within the current 4 to 8 hours in seconds; (2) Capacity prediction (control layer): Input the measured data into the pre-trained CNN-GRU-Attention hybrid model to predict the capacity change in the next 72 hours; (3) Optimization solution (control layer): The improved NSGA-II is used to perform population initialization, fitness calculation, genetic operation and constraint processing on the multi-objective optimization system, and finally solve the optimal sampling parameter sequence to obtain the optimal decision variables; (4) Command execution (execution layer): The first control quantity is sent to the variable frequency jet pump system to automatically adjust the drainage parameters; (5) Feedback correction (control layer): Compare the actual execution result with the prediction result, calculate the deviation, and feed the deviation back to the CNN-GRU-Attention hybrid model to correct the prediction result of the next cycle.

[0022] The system comprises several layers: a perception layer deploying downhole permanent pressure and temperature gauges, electromagnetic flowmeters, and online water quality monitors to achieve second-level data acquisition; a network layer employing industrial Ethernet + 5G wireless transmission technology to ensure real-time and reliable data transmission; a control layer deploying an improved NSGA-II algorithm and MPC strategy, with a control cycle of 4-8 hours for rolling optimization of drainage parameters; and an execution layer using frequency conversion control systems, electric regulating valves, and other actuators to automatically adjust drainage parameters. Safety protection mechanisms can also be implemented, including hard and soft constraints on key parameters, real-time fault diagnosis, and seamless switching between automatic / semi-automatic / manual modes, ensuring system operational safety.

[0023] The beneficial effects of this invention are: 1. The method of this invention constructs a closed-loop control system for the entire process of coalbed methane well drainage, realizing real-time perception of the drainage process, accurate production prediction, and intelligent decision-making on drainage parameters. It connects the technical links from data acquisition to control execution, achieving real-time, fully automated closed-loop control of the drainage process. Through precise coal powder production prediction and prevention measures, this invention reduces the coal powder pump jamming rate from the traditional 15%~20% to below 3%, effectively protecting reservoir integrity. The method of this invention significantly improves gas well production capacity, with the average daily production of a single well in the test well group increasing by 25%~40%, the cumulative gas production increasing by 30%~50%, and the time to reach full production shortened by more than 40%, significantly improving the gas well production capacity release efficiency. 2. This invention constructs a hybrid deep learning model that integrates an attention mechanism to achieve simultaneous capture of spatial features and temporal dependencies in production data, significantly improving the accuracy of production capacity prediction. Practice in multiple coalbed methane blocks shows that the average absolute error percentage (MAPE) of this invention for predicting gas production over the next 30 days can be stably maintained at 1.5%~2.5%, which is far superior to traditional numerical simulation methods (8%~15%) and single machine learning models (5%~8%). 3. This invention establishes a reservoir dynamic characterization method that deeply integrates data-driven and mechanism models, using mechanism models to constrain the prediction results of data-driven models, thereby enhancing the physical rationality and engineering generalization ability of the models. 4. This invention constructs a multi-objective optimization system to achieve a synergistic balance of core indicators such as output, stable production period, and pulverized coal control. It can automatically adjust the mining and drainage system according to real-time production dynamics, avoiding human judgment errors. 5. To develop differentiated drainage strategy templates that are adapted to coalbed methane wells of different production capacity levels, thereby improving the engineering practicality and field operability of the method of this invention; 6. The method of this invention is applicable to deep coalbed methane wells with a burial depth greater than 1500 meters, and can also be directly extended to optimize the drainage and production of conventional coalbed methane wells, without being significantly limited by regional geological conditions. The method of this invention only requires fine-tuning the model parameters and optimization objectives according to the reservoir characteristics, and can be used for the development and optimization of other unconventional oil and gas resources such as shale gas and tight gas, with strong technical scalability. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0025] The present invention will be further described below with reference to the embodiments and accompanying drawings, but this is not intended to limit the present invention in any way. Any modifications or substitutions made based on the teachings of the present invention shall fall within the protection scope of the present invention.

[0026] Example 1 A deep coalbed methane well in the Junggar Basin was selected as the subject of this study. The well is 1250m deep, with a coal seam thickness of 6.8m and a gas content of 18.5m³. 3 / t, initial permeability 0.35mD, unobstructed flow rate 5.2×10 4 m 3 / d, which falls under the category of high-yield wells; the traditional "seven-stage" drainage system previously used had problems such as long drainage cycles, frequent coal powder pump jamming, and large fluctuations in production; all calculations in this embodiment are based on the following parameters (see Table 1), the parameter values ​​are taken from field measurements or laboratory core analysis results, and the units are uniformly labeled with both SI units and commonly used engineering units.

[0027] Table 1 Parameters and Explanations

[0028] This embodiment of the intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning includes the following steps: S1. Data preprocessing: Collect production data for more than 2 years from the target coalbed methane well and at least 3 adjacent wells, and simultaneously collect static geological parameters and engineering parameters of the target well. The production data includes bottom hole flowing pressure of 5.2-19.8 MPa, wellhead casing pressure of 0.5-3.2 MPa, oil pressure of 0.3-2.8 MPa, and daily gas production of 0.2-5.0 × 10⁻⁶ MPa. 4 m 3 / d, daily water production 5.2-18.5m 3 / d, cumulative gas production 0.8-4.2×10 6 m 3 Cumulative water production: 1.2-3.8 × 10⁻⁶ 4 m 3 Pulverized coal concentration 50-800 mg / L; The static geological parameters include a coal seam thickness of 6.0-7.2m, a burial depth of 1200-1300m, high-rank coal, and a gas content of 16.2-19.8m³. 3 / t, initial permeability 0.30-0.40 mD, porosity 3.2-4.5%, initial reservoir pressure 18.5-20.2 MPa, critical desorption pressure 10.2-11.5 MPa, Langmuir volume 32.5 m³ 3 / t, Langmuir pressure 8.5MPa, Young's modulus 18.2GPa, Poisson's ratio 0.28; The engineering parameters include a well depth of 1250m, casing program Φ139.7mm+Φ101.6mm, open hole completion, fracture half length of 85m, conductivity of 12.5D·cm, jet pump model QYB-100, pump mounting depth of 800m, and motor power of 37kW. The collected data undergoes outlier removal, missing value imputation (using linear interpolation + random forest imputation), and normalization processing. The data time frequency is unified (daily frequency is recommended) to form a standardized multi-source database. Outlier removal: employs In principle, the average value is calculated for parameters with large fluctuations, such as gas production and pulverized coal concentration. and standard deviation Remove Outliers (such as invalid points where the coal powder concentration suddenly increases to 2000 mg / L) were identified, and a total of 12 abnormal data points were removed, leaving 8748 valid data points. Missing value imputation: Linear interpolation (short-term missing values ​​<7d) + random forest imputation (long-term missing values ​​≥7d) was used for bottom hole flowing pressure (38 missing values) and daily water production (25 missing values). After imputation, the data integrity rate was 99.97%. Normalization: Normalize all numerical parameters to eliminate the influence of dimensions. All numerical parameters are mapped to the [0, 1] interval (e.g., bottom hole flowing pressure 19.2MPa→1, 5MPa→0). Time alignment: All data sampling frequencies are unified to daily frequency, and the data is concatenated according to the "year-month-day" timestamp to form a standardized multi-source database, providing data support for subsequent feature engineering and model training.

[0029] S2. Multi-source data fusion and feature engineering calculation: Based on a multi-source database, four core feature parameters reflecting the dynamics of coal drainage and reservoir status are extracted. These include daily production efficiency, daily stable production pressure drop, pulverized coal production index, and permeability trend indicators. This step selects three sets of typical measured data from 30 days, 60 days, and 90 days of coal drainage, substitutes them into the following formulas, performs unit conversions, calculates step by step, and analyzes the results. All feature parameter calculation results serve as the core input features of the CNN-GRU-Attention hybrid model. The four core feature parameters are as follows: (1) Daily production increase efficiency = Daily production increase / Bottom flow pressure drop, the degree of increase in gas production per unit pressure drop, the larger the value, the stronger the effectiveness of the pressure reduction measures; Daily production increase (×10 4 m 3 / d), bottomhole flow pressure drop (MPa), results in units (×10). 4 m 3 / (d⋅MPa)); Detailed calculation (three sets of typical data); Results are shown in Table 2; Table 2: Calculation Results of Daily Production Increase Efficiency

[0030] (2) Daily stable production pressure drop = Flow pressure drop during stable production period / Stable production time, the daily pressure drop rate required to maintain stable production of the gas well. The smaller the value, the stronger the reservoir energy replenishment capacity; Flow pressure drop during stable production period (MPa), stable production time (d), result unit (MPa / d); Field measured data: Stable production time during the well's trial production stage =15 days, cumulative flow pressure drop during stable production period =1.2MPa; Daily stable production pressure drop =1.2 / 15 = 0.08MPa / d; The daily stable production pressure drop is 0.08MPa / d, which is lower than the pressure drop rate limit of 0.5MPa / d for high-yield wells, indicating that the reservoir energy of this well is sufficient and the potential for stable production is great; Pulverized coal production index = water production × pulverized coal concentration in water / gas production, a dimensionless index for comprehensively evaluating the degree of pulverized coal production (in this embodiment, the unit of measurement is mg / m³). 3 The higher the value, the more severe the coal powder production, which easily leads to blockage of the seepage channels; water production (m³) 3 / d)=10 6 mL / d, pulverized coal concentration (mg / L) = mg / 10 3 mL, gas production (m³) 3 / d); 60-day drainage (critical node for high-yield wells): Field measurement data shows water production =12m 3 / d, coal powder concentration =280mg / L, gas production =3.5×10 4 m 3 / d;12m 3 / d=12×10 6 mL / d, 280mg / L = 280mg / 10 3 mL; Total mass of pulverized coal = 12 × 10 6 ×(280×10 -3 ) = 3.36 × 10 6 mg / d; Pulverized coal production index = 3.36 × 10 6 / (3.5×10 4 )=96mg / m 3 Its value is lower than the high-yield well warning threshold of 500 mg / m³. 3 This indicates that the current coal powder production in the well is low during the drainage stage, and there is no risk of reservoir blockage. The permeability change trend index = (daily gas production / current flowing pressure) / (initial gas production / initial flowing pressure) is a dimensionless index that reflects the relative change in reservoir permeability. A value >1 indicates an increase in permeability, a value =1 indicates stability, and a value <1 indicates deterioration. Permeability changes were tracked for 30 / 60 / 90 days of drainage and production: the results are shown in Table 3. Table 3 Calculation Results of Penetration Rate Trend Indicators

[0031] In Table 3, This is the daily gas production volume. It is the current flow pressure. This refers to the initial gas production. This is the initial flow pressure; as can be seen from Table 3, the permeability change trend index after 90 days of drainage reached 10.56, indicating that the matrix shrinkage effect is absolutely dominant, the reservoir fractures open, and the permeability is greatly improved, so the pressure reduction rate can be appropriately increased (in line with the high-yield well strategy).

[0032] S3, CNN-GRU-Attention hybrid model pre-training: S301. Input Layer Design: Input data is a multi-dimensional time series matrix. ,in Time step (in this embodiment) =300d), For the number of features (in this embodiment) =29, including 12 categories of static geological parameters, 8 categories of dynamic production parameters, and 9 categories of engineering parameters, encompassing all standardized geological, engineering, production, and characteristic engineering parameters; static features employ a single-layer fully connected network. , , The 12-dimensional static parameters are linearly mapped to 9 dimensions, and then concatenated with the dynamic production parameters and engineering parameters in the time dimension to form the final input matrix.

[0033] S302, CNN Feature Extraction: Multi-scale convolutional kernels are used to extract spatial features from time series data, capturing local information at different time scales to form multi-scale feature maps, which serve as input for GRU time series modeling; a one-dimensional convolutional neural network is used to capture the spatial correlation between multiple parameters; let the input sequence be... ,in Convolution operation is represented as:

[0034] In the formula, The kernel size; For the first The weight matrix of each convolutional kernel; For bias terms; The ReLU activation function is used; this invention employs a multi-scale convolution kernel ( =3, 5, 7) Extract features at different time scales in parallel, and finally stitch them together to form a multi-scale feature map; This embodiment uses a 60-day timeframe for sampling. Taking =60 as an example, let's break it down step by step. The convolution process with a value of 3, The calculation methods for 5 and 7 are the same, and the specific process is as follows: (1) Convolution kernel setting: =3, 5, 7, corresponding to short-term / medium-term / long-term time scales, weight matrix. bias The number of output channels is 1 (as in simplified engineering design). Convolution window with =3: That is, the feature data at time steps 60 / 61 / 62, with a dimension of 3×29; (2) Weight and bias assignment (model training and fitting): (Initialized with a normal distribution of mean 0 and variance 0.01). =0.01; (3) Convolution calculation: The result is a 1×1 scalar, which, after being biased, is activated by ReLU (negative values ​​are removed) to obtain... (Time step 60, =3 convolutional features); (4) Multi-scale splicing: for Convolutional features of 3, 5, and 7 / / By concatenating the features along the feature dimensions, a multi-scale feature map is formed. (300 time steps × 3 convolutional features) are used as input for GRU temporal modeling in step S303.

[0035] S303, GRU Temporal Modeling: Utilizing the multidimensional spatial features output from step S302. Inputting a bidirectional GRU network (128 hidden layer dimensions), the model performs step-by-step calculations of update gates, reset gates, candidate hidden states, and final hidden states, capturing temporal dependencies. The model extracts long-term dependencies from historical data. The GRU calculation is as follows: (1) Update Gate: (2) Reset the door: (3) Candidate hidden state: (4) Final hidden state: In the formula, Output features for the CNN feature extraction step. Represents the Hadamard product; Weight / bias dimension settings (model training initialization): Hidden layer dimension 128 + CNN feature dimension 3; Initial hidden state ; Step-by-step calculation (with) For example, =60 , (From the previous hidden state): Update the door, stitch together. ,through Linear transformation + After biasing, the Sigmoid activation is obtained (Value 0~1 controls the proportion of the hidden state retained from the previous moment); reset the door, similarly, splice. through After Sigmoid is activated, the following is obtained: (Value 0~1, controls the reset ratio of the hidden state at the previous moment); Candidate hidden states are calculated first. (Hadamard product), and After splicing tanh activation yields (New hidden state candidate values); final hidden state, fused according to the formula and ,get , which represents the timing characteristics of the GRU at time step 60; Forward GRU (from ) + Reverse GRU (from The hidden states are concatenated along the feature dimension to obtain the final GRU temporal features. .

[0036] S304, Attention Mechanism Design: Introducing a Multi-Head Self-Attention Mechanism ( (Four attention heads are set up in the engineering) to capture temporal features in different feature subspaces, highlighting the contribution of key time points (such as reservoir damage, production mutations) to the current prediction. The formula is:

[0037] In the formula, , , These are obtained from the hidden state sequences through linear transformation; this invention employs four attention heads, capturing temporal features in different subspaces. In this embodiment, step S304 utilizes... The feature weighting calculation process in the attention mechanism is as follows: (1) Feature splitting: GRU features Divided into 4 subspaces This corresponds to 4 attention points; (2) Linear transformation: for each subspace Three independent weight matrices were used respectively. Linear transformation, to obtain ; (3) Attention weight calculation (taking the first head as an example): ,in For the attention score matrix, divide by To prevent gradient explosion, the attention weight matrix is ​​obtained after Softmax activation. (The sum of the rows is 1; the larger the value, the greater the contribution of the corresponding time step to the current prediction.) (4) Attention feature output: This is the first head attention feature; (5) Multi-head concatenation: The attention features of the four heads are concatenated along the feature dimension to obtain the final attention features. Highlight the key features and contributions of key drainage nodes (such as sudden changes in permeability and increases in pulverized coal concentration).

[0038] S305, Output Layer Design: The output layer is based on a two-branch design, including a regression branch and a classification branch, which incorporates the attention features obtained in step S304. Input a two-layer fully connected network (128→64→3); The regression branch output capacity forecast includes daily gas production, daily water production, and bottom hole flowing pressure for the next 30 days; the forecast values ​​are continuous. The accuracy is evaluated using the mean absolute error percentage (MAPE). ; calculated (verification set) =1752 entries): , The yield meets the requirement of ≤3%, which is far superior to traditional numerical simulation (12.3%) and single LSTM (5.6%). Prediction results: After 90 days of drainage, the gas production of this well in the next 30 days is expected to be 4.2~5.0×10⁻⁶. 4 m 3 / d, gradually reaching its peak; The classification branch is used to identify the current production stage of the gas well (preparation, depressurization, stable production, etc.) and the reservoir damage risk level (low, medium, high). The production stage identification results are: stable depressurization stage (probability 98.7%), preparation stage (0.8%), and stable production stage (0.5%). The risk level identification results are: low risk (99.2%), medium risk (0.7%), and high risk (0.1%). The calculation results show that the well is currently in the stable depressurization stage, and there is no reservoir damage risk. The depressurization rate can be appropriately increased according to the high-production well strategy.

[0039] S4. Calculation of the dynamic evolution mechanism model of reservoir permeability: The permeability change trend is identified by the drainage indicator curve, and the improved Palmer-Mansoori model is used for quantitative permeability prediction. Simultaneously, the results of this model are used as physical constraints to correct the prediction results of the CNN-GRU-Attention hybrid model, ensuring that it conforms to the physical evolution law of coal reservoirs. The specific process is as follows: S401. Permeability Trend Identification Based on Drainage Indication Curve: By analyzing the curve shape of the relationship between cumulative water production and bottom hole flowing pressure, the dynamic trend of reservoir permeability is qualitatively identified. Three typical curves and their corresponding characteristics are as follows: Horizontal curve: This indicates that the reservoir permeability remains stable, reflecting a basic balance between the effective stress effect and the matrix shrinkage effect. Convex curve: This indicates that the reservoir permeability is gradually improving, reflecting that the matrix shrinkage effect is dominant; Concave curve: This indicates that the reservoir permeability continues to deteriorate, reflecting that the effective stress effect is dominant. Step S401 involves accumulating water production. With bottom hole flowing pressure First / second derivatives determine the trend of penetration rate change: horizontal. → Stable permeability; convex shape. → Improved permeability (matrix shrinkage is dominant); concave type →Permeability deteriorates (effective stress dominates); The numerical differential-central difference method (with higher accuracy than forward difference) was used, selecting measured data from 30-90 days of sampling. : 0→8000m 3 , (19.2→10.9MPa), calculate the first / second derivative: First derivative ( Central difference formula The calculated mean is -0.0010375 MPa / m. 3 (Non-zero); Second derivative ( Central difference formula The calculated mean is -2.8 × 10⁻⁶. -7 MPa / m 6 (<0); The calculation results show that the drainage indicator curve is convex upward, the reservoir permeability continues to improve, and the matrix shrinkage effect is absolutely dominant, which is completely consistent with the calculation results of the permeability change trend index.

[0040] S402. Improved Palmer-Mansoori Quantitative Permeability Prediction Model: Based on the Palmer-Mansoori model, incorporating coal and petrological geomechanical characteristics and matrix shrinkage effects, a dynamic prediction formula for reservoir permeability considering multi-factor coupling is established to complete the quantitative calculation of permeability. The formula is as follows:

[0041] In the formula, This refers to relative penetration rate; , These are the elastic parameters of coal and rock. Initial porosity; , For current and initial pressure; The matrix shrinkage coefficient; For maximum strain; The Langmuir pressure is used as the model's output, which serves as a physical constraint and is fused with the prediction results of the CNN-GRU-Attention hybrid model to ensure that the prediction results conform to physical laws. Step S402 involves a step-by-step calculation based on a 60-day drainage period and an actual measured flow pressure of 13.5 MPa. All units are consistent with the International System of Units (SI). The calculation process is as follows: (1) Calculate the geomechanical terms Substitute the parameters: =0.28, =18.2×10 9 Pa, =0.038, =13.5×10 6 Pa, =19.2×10 6 Pa, = (1+0.28)(1−2×0.28)×(18.2×10 9 ) −1 ×(1−0.038) −1 ×(13.5×10 6 -19.2×10 6 = 1.28 × 0.44 × 5.4945 × 10 −11 ×1.0391×(−5.7×10 6 = 0.5632 × 5.4945 × 10 −11 ×1.0391×(−5.7×10 6 )≈−1.79×10 −4 ; (2) Calculate the matrix shrinkage term Substitute the parameters: =0.8, =0.005, =8.5×10 6 Pa, =0.8×0.005×(8.5 / (13.5+8.5)−8.5 / (19.2+8.5)) =0.004×(0.3864−0.3069)=0.004×0.0795=2.52×10 -4 ; (3) Calculate the relative permeability , =[1−1.79×10 −4 +2.52×10 −4 ] 3 =[1.000073] 3 ≈1.00022; (4) Calculate the current actual penetration rate , =0.35×1.00022≈0.35008mD; The calculation results show that after 60 days of drainage, the reservoir permeability of the well is 0.35008 mD, which is slightly higher than the original permeability of 0.35 mD. As drainage progresses, the matrix shrinkage effect continues to strengthen, and the permeability will be further improved. The mechanistic model results verify the physical rationality of the prediction of the hybrid deep learning model, and the conclusions of the two are consistent.

[0042] S5. Multi-objective optimization and dynamic control: S501. Production Capacity Level Determination: The differentiated production capacity strategy classifies production capacity levels based on the unobstructed flow rate of gas wells, and formulates three differentiated production capacity strategy templates for high-yield, medium-yield, and low-yield production. This achieves standardized and adaptable control of production capacity parameters. The specific templates are as follows: Unobstructed flow rate > 5 × 10 4 m 3 / d is the template for high-yield wells. High-yield well templates: rapid step-by-step pressure reduction, with an initial pressure reduction rate of 0.3MPa / d to 0.5MPa / d; stable production pressure is controlled at 70% to 80% of the critical desorption pressure; pressure reduction is slowed down when the coal powder concentration exceeds 500mg / L; Unobstructed flow rate is 2×10 4 m 3 / d~5×10 4 m 3 / d is the template for medium-yield wells. For medium-yield well templates: gradually reduce pressure in stages, with a pressure reduction rate of 0.1MPa / d to 0.3MPa / d; control the stable production flow pressure at 60% to 70% of the critical desorption pressure; adjust the working system when the coal powder concentration exceeds 300mg / L. Unobstructed flow rate < 2 × 10 4 m 3 / d is a low-production well template. Low-production well template: linear and slow pressure reduction, pressure reduction rate 0.05MPa / d~0.1MPa / d; stable production flow pressure controlled at 50%~60% of the critical desorption pressure; strict control of pressure reduction rate to prevent coal powder production from the source; The measured data in this embodiment show that the well's unobstructed flow rate is 5.2 × 10⁻⁶. 4 m 3 / d>5×10 4 m 3 / d, representing a high-yield well; the initial pressure reduction rate is taken as 0.4 MPa / d (balancing desorption efficiency and reservoir protection); the stable production flowing pressure is controlled at 75% of the critical desorption pressure, calculated as follows: =10.8×75%=8.1MPa; Coal powder concentration warning threshold: When the coal powder concentration corresponding to the coal powder production index exceeds 500mg / L, the pressure reduction rate will be automatically slowed down to 0.3MPa / d; Stage pressure reduction range: 0.8MPa (based on the step-by-step pressure reduction design for high-yield wells, stabilization for 2 days after each 0.8MPa reduction); Controlled water production: 12m³ 3 / h (< Pump maximum water production 18m³)3 / h, in compliance with equipment constraints).

[0043] S502. Constructing a multi-objective optimization system: Constructing a 4-objective function, 4 decision variables, and 4 types of constraints, with all parameters assigned specific values ​​from the example; specifically: Objective function: Taking into account production capacity, stable production period, coal dust control, and output stability, a four-objective optimization system is constructed:

[0044] Decision variable X (adjustable mining parameters): Select the core adjustable parameters during the mining process. , where ΔP is the stage pressure drop amplitude; For the rate of voltage reduction; This is the period of stabilization. To control the water production, i.e. =[0.8MPa, 0.4MPa / d, 2d, 12m 3 / h]; Constraints: Based on engineering practice, equipment performance, and reservoir characteristics, four types of hard constraints are defined: Engineering constraints: ,Right now Upper and lower limits of flow pressure; Equipment constraints: ,Right now Maximum water production of the pump; Operational constraints: ,Right now To prevent blood pressure from dropping too quickly; Geological constraints: ,Right now This ensures that methane can be desorbed.

[0045] S503, Coordination parameter fine-tuning settings: (1) Link test: Quantitative verification of data transmission performance. The patent requires a transmission delay of ≤100ms and a data accuracy of 100%. After 72 hours of continuous testing, the average data transmission delay is 85ms, the maximum is 98ms, and the delay is ≤100ms. The data acquisition / transmission accuracy is 100%, the link is uninterrupted, and the test is qualified. (2) Sensor calibration: Measurement error quantification and verification were performed. On-site calibration was conducted on three core sensors: pressure, flow rate, and pulverized coal concentration. The errors were all controlled within the allowable range for engineering applications. The results are as follows: Table 4 Sensor Calibration

[0046] (3) Collaborative operation: The model parameters are finely tuned. The system generates drainage suggestions and compares them with manual decisions for 30 days. Based on the field feedback data, the weight of the coal powder production index is finely adjusted (from 0.2 to 0.25) to improve the model's sensitivity to coal powder control. After the fine adjustment, the model's prediction error for coal powder concentration is reduced from 3.2% to 1.5%, which is more suitable for the field reservoir protection needs.

[0047] S504. Improved NSGA-II Multi-Objective Optimization Calculation: The improved NSGA-II is used to perform population initialization, fitness calculation, genetic operations, and constraint processing on the multi-objective optimization system, and finally the optimal sampling parameter sequence is solved. The calculation process is as follows: (1) Improved parameter settings for NSGA-II: Adaptive crossover mutation probability: based on population diversity Dynamic adjustment <0.2 =0.9、 =0.1; >0.6 =0.7、 =0.05; Population diversity in this embodiment =0.4, take =0.85、 =0.05; Population size: 100 (common engineering value), number of iterations: 500 generations; GPU parallel computing: Using NVIDIA A100 GPU, the fitness of 100 individuals is calculated in parallel, reducing the single-generation computing time from 20s to 0.5s, meeting the requirements of real-time optimization; (2) Solving for the optimal solution in multi-objective optimization: After 500 iterations, it converges to the Pareto optimal solution. The optimal solution that best fits the project is selected, and the optimal decision variables are obtained: =[0.9MPa, 0.45MPa / d, 2d, 13m 3 / h]; Optimization results show that increasing the depressurization rate from 0.4 MPa / d to 0.45 MPa / d (within the 0.5 MPa / d limit) is consistent with the actual situation of reservoir permeability improvement; increasing the stage pressure drop from 0.8 MPa to 0.9 MPa shortens the step depressurization time and improves production efficiency; and controlling the water production rate from 12 m³ / d... 3 / h increased to 13m 3 / h, accelerates drainage and depressurization, promotes methane desorption; all parameters meet four types of constraints, achieving the synergistic optimality of the four objective functions.

[0048] S505, Model Predictive Control (MPC) dynamically adjusts the drainage parameters in real time: the control cycle is set to 6 hours, and the refined calculation process for a single cycle is as follows (taking a typical 6-hour cycle as an example): (1) Data Acquisition: The measured data within the current 6 hours is collected in seconds. Measured data: =12.8MPa, =4.8×10 4 m 3 / d, coal powder concentration =320mg / L, =13m 3 / h; (2) Capacity prediction: Input the measured data into the pre-trained CNN-GRU-Attention hybrid model to predict the capacity change in the next 72 hours. Prediction results: =4.9~5.1×10 4 m 3 / d, The pressure decreased from 12.8 MPa to 11.2 MPa, and the pulverized coal concentration remained stable at 300~350 mg / L; (3) Optimization solution: Based on the improved NSGA-II, the multi-objective optimization problem in the 72-hour time domain is solved to obtain the optimal control sequence (the sampling parameters of 12 6-hour sub-cycles). (4) Instruction execution: Set the first control variable ( =0.45MPa / d, =13m 3 The parameters are sent to the variable frequency jet pump system at / h to automatically adjust the drainage parameters; (5) Feedback correction: Compare the actual execution result with the prediction result and calculate the deviation: the deviation is <2% for both actual and actual prediction. Feed the deviation back to the CNN-GRU-Attention hybrid model to correct the prediction result of the next cycle and achieve rolling optimization. (6) Adaptive adjustment of parameters after permeability improvement: Real-time monitoring of the drainage indicator curve. When the reservoir permeability continues to improve (convex curve + relative permeability gradually increases), the pressure reduction rate will be automatically increased from 0.45MPa / d to 0.48MPa / d (still <0.5MPa / d limit), thereby further shortening the production time and improving production efficiency.

[0049] Example 2: Comparison and Quantitative Analysis of Implementation Results The method of Example 1 was used to continuously operate the well for 730 days. The implementation effect was compared with the traditional "seven-stage" drainage method in terms of all indicators. All improvement ranges were calculated as (traditional value - invention value) / traditional value × 100%. The results are shown in Table 5. All indicators were significantly improved, which fully verified the technical feasibility and engineering superiority of the present invention. Table 5 Comparison of the effects of implementing high-yield wells in the Junggar Basin

[0050] The results show that the method of the present invention has realized the transformation of the high-yield well drainage from "experience-driven" to "model optimization", which has greatly improved the accuracy of production capacity prediction, completely solved the problem of coal powder pump jamming, increased the cumulative gas production by more than 50%, reduced the comprehensive operating cost by 20%, and shortened the investment payback period from the traditional 5.5 years to 3.5 years. The project has significant economic benefits and technical effects.

Claims

1. A method for intelligent drainage optimization of coalbed methane wells based on digital twins and deep learning, characterized in that... Includes the following steps: S1. Data Preprocessing: Collect production data for more than 2 years from the target coalbed methane well and at least 3 adjacent wells, and simultaneously collect static geological parameters and engineering parameters of the target well; perform outlier removal, missing value imputation, and normalization on the collected data, unify the data time frequency, and form a standardized multi-source database. S2. Multi-source data fusion and feature engineering calculation: Based on multi-source databases, core feature parameters reflecting drainage dynamics and reservoir status are extracted and used as input features for the CNN-GRU-Attention hybrid model. S3, CNN-GRU-Attention hybrid model pre-training: S301. Input Layer Design: Input data is a multi-dimensional time series matrix. ,in For time step, The number of features includes static features and dynamic features; Static features are encoded through a fully connected network and then concatenated with dynamic features along the time dimension. S302, CNN Feature Extraction: Multi-scale convolutional kernels are used to extract spatial features from time series data, capturing local information at different time scales to form multi-scale feature maps, which serve as input for GRU time series modeling; a one-dimensional convolutional neural network is used to capture the spatial correlation between multiple parameters; let the input sequence be... ,in Convolution operation is represented as: ； In the formula, The kernel size; For the first The weight matrix of each convolutional kernel; For bias terms; It is the ReLU activation function; S303, GRU Temporal Modeling: The multi-dimensional spatial features extracted by CNN are input into the bidirectional GRU network to capture temporal dependencies. The model extracts long-term dependencies from historical data. GRU is calculated as follows: Update Gate: ; Reset Door: ; Candidate hidden state: ; Final hidden state: ; In the formula, Output features for the CNN feature extraction step. Represents the Hadamard product; The hidden states of the forward GRU and the reverse GRU are concatenated along the feature dimension to obtain the final GRU temporal features; S304. Attention Mechanism Design: A multi-head self-attention mechanism is introduced to capture temporal features in different feature subspaces, highlighting the contribution of key time points to the current prediction. The formula is as follows: ； In the formula, , , These are obtained from the hidden state sequences through linear transformations. S305, Output Layer Design: The output layer is based on a dual-branch design, including a regression branch and a classification branch. The attention features obtained in step S304 are input into the fully connected network. The regression branch is used for production capacity prediction, and the classification branch is used to identify the current production stage of the gas well and the reservoir damage risk level. S4. Calculation of dynamic evolution mechanism model of reservoir permeability: The permeability change trend is identified by the drainage indicator curve, and the permeability is quantitatively predicted by the improved Palmer-Mansoori model. At the same time, the results of this model are used as physical constraints to correct the prediction results of the CNN-GRU-Attention hybrid model to ensure that it conforms to the physical evolution law of coal reservoir. S5. Multi-objective optimization and dynamic control: Construct a production capacity differentiated drainage strategy and determine the production capacity level of the target well. By setting objective functions, decision variables and constraints, construct a multi-objective optimization system. Use the improved NSGA-II to perform population initialization, fitness calculation, genetic operation and constraint processing on the multi-objective optimization system. Finally, solve for the optimal drainage parameter sequence and use model predictive control to dynamically regulate the drainage parameters in real time.

2. The intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning according to claim 1, characterized in that... The production data mentioned in step S1 includes bottom hole flowing pressure, wellhead casing pressure, oil pressure, daily gas production, daily water production, cumulative gas production, cumulative water production, and pulverized coal concentration; The static geological parameters include coal seam thickness, burial depth, high-rank coal, gas content, original permeability, porosity, original reservoir pressure, critical desorption pressure, Langmuir volume, Langmuir pressure, Young's modulus, and Poisson's ratio. The engineering parameters include well depth, casing program, completion method, fracture half-length, flow capacity, jet pump model, pump depth, and motor power.

3. The intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning according to claim 1, characterized in that... The core characteristic parameters of step S2 include daily production increase efficiency, daily stable production pressure reduction, pulverized coal output index, and permeability change trend indicators, among which: Daily production efficiency = Daily increase in production / Bottom flow pressure drop; Daily pressure reduction during stable production = pressure reduction during stable production period / stable production time; Pulverized coal production index = Water production × Pulverized coal concentration in water / Gas production; Permeability change trend index = (daily gas production / current flow pressure) / (initial gas production / initial flow pressure).

4. The intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning according to claim 1, characterized in that... The specific process of step S4 is as follows: S401. Permeability Trend Identification Based on Drainage Indication Curve: By analyzing the curve shape of the relationship between cumulative water production and bottom hole flowing pressure, the dynamic trend of reservoir permeability is qualitatively identified. Three typical curves and their corresponding characteristics are as follows: Horizontal curve: This indicates that the reservoir permeability remains stable, reflecting a basic balance between the effective stress effect and the matrix shrinkage effect. Convex curve: This indicates that the reservoir permeability is gradually improving, reflecting that the matrix shrinkage effect is dominant; Concave curve: This indicates that the reservoir permeability continues to deteriorate, reflecting that the effective stress effect is dominant. S402. Improved Palmer-Mansoori Quantitative Permeability Prediction Model: Based on the Palmer-Mansoori model, incorporating coal and petrological geomechanical characteristics and matrix shrinkage effects, a dynamic prediction formula for reservoir permeability considering multi-factor coupling is established to complete the quantitative calculation of permeability. The formula is as follows: ； In the formula, This refers to relative penetration rate; , These are the elastic parameters of coal and rock. Initial porosity; , For current and initial pressure; The matrix shrinkage coefficient; For maximum strain; The Langmuir pressure is used; the output of this model serves as a physical constraint, which is then fused with the prediction results of the CNN-GRU-Attention hybrid model to ensure that the prediction results conform to physical laws.

5. The intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning according to claim 1, characterized in that... The specific process of step S5 is as follows: S501. Production Capacity Level Determination: Based on the unobstructed flow rate of gas wells, production capacity levels are classified, and three differentiated drainage strategy templates for high-yield, medium-yield, and low-yield wells are formulated to achieve standardized and adaptable control of drainage parameters. The specific templates are as follows: Unobstructed flow rate > 5 × 10 4 m 3 / d is the template for high-yield wells. High-yield well templates: rapid step-by-step pressure reduction, with an initial pressure reduction rate of 0.3MPa / d to 0.5MPa / d; stable production pressure is controlled at 70% to 80% of the critical desorption pressure; pressure reduction is slowed down when the coal powder concentration exceeds 500mg / L; Unobstructed flow rate is 2×10 4 m 3 / d~5×10 4 m 3 / d is the template for medium-yield wells. For medium-yield well templates: gradually reduce pressure in stages, with a pressure reduction rate of 0.1MPa / d to 0.3MPa / d; control the stable production flow pressure at 60% to 70% of the critical desorption pressure; adjust the working system when the coal powder concentration exceeds 300mg / L. Unobstructed flow rate < 2 × 10 4 m 3 / d is a low-production well template. Low-production well template: linear and slow pressure reduction, pressure reduction rate 0.05MPa / d~0.1MPa / d; stable production flow pressure controlled at 50%~60% of the critical desorption pressure; strict control of pressure reduction rate to prevent coal powder production from the source; S502. Constructing a multi-objective optimization system: Objective function: Taking into account production capacity, stable production period, coal dust control, and output stability, a four-objective optimization system is constructed: Maximize cumulative gas production: ; Maximize stable production period: ; Minimize the risk of coal dust damage: ; Minimize output fluctuations: ; Decision variables: Select the core adjustable parameters during the sampling process. , where ΔP is the stage pressure drop amplitude; For the rate of voltage reduction; This is the period of stabilization. To control water production; Constraints: Based on engineering practice, equipment performance, and reservoir characteristics, four types of hard constraints are defined: Engineering constraints: ; Equipment constraints: ; Operational constraints: ; Geological constraints: ; S503, Cooperative parameter fine-tuning setting: Perform link testing and sensor calibration, then fine-tune the model parameters, generate mining suggestions and compare them with manual decisions, and fine-tune the weight of the coal powder production index based on field feedback data. S504. Improved NSGA-II multi-objective optimization calculation: The improved NSGA-II is used to perform population initialization, fitness calculation, genetic operations, and constraint processing on the multi-objective optimization system, and finally solves the optimal sampling parameter sequence. S505, Dynamic Control: Real-time dynamic adjustment of drainage parameters using model predictive control.

6. The intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning according to claim 5, characterized in that... The specific process of the S504 step-improved NSGA-II multi-objective optimization calculation is as follows: (1) Improved parameter settings for NSGA-II: based on population diversity Dynamically adjust crossover and mutation probabilities; set population size and number of iterations; The fitness of individuals is calculated in parallel using GPUs; (2) Solving for the optimal solution of multi-objective optimization: After iteration, it converges to the Pareto optimal solution. The optimal solution that best fits the project is selected to obtain the optimal decision variables. .

7. The intelligent drainage optimization method for coalbed methane wells based on digital twins and deep learning according to claim 5, characterized in that... The specific process of S505 is as follows: (1) Data acquisition: Set up downhole pressure sensors, electromagnetic flow meters, and coal powder concentration monitors to collect measured data; (2) Capacity forecasting: Input the measured data into the pre-trained CNN-GRU-Attention hybrid model to predict future capacity changes; (3) Optimization solution: Based on the improved NSGA-II, the multi-objective optimization problem in the 72-hour time domain is solved to obtain the optimal control sequence; (4) Command execution: The first control quantity is sent to the variable frequency jet pump system to automatically adjust the drainage parameters; (5) Feedback correction: Compare the actual execution results with the prediction results, calculate the deviation, and feed the deviation back to the CNN-GRU-Attention hybrid model to correct the prediction results of the next cycle; (6) Adaptive adjustment of parameters after permeability improvement: Real-time monitoring of the drainage indicator curve, and automatic adjustment of the depressurization rate when the reservoir permeability continues to improve.

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