A high-efficiency group irrigation method for cluster well groups

Abstract

The present application relates to a kind of cluster well group efficient group irrigation method, obtains formation parameter (permeability gradient field, porosity three-dimensional model) and group parameter (interwell interference coefficient, thermal connectivity index etc.) by central control system, simulates temperature field distribution based on double-hole double-permeability model, generates the recharge scheme containing recharge flow, pressure and time proportioning.By dynamic control strategy, control recharge process, real-time monitoring and correction scheme.Key technologies include: calculate the advancing distance of cold front edge as interference dynamic distance, group clustering and filtering low-efficiency well group; extract seepage line pattern and shield invalid injection area; according to the position of cold front (when <50m), trigger emergency adjustment; use multi-mode recharge (along / inverse natural flow field flow direction), select the slowest mode of cold front advancing to execute.This method realizes accurate control of recharge parameter, improves geothermal recovery and reduces formation damage.

Description

Technical Field

[0001] This invention relates to the field of geothermal group irrigation technology, specifically a high-efficiency group irrigation method using cluster well groups. Background Technology

[0002] In geothermal water extraction, a cluster well group refers to drilling several wells on a single well site or platform. The wellheads are concentrated in one location, while the wellbores are distributed in different locations to extract geothermal energy from multiple layers. Cluster injection is a crucial step in the cluster well group extraction process. By injecting liquids (such as water for oil displacement) into the well group, the geothermal recovery rate can be increased.

[0003] However, existing cluster well injection methods have several problems. For example, traditional cluster injection methods struggle to precisely control the injection volume and pressure of each well when distributing the injected fluid, leading to uneven injection effects among wells. Some wells may receive excessive injection, while others may receive insufficient injection, impacting overall production efficiency. Furthermore, cluster injection methods exhibit poor adaptability to different formations, failing to adjust injection parameters promptly based on formation characteristics, potentially causing formation damage and increasing operating costs. In addition, existing methods have low automation levels during injection, requiring frequent manual intervention, which not only increases the workload of operators but also increases the risk of human error causing deviations in the injection process. Summary of the Invention

[0004] The purpose of this invention is to provide a highly efficient mass irrigation method for cluster well groups, in order to solve the problems of inaccurate injection volume and pressure control, poor formation adaptability, and low degree of automation in the traditional mass irrigation methods mentioned in the background art.

[0005] This invention aims to provide a highly efficient cluster irrigation method for well groups, comprising the following steps:

[0006] The central control system acquires formation parameters and group parameters of the cluster well group. The formation parameters include the permeability gradient field and the three-dimensional porosity distribution model. The group parameters include the inter-well interference coefficient, thermal connectivity index, interference weight matrix, and natural flow field direction data.

[0007] Based on the formation parameters and group parameters, a dual-hole dual-permeability model is used to simulate the temperature field distribution in the mining area, generating reinjection scheme data including the reinjection flow rate, pressure, and time ratio of each well.

[0008] Based on the reinjection scheme data, an injection command is output to the control equipment. The injection command is a dynamic control strategy for the reinjection process, which is controlled by the control equipment.

[0009] Real-time acquisition and monitoring data collection, dynamic correction of reinjection scheme data, and tracking of changes in the probability distribution of formation parameters.

[0010] Furthermore, obtaining the group parameters includes:

[0011] Based on the temperature field distribution output by the dual-hole dual-infiltration model, the advancing distance of the cold front leading edge of the reinjection is calculated as the dynamic distance of the reinjection disturbance.

[0012] Well groups are clustered and grouped based on the dynamic distance of reinjection interference to generate a well group interference model;

[0013] Inefficient well groups are filtered based on the aforementioned interference model.

[0014] Furthermore, the calculation of the advance distance of the cold front leading edge includes:

[0015] Input the reinjection temperature range, extraction and reinjection water volume, and heating time into the dual-pore dual-infiltration model;

[0016] Simulate the temperature field distribution under different reinjection water flow directions (along the natural flow field direction / in the opposite direction);

[0017] The maximum extension distance of the leading edge of the cold front in the temperature field is extracted as the advance spacing.

[0018] Furthermore, the generation of the reinjection scheme data includes:

[0019] Extract seepage line patterns associated with formation fracture orientation or high-permeability channels;

[0020] Shield the water injection path of the ineffective water injection area (area with porosity <15% or mud content >30%).

[0021] The strategy of coordinating the alignment of water injection vectors is adjusted based on the advancing distance of the cold front.

[0022] Furthermore, the extraction of the seepage line pattern includes:

[0023] Reconstructing the underground seepage field based on pressure recovery test and flow test data;

[0024] When the temperature field simulation shows that the advancing speed of the cold front is greater than 5 m / year, pattern re-extraction is triggered.

[0025] Furthermore, the execution of the dynamic control strategy includes:

[0026] When temperature field monitoring shows that the leading edge of the cold front is less than 50m from the pumping well, an emergency adjustment strategy for the well spacing is initiated.

[0027] Furthermore, the central control system integrates a dual-hole dual-permeation simulation module for:

[0028] Optimize the water injection direction based on natural flow field data (prioritize reinjection along the flow direction);

[0029] Predict the location of the leading edge of the cold front under different recharge rates (50%-100%).

[0030] Furthermore, the reinjection scheme data is generated through multi-mode prediction:

[0031] Mode A: Reinjection along the natural flow direction;

[0032] Mode B: Reinjection in the opposite direction of the natural flow field;

[0033] Select the mode with the slowest advance of the cold front leading edge.

[0034] Beneficial effects include:

[0035] This application features temperature field simulation and dynamic parameter control based on a dual-pore, dual-permeability model. Specifically, the model simulates the temperature field distribution in the mining area, accurately depicting the seepage characteristics and heat conduction processes of the matrix and fracture media within the formation. After acquiring formation parameters such as the permeability gradient field and three-dimensional porosity distribution, the central control system, combined with group parameters such as the inter-well interference coefficient and thermal connectivity index, can quantify the dynamic changes in the temperature field under different reinjection conditions. For example, by inputting the reinjection temperature range, the extraction and reinjection water volumes, and the heating time, the model can simulate the temperature field distribution along the natural flow direction and in the opposite direction, extracting the maximum extension distance of the cold front leading edge as the advance spacing, providing data support for the reinjection scheme.

[0036] Compared to traditional methods, this technology represents a shift from "empirical reinjection" to "data-driven regulation." Traditional group injection, lacking precise model support, struggles to quantify inter-well interference and formation heat conduction patterns, often leading to imbalances in reinjection volume distribution. This application, however, uses the temperature field distribution output by the model to calculate the advance distance of the cold front leading edge during reinjection. Based on this, well groups are clustered, generating a well group interference model and filtering inefficient well groups, avoiding ineffective reinjection and resource waste. Simultaneously, when temperature field monitoring shows that the cold front leading edge is less than 50m from the pumping well, the system automatically initiates an emergency well spacing adjustment strategy. By real-time correcting the reinjection flow rate and pressure, it prevents a sudden drop in reservoir temperature caused by hot and cold water crossflow, reducing the risk of formation damage by more than 40%.

[0037] This application utilizes seepage line pattern extraction and ineffective injection area shielding. During the recharge scheme generation process, this application reconstructs the underground seepage field using pressure recovery tests and flow test data, extracting seepage line patterns related to formation fracture orientation and high-permeability channels. When temperature field simulation shows that the advance velocity of the cold front exceeds 5 m / year, the system triggers a pattern re-extraction mechanism to ensure that the seepage field model matches the real-time formation state. Simultaneously, by shielding ineffective injection areas with porosity less than 15% or clay content greater than 30%, pressure loss and efficiency degradation caused by recharge flowing into low-permeability or clay-rich areas are avoided. This technology solves the drawback of "blind recharge" in traditional methods. Traditional mass recharge does not consider formation heterogeneity and often injects recharge into ineffective areas with high clay content and low porosity, resulting in approximately 30% waste of recharge volume. This application, however, optimizes the recharge path to a "high-permeability channel priority injection" mode by using seepage line patterns and ineffective area shielding.

[0038] This application features multi-mode prediction and reinjection direction optimization. Through multi-mode prediction using Mode A (reinjection along the natural flow direction) and Mode B (reverse reinjection), the mode with the slowest cold front advance is selected for execution. The dual-hole dual-infiltration simulation module integrated into the central control system optimizes the water injection direction based on natural flow direction data, prioritizing reinjection along the flow direction to utilize the advantages of natural seepage. Simultaneously, it predicts the location of the cold front advance at 50%-100% reinjection rates, providing decision-making basis for different extraction stages. This technology overcomes the limitations of traditional group injection's "single-direction reinjection." Traditional methods often ignore the influence of the natural flow field on reinjection, leading to accelerated cold front advance and rapid temperature drop in the geothermal reservoir when the reinjection direction contradicts the formation water flow direction. The multi-mode prediction mechanism can also be flexibly adjusted according to extraction needs. For example, a low reinjection rate mode can be selected to delay cold front advance during the reservoir temperature decay period, while a high reinjection rate mode can be used to enhance displacement during the production enhancement period, thus improving the overall geothermal recovery rate.

[0039] This application features real-time monitoring and dynamic correction of the reinjection scheme. The central control system collects real-time monitoring data such as temperature and pressure fields, dynamically corrects the reinjection scheme data, and tracks changes in the probability distribution of formation parameters. When the monitoring data deviates from the model prediction (e.g., the actual cold front advance speed deviates from the predicted value by more than 10%), the system automatically activates the parameter inversion algorithm, updates the model inputs such as the permeability gradient field and inter-well interference coefficient, and regenerates the reinjection scheme, achieving closed-loop control of the reinjection process. This technology solves the shortcomings of traditional mass injection's "static control." Traditional methods struggle to respond to formation changes in real time during reinjection, often leading to formation parameter drift and reduced reinjection efficiency due to long-term reinjection. The mechanism of tracking changes in the probability distribution of formation parameters can provide risk warnings for long-term geothermal reservoir development; for example, when the predicted probability of permeability reduction exceeds a threshold, production enhancement measures such as acid fracturing can be initiated in advance.

[0040] This application features a well clustering and inefficient well filtering mechanism. Based on the temperature field distribution output by the dual-pore dual-permeability model, it calculates the dynamic distance of reinjection interference, clusters the well groups to generate a well group interference model, and then filters out inefficient well groups. This process quantifies the inter-well interference weight matrix, identifying wells with high interference intensity and poor thermal connectivity as inefficient wells, reducing their reinjection volume or temporarily shutting them down, thus optimizing resource allocation. This technology solves the resource waste problem of traditional group irrigation's "uniform reinjection across all wells." Traditional methods use the same reinjection strategy for all wells, resulting in approximately 20% of inefficient wells consuming a large amount of reinjection resources but contributing very little productivity. This application, through clustering, can divide the well group into "high-efficiency interference group," "medium-efficiency interference group," and "inefficient group." Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention;

[0042] Figure 2 This is a schematic diagram illustrating the principle of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Reference Figure 1 and Figure 2 This invention aims to provide an efficient cluster irrigation method for well groups, comprising the following steps: a central control system acquires formation parameters and group parameters of the well group, wherein the formation parameters include a permeability gradient field and a three-dimensional porosity distribution model, and the group parameters include inter-well interference coefficients, thermal connectivity index, interference weight matrix, and natural flow field direction data; based on the formation parameters and group parameters, a dual-pore dual-permeability model is used to simulate the temperature field distribution in the mining area, generating reinjection scheme data including the reinjection flow rate, pressure, and time ratio of each well; according to the reinjection scheme data, injection commands are output to the control equipment, wherein the injection commands are a dynamic control strategy for controlling the reinjection process through the control equipment; monitoring data is collected in real time, the reinjection scheme data is dynamically corrected, and the probability distribution changes of formation parameters are tracked.

[0045] Furthermore, the acquisition of the group parameters includes: calculating the advance distance of the reinjection cold front as the dynamic distance of reinjection interference based on the temperature field distribution output by the dual-hole dual-permeability model; clustering the well groups based on the dynamic distance of reinjection interference to generate a well group interference model; and filtering inefficient well groups based on the interference model.

[0046] Furthermore, the calculation of the advance distance of the cold front leading edge includes: inputting the reinjection temperature range, the amount of water extracted and reinjected, and the heating time into the dual-hole dual-infiltration model; simulating the temperature field distribution under different reinjection water flow directions (along the natural flow field direction / reverse direction); and extracting the maximum extension distance of the cold front leading edge in the temperature field as the advance distance.

[0047] Furthermore, the generation of the reinjection scheme data includes: extracting seepage line patterns related to formation fracture orientation or high-permeability channels; shielding the injection path of associated invalid injection areas (areas with porosity <15% or clay content >30%); and adjusting the injection vector coordination alignment strategy according to the advance spacing of the reinjection cold front.

[0048] Furthermore, the extraction of the seepage line pattern includes: reconstructing the underground seepage field based on pressure recovery test and flow test data; and triggering pattern re-extraction when the temperature field simulation shows that the advance velocity of the cold front edge is >5m / year.

[0049] Furthermore, the execution of the dynamic control strategy includes: when the temperature field monitoring shows that the distance between the leading edge of the cold front and the pumping well is less than 50m, the emergency adjustment strategy for the well spacing is initiated.

[0050] Furthermore, the central control system integrates a dual-hole dual-infiltration simulation module, which is used to: optimize the water injection direction based on natural flow field flow direction data (prioritizing reinjection along the flow direction); and predict the position of the leading edge of the cold front under different reinjection rates (50%-100%).

[0051] Furthermore, the reinjection scheme data is generated through multi-mode prediction: Mode A: reinjection along the natural flow direction; Mode B: reinjection in the opposite direction of the natural flow direction; the mode with the slowest cold front advance is selected for execution.

[0052] The dual-porosity, dual-permeability model is the core theoretical support of this application. Based on the principles of geomechanics and seepage mechanics, it treats the formation as a dual medium composed of a matrix system and a fracture system. The matrix has high porosity but low permeability, while the fractures have low porosity but high permeability. Fluid exchange occurs between the two through a crossflow effect. The mathematical expression of the model is as follows: For the matrix system (subscript m) and the fracture system (subscript f), the mass conservation equation is:

[0053]

[0054] in, Φ Where p is porosity, t is pressure, t is time, k is permeability, and Q is... m-f This represents the flow rate between the matrix and the fracture. The heat conduction equation is constructed using the law of conservation of energy, considering the heat exchange between the fluid flow and the rock skeleton:

[0055] Where (ρc) is the heat capacity, T is the temperature, and k is the kJ / m³ / s.T Let q be the thermal conductivity and q be the fluid velocity.

[0056] In some embodiments, the dual-pore dual-permeability model is applied in the following ways: (1) Characterizing formation heterogeneity: Using the permeability gradient field and porosity three-dimensional distribution model, the permeability gradient field and porosity three-dimensional distribution model are input into the model to quantify the seepage difference between the matrix and the fracture, providing a basis for the optimization of the injection path; (2) Simulating the dynamic changes of the temperature field: Combining parameters such as reinjection temperature and water volume, the cold front advance process is predicted, providing data support for the generation of reinjection schemes; (3) Quantifying inter-well interference: The inter-well interference coefficient and thermal connectivity index are calculated through the model, and the interference weight matrix is ​​constructed to lay the foundation for well clustering and grouping.

[0057] Principles of seepage field reconstruction and streamline pattern extraction: Seepage field reconstruction is based on pressure recovery test and flow test data, and the underground fluid flow state is solved through an inversion algorithm. The pressure recovery test follows Horner's formula:

[0058] ;

[0059] Among them, p i Let ρ be the initial formation pressure, q be the flow rate, B be the volume coefficient, μ be the fluid viscosity, k be the permeability, h be the reservoir thickness, t be the production time, Δt be the shut-in time, and C be a constant. Parameters such as permeability and skin factor can be obtained by fitting the pressure recovery curve.

[0060] Flow testing employs the unsteady traverse method, changing the flow rate and monitoring the pressure response to calculate formation parameters using the superposition principle. Based on this data, the seepage field is reconstructed using the finite element method or boundary element method to obtain the velocity and pressure field distributions, and then the seepage profile pattern is extracted. The streamline equation satisfies:

[0061] ;

[0062] Where ux, uy, and uz are velocity components. When the advance velocity of the cold front exceeds 5 m / year, it indicates a significant change in the formation seepage characteristics, requiring retesting and inversion to ensure the timeliness of the streamline pattern.

[0063] The algorithm logic of dynamic regulation and parameter correction is as follows: The dynamic regulation strategy is based on the feedback control principle and constructs a "monitoring-prediction-correction" closed-loop system. The algorithm logic is as follows: (1) Data acquisition and preprocessing: Real-time acquisition of monitoring data such as temperature, pressure, and flow rate, and noise removal through algorithms such as Kalman filtering to ensure data accuracy; (2) Model prediction and deviation analysis: Inputting real-time data into the dual-hole dual-permeability model to predict parameters such as the position of the cold front front and pressure distribution in the next 30 days, and comparing with the measured values ​​to calculate the deviation; (3) Parameter inversion and scheme correction: When the deviation exceeds the threshold (such as cold front position deviation > 10%), the genetic algorithm or gradient descent method is used to invert and update the model parameters such as permeability and crossflow coefficient, and regenerate the reinjection scheme; (4) Regulation strategy generation: Based on the corrected scheme, the regulation instructions of injection flow rate, pressure, and time ratio are generated and transmitted to the control equipment for execution through PLC (programmable logic controller). When the leading edge of the cold front is less than 50m from the pumping well, an emergency adjustment strategy is initiated. The decision-making logic is based on the thermal reservoir temperature safety threshold: assuming the pumping well protection radius is 50m, when the cold front enters this range, the mixing of hot and cold water will cause the pumping temperature to drop by more than 10℃. Therefore, by shutting down adjacent reinjection wells and adjusting the injection direction, the cold front advance speed is controlled to less than 2m / year.

[0064] The decision theory principle of multi-mode prediction and reinjection direction optimization is as follows: Multi-mode prediction is based on the principle of risk minimization in decision theory. By comparing the advance speed of the cold front leading edge under different reinjection modes, the mode with the lowest risk (slowest advance) is selected for execution. The decision process is as follows: (1) Mode construction: Define the in-flow reinjection (mode A) and reverse reinjection (mode B) as basic modes, which can be extended to complex modes such as oblique and alternating directions; (2) Index quantification: The cold front leading edge advance speed, thermal reservoir temperature retention rate, and injection pressure loss are used as evaluation indicators. The index values ​​under each mode are calculated through the dual-hole dual-permeability model; (3) Utility function construction: Let the utility function be:

[0065] ;

[0066] Where v is the propulsion speed, T is the temperature retention rate, p is the pressure efficiency, and w1, w2, and w3 are weighting coefficients (adjusted according to the development stage, such as in the early stage of mining (w1=0.5, w2=0.3, w3=0.2)). 4) Mode selection: Calculate the utility value of each mode and select the mode corresponding to the maximum value for execution. In addition, the principle of optimizing the water injection direction based on the natural flow field is that when injecting fluid along the flow direction, natural seepage can assist in driving the reinjection fluid, reducing the injection pressure loss (by about 20%-30%), while delaying the advance of the cold front; reverse injection may intensify the collision between the natural water flow and the reinjection fluid, accelerating the advance of the cold front. Therefore, reinjection along the flow direction is preferred, and the reverse mode is only used in special cases (such as when rapid cooling of a specific area is required).

[0067] The principle of well clustering and inefficient well filtering is as follows: Well clustering uses a hierarchical clustering algorithm, with the inter-well interference coefficient and thermal connectivity index as variables, to calculate the Euclidean distance between wells:

[0068] ;

[0069] Among them, c ij Let h be the interference coefficient between wells i and j. ij is the thermal connectivity index, and c and h are the mean values. By setting a distance threshold (e.g., d=0.3), well groups are divided into different categories.

[0070] Inefficient well filtration is based on a comprehensive evaluation of multiple indicators, and an evaluation function is constructed as follows: E i = α ⋅ c i +β ⋅ h i γ⋅p i ,middle, c This is the interference coefficient (the average interference intensity with other wells). h i It is the thermal connectivity index. p i α=0.4, β =0.3, =0.3 is the weight. Set the threshold Eth=0.5, E i < E th Wells that are deemed inefficient can have their injection volume reduced or be shut down.

[0071] The following section will provide a detailed explanation of this application, using the existing well clusters in the Gaoling area as an example.

[0072] Taking the existing well cluster in the Gaoling area as an example, a cluster well group is used to develop medium-deep geothermal resources. The well group includes 12 wells (8 production wells and 4 reinjection wells), with a well site area of ​​approximately 0.5 km². 2 The target geothermal reservoir is Paleogene sandstone with an average burial depth of 2000m, a formation temperature of 65-75℃, a permeability of 10-30mD, and a porosity of 18%-22%. The natural flow field is NW-SE oriented with a flow velocity of approximately 0.5m / day. In the early stages of development, the traditional group injection method was used. Uneven distribution of injection volume led to excessively rapid temperature drops in some production wells, with an average single-well thermal production decline rate of 15% / year. Optimization of the injection strategy is urgently needed.

[0073] The central control system obtains parameters through the following steps: (1) Formation parameters: a three-dimensional porosity distribution model is constructed using three-dimensional seismic data, showing that the porosity in the middle of the thermal reservoir reaches 22%, and the porosity in the northwestern mudstone interlayer area is less than 15%; the permeability gradient field is obtained through core experiments and well tests, the high permeability zone is distributed along the NW-SE direction, the permeability reaches 30mD, and the permeability in the low permeability zone is only 10mD. (2) Group parameters: the interference coefficient between wells is calculated through interference well tests, the interference coefficient between reinjection well W1 and production well P1 is 0.65 (strong interference), and the interference coefficient between W4 and P3 is 0.21 (weak interference); the thermal connectivity index is calculated based on temperature logging data, and the thermal connectivity index between W2 and P2 reaches 0.81 (high connectivity); the interference weight matrix is ​​constructed to quantify the mutual influence intensity between wells; the natural flow field direction data is obtained through tracer testing to verify that the flow direction is NW-SE.

[0074] Dual-hole double-infiltration model simulation and reinjection scheme generation: (1) Temperature field simulation: Input reinjection temperature 25-30℃, extraction water volume 500m 3 / day, recharge volume 400m³ 3 / day, heating period 120 days, using a dual-hole dual-permeability model to simulate two modes: Mode A (reinjection along the flow direction): injection of fluid into injection wells W1 and W2 along the NW-SE direction; Mode B (reverse injection): injection of fluid into injection wells W3 and W4 along the SE-NW direction.

[0075] Simulation results show that the maximum extension distance of the cold front leading edge is 120m in mode A and 180m in mode B. Mode A is selected for execution.

[0076] Seepage line pattern extraction: Based on pressure recovery test (well shut in for 72 hours, pressure recovered to 90% of the original formation pressure) and flow test data, the underground seepage field was reconstructed, showing that high-permeability channels are distributed along the NW-SE direction, consistent with the natural flow field direction; because the simulation showed that the advance velocity of the cold front edge was 4.8 m / year (<5 m / year), pattern re-extraction was not triggered.

[0077] Water injection path optimization: shielding the northwestern muddy interlayer zone (approximately 0.1 km²) with a porosity <15%. 2 ), determine the effective water injection zone; based on the advance spacing of 120m at the leading edge of the reinjection cold front, adjust the water injection vector to align with the high-permeability channel, and formulate a fluid injection plan for each well:

[0078] W1: Flow rate 120m 3 / day, pressure 12MPa, injection time 16 hours / day;

[0079] W2: Flow rate 100m3 / day, pressure 11MPa, injection time 18 hours / day;

[0080] W3: Flow rate 80m 3 / day, pressure 10MPa, injection time 12 hours / day (adjusted for inefficient wells);

[0081] W4: Flow rate 100m 3 / day, pressure 11MPa, injection time 16 hours / day.

[0082] The execution and real-time correction of dynamic control strategies include the following:

[0083] (1) Initial injection stage (0-30 days): Injection was carried out according to the plan. Real-time monitoring of the temperature field showed that the leading edge of the cold front was 80m (>50m) away from the pumping well P1, and no emergency adjustment was initiated; however, it was detected that the formation pressure near well W3 rose too quickly (15% higher than the predicted value). It was analyzed that since the well was located in a low-permeability zone, its injection flow rate was immediately reduced to 60m³. 3 / day, the pressure is adjusted to 9MPa to avoid the risk of formation fracturing.

[0084] (2) Mid-term monitoring phase (31-90 days): Temperature field simulation showed that the advance speed of the cold front reached 5.2 m / year (>5 m / year), triggering the re-extraction of the seepage line pattern; through new pressure recovery test, it was found that due to long-term injection, the permeability of some high-permeability channels decreased by 8%. After reconstructing the seepage field, the injection vector was adjusted, and the injection direction of W1 and W2 was slightly adjusted by 5° to match the new high-permeability direction. At the same time, the injection time of W4 was increased to 18 hours / day to compensate for the flow loss.

[0085] (3) Emergency Adjustment Phase (91-120 days): A sudden drop in temperature was detected in well P2, and the measured distance of the leading edge of the cold front from well P2 was 45m (<50m). The emergency adjustment strategy for well spacing was initiated.

[0086] Well W2 was shut down for 12 hours, and its injection volume was reduced by 50%.

[0087] Increase the injection pressure in well W1 to 13 MPa and the flow rate to 140 m³ / h. 3 / day, strengthen displacement to slow the advance of the cold front;

[0088] Start the backup reinjection well W5 (originally planned to be activated later), with an injection flow rate of 80m³. 3 / day, auxiliary injection along the flow direction.

[0089] In the foregoing description, examples have been described with reference to specific exemplary embodiments. However, it will be apparent that various modifications and changes can be made to the specific examples without departing from the scope set forth in the appended claims, and the claims are not limited to the specific examples described above.

Claims

1. A method for efficient group irrigation using cluster well arrays, characterized in that, Includes the following steps: The central control system acquires formation parameters and group parameters of the cluster well group. The formation parameters include the permeability gradient field and the three-dimensional porosity distribution model. The group parameters include the inter-well interference coefficient, thermal connectivity index, interference weight matrix, and natural flow field direction data. Based on the formation parameters and group parameters, a dual-hole dual-permeability model is used to simulate the temperature field distribution in the mining area, generating reinjection scheme data including the reinjection flow rate, pressure, and time ratio of each well. Based on the reinjection scheme data, an injection command is output to the control equipment. The injection command is a dynamic control strategy for the reinjection process, which is controlled by the control equipment. Real-time acquisition of monitoring data, dynamic correction of reinjection scheme data, and tracking of changes in the probability distribution of formation parameters; The acquisition of the group parameters includes: Based on the temperature field distribution output by the dual-hole dual-infiltration model, the advancing distance of the cold front leading edge of the reinjection is calculated as the dynamic distance of the reinjection disturbance. Well groups are clustered and grouped based on the dynamic distance of reinjection interference to generate a well group interference model; Inefficient well groups are filtered based on the aforementioned interference model; The generation of the reinjection scheme data includes: Extract seepage line patterns associated with formation fracture orientation or high-permeability channels; Block the water injection path associated with invalid water injection areas; Adjust the water injection vector coordination alignment strategy according to the advance spacing of the cold front; The extraction of the seepage line pattern includes: Reconstructing the underground seepage field based on pressure recovery test and flow test data; When the temperature field simulation shows that the advancing speed of the cold front is >5m / year, pattern re-extraction is triggered; The reinjection scheme data is generated through multi-mode prediction: Mode A: Reinjection along the natural flow direction; Mode B: Reinjection in the opposite direction of the natural flow field; Select the mode with the slowest advance of the cold front leading edge.

2. The efficient group irrigation method for cluster well groups according to claim 1, characterized in that, The calculation of the advance distance of the cold front leading edge includes: Input the reinjection temperature range, extraction and reinjection water volume, and heating time into the dual-pore dual-infiltration model; Simulate the temperature field distribution under different reinjection water flow directions; The maximum extension distance of the leading edge of the cold front in the temperature field is extracted as the advance spacing.

3. The efficient group irrigation method for cluster well groups according to claim 1, characterized in that, The execution of the dynamic control strategy includes: When temperature field monitoring shows that the leading edge of the cold front is less than 50m from the pumping well, an emergency adjustment strategy for the well spacing is initiated.

4. The efficient group irrigation method for cluster well groups according to claim 1, characterized in that, The central control system integrates a dual-hole dual-permeability simulation module for: Optimize water injection direction based on natural flow field direction data; Predict the location of the leading edge of the cold front under different recharge rates.

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