Cluster well group efficient group irrigation method

Abstract

The invention relates to a cluster well group efficient group irrigation method, which comprises the following steps: acquiring formation parameters (permeability gradient field and porosity three-dimensional model) and group parameters (inter-well interference coefficient, thermal connectivity index and the like) through a central control system, simulating temperature field distribution based on a double-hole double-permeability model, and generating a recharge scheme comprising recharge flow, pressure and time ratio. The recharge process is controlled through a dynamic regulation and control strategy, and the scheme is monitored and corrected in real time. The key technology comprises the following steps: calculating a cold front leading edge propulsion distance as an interference dynamic distance, clustering and grouping well groups, and filtering low-efficiency well groups; extracting a permeable streamline pattern and shielding an invalid water injection area; emergency adjustment is triggered according to the cold front position (smaller than 50 m); multi-mode recharge (along / inverse to the flow direction of a natural flow field) is adopted, and a slowest cold front propulsion mode is selected for execution. According to the method, accurate regulation and control of recharge parameters are achieved, the geothermal recovery efficiency is improved, and stratum damage is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geothermal group irrigation, in particular to a high-efficiency group irrigation method for cluster well groups. BACKGROUND

[0002] In the process of geothermal water exploitation, a cluster well group refers to drilling several wells on a well site or platform, with the wellheads concentrated in one place and the well bottoms distributed at different positions to achieve the exploitation of multiple geothermal layers. Group irrigation is an important link in the process of cluster well group exploitation, and by injecting liquid (such as water for oil displacement) into the well group, the recovery efficiency of geothermal water can be improved.

[0003] However, the existing cluster well group irrigation method has some problems. For example, in the traditional group irrigation method, it is difficult to accurately control the injection amount and injection pressure of each well when distributing the injected liquid, resulting in uneven injection effect between wells, with some wells possibly injecting too much and some wells injecting too little, affecting the overall exploitation efficiency. At the same time, the adaptability to the formation is poor during the group irrigation process, and the backflow parameters cannot be adjusted in time according to the characteristics of different formations, which may cause damage to the formation and increase the operation cost. In addition, the existing method has low automation degree in the backflow process, which requires frequent manual intervention, not only increasing the labor intensity of the operators, but also possibly causing deviation in the backflow process due to human factors. SUMMARY

[0004] The purpose of the present application is to provide a high-efficiency group irrigation method for cluster well groups to solve the problems of inaccurate injection amount and pressure control, poor formation adaptability, and low automation degree in the traditional group irrigation method as mentioned in the background.

[0005] The present application aims to provide a high-efficiency group irrigation method for cluster well groups, comprising the following steps: The central control system obtains the formation parameters and group parameters of the cluster well group, the formation parameters including the permeability gradient field, the porosity three-dimensional distribution model, and the group parameters including the interwell interference coefficient, the thermal connectivity index, the interference weight matrix, and the natural flow field flow direction data; Based on the formation parameters and group parameters, a dual-pore dual-permeability model is used to simulate the temperature field distribution of the exploitation area, and a backflow scheme data containing the backflow flow rate, pressure, and time ratio of each well is generated; According to the backflow scheme data, an injection instruction is output to the control device, which is a dynamic control strategy for controlling the backflow process through the control device; Real-time monitoring data is collected, the backflow scheme data is dynamically corrected, and the probability distribution change of the formation parameters is tracked.

[0006] Further, the acquisition of the group parameters includes: According to the temperature field distribution output by the double-hole double-permeability model, the advancing interval of the recharge cold front edge is calculated as the dynamic distance of the recharge interference; Based on the dynamic distance of the recharge interference, the well groups are clustered and grouped to generate a well group interference model; According to the interference model, the inefficient well group is filtered.

[0007] Further, the calculation of the advancing interval of the recharge cold front edge includes: The recharge temperature range, the water production and recharge amount, and the heating time are input to the double-hole double-permeability model; The temperature field distribution under different recharge water flow directions (along the natural flow field flow direction / reverse) is simulated; The maximum expansion distance of the cold front edge in the temperature field is extracted as the advancing interval.

[0008] Further, the generation of the recharge scheme data includes: The seepage line pattern related to the fracture strike or high-permeability channel of the formation is extracted; The injection path of the associated ineffective injection water area (areas with porosity <15% or shale content >30%) is shielded; The injection vector alignment strategy is adjusted according to the advancing interval of the recharge cold front edge.

[0009] Further, the extraction of the seepage line pattern includes: The underground seepage field is reconstructed based on pressure buildup test and flow test data; When the temperature field simulation shows that the cold front edge advancing speed is >5m / year, the pattern re-extraction is triggered.

[0010] Further, the execution of the dynamic regulation strategy includes: When the temperature field monitoring shows that the cold front edge is <50m from the pumping well, the well spacing emergency adjustment strategy is started.

[0011] Further, the central control system integrates a double-hole double-permeability simulation module, which is used to: Optimize the injection direction according to the natural flow field flow direction data (preferably along the flow direction for recharge); Predict the position of the cold front edge under different recharge rates (50%-100%).

[0012] Further, the recharge scheme data is generated by multi-mode prediction: Mode A: recharge along the natural flow field flow direction; Mode B: recharge along the reverse direction of the natural flow field; The mode with the slowest cold front edge advancing is selected for execution.

[0013] The beneficial effects include: The application has temperature field simulation and dynamic parameter regulation based on a double-hole double-permeation model. Specifically, the double-hole double-permeation model is used to simulate the temperature field distribution of the mining area. The model can accurately depict the seepage characteristics and heat conduction process of the dual media of matrix and fracture in the formation. After the central control system obtains formation parameters such as permeability gradient field and three-dimensional distribution of porosity, combined with interwell interference coefficients, thermal connectivity indexes and other group parameters, the dynamic changes of the temperature field under different reinjection conditions can be quantified. For example, after inputting the reinjection temperature range, mining and reinjection water volume and heating time, the model can simulate the temperature field distribution along the natural flow field flow direction and in the opposite direction, and extract the maximum expansion distance of the cold front edge as the advancing interval to provide data support for the reinjection scheme.

[0014] Compared with the traditional method, this technology realizes the transition from "empirical reinjection" to "data-driven regulation". Due to the lack of accurate model support, the traditional group reinjection is difficult to quantify the interwell interference and formation heat conduction law, often leading to unbalanced distribution of reinjection volume. The temperature field distribution output by the model can calculate the advancing interval of the cold front edge of the reinjection, and on this basis, the well groups are clustered and grouped, the well group interference model is generated, and the inefficient well groups are filtered to avoid the waste of resources caused by invalid reinjection. At the same time, when the temperature field monitoring shows that the cold front edge is less than 50m from the pumping well, the system automatically starts the interwell distance emergency adjustment strategy, and through real-time correction of the reinjection flow and pressure, it prevents the sudden drop of the thermal reservoir temperature caused by the channeling of cold and hot water, and reduces the formation damage risk by more than 40%.

[0015] The application can extract seepage line patterns and shield invalid injection areas. In the process of generating the reinjection scheme, the application reconstructs the underground seepage field by pressure buildup test and flow test data, extracts seepage line patterns related to the strike of formation fractures and high-permeability channels. When the temperature field simulation shows that the advancing speed of the cold front edge is more than 5m / year, the system triggers the pattern re-extraction mechanism to ensure that the seepage field model matches the real-time formation state. At the same time, by shielding the invalid injection areas with porosity less than 15% or shale content greater than 30%, the pressure loss and efficiency decay caused by the reinjection into low-permeability or shale-rich areas are avoided. This technology solves the drawbacks of "blind reinjection" in traditional methods. Traditional group reinjection does not consider the formation heterogeneity, often injecting reinjection into invalid areas with high shale content and low porosity, resulting in about 30% waste of reinjection volume. The application optimizes the reinjection path to the "high-permeability channel priority injection" mode through seepage line patterns and invalid area shielding.

[0016] The application has multi-mode prediction and backfill direction optimization. Through multi-mode prediction of mode A (backfill along the natural flow field) and mode B (reverse backfill), the mode with the slowest cold front edge advance is selected for execution. The central control system integrates a double-hole double-seepage simulation module to optimize the injection direction based on the natural flow field data, preferentially backfilling along the flow direction to take advantage of natural seepage, while predicting the cold front edge position at 50%-100% backfill rate to provide decision-making basis for different production stages. This technology breaks through the limitations of traditional group injection "single direction backfill". Traditional methods usually ignore the influence of natural flow field on backfill, resulting in a faster cold front edge advance when the backfill direction is opposite to the flow direction of the formation water, and a rapid decline in the temperature of the thermal reservoir. The multi-mode prediction mechanism can also be flexibly adjusted according to the production needs, such as selecting a low backfill rate mode to delay the advance of the cold front during the temperature attenuation period of the thermal reservoir, and using a high backfill rate mode to strengthen displacement during the production capacity improvement period, so as to overall improve the geothermal recovery rate.

[0017] The application has real-time monitoring and dynamic correction of backfill scheme. The central control system collects temperature field, pressure field and other monitoring data in real time, dynamically corrects the backfill scheme data and tracks the probability distribution changes of the formation parameters. When there is a deviation between the monitoring data and the model prediction (such as the actual cold front advance speed deviates more than 10% from the predicted value), the system automatically starts the parameter inversion algorithm, updates the permeability gradient field, interwell interference coefficient and other model inputs, and regenerates the backfill scheme to realize closed-loop control of the backfill process. This technology solves the defects of traditional group injection "static control". Traditional methods are difficult to respond to formation changes in real time during the backfill process, and often reduce the backfill efficiency due to long-term backfill leading to drift of formation parameters. The mechanism of tracking the probability distribution changes of formation parameters can provide risk early warning for long-term development of the thermal reservoir, such as starting acidizing and fracturing stimulation measures in advance when the probability of permeability reduction exceeds the threshold.

[0018] The application has well group clustering and grouping and low-efficiency well filtering mechanism. Based on the temperature field distribution output by the double-hole double-seepage model, the backfill interference dynamic distance is calculated, the well group is clustered and grouped to generate a well group interference model, and then the low-efficiency well group is filtered. This process quantifies the interwell interference weight matrix, identifies wells with high interference intensity and poor thermal connectivity as low-efficiency wells, reduces the backfill amount of these wells or temporarily shuts them down, and optimizes resource allocation. This technology solves the resource waste problem of traditional group injection "uniform backfill of all wells". Traditional methods use the same backfill strategy for all wells, resulting in about 20% of low-efficiency wells consuming a large amount of backfill resources but contributing little production capacity. The application can divide the well group into "high-efficiency interference group", "moderate interference group" and "low-efficiency group" through clustering and grouping. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The method flowchart of the application; Figure 2 The principle diagram in the application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be apparently and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0021] With reference to Figure 1 and Figure 2 , the present application aims to provide a high-efficiency group irrigation method for cluster well groups, comprising the following steps: a central control system acquires formation parameters and group parameters of the cluster well groups, the formation parameters including a permeability gradient field and a three-dimensional distribution model of porosity, and the group parameters including inter-well interference coefficients, thermal connectivity indexes, interference weight matrices and natural flow field flow direction data; based on the formation parameters and the group parameters, a double-hole double-permeability model is used to simulate temperature field distribution of a production area, to generate recharging scheme data containing recharging flow, pressure and time ratios of each well; injection instructions are output to control equipment according to the recharging scheme data, the injection instructions being dynamic regulation strategies for controlling the recharging process through the control equipment; real-time monitoring data are collected, the recharging scheme data are dynamically corrected, and changes in the probability distribution of the formation parameters are tracked.

[0022] Further, the acquisition of the group parameters comprises: calculating a recharging cold front leading edge advancing distance as a recharging interference dynamic distance based on the temperature field distribution output by the double-hole double-permeability model; clustering and grouping the well groups based on the recharging interference dynamic distance, to generate a well group interference model; and filtering inefficient well groups according to the interference model.

[0023] Further, the calculation of the recharging cold front leading edge advancing distance comprises: inputting a recharging temperature range, production and recharging water volume and heating time into the double-hole double-permeability model; simulating temperature field distribution under different recharging water flow directions (along the natural flow field flow direction / reverse); and extracting the maximum expansion distance of the cold front leading edge in the temperature field as the advancing distance.

[0024] Further, the generation of the recharging scheme data comprises: extracting seepage line pattern related to the strike of formation fractures or high-permeability channels; shielding water injection paths of irrelevant ineffective water injection areas (areas with porosity < 15% or argillaceous content > 30%); and adjusting water injection vector cooperative alignment strategies according to the recharging cold front leading edge advancing distance.

[0025] Further, the extraction of the seepage line pattern comprises: reconstructing an underground seepage field based on pressure buildup test and flow test data; and triggering pattern re-extraction when the temperature field simulation shows that the cold front leading edge advancing speed > 5 m / year.

[0026] Further, the execution of the dynamic regulation strategy comprises: when the temperature field monitoring shows that the cold front edge is less than 50m away from the pumping well, starting the inter-well distance emergency adjustment strategy.

[0027] Further, the central control system integrates a double-hole double-permeability simulation module, which is used to: according to the natural flow field flow direction data, optimizing the water injection direction (preferably along the flow direction for recharge); predicting the cold front edge position under different recharge rates (50%-100%).

[0028] Further, the recharge scheme data is generated through multi-mode prediction: mode A: recharge along the natural flow field flow direction; mode B: recharge along the opposite direction of the natural flow field; select the mode with the slowest cold front edge advance to execute.

[0029] The double-hole double-permeability model is the core theoretical support of the present application, which is based on the principles of geomechanics and seepage mechanics, and regards the formation as a double medium composed of matrix system and fracture system. The matrix has high porosity but low permeability, and the fracture has low porosity but high permeability, and the fluid exchange is realized through channeling 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:

[0030] wherein, Φ porosity, p is pressure, t is time, k is permeability, Q m-f is the channeling flow rate between the matrix and the fracture. The heat conduction equation is constructed by the law of conservation of energy, considering the heat exchange between fluid flow and rock skeleton: wherein, (pc) is heat capacity, T is temperature, k T is thermal conductivity, q is fluid flow rate.

[0031] In some embodiments, the double-hole double-permeability model is applied in the following ways: (1) depicting formation heterogeneity: using the permeability gradient field and the three-dimensional distribution model of porosity, inputting the model to quantify the seepage difference between the matrix and the fracture, providing basis for injection path optimization; (2) simulating the dynamic change of temperature field: combining recharge temperature, water volume and other parameters, predicting the cold front edge advance process, providing data support for recharge scheme generation; (3) quantifying inter-well interference: calculating the inter-well interference coefficient and the thermal connectivity index through the model, constructing the interference weight matrix, laying the foundation for well group clustering.

[0032] Seepage field reconstruction and streamline pattern extraction principle: seepage field reconstruction is based on pressure buildup test and flow test data, and the underground fluid flow state is solved through inversion algorithm. The pressure buildup test follows the Horner formula: ;

[0033] wherein, pi P = Poi + (qBμkh) / (t + At) + C, where Poi is the original formation pressure, q is the flow rate, B is the volume factor, μ is the fluid viscosity, k is the permeability, h is the reservoir thickness, t is the production time, At is the shut-in time, and C is a constant. By fitting the pressure buildup curve, parameters such as permeability and skin factor can be obtained.

[0034] Flowing well testing uses the unsteady-state testing method by changing the flow rate and monitoring the pressure response, and uses the superposition principle to calculate the formation parameters. Based on the above data, the finite element method or the boundary element method is used to reconstruct the seepage field, and the velocity field and pressure field distribution are obtained, and then the streamline pattern is extracted. The streamline equation satisfies: ;

[0035] where ux, uy, and uz are the velocity components. When the cold front edge advancing speed exceeds 5 m / year, it indicates that the formation seepage characteristics have changed significantly, and retesting and inversion are needed to ensure the timeliness of the streamline pattern.

[0036] The algorithm logic of dynamic regulation and parameter correction is as follows: The dynamic regulation strategy is based on the feedback control principle, and a "monitoring - prediction - correction" closed-loop system is constructed. The algorithm logic is as follows: (1) Data acquisition and preprocessing: real-time acquisition of temperature, pressure, flow rate and other monitoring data, and removal of noise by Kalman filtering and other algorithms to ensure data accuracy; (2) Model prediction and deviation analysis: input real-time data into the dual-porosity dual-permeability model to predict the cold front edge position, pressure distribution and other parameters in the next 30 days, and compare 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%), use genetic algorithm or gradient descent method to update the permeability, channeling coefficient and other model parameters, and generate a new injection scheme; (4) Regulation strategy generation: according to the corrected scheme, generate the regulation instructions of injection flow rate, pressure, and time ratio, and transmit them to the control equipment through PLC (Programmable Logic Controller) for execution. When the cold front edge is less than 50 m from the pumping well, the emergency adjustment strategy is started, and its decision logic is based on the thermal reservoir temperature safety threshold: assuming that the pumping well protection radius is 50 m, when the cold front enters this range, the mixing of cold and hot water will cause the pumping temperature drop to exceed 10°C, therefore, by shutting down adjacent injection wells, adjusting the injection direction, and other measures, the cold front advancing speed is controlled below 2 m / year.

[0037] The decision theory principle of multi-mode prediction and recharge direction optimization is as follows: multi-mode prediction is based on the risk minimization principle in decision theory, and the mode with the lowest risk (slowest advance) is selected by comparing the advance speed of the cold front edge under different recharge modes. The decision process is as follows: (1) mode construction: define the forward recharge (mode A) and reverse recharge (mode B) as basic modes, which can be extended to complex modes such as oblique cross-flow; (2) index quantification: take the advance speed of the cold front edge, the temperature retention rate of the thermal reservoir, and the injection pressure loss as evaluation indexes, and calculate the index values under each mode through the double-hole double-seepage model; (3) utility function construction: set the utility function as: ;

[0038] where v is the advance speed, T is the temperature retention rate, p is the pressure efficiency, w1, w2, w3 are weight coefficients (adjusted according to the development stage, such as w1=0.5, w2=0.3, w3=0.2 at the beginning of development). 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 injection direction based on the natural flow field flow direction is that: when injecting liquid along the flow direction, the natural seepage can assist in driving the recharge liquid, reducing the injection pressure loss (about 20%-30% reduction), and delaying the advance of the cold front; reverse injection may intensify the collision between natural water flow and recharge liquid, accelerating the advance of the cold front. Therefore, preferential selection is given to forward recharge, and reverse mode is used only in special cases (such as rapid cooling of a specific area).

[0039] The principle of well group clustering grouping and inefficient well filtering is as follows: well group clustering grouping uses hierarchical clustering algorithm, taking interference coefficient and thermal connectivity index as variables, to calculate the Euclidean distance between wells: ;

[0040] where c ij is the interference coefficient of well i and j, h ij is the thermal connectivity index, and c and h are the mean values. By setting a distance threshold (such as d=0.3), the well group is divided into different categories.

[0041] Inefficient well filtering is based on multi-index comprehensive evaluation, and an evaluation function is constructed: E i = α ⋅ c i +β ⋅ h i γ⋅p i , middle, c is the interference coefficient (average interference intensity with other wells), h i is the thermal connectivity index,p i α=0.4, β =0.3, =0.3 as weights. Set threshold Eth=0.5, E i < E th The well is determined as an inefficient well, and the liquid injection amount can be reduced or the well can be shut down.

[0042] The application will be described in detail below in combination with the group wells that have been formed in the Gaoling area.

[0043] Taking the group wells that have been formed in the Gaoling area as an example, the cluster well group is used to develop the middle-deep geothermal resources, the well group contains 12 wells (8 production wells and 4 backfilling wells), the well site area is about 0.5km 2 , the target geothermal reservoir is the Paleogene sandstone, the average buried depth is 2000m, the formation temperature is 65-75℃, the permeability is 10-30mD, the porosity is 18%-22%, the natural flow field flow direction is NW-SE, and the flow rate is about 0.5m / day. During the initial development, the traditional group irrigation method is used, and the uneven distribution of liquid injection amount leads to the rapid temperature drop of some production wells, and the average single well heat production decline rate reaches 15% / year, so it is urgent to optimize the liquid irrigation strategy.

[0044] The central control system obtains the parameters through the following steps: (1) formation parameters: the porosity three-dimensional distribution model is constructed by using three-dimensional seismic data, and it is shown that the porosity in the middle of the geothermal reservoir reaches 22%, and the porosity in the northwest argillaceous interlayer area is lower than 15%; the permeability gradient field is obtained through core experiment and well test, and the high permeability zone is distributed along the NW-SE direction, and the permeability reaches 30mD, and the permeability in the low permeability zone is only 10mD. (2) group parameters: the interwell interference coefficient is calculated through interference well testing, and the interference coefficient of backfilling well W1 and production well P1 is 0.65 (strong interference), and the interference coefficient of W4 and P3 is 0.21 (weak interference); the thermal connectivity index is calculated based on temperature logging data, and the thermal connectivity index of W2 and P2 reaches 0.81 (high connectivity); the interference weight matrix is constructed to quantify the mutual influence strength of each well; the natural flow field flow direction data is obtained through tracer test to verify that the flow direction is NW-SE.

[0045] Double-hole double-permeability model simulation and backfilling scheme generation: (1) temperature field simulation: input backfilling temperature 25-30℃, production water amount 500m 3 / day, backfilling water amount 400m 3 / day, heating time 120 days, and two modes are simulated by using the double-hole double-permeability model: mode A (backfilling along the flow direction): backfilling wells W1 and W2 inject liquid along the NW-SE direction; mode B (reverse backfilling): backfilling wells W3 and W4 inject liquid along the SE-NW direction.

[0046] The simulation results show that the maximum expansion distance of the cold front edge under mode A is 120 m, and that under mode B is 180 m. Mode A is selected for execution.

[0047] Seepage streamline pattern extraction: Based on pressure buildup test (shut-in for 72 hours, pressure recovery to 90% of the original formation pressure) and flow test data, the underground seepage field is reconstructed, showing that the high permeability channel is distributed along the NW-SE direction, consistent with the direction of natural flow field; since the simulation shows that the cold front edge advances at a speed of 4.8 m / year (<5 m / year), the pattern re-extraction is not triggered.

[0048] Water injection path optimization: The northwestern argillaceous interlayer area (area about 0.1km 2 ) with shield porosity <15% is determined as the effective water injection area; according to the 120m injection interval of the cold front edge, the water injection vector is adjusted to align with the high permeability channel, and the injection plan for each well is formulated: W1: flow rate 120m 3 / day, pressure 12 MPa, injection time 16 hours / day; W2: flow rate 100m 3 / day, pressure 11 MPa, injection time 18 hours / day; W3: flow rate 80m 3 / day, pressure 10 MPa, injection time 12 hours / day (low efficiency well adjustment); W4: flow rate 100m 3 / day, pressure 11 MPa, injection time 16 hours / day.

[0049] Dynamic control strategy execution and real-time correction includes the following: (1) Initial injection stage (0-30 days): execute injection according to the plan, real-time monitor temperature field shows that the cold front edge is 80m away from the water extraction well P1 (>50m), no emergency adjustment is started; but it is monitored that the formation pressure near W3 well rises too fast (15% higher than the predicted value), analysis shows that the well is located in a low permeability area, immediately reduce its injection flow rate to 60m 3 / day, pressure to 9 MPa, to avoid the risk of formation fracture.

[0050] (2) Mid-term monitoring stage (31-90 days): Temperature field simulation shows that the front edge of the cold front advances at a speed of 5.2 m / year (> 5 m / year), triggering the re-extraction of the seepage line pattern; through new pressure recovery tests, it is found that due to long-term injection, the permeability of part of the high-permeability channel decreases by 8%, and after the seepage field is rebuilt, the injection vector is adjusted, the injection direction of W1 and W2 is fine-tuned by 5° to match the new high-permeability direction, and at the same time, the injection time of W4 is increased to 18 hours / day, compensating for the flow loss.

[0051] (3) Emergency adjustment stage (91-120 days): It is monitored that the temperature of P2 well drops suddenly, and the measured front edge of the cold front is 45 m away from P2 well (< 50 m), and the interwell distance emergency adjustment strategy is started: W2 well is shut down for 12 hours, and its injection volume is reduced by 50%; The injection pressure of W1 well is increased to 13 MPa, and the flow rate is 140 m 3 / day, and the displacement is strengthened to delay the advance of the cold front; The standby injection well W5 (originally planned to be used later) is started, and the injection flow rate is 80 m 3 / day, and the injection is assisted along the flow direction.

[0052] In the foregoing specification, examples have been described with reference to specific example implementations. It will, however, be evident that various modifications and changes can be made without departing from the scope as set forth in the following claims. The claims are not limited to the 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 and monitoring data collection, dynamic correction of reinjection scheme data, and tracking of changes in the probability distribution of formation parameters.

2. The efficient group irrigation method for cluster well groups according to claim 1, characterized in that, 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.

3. The efficient group irrigation method using cluster well groups according to claim 2, 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.

4. The efficient group irrigation method using cluster well groups according to claim 1, characterized in that, 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; The strategy of coordinating the alignment of water injection vectors is adjusted based on the advancing distance of the cold front.

5. The efficient group irrigation method using cluster well groups according to claim 1, characterized in that, 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 greater than 5 m / year, pattern re-extraction is triggered.

6. The efficient group irrigation method using 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.

7. 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.

8. The efficient group irrigation method for cluster well groups according to claim 1, characterized in that, 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.

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