A method and system for managing geothermal heat replenishment energy balance based on multi-energy complementarity

Abstract

This application belongs to the field of sustainable development and multi-energy complementary energy utilization technology of medium-deep geothermal energy. Specifically, it discloses a method and system for managing geothermal energy balance based on multi-energy complementarity. The method includes the following steps: identifying cold accumulation zones in underground geothermal reservoirs and calculating the total heat deficit required for reservoir repair, while simultaneously assessing the availability characteristics of multi-source thermal energy; constructing a full-link dynamic energy balance master equation, which includes targeted repair weight coefficients and reservoir health correction coefficients; constructing a three-field coupled numerical model and setting targeted heat replenishment source terms, and verifying the heat replenishment effect through simulation; using the master equation as constraints and combining the simulation verification results, constructing an optimization model with the dual objectives of maximizing the long-term health of the geothermal reservoir and minimizing heat replenishment costs, and solving for the heat replenishment parameter combination; executing heat replenishment according to the heat replenishment parameter combination, and iteratively updating the constraints by transmitting data back. This application can achieve precise matching between multi-source thermal energy input and geothermal reservoir repair needs.

Description

Technical Field

[0001] This application belongs to the field of sustainable development and multi-energy complementary energy utilization technology of medium-deep geothermal energy, and more specifically, relates to a method and system for geothermal heat replenishment energy balance management based on multi-energy complementarity. Background Technology

[0002] Medium-deep geothermal energy, as a stable and reliable baseload energy source, has ushered in opportunities for large-scale development. Clean development, where "heat is extracted without water extraction," can be achieved through U-shaped closed-loop well technology. However, the problem of energy imbalance in the geothermal reservoir caused by long-term continuous heat extraction is becoming increasingly prominent. Monitoring data shows that after three years of operation, the annual decline rate of heat exchange efficiency in U-shaped geothermal wells reaches 4.7%, the well outlet temperature decreases by an average of 1.2~1.8℃ per year, and the system's total energy efficiency ratio (COP, i.e., the ratio of system output heat to total input energy consumption) drops from the initial 3.2 to 2.6. The fundamental reason is that the natural recovery rate of the geothermal reservoir is far lower than the extraction intensity of a single well, thus forming a "geothermal drop funnel" with a diameter of approximately 500~800 meters.

[0003] The existing medium-deep geothermal replenishment technologies have the following defects: (1) Energy balance only focuses on the static supply and demand balance of "supply-consumption" of multiple energy sources on the ground, without taking the long-term health status of underground thermal reservoirs as the core constraint of energy balance, and without establishing a quantitative correlation between the replenishment energy input and the spatiotemporal evolution characteristics of the thermal reservoir cold accumulation area, resulting in a serious mismatch between the replenishment intensity and the thermal reservoir repair demand, resulting in under-replenishment or over-replenishment of energy; (2) Existing studies mostly focus on the replenishment effect of a single heat source or the optimization of the downhole heat exchange structure, lacking a full-link coupling model of ground heat source input, wellbore heat transfer process and underground thermal reservoir response, and cannot accurately predict the temperature field evolution law under different replenishment conditions; (3) The optimization of replenishment parameters in existing technologies only focuses on the lowest short-term operating cost and the highest instantaneous energy efficiency, without taking the long-term health of the thermal reservoir as the mandatory core constraint of optimization, and the optimization results cannot curb the long-term decline trend of the thermal reservoir, making it difficult to support the long-term stable operation of the geothermal system.

[0004] Therefore, how to accurately match the input of multiple heat sources with the needs of geothermal storage repair during the geothermal replenishment process is an urgent problem to be solved. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this application is to provide a method and system for managing geothermal energy balance based on multi-energy complementarity, which can achieve precise matching between multi-source heat energy input and geothermal storage repair needs during geothermal replenishment.

[0006] To achieve the above objectives, in a first aspect, this application provides a method for managing geothermal heat replenishment energy balance based on multi-energy complementarity, comprising the following steps: S10 identifies cold accumulation zones in underground thermal reservoirs and calculates the total heat loss required for reservoir remediation, while also assessing the total available amount, spatiotemporal distribution characteristics, energy supply characteristics, and cost characteristics of multi-source thermal energy. S20. Based on the total heat deficit and the total available amount, spatiotemporal distribution characteristics, energy supply characteristics and cost characteristics of the multi-source thermal energy, a full-link dynamic energy balance master control equation is constructed. The master control equation includes a targeted repair weight coefficient for spatially targeted allocation of heat replenishment energy according to the real-time status of the cold accumulation zone, and a heat reservoir health correction coefficient for dynamically adjusting the priority of heat replenishment input according to the health status of the thermal reservoir. S30. Based on the energy balance scheme determined by the master equation of the full-link dynamic energy balance, a three-field coupled numerical model of seepage field-temperature field-stress field is constructed and a position-dependent targeted heat source term is set. The heat replenishment effect of the energy balance scheme is verified by simulation prediction. S40. Using the full-link dynamic energy balance master equation as a constraint and combining the simulation verification results of the three-field coupled numerical model, an optimization model with the dual objectives of maximizing the long-term health of the thermal storage and minimizing the full life cycle cost of heat replenishment is constructed. The optimal combination of heat replenishment parameters that satisfies the long-term health objective and the full life cycle cost of the thermal storage is obtained by solving the model. S50, perform heat replenishment according to the heat replenishment parameter combination, and transmit the running data back to iteratively update the main control equation of the full-link dynamic energy balance and the constraints of the three-field coupled numerical model.

[0007] As a further preferred embodiment, step S10, which involves identifying the cold accumulation zone of the underground thermal reservoir and calculating the total heat loss required for reservoir repair, specifically includes: Temperature data of the entire well section of the target thermal reservoir was collected using a distributed fiber optic temperature measurement system to identify the boundaries and volume (V) of the cold accumulation zone. cold and the average temperature decay ΔT; Combining the density ρ and specific heat capacity c of the thermal reservoir rock p The total heat loss required to repair the thermal reservoir to the target temperature is calculated using the formula ΔQ. res =ρc p V cold ΔT / τ recovery , where ΔQ res τ represents the heat deficit in the thermal reservoir. recovery The set heating cycle is used; at the same time, the natural recovery rate of the thermal reservoir is quantified to determine the minimum thermal power gap required for artificial heating.

[0008] As a further preferred embodiment, in step S20, the master equation for the end-to-end dynamic energy balance is: Q in (t)=η sys [qsolar (t) α solar +q waste (t) α waste +q green (t) α green ]=ΔQ res (t) β(t)+Q loss (t) γ(t)+Q out (t) In the formula, Q in (t) represents the total input thermal power of the system; η sys For overall system efficiency; q solar q waste q green These are the instantaneous input power from solar energy, industrial waste heat, and waste green electricity, respectively; α solar α waste α green These are the priority weighting coefficients for each heat source, dynamically adjusted based on seasonal solar radiation intensity, industrial waste heat availability, and grid curtailment patterns; ΔQ res (t) represents the thermal deficit of the reservoir calculated based on the volume and temperature difference of the cold accumulation zone; β(t) is the targeted remediation weighting coefficient of the cold accumulation zone, which is positively correlated with the real-time identified volume and temperature decay of the cold accumulation zone; Q loss (t) represents the heat loss between the system pipeline and the wellbore; γ(t) is the dynamic correction coefficient for the health of the thermal reservoir, dynamically adjusted based on the real-time decay rate of the thermal reservoir and the recovery rate of the cold accumulation zone; Q out (t) represents the effective heat output by the system.

[0009] As a further preferred embodiment, the targeted repair weight coefficient β(t) of the cold accumulation zone is configured as follows: only the cold accumulation zone is assigned a heat replenishment weight, and the normal thermal storage zone that has reached the target temperature has β=0; the dynamic correction coefficient γ(t) of the thermal storage health is configured as follows: when the thermal storage health is lower than the set threshold, the priority of heat replenishment input is forcibly increased.

[0010] As a further preferred embodiment, in step S30, the three-field coupled numerical model of seepage field-temperature field-stress field includes the fluid mass conservation equation, the rock mass-fluid coupled energy conservation equation, and the thermal stress coupled equation. The fluid mass conservation equation is: + (ρu)=0 The energy conservation equation for the rock mass-fluid coupling is:

[0011] The thermal stress coupling equation is:

[0012] In the formula, The density of the solid rock skeleton of the thermal reservoir; Porosity of the thermal reservoir rock mass; The velocity vector of the heat-replenishing circulating medium in the pores of the thermal reservoir; The density of the fluid phase (heat-replenishing circulation medium); This refers to the isobaric specific heat capacity of the fluid phase (heat-replenishing circulating medium); The instantaneous temperature of the thermal reservoir rock mass-fluid coupling system; These are the stress tensor components of the thermal reservoir rock mass; For the elastic stiffness tensor components of the thermal reservoir rock mass; For the strain tensor components of the thermal reservoir rock mass; Kronecker function, used for tensor simplification calculations of the thermal stress equation; is the linear thermal expansion coefficient of the thermal reservoir rock mass; The equivalent heat capacity of rock mass-fluid; It is the equivalent thermal conductivity; Heat source supplement.

[0013] As a further preferred embodiment, in step S40, the bi-objective optimization model is specifically as follows:

[0014] In the formula, As a comprehensive indicator of the long-term health of thermal storage; Recovery rate of the cold deposit area; This represents the decrease in the thermal storage degradation rate; To extend the service life of thermal storage; , , These are the weighting coefficients; Total cost of the entire heating cycle; The unit electricity price for the operation of the supplementary heating system pump set; The instantaneous operating power of the circulating pump and booster pump of the heat replenishment system at time t; The unit energy supply cost for supplementing solar thermal heating; Let t be the instantaneous thermal power input from the solar thermal system to the supplementary heating system. The unit energy cost of supplementing industrial waste heat; Let t be the instantaneous thermal power input from the industrial waste heat recovery system to the supplementary heating system at time t; The unit energy supply cost for supplementing heat from waste green electricity; Let t be the instantaneous thermal power input from the abandoned green electric heating system to the regenerative heating system; These represent the termination and start times of the full lifecycle optimization calculation for heat replenishment.

[0015] As a further preferred embodiment, in step S40, the constraints of the dual-objective optimization model include: injection temperature ≤ 80℃ ≤120℃, injection flow rate 0.5≤ ≤2.0m³ / h, injection pressure ≤50MPa; Mandatory constraints for thermal storage health: cold accumulation zone recovery rate ≥90%, thermal storage attenuation rate reduction ≥50%, target temperature of cold accumulation zone ≥55℃; and the constraints of the master equation for dynamic energy balance across the entire link.

[0016] As a further optimization, in step S40, the optimal combination of heat replenishment parameters that satisfies the long-term health goals of the thermal storage and the optimal cost over the entire life cycle is obtained. Specifically, the NSGA-II multi-objective genetic algorithm is used to solve the problem, and the Pareto optimal solution set is obtained through iterative calculation. The selected combination of heat replenishment parameters includes the dynamic output ratio of the three types of heat sources, the injection temperature, flow rate, pressure, heat replenishment cycle, and daily heat replenishment duration of the heat replenishment medium.

[0017] As a further preferred option, in step S50, the iterative update specifically includes: after each round of optimization, calling the three-field coupled numerical model to verify the cold accumulation zone repair effect of the optimized parameter combination, dynamically adjusting the constraint conditions, forming a closed-loop iteration of simulation-optimization-verification, until the final optimal heat replenishment scheme is output.

[0018] Secondly, this application provides a geothermal energy balance management system based on multi-energy complementarity, used to execute the geothermal energy balance management method based on multi-energy complementarity described in any of the above claims, the system comprising: The multi-source energy capture and dynamic ratio subsystem is used to capture three types of heat sources: solar energy, industrial waste heat and waste green electricity, and output a stable heat medium according to the dynamic ratio. A medium circulation and pressurization injection device is used to transport hot media underground; The underground U-shaped well heat exchange system is used to conduct heat through the casing wall to the dense rock layer in the heat replenishment mode, so as to repair the low temperature zone of cold accumulation. The intelligent monitoring and optimization control system is used to collect system operation data, solve the master equation of dynamic energy balance across the entire link and optimize the heat compensation parameters, and adjust the output ratio and injection parameters of each energy unit.

[0019] The beneficial effects of this application are as follows: (1) Significantly enhanced thermal reservoir repair capability: By constructing a dynamic coupling equation between multi-source thermal energy and thermal reservoir response, precise matching between external heat source input and thermal reservoir deficiency is achieved. During the reheating cycle, the cold accumulation low-temperature zone around the wellbore can be effectively repaired, significantly reducing the range of low temperature influence. The wellbore outlet temperature is significantly restored during the heating season, and the system energy efficiency ratio is steadily improved. (2) The heating effect is predictable and controllable: Based on the multi-physics coupled numerical model, the evolution law of the temperature field of the thermal reservoir and the boundary migration characteristics of the cold accumulation zone can be predicted, providing a high-precision simulation basis for heating decision-making. By solving the optimal combination of technical and economic parameters through multi-objective optimization algorithm, the heating scheme is transformed from traditional experience-driven to model-driven, which significantly improves the scientificity and reliability of the heating process; (3) Multi-source collaborative intelligence: Establish a three-element coupling mechanism and a dynamic scheduling strategy for heat source priority of solar energy, industrial waste heat and abandoned green electricity, and realize the spatiotemporal complementarity and continuous and stable supply of multi-source heat energy according to seasonal characteristics and energy availability. While realizing the restoration of heat storage, it effectively absorbs industrial waste heat and abandoned wind and solar power, reduces the unit heat replenishment cost, and helps the efficient utilization of regional renewable energy and carbon emission reduction; (4) The system has wide adaptability and outstanding technical promotion value: This scheme relies entirely on the existing U-shaped well facilities for modification, without the need to build new dedicated heat replenishment wells or implement large-scale hydraulic fracturing, which significantly reduces the engineering modification cost and implementation difficulty. This method is applicable to conductive tight thermal reservoirs such as the Songliao Basin, and can also be extended to depleted oil and gas reservoirs, dry hot rocks and other diverse geological scenarios, providing a replicable and scalable technical path for the large-scale and sustainable development of medium and deep geothermal energy. Attached Figure Description

[0020] Figure 1 A schematic diagram of the structure of the U-shaped deep well geothermal heat replenishment energy balance control system based on multi-energy complementarity provided in this application; Figure 2 A flowchart illustrating the geothermal heat replenishment energy balance control method based on multi-energy complementarity provided in this application; Figure 3 The graph shows the change of wellhead temperature over time (months) obtained from numerical simulation calculations provided in this application embodiment. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0022] This application addresses the core deficiencies of existing geothermal supplementation technologies by proposing two core components: a multi-energy complementary geothermal supplementation energy balance management and control system, which serves as the execution carrier for the energy balance method. Through multi-module collaborative design, it achieves closed-loop execution of the entire process, including multi-source energy capture, medium transport, underground heat exchange, and intelligent management, providing hardware support for energy balance management and control. The multi-energy complementary geothermal supplementation full-link energy balance management and control method, the core innovation of this application, is the logical kernel driving the system to achieve optimal operation. Through full-link closed-loop management and control, it clarifies the energy balance logic, input and output control methods, and optimal operating condition search path for the entire supplementation process, ultimately achieving the dual objectives of maximizing the long-term health of the geothermal storage and minimizing supplementation costs.

[0023] The two are strongly related as "core and carrier": the design of each module of the system serves the implementation of the energy balance method, and each step of the method corresponds to the coordinated action of a specific module of the system. Through the closed loop of "the method outputs the optimal strategy, the system executes the strategy and implements it, and the running data is fed back to iterate the method", the precise energy balance control of the entire heating cycle is realized.

[0024] (1) Geothermal supplementation energy balance control system based on multi-energy complementarity This system is a closed-loop design with a complete link, such as Figure 1 As shown, it is divided into 4 core subsystems. Each subsystem has a clear functional boundary and operates collaboratively to jointly support the implementation of the energy balance method. The specific composition and functions are as follows: Multi-source energy capture and dynamic proportioning subsystem: Breaking through the limitations of single-source heat supplementation, it pioneers a three-element coupling mechanism of "solar energy - industrial waste heat - waste green electricity". The solar thermal collection unit adopts a parabolic trough or tower-type solar thermal system to convert solar energy into 120-150℃ heat energy; the industrial waste heat recovery unit captures waste heat from oilfield produced fluids or refining processes through plate heat exchangers, with a temperature range of 90-130℃; the waste green electricity conversion unit utilizes grid-cured wind and solar power to drive electric heaters, achieving zero-carbon heat supplementation. The three types of heat sources are dynamically proportioned and coupled according to seasonal changes through proportional valves, and output a stable heat medium of 80-120℃ through a heat storage buffer tank, achieving a continuous and stable supply of multi-source heat energy.

[0025] Medium Circulation and Pressurized Injection Device: As the core unit for transferring thermal energy underground, this device consists of a closed-loop system comprising a storage tank, a variable frequency circulation pump, a high-pressure pressurization pump, and intelligent valves. The storage tank stores deionized water or softened water with added corrosion inhibitors; the variable frequency circulation pump dynamically adjusts the flow rate according to optimized commands to achieve precise control of the heat replenishment intensity; the high-pressure pressurization pump pressurizes the medium and ensures smooth injection into the target thermal reservoir at a depth of 2500-3500 meters; the intelligent valves automatically adjust their opening based on fiber optic temperature feedback, automatically adjusting the injection opening of each perforation section to achieve targeted heat replenishment and energy saving.

[0026] Underground U-shaped well heat exchange system: Designed for the tight sandstone reservoir characteristics of Daqing Oilfield, a closed-loop structure is incorporated, including an injection well, a horizontal section, and a production well. The horizontal section is located within the target thermal reservoir at a depth of 2500-3500 meters, with a length of no less than 800 meters. The casing employs a double-layer vacuum insulation structure, with the outer casing coated with a high thermal conductivity nano-coating (thermal conductivity ≥5.0 W / (m·K)) at the contact surface with the rock formation. During the non-heating season (April-October), a heat replenishment mode is operated, transferring heat to the tight rock formation through the casing wall to repair the cold accumulation low-temperature zone. During the heating season (November-March), a heat extraction mode is operated, utilizing the repaired thermal reservoir to enhance heat extraction power, achieving cyclical synergy of "summer replenishment and winter extraction."

[0027] Intelligent monitoring and optimization control system: Adopting a three-tiered architecture of "sensor monitoring - data analysis - execution control," it achieves precise regulation of the heat replenishment process. The sensor monitoring module deploys a distributed fiber optic temperature measurement system (spatial resolution up to 1 meter) and ground temperature, pressure, and flow sensors to collect system operation data in real time. The data analysis module incorporates a proxy model built based on a multi-field coupled simulation model, executes the dynamic coupling equation of multi-source thermal energy-reservoir response, calculates the optimal heat replenishment conditions, and derives relevant parameters. The execution control module issues instructions based on the optimization results, adjusting the output ratio of each energy unit, the frequency of the circulating pump, the pressure of the booster pump, and the valve opening. This achieves dual-objective optimization, aiming to minimize the levelized thermal cost over the entire life cycle and maximize the long-term health of the thermal reservoir, thereby reducing the unit heat replenishment cost and steadily increasing the wellbore outlet temperature.

[0028] (2) A method for energy balance management and control of the entire geothermal heat replenishment chain based on multi-energy complementarity This method employs a closed-loop control logic across the entire chain, which is the core driving force for the aforementioned system to achieve optimal energy balance. Figure 2 As shown, it consists of four core steps, each of which clearly defines the control objectives, energy balance logic, corresponding system modules invoked, and the implementation path for the two objectives, as detailed below: S1, energy supply and demand baseline diagnosis and quantification (balanced base layer).

[0029] Clearly define the energy demand baseline and supply capacity baseline for the entire heating cycle, addressing the core questions of "how much heat is needed and how much heat is available for supplementation," thus establishing a quantitative foundation for end-to-end energy balance. The specific process is as follows: Precise quantification of the energy gap in thermal reservoirs: Utilizing the distributed fiber optic temperature measurement system of the underground U-shaped well system, temperature data of the entire well section of the target thermal reservoir is collected, identifying the boundaries and volume V of the cold accumulation zone. cold The average temperature decay ΔT; combined with thermal properties such as reservoir rock density and specific heat capacity, the total heat loss ΔQ required to restore the reservoir to the target temperature is calculated. res The formula is: ΔQres =ρc p V cold ΔT / τ recovery In the formula, τ recovery To determine the minimum thermal power gap ΔP required for artificial heating, the set heating cycle is used, and the natural recovery rate of the thermal reservoir is quantified.

[0030] The monitoring unit of the multi-source energy capture subsystem is invoked to assess the total available amount, spatiotemporal distribution characteristics, energy conversion efficiency, unit energy supply cost, and energy supply stability of three types of heat sources: solar thermal, industrial waste heat, and waste green electricity. A multi-source energy characteristic database is established to clarify the maximum available energy supply in different seasons and time periods.

[0031] S2, Construction of dynamic energy balance equations for the entire energy-storage-use chain (balance core layer).

[0032] Establish a mathematical balance relationship between multi-source energy input, system loss, thermal storage repair needs, and effective energy output, clarify the energy expenditure constraints of the entire heat replenishment process, solve the core problem of "how to allocate energy and how to match thermal storage repair needs", and achieve precise linkage between "source-storage-use".

[0033] The master equations for the dynamic energy balance across the entire energy chain are constructed as follows: Q in (t)=η sys [q solar (t) α solar +q waste (t) α waste +q green (t) α green ]=ΔQ res (t) β(t)+Q loss (t) γ(t)+Q out (t) Among them, Q in (t) represents the total input thermal power of the system; η sys For the overall system efficiency, a value of 0.75~0.85 is used, considering multi-source coupling and heat exchange losses; q solar q waste q greenThese represent the instantaneous input power from solar energy, industrial waste heat, and waste green electricity, respectively; α is the priority weight coefficient for each heat source (dynamically adjusted based on seasonal solar radiation intensity, industrial waste heat availability, and grid curtailment patterns, with the baseline dispatch priority being: direct solar power > industrial waste heat exchange > waste green electricity off-peak electricity supplementation); β(t) is the targeted repair weight coefficient for cold accumulation areas, positively correlated with the real-time identified volume and temperature decay of cold accumulation areas, allocating supplementation weight only to cold accumulation areas, with β=0 for normal thermal storage areas that have reached the target temperature, achieving spatial targeted allocation of supplementation energy; γ(t) is the dynamic correction coefficient for thermal storage health, dynamically adjusted based on the real-time decay rate of the thermal storage layer and the recovery rate of the cold accumulation area, forcibly increasing the priority of supplementation input when the thermal storage health is below a set threshold to ensure the long-term sustainable operation of the thermal storage; ΔQ res (t) represents the thermal deficit of the reservoir (based on the volume V of the cold accumulation zone). cold Calculation of ΔQ with temperature difference ΔT: res =ρc p V cold ΔT / τ recovery ), Q loss (t) represents the heat loss between the system piping and the wellbore, Q out (t) represents the effective heat output by the system.

[0034] By implementing a closed-loop constraint on energy income and expenditure throughout the entire heat replenishment process through the master control equation, the problem of insufficient or excessive energy replenishment is fundamentally avoided. By using the targeted repair weight coefficient β, the spatial precision of heat replenishment energy is achieved, avoiding ineffective heat dissipation. By using the thermal storage health correction coefficient γ, the long-term health of the thermal storage is dynamically managed, addressing the industry pain point of continuous thermal storage degradation at its root. By dynamically adjusting the priority weight coefficient α, low-cost heat sources are prioritized, minimizing the total life-cycle cost of heat replenishment while meeting the thermal storage repair needs.

[0035] S3, simulation and prediction of energy transfer through multi-field coupling between the ground and underground (equilibrium verification layer).

[0036] Based on the constructed three-field coupled numerical model of seepage field, temperature field, and stress field, the dynamic parameters of multi-source energy input in S2 are used as the model boundary conditions, and the model is fully coupled with the casing heat transfer and rock mass heat conduction processes of the underground U-shaped well-thermal reservoir system for solution. The core governing equations include: Fluid mass conservation equation: + (ρu)=0 Rock mass-fluid coupling energy conservation equation:

[0037] in, The equivalent heat capacity of rock mass-fluid is given. The equivalent thermal conductivity is Heat source term (location-dependent function, focusing on cold accumulation region); Thermal stress coupling equation:

[0038] Used to assess changes in rock mass stress caused by thermal injection.

[0039] By using a downhole distributed fiber optic temperature measurement system to accurately locate the spatial distribution and temperature characteristics of the cold accumulation zone, a location-dependent targeted heat replenishment term is set in a multi-field coupled numerical model. Simulations optimize key parameters such as injection temperature, flow rate, and injection-production cycle, allowing the heat replenishment energy to preferentially complete the rock-fluid heat exchange in the low-temperature zone of the cold accumulation zone, avoiding ineffective heat dissipation in the high-temperature reservoir zone. At the same time, simulation predictions are used to verify the heat replenishment effect of the energy balance scheme in advance, avoiding cost waste caused by ineffective heat replenishment from the source, achieving precise focusing of input energy on the cold accumulation zone, and maximizing the efficiency of reservoir temperature recovery.

[0040] S4, dual-objective driven multi-dimensional optimization of heat compensation parameters (balance optimization layer).

[0041] Based on energy balance equations and simulation models, we seek the optimal combination of heat replenishment parameters that achieves both technical and economic benefits and long-term health of thermal storage, thereby solving the core problem of "how to achieve the best heat replenishment effect and the lowest cost" and realizing the precise output of the optimal heat replenishment conditions.

[0042] Constructing a dual-objective optimization model: Using the S2 energy balance equation as the core constraint, and with the dual optimization objectives of maximizing the long-term health of the thermal storage and minimizing the total life-cycle cost of heat replenishment, a mathematical model is constructed as follows:

[0043] In the formula, As a comprehensive indicator of the long-term health of thermal storage, The recovery rate of the cold deposit area. For the decrease in thermal storage degradation rate, To extend the service life of thermal storage; , , These are the weighting coefficients, and + + =1, which can be adjusted according to project requirements; The total cost of the entire heating cycle; constraints include: injection temperature ≤ 80℃ ≤120℃, injection flow rate 0.5≤ ≤2.0m³ / h, injection pressure ≤50MPa; Mandatory constraints for thermal storage health: cold accumulation zone recovery rate ≥90%, thermal storage attenuation rate reduction ≥50%, target temperature of cold accumulation zone ≥55℃, and the energy balance equation constraint of S2.

[0044] The NSGA-II multi-objective genetic algorithm was used to solve the above model. Iterative calculations yielded the Pareto optimal solution set, and the optimal combination of heating parameters that simultaneously met the long-term health goals of the thermal storage and the optimal life-cycle cost was selected. This included: the dynamic output ratio of the three types of heat sources, the injection temperature, flow rate, pressure, heating cycle, and daily heating duration of the heating medium. After each round of optimization, the S3 multi-field coupling model was invoked to verify the repair effect of the optimized parameter combination on the cold accumulation zone. Constraints were dynamically adjusted to form a closed-loop iteration of "simulation-optimization-verification" until the final optimal heating scheme was output.

[0045] The beneficial effects of this application are as follows: (1) Significantly enhanced thermal reservoir repair capability: By constructing a dynamic coupling equation between multi-source thermal energy and thermal reservoir response, precise matching between external heat source input and thermal reservoir deficiency is achieved. During the reheating cycle, the cold accumulation low-temperature zone around the wellbore can be effectively repaired, significantly reducing the range of low temperature influence. The wellbore outlet temperature is significantly restored during the heating season, and the system energy efficiency ratio is steadily improved. (2) The heating effect is predictable and controllable: Based on the multi-physics coupled numerical model, the evolution law of the temperature field of the thermal reservoir and the boundary migration characteristics of the cold accumulation zone can be predicted, providing a high-precision simulation basis for heating decision-making. By solving the optimal combination of technical and economic parameters through multi-objective optimization algorithm, the heating scheme is transformed from traditional experience-driven to model-driven, which significantly improves the scientificity and reliability of the heating process; (3) Multi-source collaborative intelligence: Establish a three-element coupling mechanism and a dynamic scheduling strategy for heat source priority of solar energy, industrial waste heat and abandoned green electricity, and realize the spatiotemporal complementarity and continuous and stable supply of multi-source heat energy according to seasonal characteristics and energy availability. While realizing the restoration of heat storage, it effectively absorbs industrial waste heat and abandoned wind and solar power, reduces the unit heat replenishment cost, and helps the efficient utilization of regional renewable energy and carbon emission reduction; (4) The system has wide adaptability and outstanding technical promotion value: This scheme relies entirely on the existing U-shaped well facilities for modification, without the need to build new dedicated heat replenishment wells or implement large-scale hydraulic fracturing, which significantly reduces the engineering modification cost and implementation difficulty. This method is applicable to conductive tight thermal reservoirs such as the Songliao Basin, and can also be extended to depleted oil and gas reservoirs, dry hot rocks and other diverse geological scenarios, providing a replicable and scalable technical path for the large-scale and sustainable development of medium and deep geothermal energy.

[0046] The key technology of this application lies in: (1) The first dynamic energy balance master equation of the entire "source-storage-use" chain with the spatiotemporal evolution of the cold accumulation zone as the core constraint was created. Establish external heat source input power With thermal reservoir heat loss The dynamic equilibrium equation, for the first time, integrates the spatiotemporal characteristics (intermittency, fluctuation, continuity) of solar thermal energy, industrial waste heat, and waste green electricity with the characteristics (volume) of thermal storage and cold storage accumulation zones. Temperature difference Quantitative correlation is performed. A heat source priority weighting coefficient is introduced into the equation. The system dynamically adjusts the heat supply based on seasonal solar radiation intensity and industrial waste heat availability, achieving precise matching between heat source and storage. This equation overcomes the limitations of traditional heat replenishment technologies that rely on experience-based judgment, enabling the control of heat replenishment intensity to shift from qualitative to quantitative methods, thus significantly improving energy utilization efficiency.

[0047] (2) Multi-field coupling modeling method for surface-underground systems A three-field coupled numerical model of seepage field, temperature field, and stress field was developed. Innovatively, the surface heat source system (solar collector outlet temperature, industrial waste heat flow, waste green electricity, etc.) was used as boundary conditions, and a fully coupled solution was performed with the underground U-shaped well-heat reservoir system (casing heat transfer, rock mass heat conduction). The model includes a heat source term. By employing a position-dependent function and focusing on the cold accumulation zone identified by fiber optic temperature measurement, "targeted heat replenishment" simulation is achieved. This method can predict the evolution of the thermal reservoir temperature field and the boundary migration characteristics of the cold accumulation zone under different injection parameters, providing a high-precision simulation platform for optimizing heat replenishment parameters.

[0048] (3) A multi-objective and multi-constraint dynamic optimization algorithm with thermal reservoir health as the mandatory core constraint was developed. This application breaks through the limitations of existing technologies that only focus on short-term costs and instantaneous energy efficiency. For the first time, it takes the long-term health of thermal storage as the core objective and mandatory hard constraint of the optimization algorithm, and constructs a dual-core optimization model that maximizes the long-term health of thermal storage and minimizes the cost of the entire life cycle of heat replenishment. It innovatively incorporates the recovery rate of cold accumulation zone, the reduction rate of thermal storage decay rate, and the extension rate of thermal storage service life into the optimization objectives, and sets a mandatory constraint threshold for thermal storage health.

[0049] The NSGA-II algorithm is used to solve for the Pareto optimal solution set, outputting the most technically and economically efficient combination of reheating parameters (injection temperature, flow rate, and cycle). This algorithm innovatively uses the simulation results of the thermal reservoir temperature field as the objective function input, achieving a closed-loop iteration of "simulation-optimization": after each round of optimization, the numerical model is called to verify the temperature recovery effect, and the constraints (such as T) are dynamically adjusted. cold_zone ≥55℃), until the termination condition is met (e.g., cumulative heat supplementation ≥1.2×10). 4 (GJ or target layer temperature rise ≥8℃). This algorithm shifts the heating scheme from "experience-driven" to "model-driven", significantly reducing the unit heating cost.

[0050] The following is a specific implementation example of this application: This embodiment focuses on the application of the heat replenishment energy balance method for a 2850-meter-deep U-shaped geothermal well in a certain block in Northeast my country. The core of the system is composed of an energy load analysis module, a coupled equation solving module, a multi-field coupled simulation module, and a parameter optimization execution module.

[0051] (1) Energy load analysis module As the "data foundation" for energy balance, the heat deficit of the thermal reservoir is first quantified: cold accumulation zones (temperature <45℃) are identified based on distributed fiber optic temperature measurement data, and their volume is calculated. =3.2×10 5 m³ and average temperature difference =12℃; based on the thermal properties of the reservoir rock (rock density ρ=2650kg / m³ and specific heat capacity c) p =920J / (kg·K)), calculate the heat deficit. ( (For the heat replenishment cycle, 180 days), substituting these values ​​into the calculation yields ΔQ. res ≈2.45MW.

[0052] Simultaneously, the availability of multiple thermal energy sources is assessed: a parabolic trough solar collector field (peak power 350kW, daily effective irradiance 4.2h), an industrial waste heat exchange station (800kW of produced liquid waste heat, 24h continuous operation), and waste green electricity (200kW during off-peak hours, 22:00-6:00). This module outputs a list of heat deficit and heat source availability, providing input for the construction of coupled equations.

[0053] (2) Coupled Equation Solving Module As the "theoretical core" of energy balance, a dynamic equilibrium equation is constructed:

[0054] Among them, the overall system efficiency η sys =0.82; The initial value of the targeted repair weight coefficient β(t) is set to 1.0, and is dynamically adjusted downwards as the cold accumulation area repair progresses. β=0 after the cold accumulation area recovery rate reaches 90%; The initial value of the thermal reservoir health correction coefficient γ(t) is set to 1.2, and is adjusted to 1.0 after the thermal reservoir attenuation rate decreases by 50%; The heat source priority weight coefficient is dynamically set according to seasonal characteristics: during the period of abundant sunshine from May to August, solar energy is the primary source (α). solar =0.6, α waste =0.3, α green =0.1), the transition period from September to October is mainly characterized by industrial waste heat (α). solar =0.2, α waste =0.7, α green =0.1), supplementing waste green electricity during off-peak hours at night (α) solar =0, α waste =0.5, αgreen =0.5).

[0055] The equation solution shows that an average of 14.5 hours of supplemental heating is required daily (solar energy + industrial waste heat from 9:00 to 17:00, and industrial waste heat + waste green electricity from 22:00 to 6:00), which can meet the heat deficit demand of 2.45MW, and reduce the heat loss from the well shaft. The power output is controlled within 0.35MW. This module outputs the daily supplementary heating duration, heat source ratio, and target injection power, providing constraints for parameter optimization.

[0056] (3) Multi-field coupling simulation module As a "prediction engine" for energy balance, a U-shaped well-reservoir coupling model was constructed: the geometric model includes a U-shaped well with a vertical depth of 2850m and a horizontal section length of 1000m. The reservoir is the Quanzhou No. 2 Formation tight sandstone, which is the main target reservoir layer, belonging to the second member of the Lower Cretaceous Quanzhou Formation. The lithology is mainly fine sandstone and siltstone with high clay content (its permeability is 5×10⁻⁶). -15 m 2 The thermal conductivity is 2.5 W / (m·K); the grid is densified by boundary layer (50 layers near the wellbore, minimum size 0.1m), with a total of 128,000 grids; the boundary conditions are set as follows: injection temperature 95~105℃ (solar energy enrichment period from May to August), 85~95℃ (waste heat dominance period from September to October), flow rate 15 m³ / h, and geothermal gradient 4.2℃ / 100m.

[0057] Simulation results are as follows Figure 3 The results show that before the start of reheating in the non-heating season (January to June), the wellbore outlet temperature continued to decline due to the long-term heat extraction in the previous period, dropping from an initial 55℃ to 47℃ in July, a decrease of 8℃, verifying the existence of the cold accumulation effect. After the implementation of reheating measures in May, the temperature decline was curbed. From August onwards, during the heating season heat extraction phase, thanks to the reservoir reheating in the non-heating season, the wellbore outlet temperature rebounded significantly, reaching a peak of 73℃ in November, an increase of 26℃ from the lowest point before reheating (47℃). The average outlet temperature during the heating season remained above 66℃. Throughout the entire cycle, the temperature steadily increased during the reheating phase and remained high during the heat extraction phase, verifying the effective remediation effect of the proposed method on the cold accumulation zone.

[0058] (4) Parameter optimization execution module As the "decision-making center" for energy balance, a dual-objective optimization model is constructed: The NSGA-II algorithm was used for 50 iterations to obtain the Pareto optimal solution set. The technically and economically optimal solution was selected: injection temperature 95℃, flow rate 1.3 m³ / h, and reheating cycle 120 days. When this parameter combination was implemented in the field, the well outlet temperature increased by 6.8℃ (18.2% increase) during the heating season compared to the previous cycle, the heat exchange increased by 23.5%, the system COP value recovered to 3.15, and the energy balance method showed 87.6% agreement with the measured data.

[0059] The proposed method and system for dynamic energy balance based on multi-energy complementarity in geothermal heat replenishment, through three core innovations—a dynamic energy balance master equation centered on the health of the geothermal reservoir, a targeted heat replenishment closed-loop management method, and a multi-objective optimization algorithm oriented towards the health of the geothermal reservoir—systematically solves the core problems of mismatch between the source and the reservoir in traditional geothermal heat replenishment, continuous decay of the geothermal reservoir, uncontrollable heat replenishment effect, and poor long-term economic efficiency.

[0060] This application fundamentally changes the core logic of geothermal recharge energy balance, breaking through the limitations of existing technologies that only focus on surface supply and demand balance. It is the first to achieve a paradigm shift in geothermal recharge from "short-term experience-driven" to "long-term reservoir health-oriented model-driven," constructing an integrated technical path of "identification-modeling-simulation-optimization-execution-iteration." This method is perfectly adaptable to medium- and low-temperature conductive tight geothermal reservoirs such as the Songliao Basin, and can also be extended to diverse geological scenarios such as depleted oil and gas reservoirs and hot dry rocks. It provides replicable and scalable theoretical tools and engineering methods for the transformation of regional energy structure and the achievement of "dual carbon" goals in oilfields, possessing significant academic value and industrialization prospects.

[0061] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for managing geothermal heat replenishment energy balance based on multi-energy complementarity, characterized in that, Includes the following steps: S10 identifies cold accumulation zones in underground thermal reservoirs and calculates the total heat loss required for reservoir remediation, while also assessing the total available amount, spatiotemporal distribution characteristics, energy supply characteristics, and cost characteristics of multi-source thermal energy. S20. Based on the total heat deficit and the total available amount, spatiotemporal distribution characteristics, energy supply characteristics and cost characteristics of the multi-source thermal energy, a full-link dynamic energy balance master control equation is constructed. The master control equation includes a targeted repair weight coefficient for spatially targeted allocation of heat replenishment energy according to the real-time status of the cold accumulation zone, and a heat reservoir health correction coefficient for dynamically adjusting the priority of heat replenishment input according to the health status of the thermal reservoir. S30. Based on the energy balance scheme determined by the master equation of the full-link dynamic energy balance, a three-field coupled numerical model of seepage field-temperature field-stress field is constructed and a position-dependent targeted heat source term is set. The heat replenishment effect of the energy balance scheme is verified by simulation prediction. S40. Using the full-link dynamic energy balance master equation as a constraint and combining the simulation verification results of the three-field coupled numerical model, an optimization model with the dual objectives of maximizing the long-term health of the thermal storage and minimizing the full life cycle cost of heat replenishment is constructed. The optimal combination of heat replenishment parameters that satisfies the long-term health objective and the full life cycle cost of the thermal storage is obtained by solving the model. S50, perform heat replenishment according to the heat replenishment parameter combination, and transmit the running data back to iteratively update the main control equation of the full-link dynamic energy balance and the constraints of the three-field coupled numerical model.

2. The geothermal heat replenishment energy balance management method based on multi-energy complementarity as described in claim 1, characterized in that, In step S10, the cold accumulation zone of the underground thermal reservoir is identified and the total heat loss required for reservoir repair is calculated, specifically including: Temperature data of the entire well section of the target thermal reservoir was collected using a distributed fiber optic temperature measurement system to identify the boundaries and volume (V) of the cold accumulation zone. cold and the average temperature decay ΔT; Combining the density ρ and specific heat capacity c of the thermal reservoir rock p The total heat loss required to repair the thermal reservoir to the target temperature is calculated using the formula ΔQ. res =ρc p V cold ΔT / τ recovery , where ΔQ res τ represents the heat deficit in the thermal reservoir. recovery The set heating cycle is used; at the same time, the natural recovery rate of the thermal reservoir is quantified to determine the minimum thermal power gap required for artificial heating.

3. The geothermal heat replenishment energy balance management method based on multi-energy complementarity as described in claim 1, characterized in that, In step S20, the master equation for the end-to-end dynamic energy balance is: Q in (t)=η sys [q solar (t) α solar +q waste (t) α waste +q green (t) α green ]=ΔQ res (t) β(t)+Q loss (t) γ(t)+Q out (t) In the formula, Q in (t) represents the total input thermal power of the system; η sys For overall system efficiency; q solar q waste q green These are the instantaneous input power from solar energy, industrial waste heat, and waste green electricity, respectively; α solar α waste α green These are the priority weighting coefficients for each heat source, dynamically adjusted based on seasonal solar radiation intensity, industrial waste heat availability, and grid curtailment patterns; ΔQ res (t) represents the thermal deficit of the reservoir calculated based on the volume and temperature difference of the cold accumulation zone; β(t) is the targeted remediation weighting coefficient of the cold accumulation zone, which is positively correlated with the real-time identified volume and temperature decay of the cold accumulation zone; Q loss (t) represents the heat loss between the system pipeline and the wellbore; γ(t) is the dynamic correction coefficient for the health of the thermal reservoir, dynamically adjusted based on the real-time decay rate of the thermal reservoir and the recovery rate of the cold accumulation zone; Q out (t) represents the effective heat output by the system.

4. The geothermal heat replenishment energy balance management method based on multi-energy complementarity as described in claim 3, characterized in that, The targeted repair weight coefficient β(t) for the cold accumulation zone is configured as follows: only the cold accumulation zone is assigned a heat replenishment weight, and the normal thermal storage zone that has reached the target temperature has β=0; the dynamic correction coefficient γ(t) for thermal storage health is configured as follows: when the thermal storage health is lower than the set threshold, the priority of heat replenishment input is forcibly increased.

5. The geothermal heat replenishment energy balance management method based on multi-energy complementarity as described in claim 1, characterized in that, In step S30, the three-field coupled numerical model of seepage field-temperature field-stress field includes the fluid mass conservation equation, the rock mass-fluid coupled energy conservation equation, and the thermal stress coupling equation. The fluid mass conservation equation is: + (ρu)=0 The energy conservation equation for the rock mass-fluid coupling is: The thermal stress coupling equation is: In the formula, The density of the solid rock skeleton of the thermal reservoir; Porosity of the thermal reservoir rock mass; The velocity vector of the heat-replenishing circulating medium in the pores of the thermal reservoir; The density of the heat exchange medium; The isobaric specific heat capacity of the heat exchanger; The instantaneous temperature of the thermal reservoir rock mass-fluid coupling system; These are the stress tensor components of the thermal reservoir rock mass; For the elastic stiffness tensor components of the thermal reservoir rock mass; For the strain tensor components of the thermal reservoir rock mass; Kronecker function, used for tensor simplification calculations of the thermal stress equation; is the linear thermal expansion coefficient of the thermal reservoir rock mass; The equivalent heat capacity of rock mass-fluid; It is the equivalent thermal conductivity; Heat source supplement.

6. The geothermal heat replenishment energy balance management method based on multi-energy complementarity as described in claim 1, characterized in that, In step S40, the bi-objective optimization model is specifically as follows: In the formula, As a comprehensive indicator of the long-term health of thermal storage; Recovery rate of the cold deposit area; This represents the decrease in the thermal storage degradation rate; To extend the service life of thermal storage; , , These are the weighting coefficients; Total cost of the entire heating cycle; The unit electricity price for the operation of the supplementary heating system pump set; The instantaneous operating power of the circulating pump and booster pump of the heat replenishment system at time t; The unit energy supply cost for supplementing solar thermal heating; Let t be the instantaneous thermal power input from the solar thermal system to the supplementary heating system. The unit energy cost of supplementing industrial waste heat; Let t be the instantaneous thermal power input from the industrial waste heat recovery system to the supplementary heating system at time t; The unit energy supply cost for supplementing heat from waste green electricity; Let t be the instantaneous thermal power input from the abandoned green electric heating system to the regenerative heating system; These represent the termination and start times of the full lifecycle optimization calculation for heat replenishment.

7. The geothermal heat replenishment energy balance management method based on multi-energy complementarity as described in claim 1 or 6, characterized in that, In step S40, the constraints of the bi-objective optimization model include: injection temperature ≤ 80℃ ≤120℃, injection flow rate 0.5≤ ≤2.0m³ / h, injection pressure ≤50MPa; Mandatory constraints for thermal storage health: cold accumulation zone recovery rate ≥90%, thermal storage attenuation rate reduction ≥50%, target temperature of cold accumulation zone ≥55℃; and the constraints of the master equation for dynamic energy balance across the entire link.

8. The geothermal heat replenishment energy balance management method based on multi-energy complementarity as described in claim 1, characterized in that, In step S40, the optimal combination of heat replenishment parameters that satisfies the long-term health goals of the thermal storage and the optimal cost over the entire life cycle is obtained. Specifically, the NSGA-II multi-objective genetic algorithm is used to solve the problem, and the Pareto optimal solution set is obtained through iterative calculation. The selected combination of heat replenishment parameters includes the dynamic output ratio of the three types of heat sources, the injection temperature, flow rate, pressure, heat replenishment cycle, and daily heat replenishment duration of the heat replenishment medium.

9. The geothermal heat replenishment energy balance management method based on multi-energy complementarity as described in claim 1, characterized in that, In step S50, the iterative update specifically includes: after each round of optimization, calling the three-field coupled numerical model to verify the cold accumulation zone repair effect of the optimized parameter combination, dynamically adjusting the constraint conditions, forming a closed-loop iteration of simulation-optimization-verification, until the final optimal heat replenishment scheme is output.

10. A geothermal heat replenishment energy balance management system based on multi-energy complementarity, characterized in that, The system is used to implement the geothermal heat replenishment energy balance control method based on multi-energy complementarity as described in any one of claims 1 to 9, the system comprising: The multi-source energy capture and dynamic ratio subsystem is used to capture three types of heat sources: solar energy, industrial waste heat and waste green electricity, and output a stable heat medium according to the dynamic ratio. A medium circulation and pressurization injection device is used to transport hot media underground; The underground U-shaped well heat exchange system is used to conduct heat through the casing wall to the dense rock layer in the heat replenishment mode, so as to repair the low temperature zone of cold accumulation. The intelligent monitoring and optimization control system is used to collect system operation data, solve the master equation of dynamic energy balance across the entire link and optimize the heat compensation parameters, and adjust the output ratio and injection parameters of each energy unit.

Images