Hierarchical heat extraction and heat recovery system of middle-deep ground buried pipe group and its coordinated scheduling method

Abstract

The present application belongs to the technical field of geothermal energy development and utilization, and relates to a layered heat extraction and heat recovery system for a medium-deep buried pipe group and a collaborative scheduling method thereof. The system comprises multiple buried pipe wells of different depths. A controllable isolation device is arranged in each buried pipe well to divide the buried pipe well into at least two heat exchange sections that can be independently circulated in the longitudinal direction. The heat exchange sections have mutually independent fluid inlets and outlets or flow channels that can be switched by valves, so as to realize independent heat extraction or shutdown of different depth formations in the same well. The scheduling method comprises two unique scheduling modes, i.e., annual rotation and seasonal layering. The present application breaks the temperature drop trend caused by continuous heat extraction and optimizes the energy consumption distribution during the entire heating season. At the same time, closed-loop optimization is realized by using a high-precision numerical model and real-time monitoring, thereby ensuring the scientificity and accuracy of system scheduling.

Description

Technical Field

[0001] This invention belongs to the field of geothermal energy development and utilization technology, specifically involving a medium-deep buried pipe group layered heat extraction and heat recovery system and its coordinated scheduling method. Background Technology

[0002] In current applications of medium-deep coaxial buried pipe networks for building heating in frigid regions, a "synchronous operation, full-load heat extraction" mode is commonly adopted. This means that throughout the entire heating season (typically 120-150 days), all buried pipe heat exchangers within the network operate continuously or at high loads, extracting heat from the soil and rock mass at a depth of 2000-3000 meters. The technical logic is based on steady-state or quasi-steady-state heat transfer assumptions. The design primarily focuses on the heat exchange capacity of individual wells and the spacing of the pipe network to avoid thermal interference (thermal short-circuiting), thus determining a reasonable well spacing and system configuration. Operational control adjusts the circulation pump frequency and heat pump start-up and shutdown based on the building's load demand. From the ground source side, this is essentially a "passive response, continuous extraction" approach. In this mode, the pipe network is considered a single heat source, and its long-term performance evaluation focuses on predicting the attenuation trend of the average temperature of the soil and rock mass under continuous heat extraction, thereby determining the system's lifespan (e.g., 20-30 years). This model does not differentiate the operation of the pipe group, nor does it proactively create a concentrated thermal recovery period for the soil and rock mass.

[0003] The disadvantages of existing medium-deep coaxial casing underground pipe networks are as follows: 1. Increased thermal accumulation effect in the soil and rock mass, leading to rapid long-term performance degradation: All buried pipes operate at high intensity simultaneously throughout the year, resulting in heat extraction far exceeding natural heat replenishment (geothermal heat flow, lateral conduction). Heat is continuously drawn from the soil and rock mass, especially in the underlying high-temperature rock layers in the central area of ​​the pipe group, forming a large-scale, continuous "cold accumulation zone." The temperature of the soil and rock mass decreases year by year, leading to a decrease in the outlet water temperature of the buried pipes, a decrease in the evaporation temperature of the heat pump unit, a decline in the system's coefficient of performance (COP), and a significant decline in heating capacity with increasing years of operation.

[0004] 2. Insufficient heat recovery period and poor sustainability: The current operating model relies on extremely slow heat recovery through natural heat conduction for only about 7-8 months during the non-heating season. For deep soil and rock layers, the thermal diffusivity is low, making it difficult to fully replenish the heat extraction area from surrounding undisturbed areas within a limited time. This is essentially a "depletionist" extraction method, lacking guarantees for the long-term (over 20 years) thermal stability of the system, which contradicts the concept of sustainable use of renewable energy.

[0005] 3. Inability to cope with load fluctuations and insufficient system resilience: During peak load periods in severe cold weather, the system can only rely on all buried pipes to operate at full capacity, lacking flexible adjustment mechanisms. Once the temperature of the soil and rock mass is already at a low level, the system's heating supply may not be able to meet the demands of extreme weather. The existing model does not utilize the differences in thermal response rates at different strata depths to optimize load distribution.

[0006] 4. Disconnect between design and operation, and low level of intelligence: Existing designs are one-off and static. The pipe network layout and operation strategies determined in the design phase are rarely dynamically adjusted according to the actual evolution of the underground thermal field over decades of operation. There is a lack of feedback control mechanisms based on real-time geothermal monitoring, resulting in extensive operation and management.

[0007] Therefore, a medium-deep underground pipe network that can suppress cold deposition of soil and rock and thus improve long-term sustainability is needed to solve the above-mentioned technical problems. Summary of the Invention

[0008] This invention aims to revolutionize the traditional "planar and synchronized" pipe network operation mode into a "three-dimensional, hierarchical, and time-coordinated" intelligent scheduling mode. Instead of treating the pipe network as a homogeneous whole, it functionally layers it according to depth. Through cross-year and seasonal collaborative scheduling strategies, it creates alternating "working periods" and "fallow recovery periods" for soil and rock layers at different depths, thereby creating space for thermal recovery over time and achieving long-term stability and improvement in system performance.

[0009] The present invention provides the following technical solution: a medium-deep buried pipe group layered heat extraction and heat recovery system, including multiple buried pipe wells at different depths; a controllable isolation device is provided in the buried pipe well, which is used to divide the buried pipe well into at least two independently circulating heat exchange sections in the longitudinal direction; the heat exchange sections have mutually independent fluid inlets and outlets or flow channels that can be switched by valves, thereby realizing independent heat extraction or shutdown of strata at different depths in the same well.

[0010] Preferably, the depth range of the buried well includes: shallow wells 1-800 meters, medium wells 800-2000 meters, and deep wells 2000-4000 meters.

[0011] Preferably, the controllable isolation device includes: a switchable downhole packer and a concentric dual-loop pipe structure.

[0012] This invention also discloses a collaborative scheduling method for a medium-deep buried pipe group layered heat extraction and heat recovery system. This collaborative scheduling method is used to schedule the aforementioned system and includes: Mode A: Divide the buried wells in the pipe group into at least two functional clusters according to their depth range; in the Nth operating cycle: during the heating season, set one functional cluster as the main heat extraction cluster to operate at high load; set the remaining functional clusters as auxiliary or backup clusters to operate at low load or only be activated briefly during peak load periods; thus, the soil and rock layers corresponding to the remaining functional clusters are mainly in a "heat recovery" or "slight heat extraction" state during this operating cycle. In the N+1th operating cycle: the roles are reversed; this rotation is repeated cycle by cycle, providing a complete concentrated heat recovery cycle for strata at different depths, fundamentally breaking the temperature drop trend caused by continuous heat extraction.

[0013] Mode B: During a heating season, the number of underground heat exchange wells is dynamically adjusted based on changes in outdoor temperature and building load. Early / Late Cold Season: The shallow and medium-depth heat exchange sections of all underground heat exchange wells are activated for heat extraction. During this stage, the initial temperature of the soil and rock is relatively low, but the load demand is also small, allowing the system to operate efficiently and stably. Severe Cold Season: In addition to activating the shallow and medium-depth heat exchange sections, all or enhanced deep heat exchange sections of all underground heat exchange wells are activated. The high-grade heat energy stored in the deep, high-temperature soil and rock is utilized to cope with peak loads and ensure heating performance. Once the peak load has passed, the deep heat extraction can be reduced or shut down.

[0014] Preferably, the collaborative scheduling method is based on a high-precision numerical model and a closed-loop optimization system for real-time monitoring. The numerical model includes a three-dimensional transient thermo-hydraulic-mechanical coupling numerical model established on an open-source platform, covering non-uniform strata, groundwater seepage, heat exchange of buried pipe groups, and hierarchical scheduling logic. The model is used to simulate the dynamic evolution of the regional geothermal field under different scheduling strategies. The closed-loop optimization system includes a distributed optical fiber temperature measurement system deployed in buried pipe wells and observation wells. The distributed optical fiber temperature measurement system is used to acquire real-time soil and rock temperature profile data along the well depth to facilitate: model correction, state assessment and early warning, and triggering strategy switching.

[0015] More preferably, the governing equations of the three-dimensional transient thermo-hydraulic-mechanical coupled numerical model include: The governing equation for heat transfer in soil and rock:

[0016] in, Indicates the temperature of the rock and soil mass. The effective volumetric heat capacity of the system is represented by t, where t represents time. These represent fluid density and specific heat capacity at constant pressure, respectively. Indicates Darcy velocity, For Hamiltonian operators, The tensor represents the effective thermal conductivity of the system. This indicates a heat source sink.

[0017] Groundwater flow control equations:

[0018] in, Indicates water head. Indicates the water storage coefficient. Indicates source and sink terms. This represents the inherent permeability tensor of the rock and soil mass. Indicates fluid dynamic viscosity, Indicates pore pressure, Represents gravitational acceleration. Represents the vertical coordinate.

[0019] Control equation for heat transfer of fluid inside buried pipes: For fluid in the inner tube:

[0020] For annular fluid:

[0021] in, These represent the temperatures of the inner tube and the annulus fluid, respectively. These represent the cross-sectional areas of the inner tube and the annulus, respectively. These represent the fluid velocities in the inner tube and the annulus, respectively. Indicates the thermal resistance between the fluids inside and outside the pipes. Calculated heat transfer coefficient, This indicates the thermal resistance between the annular fluid and the backfill material. Calculated heat transfer coefficient, This indicates the temperature of the backfill material.

[0022] Backfill material heat transfer control equation:

[0023] in, , , These represent the density, specific heat capacity, and thermal conductivity of the backfill material, respectively.

[0024] The governing equations of the soil and rock mechanics field are as follows:

[0025] in, Represents the effective stress tensor. Represents the Biot coefficient. This represents the elastic stiffness tensor. Represents the strain tensor. Indicates the coefficient of thermal expansion. Indicates the initial temperature. Represents a unit tensor.

[0026] More preferably, the three-dimensional transient thermo-hydraulic-mechanical coupled numerical model is a multi-objective optimization of the hierarchical scheme and scheduling parameters guided by an optimization objective function, the objective function including: Minimize: [ Total temperature drop over system lifecycle + Total operating power consumption Maximize: [Heating supply guarantee rate] Subject to: [Maximum heat extraction capacity, equipment constraints, and minimum outlet temperature limits for each well / section] Design variables include: well grouping, rotation cycle, and activation thresholds for different levels in different seasons.

[0027] More preferably, the boundary condition functions of the three-dimensional transient thermo-hydraulic-mechanical coupled numerical model include: state variables of the buried well or heat exchange section. .

[0028] For the first in the pipe group Buried well or the first Each heat exchange section defines state variables. ; This indicates that the buried well or heat exchange section is in the "primary heat extraction" state at time t, based on the design flow rate. and design inlet temperature run; This indicates that the buried pipe well or heat exchange section is in an "auxiliary / low load" state, with reduced flow rate. run; This indicates that the buried well or heat exchange section is in a "heat recovery / shutdown" state, with a flow rate of zero.

[0029] The beneficial effects of this invention are: 1. This invention can significantly suppress cold deposition in soil and rock masses, improving long-term sustainability: Through annual rotation, it provides each soil and rock layer with a concentrated thermal recovery period of several months to a year, allowing the soil and rock mass temperature to effectively recover. Numerical simulations show that, compared with the traditional synchronous operation mode, this invention can reduce the average temperature drop of soil and rock masses after 20 years of operation by 30%-50%, extending the effective service life of the system, which is expected to be extended from the traditional 20-30 years to more than 40 years.

[0030] 2. This invention can improve the overall energy efficiency and stability of the system: Because the main heat extraction layer is always in a relatively good thermal state (after recovery), the average water temperature at the outlet of the buried pipe can be increased by 1.5-4.0°C. This increases the evaporation temperature of the heat pump unit, and the coefficient of performance (COP) is expected to increase by 5%-15%. The system's heating capacity fluctuates less throughout its entire life cycle, and its operation is more stable.

[0031] 3. This invention can enhance the system's resilience to peak loads: The seasonal tiered scheduling mode uses high-grade deep geothermal resources to cope with extreme cold weather, which is equivalent to building an "underground tiered heat storage and peak-shaving system", improving the heating security rate and enhancing the resilience of the energy system.

[0032] 4. This invention enables intelligent and refined management of geothermal resources: It upgrades buried pipe networks from simple "heat extraction equipment" to an intelligent system for "recyclable resource management." Through a closed-loop process of "monitoring-simulation-optimization-control," it achieves on-demand, efficient, and sustainable development of underground thermal resources, representing an advanced operational concept in the field of geothermal energy utilization. Attached Figure Description

[0033] Figure 1 This is the core process and control logic diagram of the deep buried pipe group layered heat extraction and heat recovery system and its collaborative scheduling method in this invention; Figure 2 This is a flowchart illustrating the design of the hierarchical scheme based on numerical models and optimization algorithms of the present invention. Detailed Implementation

[0034] The relevant technologies of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0035] like Figures 1-2 As shown, the specific details of the medium-deep buried pipe group layered heat extraction and heat recovery system and its coordinated scheduling method in this embodiment are as follows: 1. The structure of the medium-deep buried pipe group layered heat extraction and heat recovery system includes physical layering and controllable circulation.

[0036] Differentiated configuration of pipeline depths: During the pipeline design phase, buried pipelines of varying depths are configured. For example, some buried pipelines are drilled to a depth of 2000 meters (referred to as "shallow-medium-deep wells"), primarily utilizing geothermal resources in the shallow to medium depths (e.g., 800-2000 meters); other buried pipelines are drilled to a depth of 3000 meters (referred to as "full-deep wells"), fully utilizing geothermal resources in the shallow, medium, and deep depths. This physical depth difference provides the basis for tiered scheduling.

[0037] Single-well segmented controllable technology (optional enhancement scheme): In deep wells (especially full-depth wells), by installing downhole controllable isolation devices (such as switchable downhole packers or concentric dual-loop pipe structures), a single well is vertically divided into at least two independently circulating heat exchange sections (e.g., upper section 0-1500 meters, lower section 1500-3000 meters). Each section has independent fluid inlets and outlets or the flow channels can be switched via valves, thereby achieving independent heat extraction or shutdown at different formation depths within the same well.

[0038] 2. Core scheduling strategy: Spatiotemporal coordinated heat extraction-recovery rotation: The scheduling strategy of this invention is not a simple start-stop process, but an intelligent decision-making process based on geological characteristics, thermal dynamics, and load demand. It mainly includes two modes that can be used individually or in combination: Mode A: Interannual rotation scheduling. The wells in the pipeline group are divided into at least two functional clusters (e.g., Cluster I: full-depth wells; Cluster II: shallow-to-medium-depth wells).

[0039] In the Nth operating year: During the heating season, Cluster I (full-depth wells) will be designated as the primary heat extraction cluster, operating at high load to extract high-grade heat energy from its deep layers (e.g., 2000-3000 meters). Simultaneously, Cluster II (shallow-medium-deep wells) will be designated as an auxiliary or backup cluster, operating at lower load or only briefly activated during peak load periods. Thus, the shallow to medium-depth soil layers corresponding to Cluster II will primarily be in a state of "heat recovery" or "minor heat extraction" during this year.

[0040] In the N+1th operating year: role swapping will take place. Cluster II (shallow-medium-deep wells) will be switched to become the main heat extraction cluster, utilizing the shallow and medium-deep thermal energy that was well recovered in the previous year; while Cluster I (full-deep wells) will enter the "thermal recovery period", during which its deep soil and rock will receive significant heat replenishment through terrestrial heat flow and lateral heat conduction during a year-long fallow period.

[0041] This annual rotation provides a concentrated heat recovery cycle of up to one year for strata at different depths, fundamentally breaking the temperature drop trend caused by continuous heat extraction.

[0042] Mode B: Seasonal tiered scheduling. Within a heating season, the main heat extraction depth is dynamically adjusted based on changes in outdoor temperature and building load.

[0043] Early / late cold season (outdoor temperature is relatively high, heating load is relatively low): Heat extraction is mainly carried out in the shallow to medium-depth heat exchange sections (e.g., 0-2000 meters) of buried pipes in various locations. During this stage, the initial temperature of the soil and rock is relatively low, but the load demand is also small, allowing the system to operate efficiently and stably.

[0044] During periods of severe cold (extremely low outdoor temperatures and peak heating load): In addition to activating the shallow and medium-depth heating layers, all or enhanced deep heat exchange sections (e.g., 2000-3000 meters) of buried pipes in various locations will be activated. This utilizes the high-grade heat energy stored in the deep, high-temperature soil and rock layers to cope with peak loads and ensure heating performance. Once the peak load has passed, heat extraction from the deep sections can be reduced or shut down.

[0045] This model utilizes the high thermal inertia and high temperature of deep strata, using them as "strategic reserves" and "peak-shaving power sources" to optimize energy consumption distribution throughout the heating season.

[0046] 3. Optimization and Control Systems: Intelligent Decision-Making Based on Digital Twins The scheduling in this invention is not based on experience, but on a closed-loop optimization system based on a high-precision numerical model and real-time monitoring.

[0047] A high-fidelity long-term coupled prediction model: Based on the open-source platform OpenGeoSys, a three-dimensional transient thermo-hydraulic-mechanical (THM) coupled numerical model is established, encompassing non-uniform formations, groundwater seepage, heat exchange from buried pipe networks, and hierarchical scheduling logic. The model can simulate the dynamic evolution of the regional geothermal field over a period of 10-20 years under different scheduling strategies. The core equations of the governing equations are as follows, with the addition of time-varying boundary condition functions describing the operating states (on / off, flow rate, inlet temperature) of different wells or well sections.

[0048] Multi-objective optimization design: This approach uses an optimization objective function to guide the design of hierarchical schemes and scheduling parameters. The objective function can be set as follows: Minimize: [ Total temperature drop over system lifecycle + Total operating power consumption Maximize: [Heating supply guarantee rate] Subject to: [Maximum heat extraction capacity, equipment constraints, minimum outlet temperature limits, etc. for each well / section] Design variables include: how the well cluster is grouped, the rotation cycle (1 year, 2 years, or longer), and the activation thresholds for different levels in each season (e.g., based on average daily outdoor temperature or predicted load). Multi-objective optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are coupled iteratively with the numerical model to automatically find the optimal solution.

[0049] Real-time monitoring and dynamic feedback control: Distributed fiber optic temperature measurement systems (DTS) are deployed in key buried pipes and observation wells to acquire real-time soil and rock temperature profiles along the well depth. This data is used for: (a) Model calibration: The measured temperature field is compared with the model prediction periodically, and the geothermal parameters of the soil and rock are inverted and corrected to ensure that the "digital twin" model always maintains high accuracy.

[0050] (b) Status assessment and early warning: The system calculates the degree of thermal recovery in each layer of the region in real time (e.g., the ratio of the current temperature to the initial temperature or the temperature at the end of the previous cycle). The system can issue an early warning when the recovery of a region does not meet expectations.

[0051] (c) Triggering strategy switching: Based on the model's prediction of system performance in the next 7-15 days, combined with the real-time monitoring of the soil and rock "heat storage status", dynamically decide whether to switch the scheduling mode (such as switching from shallow heat extraction as the main mode B to mixed heat extraction including deep layers), or determine the best start time for interannual rotation.

[0052] The governing equations of the three-dimensional transient thermal-hydraulic-mechanical (THM) coupled numerical model based on OpenGeoSys are as follows: The core of the model consists of the following set of mutually coupled partial differential equations, which are spatially discretized on a three-dimensional unstructured mesh and temporally advanced using an implicit backward difference method.

[0053] The governing equations for heat transfer in soil and rock masses (temperature field, T): Considering that the soil and rock mass is a porous medium, its heat transfer follows the energy conservation equation that includes heat conduction and heat convection (caused by groundwater seepage):

[0054] in: : Temperature of soil and rock mass (K); The effective volumetric heat capacity of the system (J / (m³·K)) is calculated using the following formula: ; Porosity; : Density of soil and rock skeleton (kg / m³) and specific heat capacity (J / (kg·K)); Fluid density (kg / m³) and specific heat capacity at constant pressure (J / (kg·K)); Darcy velocity (m / s) is obtained by solving the flow equation, realizing heat-water coupling; The effective thermal conductivity tensor of the system (W / (m·K)) includes both solid thermal conductivity and fluid thermal dispersion effects; Heat source sink (W / m³) is used to characterize the heat exchange between the buried pipe and the soil and rock, and serves as a bridge connecting the buried pipe model and the soil and rock model.

[0055] Groundwater flow governing equations (hydrodynamic field, H): The modified Darcy's law is used to describe the transient flow of groundwater in porous soil and rock media:

[0056] in: Water head (m); : Water storage coefficient (1 / Pa); Darcy velocity vector (m / s); Source and sink items (kg / (m³·s)); : Intrinsic permeability tensor of rock and soil mass (m²); : Fluid dynamic viscosity (Pa·s); Pore ​​pressure (Pa). ; : Gravitational acceleration (m / s²); : Vertical coordinate (m).

[0057] Solving this equation yields the pore pressure field. Darcy velocity field This provides the convection velocity for the heat transport equation, enabling heat-water (TH) coupling.

[0058] Control equations for heat transfer of fluid inside buried pipes (well model): The circulating fluid within the coaxial sleeve is considered a one-dimensional flow, embedded as line elements in a three-dimensional model, exchanging heat with the soil and rock through the pipe wall. Its energy equation simplifies to: For fluid in the inner tube:

[0059] For annular fluid:

[0060] in: The temperature (K) of the fluid in the inner tube and annulus is a function of depth s and time t; : Cross-sectional area of ​​inner tube and annulus (m²); : Flow velocity of fluid in the inner tube and annulus (m / s); The fluid flow between the inner and outer pipes is based on thermal resistance. Calculated heat transfer coefficient (W / (m·K)). ; The interaction between the annular fluid and the backfill material is based on thermal resistance. Calculated heat transfer coefficient (W / (m·K)). ; : Temperature of backfill material (K).

[0061] Backfill material heat transfer control equation:

[0062] At the borehole wall ( ), backfill material temperature With the temperature of the rock and soil Coupling through interfacial heat flow continuity conditions:

[0063] The governing equations of the soil and rock mechanics field (used for long-term deformation assessment): To more accurately assess the impact of long-term operation, porosity elasticity theory is introduced to consider thermal stress caused by temperature changes and effective stress changes caused by fluid pressure changes:

[0064] in: Effective stress tensor (Pa); Biot coefficient; : Elastic stiffness tensor (Pa); : Strain tensor; : Coefficient of thermal expansion (1 / K); Initial temperature (K).

[0065] The feedback of the mechanical field mainly affects permeability. and porosity (and thus affect) and This achieves full THM coupling.

[0066] This invention embeds "hierarchical scheduling logic" as a control variable into the aforementioned physical field model. This is achieved by defining a set of time-varying boundary condition functions that are related to the state of the well or well section.

[0067] Define the state variables of the buried pipe (or well section) : First, regarding the first in the management group Koujing (or the first) (each well section), define state variables. .

[0068] two, This indicates that the well (section) is in the "main heat extraction" state at time t, with the design flow rate. and design inlet temperature run.

[0069] three, This indicates that the system is in a "secondary / low load" state, with reduced flow. run.

[0070] Four, This indicates that the system is in a "hot recovery / shutdown" state with zero flow.

[0071] Embed state variables into the boundary conditions of the buried pipe model: The boundary conditions (mass flow rate and temperature) at the inlet of the buried pipe are no longer fixed values, but time-varying functions determined by the scheduling strategy:

[0072] These functions The specific form is determined by the scheduling strategy (such as the interannual rotation of mode A and the seasonal stratification of mode B).

[0073] Implementation in the numerical model: Within the OGS platform, through its flexible "boundary conditions" and "source / sink" management interfaces, and Defined as a time-varying Dirichlet boundary condition or Neumann boundary condition.

[0074] The temporal variation pattern can be input using a table function or a user-defined script function (Python Interface). For example, for "annual cycle", a piecewise constant function with a 365-day cycle can be defined; for "seasonal stratification", a logical function triggered by the daily average outdoor temperature can be defined.

[0075] Model initialization and long-term simulation: Initial conditions: The initial temperature field of the entire computational domain is set according to the geothermal gradient and the annual average surface temperature; the initial pore pressure field is set according to the hydrostatic pressure distribution.

[0076] Boundary conditions: Flow boundary: The sides of the model can be constant head or zero flux boundary; the bottom is a watertight boundary; the top is a free surface or constant head boundary.

[0077] Thermal boundary: The bottom of the model is a constant heat flux (reflecting the heat flux density of the earth) or isothermal boundary; the sides of the model are adiabatic or isothermal boundaries; the surface is a third type of boundary condition that exchanges temperature with the atmosphere.

[0078] Simulation duration: The model can be set to a simulation duration of 10-20 years, and the time step can be dynamically adjusted as needed (e.g., a smaller step during the heating season and a larger step during the non-heating season).

[0079] To ensure model reliability, the following methods were used for verification and calibration: 1. Analytical solution verification: Under simple boundary conditions (such as a constant line heat source in a homogeneous medium), the numerical solution of the model is compared with the analytical solution of the classic moving line heat source to ensure the correctness of the core algorithm.

[0080] 2. Verification using measured engineering data: Long-term soil and rock temperature field data obtained from distributed fiber optic thermometry (DTS) in collaborative projects (such as the Jilin Da'an project), as well as inlet and outlet water temperature and flow rate data during the operation of the buried pipe, are used to perform inversion correction on the model. Specifically, the Levenberg-Marquardt algorithm or Bayesian inversion method is used to automatically adjust key formation parameters (such as the thermal conductivity of each layer). The model is then used to calculate the permeability coefficient (K) until the simulation results and measured data are within an acceptable error range (e.g., mean absolute error (MAE) < 0.5°C). The calibrated model then becomes the "digital twin" of the site and is used for subsequent optimization and prediction.

[0081] Alternatives to this implementation: (1) Physical stratification substitution: In addition to configuring wells at different depths, "functional stratification" can also be achieved in a well group with the same depth standard by adjusting the operating flow rate and inlet temperature of different wells. For example, some wells can be operated with high flow rate and low temperature difference to mainly extract heat from the shallow and middle parts; while other wells can be operated with low flow rate and high temperature difference to mainly stimulate deep heat exchange. This can also create differentiated heat-affected zones and recovery conditions.

[0082] (2) Alternatives to segmented technology: In addition to mechanical packers, downhole segmented controllable technology can also use "smart materials" or fluids with controllable viscosity to temporarily block specific well sections, or adopt a more complex double concentric tube heat exchanger design.

[0083] Alternatives to scheduling modes: (1) Periodic rotation substitution: The annual rotation is not limited to 1 year, and can be adjusted to a cycle of 2 years or longer based on the simulation optimization results. It can also be more complex than two sets of rotation, but design more complex multi-set (such as three sets A, B, and C) cyclic sequences.

[0084] (2) Replacement of triggering conditions: In addition to fixed calendar time and outdoor temperature, the triggering conditions for mode switching can also be based on the real-time calculated "thermal storage index" of the soil and rock, the predicted building load curve, or even the time-of-use electricity price in the electricity market (combined with electric drive heat pumps) to achieve the most economical scheduling.

[0085] Alternative optimization methods: In addition to optimization based on accurate physical models, data-driven reinforcement learning (RL) algorithms can be used during the runtime phase. The system learns the optimal scheduling strategy autonomously by continuously interacting with the real environment (underground thermal field), which is particularly suitable for sites with high uncertainty in geological parameters.

[0086] Alternative technologies: This invention can be combined with "asymmetric layout." That is, in areas with seepage, the pipe group layout is first optimized according to the seepage direction, and then the optimized pipe group is subject to tiered scheduling, forming a dual synergy of "spatial layout optimization" and "time scheduling optimization." It can also be combined with "variable thermal conductivity backfilling" to enhance vertical thermal control capabilities within a single well.

[0087] In summary, this invention provides sufficient heat recovery cycles for different geological depths through two unique scheduling modes: interannual rotation and seasonal stratification. This breaks the temperature drop trend caused by continuous heat extraction and optimizes energy consumption distribution throughout the heating season. Simultaneously, the intelligent decision-making system based on digital twins utilizes high-precision numerical models and real-time monitoring to achieve closed-loop optimization, ensuring the scientific and precise nature of system scheduling.

[0088] This invention demonstrates significant advantages and innovations in several aspects. Technically, its established three-dimensional transient thermo-hydraulic-mechanical (THM) coupled numerical model encompasses a variety of complex factors, capable of simulating the dynamic evolution of regional geothermal fields over 10-20 years, providing strong theoretical support for the long-term stable operation of the system. By embedding "hierarchical scheduling logic" as a control variable into the physical field model, flexible control of the system's operating state is achieved.

[0089] In practical applications, this invention exhibits strong adaptability and operability. Through various verification and calibration methods, such as analytical solution verification and engineering measured data verification, the reliability and accuracy of the model are ensured. Simultaneously, it provides a wealth of alternative solutions, including physical layering substitution, segmented technology substitution, scheduling mode substitution, optimization method substitution, and combinations with other technologies, enabling the system to be flexibly adjusted and optimized according to different geological conditions, economic factors, and actual needs.

[0090] Furthermore, this invention also offers significant economic and social benefits. By optimizing energy consumption distribution, reducing total operating power consumption, and improving heating reliability, it effectively lowers heating costs and reduces environmental impact. Moreover, its innovative design and technical solutions provide new ideas and methods for the development and utilization of geothermal energy, promoting industry development and progress. In the future, this invention is expected to be promoted and applied in more regions, providing strong technical support for achieving green and efficient heating.

[0091] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A layered heat extraction and heat recovery system for medium-deep underground pipe networks, characterized in that, It includes multiple buried pipe wells at different depths; each buried pipe well is equipped with a controllable isolation device, which is used to divide the buried pipe well longitudinally into at least two heat exchange sections that can circulate independently; each heat exchange section has independent fluid inlets and outlets or flow channels that can be switched by valves, thereby realizing independent heat extraction or shutdown of strata at different depths within the same well.

2. The medium-deep buried pipe group layered heat extraction and heat recovery system according to claim 1, characterized in that, The depth range of the buried wells includes: shallow wells 1-800 meters, medium wells 800-2000 meters, and deep wells 2000-4000 meters.

3. The medium-deep buried pipe group layered heat extraction and heat recovery system according to claim 1, characterized in that, The controllable isolation device includes: a switchable downhole packer and a concentric double-loop pipe structure.

4. A collaborative scheduling method for a medium-deep buried pipe group layered heat extraction and heat recovery system, characterized in that, The cooperative scheduling method is used to schedule the system according to any one of claims 1 to 3, and the cooperative scheduling method includes: Mode A: Divide the buried pipe wells in the pipe group into at least two functional clusters according to their depth range; In the Nth operating cycle: During the heating season, set one functional cluster to operate at high load as the main heating cluster; set the remaining functional clusters to operate at low load as auxiliary or backup clusters or only activate them briefly during peak load periods; In the N+1th operating cycle: Reverse the roles. Mode B: Dynamically adjust the underground heating pipe wells according to changes in outdoor temperature and building load during a heating season; Early / late cold period: Start the shallow and medium-depth heat exchange sections of buried wells in various locations to extract heat; During the severe cold period: On the basis of activating the shallow heat exchange section, activate or strengthen the deep heat exchange section of buried pipe wells in various places.

5. The collaborative scheduling method for a medium-deep buried pipe group layered heat extraction and heat recovery system according to claim 4, characterized in that, The collaborative scheduling method is based on a high-precision numerical model and a closed-loop optimization system with real-time monitoring. The numerical model includes: a three-dimensional transient thermo-hydraulic-mechanical coupling numerical model based on an open-source platform, which covers non-uniform strata, groundwater seepage, heat exchange of buried pipe groups and hierarchical scheduling logic. The model is used to simulate the dynamic evolution of the regional geothermal field under different scheduling strategies. The closed-loop optimization system includes a distributed fiber optic temperature measurement system deployed in the buried well and observation well. The distributed fiber optic temperature measurement system is used to acquire real-time soil and rock temperature profile data along the well depth to facilitate: model correction, state assessment and early warning, and triggering strategy switching.

6. The collaborative scheduling method for a medium-deep buried pipe group layered heat extraction and heat recovery system according to claim 5, characterized in that, The governing equations of the three-dimensional transient thermo-hydraulic-mechanical coupled numerical model include: The governing equation for heat transfer in soil and rock: in, Indicates the temperature of the rock and soil mass. The effective volumetric heat capacity of the system is represented by t, where t represents time. These represent fluid density and specific heat capacity at constant pressure, respectively. Indicates Darcy velocity, For Hamiltonian operators, The tensor represents the effective thermal conductivity of the system. Indicates a heat source sink; Groundwater flow governing equations: in, Indicates water head. Indicates the water storage coefficient. Indicates source and sink terms. This represents the inherent permeability tensor of the rock and soil mass. Indicates fluid dynamic viscosity, Indicates pore pressure, Represents gravitational acceleration. Represents the vertical coordinate; Control equation for heat transfer of fluid inside buried pipes: For fluid in the inner tube: For annular fluid: in, These represent the temperatures of the inner tube and the annulus fluid, respectively. These represent the cross-sectional areas of the inner tube and the annulus, respectively. These represent the fluid velocities in the inner tube and the annulus, respectively. Indicates the thermal resistance between the fluids inside and outside the pipes. Calculated heat transfer coefficient, This indicates the thermal resistance between the annular fluid and the backfill material. Calculated heat transfer coefficient, Indicates the temperature of the backfill material; Heat transfer control equation for backfill material: in, , , These represent the density, specific heat capacity, and thermal conductivity of the backfill material, respectively. The governing equations of the soil and rock mechanics field are as follows: in, Represents the effective stress tensor. Represents the Biot coefficient. This represents the elastic stiffness tensor. Represents the strain tensor. Indicates the coefficient of thermal expansion. Indicates the initial temperature. Represents a unit tensor.

7. The collaborative scheduling method for a medium-deep buried pipe group layered heat extraction and heat recovery system according to claim 5, characterized in that, The three-dimensional transient thermo-hydraulic-mechanical coupled numerical model is a multi-objective optimization of the hierarchical scheme and scheduling parameters guided by an optimization objective function, which includes: Minimize: [ Total temperature drop over system lifecycle + Total operating power consumption Maximize: [Heating supply guarantee rate] Subject to: [Maximum heat extraction capacity, equipment constraints, and minimum outlet temperature limits for each well / section]; Design variables include: well grouping, rotation cycle, and activation thresholds for different levels in different seasons.

8. The collaborative scheduling method for a medium-deep buried pipe group layered heat extraction and heat recovery system according to claim 5, characterized in that, The boundary condition functions of the three-dimensional transient thermo-hydraulic-mechanical coupling numerical model include: state variables of the buried well or heat exchange section. ; For the first in the pipe group Buried well or the first Each heat exchange section defines state variables. ; This indicates that the buried pipe well or heat exchange section is in the "main heat extraction" state at time t, based on the design flow rate. and design inlet temperature run; This indicates that the buried pipe well or heat exchange section is in an "auxiliary / low load" state, with reduced flow rate. run; This indicates that the buried pipe well or heat exchange section is in a "heat recovery / shutdown" state, and the flow rate is zero.

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