A method for predicting the thermal properties of a layered soil for a pipe-in-tube ground heat exchanger

Abstract

The application discloses a kind of casing type middle-deep buried pipe geotechnical layering thermophysical property prediction method, utilize distributed heat response test experimental data to carry out prediction to geotechnical layering thermophysical property, belong to ground source heat pump technical field.First, casing type middle-deep buried pipe is divided into several layers along depth direction;Then, boundary condition is constructed based on distributed heat response test experimental data, and then heat transfer model is respectively established to each layer casing type middle-deep buried pipe;Root mean square error of distributed heat response test experimental data and heat transfer model calculation value is used as the objective function of each layer casing type middle-deep buried pipe;Finally, by solving the minimum objective function of each layer casing type middle-deep buried pipe, geotechnical thermophysical property (thermal conductivity and heat capacity) is predicted in turn.The precision of each layer geotechnical thermophysical property predicted by the application is higher, solve the problem that existing distributed heat response test technology measures shallow, modeling is complex or measurement parameter is single, has higher popularization value.

Description

Technical Field

[0001] This invention belongs to the field of ground source heat pump technology, and more specifically, relates to a method for predicting the thermal properties of soil and rock layers in a casing-type medium-deep buried pipe. Background Technology

[0002] Coil-and-tube medium-deep geothermal heat exchangers (hereinafter referred to as coil-and-tube medium-deep geothermal pipes) can effectively extract medium-deep geothermal energy. When combined with a heat pump, they form a ground source heat pump, providing a low-carbon and energy-saving solution for winter heating in my country. The thermal properties of the soil and rock are important parameters for performance prediction and optimized design of coil-and-tube medium-deep geothermal pipes. Due to the considerable depth of coil-and-tube medium-deep geothermal pipes, the thermal properties of the soil and rock are likely to change with depth, and the variation can be significant. Currently, distributed thermal response testing is commonly used in engineering to measure the stratified thermal properties of the soil and rock.

[0003] In February 2018, Zhu Bingsen, Bi Wenming, Guo Jianfeng, and Li Zhonglan published an article entitled "Application Analysis of Layered Thermal Property Testing in Heat Pump Engineering" in Volume 39, Issue 2, pp. 468-474 of the *Journal of Energiae Solaris Sinica*. This article focused on the vertical distribution of thermal property parameters of soil and rock within shallow U-shaped buried pipe test holes at engineering sites. A distributed fiber optic temperature measurement system was added to traditional thermal property testing instruments to conduct initial temperature and thermal property tests on the soil and rock. The initial temperature of the soil and rock at different depths and the average temperature of the circulating fluid at different depths during heating experiments were measured and compared with the results of traditional testing. The results showed that the layered thermal property testing was more accurate, allowing analysis of the vertical temperature distribution of the soil and rock, and thus determining the thermal conductivity and heat transfer coefficient of the buried pipe heat exchanger in the vertical direction.

[0004] In April 2020, Wang Qilong, Zhang Weidong, and Tian Dongmeng published an article titled "Research on Layered Thermal Properties of Buried Pipes Based on Thermal Response Tests" in Volume 48, Issue 350, pages 68-72 of *Building Energy Conservation*. This article focuses on the layered thermal properties of shallow U-shaped buried pipes in soil and rock. Using the thermal property testing of soil and rock under heat exchange conditions in a test borehole of a shallow soil source heat pump project as background, temperature measuring devices were deployed in layers within the test borehole. Data on the temperature field during the thermal response process and the recovery state of the temperature field after disturbance were recorded through monitoring the original temperature field within the borehole. The study investigated the temperature field variation law during the thermal response process of the soil source heat pump system, providing a reference for the analysis of ground temperature field changes during operation. Combined with indoor soil and rock thermal property tests, and through on-site soil and rock thermal response tests, different calculation methods in heat transfer were used to calculate and analyze the layered soil and rock thermal property parameters of the shallow soil source heat pump system. The comparison showed that the layered thermal property test parameter values ​​are relatively reliable, providing a theoretical basis for optimized design.

[0005] In October 2022, Zhang Changxing, Lu Jiahui, Lu Xizheng, and Liu Yufeng disclosed an invention patent entitled "A Method for Identifying Thermal Properties of Soil and Rock Based on Geological Stratification" (application number: 202211256562.3). This patent constructs an experimental model of a buried pipe heat exchanger, conducts thermal response experiments on the soil and rock to be identified using the buried pipe heat exchanger experimental model, obtains the measured temperature values ​​of each temperature sensor in the buried pipe heat exchanger experimental model during the thermal response experiment, and then constructs a numerical simulation model of the thermal response experiment of soil and rock using finite element simulation software based on the buried pipe heat exchanger experimental model. The numerical simulation model of the thermal response experiment of soil and rock is used to simulate the thermal response experiment, obtain the simulated temperature of each temperature sensor at different times, construct an objective function, and use the LM algorithm to optimize the objective function to determine the optimal solution of the thermal properties of the soil and rock to be identified. This patent combines geotechnical thermal response experiments with finite element numerical simulation methods to realistically reproduce the working conditions of buried pipe heat exchangers, and achieves accurate acquisition of the thermal property parameters of multi-layer geotechnical materials, laying the foundation for guiding the design scheme of buried pipe heat exchangers.

[0006] In December 2020, Nian Yongle, Wang Xiangyang, and Cheng Wenlong disclosed an invention patent entitled "A Thermal Response Test Method for the Distribution of Thermal Conductivity in Soil and Rock" (application number: 202011578350.8). This patent first establishes a multidimensional transient heat transfer model for a buried pipe heat exchanger by considering heat transfer along the depth direction of the soil and rock, simulating the longitudinal temperature distribution curve of the fluid in the heat exchanger. Then, it experimentally tests the fluid temperature distribution data at different depths within the heat exchanger. Finally, it uses a multi-objective optimization algorithm and directly employs the fluid temperature distribution data to simultaneously predict the thermal conductivity distribution data of the soil and rock within multiple spatial layers. The soil and rock thermal conductivity distribution tested using this patent not only has high accuracy but also overcomes the problem of poor convergence in traditional layered testing methods. Furthermore, the test accuracy is not affected by depth, making it widely applicable and highly efficient.

[0007] However, the first three studies mentioned above all focus on the stratified thermal properties of shallow U-shaped buried pipes, and their techniques cannot be applied to the measurement of stratified thermal properties of casing-type medium-deep buried pipes. The fourth study focuses on the stratified thermal properties of casing-type buried pipes, but it suffers from complex modeling and a single measurement parameter (only the thermal conductivity of the soil and rock). Furthermore, existing methods for measuring the stratified thermal properties of medium-deep soil and rock (including table lookup methods and laboratory measurement methods) suffer from low measurement accuracy or high cost. Summary of the Invention

[0008] 1. The problem to be solved

[0009] To address the issues of low accuracy and high cost in existing methods for measuring the thermal properties of soil and rock layers in casing-type medium-deep buried pipes, this invention provides a method for predicting the thermal properties of soil and rock layers in casing-type medium-deep buried pipes. Based on distributed thermal response test data, this method predicts the thermal conductivity and heat capacity of soil and rock at different depths, laying the foundation for performance prediction and optimized design of casing-type medium-deep buried pipes.

[0010] 2. Technical Solution

[0011] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0012] This invention discloses a method for predicting the thermal properties of soil and rock layers in a casing-type medium-deep buried pipe. Based on experimental data of fluid temperature distribution from a distributed thermal response test, the method predicts the thermal properties of soil and rock at different depths. First, according to the distribution of measurement points in the distributed thermal response test, the casing-type medium-deep buried pipe is divided into several layers along the depth direction (with measurement points only distributed at the top and bottom of each layer). Then, a heat transfer model and objective function are established for each layer. Finally, the thermal properties of soil and rock in each layer are predicted sequentially based on the distributed thermal response test data. The distributed thermal response test is a method for in-situ measurement of the thermal properties of soil and rock layers. It measures the fluid temperature that changes with depth and time during the actual operation of the buried pipe, and then uses the measured fluid temperature distribution experimental data to inversely calculate the thermal properties of soil and rock layers.

[0013] According to one aspect of the present invention, a heat transfer model for an arbitrary k-th layer casing-type medium-deep buried pipe is provided:

[0014] Ignoring the influence between layers, the heat transfer process of any k-th layer of the casing-type deep buried pipe is modeled separately, and the boundary conditions of the heat transfer model are constructed based on distributed thermal response test data. The relevant equations are as follows:

[0015]

[0016]

[0017]

[0018]

[0019] T i =T a =T0=T sur +az,(Z k-1 ≤z≤Z k (5)

[0020]

[0021]

[0022] Equations (1) and (2) are the energy conservation equations for the fluid in the inner pipe and the fluid in the outer pipe, respectively; Equation (3) is the steady-state heat transfer equation between the fluid in the outer pipe and the outer wall of the outer pipe; Equation (4) is the transient heat transfer equation between the outer wall of the outer pipe and the rock and soil at infinity; Equation (5) is the initial condition; Equations (6) and (7) are the boundary conditions at the top and bottom of the deep buried pipe in the k-th layer of the casing type, respectively.

[0023] r i —Inner radius of the inner tube;

[0024] r o —Outer radius of the inner tube;

[0025] R i —Inner radius of the outer tube;

[0026] R o —Outer radius of the outer tube;

[0027] T i —Inner tube fluid temperature;

[0028] T a —Outer pipe fluid temperature;

[0029] T0—Initial temperature;

[0030] T eo —Temperature of the outer wall surface of the outer tube;

[0031] T sur —Surface temperature;

[0032] a—Geothermal gradient;

[0033] m—mass flow rate of the fluid;

[0034] C f —The heat capacity of the fluid;

[0035] c f —Specific heat capacity of the fluid;

[0036] C ip —Heat capacity of the inner tube;

[0037] C op —The heat capacity of the outer tube;

[0038] z — Depth;

[0039] t — time;

[0040] j — time node number, i.e., the j-th time node;

[0041] n — the time node number, i.e., the nth time node;

[0042] G—The G function of the composite medium column heat source model is a function of the thermal conductivity and heat capacity of the soil and rock.

[0043] q — the heat flow from the fluid in the outer tube to the heat flow in the outer tube;

[0044] Z k-1 —The depth of the bottom of the (k-1)th layer of soil and rock, which is the depth of the top of the kth layer of soil and rock;

[0045] Z k —The depth of the bottom of the k-th layer of soil and rock;

[0046] R ia —The thermal resistance between the fluid in the inner tube and the fluid in the outer tube;

[0047] R ae —The thermal resistance between the fluid in the outer tube and the outer wall of the outer tube;

[0048] K g —The thermal conductivity of the backfill soil;

[0049] —The experimental value of the fluid top temperature of the k-th layer outer tube at the nth time node in the distributed thermal response test;

[0050] —The experimental value of the top temperature of the fluid in the k-th layer of the inner tube at the nth time node in the distributed thermal response test;

[0051] —The experimental value of the fluid bottom temperature at the k-th layer of the outer pipe at the nth time point in the distributed thermal response test;

[0052] —The experimental value of the bottom temperature of the fluid in the k-th layer of the inner tube at the nth time node in the distributed thermal response test;

[0053] Given the thermal properties of the soil and rock in the deep buried pipe of the k-th layer casing, the distribution of fluid temperature in the inner and outer pipes with time and depth can be calculated by discretely solving the above equations.

[0054] According to another aspect of the present invention, an objective function is provided for any k-th layer casing-type deep buried pipe:

[0055]

[0056] F k —The objective function of the deep buried pipe in the k-th layer casing type is the root mean square error between the experimental data of the fluid temperature in the k-th layer and the calculated value of the heat transfer model in the distributed thermal response test; if F k The smaller the value, the better the experimental and calculated values ​​of the fluid temperature in the k-th layer match.

[0057] N—The total number of time points in the distributed thermal response test;

[0058] s——Time node number corresponding to 10 hours in the distributed thermal response test;

[0059] —The calculated temperature at the top of the fluid in the k-th layer of the outer tube at the nth time point, calculated by the heat transfer model;

[0060] —Calculated value of the temperature at the top of the fluid in the k-th layer of the inner tube at the nth time point calculated by the heat transfer model;

[0061] —Calculated value of the bottom temperature of the fluid in the k-th layer of the outer tube at the nth time point calculated by the heat transfer model;

[0062] —Calculated value of the bottom temperature of the fluid in the inner tube of the kth layer at the nth time point calculated by the heat transfer model.

[0063] According to another aspect of the present invention, a step for predicting the thermal properties of any k-th layer of rock and soil is provided:

[0064] (1) Roughly determine the thermal properties of the k-th layer of soil and rock (thermal conductivity K). s,k and heat capacity C s,k The range of values ​​for ), where K s,k The value range of C is 0-7 W / (m·K). s,k The value range is 0-5 MJ / (m 3 ·K).

[0065] (2) In C s,k Generate a random value within the range of values ​​of C, and use it as C. s,k The initial value of the optimal value.

[0066] (3) In K s,k X K values ​​are generated within the range of values. s,k Random values, respectively X K s,k Random value and C s,k Substituting the optimal value into the heat transfer model of the k-th layer casing-type deep underground pipe, calculating the fluid temperature distribution of the k-th layer casing-type deep underground pipe, and then calculating the objective function F of the k-th layer. k Then the smallest F k The corresponding K s,k A random value is assigned to K s,k Optimal value.

[0067] (4) In C s,k X C values ​​are generated within the range of values. s,k Random values, respectively X Cs,k Random value and K s,k Substituting the optimal value into the heat transfer model of the k-th layer casing-type deep underground pipe, calculating the fluid temperature distribution of the k-th layer casing-type deep underground pipe, and then calculating F. k Then the smallest F k The corresponding C s,k Random value assigned to C s,k Optimal value.

[0068] (5) Determine the C of two adjacent iterations s,k Is the difference between the optimal values ​​less than the set value (0.02 MJ / (m))? 3 ·K): If no, return to step (3); if yes, end and output the predicted value of the thermal properties of the k-th layer of soil and rock (i.e., the final K). s,k Optimal value and C s,k (Optimal value).

[0069] By performing the above steps on each layer of the casing-type deep underground pipe, the geothermal properties of all layers can be predicted.

[0070] 3. Beneficial effects

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0072] (1) The present invention provides a method for predicting the thermal properties of medium-deep buried soil and rock layers using a casing-type method. This method can predict the thermal conductivity and heat capacity of medium-deep soil and rock layers in layers. The method is simple to model and solves the problems of shallow measurement depth, complex modeling, or single measurement parameters in existing distributed thermal response testing technologies.

[0073] (2) The method for predicting the thermal properties of soil and rock stratification in a casing-type medium-deep buried pipe of the present invention only uses distributed thermal response test data to predict the thermal properties of soil and rock stratification, so its cost is much lower than that of laboratory measurement method.

[0074] (3) The method for predicting the thermal properties of soil and rock layers in the casing-type medium-deep buried pipe of the present invention has a small error in the predicted thermal properties of soil and rock layers by matching with the in-situ experimental data. Therefore, the accuracy of the present invention is much higher than that of the table lookup method. Attached Figure Description

[0075] Figure 1 This is a flowchart of the present invention;

[0076] Figure 2 This is a schematic diagram of the k-th layer sleeve-type medium-deep underground pipe structure in the distributed thermal response test of Embodiment 1 of the present invention;

[0077] Figure 3 This is an experimental data diagram of the fluid temperature distribution in the inner tube during the distributed thermal response test of Embodiment 1 of the present invention;

[0078] Figure 4 This is an experimental data diagram of the fluid temperature distribution in the outer tube during the distributed thermal response test of Embodiment 1 of the present invention;

[0079] Figure 5 This is an experimental data diagram of the fluid temperature distribution in the inner tube during the distributed thermal response test of Embodiment 2 of the present invention;

[0080] Figure 6 This is an experimental data diagram of the temperature distribution of the fluid in the outer pipe during the distributed thermal response test in Embodiment 2 of the present invention.

[0081] Explanation of the labels in the diagram:

[0082] 1. Fluid temperature measuring point; 2. Fluid in inner pipe; 3. Inner pipe; 4. Fluid in outer pipe; 5. Outer pipe; 6. Backfill soil; 7. Rock and soil. Detailed Implementation

[0083] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings and embodiments.

[0084] Example 1

[0085] For a certain casing-type medium-deep buried pipe (relevant parameters are shown in Tables 1 and 2), a three-dimensional numerical model was established using the commercial software FLUENT. Figure 2 The diagram shows the structure of the k-th layer of the casing-type deep underground pipe in the established three-dimensional numerical model. Fluid 2 flows from bottom to top in the inner pipe 3, and fluid 4 flows from top to bottom in the outer pipe 5. The outer pipe 5 is surrounded by backfill soil 6 and rock / soil 7. Fluid temperature measuring points 1 are only distributed at the top and bottom of each layer of the casing-type deep underground pipe. The distributed thermal response test under known heat output power conditions was simulated using this three-dimensional numerical model. The results are shown in [Figure number missing]. Figure 3 and Figure 4 In this distributed thermal response test, the temperature measurement points for the fluid in the inner and outer pipes are uniformly distributed along the depth direction (with a measurement point interval of 400m), and there are 6 measurement points in each pipe. The temperature measurement interval is 4 minutes, and the total test time is 80 hours. Then, based on the experimental data of the above-mentioned distributed thermal response test of the casing-type medium-deep buried pipe, the method proposed in this invention is used to predict the stratified thermal properties of soil and rock.

[0086] Table 1. Relevant parameters of casing-type medium-deep buried pipes

[0087]

[0088]

[0089] Table 2. Geophysical properties of casing-type medium-deep buried pipes (true values)

[0090]

[0091] Based on the distribution of measuring points in the distributed thermal response test, the soil and rock are divided into 5 layers. Similarly, the casing-type medium-deep buried pipe is also divided into 5 layers in the depth direction. Heat transfer models and objective functions are established for each of these 5 layers of casing-type medium-deep buried pipe. Since the technical solution provides a detailed explanation of the heat transfer models and objective functions, they will not be elaborated upon here. It is worth noting that the establishment of the heat transfer models and objective functions is based on the experimental data from the distributed thermal response test of the casing-type medium-deep buried pipe; the total number of time nodes in the distributed thermal response test N = 80 hours / 4 minutes = 1200; the time node number corresponding to 10 hours in the distributed thermal response test s = 10 hours / 4 minutes = 150; and the depth Z at the bottom of the (k-1)th layer of soil and rock... k-1 = 400(k-1)m, and the depth Z at the bottom of the k-th layer of soil and rock is... k = 400 km; the time of the nth time node is t n = 4n minutes. The thermal properties of the soil and rock are predicted sequentially for layers 1, 2, 3, 4, and 5. The steps for predicting the thermal properties of any k-th layer are as follows:

[0092] (1) Determine the thermal conductivity of the k-th layer of soil and rock (K) s,k The value of ) ranges from 0 to 7 W / (m·K), and the heat capacity (C) of the k-th layer of soil and rock is determined. s,k The value range is 0-5 MJ / (m 3 ·K).

[0093] (2) In the range of 0-5 MJ / (m 3 Generate a random value within the range of K, and use it as C. s,k The initial value of the optimal value;

[0094] (3) Generate 500 K units within the range of 0-7 W / (m·K). s,k Random values, respectively 500 K s,k Random value and C s,k Substituting the optimal value into the heat transfer model of the k-th layer casing-type deep underground pipe, calculating the fluid temperature distribution of the k-th layer casing-type deep underground pipe, and then calculating F. k Then the smallest F k The corresponding K s,k A random value is assigned to K s,k Optimal value;

[0095] (4) In the range of 0-5 MJ / (m 3 500 C's are generated within the range of K. s,k Random values, respectively 500 C s,k Random value and K s,kSubstituting the optimal value into the heat transfer model of the k-th layer casing-type deep underground pipe, calculating the fluid temperature distribution of the k-th layer casing-type deep underground pipe, and then calculating F. k Then the smallest F k The corresponding C s,k Random value assigned to C s,k Optimal value;

[0096] (5) Determine the C of two adjacent iterations s,k Is the difference between the optimal values ​​less than 0.02 MJ / (m)? 3 •K): If no, return to step (3); if yes, end and output the predicted value of the thermal properties of the k-th layer of soil and rock (i.e., the final K). s,k Optimal value and C s,k (Optimal value).

[0097] The predicted thermal properties of each layer of soil and rock in this invention are compared with the true values, as shown in Table 3. It can be seen that the relative error of the predicted thermal conductivity of the soil and rock is basically within 2%, and the relative error of the predicted heat capacity of the soil and rock is basically within 4%, with only the error in the heat capacity of the first layer being relatively large. This indicates that the accuracy of the predicted thermal properties of each layer of soil and rock in this invention is relatively high.

[0098] Table 3 Comparison of predicted thermal properties of each layer of rock and soil with true values ​​in Example 1 of this invention.

[0099]

[0100] Example 2

[0101] Distributed thermal response tests were simulated using a three-dimensional numerical model under known inlet temperature conditions (inlet temperature 20℃). Results are shown in [Table / Reference]. Figure 5 and Figure 6 Example 1 predicted the stratified thermal properties of soil and rock based on distributed thermal response test data under known heat output power boundary conditions. This example, however, predicts the stratified thermal properties of soil and rock based on distributed thermal response test data under known inlet temperature boundary conditions. The difference lies in the distributed thermal response test data used. The parameters of the casing-type medium-deep buried pipe in this example are basically the same as in Example 1. The difference is that the inlet temperature of the casing-type medium-deep buried pipe is known in the distributed thermal response test of this example, while the heat output power Q is unknown. The steps of this example are exactly the same as those of Example 1, and will not be repeated here.

[0102] The predicted thermal properties of each layer of soil and rock in this invention are compared with the true values, as shown in Table 4. It can be seen that the relative error of the predicted thermal conductivity of the soil and rock is within 3%, and the relative error of the predicted heat capacity of the soil and rock is basically within 1%, with only the error in the heat capacity of the first layer being relatively large. This indicates that the accuracy of the predicted thermal properties of each layer of soil and rock in this invention is also relatively high.

[0103] Table 4 Comparison of predicted thermal properties of each layer of rock and soil with true values ​​in Example 2 of the present invention.

[0104]

[0105]

Claims

1. A method for predicting the thermal properties of soil and rock layers in a casing-type medium-deep buried pipe, characterized in that, Includes the following steps: Layering steps: Based on the distribution of measuring points for distributed thermal response testing, the sheathed medium-deep buried pipe is divided into several layers along the depth direction, with fluid temperature measuring points at the top and bottom of each layer. Modeling steps: Establish a layered heat transfer model for each layer, where: The measured fluid temperatures from the distributed thermal response tests at the top and bottom of the layer were used as boundary conditions. The heat transfer of the fluid in the inner tube and the fluid in the outer tube are described by transient energy conservation equations, respectively. The transient heat transfer between the outer wall of the outer tube and the infinitely distant rock and soil is described by the G function based on the composite medium column heat source model; Objective function construction steps: The root mean square error between the experimental value of the distributed thermal response test fluid temperature and the calculated value of the layered heat transfer model is used as the objective function of this layer; Inversion solution steps: An alternating iterative random search algorithm is used to optimize the thermal conductivity and heat capacity of each layer of soil and rock to minimize the objective function. The alternating iterative random search algorithm includes: Under the condition of a fixed optimal heat capacity, the optimal value of thermal conductivity is randomly searched within a predetermined range. Under the condition of fixing the optimal value of thermal conductivity, randomly search for the optimal value of heat capacity within a predetermined range; Repeat the above two random search steps and iterate alternately until the difference between the optimal heat capacity values ​​of two adjacent iterations is less than the set threshold, and then output the predicted values ​​of thermal conductivity and heat capacity of each layer of soil and rock.

2. The method for predicting the thermal properties of soil and rock layers in a casing-type medium-deep buried pipe according to claim 1, characterized in that, The heat transfer process of the k-th layer of the casing-type medium-deep buried pipe is modeled separately, and the boundary conditions of the heat transfer model are constructed based on distributed thermal response test data. The relevant equations are as follows: （1） （2） （3） （4） （5） （6） （7） Equations (1) and (2) are the energy conservation equations for the fluid in the inner tube and the fluid in the outer tube, respectively; Equation (3) is the steady-state heat transfer equation between the fluid in the outer tube and the outer wall of the outer tube; Equation (4) is the transient heat transfer equation between the outer wall of the outer tube and the rock and soil at infinity; Equation (5) is the initial condition. Equations (6) and (7) are the boundary conditions for the top and bottom of the deep buried pipe in the k-th layer casing type, respectively; G—The G function of the composite medium column heat source model is a function of the thermal conductivity and heat capacity of the soil and rock. Z k-1 —The depth of the bottom of the (k-1)th layer of soil and rock, which is the depth of the top of the kth layer of soil and rock; Z k —The depth of the bottom of the k-th layer of soil and rock; —The experimental value of the fluid top temperature at the k-th layer of the outer pipe at the nth time point in the distributed thermal response test; —The experimental value of the top temperature of the fluid in the k-th layer of the inner tube at the nth time node in the distributed thermal response test; —The experimental value of the fluid bottom temperature at the k-th layer of the outer pipe at the nth time node in the distributed thermal response test; —The experimental value of the bottom temperature of the fluid in the inner tube of the kth layer at the nth time point in the distributed thermal response test; Given the thermal properties of the soil and rock in the deep buried pipe of the k-th layer, the distribution of fluid temperature in the inner and outer pipes of the k-th layer with time and depth can be calculated by discretizing and solving the above equations.

3. The method for predicting the thermal properties of soil and rock layers in a casing-type medium-deep buried pipe according to claim 2, characterized in that, The objective function for any k-th layer casing-type deep buried pipe is as follows: （8） F k —The objective function of the deep buried pipe in the k-th layer casing type is the root mean square error between the experimental data of the fluid temperature in the k-th layer and the calculated value of the heat transfer model in the distributed thermal response test. N—The total number of time points in the distributed thermal response test; s——Time node number corresponding to 10 hours in the distributed thermal response test; —The calculated temperature at the top of the fluid in the k-th layer of the outer tube at the nth time point, calculated by the heat transfer model; —Calculated value of the temperature at the top of the fluid in the k-th layer of the inner tube at the nth time point calculated by the heat transfer model; —Calculated value of the bottom temperature of the fluid in the k-th layer of the outer tube at the nth time point calculated by the heat transfer model; —Calculated value of the bottom temperature of the fluid in the inner tube of the kth layer at the nth time point calculated by the heat transfer model.

4. The method for predicting the thermal properties of soil and rock layers in a casing-type medium-deep buried pipe according to claim 3, characterized in that, The steps for predicting the thermal properties of any k-th layer of soil and rock are as follows: (1) Determine the thermal properties of the k-th layer of soil and rock, specifically the thermal conductivity K. s,k and heat capacity C s,k The range of values ​​for; (2) In the heat capacity C s,k A random value is generated within the range of values ​​of and this value is used as the heat capacity C. s,k The initial value of the optimal value; (3) At thermal conductivity K s,k X K values ​​are generated within the range of values. s,k Random values, respectively X K s,k Random value and C s,k The optimal value is substituted into the heat transfer model of the k-th layer casing-type deep underground pipe to calculate the fluid temperature distribution of the k-th layer casing-type deep underground pipe, and then the objective function F of the k-th layer is calculated. k Then the smallest F k The corresponding K s,k A random value is assigned to K s,k Optimal value; (4) In the heat capacity C s,k X C values ​​are generated within the range of values. s,k Random values, respectively X C s,k Random value and K s,k The optimal value is substituted into the heat transfer model of the k-th layer casing-type deep underground pipe to calculate the fluid temperature distribution of the k-th layer casing-type deep underground pipe, and then the objective function F of the k-th layer is calculated. k Then the smallest F k The corresponding C s,k Random value assigned to C s,k Optimal value; (5) Determine the C of two consecutive iterations s,k Is the difference between the optimal values ​​less than the set value? If not, return to step (3); if yes, end and output the predicted value of the thermal properties of the k-th layer of soil and rock, i.e., the final K. s,k Optimal value and C s,k Optimal value.

5. The method for predicting the thermal properties of soil and rock layers in a casing-type medium-deep buried pipe according to claim 4, characterized in that: The thermal conductivity K of the k-th layer of soil and rock determined in step (1) s,k The value range is 0-7 W / (m·K), and the heat capacity C of the k-th layer of soil and rock is... s,k The value range is 0-5 MJ / (m 3 ·K).

6. The method for predicting the thermal properties of soil and rock layers in a casing-type medium-deep buried pipe according to claim 5, characterized in that: The set value in step (5) is 0.02 MJ / (m 3 ·K).

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