Methods, apparatus, equipment and storage media for predicting the high-temperature thermal conductivity of rocks

Abstract

This invention discloses a method, apparatus, device, and storage medium for predicting the high-temperature thermal conductivity of rocks. The method includes: establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory, and using the high-temperature mineral thermal conductivity model to determine the thermal conductivity of each mineral in a multiphase rock mixture model; determining the number of cracks in the multiphase rock mixture model at high temperatures using fracture mechanics theory; constructing a series-parallel thermal resistance model of the basic units in the multiphase rock mixture model based on the number of cracks; and predicting the high-temperature thermal conductivity of the rock in the multiphase rock mixture model using an effective medium theory model and a series-parallel thermal resistance model. Because this invention determines the thermal conductivity of each mineral in the multiphase rock mixture model based on phonon scattering theory and characterizes the influence of cracks using fracture mechanics theory and a series-parallel thermal resistance model, compared to existing technologies, this invention improves the reliability and accuracy of predicting the thermal conductivity of rocks at high temperatures.

Description

Technical Field

[0001] This invention relates to the field of geological resource extraction technology, and in particular to a method, apparatus, equipment and storage medium for predicting the high-temperature thermal conductivity of rocks. Background Technology

[0002] Heat transfer is a crucial process in various underground engineering projects. Many fields, including geothermal energy development, underground thermal energy storage, oil and gas extraction, nuclear waste disposal, and thermal management in civil engineering, require consideration of this process. Given these widespread applications, studying the thermal conductivity of rocks remains essential. However, current theories for predicting rock thermal conductivity primarily target ambient temperature environments, and various multiphase mixing models (such as arithmetic / geometric mean models and effective medium theory) and empirical formulas have been established. These models achieve predictions by analyzing the relationship between mineral composition, pore structure, and fluid saturation, but none of them consider the enhanced phonon scattering and microcrack formation effects caused by temperature increases, thus failing to extend to high-temperature scenarios.

[0003] Existing research on the thermal conductivity of high-temperature rocks mainly relies on empirical models (such as inverse proportional relationships or power-law relationships). These models are obtained by fitting experimental data, but they suffer from two major drawbacks: 1. The fitting parameters cannot be correlated with mineral properties or rock mechanical characteristics, causing the model to fail when applied across different stratigraphic layers. 2. The coupling effect between the thermal conductivity attenuation of the solid matrix at high temperatures (mineral phonon scattering) and the increase in fracture thermal resistance is not quantified. Consequently, the reliability and accuracy of predicting the high-temperature thermal conductivity of rocks are relatively low.

[0004] Therefore, there is an urgent need for a method to predict the thermal conductivity of rocks at high temperatures, which can improve the reliability and accuracy of predicting the thermal conductivity of rocks at high temperatures. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, and storage medium for predicting the thermal conductivity of rocks at high temperatures, aiming to solve the technical problem of low accuracy and reliability in the prediction of the thermal conductivity of rocks at high temperatures in existing technologies.

[0006] To achieve the above objectives, the present invention provides a method for predicting the high-temperature thermal conductivity of rocks, the method comprising the following steps:

[0007] A model for the thermal conductivity of high-temperature minerals was established based on phonon scattering theory, and the thermal conductivity of each mineral in the multiphase rock mixture model was determined using the model.

[0008] The number of cracks in the multiphase rock mixture model at high temperature was determined using fracture mechanics theory.

[0009] Based on the number of cracks, a series and parallel thermal resistance model of the basic unit in the multiphase rock mixing model is constructed;

[0010] The high-temperature thermal conductivity of the multiphase rock mixture model is predicted using the effective medium theory model and the series-parallel thermal resistance model.

[0011] Optionally, before the step of establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and using the high-temperature mineral thermal conductivity model to determine the thermal conductivity of each mineral in the multiphase rock mixing model, the method further includes:

[0012] Obtain rock samples and construct a multiphase rock mixture model based on the rock samples, which consists of uniform cubic basic units, the boundaries of which include randomly distributed pores and cracks.

[0013] The rock sample is analyzed, and the data information of the rock sample is determined based on the analysis results.

[0014] Optionally, the step of establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and using the high-temperature mineral thermal conductivity model to determine the thermal conductivity of each mineral in the multiphase rock mixture model includes:

[0015] A model for the thermal conductivity of high-temperature minerals was established based on phonon scattering theory and data from the rock samples.

[0016] When the target temperature exceeds the preset temperature threshold, a thermal radiation correction term is added to the high-temperature mineral thermal conductivity model to obtain the corrected high-temperature mineral thermal conductivity model.

[0017] The thermal conductivity of each mineral in the multiphase rock mixture model is determined using the modified high-temperature mineral thermal conductivity model.

[0018] Optionally, the step of determining the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory includes:

[0019] The strain energy density of the rock sample is determined based on the thermal expansion coefficient of each mineral in the rock sample.

[0020] The strain energy required for the formation of a single crack in the rock sample is determined by the critical stress intensity coefficient.

[0021] The number of cracks in the multiphase rock mixing model is determined based on the ratio of strain energy density to strain energy.

[0022] Optionally, the step of constructing the series-parallel thermal resistance model of the basic unit in the multiphase rock mixing model based on the number of cracks includes:

[0023] Based on the random distribution of cracks, the probability distribution of boundary cracks in the basic units of the multiphase rock mixing model is determined.

[0024] The crack normal direction is determined based on the crack direction in the basic unit, and various basic units are determined according to the relative relationship between the crack normal direction and the heat flow direction.

[0025] Based on the various basic units, a variety of corresponding series and parallel thermal resistance models are constructed.

[0026] Optionally, the step of predicting the high-temperature thermal conductivity of the multiphase rock mixture model using the effective medium theory model and the series-parallel thermal resistance model includes:

[0027] Based on the probability distribution of the boundary cracks, the probabilities of various series and parallel thermal resistance models are derived.

[0028] Using an effective medium theory model, the high-temperature thermal conductivity of the multiphase rock mixture model is predicted based on the probabilities of various series and parallel thermal resistance models and the thermal conductivity of the basic unit.

[0029] Optionally, before the step of predicting the high-temperature thermal conductivity of the multiphase rock mixture model based on the probabilities of various series and parallel thermal resistance models and the thermal conductivity of the basic unit using an effective medium theory model, the method further includes:

[0030] Based on the thermal conductivity and corresponding volume fraction of each mineral, the thermal conductivity of the solid matrix in the multiphase rock mixing model is determined by the effective medium theory model.

[0031] The thermal resistance of the crack is determined based on the crack thermal conductivity and crack geometric parameters.

[0032] The thermal resistance of the basic unit is determined based on the thermal resistance of the crack, the thermal conductivity of the solid matrix, and the series-parallel thermal resistance model corresponding to the basic unit.

[0033] The thermal conductivity of the basic unit is determined based on the thermal resistance of the basic unit and the geometric parameters of the crack.

[0034] Furthermore, to achieve the above objectives, the present invention also proposes a device for predicting the high-temperature thermal conductivity of rocks, the device comprising:

[0035] The mineral thermal conductivity determination module is used to establish a high-temperature mineral thermal conductivity model based on phonon scattering theory, and to determine the thermal conductivity of each mineral in the multiphase rock mixture model using the high-temperature mineral thermal conductivity model.

[0036] The crack number determination module is used to determine the number of cracks in the multiphase rock mixture model at high temperatures using fracture mechanics theory.

[0037] The thermal resistance model construction module is used to construct the series and parallel thermal resistance models of the basic units in the multiphase rock mixture model based on the number of cracks.

[0038] The high-temperature thermal conductivity determination module is used to predict the high-temperature thermal conductivity of the rock in the multiphase rock mixture model using the effective medium theory model and the series-parallel thermal resistance model.

[0039] Furthermore, to achieve the above objectives, the present invention also proposes a rock high-temperature thermal conductivity prediction device, the device comprising: a memory, a processor, and a rock high-temperature thermal conductivity prediction program stored in the memory and executable on the processor, the rock high-temperature thermal conductivity prediction program being configured to implement the steps of the rock high-temperature thermal conductivity prediction method as described above.

[0040] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a rock high-temperature thermal conductivity prediction program, wherein when the rock high-temperature thermal conductivity prediction program is executed by a processor, the rock high-temperature thermal conductivity prediction program implements the steps of the rock high-temperature thermal conductivity prediction method described above.

[0041] This invention discloses a high-temperature mineral thermal conductivity model based on phonon scattering theory, and uses this model to determine the thermal conductivity of each mineral in a multiphase rock mixture model. It also utilizes fracture mechanics theory to determine the number of cracks in the multiphase rock mixture model at high temperatures; constructs a series-parallel thermal resistance model for the basic units in the multiphase rock mixture model based on the number of cracks; and predicts the high-temperature thermal conductivity of the rock in the multiphase rock mixture model using an effective medium theory model and the series-parallel thermal resistance model. Because this invention determines the thermal conductivity of each mineral in the multiphase rock mixture model based on phonon scattering theory and characterizes the influence of cracks using fracture mechanics theory and the series-parallel thermal resistance model, compared to existing technologies, this invention improves the reliability and accuracy of predicting the thermal conductivity of rocks at high temperatures. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the first embodiment of the method for predicting the high-temperature thermal conductivity of rocks according to the present invention.

[0043] Figure 2 This is a schematic diagram of a multiphase rock mixing model in the rock high-temperature thermal conductivity prediction method of the present invention;

[0044] Figure 3 This is a schematic diagram of the mineral composition of six different sedimentary rocks involved in this invention;

[0045] Figure 4 This is a schematic diagram showing the characteristic parameters of six different sedimentary rocks involved in this invention;

[0046] Figure 5 This is a schematic diagram of six series and parallel thermal resistance models in the rock high-temperature thermal conductivity prediction method of this invention;

[0047] Figure 6 This is a schematic diagram illustrating the thermal resistance and probability calculation of six different basic units in the multiphase rock mixing model of this invention.

[0048] Figure 7 This is a schematic diagram comparing the predicted results of the high-temperature thermal conductivity of rocks obtained by the rock high-temperature thermal conductivity prediction method of the present invention with the experimental results.

[0049] Figure 8 A schematic diagram illustrating the effect of quartz content on thermal conductivity in rock characteristics;

[0050] Figure 9 This is a schematic diagram showing the influence of Young's modulus and Poisson's ratio on thermal conductivity in rock mechanics parameters.

[0051] Figure 10 This is a flowchart illustrating the second embodiment of the method for predicting the high-temperature thermal conductivity of rocks according to the present invention.

[0052] Figure 11 This is a structural block diagram of the first embodiment of the rock high-temperature thermal conductivity prediction device of the present invention;

[0053] Figure 12 This is a schematic diagram of the structure of a rock high-temperature thermal conductivity prediction device for the hardware operating environment involved in the embodiments of the present invention.

[0054] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0055] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0056] This invention provides a method for predicting the high-temperature thermal conductivity of rocks, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for predicting the high-temperature thermal conductivity of rocks according to the present invention.

[0057] In this embodiment, the method for predicting the high-temperature thermal conductivity of rocks includes steps S10 to S40:

[0058] Step S10: Establish a high-temperature mineral thermal conductivity model based on phonon scattering theory, and use the high-temperature mineral thermal conductivity model to determine the thermal conductivity of each mineral in the multiphase rock mixture model.

[0059] It should be noted that the executing entity in this embodiment can be a computer server device with data processing, network communication, and program execution functions applied in geological resource mining scenarios, such as a server, tablet computer, or personal computer, or an electronic device capable of performing the above functions (such as a rock high-temperature thermal conductivity prediction device). The following uses a system including a rock high-temperature thermal conductivity prediction device (hereinafter referred to as the system) as an example to illustrate this embodiment and the following embodiments.

[0060] It should be understood that rocks are aggregates of mineral particles. When constructing thermal conductivity analysis models, it is usually assumed that rock particles are uniform in size and have various geometric shapes, such as hexahedrons, cylinders, rectangular prisms, spheres, hemispheres, etc. The specific shape of these basic units is determined by their physical meaning. This embodiment assumes that the rock is composed of uniform cubic basic units representing mineral particles, with clear boundaries between them, and exhibits homogeneity and isotropy on a macroscopic scale.

[0061] Rocks are generally considered to consist of three phases: solid, liquid, and gas. The solid phase is composed of minerals and organic matter, and its thermal conductivity depends on petrological characteristics such as mineral composition, grain size, and contact relationships. The liquid phase refers to formation water present in the rock. Since the liquid phase transforms into the gas phase at certain temperatures, formation water is not considered in this embodiment, as the focus is on the thermal conduction of the rock at high temperatures. The gas phase typically represents air within the pores and fractures of the rock. Mineral crystal defects and specific diagenetic processes may lead to the formation of pores or fractures within mineral grains, but in small numbers. Therefore, this embodiment assumes that the gas phase is primarily located at the boundaries between mineral grains, i.e., pores and fractures exist only at the boundaries of basic units. For example, refer to... Figure 2 Cracks at the boundaries of basic units increase the thermal resistance between grains, thereby reducing the overall thermal conductivity of the rock. Since the rock is assumed to be homogeneous and isotropic on a macroscopic scale, the cracks are randomly distributed in the three-dimensional XYZ directions, forming parallel and series heat conduction paths with the grains.

[0062] In a specific implementation, before step S10, the method further includes: acquiring a rock sample and constructing a multiphase rock hybrid model composed of uniform cubic basic units based on the rock sample, wherein the boundaries of the basic units include randomly distributed pores and cracks; analyzing the rock sample and determining the data information of the rock sample based on the analysis results.

[0063] It should be noted that the data information for the aforementioned rock samples may include reference parameters, experimental parameters, and iterative parameters. Experimental parameters can be obtained through laboratory experiments and include the mineral volume fraction, grain size, Young's modulus, Poisson's ratio, and high-temperature thermal conductivity of the rock samples. Reference parameters mainly include mineral properties, such as the mineral's coefficient of thermal expansion, bulk modulus, density, specific heat capacity, and thermal conductivity at 25°C, which can be reliably obtained from existing studies without the need for additional testing. Iterative parameters include fracture width. and the difference in thermal expansion coefficient Both of these parameters are difficult to measure directly experimentally. Therefore, these parameters can be determined iteratively using thermal conductivity measurements at 25°C and 200°C.

[0064] In this embodiment, six sedimentary rocks were selected for experiments. The mineral composition of the rock samples was determined by X-ray diffraction (XRD) analysis, such as... Figure 3 As shown. Analysis was performed using a Rigaku TTR III X-ray diffractometer, conforming to ISO 23071:2021. After obtaining the mineral composition, the volume composition of the rocks was calculated using mineral density. The grain size and texture of the rocks were analyzed using optical microscopy and scanning electron microscopy. The coarse sandstone has a relatively large grain size of 546.4 micrometers. Fine sandstone 1 and fine sandstone 2 have similar grain sizes, mainly composed of quartz grains, with a small amount of feldspar grains. Mudstone 1 and mudstone 2 have finer grains, with grain sizes of 2.8 micrometers and 2.3 micrometers, respectively. Mudstone 1 is mainly composed of quartz grains, while mudstone 2 has a lower quartz content and a higher proportion of clay minerals. The limestone was classified as microcrystalline limestone, exhibiting a fine grain size of 1.9 micrometers. Grain size was considered as the basic unit grain size. The Young's modulus and Poisson's ratio of the samples were determined by uniaxial compression tests according to the ISRM recommended method. Since some model parameters require iteration, the thermal conductivity of the rock was tested at 200°C and 25°C to derive iterative parameters. Thermal conductivity was measured using a NETZSCH LFA 467 laser flash analyzer with a heating rate set to 5°C / min. Samples were tested in an argon atmosphere to simulate the oxygen-free conditions of underground engineering. Before testing, both sides of each sample were coated with carbon to improve measurement accuracy. Tests were performed according to ASTM E1461 standards, with each sample measured three times and the average value recorded. Figure 4 As shown.

[0065] It should be understood that, with the exception of a few minerals such as opal and agate, most minerals are crystals with periodically arranged lattices. Modern materials science uses a particle-like theoretical framework to describe this property, referring to lattice vibration quanta as phonons. In minerals, thermal energy is primarily transferred through phonons. Since increasing temperature enhances phonon scattering and lowers vibration frequencies, thermal conductivity decreases with increasing temperature.

[0066] It should be noted that phonons are quantized quasiparticles of lattice vibrations. Their scattering refers to the interaction of phonons with other particles (such as electrons and defects) or quasiparticles (such as other phonons) in the crystal, resulting in changes in energy, momentum, or propagation direction. This scattering has a significant impact on the thermal conduction of solids.

[0067] It should be explained that the thermal conductivity model of high-temperature minerals is established based on phonon scattering theory. The thermal conductivity model of high-temperature minerals is as follows:

[0068] ;

[0069] in, It's temperature; Representing the The thermal conductivity of a mineral is related to temperature. The function; It is the first The fitting parameters for the minerals are 0.75 for quartz, 0.51 for carbonate minerals, and 0.33 for silicate minerals. It is the coefficient of thermal expansion of the i-th mineral, and is also a function of temperature; It is the integral variable of temperature and has the same physical meaning as temperature. This represents the Grüneisen parameter, which quantitatively links thermal properties to mechanical parameters.

[0070] ;

[0071] in, It is the bulk modulus of the i-th mineral; It is density; It is heat capacity.

[0072] Step S20: Determine the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory.

[0073] It should be understood that fracture mechanics theory studies the influence of the presence and propagation of cracks in materials on the structural strength, and its core is to understand the energy or stress conditions for crack propagation.

[0074] It should be noted that due to the differences in thermal expansion between mineral grains within a rock, these varying degrees of expansion generate tensile stress at high temperatures. When this tensile stress exceeds the tensile strength of the grain boundaries, intergranular cracks will form and propagate. Before fracture formation, strain energy accumulates within the rock. As the crack propagates, most of the strain energy is converted into surface energy, driving crack growth, while a small portion of the strain energy is dissipated as heat, which is negligible. The strain energy density of the rock... The quantification formula is:

[0075] ;

[0076] in, It is Young's modulus, measured in Pa. It is Poisson's ratio; It is the difference in the coefficients of thermal expansion between minerals; That is the initial temperature.

[0077] Analysis of side length is In a multiphase rock mixture model, a single basic unit, when its boundary cracks, accumulates strain energy and converts it into fracture surface energy, leading to fracture formation. The fracture width is expressed as... The area of ​​the fractured surface is (like Figure 2 (b) Fracture width The thermal resistance of the unit boundary is mainly affected by introducing local interruptions in the heat transfer path. The strain energy required to form a single fracture is...

[0078] ;

[0079] in, It is the critical stress intensity coefficient, and its unit is 1000 kJ / m². The formula for calculating the critical stress intensity coefficient is as follows:

[0080] .

[0081] The number of cracks generated in a rock sample at high temperatures can be determined based on strain energy density and the strain energy required for a single fracture. N(T), That is, to determine the number of cracks in the multiphase rock mixture model.

[0082] .

[0083] In a specific implementation, the strain energy density of the rock sample can be determined based on the thermal expansion coefficient of each mineral in the rock sample; the strain energy required for the formation of a single crack in the rock sample can be determined by the critical stress intensity coefficient; and the number of cracks in the multiphase rock hybrid model can be determined according to the ratio of the strain energy density to the strain energy.

[0084] Step S30: Construct a series-parallel thermal resistance model of the basic unit in the multiphase rock mixing model based on the number of cracks.

[0085] It should be understood that the resulting cracks increase the thermal resistance of the rock. This is due to the crack width. Much smaller than the basic unit side length of rock Therefore, the basic unit size remains unchanged after the crack forms, that is... In a unit volume of rock, the number of boundaries in the x, y, and z directions are respectively... Assuming the cracks are randomly distributed, the number of cracks in each direction is... Therefore, the probability that the boundary contains cracks in each direction, i.e., the probability distribution of boundary cracks in the basic unit of the multiphase rock mixture model, is as follows:

[0086] .

[0087] Therefore, each basic unit in a multiphase rock mixing model may contain fractures in three directions. When the fracture normal is parallel to the heat flow direction, the fracture and solid matrix form a series structure. When the fracture normal is perpendicular to the heat flow direction, the fracture and solid matrix form a parallel structure. Series and parallel structures may coexist, resulting in six possible cases, for example, referencing... Figure 5 , Figure 5 The diagram illustrates six possible configurations of the basic unit's fracture and solid matrix: I, no fracture; II, fracture and solid matrix in series; III, fracture and solid matrix in parallel; IV, two fractures and solid matrix in parallel; V, a combination of series and parallel (one in series, one in parallel); VI, a combination of series and parallel (one in series, two in parallel).

[0088] In a practical implementation, the probability distribution of boundary cracks in the basic units of the multiphase rock mixing model can be determined based on the random distribution of cracks; the crack normal direction can be determined based on the crack direction in the basic unit, and various basic units can be determined based on the relative relationship between the crack normal direction and the heat flow direction; and various corresponding series and parallel thermal resistance models can be constructed based on the various basic units.

[0089] Step S40: Predict the high-temperature thermal conductivity of the multiphase rock mixture model using the effective medium theory model and the series-parallel thermal resistance model.

[0090] It should be explained that the Effective Medium Theory (EMT) is a theoretical model used to predict the macroscopic physical properties (such as electrical conductivity, dielectric constant, and thermal conductivity) of multiphase composite materials or non-homogeneous media. Its core idea is to equate complex multi-component media to hypothetical single-phase media with homogeneous properties, thereby simplifying analysis and calculation.

[0091] It should be understood that the high-temperature thermal conductivity of rock can represent the thermal conductivity of rock at high temperatures. In this embodiment and the following embodiments, a temperature greater than 600°C is used as an example of high temperature for illustration.

[0092] It should be noted that, due to the anisotropy of fracture compared to a solid matrix, the formulas for calculating the fracture thermal resistance differ for different basic unit cells. Given the known probabilities of fracture occurring in different directions, it is also possible to derive formulas for different types of basic unit cells. ( The probability of the basic unit type, i.e., the probability of various series and parallel thermal resistance models, such as Figure 6 As shown. By Figure 6 It is known that the thermal conductivity of the crack at high temperatures is... The thermal resistance of a crack is derived from experimental values ​​of the thermal conductivity of air at high temperatures. and crack geometry parameters The calculated thermal resistance of the basic unit. It is calculated based on the series and parallel configurations of the crack thermal resistance and the solid matrix thermal resistance. The thermal conductivity of the basic unit... It is based on the thermal resistance and geometric parameters of the basic unit. The conclusion was reached. Figure 6 The symbol ~ represents the relationship between the thermal resistance of the basic unit and its constituent components (i.e., the thermal resistance of the solid matrix and the thermal resistance at break), rather than a strict equation. It is the probability that the boundary contains a break in each direction of the equation. It is the thermal conductivity of the solid matrix. For example, Figure 5 The probability of basic unit I in the array is: ; The thermal resistance of basic unit I is: The thermal conductivity of basic unit I is Correspondingly, the calculation methods for the probability, thermal resistance, and thermal conductivity of other types of basic units can be found by referring to [the relevant documentation / reference]. Figure 6 .

[0093] In a specific implementation, the probabilities of various series and parallel thermal resistance models can be derived based on the probability distribution of the boundary cracks; and the high-temperature thermal conductivity of the multiphase rock mixture model can be predicted based on the probabilities of various series and parallel thermal resistance models and the thermal conductivity of the basic unit using an effective medium theory model.

[0094] To determine the thermal conductivity of the basic unit in a multiphase rock mixture model, prior to the step of predicting the high-temperature thermal conductivity of the rock in the multiphase rock mixture model using an effective medium theory model based on the probabilities of various series and parallel thermal resistance models and the thermal conductivity of the basic unit, the method further includes: determining the thermal conductivity of the solid matrix in the multiphase rock mixture model using an effective medium theory model based on the thermal conductivity and corresponding volume fraction of each mineral; determining the fracture thermal resistance based on the fracture thermal conductivity and fracture geometric parameters; determining the basic unit thermal resistance based on the fracture thermal resistance, the thermal conductivity of the solid matrix, and the series and parallel thermal resistance models corresponding to the basic unit; and determining the thermal conductivity of the basic unit based on the basic unit thermal resistance and the fracture geometric parameters.

[0095] It should be noted that the Effective Medium Theory (EMT) model is an effective tool for calculating the thermal conductivity of multi-component solid mixtures with completely random distributions. Assuming that the rock is macroscopically homogeneous and isotropic, the EMT model can be used to estimate the thermal conductivity of the solid matrix. The formula for calculating the thermal conductivity of the solid matrix is ​​shown below:

[0096] ;

[0097] in, It refers to the number of mineral species in a rock sample; It is the first Volume fraction of a mineral; It is the thermal conductivity of the solid matrix, and it is the temperature. The function.

[0098] By applying the thermal conductivity of different random basic units in the EMT model, the high-temperature thermal conductivity of a multiphase rock mixture model can be calculated. The calculation formula is:

[0099] .

[0100] It should be understood that, in order to verify the predicted high-temperature thermal conductivity of rock, the predicted high-temperature thermal conductivity of rock can be compared with the experimentally obtained high-temperature thermal conductivity of rock. The effectiveness and applicability of the rock high-temperature thermal conductivity prediction method in this embodiment can be determined based on the comparison results. For example, refer to... Figure 7In the figure, the horizontal axis represents temperature, and the vertical axis represents the high-temperature thermal conductivity of the rock; red represents experimental results, and blue represents the prediction results of this embodiment. a represents coarse sandstone; b represents fine sandstone 1; c represents fine sandstone 2; d represents mudstone 1; e represents mudstone 2; and f represents limestone. Mean absolute error (MAE) and mean relative error (MRE) are also shown. The comparison between the prediction results and experimental results in the figure shows that this embodiment has strong prediction performance for coarse sandstone, fine sandstone 1, fine sandstone 2, and mudstone 1, with mean relative errors (MRE) of 1.041%, 0.446%, 2.176%, and 1.826%, respectively. However, the prediction accuracy for mudstone 2 and limestone is relatively low, with mean relative errors of 17.418% and 8.516%, respectively.

[0101] The differences observed in mudstone 2 are primarily attributed to the high-temperature reaction of clay minerals, leading to the formation of new mineral phases. This reaction process generates a liquid phase, reducing rock porosity and thus thermal resistance. Since the theoretical model does not account for these reactions, the predicted values ​​are lower than the experimental results. In limestone, the difference between the predicted and experimental results in this embodiment is due to structural differences between limestone and other sedimentary rocks. This embodiment assumes that fractures in limestone propagate along microcrystalline boundaries at high temperatures. However, due to the complex diagenetic processes of limestone, microcrystalline material often forms aggregates such as oolitic grains and nodules. Fractures in limestone often appear at aggregate boundaries or within aggregates, leading to inaccurate estimates of grain size when calculating thermal conductivity. Currently, there is no accurate method to predict the location of fractures in limestone at high temperatures.

[0102] Therefore, this embodiment is applicable to sandstone, mudstone with low clay content, and other rocks with clearly defined fracture locations and slight high-temperature reactions. However, the prediction accuracy is lower for limestone with difficult-to-determine fracture locations and clay-rich mudstone with severe high-temperature reactions. The theoretical prediction model corresponding to this embodiment can be used to determine the thermal conductivity of rocks in numerical simulations of high-temperature geological engineering projects such as ultra-high-temperature geothermal systems, underground coal gasification, and nuclear waste treatment.

[0103] To investigate the influence of rock characteristics on the high-temperature thermal conductivity of rocks, a sensitivity analysis was conducted using parameters of coarse sandstone, based on this embodiment. The mineral composition of the coarse sandstone was adjusted, and it can include quartz, potassium feldspar, plagioclase, calcite, dolomite, illite, and kaolinite. The contents of potassium feldspar and plagioclase remained unchanged. Initially, the quartz content was 56.2%, which was later adjusted to 30%, 40%, 50%, and 60%. The illite content was also adjusted accordingly to ensure that the total mineral composition remained at 100%. Figure 8 In this context, 'a' represents the result of a sensitivity analysis of quartz content. Figure 8 In this context, 'b' represents the thermal conductivity of the mineral at high temperatures.

[0104] The results show that quartz content has a significant impact on the thermal conductivity of rocks at both room temperature and high temperature. Higher quartz content results in higher thermal conductivity of the rock at both room temperature and high temperatures (e.g., ...). Figure 8 (As shown in a). Quartz is a typical mineral with high thermal conductivity. Compared with other minerals, quartz has a relatively high thermal conductivity at room temperature. As temperature increases, the thermal conductivity of most minerals decreases, while the thermal conductivity of quartz remains higher than that of other minerals (such as...). Figure 8 (As shown in b) The quartz content affects the thermal conductivity of the solid matrix, thus affecting the overall thermal conductivity of the rock at high temperatures. This relationship indicates that rocks with a higher proportion of high thermal conductivity minerals will exhibit higher thermal conductivity at high temperatures.

[0105] The mineral composition and content of coarse sandstone, fine sandstone, and mudstone are similar. However, compared to coarse and fine sandstone, mudstone consistently exhibits a lower experimental thermal conductivity at high temperatures (e.g., ...). Figure 7 (As shown in the image). This observation highlights the strong influence of grain size on high-temperature thermal conductivity; larger grains result in higher thermal conductivity.

[0106] To investigate the influence of rock mechanical parameters on thermal conductivity, a sensitivity analysis was conducted on Young's modulus and Poisson's ratio using coarse sandstone as a reference material. While keeping other parameters constant, Young's modulus was set to 40 GPa, 50 GPa, 60 GPa, and 70 GPa, and Poisson's ratio to 0.1, 0.2, 0.3, and 0.4, respectively. For example, the reference... Figure 9 , Figure 9 In this context, 'a' represents Young's modulus and 'b' represents Poisson's ratio. The results of sensitivity analysis on Young's modulus and Poisson's ratio are as follows: Figure 9 As shown, rock mechanical parameters do not affect thermal conductivity at room temperature, but they have a significant impact at high temperatures. This is because Young's modulus and Poisson's ratio determine the density of strain energy stored in the rock, thus controlling the fracture formation process at high temperatures. The higher the Young's modulus and Poisson's ratio, the lower the thermal conductivity at high temperatures. The mechanism is that Young's modulus represents the stiffness of a material. A higher Young's modulus results in a greater strain energy density in the rock, promoting the formation of more cracks, increasing thermal resistance, and ultimately reducing thermal conductivity. Similarly, a higher Poisson's ratio increases the strain energy density at high temperatures, further reducing thermal conductivity. The effect of Young's modulus on high-temperature thermal conductivity is stronger than that of Poisson's ratio.

[0107] This embodiment discloses a high-temperature mineral thermal conductivity model based on phonon scattering theory, and uses this model to determine the thermal conductivity of each mineral in a multiphase rock mixture model. It also utilizes fracture mechanics theory to determine the number of cracks in the multiphase rock mixture model at high temperatures; constructs a series-parallel thermal resistance model for the basic units in the multiphase rock mixture model based on the number of cracks; and predicts the high-temperature thermal conductivity of the rock in the multiphase rock mixture model using an effective medium theory model and the series-parallel thermal resistance model. Because this embodiment determines the thermal conductivity of each mineral in the multiphase rock mixture model based on phonon scattering theory and uses fracture mechanics theory and the series-parallel thermal resistance model to characterize the influence of cracks, compared to existing technologies, this embodiment improves the reliability and accuracy of predicting the thermal conductivity of rocks at high temperatures.

[0108] refer to Figure 10 , Figure 10 This is a flowchart illustrating the second embodiment of the method for predicting the high-temperature thermal conductivity of rocks according to the present invention.

[0109] Based on the first embodiment described above, in this embodiment, step S10 includes steps S101 to S103:

[0110] Step S101: Establish a thermal conductivity model for high-temperature minerals based on phonon scattering theory and data from the rock sample.

[0111] Step S102: When the target temperature exceeds the preset temperature threshold, add a thermal radiation correction term to the high-temperature mineral thermal conductivity model to obtain the corrected high-temperature mineral thermal conductivity model.

[0112] Step S103: Determine the thermal conductivity of each mineral in the multiphase rock mixture model using the modified high-temperature mineral thermal conductivity model.

[0113] It should be noted that the above-mentioned preset temperature threshold can be set by the user. This embodiment does not limit this setting. The following description takes a preset temperature threshold of 600°C as an example.

[0114] When the temperature exceeds 600°C, the effect of thermal radiation on the thermal conductivity of solids becomes non-negligible. Therefore, a thermal radiation correction term must be added to the thermal conductivity model of high-temperature minerals, as shown below:

[0115] .

[0116] Therefore, the modified model for the thermal conductivity of high-temperature minerals is:

[0117] .

[0118] This embodiment discloses a high-temperature mineral thermal conductivity model established based on phonon scattering theory and data information from the rock sample. When the target temperature exceeds a preset temperature threshold, a thermal radiation correction term is added to the high-temperature mineral thermal conductivity model to obtain a corrected high-temperature mineral thermal conductivity model. The corrected high-temperature mineral thermal conductivity model is then used to determine the thermal conductivity of each mineral in the multiphase rock mixture model. Compared to existing technologies that add a thermal radiation correction term to the high-temperature mineral thermal conductivity model when the target temperature exceeds a preset temperature threshold, this method improves the accuracy of calculating the thermal conductivity of each mineral in the multiphase rock mixture model.

[0119] Furthermore, this embodiment of the invention also proposes a storage medium storing a rock high-temperature thermal conductivity prediction program, which, when executed by a processor, implements the steps of the rock high-temperature thermal conductivity prediction method described above.

[0120] Reference Figure 11 , Figure 11 This is a structural block diagram of the first embodiment of the rock high-temperature thermal conductivity prediction device of the present invention.

[0121] like Figure 11 As shown, the rock high-temperature thermal conductivity prediction device proposed in this embodiment of the invention includes: a mineral thermal conductivity determination module 801, a crack number determination module 802, a thermal resistance model construction module 803, and a high-temperature thermal conductivity determination module 804.

[0122] The mineral thermal conductivity determination module 801 is used to establish a high-temperature mineral thermal conductivity model based on phonon scattering theory, and to determine the thermal conductivity of each mineral in the multiphase rock mixture model using the high-temperature mineral thermal conductivity model.

[0123] The crack number determination module 802 is used to determine the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory.

[0124] The thermal resistance model construction module 803 is used to construct the series and parallel thermal resistance models of the basic units in the multiphase rock mixture model based on the number of cracks.

[0125] The high-temperature thermal conductivity determination module 804 is used to predict the high-temperature thermal conductivity of the rock in the multiphase rock mixture model through the effective medium theory model and the series-parallel thermal resistance model.

[0126] The mineral thermal conductivity determination module 801 is also used to acquire rock samples and construct a multiphase rock mixture model composed of uniform cubic basic units based on the rock samples, wherein the boundaries of the basic units include randomly distributed pores and cracks; analyze the rock samples and determine the data information of the rock samples based on the analysis results.

[0127] The crack number determination module 802 is also used to determine the strain energy density of the rock sample based on the thermal expansion coefficient of each mineral in the rock sample; determine the strain energy required for the formation of a single crack in the rock sample through the critical stress intensity coefficient; and determine the number of cracks in the multiphase rock mixture model according to the ratio of the strain energy density to the strain energy.

[0128] The thermal resistance model construction module 803 is also used to determine the boundary crack probability distribution of the basic unit in the multiphase rock mixture model based on the random distribution characteristics of cracks; determine the crack normal direction based on the crack direction in the basic unit, and determine a variety of basic units based on the relative relationship between the crack normal direction and the heat flow direction; and construct a variety of corresponding series and parallel thermal resistance models based on the various basic units.

[0129] The high-temperature thermal conductivity determination module 804 is also used to derive the probabilities of various series and parallel thermal resistance models based on the probability distribution of the boundary cracks; and to predict the high-temperature thermal conductivity of the multiphase rock mixture model based on the probabilities of various series and parallel thermal resistance models and the thermal conductivity of the basic unit through the effective medium theory model.

[0130] The high-temperature thermal conductivity determination module 804 is further configured to determine the thermal conductivity of the solid matrix in the multiphase rock mixture model based on the thermal conductivity and corresponding volume fraction of each mineral using an effective medium theory model; determine the fracture thermal resistance based on the fracture thermal conductivity and fracture geometric parameters; determine the basic unit thermal resistance based on the fracture thermal resistance, the thermal conductivity of the solid matrix, and the series-parallel thermal resistance model corresponding to the basic unit; and determine the thermal conductivity of the basic unit based on the basic unit thermal resistance and the fracture geometric parameters.

[0131] This embodiment of the device discloses a high-temperature mineral thermal conductivity model established based on phonon scattering theory, and uses this model to determine the thermal conductivity of each mineral in a multiphase rock mixture model. It also utilizes fracture mechanics theory to determine the number of cracks in the multiphase rock mixture model at high temperatures; constructs a series-parallel thermal resistance model for the basic units in the multiphase rock mixture model based on the number of cracks; and predicts the high-temperature thermal conductivity of the rock in the multiphase rock mixture model using an effective medium theory model and the series-parallel thermal resistance model. Because this embodiment of the device determines the thermal conductivity of each mineral in the multiphase rock mixture model based on phonon scattering theory and uses fracture mechanics theory and the series-parallel thermal resistance model to characterize the influence of cracks, compared to existing technologies, this embodiment of the device improves the reliability and accuracy of predicting the thermal conductivity of rocks at high temperatures.

[0132] Based on the first embodiment of the rock high-temperature thermal conductivity prediction device of the present invention, a second embodiment of the rock high-temperature thermal conductivity prediction device of the present invention is proposed.

[0133] In this embodiment, the mineral thermal conductivity determination module 801 is further used to establish a high-temperature mineral thermal conductivity model based on phonon scattering theory and data information of the rock sample; when the target temperature exceeds a preset temperature threshold, a thermal radiation correction term is added to the high-temperature mineral thermal conductivity model to obtain a corrected high-temperature mineral thermal conductivity model; and the thermal conductivity of each mineral in the multiphase rock mixture model is determined using the corrected high-temperature mineral thermal conductivity model.

[0134] Other embodiments or specific implementations of the rock high-temperature thermal conductivity prediction device of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0135] This application provides a rock high-temperature thermal conductivity prediction device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the rock high-temperature thermal conductivity prediction method in the above embodiment 1.

[0136] The following is for reference. Figure 12 The diagram illustrates a structural schematic suitable for implementing the rock high-temperature thermal conductivity prediction device in the embodiments of this application. The rock high-temperature thermal conductivity prediction device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 12 The rock high-temperature thermal conductivity prediction device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0137] like Figure 12As shown, the rock high-temperature thermal conductivity prediction device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the rock high-temperature thermal conductivity prediction device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the rock high-temperature thermal conductivity prediction device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows rock high-temperature thermal conductivity prediction devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0138] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0139] The rock high-temperature thermal conductivity prediction device provided in this application, employing the rock high-temperature thermal conductivity prediction method in the above embodiments, can solve the technical problem of low accuracy and reliability in the prediction of rock thermal conductivity at high temperatures in the prior art. Compared with the prior art, the beneficial effects of the rock high-temperature thermal conductivity prediction device provided in this application are the same as those of the rock high-temperature thermal conductivity prediction method provided in the above embodiments, and other technical features in this rock high-temperature thermal conductivity prediction device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0140] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0141] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0142] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0143] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0144] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0145] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for predicting the high-temperature thermal conductivity of rocks, characterized in that, The method includes: A model for the thermal conductivity of high-temperature minerals was established based on phonon scattering theory, and the thermal conductivity of each mineral in the multiphase rock mixing model was determined using the model. The number of cracks in the multiphase rock mixture model at high temperature was determined using fracture mechanics theory. Based on the number of cracks, a series and parallel thermal resistance model of the basic unit in the multiphase rock mixing model is constructed; The high-temperature thermal conductivity of the multiphase rock mixture model is predicted using the effective medium theory model and the series-parallel thermal resistance model. The thermal conductivity model for the high-temperature minerals is as follows: ； in, It's temperature. Represents the thermal conductivity of the i-th mineral. These are the fitting parameters for the i-th mineral. It is the coefficient of thermal expansion of the i-th mineral. It is the integral variable of temperature. Represents the Grüneisen parameter; ； in, It is the bulk modulus of the i-th mineral. It's density. It is heat capacity; The step of constructing the series-parallel thermal resistance model of the basic unit in the multiphase rock mixing model based on the number of cracks includes: Based on the random distribution of cracks, the probability distribution of boundary cracks in the basic units of the multiphase rock mixing model is determined. The crack normal direction is determined based on the crack direction in the basic unit, and various basic units are determined according to the relative relationship between the crack normal direction and the heat flow direction. Based on the various basic units, a variety of corresponding series and parallel thermal resistance models are constructed.

2. The method for predicting the high-temperature thermal conductivity of rocks as described in claim 1, characterized in that, Before the step of establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and using the high-temperature mineral thermal conductivity model to determine the thermal conductivity of each mineral in the multiphase rock mixture model, the method further includes: Obtain rock samples and construct a multiphase rock mixture model based on the rock samples, which consists of uniform cubic basic units, the boundaries of which include randomly distributed pores and cracks. The rock sample is analyzed, and the data information of the rock sample is determined based on the analysis results.

3. The method for predicting the high-temperature thermal conductivity of rocks as described in claim 2, characterized in that, The steps of establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and determining the thermal conductivity of each mineral in a multiphase rock mixture model using the high-temperature mineral thermal conductivity model include: A model for the thermal conductivity of high-temperature minerals was established based on phonon scattering theory and data from the rock samples. When the target temperature exceeds the preset temperature threshold, a thermal radiation correction term is added to the high-temperature mineral thermal conductivity model to obtain the corrected high-temperature mineral thermal conductivity model. The thermal conductivity of each mineral in the multiphase rock mixture model is determined using the modified high-temperature mineral thermal conductivity model.

4. The method for predicting the high-temperature thermal conductivity of rocks as described in claim 2, characterized in that, The step of determining the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory includes: The strain energy density of the rock sample is determined based on the thermal expansion coefficient of each mineral in the rock sample. The strain energy required for the formation of a single crack in the rock sample is determined by the critical stress intensity coefficient. The number of cracks in the multiphase rock mixing model is determined based on the ratio of strain energy density to strain energy.

5. The method for predicting the high-temperature thermal conductivity of rocks as described in claim 1, characterized in that, The step of predicting the high-temperature thermal conductivity of the multiphase rock mixture model using the effective medium theory model and the series-parallel thermal resistance model includes: Based on the probability distribution of the boundary cracks, the probabilities of various series and parallel thermal resistance models are derived. Using an effective medium theory model, the high-temperature thermal conductivity of the multiphase rock mixture model is predicted based on the probabilities of various series and parallel thermal resistance models and the thermal conductivity of the basic unit.

6. The method for predicting the high-temperature thermal conductivity of rocks as described in claim 5, characterized in that, Before the step of predicting the high-temperature thermal conductivity of the multiphase rock mixture model based on the probabilities of various series and parallel thermal resistance models and the thermal conductivity of the basic unit using an effective medium theory model, the method further includes: Based on the thermal conductivity and corresponding volume fraction of each mineral, the thermal conductivity of the solid matrix in the multiphase rock mixing model is determined by the effective medium theory model. The thermal resistance of the crack is determined based on the crack thermal conductivity and crack geometric parameters. The thermal resistance of the basic unit is determined based on the thermal resistance of the crack, the thermal conductivity of the solid matrix, and the series-parallel thermal resistance model corresponding to the basic unit. The thermal conductivity of the basic unit is determined based on the thermal resistance of the basic unit and the geometric parameters of the crack.

7. A device for predicting the high-temperature thermal conductivity of rocks, characterized in that, The device includes: The mineral thermal conductivity determination module is used to establish a high-temperature mineral thermal conductivity model based on phonon scattering theory, and to determine the thermal conductivity of each mineral in the multiphase rock mixture model using the high-temperature mineral thermal conductivity model. The crack number determination module is used to determine the number of cracks in the multiphase rock mixture model at high temperatures using fracture mechanics theory. The thermal resistance model construction module is used to construct the series and parallel thermal resistance models of the basic units in the multiphase rock mixture model based on the number of cracks. The high-temperature thermal conductivity determination module is used to predict the high-temperature thermal conductivity of the rock in the multiphase rock mixture model through the effective medium theory model and the series-parallel thermal resistance model. The thermal conductivity model for the high-temperature minerals is as follows: ； in, It's temperature. Represents the thermal conductivity of the i-th mineral. These are the fitting parameters for the i-th mineral. It is the coefficient of thermal expansion of the i-th mineral. It is the integral variable of temperature. Represents the Grüneisen parameter; ； in, It is the bulk modulus of the i-th mineral. It's density. It is heat capacity; The thermal resistance model construction module is also used to determine the boundary crack probability distribution of the basic unit in the multiphase rock mixture model based on the random distribution of cracks; determine the crack normal direction based on the crack direction in the basic unit, and determine a variety of basic units based on the relative relationship between the crack normal direction and the heat flow direction; and construct a variety of corresponding series and parallel thermal resistance models based on the various basic units.

8. A device for predicting the high-temperature thermal conductivity of rocks, characterized in that, The device includes: a memory, a processor, and a rock high-temperature thermal conductivity prediction program stored in the memory and executable on the processor, the rock high-temperature thermal conductivity prediction program being configured to implement the steps of the rock high-temperature thermal conductivity prediction method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a rock high-temperature thermal conductivity prediction program, which, when executed by a processor, implements the steps of the rock high-temperature thermal conductivity prediction method as described in any one of claims 1 to 6.

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