Rock high-temperature heat conductivity coefficient prediction method, device and equipment and storage medium

Abstract

The invention discloses a rock high-temperature heat conductivity coefficient prediction method, device and equipment and a storage medium, and the method comprises the steps: building a high-temperature mineral heat conductivity coefficient model based on a phonon scattering theory, and determining the heat conductivity coefficient of each mineral in a multiphase rock mixed model through the high-temperature mineral heat conductivity coefficient model; determining the number of cracks in the multi-phase rock mixed model at high temperature by using a fracture mechanics theory; constructing a series-parallel thermal resistance model of a basic unit in the multiphase rock mixing model based on the number of the cracks; and predicting the rock high-temperature heat conductivity coefficient of the multiphase rock mixing model through the effective medium theory model and the series-parallel thermal resistance model. According to the method, the heat conductivity coefficient of each mineral in the multiphase rock mixed model is determined based on the phonon scattering theory, and the fracture influence is represented by using the fracture mechanics theory and the series-parallel thermal resistance model, so that compared with the prior art, the reliability and the accuracy of predicting the heat conductivity coefficient of the rock at high temperature are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological resource mining, and in particular to a method, device, equipment and storage medium for predicting high-temperature thermal conductivity of rock. Background Art

[0002] Heat transfer is a crucial process in various underground engineering projects. This process is considered in many fields, including geothermal energy development, underground thermal energy storage, oil and gas extraction, nuclear waste disposal, and thermal management in civil engineering. Given these widespread applications, studying the thermal conductivity of rocks remains crucial. However, current rock thermal conductivity prediction theories primarily focus on ambient temperature environments, with a variety of multiphase mixing models (such as arithmetic / geometric mean models and effective medium theory) and empirical formulas established. These models achieve predictions by analyzing the relationship between mineral composition, pore structure, and fluid saturation. However, none of these models consider the enhanced phonon scattering and microcrack formation in minerals caused by elevated temperatures, making them incapable of scalability to high-temperature environments.

[0003] Existing studies of high-temperature rock thermal conductivity primarily rely on empirical models (such as inverse or power-law relationships). These models, derived by fitting experimental data, suffer from two major flaws: 1. The fitting parameters cannot be correlated with mineral properties or rock mechanical characteristics, rendering the models ineffective when applied across formations. 2. They fail to quantify the coupling effect of solid matrix thermal conductivity attenuation (mineral phonon scattering) and fracture thermal resistance increments at high temperatures. This, in turn, results in low reliability and accuracy in predictions of high-temperature rock thermal conductivity.

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

[0005] The main purpose of the present invention is to provide a method, device, equipment and storage medium for predicting the high-temperature thermal conductivity of rocks, aiming to solve the technical problem of low accuracy and reliability of the existing technology in predicting the thermal conductivity of rocks at high temperatures.

[0006] To achieve the above object, the present invention provides a method for predicting the high-temperature thermal conductivity of rock, the method comprising the following steps: 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; Determining the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory; Constructing a series-parallel thermal resistance model of a basic unit in the multiphase rock mixture model based on the number of cracks; The high-temperature thermal conductivity of rock in the multiphase rock mixture model is predicted by the effective medium theory model and the series-parallel thermal resistance model.

[0007] Optionally, before the step of establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and determining the thermal conductivity of each mineral in the multiphase rock mixture model using the high-temperature mineral thermal conductivity model, the method further includes: Obtaining a rock sample, and constructing a multiphase rock mixture model composed of uniform cubic basic units based on the rock sample, wherein the basic unit boundaries include randomly distributed pores and cracks; The rock sample is analyzed, and data information of the rock sample is determined according to the analysis result.

[0008] Optionally, the step of establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and determining the thermal conductivity of each mineral in the multiphase rock mixture model using the high-temperature mineral thermal conductivity model includes: Establishing 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; The thermal conductivity of each mineral in the multiphase rock mixture model is determined using the modified high-temperature mineral thermal conductivity model.

[0009] Optionally, the step of determining the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory includes: determining the strain energy density of the rock sample based on the thermal expansion coefficient of each mineral in the rock sample; determining the strain energy required for the formation of a single crack in the rock sample using a critical stress intensity coefficient; The number of cracks in the multiphase rock mixture model is determined according to a proportional relationship between the strain energy density and the strain energy.

[0010] Optionally, the step of constructing a series-parallel thermal resistance model of a basic unit in the multiphase rock mixture model based on the number of cracks includes: Determining the probability distribution of boundary cracks of the basic unit in the multiphase rock mixture model according to the random distribution characteristics of the cracks; Determining a crack normal direction based on a crack direction in the basic unit, and determining multiple basic units according to a relative relationship between the crack normal direction and a heat flow direction; A variety of corresponding series-parallel thermal resistance models are constructed based on the various basic units.

[0011] Optionally, the step of predicting the high-temperature thermal conductivity of rock in the multiphase rock mixture model using the effective medium theory model and the series-parallel thermal resistance model includes: Derived the probabilities of various series-parallel thermal resistance models based on the probability distribution of the boundary cracks; The high-temperature thermal conductivity of the rock in the multiphase rock mixture model is predicted based on the probabilities of the various series-parallel thermal resistance models and the thermal conductivity of the basic unit through the effective medium theory model.

[0012] Optionally, before the step of predicting the high-temperature thermal conductivity of the rock in the multiphase rock mixture model based on the probabilities of the various series-parallel thermal resistance models and the thermal conductivity of the basic unit using the effective medium theory model, the step 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; Determine the crack thermal resistance based on the crack thermal conductivity and crack geometric parameters; Determining a basic unit thermal resistance based on the crack thermal resistance, the thermal conductivity of the solid matrix, and a series-parallel thermal resistance model corresponding to the basic unit; The thermal conductivity of the basic unit is determined according to the basic unit thermal resistance and the crack geometric parameters.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also proposes a device for predicting high-temperature thermal conductivity of rock, the device comprising: A mineral thermal conductivity determination module is used to 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; a crack number determination module, configured to determine the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory; A thermal resistance model construction module, configured to construct a series-parallel thermal resistance model of a basic unit 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.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also proposes a rock high-temperature thermal conductivity prediction device, which includes: a memory, a processor, and a rock high-temperature thermal conductivity prediction program stored in the memory and runnable on the processor, and the rock high-temperature thermal conductivity prediction program is configured to implement the steps of the rock high-temperature thermal conductivity prediction method as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a rock high-temperature thermal conductivity prediction program is stored. When the rock high-temperature thermal conductivity prediction program is executed by a processor, the steps of the rock high-temperature thermal conductivity prediction method as described above are implemented.

[0016] The present invention discloses a method for establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory, using the high-temperature mineral thermal conductivity model to determine the thermal conductivity of each mineral in a multiphase rock mixture model; using fracture mechanics theory to determine the number of cracks in the multiphase rock mixture model at high temperatures; 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 the series-parallel thermal resistance model. Because the present 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, the present invention improves the reliability and accuracy of rock thermal conductivity prediction at high temperatures. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic flow chart of the first embodiment of the method for predicting high-temperature thermal conductivity of rock according to the present invention; Figure 2 Schematic diagram of a multiphase rock mixing model in the method for predicting high-temperature thermal conductivity of rock according to the present invention; Figure 3 Schematic diagram of the mineral mass composition of six different sedimentary rocks involved in the present invention; Figure 4 Schematic diagram of characteristic parameters of six different sedimentary rocks involved in the present invention; Figure 5 Schematic diagram of six series-parallel thermal resistance models in the method for predicting high-temperature thermal conductivity of rock of the present invention; Figure 6 Schematic diagram of thermal resistance and probability calculation of six different basic units in the multiphase rock mixture model of the present invention; Figure 7 Schematic diagram comparing the rock high-temperature thermal conductivity prediction results obtained by the rock high-temperature thermal conductivity prediction method of the present invention and the experimental results; Figure 8 Schematic diagram of the effect of quartz content on thermal conductivity in rock characteristics; Figure 9 Schematic diagram of the effect of Young's modulus and Poisson's ratio on thermal conductivity in rock mechanics parameters; Figure 10 This is a schematic flow chart of a second embodiment of a method for predicting high-temperature thermal conductivity of rock according to the present invention; Figure 11This is a structural block diagram of the first embodiment of the rock high-temperature thermal conductivity prediction device of the present invention; Figure 12 It is a structural diagram of a rock high-temperature thermal conductivity prediction device in a hardware operating environment involved in an embodiment of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] The embodiment of the present invention provides a method for predicting the high-temperature thermal conductivity of rock, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for predicting high-temperature thermal conductivity of rock according to the present invention.

[0021] In this embodiment, the rock high-temperature thermal conductivity prediction method includes steps S10 to S40: Step S10: 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.

[0022] It should be noted that the execution subject of this embodiment can be a computer server device with data processing, network communication, and program execution functions used in geological resource extraction 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.

[0023] It should be understood that rock is an aggregate of mineral particles. When constructing thermal conductivity analysis models, it is generally assumed that rock particles are uniform in size and have various geometric shapes, such as hexahedrons, cylinders, rectangular prisms, spheres, and hemispheres. The specific shapes of these basic units are determined by their physical meaning. This example assumes that rock is composed of uniform cubic basic units representing mineral particles, with clear boundaries between them and exhibiting homogeneity and isotropy on a macroscopic scale.

[0024] Rocks are generally considered to consist of three phases: solid, liquid, and gas. The solid phase (Solid) is composed of minerals and organic matter, and its thermal conductivity depends on petrological characteristics such as mineral composition, grain size, and contact relationship. The liquid phase refers to the formation water present in the rock. Since the liquid phase will transform into the gas phase (Gas) at a certain temperature, formation water is not considered in this embodiment because the focus is on the heat conduction of rocks at high temperatures. The gas phase usually represents the air in the pores and cracks inside the rock. Mineral crystal defects and specific diagenetic processes may cause pores or cracks to form inside mineral particles, but the number is very small. Therefore, this embodiment assumes that the gas phase is mainly located at the boundaries between mineral grains, that is, pores and cracks only exist at the boundaries of basic units. For example, refer to Figure 2 , cracks on 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 at the macroscale, the cracks are randomly distributed in the three-dimensional XYZ directions, forming parallel and series heat conduction pathways with the grains.

[0025] In a specific implementation, before step S10, the method further includes: obtaining a rock sample, and constructing a multiphase rock mixture model composed of uniform cubic basic units based on the rock sample, wherein the basic unit boundaries include randomly distributed pores and cracks; analyzing the rock sample, and determining data information of the rock sample according to the analysis results.

[0026] It should be noted that the data information of the above rock samples can include reference parameters, experimental parameters and iteration parameters. The experimental parameters can be obtained through laboratory experiments, including the mineral volume content, particle size, Young's modulus, Poisson's ratio and high-temperature thermal conductivity of the rock sample. The reference parameters mainly include mineral properties, including mineral thermal expansion coefficient, mineral bulk modulus, mineral density, mineral specific heat capacity and mineral thermal conductivity at 25°C, which can be reliably obtained from existing research without the need for additional testing. The iteration parameters include fracture width and thermal expansion coefficient difference , both of which are difficult to measure directly experimentally. Therefore, these parameters can be determined iteratively using the thermal conductivity measurements at 25°C and 200°C.

[0027] In this example, six sedimentary rocks were selected for the experiment. The mineral composition of the rock samples was determined by X-ray diffraction (XRD) analysis. Figure 3The analysis was performed using a Rigaku TTR III X-ray diffractometer in accordance with ISO 23071:2021. After obtaining the mineral mass composition, the volumetric composition of the rock was calculated using the mineral density. The grain size and structure of the rock were analyzed using optical microscopy and scanning electron microscopy. The coarse sandstone has a larger grain size, with a grain size of 546.4 μm. Fine sandstones 1 and 2 have similar grain sizes and are composed primarily of quartz grains with minor feldspar grains. Mudstones 1 and 2 have finer grains, with grain sizes of 2.8 μm and 2.3 μm, respectively. Mudstone 1 is primarily composed of quartz grains, while mudstone 2 has a lower quartz content and a higher proportion of clay minerals. The limestone is classified as microcrystalline limestone and exhibits fine grains with a grain size of 1.9 μm. Grain size is considered the basic unit particle 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 at 200°C and 25°C was tested to obtain the iterative parameters. The thermal conductivity was measured using a NETZSCH LFA 467 laser flash analyzer with a heating rate set to 5°C / min. The samples were tested in an argon environment to simulate the oxygen-free conditions of underground engineering. Before testing, both sides of each sample were coated with carbon to improve the measurement accuracy. The test was carried out in accordance with the ASTM E1461 standard, and each sample was measured three times, and the average value was recorded, as shown in Figure 2. Figure 4 shown.

[0028] It should be understood that, with the exception of a few minerals like opal and agate, most minerals are crystalline, exhibiting a periodically arranged lattice. Modern materials science uses a particle-like theoretical framework to describe this property, calling the lattice vibration quanta phonons. In minerals, thermal energy is primarily transported via phonons. Because increasing temperature enhances phonon scattering and reduces vibration frequency, thermal conductivity decreases with increasing temperature.

[0029] 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 conductivity of solids.

[0030] It should be explained that a high-temperature mineral thermal conductivity model is established based on the phonon scattering theory, and the high-temperature mineral thermal conductivity model is: ; in, It is the temperature; Representative The thermal conductivity of a mineral is the temperature function; It is The fitting parameters of the minerals are 0.75 for quartz, 0.51 for carbonate minerals, and 0.33 for silicate minerals. is the thermal expansion coefficient of the i-th mineral, which is also a function of temperature; It is the temperature integral variable and has the same physical meaning as temperature. stands for Grüneisen parameter, which quantitatively relates thermal properties to mechanical parameters.

[0031] ; in, is the bulk modulus of the i-th mineral; is the density; is the heat capacity.

[0032] Step S20: using fracture mechanics theory to determine the number of cracks in the multiphase rock mixture model at high temperature.

[0033] It should be understood that fracture mechanics theory studies the effects of the presence and propagation of cracks in materials on structural strength, and its core is to understand the energy or stress conditions for crack propagation.

[0034] It should be noted that due to the difference in thermal expansion between mineral grains within the rock, the different expansions will produce tensile stress at high temperatures. When the tensile stress exceeds the tensile strength of the grain boundary, intergranular cracks will be generated and expand. Before the fracture is formed, strain energy accumulates in the rock. When the crack expands, most of the strain energy is converted into surface energy, driving the crack growth, while a small part of the strain energy is dissipated in the form of heat energy, which is almost negligible. The strain energy density of the rock is The quantitative formula is: ; in, is Young's modulus, unit: Pa; is Poisson's ratio; is the difference in thermal expansion coefficients between minerals; is the initial temperature.

[0035] The analysis side length is When the unit boundary cracks, the accumulated strain energy is converted into fracture surface energy, leading to the formation of fracture. The fracture width is expressed as The single-sided fracture area is (like Figure 2 b). Fracture width The thermal resistance of the cell boundary is affected primarily by introducing local interruptions in the heat transfer path. The strain energy required to form a single fracture is ; in, is the critical stress intensity factor, in units of , the calculation formula of the critical stress intensity coefficient is as follows: .

[0036] The number of cracks generated in a rock sample at high temperature can be determined based on the strain energy density and the strain energy required for a single fracture. N(T), That is, determine the number of fractures in the multiphase rock mixture model.

[0037] .

[0038] 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 through the critical stress intensity coefficient; and the number of cracks in the multiphase rock mixture model can be determined based on the proportional relationship between the strain energy density and the strain energy.

[0039] Step S30: constructing a series-parallel thermal resistance model of the basic unit in the multiphase rock mixture model based on the number of cracks.

[0040] It should be understood that the cracks created will increase the thermal resistance of the rock. Much smaller than the basic unit side length of rock , so the basic unit size remains unchanged after the crack is formed, that is In a unit volume of rock, the number of boundaries in the x, y, and z directions is Assuming that the cracks are randomly distributed, the number of cracks in each direction is Therefore, the probability of a boundary containing cracks in each direction, that is, the probability distribution of boundary cracks of the basic unit in the multiphase rock mixture model is: .

[0041] Therefore, each basic unit in the multiphase rock mixture model may contain fractures in three directions. When the fracture normal is parallel to the heat flow direction, the fracture and the solid matrix form a series structure. When the fracture normal is perpendicular to the heat flow direction, the fracture and the solid matrix form a parallel structure. Series and parallel structures may exist at the same time, so there are six possible situations. For example, Figure 5 , Figure 5The figure shows six possible configurations of the fracture and solid matrix of the basic unit: I, no fracture; II, the fracture and the solid matrix in series; III, the fracture and the solid matrix in parallel; IV, two fractures and the solid matrix in parallel; V, a combination of series and parallel (one in series and one in parallel); VI, a combination of series and parallel (one in series and two in parallel).

[0042] In a specific implementation, the probability distribution of boundary cracks of the basic unit in the multiphase rock mixture model can be determined based on the characteristics of random distribution of cracks; the crack normal direction is determined based on the crack direction in the basic unit, and multiple basic units are determined based on the relative relationship between the crack normal direction and the heat flow direction; and multiple corresponding series-parallel thermal resistance models are constructed based on various basic units.

[0043] Step S40: predicting the high-temperature thermal conductivity of rock in the multiphase rock mixture model using the effective medium theory model and the series-parallel thermal resistance model.

[0044] It's important to explain that 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 inhomogeneous media. Its core concept is to equate complex multicomponent media to a hypothetical single-phase medium with homogeneous properties, thereby simplifying analysis and calculations.

[0045] It should be understood that the high-temperature thermal conductivity of rock can represent the thermal conductivity of rock at high temperature. This embodiment and the following embodiments use a temperature greater than 600°C as an example of high temperature for illustration.

[0046] It should be noted that due to the anisotropy of fracture compared to the solid matrix, the fracture thermal resistance calculation formulas of different basic units are also different. Given the known probability of fracture occurring in different directions, it is also possible to deduce the fracture thermal resistance of different types of basic units. ( is the basic unit type), that is, the probability of various series and parallel thermal resistance models, such as Figure 6 As shown. Figure 6 It can be seen that the thermal conductivity of cracks at high temperatures is The crack thermal resistance is based on the experimental value of the thermal conductivity of air at high temperature. and crack geometry parameters Calculated. Thermal resistance of the basic unit It is calculated based on the series and parallel configuration of the crack thermal resistance and the solid matrix thermal resistance. It is based on the thermal resistance and geometric parameters of the basic unit Obtained. Figure 6 The ~ represents the relationship between the thermal resistance of the basic unit and its components (i.e., the solid matrix thermal resistance and the fracture thermal resistance), rather than a strict equation. is the probability that the boundary contains a break in each direction in the equation. is the thermal conductivity of the solid matrix. For example, Figure 5 The probability of the basic unit I in is: ; , the thermal resistance of basic unit I is: ; The thermal conductivity of basic unit I is Correspondingly, the calculation methods of probability, thermal resistance and thermal conductivity of other types of basic units can refer to Figure 6 .

[0047] In a specific implementation, the probabilities of the various series-parallel thermal resistance models can be derived based on the probability distribution of the boundary cracks; and the high-temperature thermal conductivity of the rock in the multiphase rock mixture model can be predicted based on the probabilities of the various series-parallel thermal resistance models and the thermal conductivity of the basic unit through the effective medium theory model.

[0048] In order to determine the thermal conductivity of the basic unit in the multiphase rock mixture model, before the step of predicting the high-temperature thermal conductivity of the rock in the multiphase rock mixture model through the effective medium theory model based on the probabilities of the various series-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 through the effective medium theory model according to the thermal conductivity and corresponding volume fraction of each mineral; determining the fracture thermal resistance according to 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-parallel thermal resistance model corresponding to the basic unit; and determining the thermal conductivity of the basic unit according to the basic unit thermal resistance and the fracture geometric parameters.

[0049] It should be noted that the effective medium theory (EMT) model is an effective tool for calculating the thermal conductivity of completely randomly distributed multi-component solid mixtures. 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 calculation formula for the thermal conductivity of the solid matrix is as follows: ; in, is the number of mineral species in a rock sample; It is Volume fraction of the minerals; is the thermal conductivity of the solid matrix, is the temperature function.

[0050] Applying the EMT model to mix the thermal conductivity of different random basic units, the high-temperature thermal conductivity of the rock in the multiphase rock mixture model can be calculated. , the calculation formula is .

[0051] 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 high-temperature thermal conductivity of rock obtained from experimental results, and the effectiveness and applicability of the high-temperature thermal conductivity prediction method of rock in this embodiment can be determined based on the comparison results. Figure 7 In the figure, the horizontal axis represents temperature, and the vertical axis represents the high-temperature thermal conductivity of rock. Red represents experimental results, and blue represents the predicted results of this example. 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 shown. A comparison of the predicted results with the experimental results in the figure shows that this example 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.

[0052] The differences observed in mudstone 2 are primarily attributed to high-temperature reactions of clay minerals, which lead to the formation of new mineral phases. This reaction process produces a liquid phase, reducing the rock's porosity and, therefore, its thermal resistance. Because the theoretical model does not account for these reactions, the predicted values are lower than the experimental results. In limestone, the differences between the predicted values in this example and the experimental results are due to structural differences between limestone and other sedimentary rocks. This example assumes that cracks 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 ooids and nodules. Cracks in limestone often occur at aggregate boundaries or within aggregates, resulting in inaccurate estimates of grain size when calculating thermal conductivity. Currently, there is no accurate method to predict fracture locations in limestone at high temperatures.

[0053] Therefore, this embodiment is suitable for sandstone, low-clay mudstone, and other rocks with well-defined fracture locations and mild high-temperature reactions. However, prediction accuracy is lower for limestone, where fracture locations are difficult to determine, and clay-rich mudstone, where high-temperature reactions are severe. 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 disposal.

[0054] To investigate the impact of rock characteristics on high-temperature thermal conductivity, a sensitivity analysis was conducted using the parameters of coarse sandstone, based on the present example. The mineral composition of the coarse sandstone was adjusted. The mineral composition of the coarse sandstone can include quartz, potassium feldspar, plagioclase, calcite, dolomite, illite, and kaolinite. The potassium feldspar and plagioclase contents remained unchanged. Initially, the quartz content was 56.2%, which was subsequently adjusted to 30%, 40%, 50%, and 60%. The illite content was also adjusted accordingly to ensure a 100% total mineral composition. Figure 8 a in represents the sensitivity analysis result of quartz content. Figure 8 The b in it represents the thermal conductivity of the mineral at high temperature.

[0055] The results show that the quartz content has a great influence on the thermal conductivity of rocks at both room temperature and high temperature. The higher the quartz content, the higher the thermal conductivity of the rock at room temperature and high temperature (e.g. Figure 8 As shown in a in Figure 1). Quartz is a typical high thermal conductivity mineral. Compared with other minerals, the thermal conductivity of quartz at room temperature is relatively high. As the temperature rises, the thermal conductivity of most minerals will decrease, while the thermal conductivity of quartz is still higher than that of other minerals (such as Figure 8 (b) The quartz content affects the thermal conductivity of the solid matrix, and thus the overall thermal conductivity of the rock at high temperatures. This relationship indicates that rocks with a higher proportion of high-thermal-conductivity minerals exhibit higher thermal conductivity at high temperatures.

[0056] Coarse sandstone, fine sandstone and mudstone have similar mineral compositions and contents. However, the experimental thermal conductivity of mudstone consistently shows lower high-temperature thermal conductivity (e.g. Figure 7 This observation highlights the strong influence of grain size on high-temperature thermal conductivity, with larger grains resulting in higher thermal conductivity.

[0057] In order to study the influence of rock mechanical parameters on thermal conductivity, a sensitivity analysis of Young's modulus and Poisson's ratio was conducted using coarse sandstone as a reference material. While keeping other parameters unchanged, the Young's modulus was set to 40 GPa, 50 GPa, 60 GPa and 70 GPa, and the Poisson's ratio was set to 0.1, 0.2, 0.3 and 0.4 respectively. For example, Figure 9 , Figure 9 Where 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 9As shown, rock mechanical parameters do not affect thermal conductivity at room temperature, but they have a significant impact at high temperatures. This effect occurs because Young's modulus and Poisson's ratio determine the density of strain energy stored in the rock, thereby controlling the fracture formation process at high temperatures. The larger the Young's modulus and Poisson's ratio, the lower the thermal conductivity at high temperatures. The mechanism is that the Young's modulus represents the stiffness of the material. A higher Young's modulus increases the strain energy density of the rock, which promotes the formation of more cracks, increases thermal resistance, and ultimately reduces thermal conductivity. Similarly, a higher Poisson's ratio increases the strain energy density at high temperatures, further reducing thermal conductivity. The influence of Young's modulus on high-temperature thermal conductivity is stronger than that of Poisson's ratio.

[0058] This embodiment discloses establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory, using the high-temperature mineral thermal conductivity model to determine the thermal conductivity of each mineral in a multiphase rock mixture model; using fracture mechanics theory to determine the number of cracks in the multiphase rock mixture model at high temperatures; constructing a series-parallel thermal resistance model of the basic unit 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 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 impact of cracks, compared to existing technologies, this embodiment improves the reliability and accuracy of rock thermal conductivity prediction at high temperatures.

[0059] refer to Figure 10 , Figure 10 This is a flow chart of the second embodiment of the method for predicting high-temperature thermal conductivity of rock according to the present invention.

[0060] Based on the first embodiment described above, in this embodiment, step S10 includes steps S101 to S103: Step S101: establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and data information of the rock sample.

[0061] Step S102: 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.

[0062] Step S103: using the modified high-temperature mineral thermal conductivity model to determine the thermal conductivity of each mineral in the multiphase rock mixture model.

[0063] It should be noted that the above-mentioned preset temperature threshold can be a custom setting, which is not limited in this embodiment. The following description is made using the preset temperature threshold of 600°C as an example.

[0064] When the temperature exceeds 600°C, the effect of thermal radiation on solid thermal conductivity becomes non-negligible. Therefore, a thermal radiation correction term must be added to the high-temperature mineral thermal conductivity model to make corrections. The thermal radiation correction term is as follows: .

[0065] Then, the revised high-temperature mineral thermal conductivity model is: .

[0066] This embodiment discloses establishing a high-temperature mineral thermal conductivity model 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 the prior art, which adds 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 thermal conductivity calculations for each mineral in the multiphase rock mixture model.

[0067] In addition, an embodiment of the present invention also proposes a storage medium, which stores a rock high-temperature thermal conductivity prediction program. When the rock high-temperature thermal conductivity prediction program is executed by a processor, the steps of the rock high-temperature thermal conductivity prediction method described above are implemented.

[0068] 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.

[0069] like Figure 11 As shown, the rock high-temperature thermal conductivity prediction device proposed in the embodiment of the present 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.

[0070] The mineral thermal conductivity determination module 801 is used to 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.

[0071] 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.

[0072] The thermal resistance model construction module 803 is used to construct a series-parallel thermal resistance model of the basic unit in the multiphase rock mixture model based on the number of fractures.

[0073] 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 using the effective medium theory model and the series-parallel thermal resistance model.

[0074] The mineral thermal conductivity determination module 801 is further configured to obtain a rock sample and construct, based on the rock sample, a multiphase rock mixture model consisting of uniform cubic basic units, wherein the basic unit boundaries include randomly distributed pores and cracks; analyze the rock sample and determine data information of the rock sample based on the analysis results.

[0075] The crack number determination module 802 is further configured 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 a critical stress intensity coefficient; and determine the number of cracks in the multiphase rock mixture model based on a proportional relationship between the strain energy density and the strain energy.

[0076] The thermal resistance model construction module 803 is also used to determine the probability distribution of boundary cracks of the basic unit in the multiphase rock mixture model based on the characteristics of the random distribution of cracks; determine the crack normal direction based on the crack direction in the basic unit, and determine multiple basic units based on the relative relationship between the crack normal direction and the heat flow direction; and construct multiple corresponding series-parallel thermal resistance models based on various basic units.

[0077] The high-temperature thermal conductivity determination module 804 is also used to derive the probabilities of various series-parallel thermal resistance models based on the probability distribution of the boundary cracks; and predict the high-temperature thermal conductivity of the rock in the multiphase rock mixture model based on the probabilities of various series-parallel thermal resistance models and the thermal conductivity of the basic unit through the effective medium theory model.

[0078] 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 using an effective medium theory model based on the thermal conductivity and corresponding volume fraction of each mineral; 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.

[0079] This device embodiment discloses 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; using fracture mechanics theory to determine the number of cracks in the multiphase rock mixture model at high temperatures; constructing a series-parallel thermal resistance model of the basic unit 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 the series-parallel thermal resistance model. Because this device 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 impact of cracks, compared to existing technologies, this device embodiment improves the reliability and accuracy of rock thermal conductivity prediction at high temperatures.

[0080] Based on the first embodiment of the device for predicting high-temperature thermal conductivity of rock of the present invention, a second embodiment of the device for predicting high-temperature thermal conductivity of rock of the present invention is proposed.

[0081] In this embodiment, the mineral thermal conductivity determination module 801 is also used to establish a high-temperature mineral thermal conductivity model based on phonon scattering theory and the 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.

[0082] Other embodiments or specific implementations of the rock high-temperature thermal conductivity prediction device of the present invention can refer to the above-mentioned method embodiments and will not be repeated here.

[0083] The present application provides a device for predicting the high-temperature thermal conductivity of rock, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for predicting the high-temperature thermal conductivity of rock in the above-mentioned embodiment one.

[0084] Reference below Figure 12, which shows a schematic diagram of the structure of a device suitable for implementing the embodiments of the present application for predicting the high-temperature thermal conductivity of rock. The device for predicting the high-temperature thermal conductivity of rock in the embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 12 The rock high-temperature thermal conductivity prediction device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0085] like Figure 12 As shown, the rock high-temperature thermal conductivity prediction device may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or 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 device 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: an input device 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape or hard disk; and a communication device 1009. The communication device 1009 can allow the rock high-temperature thermal conductivity prediction device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a rock high-temperature thermal conductivity prediction device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0086] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0087] The device for predicting high-temperature thermal conductivity of rock provided in this application utilizes the method for predicting high-temperature thermal conductivity of rock described in the aforementioned embodiment, resolving the technical issues of low accuracy and reliability in prior art for predicting thermal conductivity of rock at high temperatures. Compared to prior art, the device for predicting high-temperature thermal conductivity of rock provided in this application achieves the same beneficial effects as the method for predicting high-temperature thermal conductivity of rock provided in the aforementioned embodiment. Other technical features of the device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0088] 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 one or more embodiments or examples in a suitable manner.

[0089] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0090] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0091] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0092] Through the above description of the embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented by means of software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion 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, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (which can 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.

[0093] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for predicting high-temperature thermal conductivity of rock, characterized in that: The method comprises: 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; Determining the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory; Constructing a series-parallel thermal resistance model of a basic unit in the multiphase rock mixture model based on the number of cracks; The high-temperature thermal conductivity of rock in the multiphase rock mixture model is predicted by the effective medium theory model and the series-parallel thermal resistance model.

2. The method for predicting high-temperature thermal conductivity of rock according to claim 1, wherein: Before the step of establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and determining the thermal conductivity of each mineral in the multiphase rock mixture model using the high-temperature mineral thermal conductivity model, the method further includes: Obtaining a rock sample, and constructing a multiphase rock mixture model composed of uniform cubic basic units based on the rock sample, wherein the basic unit boundaries include randomly distributed pores and cracks; The rock sample is analyzed, and data information of the rock sample is determined according to the analysis result.

3. The method for predicting high-temperature thermal conductivity of rock according to claim 2, wherein: The step of establishing a high-temperature mineral thermal conductivity model based on phonon scattering theory and determining the thermal conductivity of each mineral in the multiphase rock mixture model using the high-temperature mineral thermal conductivity model includes: Establishing 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; 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 high-temperature thermal conductivity of rock according to claim 2, wherein: The step of determining the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory includes: determining the strain energy density of the rock sample based on the thermal expansion coefficient of each mineral in the rock sample; determining the strain energy required for the formation of a single crack in the rock sample using a critical stress intensity coefficient; The number of cracks in the multiphase rock mixture model is determined according to a proportional relationship between the strain energy density and the strain energy.

5. The method for predicting high-temperature thermal conductivity of rock according to claim 1, wherein: The step of constructing a series-parallel thermal resistance model of a basic unit in the multiphase rock mixture model based on the number of cracks includes: Determining the probability distribution of boundary cracks of the basic unit in the multiphase rock mixture model according to the random distribution characteristics of the cracks; Determining a crack normal direction based on a crack direction in the basic unit, and determining multiple basic units according to a relative relationship between the crack normal direction and a heat flow direction; A variety of corresponding series-parallel thermal resistance models are constructed based on the various basic units.

6. The method for predicting high-temperature thermal conductivity of rock according to claim 5, wherein: The step of predicting the high-temperature thermal conductivity of rock in the multiphase rock mixture model using the effective medium theory model and the series-parallel thermal resistance model includes: Derived the probabilities of various series-parallel thermal resistance models based on the probability distribution of the boundary cracks; The high-temperature thermal conductivity of the rock in the multiphase rock mixture model is predicted based on the probabilities of the various series-parallel thermal resistance models and the thermal conductivity of the basic unit through the effective medium theory model.

7. The method for predicting high-temperature thermal conductivity of rock according to claim 6, wherein: Before the step of predicting the high-temperature thermal conductivity of the rock in the multiphase rock mixture model using the effective medium theory model based on the probabilities of the various series-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; Determine the crack thermal resistance based on the crack thermal conductivity and crack geometric parameters; Determining a basic unit thermal resistance based on the crack thermal resistance, the thermal conductivity of the solid matrix, and a series-parallel thermal resistance model corresponding to the basic unit; The thermal conductivity of the basic unit is determined according to the basic unit thermal resistance and the crack geometric parameters.

8. A device for predicting high-temperature thermal conductivity of rock, characterized in that: The device comprises: A mineral thermal conductivity determination module is used to 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; a crack number determination module, configured to determine the number of cracks in the multiphase rock mixture model at high temperature using fracture mechanics theory; A thermal resistance model construction module, configured to construct a series-parallel thermal resistance model of a basic unit 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.

9. A device for predicting high-temperature thermal conductivity of rock, 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 is configured to implement the steps of the rock high-temperature thermal conductivity prediction method according to any one of claims 1 to 7.

10. 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 according to any one of claims 1 to 7.

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