Coalbed pore evolution prediction method under action of low-temperature liquid nitrogen

Abstract

This invention discloses a method for predicting coal seam pore evolution under cryogenic liquid nitrogen, belonging to the field of coal seam pore evolution technology. The method includes the following steps: Step S1, obtaining the three-dimensional pore structure of coal samples through micro-CT scanning and calculating initial pore parameters; Step S2, collecting coal seam sample parameters and calibrating key parameters of liquid nitrogen action through liquid nitrogen experiments, including pore freezing point, ice crystal growth rate, and ice expansion coefficient; Step S3, constructing pore evolution equations and solving for predicted porosity and pore size distribution functions; Step S4, defining pore types and calculating pore type transformation; Step S5, generating the final predicted result of coal seam pore structure evolution under cryogenic liquid nitrogen action. This invention combines numerical analysis and experimental research to construct a dynamic evolution prediction model of reservoir micropore structure under liquid nitrogen injection disturbance, revealing the law and mechanism of the effect of cryogenic liquid nitrogen fluid on the adsorption performance of coal.

Description

Technical Field

[0001] This invention relates to the field of coal seam porosity evolution technology, specifically to a method for predicting coal seam porosity evolution under the action of cryogenic liquid nitrogen. Background Technology

[0002] With the continuous development of coal seam permeability enhancement technology, anhydrous fracturing technology has attracted significant attention from experts and scholars as an emerging technology. The key feature of anhydrous fracturing technology is the use of non-aqueous substances as the medium for fracturing and permeating the coal seam. This technology avoids water lock damage, water resource waste, and coal reservoir pollution, thus it is expected to become a future trend in coal seam permeability enhancement. Among anhydrous fracturing technologies, liquid nitrogen-based fracturing technology has received widespread attention. Under normal temperature and pressure, when liquid nitrogen is injected into the coal seam, the temperature decreases, causing the liquid water in the pores and fractures to freeze and undergo a phase change, expanding in volume. When the frost heave force reaches the strength limit of the coal body, it can cause the coal body to fracture. On the other hand, the decrease in coal temperature generates thermal stress, which exacerbates the damage to the pore and fracture structures of the coal body, accelerating its fracture and creating seepage channels, which facilitates the diffusion and seepage of coalbed methane. Liquid nitrogen fracturing can greatly alleviate the dependence of conventional hydraulic fracturing on water resources and the pressure in water-scarce areas. It will not pollute coalbed methane reservoirs and the surrounding environment, and has a very good application prospect in underground gas extraction in coal mines.

[0003] Currently, conventional downhole coal seam liquid nitrogen fracturing technology has the following main problems: the coal seam is highly heterogeneous and has complex pore and fracture development characteristics. There is insufficient research on the triple fracturing mechanism of liquid nitrogen on coal and rock mass, namely gasification expansion fracturing, freezing fracturing and low temperature fracturing. It is difficult to intuitively and effectively obtain the pore structure characteristics of liquid nitrogen-fracturing coal seams. The pore and fracture system is not well understood, which seriously restricts the industrial application of liquid nitrogen fracturing technology.

[0004] For example, Chinese patent CN114577695B discloses a monitoring device and method for monitoring the porosity evolution of hydraulic concrete before initial setting in cold regions. The device includes an environmental chamber with a concrete vibration table at the bottom and a concrete mixing device on the vibration table. An air compressor, a humidifier, and liquid nitrogen are located outside the environmental chamber. The air compressor is connected to the environmental chamber via pipe A, and the humidifier is connected via pipe B. The inlet and outlet of the liquid nitrogen are connected via a liquid nitrogen circulation pipe located inside the environmental chamber. Multiple high-speed cameras are installed around the environmental chamber, and a temperature, pressure, and humidity meter is installed inside the chamber. This invention solves the problem that existing devices cannot obtain the porosity evolution of concrete before initial setting.

[0005] All of the above patents share the problem raised in the background: the evolution of pore and fracture structure damage and the permeability enhancement mechanism of coal under liquid nitrogen freeze-thaw action are currently unclear. Therefore, it is necessary to study the evolution of pore and fracture damage and the permeability enhancement mechanism of coal under liquid nitrogen freeze-thaw action, to provide a theoretical basis for liquid nitrogen freeze-thaw-induced fracture and permeability enhancement of coal bodies, and to provide new methods for coalbed methane extraction and reducing coal mine gas disasters. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the shortcomings of the existing technology by providing a multi-level network congestion control method for intelligent computing centers.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for predicting coal seam porosity evolution under cryogenic liquid nitrogen conditions includes the following steps:

[0009] Step S1: Obtain the three-dimensional pore structure of the coal sample and calculate the initial pore parameters by micro-CT scanning;

[0010] Step S2: Collect coal seam sample parameters and calibrate key parameters of liquid nitrogen action through liquid nitrogen experiments, including pore freezing point, ice crystal growth rate and ice expansion coefficient;

[0011] Step S3: Construct the pore evolution equation and solve for the predicted porosity and pore size distribution function;

[0012] Step S4: Define pore type and calculate pore type conversion;

[0013] Step S5: Generate the final prediction results of coal seam pore structure evolution under the action of cryogenic liquid nitrogen.

[0014] Furthermore, step S1 specifically includes the following steps:

[0015] Step S1.1: Drill a standard cylindrical core sample from the target coal seam;

[0016] Step S1.2: Use a micro-CT scanner to perform tomographic scanning on the sample to obtain a three-dimensional grayscale image dataset;

[0017] Step S1.3: Use software to process images and reconstruct the pore network model, specifically including: threshold segmentation to distinguish between solid matrix and pores; pore space binarization; and construction of a three-dimensional pore topology network model.

[0018] Step S1.4: Calculate the initial pore parameters based on the reconstructed pore network model;

[0019] The initial pore parameters include: porosity, fractal dimension, and pore internal surface area;

[0020] Among them, porosity is the percentage of total pore volume to total sample volume; fractal dimension is calculated based on the natural logarithm of the ratio of the maximum pore radius to the minimum pore radius and is used to quantify the complexity of pore structure; and pore internal surface area is the sum of the surface areas of all pore walls per unit volume.

[0021] Furthermore, the formula for calculating the initial pore parameters is as follows:

[0022]

[0023] Where φ0 represents the initial porosity of the coal seam sample, V pore V represents the total volume of all pores in a coal seam sample. total D represents the total volume of the coal seam sample. f The fractal dimension represents the pore structure and describes its complexity; a larger value indicates a more complex pore distribution. and S represents the maximum pore radius and the minimum pore radius, respectively. pore R represents the internal surface area of ​​all pores within a unit volume of coal sample, where N represents the total number of pores, and r represents the total surface area of ​​pores within the sample. p,i L represents the radius of the i-th pore. p,i This represents the length of the throat of the i-th pore.

[0024] Furthermore, step S2 specifically includes the following steps:

[0025] A liquid nitrogen circulation experiment was conducted in a cryogenic reactor;

[0026] The actual solidification point of nanopores was calculated using the Gibbs-Thomson equation, which shows an inverse relationship with the pore radius.

[0027] Ice crystal growth was observed and the growth rate was calculated by cryogenic scanning electron microscopy, and the result was obtained by dividing the rate of change of ice crystal radius by the square of the supercooling.

[0028] The ice expansion coefficient is determined by strain measurement and is calculated by dividing the sample volume change by the product of water saturation and ice volume expansion rate.

[0029] Furthermore, in step S3, the specific formula for the dynamic pore evolution equation is as follows:

[0030]

[0031] in, α represents the rate of change of porosity over time. T This represents the thermal fracture coefficient, used to characterize the intensity of the effect of temperature on thermal fracture. ΔT represents the difference between the current temperature and the initial temperature. The temperature gradient is represented by t, the duration of liquid nitrogen treatment is represented by τ, and the saturation time for ice crystal growth is represented by e. eff This represents the net stress borne by the coal seam sample, which is the total stress minus the pore pressure.

[0032] Furthermore, in step S3, solving the pore evolution equation specifically includes the following steps:

[0033] The coal body is discretized into 1mm segments. 3 Finite element mesh;

[0034] The explicit time-stepping method is used for iterative calculation, with a time step of 0.1 seconds. The updated porosity is equal to the current porosity plus the change in porosity multiplied by the time step.

[0035] Among them, the change in porosity includes thermal fracturing, ice expansion, and compression. Thermal fracturing represents the amount of coal body fracturing and expansion caused by temperature gradient, ice expansion represents the volume increase caused by ice crystal growth compressing the pore walls, and compression represents the compressive effect of geostress on the pores.

[0036] The pore size distribution is updated synchronously. The updated pore distribution function is equal to the current pore distribution function plus the change in porosity distribution multiplied by the time step.

[0037] The changes in porosity distribution include a temperature-driven term and an ice expansion-driven term. The temperature-driven term is proportional to the temperature difference and the temperature-related exponential decay function, while the ice expansion-driven term is proportional to the product of the negative fractal dimension of the pore radius and the rate of change of ice volume.

[0038] Furthermore, in step S4, the calculation formula for pore type conversion is:

[0039]

[0040] in, and These represent the proportions of micropores, mesopores, and macropores at time m, respectively, and X represents the pore type transformation matrix. and represent the initial proportions of micropores, mesopores, and macropores, respectively, and b represents the driving term vector;

[0041] Among them, micropores are pores with a diameter of less than 2 nm, mesopores are pores with a diameter of 2-50 nm, and macropores are pores with a diameter of more than 50 nm.

[0042] Furthermore, in the pore type conversion matrix, the diagonal elements represent the pore type self-retention probability, and the off-diagonal elements represent the conversion probability between types: where X[1][1] represents the proportion of a pore that was originally a micropore and remains a micropore after conversion, X[1][2] represents the proportion of a pore that was originally a mesopore and becomes a micropore after conversion, X[1][3] represents the proportion of a pore that was originally a macropore and becomes a micropore after conversion, X[2][1] represents the proportion of a pore that was originally a micropore and becomes a mesopore after conversion, X[2][2] represents the proportion of a pore that was originally a mesopore and remains a mesopore after conversion, X[2][3] represents the proportion of a pore that was originally a macropore and becomes a mesopore after conversion, X[3][1] represents the proportion of a pore that was originally a micropore and directly becomes a macropore after conversion, where micropores are not allowed to directly jump to macropores, so it is 0, X[3][2] represents the proportion of a pore that was originally a mesopore and becomes a macropore after conversion, and X[3][3] represents the proportion of a pore that was originally a macropore and remains a macropore after conversion.

[0043] Furthermore, the driving vector includes three driving factors: temperature difference, ice expansion, and damage degree. Among them, the change in the micropore ratio is negatively correlated with the temperature difference, the change in the mesopore ratio is positively correlated with the ice expansion, and the change in the macropore ratio is positively correlated with the damage degree.

[0044] Furthermore, in step S5, the final prediction result of the coal seam pore structure evolution under the action of cryogenic liquid nitrogen includes: the predicted porosity value calculated in step S3, the pore size distribution function, and the pore type conversion result calculated in step S4.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] 1. This invention integrates the three-dimensional pore network of micro-CT with the phase change dynamics parameters of liquid nitrogen to establish a coupled equation of thermal rupture-ice expansion-compression, thereby achieving high-precision prediction of the spatiotemporal evolution of porosity.

[0047] 2. This invention uses a combination of numerical analysis and experimental research to construct a dynamic evolution prediction model of reservoir micropore structure under liquid ammonia injection disturbance, revealing the law and mechanism of the effect of low temperature liquid nitrogen fluid on the adsorption performance of coal.

[0048] 3. The porosity prediction, pore size distribution function and pore type conversion results calculated and predicted by this invention can guide the optimization of liquid nitrogen engineering parameters and provide early warning of the risk of crack runaway. Attached Figure Description

[0049] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0050] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the heat-ice-force coupling mechanism in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] like Figure 1 As shown, the method for predicting coal seam porosity evolution under the action of cryogenic liquid nitrogen includes the following steps:

[0054] Step S1: Obtain the three-dimensional pore structure of the coal sample and calculate the initial pore parameters by micro-CT scanning;

[0055] Step S2: Collect coal seam sample parameters and calibrate key parameters of liquid nitrogen action through liquid nitrogen experiments, including pore freezing point, ice crystal growth rate and ice expansion coefficient;

[0056] Step S3: Construct the pore evolution equation and solve for the predicted porosity and pore size distribution function;

[0057] Step S4: Define pore type and calculate pore type conversion;

[0058] Step S5: Generate the final prediction results of coal seam pore structure evolution under the action of cryogenic liquid nitrogen.

[0059] Porosity: The percentage of total pore volume to total sample volume;

[0060] Fractal dimension: Based on the ratio of the maximum to the minimum value of the pore size distribution, it quantifies the complexity of the pore structure (the larger the value, the more irregular the structure).

[0061] Pore ​​internal surface area: the total surface area of ​​all pore walls per unit volume (determines the liquid nitrogen-coal contact efficiency).

[0062] Step S1 specifically includes the following steps:

[0063] Step S1.1: Drill a standard cylindrical core sample from the target coal seam;

[0064] Step S1.2: Use a micro-CT scanner to perform tomographic scanning on the sample to obtain a three-dimensional grayscale image dataset;

[0065] Step S1.3: Use software to process images and reconstruct the pore network model, specifically including: threshold segmentation to distinguish between solid matrix and pores; pore space binarization; and construction of a three-dimensional pore topology network model.

[0066] Step S1.4: Calculate the initial pore parameters based on the reconstructed pore network model;

[0067] The initial pore parameters include porosity, fractal dimension, and pore internal surface area.

[0068] Pore ​​solidification point calibration:

[0069] The actual freezing point of water in pores of different sizes was measured by low-temperature differential scanning calorimetry (DSC), and a "pore size-freezing point" relationship model was established (the smaller the pore size, the lower the freezing point).

[0070] Ice crystal growth rate calibration:

[0071] Liquid nitrogen was injected into a cryogenic reactor, and the ice crystal growth process was observed in situ using cryo-electron microscopy (Cryo-SEM). The radial expansion rate of ice crystals per unit supercooling was calculated.

[0072] Ice expansion coefficient calibration:

[0073] Liquid nitrogen freeze-thaw experiments were conducted in a true triaxial pressure system. The volume expansion rate of coal samples was measured using strain sensors, and the ice expansion efficiency coefficient was calculated in combination with the water saturation.

[0074] The formula for calculating the initial pore parameters is as follows:

[0075]

[0076] Where φ0 represents the initial porosity of the coal seam sample, V pore V represents the total volume of all pores in a coal seam sample. total D represents the total volume of the coal seam sample. f The fractal dimension represents the pore structure and describes its complexity; a larger value indicates a more complex pore distribution. and S represents the maximum pore radius and the minimum pore radius, respectively. pore R represents the internal surface area of ​​all pores within a unit volume of coal sample, where N represents the total number of pores, and r represents the total surface area of ​​pores within the sample. p,i L represents the radius of the i-th pore. p,i This represents the length of the throat of the i-th pore.

[0077] Determination of basic parameters for coal seam samples: providing necessary initial formation condition parameters for liquid nitrogen experiments (S2) and pore evolution modeling (S3-S4).

[0078] Key parameters and measurement methods:

[0079] Initial water saturation: Coal samples were sealed immediately after drilling, and the laboratory used a weighing method (drying method). The original mass of the coal sample was measured, and the dry coal mass was measured after drying at 105℃ to constant weight. The initial water saturation was calculated by dividing the water content by (pore volume * water density).

[0080] Initial pore pressure: The pressure recovered under simulated formation conditions in the laboratory (triaxial pressure chamber) is used to calculate the net stress.

[0081] Coal mechanical parameters: Elastic modulus, Poisson's ratio, and uniaxial compressive strength were determined by laboratory uniaxial / triaxial compression tests.

[0082] Step S2 specifically includes the following steps:

[0083] A liquid nitrogen circulation experiment was conducted in a cryogenic reactor;

[0084] The formula for calculating the actual solidification point of nanopores is as follows:

[0085]

[0086] Among them, T f This indicates that in a radius of r p The actual freezing point of water in the pores. σ represents the Kelvin temperature corresponding to the standard freezing point of water. iw ρ represents the interfacial tension between ice and water. ice L represents the density of ice. f r represents the amount of heat released when a unit mass of water freezes. p Indicates the pore radius;

[0087] The specific parameter values ​​are shown in Table 1:

[0088] Table 1

[0089]

[0090] Ice crystal growth was observed using cryogenic scanning electron microscopy, and the growth rate was calculated using the following formula:

[0091]

[0092] Where, k g The ice crystal growth rate constant, r, is used to quantify the rate of ice crystal growth. ice The radius of the ice crystal is represented by T, and the real-time temperature of the environment in which the pores are located is represented by T.

[0093] The coefficient of ice expansion is determined by strain measurement, and the calculation formula is as follows:

[0094]

[0095] Where β represents the ice expansion coefficient, used to quantify the volumetric strain caused by unit water saturation and unit ice volume expansion rate, ε v φ represents the volume change of a coal seam sample measured in an experiment. w ΔV represents the water saturation of a coal seam sample, i.e., the volume percentage of water in the pores. iceThis represents the volume expansion rate of water when it freezes, with a fixed value of 0.09.

[0096] In step S3, the specific formula for the dynamic pore evolution equation is as follows:

[0097]

[0098] in, α represents the rate of change of porosity over time. T The coefficient of thermal deterioration is 0.018, which characterizes the intensity of the effect of temperature on thermal deterioration. ΔT represents the difference between the current temperature and the initial temperature. The temperature gradient is represented by t, the duration of liquid nitrogen treatment is represented by τ, and the saturation time for ice crystal growth is represented by e. eff This represents the net stress borne by the coal seam sample, which is the total stress minus the pore pressure.

[0099] Thermal decomposition term: Pore expansion caused by temperature gradient (positively correlated with the amount of temperature change and gradient intensity);

[0100] Ice expansion term: The increase in pore volume caused by ice crystal growth (positively correlated with ice expansion coefficient, water saturation and ice growth rate);

[0101] Compression term: Pore closure caused by geostress (negatively correlated with effective stress intensity).

[0102] In step S3, solving the pore evolution equation specifically includes the following steps:

[0103] The coal body is discretized into 1mm segments. 3 Finite element mesh;

[0104] The explicit time-stepping method is used for iterative calculation, with a time step of 0.1 seconds. The calculation formula is as follows:

[0105]

[0106] Where, φ n+1 φ represents the predicted porosity at time n+1. n ΔT represents the porosity at the current time, i.e., time n. n This indicates the temperature difference at the current moment. This represents the temperature gradient at the current moment. Δt represents the net stress borne by the coal seam sample at the current moment, and Δt represents the time step.

[0107] The aperture distribution is updated synchronously, and the calculation formula is as follows:

[0108]

[0109] in, Let the aperture distribution function at time n+1 be an example. Let R represent the aperture distribution function at the current moment, and let R represent the gas constant. This represents the rate of change of ice volume.

[0110] In step S4, the calculation formula for pore type conversion is as follows:

[0111]

[0112] in, and These represent the proportions of micropores, mesopores, and macropores at time m, respectively, and X represents the pore type transformation matrix. and represent the initial proportions of micropores, mesopores, and macropores, respectively, and b represents the driving term vector;

[0113] Among them, micropores are pores with a diameter of <2nm, mesopores are pores with a diameter of 2-50nm, and macropores are pores with a diameter of >50nm.

[0114] The specific formula for the pore type transformation matrix in the pore type transformation calculation formula is as follows:

[0115]

[0116] The formula for calculating the driving term vector is:

[0117]

[0118] Among them, D d This indicates the degree of damage, describing the extent of damage to the coal body caused by thermal stress.

[0119] The formula for calculating the degree of damage is:

[0120]

[0121] Where C represents the damage accumulation rate constant, typically 0.28, and T ref This represents the temperature scale factor, with a typical value of 293K, or 20℃.

[0122] In step S5, the final prediction result of the coal seam pore structure evolution under the action of cryogenic liquid nitrogen includes: the predicted porosity value calculated in step S3, the pore size distribution function, and the pore type transformation result calculated in step S4.

[0123] The specific settings for the pore type conversion matrix are shown in Table 2.

[0124] Table 2

[0125]

[0126]

[0127] like Figure 2 The diagram shown illustrates the heat-ice-force coupling mechanism:

[0128] 1. Liquid nitrogen was injected into the coal seam, causing a sudden drop in temperature (-196℃).

[0129] 2. Low-temperature activation of phase transition, acting in two directions:

[0130] Pore ​​water transforms into ice → volume expansion → generating frost heave force

[0131] Non-uniform shrinkage of coal body → thermal stress fracture → formation of new fractures

[0132] 3. Frost heave force and thermal stress work together to drive porosity expansion:

[0133] The original pore diameter increases and micro-fractures connect to form seepage channels.

[0134] 4. Optimized pore structure significantly enhances adsorption / diffusion capabilities:

[0135] The methane desorption rate is increased and the gas diffusion efficiency is enhanced.

[0136] The architecture of the liquid nitrogen-coal rock interaction experimental platform in this embodiment:

[0137] 1. The liquid nitrogen supply system delivers -196℃ liquid nitrogen to the cryogenic reactor;

[0138] 2. The coal and rock samples inside the reactor undergo a thermal response due to the sudden drop in temperature;

[0139] 3. Low-temperature-triggered phase transition, with dual pathways jointly driving pore structure evolution;

[0140] 4. Real-time monitoring system synchronously collects data:

[0141] Distributed optical fiber → spatial distribution of temperature field; strain gauge → volume expansion; microseismic sensor → crack development event.

[0142] 5. The imaging analysis system performs final-state detection:

[0143] Micro-CT scanning → 3D pore reconstruction, cryogenic electron microscopy → ice crystal morphology observation, NMR → pore size distribution inversion.

[0144] Experimental workflow:

[0145] Sample loading: Place a 50×100mm coal sample into the reactor and install strain gauges and optical fibers;

[0146] Initial scan: Perform a micro-CT baseline scan to obtain the initial pore structure;

[0147] Liquid nitrogen injection: Inject liquid nitrogen at the set flow rate (e.g., 3L / min) and simultaneously turn on DTS monitoring;

[0148] Dynamic monitoring: Temperature / strain field is recorded every 5 minutes, and rapid imaging of cryogenic electron microscopy is triggered when the temperature is <-100℃;

[0149] Process scanning: Micro-CT in-situ scanning was performed at t = 10 / 30 / 60 min, and the frozen sample was transferred to NMR to measure the pore size distribution;

[0150] Data analysis: By comparing the changes in porosity before and after freeze-thaw cycles, a quantitative relationship between ice crystal growth and pore expansion was established.

[0151] Experimental comparison and verification:

[0152] The predicted porosity spatial distribution map was compared with the measured porosity by CT scan after freeze-thaw cycles, and the relative error was calculated as follows: Porosity error = |predicted value - measured value| / measured value × 100%;

[0153] Requirements: Average error ≤ 8%, and maximum local error ≤ 15%;

[0154] Optimization mechanism: When the average error is greater than 8%, the ice expansion coefficient is automatically adjusted.

[0155] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.

Claims

1. A method for predicting the evolution of coal bed porosity under the action of low-temperature liquid nitrogen, characterized in that, Includes the following steps: Step S1: Obtain the three-dimensional pore structure of the coal sample and calculate the initial pore parameters by micro-CT scanning; Step S2: Collect coal seam sample parameters and calibrate key parameters of liquid nitrogen action through liquid nitrogen experiments, including pore freezing point, ice crystal growth rate and ice expansion coefficient; Step S3: Construct the pore evolution equation and solve for the predicted porosity and pore size distribution function; Step S4: Define pore type and calculate pore type conversion; Step S5: Generate the final prediction results of coal seam pore structure evolution under the action of cryogenic liquid nitrogen; Specifically, step S2 includes the following steps: A liquid nitrogen circulation experiment was conducted in a cryogenic reactor; The actual solidification point of nanopores was calculated using the Gibbs-Thomson equation, which shows an inverse relationship with the pore radius. Ice crystal growth was observed and the growth rate was calculated by cryogenic scanning electron microscopy, and the result was obtained by dividing the rate of change of ice crystal radius by the square of the supercooling. The ice expansion coefficient is determined by strain measurement and is calculated by dividing the sample volume change by the product of water saturation and ice volume expansion rate. In step S3, the specific formula for the dynamic pore evolution equation is as follows: in, This indicates the rate of change of porosity over time. This represents the thermal decomposition coefficient, used to characterize the intensity of the effect of temperature on thermal decomposition. This represents the difference between the current temperature and the initial temperature. Represents the temperature gradient. Indicates the coefficient of ice expansion. This indicates the water saturation of a coal seam sample. This represents the rate of volume expansion when water freezes. Indicates the duration of liquid nitrogen action. Indicates the saturation time of ice crystal growth. This represents the net stress borne by the coal seam sample, which is the total stress minus the pore pressure. In step S3, solving the pore evolution equation specifically includes the following steps: The coal body is discretized into a finite element mesh of 1 mm³; The explicit time-stepping method is used for iterative calculation, with a time step of 0.1 seconds. The updated porosity is equal to the current porosity plus the change in porosity multiplied by the time step. Among them, the change in porosity includes thermal fracturing, ice expansion, and compression. Thermal fracturing represents the amount of coal body fracturing and expansion caused by temperature gradient, ice expansion represents the volume increase caused by ice crystal growth compressing the pore walls, and compression represents the compressive effect of geostress on the pores. The pore size distribution is updated synchronously. The updated pore distribution function is equal to the current pore distribution function plus the change in porosity distribution multiplied by the time step. Among them, the change in porosity distribution includes: temperature-driven term and ice expansion-driven term. The temperature-driven term is proportional to the temperature difference and the temperature-related exponential decay function, while the ice expansion-driven term is proportional to the product of the negative fractal dimension of the pore radius and the rate of change of ice volume. In step S4, the calculation formula for pore type conversion is as follows: in, , and These represent the proportions of micropores, mesopores, and macropores at time m, respectively. This represents the pore type transformation matrix. , and These represent the initial proportions of micropores, mesopores, and macropores, respectively. Represents the driving term vector; Among them, micropores are pores with a diameter of less than 2 nm, mesopores are pores with a diameter of 2-50 nm, and macropores are pores with a diameter of more than 50 nm. In the pore type transformation matrix, the diagonal elements represent the pore type self-retention probability, and the off-diagonal elements represent the inter-type transformation probability: where X[1][1] represents the proportion of a pore that was originally a micropore and remains a micropore after transformation, X[1][2] represents the proportion of a pore that was originally a mesopore and becomes a micropore after transformation, X[1][3] represents the proportion of a pore that was originally a macropore and becomes a micropore after transformation, X[2][1] represents the proportion of a pore that was originally a micropore and becomes a mesopore after transformation, X[2][2] represents the proportion of a pore that was originally a mesopore and remains a mesopore after transformation, X[2][3] represents the proportion of a pore that was originally a macropore and becomes a mesopore after transformation, X[3][1] represents the proportion of a pore that was originally a micropore and directly becomes a macropore after transformation, where micropores are not allowed to directly jump to macropores, so it is 0, X[3][2] represents the proportion of a pore that was originally a mesopore and becomes a macropore after transformation, and X[3][3] represents the proportion of a pore that was originally a macropore and remains a macropore after transformation.

2. The method of claim 1, wherein, Step S1 specifically includes the following steps: Step S1.1: Drill a standard cylindrical core sample from the target coal seam; Step S1.2: Use a micro-CT scanner to perform tomographic scanning on the sample to obtain a three-dimensional grayscale image dataset; Step S1.3: Use software to process images and reconstruct the pore network model, specifically including: threshold segmentation to distinguish between solid matrix and pores; pore space binarization; and construction of a three-dimensional pore topology network model. Step S1.4: Calculate the initial pore parameters based on the reconstructed pore network model; The initial pore parameters include: porosity, fractal dimension, and pore internal surface area; Among them, porosity is the percentage of total pore volume to total sample volume; fractal dimension is calculated based on the natural logarithm of the ratio of the maximum pore radius to the minimum pore radius and is used to quantify the complexity of pore structure; and pore internal surface area is the sum of the surface areas of all pore walls per unit volume.

3. The method of claim 2, wherein, The formula for calculating the initial pore parameters is as follows: in, This indicates the initial porosity of the coal seam sample. This represents the total volume of all pores in a coal seam sample. This represents the total volume of the coal seam sample. The fractal dimension represents the pore structure and describes its complexity; a larger value indicates a more complex pore distribution. and These represent the maximum pore radius and the minimum pore radius, respectively. This represents the internal surface area of ​​all pores within a unit volume of coal sample. Indicates the total number of pores. This represents the radius of the i-th pore. This represents the length of the throat of the i-th pore.

4. The method of claim 3, wherein, The driving vector includes three driving factors: temperature difference, ice expansion, and damage degree. Among them, the change in the micropore ratio is negatively correlated with the temperature difference, the change in the mesopore ratio is positively correlated with the ice expansion, and the change in the macropore ratio is positively correlated with the damage degree.

5. The method of claim 4, wherein, In step S5, the final prediction result of the coal seam pore structure evolution under the action of cryogenic liquid nitrogen includes: the predicted porosity value calculated in step S3, the pore size distribution function, and the pore type transformation result calculated in step S4.

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