Heat conductivity coefficient prediction method of three-dimensional mixed medium model based on phase change condition
By constructing a 3-dimensional mixed media model based on phase change conditions in geological energy mining, a multi-component mixed media model is generated and numerical simulation is performed, the problem of insufficient calculation accuracy of the thermal conductivity coefficient of mixed media in the prior art is solved, and a higher precision thermal conductivity prediction is achieved.
Patent Information
- Application Number
- CN202411994480.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2025-04-18
AI Technical Summary
The calculation results of the existing theoretical method of thermal conductivity of mixed media are insufficient under phase change conditions, and cannot effectively reflect the influence of phase change on the thermal conductivity of multiphase mixed media.
A 3-dimensional mixed medium model based on phase change conditions was used to construct a matrix-pore 3-dimensional porous medium model through CT scanning images, and a gas phase, aqueous phase, hydrate phase and ice phase were generated by a four-parameter random method to form a multi-component mixed medium model, and the thermal conductivity equation was solved through numerical simulation method to predict the thermal conductivity coefficient.
It improves the accuracy of thermal conductivity prediction, can more accurately reflect the impact of phase change on the thermal conductivity of mixed media, and enhances the understanding of reservoir temperature field evolution laws in geological energy mining.
Smart Images
Figure CN119846012A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of geological resource exploitation, and in particular to a method for predicting thermal conductivity of a three-dimensional mixed medium model based on phase change conditions. Background Art
[0002] Natural gas hydrates (commonly known as "combustible ice", a cage-type crystalline hydrate formed under low temperature and high pressure conditions) have huge economic potential. However, the geological energy extraction process is accompanied by phase changes (for example: hydrate decomposition and secondary synthesis during natural gas hydrate extraction; water phase change to ice at low temperature, etc.), resulting in unclear evolution of reservoir temperature field, which affects the efficient extraction of geological energy.
[0003] The effective thermal conductivity (ETC) of the reservoir plays a vital role in controlling the temperature of geological energy extraction and also affects the evolution law of the reservoir temperature field.
[0004] It is difficult to conduct experiments to determine the effective thermal conductivity of rock and soil under phase change conditions in the laboratory. The current experimental methods mainly include the thermal probe method and the transient plane heat source method to determine the thermal conductivity of rock and soil media. However, the experimental methods cannot accurately control the pore morphology of hydrates and ice in rock and soil media, which makes it very difficult to repeat related experiments and quantitatively analyze the effects of various factors on the ETC of matrix particles-gas phase-water phase-natural gas hydrate-ice phase mixed media.
[0005] Therefore, domestic and foreign scholars have proposed many theoretical methods to calculate the ETC of mixed media, which is also one of the current research hotspots in the heat transfer process of geological energy mining reservoirs. However, due to the complex distribution pattern of matrix particles-gas phase-water phase-natural gas hydrate-ice phase in the rock and soil medium under phase change conditions, the existing rock and soil medium micro-geometry structure constructed based on the porous random mixed medium model is highly random, and the statistical characteristics of the geometric structure are quite different from the actual multi-component mixed medium, and the representativeness needs to be strengthened.
[0006] In addition, the calculation results of existing mixed medium ETC theoretical methods (such as the weighted harmonic mean method of thermal conductivity of each phase and the weighted arithmetic mean method of each phase) are not accurate enough and cannot reflect the impact of phase change on the ETC of multiphase mixed media. Summary of the invention
[0007] The purpose of the present invention is to provide a thermal conductivity prediction method for a 3D mixed medium model based on phase change conditions in order to solve the problem of insufficient calculation accuracy of the existing mixed medium ETC theoretical method.
[0008] The above-mentioned purpose of the present application is achieved through the following technical solutions: S1: Obtain CT scan images of heterogeneous cores and perform preprocessing; construct a matrix-pore 3D porous media model through the preprocessed CT scan images; S2: Based on the reconstructed matrix-pore 3D porous media model, the pore structure parameters of the heterogeneous core are obtained; S3: Based on the pore structure parameters, the gas phase and water phase are generated in the pore region of the matrix-pore 3D porous media model in combination with the four-parameter stochastic method; S4: Based on the hydrate formation phase transition conditions, combined with the four-parameter stochastic method, the hydrate phase is generated at the gas-water interface of the matrix-pore 3D porous media model; S5: Based on the phase transition conditions of ice formation, combined with the four-parameter stochastic method, ice phase is generated at the water-rock particle interface of the matrix-pore 3D porous media model to obtain a 3D multi-component mixed media model; S6: Based on the 3D multi-component mixed medium model, thermal conductivity simulation is carried out to predict the thermal conductivity of the 3D multi-component mixed medium under phase change conditions.
[0009] Optionally, step S1 includes: Preprocessing includes: cropping, binary segmentation, filtering and denoising; The matrix-pore three-dimensional porous media model was constructed by combining the preprocessed CT scan images with image overlay and interpolation algorithms.
[0010] Optionally, step S2 includes: Pore structure parameters include: core pore volume , Porosity , pore size distribution, maximum pore radius , minimum pore radius and pore fractal dimension .
[0011] Optionally, step S3 includes: Set initial gas saturation and initial water saturation , ; According to the core pore volume in the pore structure parameters , initial gas saturation and initial water saturation , calculate the initial gas volume in the core and the initial water volume in the core ,as follows:
[0012]
[0013] Using the four-parameter stochastic method, the pore region in the matrix-pore 3D porous media model is generated. Volume of gas phase and volume of aqueous phase; The gas phase is distributed in the central area of the pores, and the water phase is distributed in the wall area of the pores.
[0014] Optionally, step S4 includes: When the gas-water interface of the matrix-pore 3D porous media model meets the phase change condition for hydrate formation, the gas-water interface region is subjected to gas phase expansion or water phase expansion treatment to determine the hydrate formation region; The phase transition conditions for hydrate formation are: the gas-water interface reaches the preset temperature-pressure conditions, which will cause the gas phase and the water phase to form natural gas hydrates; The hydrate saturation is set, and the hydrate phase is generated in the hydrate formation area using the four-parameter random method.
[0015] Optionally, step S5 includes: When the water-rock particle interface of the matrix-pore 3D porous media model meets the phase transition conditions for ice formation, the water-rock particle interface region is subjected to water phase expansion treatment to determine the ice formation region; The phase transition conditions for ice formation are: the water-rock particle interface reaches the preset temperature-pressure conditions, which will cause the water phase to form ice; The ice saturation is set and the ice phase is generated in the ice generation area using a four-parameter random method.
[0016] Optionally, step S6 includes: The multi-component mixed medium includes: matrix particles, gas phase, water phase, natural gas hydrate phase and ice phase; The heat conduction equation in the matrix-pore 3D porous media model is solved by numerical simulation method to obtain the thermal conductivity of the matrix-pore 3D porous media model and predict the thermal conductivity of the 3D multi-component mixed media under phase change conditions. Numerical simulation methods include: finite volume method and finite element method.
[0017] An electronic device comprises a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a thermal conductivity prediction method for a 3D mixed medium model based on phase change conditions.
[0018] A computer-readable storage medium stores instructions. When the instructions are executed, a thermal conductivity prediction method of a three-dimensional mixed medium model based on phase change conditions is performed.
[0019] The beneficial effects of the technical solution provided by this application are: Based on the CT scan images of heterogeneous cores, combined with image processing methods and mathematical statistics, a matrix-pore 3D porous media model is generated; the random four-parameter method is used to generate gas and water phases with specific saturation in the pores. On this basis, considering the real ambient temperature and pressure of the porous medium, combined with thermodynamics and the random four-parameter method, the fourth phase (gas hydrate phase) or the fifth phase (ice phase) with specific saturation is generated in the pore grid occupied by the gas phase or water phase, forming a 3D multi-component mixed medium model based on the digital core. Compared with the porous random mixed medium model, this mixed medium model comprehensively considers the distribution of matrix particles-gas phase-water phase-gas hydrate-ice in the core, pore size distribution, occurrence mode of each phase and saturation of each phase. The constructed digital core micro-geometric structure statistical characteristics are consistent with the actual multi-component mixed medium, and the model accuracy is higher. Furthermore, based on the generated multi-component mixed medium model, the heat conduction equation in the mixed medium is solved by numerical simulation method to obtain the thermal conductivity of the mixed medium, thereby realizing the three-dimensional multi-component mixed medium modeling and thermal conductivity prediction under phase change conditions. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The present application will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 is a step diagram in an embodiment of the present application; Figure 2 is a first matrix-pore 3D porous medium model diagram in an embodiment of the present application; Figure 3 is a CT scan image in an embodiment of the present application; Figure 4 is a diagram of a second matrix-pore 3D porous medium model in an embodiment of the present application; Figure 5 is a diagram of a third matrix-pore 3D porous medium model in an embodiment of the present application; Figure 6 It is a 3D multi-component mixed medium model diagram in the embodiment of the present application; Figure 7 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION
[0021] In order to have a clearer understanding of the technical features, purposes and effects of the present application, the specific implementation methods of the present application are now described in detail with reference to the accompanying drawings.
[0022] The embodiments of the present application provide a method for predicting thermal conductivity of a 3D mixed medium model based on phase change conditions.
[0023] Please refer to Figure 1 , Figure 1 : is a step diagram of a thermal conductivity prediction method of a 3D mixed medium model based on phase change conditions in an embodiment of the present application, comprising: S1: Obtain CT scan images of heterogeneous cores and perform preprocessing; construct a matrix-pore 3D porous media model through the preprocessed CT scan images; S2: Based on the reconstructed matrix-pore 3D porous media model, the pore structure parameters of the heterogeneous core are obtained; S3: Based on the pore structure parameters, the gas phase and water phase are generated in the pore region of the matrix-pore 3D porous media model in combination with the four-parameter stochastic method; S4: Based on the hydrate formation phase transition conditions, combined with the four-parameter stochastic method, the hydrate phase is generated at the gas-water interface of the matrix-pore 3D porous media model; S5: Based on the phase transition conditions of ice formation, combined with the four-parameter stochastic method, ice phase is generated at the water-rock particle interface of the matrix-pore 3D porous media model to obtain a 3D multi-component mixed media model; S6: Based on the 3D multi-component mixed medium model, thermal conductivity simulation is carried out to predict the thermal conductivity of the 3D multi-component mixed medium under phase change conditions.
[0024] Step S1 includes: Preprocessing includes: cropping, binary segmentation, filtering and denoising; The matrix-pore three-dimensional porous media model was constructed by combining the preprocessed CT scan images with image overlay and interpolation algorithms.
[0025] As an example, a CT scan is performed on a heterogeneous core to determine the pore structure of the heterogeneous core. A series of two-dimensional grayscale slice images obtained by the core CT scan are cropped (to obtain the middle rectangular area), binarized (watershed algorithm), filtered and denoised, and the processed images are imported into a three-dimensional image reconstruction and analysis software (such as Avizo, etc.), and a matrix-pore three-dimensional porous medium model is reconstructed through image superposition and interpolation algorithms, such as Figure 2 -a, where gray represents matrix particles and blue represents pores, and the pore structure of the core is extracted, as shown in Figure 2 -b as shown.
[0026] As an example, the idea of the watershed algorithm is: first, classify all pixels in the gradient image according to their grayscale values and set a grayscale threshold. Then find the pixel with the smallest grayscale value (starting point) within the range, and let the calculation range grow from the minimum value. During the growth of the horizontal plane, it will encounter the surrounding neighborhood voxels. If the grayscale value of the voxel is less than the set threshold, these pixels will be submerged. Otherwise, a dam will be set on these pixels. In this way, the voxels within the range are classified. Figure 3 The watershed algorithm can be used to distinguish matrix particles and pores in the CT scan image.
[0027] Step S2 includes: Pore structure parameters include: core pore volume , Porosity , pore size distribution, maximum pore radius , minimum pore radius and pore fractal dimension .
[0028] Step S3 includes: Set initial gas saturation and initial water saturation , ; According to the core pore volume in the pore structure parameters , initial gas saturation and initial water saturation , calculate the initial gas volume in the core and the initial water volume in the core ,as follows:
[0029]
[0030] Using the four-parameter stochastic method, the pore region in the matrix-pore 3D porous media model is generated. Volume of gas phase and volume of aqueous phase; The gas phase is distributed in the central area of the pores, and the water phase is distributed in the wall area of the pores.
[0031] Step S4 includes: When the gas-water interface of the matrix-pore 3D porous media model meets the phase change condition for hydrate formation, the gas-water interface region is subjected to gas phase expansion or water phase expansion treatment to determine the hydrate formation region; The phase transition conditions for hydrate formation are: the gas-water interface reaches the preset temperature-pressure conditions, which will cause the gas phase and the water phase to form natural gas hydrates; The hydrate saturation is set, and the hydrate phase is generated in the hydrate formation area using the four-parameter random method.
[0032] As an example, the hydrate formation phase change process is: according to the natural gas hydrate phase equilibrium curve, the experimental temperature-pressure conditions are set. For example, according to the methane hydrate phase equilibrium curve, when the temperature is 275 K and the pressure is 4 MPa, methane hydrate can be stably present. Therefore, when the gas-water interface reaches the preset temperature-pressure conditions (temperature 275 K, pressure 5 MPa), it will cause the gas phase and the water phase to generate natural gas hydrates; As an embodiment, the idea of generating hydrates by the four-parameter random algorithm is: first, the initial growth point of the hydrate is randomly generated in the expansion area; then, based on the hydrate growth rate obtained under laboratory conditions (for example, 0.01), starting from the initial growth point, the hydrate grows in 26 directions with the growth probability, and when the preset hydrate saturation is reached, the growth process stops. Figure 4 (a) is a digital core map of matrix particle-water-gas distribution; Figure 4 (b) is a digital core map of matrix particle-water-hydrate-gas distribution generated in the gas-water expansion region using a four-parameter random algorithm, where gray represents matrix particles, blue represents water, red represents gas, and yellow represents hydrate.
[0033] Step S5 includes: When the water-rock particle interface of the matrix-pore 3D porous media model meets the phase transition conditions for ice formation, the water-rock particle interface region is subjected to water phase expansion treatment to determine the ice formation region; The phase transition conditions for ice formation are: the water-rock particle interface reaches the preset temperature-pressure conditions, which will cause the water phase to form ice; The ice saturation is set and the ice phase is generated in the ice generation area using a four-parameter random method.
[0034] As an example, the water-rock particle interface reaches the preset temperature-pressure condition with the pressure maintained at 4 MPa and the temperature reduced to 273 K. As an embodiment, the idea of generating ice by the four-parameter random algorithm is: first, the initial growth point of the ice phase is randomly generated in the expansion area; then, according to the growth rate of ice under laboratory conditions (for example, 0.05), from the initial growth point of the ice, the ice grows in 26 directions at this growth rate, and when the preset ice saturation is reached, the growth process stops. Figure 5 This is a digital core map of matrix particle-ice-water-gas distribution generated in the area where the ice phase is to grow using a four-parameter random algorithm. Gray represents matrix particles, blue represents water, red represents gas, and the yellow area represents the ice phase.
[0035] Step S6 includes: The multi-component mixed medium includes: matrix particles, gas phase, water phase, natural gas hydrate phase and ice phase; The heat conduction equation in the matrix-pore 3D porous media model is solved by numerical simulation method to obtain the thermal conductivity of the matrix-pore 3D porous media model and predict the thermal conductivity of the 3D multi-component mixed media under phase change conditions. Numerical simulation methods include: finite volume method and finite element method.
[0036] As an example, in a 3D multi-component mixed medium model consisting of matrix particles-gas phase-water phase-natural gas hydrate-ice ( Figure 6 ) to perform numerical simulation of heat conduction. If the thermal conductivity of the mixed medium in the x direction is required, the boundary conditions can be defined as: the y and z directions are adiabatic boundaries; the mixed medium satisfies the heat inflow boundary , at the heat outflow boundary .
[0037] The mixed medium model is meshed to ensure that each grid contains only single-phase material. According to the corresponding material in each grid, the corresponding thermal conductivity value is assigned to the material. For example, if the grid contains matrix particles, then the thermal conductivity of the grid is the thermal conductivity of the matrix particles (8.8 ); if the grid is gas phase, then the thermal conductivity of the grid is the thermal conductivity of the gas phase (0.034 ); if the grid contains water phase, the thermal conductivity of the grid is the thermal conductivity of the water phase (0.6 ); if the grid is in ice phase, then the thermal conductivity of the grid is the thermal conductivity of the ice phase (2.24 ); if the grid contains hydrate, the thermal conductivity of the grid is the thermal conductivity of hydrate (0.57 ).
[0038] According to the steady-state heat conduction control equation of porous media
[0039] Where: is the thermal conductivity of the medium; is the total heat flux on the boundary.
[0040] The above equation is solved using numerical simulation methods (finite volume method, finite element method). The effective thermal conductivity of the mixed medium obtained according to Fourier's law The calculation formula is:
[0041] Where: is the core length through which the heat flows, is the cross-sectional area of the core through which the heat flows, For the temperature difference.
[0042] The present application also discloses an electronic device. Figure 7 , Figure 7 The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .
[0043] The communication bus 502 is used to realize the connection and communication between these components.
[0044] The user interface 503 may include a display screen, and the optional user interface 503 may also include a standard wired interface or a wireless interface.
[0045] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).
[0046] The present application also discloses a computer-readable storage medium storing a plurality of instructions suitable for loading by a processor to execute the above-mentioned thermal conductivity prediction method of a 3D mixed medium model based on phase change conditions.
[0047] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure.
[0048] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.
Claims
1. A method for predicting thermal conductivity of a three-dimensional mixed medium model based on phase change conditions, characterized in that: The method comprises the following steps: S1: Obtain CT scan images of heterogeneous cores and perform preprocessing; construct a matrix-pore 3D porous media model through the preprocessed CT scan images; S2: Based on the reconstructed matrix-pore 3D porous media model, the pore structure parameters of the heterogeneous core are obtained; S3: Based on the pore structure parameters, the gas phase and water phase are generated in the pore region of the matrix-pore 3D porous media model in combination with the four-parameter stochastic method; S4: Based on the hydrate formation phase transition conditions, combined with the four-parameter stochastic method, the hydrate phase is generated at the gas-water interface of the matrix-pore 3D porous media model; S5: Based on the phase transition conditions of ice formation, combined with the four-parameter stochastic method, ice phase is generated at the water-rock particle interface of the matrix-pore 3D porous media model to obtain a 3D multi-component mixed media model; S6: Based on the 3D multi-component mixed medium model, thermal conductivity simulation is carried out to predict the thermal conductivity of the 3D multi-component mixed medium under phase change conditions.
2. A method for predicting thermal conductivity of a three-dimensional mixed medium model based on phase change conditions as claimed in claim 1, characterized in that: Step S1 includes: Preprocessing includes: cropping, binary segmentation, filtering and denoising; The matrix-pore three-dimensional porous media model was constructed by combining the preprocessed CT scan images with image overlay and interpolation algorithms.
3. The method for predicting thermal conductivity of a three-dimensional mixed medium model based on phase change conditions according to claim 1, characterized in that: Step S2 includes: The pore structure parameter is the core pore volume .
4. The method for predicting thermal conductivity of a three-dimensional mixed medium model based on phase change conditions according to claim 1, characterized in that: Step S3 includes: Set initial gas saturation and initial water saturation , ; According to the core pore volume in the pore structure parameters , initial gas saturation and initial water saturation , calculate the initial gas volume in the core and the initial water volume in the core ,as follows: Using the four-parameter stochastic method, the pore region in the matrix-pore 3D porous media model is generated. Volume of gas phase and volume of aqueous phase; The gas phase is distributed in the central area of the pores, and the water phase is distributed in the wall area of the pores.
5. The method for predicting thermal conductivity of a three-dimensional mixed medium model based on phase change conditions according to claim 1, characterized in that: Step S4 includes: When the gas-water interface of the matrix-pore 3D porous media model meets the phase change condition for hydrate formation, the gas-water interface region is subjected to gas phase expansion or water phase expansion treatment to determine the hydrate formation region; The phase transition conditions for hydrate formation are: the gas-water interface reaches the preset temperature-pressure conditions, which will cause the gas phase and the water phase to form natural gas hydrates; The hydrate saturation is set, and the hydrate phase is generated in the hydrate formation area using the four-parameter random method.
6. The method for predicting thermal conductivity of a three-dimensional mixed medium model based on phase change conditions according to claim 1, characterized in that: Step S5 includes: When the water-rock particle interface of the matrix-pore 3D porous media model meets the phase transition conditions for ice formation, the water-rock particle interface region is subjected to water phase expansion treatment to determine the ice formation region; The phase transition conditions for ice formation are: the water-rock particle interface reaches the preset temperature-pressure conditions, which will cause the water phase to form ice; The ice saturation is set and the ice phase is generated in the ice generation area using a four-parameter random method.
7. The method for predicting thermal conductivity of a three-dimensional mixed medium model based on phase change conditions according to claim 1, characterized in that: Step S6 includes: The multi-component mixed medium includes: matrix particles, gas phase, water phase, natural gas hydrate phase and ice phase; The heat conduction equation in the matrix-pore 3D porous media model is solved by numerical simulation method to obtain the thermal conductivity of the matrix-pore 3D porous media model and predict the thermal conductivity of the 3D multi-component mixed media under phase change conditions. Numerical simulation methods include: finite volume method and finite element method.
8. An electronic device, characterized in that: The electronic device comprises a processor (501), a memory (505), a user interface (503) and a network interface (504), wherein the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device executes the method according to any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by a computer, the method according to any one of claims 1 to 7 is executed.