Method and system for predicting pore change tendency of porous medium

Abstract

The invention discloses a method and system for predicting the pore change tendency of a porous medium, and relates to the technical field of porous medium pore evolution analysis, and the method comprises the following steps: obtaining a two-dimensional slice image and a three-dimensional image of the porous medium through a nondestructive testing technology in advance; based on the two-dimensional slice imaging, calculating a two-dimensional fractal dimension by using a box counting dimension method, and calculating two-dimensional stability through the two-dimensional fractal dimension; and calculating a three-dimensional fractal dimension by using a box counting dimension method based on the three-dimensional imaging, and calculating the three-dimensional stability through the three-dimensional fractal dimension. According to the method, the limitation of static characterization is broken through, the limitation of traditional pore structure static characterization is broken through by innovatively introducing stability parameters and calculating the stability change rate, the change tendency of the pores of the porous medium can be dynamically and quantitatively evaluated, and a new view angle and method are provided for researching pore evolution.

Description

Technical Field

[0001] The present invention relates to the technical field of porous medium pore evolution analysis, and in particular to a method and system for predicting pore change tendency of porous media. Background Art

[0002] In many scientific research and engineering fields involving the analysis of pore evolution in porous media, such as geological engineering, groundwater recharge, and energy reservoir development, it is crucial to accurately understand the changing patterns of pores in porous media. However, traditional techniques for measuring parameters such as porosity have significant drawbacks, as follows:

[0003] 1. Destructive sampling. Traditional physical experimental methods, such as mercury porosimetry and gas adsorption, typically require the destruction of the medium sample to obtain pore structure information. This method not only fails to dynamically monitor the medium in situ and capture pore changes over time, but also makes it difficult to replicate experimental results due to sample destruction, reducing the reliability and scientific nature of the research.

[0004] 2. Lack of dynamic prediction capabilities. Existing analysis methods mostly focus on the instantaneous description of pore morphology, such as characterizing the geometric complexity of pore boundaries through calculation of fractal dimension. However, fractal dimension is essentially a static parameter that can only reflect the complexity of the current pore morphology and cannot directly correlate with the future dynamic evolution trend of the medium's pores. Therefore, it cannot meet the needs of forward-looking research on pore changes.

[0005] 3. Inadequate scale adaptability. In macro-engineering scenarios, the size of porous media often exceeds the limits of detection equipment. For example, industrial CT scans are generally suitable for inspection objects within a scale of 1 meter. When dealing with larger porous media, existing methods struggle to accurately reflect the overall pore evolution through local sampling, resulting in significant limitations in practical applications.

[0006] In recent years, fractal theory has been introduced into the field of porous media pore analysis due to its ability to quantify complex structures, but its application has remained limited to static characterization using fractal dimensions. Furthermore, while nondestructive testing techniques, such as nuclear magnetic resonance (NMR) and industrial CT, have enabled nondestructive imaging of pore structures, the data obtained by these techniques are mostly used only to reconstruct pore network models or calculate porosity. Effective methods for integrating these techniques with theories for predicting media pore evolution have yet to emerge.

[0007] Therefore, it is urgent to develop a method that can integrate non-destructive testing technology with the dynamic evolution theory of medium pores to meet the needs of predicting pore changes in porous media in scientific research and engineering practice.

[0008] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0009] In response to the problems in the related art, the present invention proposes a method and system for predicting the pore change tendency of porous media to overcome the above-mentioned technical problems existing in the existing related art.

[0010] The technical solution of the present invention is achieved as follows:

[0011] In one aspect, the present invention:

[0012] A method for predicting pore change tendency of a porous medium comprises the following steps:

[0013] Step S1, obtaining two-dimensional slice imaging and three-dimensional stereo imaging of the porous medium by non-destructive testing technology in advance;

[0014] Step S2, based on the two-dimensional slice imaging, calculating the two-dimensional fractal dimension using a box counting method, and calculating the two-dimensional stability using the two-dimensional fractal dimension;

[0015] Step S3, based on the three-dimensional stereoscopic imaging, calculating the three-dimensional fractal dimension using a box counting method, and calculating the three-dimensional stability using the three-dimensional fractal dimension;

[0016] Step S4, repeating the above steps at multiple characteristic time points or time segments divided in equal proportions, and calculating the stability change rate between different time points or in different time segments;

[0017] Step S5: predicting the evolution tendency of the pores in the porous medium according to the calculated stability change rate.

[0018] Wherein, the non-destructive testing technology includes magnetic resonance imaging or industrial CT scanning.

[0019] The two-dimensional stability is calculated as follows:

[0020] S = |1.5-D|;

[0021] Here, S represents two-dimensional stability, and D represents the two-dimensional fractal dimension. The range of D is 1.0 < D < 2.0. A D value of 1.0 marks the boundary between a one-dimensional image and a two-dimensional image, while a D value of 2.0 marks the boundary between a two-dimensional image and a three-dimensional image. It can be deduced that the closer the fractal dimension is to the three-dimensional critical point of 2.0 or the one-dimensional critical point of 1.0, the more stable the image boundary tends to be. However, the closer the fractal dimension is to the intermediate value of 1.5 between 1.0 and 2.0, the more complex and chaotic the boundary of the two-dimensional image being analyzed tends to be, i.e., unstable. Therefore, the range of S is 0 < S < 0.5. Smaller S values ​​indicate lower stability of the two-dimensional image, while larger values ​​indicate higher stability.

[0022] The calculation of three-dimensional stability is expressed as:

[0023] S = |2.5-D|;

[0024] Here, S represents three-dimensional stability, and D represents the three-dimensional fractal dimension. The range of the three-dimensional fractal dimension D is 2.0 < D < 3.0. When the fractal dimension approaches the four-dimensional critical point of 3.0 or the two-dimensional critical point of 2.0, the image boundary tends to be stable. However, as the fractal dimension approaches the intermediate value of 2.5 between 2.0 and 3.0, the three-dimensional image boundary tends to be complex and chaotic, i.e., unstable. Therefore, the range of S values ​​is also 0 < S < 0.5. Smaller S values ​​indicate lower stability of the three-dimensional image, while larger S values ​​indicate higher stability.

[0025] Current nondestructive testing technology can generally accommodate inspection objects with three-dimensional dimensions within 1 meter. For projects or research objects involving porous media with dimensions of less than 1 meter in length, width and height, they can be directly placed in nondestructive testing devices (such as nuclear magnetic resonance testing devices, industrial CT scanning testing devices) for scanning and imaging. For projects or research objects involving porous media with dimensions greater than 1 meter or far exceeding 1 meter, one or more representative unit bodies can be taken and placed in nondestructive testing devices for scanning and imaging.

[0026] At different characteristic time points, or in time segments divided proportionally according to the test or construction period, non-destructive testing is performed and stability is calculated. The rate of change of stability in the time segment can be used to understand the subsequent porosity change trend over time in the same scientific research or engineering scenario. The stability change rate is calculated as:

[0027] R s =(S t -S0) / S0;

[0028] Among them, R s is the image stability change rate, S0 is the stability at the previous time node or before the start of the time segment, S t It refers to the stability after the end of the next time node or time segment.

[0029] The method of predicting the evolution tendency of pores in a porous medium comprises the following steps:

[0030] If the stability change rate R s If it is a negative value, the pore morphology is judged to be unstable;

[0031] If the stability change rate R s If it is a positive value, it is judged that the pore morphology tends to be stable.

[0032] Another aspect of the present invention is:

[0033] A prediction system for pore change tendency of porous media, comprising:

[0034] Nondestructive testing module for obtaining two-dimensional and three-dimensional images of porous media;

[0035] Fractal dimension calculation module, which analyzes imaging data based on box counting dimension method and outputs fractal dimension value;

[0036] Stability calculation module, which calculates two-dimensional and three-dimensional stability based on fractal dimension values;

[0037] The dynamic prediction module generates a pore evolution tendency report based on the stability change rate.

[0038] Beneficial effects of the present invention:

[0039] 1. This invention breaks through the limitations of static characterization by innovatively introducing stability parameters and calculating the stability change rate. It breaks the limitations of traditional static characterization of pore structure and can dynamically and quantitatively evaluate the changing tendency of pores in porous media, providing a new perspective and method for studying pore evolution.

[0040] 2. The present invention achieves full-scale coverage. By proposing a representative unit-level detection strategy for porous media of different scales, it is suitable for the study of overall imaging of media within 1 m at the laboratory scale, and can also meet the needs of local precise analysis of media exceeding 1 m at the engineering macroscale. It achieves coverage of the entire scene from micro to macro, and improves the versatility and practicality of the method.

[0041] 3. The present invention provides a predictive basis. By accurately predicting the pore evolution tendency through the stability change rate, it provides a reliable basis for predicting the dynamic evolution of pores for scientific research and engineering scenarios involving pore changes in porous media, especially porosity changes. This helps to optimize engineering design, improve engineering safety and resource utilization efficiency, and promote the development of related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 1 is a flow chart of a method for predicting pore change tendency of porous media according to an embodiment of the present invention;

[0044] Figure 2 2. It is a schematic diagram of non-destructive testing two-dimensional imaging of a method for predicting pore change tendency of porous media according to an embodiment of the present invention;

[0045] Figure 3 3D imaging schematic diagram of nondestructive testing of a method for predicting pore change tendency of porous media according to an embodiment of the present invention;

[0046] Figure 4 3. It is a schematic diagram of the stability of a quartz sand medium at different depths before a reinjection test according to a method for predicting pore change tendency of a porous medium according to an embodiment of the present invention;

[0047] Figure 5 Schematic diagram of stability at different depths of a glass bead medium before a reinjection test according to a method for predicting pore change tendency of a porous medium according to an embodiment of the present invention;

[0048] Figure 6 Schematic diagram of stability at different depths of a quartz sand medium after a reinjection test according to a method for predicting pore change tendency of a porous medium according to an embodiment of the present invention;

[0049] Figure 7 Schematic diagram of stability at different depths of a glass bead medium after a reinjection test according to a method for predicting pore change tendency of a porous medium according to an embodiment of the present invention;

[0050] Figure 8 1 is a schematic diagram of the stability change rate at different depths before and after a reinjection test of a quartz sand medium according to a method for predicting the pore change tendency of a porous medium according to an embodiment of the present invention;

[0051] Figure 9 Schematic diagram of the stability change rate at different depths before and after a reinjection test of a glass bead medium according to a method for predicting pore change tendency of a porous medium according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention are within the scope of protection of the present invention.

[0053] According to an embodiment of the present invention, a method for predicting pore change tendency of a porous medium is provided.

[0054] like Figure 1 As shown, the method for predicting the pore change tendency of a porous medium according to an embodiment of the present invention includes the following steps:

[0055] Imaging acquisition: The medium in the groundwater artificial recharge particle blockage test was placed in a non-destructive testing device for scanning and imaging before the start of recharge and 127 hours after recharge (this time point was determined by preliminary experiments to be the time point when the blockage develops to the maximum value), and two-dimensional slice imaging was obtained, such as Figure 2 As shown in the figure, the three-dimensional whole image is obtained by superimposing the two-dimensional slice images, as shown in the figure. Figure 3 shown.

[0056] Stability calculation: The two-dimensional fractal dimension is obtained by using the box counting method. Substituting it into the two-dimensional stability calculation formula S = |1.5-D|, the two-dimensional stability of the sand column with quartz sand and glass beads at different depths before and after the groundwater recharge test is obtained, and displayed in the form of a scatter plot, as shown in the figure below. Figure 4-Figure 7 At the same time, the three-dimensional fractal dimension was calculated and substituted into the three-dimensional stability calculation formula S = |2.5-D| to obtain the three-dimensional stability of the two media sand columns before and after the reinjection test as a whole and in different sections, as shown in Table 1.

[0057] Table 1 Stability data of two media particles before and after re-injection

[0058]

[0059] Change rate analysis: Calculate the stability change rate, the formula is R s =(S t -S0) / S0. Through comparative analysis, it was found that the stability of the sand columns of quartz sand medium and glass bead medium at different depths after the particle recharge test mostly decreased, and a small part increased; from the macro data, the stability of the whole and each segment of the sand column of the two media decreased after the recharge test, as shown in Table 2. This is because as the particle recharge test progresses, the particles are deposited in the pores inside the sand column, which changes the size, shape structure and spatial distribution of the pores, resulting in a decrease in the overall stability. Although the flow field inside the medium is complex and the stability of some spatial pores will occasionally increase, it does not affect the overall trend of the macroscopic stability decrease. In order to facilitate comparative analysis, the amplitude of the stability change after the groundwater recharge test is plotted as Figure 8 and Figure 9 .

[0060] Table 2 Stability change rate of two medium sand column particles before and after reinjection test Unit: %

[0061]

[0062] Summary and Analysis Figure 8 、 Figure 9As can be seen from the data in Table 2, both the two-dimensional image stability and the three-dimensional image stability proposed by the present invention can be used to characterize the trend of changes in the morphological features in non-destructive testing images. This is because during the groundwater recharge test, a certain number of recharged particles are deposited on the pore walls inside the medium, changing the morphology of the pores inside the medium. This is reflected in the non-destructive testing as a change in the shape characteristics of the test image. The particle deposition that occurs during the groundwater recharge test will continue to change the shape of the pores inside the medium over the next period of time, which means that it will continue to have a direct impact on the non-destructive testing image. Stability is data that can characterize the tendency of image stability changes. The stability data after the groundwater recharge test and the rate of change of stability before and after the recharge test can accurately reflect the process of particle deposition changing the pore shape of the medium. In summary, stability as a parameter to describe non-destructive testing images can characterize the trend of changes in the pores inside the medium caused by the groundwater recharge test.

[0063] According to an embodiment of the present invention, a system for predicting pore change tendency of porous media is provided.

[0064] A system for predicting pore change tendency of porous media according to an embodiment of the present invention includes:

[0065] Nondestructive testing module for obtaining two-dimensional and three-dimensional images of porous media;

[0066] Fractal dimension calculation module, which analyzes imaging data based on box counting dimension method and outputs fractal dimension value;

[0067] Stability calculation module, which calculates two-dimensional and three-dimensional stability based on fractal dimension values;

[0068] The dynamic prediction module generates a pore evolution tendency report based on the stability change rate.

[0069] In summary, with the help of the above technical solution of the present invention, the following effects can be achieved:

[0070] 1. This invention breaks through the limitations of static characterization by innovatively introducing stability parameters and calculating the stability change rate. It breaks the limitations of traditional static characterization of pore structure and can dynamically and quantitatively evaluate the changing tendency of pores in porous media, providing a new perspective and method for studying pore evolution.

[0071] 2. The present invention achieves full-scale coverage. By proposing a representative unit-level detection strategy for porous media of different scales, it is suitable for the study of overall imaging of media within 1 m at the laboratory scale, and can also meet the needs of local precise analysis of media exceeding 1 m at the engineering macroscale. It achieves coverage of the entire scene from micro to macro, and improves the versatility and practicality of the method.

[0072] 3. The present invention provides a predictive basis. By accurately predicting the pore evolution tendency through the stability change rate, it provides a reliable basis for predicting the dynamic evolution of pores for scientific research and engineering scenarios involving pore changes in porous media, especially porosity changes. This helps to optimize engineering design, improve engineering safety and resource utilization efficiency, and promote the development of related fields.

[0073] The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention. A person skilled in the art will readily appreciate other embodiments of the present invention after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely exemplary, and the true scope and spirit of the present invention are indicated by the claims.

[0074] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for predicting pore change tendency of porous media, characterized in that: The following steps are involved: Obtain two-dimensional slice imaging and three-dimensional stereo imaging of porous media in advance through non-destructive testing technology; Based on the two-dimensional slice imaging, a two-dimensional fractal dimension is calculated using a box counting method, and a two-dimensional stability is calculated using the two-dimensional fractal dimension; Based on the three-dimensional stereo imaging, a three-dimensional fractal dimension is calculated using a box counting method, and a three-dimensional stability is calculated using the three-dimensional fractal dimension; Repeat the above steps at multiple characteristic time points or time segments divided in equal proportions to calculate the stability change rate between different time points or within different time segments; The evolution tendency of the pores in porous media is predicted based on the calculated stability change rate.

2. The method for predicting pore change tendency of porous media according to claim 1, characterized in that: The non-destructive testing technology includes magnetic resonance imaging or industrial CT scanning.

3. The method for predicting pore change tendency of porous media according to claim 1, characterized in that: The two-dimensional stability is calculated as: S = |1.5-D|; Wherein, S represents two-dimensional stability, D represents two-dimensional fractal dimension, and the value range of two-dimensional fractal dimension D is 1.0<D<2.

0.

4. The method for predicting pore change tendency of porous media according to claim 3, characterized in that: The three-dimensional stability is calculated as: S = |2.5-D|; Wherein, S represents three-dimensional stability, D represents three-dimensional fractal dimension, and the value range of three-dimensional fractal dimension D is 2.0<D<3.

0.

5. The method for predicting pore change tendency of porous media according to claim 1, characterized in that: The stability change rate is calculated and expressed as: R s =(S t -S0) / S0; Among them, R s is the image stability change rate, S0 is the stability at the previous time node or before the start of the time segment, S t It refers to the stability after the end of the next time node or time segment.

6. The method for predicting pore change tendency of porous media according to claim 5, characterized in that: The method of predicting the evolution tendency of pores in a porous medium comprises the following steps: If the stability change rate R s If it is a negative value, the pore morphology is judged to be unstable; If the stability change rate R s If it is a positive value, it is judged that the pore morphology tends to be stable.

7. A system for predicting pore change tendency of porous media, used in the method for predicting pore change tendency of porous media according to any one of claims 1 to 6, characterized in that: include: Nondestructive testing module for obtaining two-dimensional and three-dimensional images of porous media; Fractal dimension calculation module, which analyzes imaging data based on box counting dimension method and outputs fractal dimension value; Stability calculation module, which calculates two-dimensional and three-dimensional stability based on fractal dimension values; The dynamic prediction module generates a pore evolution tendency report based on the stability change rate.

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