PRB permeability prediction method considering dynamic non-uniform mineral precipitation blockage process

Abstract

This invention discloses a method for predicting the permeability coefficient of a permeable reactive barrier (PRB) considering the dynamic non-uniform mineral precipitation and blockage process. It belongs to the technical field of permeable reactive barriers and includes: conducting a visualized microfluidic experiment on the non-uniform precipitation of CaCO3; extracting parameters based on the results of the visualized microfluidic experiment; constructing a multi-parameter correction function and correcting the K-C equation based on the results of the visualized microfluidic experiment and the extracted parameters; dynamically updating the K-C equation based on the multi-parameter correction function, thereby completing the prediction of the permeability coefficient. This invention utilizes a visualized microfluidic experiment to simultaneously and in-situ acquire pore structure images and permeability data during the non-uniform precipitation process, thereby establishing a quantitative relationship between precipitation characteristics and key constants in the K-C equation, and dynamically correcting it to achieve accurate prediction of the PRB permeability coefficient.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of permeable reactive barrier, and particularly relates to a PRB permeability coefficient prediction method considering dynamic non-uniform mineral precipitation plugging process. BACKGROUND

[0002] Compared with remediation technologies such as extraction treatment, electrokinetic remediation, chemical injection remediation, bioremediation and natural attenuation, the permeable reactive barrier (PRB) becomes the main technical choice for in-situ remediation of groundwater in various sites such as industrial parks, mines and solid waste landfills due to its green sustainability, small environmental interference, low operation and maintenance cost and low equipment demand. The PRB sets specific high-permeability active fillers in the seepage path to capture the surrounding low-permeability soil layer of contaminated groundwater, and realizes purification based on physical, chemical and biological reactions. Although reactivity and permeability are both key indicators of the long-term service performance of PRB, people in the early stage paid more attention to the selection of active fillers on reactivity, mainly by backfilling particles with relatively uniform particle size to ensure permeability as much as possible. Among them, zero-valent iron (ZVI) becomes the preferred filler for more than 60% of PRBs because it can efficiently remove various groundwater pollutants such as heavy metals, chlorinated hydrocarbons, dyes, nitrate and pesticides. The corrosion and expansion of ZVI and the precipitation of carbonates are the main reasons for the plugging of PRB, and the porosity loss rate is 0.003-0.03 per year.

[0003] Affected by various physical and chemical conditions, such as Figure 1As shown, mineral precipitation in porous media mainly exhibits three basic modes: ① homogeneous precipitation, primarily occurring in homogeneous porous structures, where permeability changes are positively correlated with porosity changes; ② preferential precipitation in small pores / pore throats, where precipitation preferentially occurs at small pores or pore throats due to lower thermodynamic barriers, resulting in smaller porosity changes but a significant decrease in permeability; ③ preferential precipitation in macropores, where precipitation preferentially occurs in macropores due to smaller nucleation curvature radii and lower solubility, resulting in smaller permeability changes but a significant decrease in porosity. ZVI corrosion expansion is a homogeneous precipitation, but CaCO3 precipitation involves complex processes of amorphous phase nucleation, crystal transformation, and crystal growth, potentially occurring preferentially in either macropores or small pores, depending on hydrochemical and hydrodynamic conditions. In microfluidic models with simple geometries, multiple studies have shown that calcite tends to form stable precipitation at pore throats, mainly due to stronger fluid mixing, greater lateral convection, and longer fluid-particle surface contact time in pore throats. However, early-stage, smaller-sized, lower-density ACC migrates with the water flow, leading to intermittent blockage at pore throats and premature decrease in permeability. Furthermore, the effects of precipitation evolution on porosity and permeability are dynamic. Precipitation not only promotes heterogeneity at the pore scale and the redistribution of seepage paths, but also further influences precipitation patterns through anisotropy and the formation of dominant channels.

[0004] Existing technologies include the Kozeny-Carman (KC) equation, power-law / exponential models, and pore network models. Among these, the classic Kozeny-Carman (KC) equation is often used to establish the relationship between porosity (n) and permeability (k). The most commonly used form of the KC equation is as follows:

[0005]

[0006] Among them, C KC γ is a dimensionless pore geometry constant. τ is the tortuosity factor, representing the ratio of the actual flow path of the fluid to the thickness of the medium. S is the specific surface area of ​​the particles (unit: 1 / m), referring to the total surface area of ​​solid particles per unit volume; the smaller the particles, the larger the specific surface area. γ is the specific gravity of the fluid (unit: N / m³); μ is the dynamic viscosity of the fluid (unit: Pa·s).

[0007] Over the past thirty years, researchers have been working to refine the four key parameters mentioned above in order to achieve more accurate fitting results under different operating conditions:

[0008] (1) Porosity n is the easiest parameter to measure. It only needs to be corrected by the ratio of free water to non-free water when considering clay particles.

[0009] (2) Specific surface area S is used to describe the influence of microstructure surface on fluid resistance during seepage. It is especially important when it involves changes in the permeability of media with obvious particle size distribution, such as soil. The correction method based on fractal dimension is relatively mature, and it can be accurately obtained in most cases based on micro-characterization technology.

[0010] (3) The tortuosity factor τ is relatively abstract, and it is defined as the ratio of the actual seepage path length to the medium length. The tortuosity factor obtained by fitting the results of column experiments usually leads to the predicted permeability being one to two orders of magnitude higher than the actual value. A more accurate tortuosity factor can be obtained based on CT scan reconstruction of digital models and flow field simulation.

[0011] (4) Pore geometry C KC The most difficult aspect to quantify is that constants are commonly used in the KC equation to describe pore shape (e.g., a value of 2 for circular tubes and 3 for thin cracks). However, the actual pore shape is complex and irregular, making its value selection and correction extremely difficult. The method of using a piecewise function of tortuosity-porosity to replace the pore geometric constants, based on homogeneous precipitation of a single crystal form, can currently achieve fitting of column test results.

[0012] The drawback of this technology is:

[0013] (1) Poor predictive extrapolation: The model is heavily dependent on specific experimental conditions (such as packing type, solution chemical composition, and flow rate). Once the operating environment of the PRB (such as Ca²⁺ in groundwater) is affected, the model will be significantly affected. + HCO3 - When the concentration changes or the geometric parameters of the packing material (such as particle size and gradation) change, the original empirical constants become invalid and cannot be accurately predicted.

[0014] (3) The special characteristics of CaCO3 precipitation were not considered: the classic KC equation and its conventional correction method did not take into account the pore geometric constant C KC Treating it as a fixed value or a simple function related only to porosity completely fails to reflect the drastically different dynamic effects of different crystal forms such as calcite, aragonite, and spheroidal aragonite on pore shape and connectivity due to their unique growth habits (such as uniform coating, needle-like crystals, and clustered aggregation), leading to huge biases in predicting permeability changes caused by complex CaCO3 precipitation. Summary of the Invention

[0015] The purpose of this invention is to address the aforementioned shortcomings in the prior art by providing a PRB permeability coefficient prediction method that considers the dynamic non-uniform mineral precipitation blockage process. This solves the problem that existing PRB permeability coefficient prediction methods cannot efficiently and accurately quantify the dynamic influence of non-uniform precipitation on pore geometry, resulting in a large deviation between the predicted results and the actual values.

[0016] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0017] A method for predicting PRB permeability coefficient considering dynamic non-uniform mineral precipitation and blockage processes includes the following steps:

[0018] S1. Conduct a visual microfluidic experiment on the non-uniform precipitation of CaCO3;

[0019] S2. Extract parameters based on the results of the visualized microfluidic experiments;

[0020] S3. Based on the visualized microfluidic experimental results and extracted parameters, construct a multi-parameter correction function and correct the KC equation;

[0021] S4. The KC equation is dynamically updated based on a multi-parameter correction function, thereby completing the prediction of the permeability coefficient.

[0022] Furthermore, step S1 specifically includes the following sub-steps:

[0023] S11. Fabricate microfluidic chips with different geometric features to simulate different PRB filler pore environments;

[0024] S12. Inject a mixed solution of Na2CO3 and CaCl2 into the microfluidic chip to induce CaCO3 precipitation;

[0025] S13. Real-time acquisition of images of the CaCO3 precipitation process;

[0026] S14. Connect micro differential pressure sensors at the inlet and outlet of the microfluidic chip to monitor and record the pressure difference in real time throughout the CaCO3 precipitation process; then calculate and obtain the inherent permeability of the microfluidic chip in real time.

[0027] Furthermore, step S2 specifically includes the following sub-steps:

[0028] S21. Process images of the CaCO3 precipitation process and calculate the local and overall porosity of the microfluidic chip in real time.

[0029] S22. Identify images of the CaCO3 precipitation process, quantify and extract precipitation characteristic parameters.

[0030] Furthermore, S21 specifically includes:

[0031] Thresholding and binarization are performed on images of the CaCO3 precipitation process to distinguish between the solid phase and the porous phase. By statistically analyzing the proportion of porous phase pixels to the total number of pixels, the local and overall porosity of the chip is calculated in real time.

[0032] Furthermore, in step S22, the precipitation characteristic parameters include:

[0033] The volume fractions of different crystalline precipitates are as follows: volume fraction of amorphous calcium carbonate, volume fraction of aragonite, volume fraction of aragonite, and volume fraction of calcite.

[0034] The spatial distribution factors of precipitates with different crystal forms are as follows:

[0035] The non-uniform precipitation factor is calculated by the ratio of the volume of crystal precipitate located in the pore throat region to the total volume of precipitate of that crystal form.

[0036] The uniform precipitation factor is calculated as the ratio of the area of ​​crystalline precipitates attached to the particle surface to the total particle surface area.

[0037] Furthermore, step S3 specifically includes the following sub-steps:

[0038] S31. Determine the key parameters of the KC equation;

[0039] S32. Substitute the key parameter into the KC equation for inversion, and construct a multi-parameter correction function based on the precipitation characteristic parameters, and use the multi-parameter correction function to correct the KC equation.

[0040] Furthermore, in step S31, determining the key parameters of the KC equation specifically includes:

[0041] Specific surface area is determined based on the outer contour line of the binarized image of the CaCO3 precipitation process.

[0042] The bending factor is determined by the ratio of the actual seepage path length to the microfluidic chip length L in the binarized image of the CaCO3 precipitation process.

[0043] The measured values ​​include the porosity calculated in real time in S21 and the intrinsic permeability calculated in real time in S14.

[0044] Furthermore, step S32 specifically includes the following sub-steps:

[0045] S321. Substitute the specific surface area, tortuosity, porosity and intrinsic permeability into the KC equation to invert and calculate the actual pore geometric constants corresponding to each time point.

[0046] S322. Based on the correlation between the pore geometric constant and precipitation characteristic parameters obtained from the inversion, regression analysis is used to construct a multi-parameter correction function to correct the KC equation, which is specifically expressed as follows:

[0047]

[0048]

[0049] In the formula, This represents the dynamically corrected porosity geometric coefficients in the KC equation; This represents the dimensionless porosity geometric constant in the KC equation. This represents a multi-parameter correction function; α is the global bridging effect coefficient; β represents the non-uniform precipitation factor; β is the uniform precipitation coefficient. Indicates the uniform precipitation factor; V is the ACC transfer effect coefficient; ACC T represents the volume fraction of amorphous calcium carbonate. ACC These are parameters related to time or flow field intensity.

[0050] Furthermore, non-uniform precipitation factor Represented as:

[0051]

[0052] Uniform precipitation factor express:

[0053]

[0054] In the formula, This represents the flow impediment weighting coefficient per unit bridging volume of clustered aragonite; Indicates the volume fraction of aragonite; Indicates the non-uniform precipitation factor of aragonite; This represents the flow impediment weighting coefficient per unit bridging volume of spherical aragonite; This represents the volume fraction of aragonite; Indicates the non-uniform precipitation factor of spheroidal aragonite; This represents the flow impediment weighting coefficient per unit bridging volume of rhombohedral calcite. This indicates the volume fraction of calcite. Indicates the non-uniform precipitation factor of calcite; This represents the weighting coefficient for uniform precipitation of amorphous calcium carbonate. This indicates the uniform precipitation factor of amorphous calcium carbonate.

[0055] Furthermore, in S4, the prediction of the permeability coefficient is expressed as:

[0056]

[0057] In the formula, Indicates the permeability coefficient; Indicates the dynamic viscosity of a fluid; Indicates porosity; Represents specific surface area; This represents the bending factor.

[0058] The PRB permeability coefficient prediction method considering the dynamic non-uniform mineral precipitation blockage process provided by this invention has the following beneficial effects:

[0059] 1. This invention utilizes visualized microfluidic experiments to synchronously and in situ acquire pore structure images and permeability data during non-uniform precipitation, thereby establishing a quantitative relationship between precipitation characteristics and key constants in the KC equation, and dynamically correcting them.

[0060] 2. The mechanism of this invention is clear and the accuracy is high: For the first time, the polymorphic characteristics of CaCO3 are introduced into the KC equation in the form of quantitative parameters (such as λ) to correct the model, so that the physical meaning of the model is clear and can more accurately reflect the differential effects of different precipitation modes on permeability.

[0061] 3. This invention is direct and reliable: By simultaneously and in situ measuring permeability and porosity through microfluidic experiments, it avoids the cumulative error caused by indirect parameter fitting in traditional methods. The correction of the KC constant is based on the inversion analysis of a large amount of real-time experimental data, rather than subjective assumptions.

[0062] 4. This invention overcomes the core defects of the prior art: Compared with the power law / exponential model and the traditional KC equation, this invention solves the problem that its constants are fixed and cannot adapt to the dynamic changes of polymorphic precipitation; Compared with the pore network model, the method of this invention does not rely on complex and idealized network construction, and the calculation is simple and efficient, making it more suitable for rapid prediction and evaluation in engineering. Attached Figure Description

[0063] Figure 1 This is a diagram of mineral precipitation in porous media in the prior art, where, Figure 1 (a) shows the characteristics of polycrystalline CaCO3 precipitation. Figure 1 (b) shows that multiple precipitation modes cause the actual seepage path to lengthen and porosity and permeability to decrease to varying degrees.

[0064] Figure 2 The flowchart illustrates a method for predicting the permeability coefficient of PRB (Potentially Differentiated Biofilm) considering a dynamic, non-uniform mineral precipitation blockage process, as described in an embodiment of the present invention. Detailed Implementation

[0065] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0066] This embodiment considers the PRB permeability coefficient prediction method based on the dynamic non-uniform mineral precipitation and blockage process. By introducing a correction function related to the non-uniform precipitation characteristics, it dynamically corrects the pore geometry constant in the KC equation, thereby establishing a PRB permeability coefficient prediction method with a clearer mechanism and more accurate prediction. (Refer to...) Figure 2 Specifically, it includes the following:

[0067] S1. Conduct a visualization microfluidic experiment on the non-uniform precipitation of CaCO3, which includes the following steps:

[0068] S11, Microfluidic chip design; fabrication of microfluidic chips with different geometric features (such as homogeneous and heterogeneous pore size distribution) to simulate different PRB filler pore environments.

[0069] S12. Experimental Procedure: A mixed solution of Na₂CO₃ and CaCl₂ is injected into the microfluidic chip to induce CaCO₃ precipitation; specifically, this is achieved by controlling the solution concentration and ion ratio (e.g., Mg²⁺). + / Ca² + The precipitation advantages and spatial distribution patterns of different crystal forms (calcite, aragonite, spheroidal aragonite, and amorphous calcium carbonate ACC) are controlled by adjusting the flow rate and flow velocity.

[0070] S13, Image Acquisition; Real-time acquisition of images of the CaCO3 precipitation process for subsequent analysis of the crystal form, spatial location, size, and volume fraction of the precipitate;

[0071] S14. Hydraulic data acquisition: Connect micro-differential pressure sensors at the inlet and outlet of the microfluidic chip to monitor and record the pressure difference ΔP change in real time throughout the CaCO3 precipitation process; and combine the known flow rate q per unit time and the geometric dimensions of the microfluidic chip (length L, width W, depth H) to directly calculate and obtain the inherent permeability k of the microfluidic chip in real time according to the formula k = (q * L) / (W * H * ΔP).

[0072] S2. Based on the results of the visualized microfluidic experiments, parameter extraction is performed; this specifically includes the following steps:

[0073] S21. Porosity n calculation: Process images of the CaCO3 precipitation process, perform threshold segmentation and binarization on the acquired images to distinguish between the solid phase and the porous phase. Then, by statistically analyzing the proportion of porous phase pixels to the total pixels, calculate the local and overall porosity n of the microfluidic chip in real time.

[0074] S22. Identify images of the CaCO3 precipitation process. By performing image recognition on images of the CaCO3 precipitation process, quantify and extract precipitation characteristic parameters, including quantifying the volume fraction V of different crystalline precipitates. iAnd the spatial distribution factor λ, which is as follows:

[0075] The volume fraction of different crystalline precipitates is specifically: the volume fraction V of amorphous calcium carbonate. ACC Volume fraction V of spheroidal aragonite Vat Volume fraction of aragonite V Arg Volume fraction of calcite V Cal ;

[0076] Among them, V ACC + V Vat + V Arg + V Cal ≈ 1;

[0077] The spatial distribution factors of precipitates with different crystal forms are as follows:

[0078] Non-uniform precipitation factor λ b It is calculated by the ratio of the volume of the precipitate of this crystal form located in the pore throat region to the total volume of the precipitate of this crystal form; it is mainly used to characterize the tendency of aragonite (acicular, clustered), fine spheroidal aragonite, and calcite to form bridging and blockage at the throat.

[0079] Uniform precipitation factor λ c It is calculated by the ratio between the area of ​​the crystalline precipitate attached to the particle surface and the total particle surface area; it is mainly used to characterize the tendency of ACC to form a uniform precipitate on the particle surface.

[0080] S3. Based on the visualized microfluidic experimental results and extracted parameters, construct a multi-parameter correction function and correct the KC equation;

[0081] S31. Determine the key parameters of the KC equation, specifically including:

[0082] The specific surface area S is determined based on the outer contour line of the binarized image of the CaCO3 precipitation process.

[0083] The bending factor τ is determined based on the ratio of the actual seepage path length in the binarized image of the CaCO3 precipitation process to the length L of the microfluidic chip.

[0084] The measured values ​​include the porosity n calculated in real time in S21 and the intrinsic permeability k calculated in real time in S14.

[0085] S32. Substitute the key parameter into the KC equation for inversion, and based on the sedimentation characteristic parameters, construct a multi-parameter correction function, and use the multi-parameter correction function to correct the KC equation. The specific steps include the following:

[0086] S321. Substitute the specific surface area, tortuosity, porosity, and intrinsic permeability into the KC equation to invert and calculate the actual pore geometric constant C at each time point. KC ;

[0087] S322. Based on the correlation between the pore geometric constant and precipitation characteristic parameters obtained from the inversion, regression analysis is used to construct a multi-parameter correction function to correct the KC equation, which is specifically expressed as follows:

[0088]

[0089]

[0090] In the formula, This represents the dynamically corrected porosity geometric coefficients in the KC equation; This represents the dimensionless pore geometry constant (empirical value) in the KC equation. The multi-parameter correction function is a multi-parameter weighted superposition function. The larger its value, the more severe the permeability decay. α is the global bridging effect coefficient, which directly and drastically increases the flow resistance. Therefore, it is usually the main contributor to F. β represents the non-uniform precipitation factor; β is the uniform precipitation coefficient. This term increases resistance by uniformly reducing pore size and increasing surface roughness, and its effect is usually weaker than bridging. Indicates the uniform precipitation factor; V is the ACC transfer effect coefficient; ACC T represents the volume fraction of amorphous calcium carbonate. ACC α * λ are parameters related to time or flow field intensity. b It is a non-uniform precipitation factor phase;

[0091] Non-uniform precipitation factor Represented as:

[0092]

[0093] Uniform precipitation factor express:

[0094]

[0095] In the formula, This represents the flow impediment weighting coefficient per unit bridging volume of clustered aragonite; Indicates the non-uniform precipitation factor of aragonite; This represents the flow impediment weighting coefficient per unit bridging volume of spherical aragonite; Indicates the non-uniform precipitation factor of spheroidal aragonite; This represents the flow impediment weighting coefficient per unit bridging volume of rhombohedral calcite. Indicates the non-uniform precipitation factor of calcite; This represents the weighting coefficient for uniform precipitation of amorphous calcium carbonate. Indicates the homogeneous precipitation factor of amorphous calcium carbonate; γ(V) ACC / T ACC ) represents the ACC migration-temporary blockage effect term, used to describe the special dynamic behavior of the ACC temporary blockage orifice. Where T... ACC T is a parameter related to time or flow field intensity, namely the "stability threshold" of the ACC. When the flow field shear force is strong or the ACC itself is unstable, T ACC The value is relatively small. γ is the ACC migration effect coefficient, which describes the transient and random blockage caused by ACC particle migration. Its contribution is inversely proportional to the relative amount and stability of ACC. When ACC is abundant and stable (V... ACC / T ACC When the ACC value is large, this contribution is significant; when the ACC is converted into a crystal, this contribution weakens or disappears.

[0096] S4. Dynamically update the KC equation based on the multi-parameter correction function to complete the prediction of the permeability coefficient;

[0097] For a new PRB system, once the initial KC constant and the predicted / monitored CaCO3 precipitate crystal form distribution (λ can be estimated through geochemical simulation or water quality monitoring) are known, the correction function F established by this method can be called to dynamically update the KC constant.

[0098] The predicted permeability coefficient is expressed as:

[0099]

[0100] In the formula, Indicates the permeability coefficient; Indicates the dynamic viscosity of a fluid; Indicates porosity; Represents specific surface area; This represents the bending factor.

[0101] Although specific embodiments of the invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of this patent. Various modifications and variations that can be made by a person skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of this patent.

Claims

1. A PRB permeability coefficient prediction method considering dynamic non-uniform mineral precipitation plugging process, characterized in that, The method comprises the following steps: S1, carrying out a visual microfluidic experiment of CaCO3 non-uniform precipitation; S2, extracting parameters according to the visual microfluidic experiment result; The method comprises the following steps: S21, processing CaCO3 precipitation process images and calculating the local and overall porosity of the microfluidic chip in real time; S22, identifying CaCO3 precipitation process images, quantifying and extracting precipitation characteristic parameters; The precipitation characteristic parameters comprise: The volume fraction of different crystal form precipitates, specifically: the volume fraction of amorphous calcium carbonate, the volume fraction of vaterite, the volume fraction of aragonite, and the volume fraction of calcite; The spatial distribution factor of different crystal form precipitates, specifically: The non-uniform precipitation factor, which is calculated by the ratio of the crystal form precipitate volume located in the pore throat region to the total crystal form precipitate volume; The uniform precipitation factor, which is calculated by the ratio of the crystal form precipitate area attached to the particle surface to the total particle surface area; S3, constructing a multi-parameter correction function and correcting the K-C equation based on the visual microfluidic experiment result and the extracted parameters; the method comprises the following steps: S31, determining the key parameters of the K-C equation; S32, bringing the key parameters into the K-C equation for inversion, constructing a multi-parameter correction function based on the precipitation characteristic parameters, and correcting the K-C equation using the multi-parameter correction function; the method comprises the following steps: S321, substituting the specific surface area, the tortuosity factor, the porosity, and the intrinsic permeability into the K-C equation to inversely calculate the actual pore geometry constant corresponding to each time point; S322, using regression analysis to construct a multi-parameter correction function based on the correlation between the inversely calculated pore geometry constant and the precipitation characteristic parameters, to correct the K-C equation; S4, dynamically updating the K-C equation based on the multi-parameter correction function, and then completing the prediction of the permeability coefficient. The S1 specifically comprises the following steps: S11, preparing microfluidic chips with different geometric characteristics to simulate different PRB filler pore environments; 2. The method for predicting PRB permeability considering dynamic non-uniform mineral precipitation plugging process according to claim 1, characterized in that, S12, injecting a mixed solution of Na2CO3 and CaCl2 into the microfluidic chip to induce CaCO3 precipitation; S13, real-time acquisition of CaCO3 precipitation process images; S14, connecting micro pressure difference sensors at the inlet and outlet of the microfluidic chip to monitor and record the pressure difference during the entire CaCO3 precipitation process in real time; and then calculating and obtaining the intrinsic permeability of the microfluidic chip in real time. The S21 specifically comprises: Threshold segmentation and binary processing of the CaCO3 precipitation process images to distinguish between solid and pore phases, and real-time calculation of the local and overall porosity of the chip by counting the proportion of pore phase pixels in the total pixels.

3. The method for predicting PRB permeability considering dynamic non-uniform mineral precipitation plugging process according to claim 1, characterized in that, In the S31, the key parameters of the K-C equation are determined, specifically including: The specific surface area is determined based on the outer contour line of the binary image of the CaCO3 precipitation process image; 4. The method for predicting PRB permeability considering dynamic non-uniform mineral precipitation plugging process according to claim 1, characterized in that, The tortuosity factor is determined based on the ratio of the real seepage path length of the binary image of the CaCO3 precipitation process image to the length L of the microfluidic chip; The measured value includes the porosity calculated in real time in S21 and the intrinsic permeability calculated in real time in S14. ​ ​ 5. The method for predicting PRB permeability considering the process of dynamic non-uniform mineral precipitation clogging according to claim 4, characterized in that, In the S322, according to the correlation between the pore geometry constant obtained by inversion and the sedimentation characteristic parameter, a multi-parameter correction function is constructed by using regression analysis to correct the K-C equation, and the specific representation is as follows: wherein, represents the dynamic corrected KC equation pore geometry factor; represents the dimensionless pore geometry constant in the KC equation, represents the multi-parameter correction function; a is the global bridging effect coefficient; represents the non-uniform settling factor; b is the uniform settling coefficient; represents the uniform settling factor; is the ACC migration effect coefficient; V ACC is the volume fraction of amorphous calcium carbonate; T ACC is a parameter related to time or flow field intensity.

6. The method for predicting PRB permeability considering dynamic non-uniform mineral precipitation plugging process according to claim 5, characterized in that, Non-uniform precipitation factor is represented as: Uniform precipitation factor Indicates: wherein represents the hindering weight coefficient of the volume of the cluster-shaped aragonite unit bridge to the flow; represents the volume fraction of aragonite; represents the non-uniform precipitation factor of aragonite; represents the hindering weight coefficient of the volume of the spherical vaterite unit bridge to the flow; represents the volume fraction of vaterite; represents the non-uniform precipitation factor of vaterite; represents the hindering weight coefficient of the volume of the rhombohedral calcite unit bridge to the flow; represents the volume fraction of calcite; represents the non-uniform precipitation factor of calcite; represents the uniform precipitation weight coefficient of amorphous calcium carbonate; represents the uniform precipitation factor of amorphous calcium carbonate.

7. The method for predicting PRB permeability considering dynamic non-uniform mineral precipitation plugging process according to claim 5, characterized in that, In the S4, the prediction of the permeability coefficient is represented as follows: wherein represents the permeability coefficient; represents the dynamic viscosity of the fluid; represents the porosity; represents the specific surface area; represents the tortuosity factor.

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