Calculation method for precipitation rate coefficient of carbonate induced by microscale mixing effect

Abstract

The invention discloses a micro-scale mixing effect induced carbonate precipitation rate coefficient calculation method, which comprises the following steps: S1, carrying out a micro-scale mixing effect induced carbonate precipitation visualization experiment to obtain a carbonate precipitation image data set; s2, establishing a carbonate precipitation identification model, wherein the carbonate precipitation identification model sequentially comprises three steps of image calibration, gray scale iteration adjustment and precipitation identification; s3, establishing a carbonate precipitation rate calculation model, wherein the carbonate precipitation rate calculation model sequentially comprises three steps of pixel point statistics, precipitation amount calculation and precipitation rate calculation; and S4, establishing a carbonate precipitation rate coefficient calculation model, wherein the model comprises precipitation rate analysis and precipitation rate coefficient calculation. The calculation of the rate coefficient of the carbonate precipitation induced by the mixing effect under the microscale is realized, and support is provided for understanding the carbonate precipitation mechanism in the karst area and improving the geological storage efficiency of carbon dioxide and the efficiency of calcium carbonate precipitation induced by microorganisms.

Description

Technical Field

[0001] The invention belongs to the field of geological storage of carbon dioxide and microbial induced calcium carbonate precipitation, and particularly relates to a method for calculating a precipitation rate coefficient of carbonate induced by microscale mixing. Background Art

[0002] Mixing-induced carbonate precipitation is widely involved in natural processes and practical engineering such as the formation and migration of highly mineralized groundwater in karst, geological storage of carbon dioxide, and microbial-induced calcium carbonate precipitation to strengthen rock and soil. In karst areas, groundwater with different chemical compositions in the fracture network of carbonate rock mass will mix under the influence of the cross-fractures, and Ca 2+ Mg 2+ Metal cations and CO3 2- Anion mixing will induce carbonate precipitation and thus change the medium structure and seepage process. In the geological storage of carbon dioxide, the mixing of injection fluid and highly mineralized groundwater near the well wall may induce carbonate precipitation, thereby reducing the efficiency of the project. In microbial reinforcement technology, CO3 2- Ions and Ca in groundwater 2+ Calcium carbonate precipitation is generated after ion mixing. Therefore, the calculation of the precipitation rate coefficient of carbonate induced by microscale mixing is the key to understanding the carbonate precipitation mechanism and promoting the development of related engineering technologies.

[0003] Many studies have focused on estimating precipitation rates through methods such as "black box" experiments, chemical penetration curves, rock and soil mechanical parameters, and pressure inversion, but less attention has been paid to directly obtaining the carbonate precipitation rate coefficient in a mixed environment. This has brought great challenges to understanding the mesoscopic mechanism of mixing-induced carbonate precipitation and improving engineering efficiency at the site scale. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to solve the deficiency that the existing method cannot directly obtain the precipitation rate coefficient of carbonate in a mixed environment. To this end, the present invention provides a method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing.

[0005] Technical solution, in order to achieve the above purpose, the present invention proposes a method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing, comprising the following steps:

[0006] S1 conducts microscale mixing-induced carbonate precipitation experiments, uses a camera to record images of the carbonate precipitation process, and obtains a carbonate precipitation image dataset;

[0007] S2 establishes a carbonate precipitation recognition model, adjusts the position, size and brightness of the original image to a unified style through image calibration, uses a preset grayscale threshold to recognize carbonate precipitation, and continuously iterates the grayscale according to the recognition effect until accurate recognition is possible, and finally accurately recognizes carbonate precipitation according to the iterated grayscale threshold;

[0008] S3 establishes a carbonate precipitation rate calculation model, and based on the carbonate precipitation recognition results, counts the number of pixels occupied by carbonate precipitation in the calibrated image, calculates the amount of carbonate precipitation induced by mixing according to the number of pixels, and calculates the carbonate precipitation rate in combination with the mixing time;

[0009] S4 established a carbonate precipitation rate coefficient calculation model, calculated the carbonate precipitation amount and precipitation rate at different times, and finally calculated the precipitation rate coefficient of carbonate induced by mixing.

[0010] Furthermore, the step S1 specifically includes the following steps:

[0011] 1) Conduct microscale mixing-induced carbonate precipitation experiments: First, prepare a two-dimensional microscale transparent medium according to the research objectives, and inject two or more miscible fluids that can produce carbonate precipitation reactions into the microscale transparent medium at a flow rate to provide a basis for creating a carbonate precipitation image dataset.

[0012] 2) Establish a microscale mixing-induced carbonate precipitation image dataset: Use a CMOS camera to record images of the mixing-induced carbonate precipitation process in real time, and use all images of a single experiment as the image dataset of a single experiment, and use images of all experiments as the image dataset of all experiments.

[0013] Furthermore, the step S2 specifically includes the following steps:

[0014] 1) Carbonate precipitation image calibration: According to the research requirements, the original image captured by the CMOS camera is rotated to an orthogonal direction, the original image is cropped to the target field of view, and the image brightness is adjusted to the same value;

[0015] 2) Iterative adjustment of grayscale threshold: carbonate precipitation is identified on the calibrated image according to the preset grayscale threshold. If the boundary overlap rate between the original image and the calibrated image is greater than 98%, the identification result is judged to be accurate. If not, the grayscale threshold is adjusted iteratively until the identification is accurate.

[0016] 3) Carbonate precipitation identification: Carbonate precipitation is identified in the calibrated image according to the iterative grayscale threshold, and the pixels occupied by the precipitation are modified to black and the remaining positions are modified to white.

[0017] Furthermore, the step S3 specifically includes the following steps:

[0018] 1) Carbonate precipitation pixel statistics: According to the carbonate precipitation recognition results, the number of pixels N occupied by precipitation in the pixel matrix of the calibrated image is counted. p To conduct statistics;

[0019] 2) Calculation of carbonate precipitation: Based on the number of pixels counted, calculate the mass M of carbonate precipitation induced by mixing T , and its calculation is shown as:

[0020] M T =N p δhρ c (1)

[0021] Where N p is the number of pixels occupied by carbonate precipitation ( / ); h is the depth of the two-dimensional microscale transparent medium flow channel (L); ρ c is the density of carbonate precipitation (ML -3 ); δ is the pixel resolution (L 2 ), the calculation formula is,

[0022] δ=S0 / N0 (2)

[0023] Where S0 is the actual viewing area represented by the calibrated image (L 2 ); N0 is the total number of pixels in the calibrated image ( / );

[0024] 3) Calculation of carbonate precipitation rate: Based on the calculated carbonate precipitation amount, calculate the mixing-induced carbonate precipitation rate R T (molT -1 ), the calculation formula is (),

[0025]

[0026] Where M T is the mass of carbonate precipitation (M); M c is the molar mass of carbonate minerals (Mmol -1 );t is the mixing time (T), and its calculation formula is,

[0027]

[0028] Where V T is the total injection volume of the solution (M); Q is the injection flow rate (L 3 T -1 );

[0029] Furthermore, the step S4 specifically includes the following steps:

[0030] 1) Calculate the carbonate precipitation amount and precipitation rate at different times: Calculate the carbonate precipitation amount M at different times according to equations (1) and (3): T and the precipitation rate R T .

[0031] 2) Calculate the carbonate precipitation rate coefficient: Based on the calculated carbonate precipitation rate, the precipitation rate coefficient is calculated in combination with the carbonate precipitation rate. The carbonate precipitation rate is calculated as follows:

[0032]

[0033] Therefore, the precipitation rate constant k is calculated as:

[0034]

[0035] In the formula, R T is the precipitation rate (mol·L·T -1 ); k is the precipitation rate constant (mol·L·T -1 );ΔG is the change in Gibbs free energy of the entire reaction (J·mol -1 );R is the ideal gas constant (J·K -1 ·mol -1 ); T is the absolute temperature (K); m and n are empirical coefficients depending on the chemical kinetic process; Ω represents the saturation state, which can be calculated as:

[0036] Ω=IAP / K sp (7)

[0037] In the formula, K sp is the solubility product constant of carbonate; IAP is the ion activity product, which can be calculated as:

[0038] IAP=c + γ + c - γ - (8)

[0039]

[0040] In the formula, c - 、c + are the anion and cation concentrations of carbonate minerals (mol·L -3 ); γ i is the activity coefficient of ion i in the solution ( / ), where γ - , γ + is the corresponding anion and cation activity coefficient ( / ); I is the ionic strength of the solution (mol·L -3 );Z i is the charge number of ion i ( / ); c iis the concentration of ion i (mol·L -3 ) ; N is the total number of ions in the solution ( / ); A is a constant that depends on the dielectric constant, density and temperature of water.

[0041] Beneficial effects: Compared with the prior art, the present invention has the following beneficial technical effects:

[0042] (1) The present invention proposes a method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing. By conducting a microscale mixing-induced carbonate precipitation experiment, a carbonate precipitation image data set is established, which has the advantages of strong operability and low price; by establishing a carbonate precipitation recognition model and a carbonate precipitation rate calculation model, accurate identification of microscale mixing-induced carbonate precipitation and direct rate quantification are achieved, which has the advantages of simple process, high precision and fast calculation; by establishing a carbonate precipitation rate coefficient calculation model, the calculation of the precipitation rate coefficient of carbonate induced by microscale mixing is achieved, which has the advantages of good innovation and high precision. In general, the present invention proposes a method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing, which has the advantages of strong operability, simple process, low price, high precision and fast calculation.

[0043] (2) The present invention proposes a method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing, which can realize the calculation of the precipitation rate coefficient of carbonate induced by microscale mixing, and is of great significance to the geological storage of carbon dioxide and microbial induction of calcium carbonate. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic flow chart of a method for calculating a precipitation rate coefficient of carbonate induced by micro-scale mixing according to the present invention;

[0045] Figure 2 A two-dimensional microscale transparent medium (left) and its internal structure (right);

[0046] Figure 3 The image calibration result;

[0047] Figure 4 Identify the results for calcium carbonate precipitation;

[0048] Figure 5 is the evolution curve of calcium carbonate precipitation;

[0049] Figure 6 This is the evolution curve of calcium carbonate precipitation rate.

[0050] Specific embodiment

[0051] The present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation methods, and the present invention will be further explained below in conjunction with specific implementation methods.

[0052] Aiming at the problem that the existing methods cannot directly obtain the carbonate precipitation rate coefficient in a mixed environment, the present invention proposes a method for calculating the precipitation rate coefficient of carbonate induced by micro-scale mixing.

[0053] like Figure 1 As shown, the present invention proposes a method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing, comprising the following steps:

[0054] S1 conducts microscale mixing-induced carbonate precipitation experiments, uses a camera to record images of the carbonate precipitation process, and obtains a carbonate precipitation image dataset;

[0055] S2 establishes a carbonate precipitation recognition model, adjusts the position, size and brightness of the original image to a unified style through image calibration, uses a preset grayscale threshold to recognize carbonate precipitation, and continuously iterates the grayscale according to the recognition effect until accurate recognition is possible, and finally accurately recognizes carbonate precipitation according to the iterated grayscale threshold;

[0056] S3 establishes a carbonate precipitation rate calculation model, and based on the carbonate precipitation recognition results, counts the number of pixels occupied by carbonate precipitation in the calibrated image, calculates the amount of carbonate precipitation induced by mixing according to the number of pixels, and calculates the carbonate precipitation rate in combination with the mixing time;

[0057] S4 established a carbonate precipitation rate coefficient calculation model, calculated the carbonate precipitation amount and precipitation rate at different times, and finally calculated the precipitation rate coefficient of carbonate induced by mixing.

[0058] Among them, S1 establishes a carbonate precipitation image dataset:

[0059] 1) Conduct microscale mixing to induce carbonate precipitation experiments: Figure 2 As shown, a mixing-induced carbonate precipitation micro-scale experiment is designed according to the research objectives. This example provides a two-dimensional micro-scale transparent porous medium, which has two completely symmetrical injection ports on the left side to ensure that the mixing center line of the solution coincides with the center line of the micro-scale transparent porous medium. Two outlets are designed on the right side. The first outlet is used to inject dilute hydrochloric acid to ensure that the outlet is not blocked, and the second outlet is used to flow out the mixed solution. The medium flow channel depth is 0.01mm; This example conducts a mixing-induced calcium carbonate precipitation micro-scale experiment. The concentrations of CaCl2 solution and NaCO3 solution are both 0.1mol / L. Both are injected at the same time, and the injection flow rate is 5μL / min. The image resolution of the CMOS camera is 7.5μm×7.5μm.

[0060] 2) Establish a high-precision image dataset of micro-scale mixing-induced carbonate precipitation: Use a CMOS camera to record images of the mixing-induced carbonate precipitation process in real time, and use all images of the experiment in which CaCl2 solution and NaCO3 solution are mixed to form calcium carbonate precipitation as the image dataset.

[0061] S2 establishes a carbonate precipitation identification model:

[0062] 1) Carbonate precipitation image calibration: Figure 3 As shown, the original image captured by the CMOS camera is rotated to an orthogonal direction, the original image is cropped to the target field of view, and then the image brightness is adjusted to the same value;

[0063] 2) Iterative adjustment of grayscale threshold: carbonate precipitation is identified on the calibrated image according to the preset grayscale threshold. If the boundary overlap rate between the original image and the calibrated image is greater than 98%, the identification result is judged to be accurate. If not, the grayscale threshold is adjusted iteratively until the identification is accurate.

[0064] 3) Identification of carbonate precipitation: Figure 4 As shown, calcium carbonate precipitation is identified in the calibrated image according to the iterative grayscale threshold, and the pixels occupied by the precipitation are modified to black, and the remaining positions are modified to white.

[0065] S3 establishes a carbonate precipitation rate calculation model:

[0066] 1) Carbonate precipitation pixel statistics: According to the calcium carbonate precipitation recognition results, the number of pixels N occupied by precipitation in the pixel matrix of the calibrated image is counted. p To conduct statistics;

[0067] 2) Calculation of carbonate precipitation: Based on the number of pixels counted, calculate the precipitation mass M of calcium carbonate induced by mixing T , and its calculation is shown as:

[0068] M T =N p δhρ c (1)

[0069] Where N p is the number of pixels occupied by calcium carbonate precipitation ( / ); h is the depth of the two-dimensional microscale transparent medium flow channel (L); ρ c is the density of calcium carbonate precipitation (ML -3 ); δ is the pixel resolution (L 2 ), the calculation formula is,

[0070] δ=S0 / N0(2)

[0071] Where S0 is the actual viewing area represented by the calibrated image (L 2 ); N0 is the total number of pixels in the calibrated image ( / ).

[0072] In this example, the pixel resolution is 7.5 μm × 7.5 μm;

[0073] like Figure 5 As shown, the amount of calcium carbonate precipitation at any time can be calculated;

[0074] 3) Calculation of carbonate precipitation rate: Based on the calculated amount of calcium carbonate precipitation, calculate the precipitation rate R of calcium carbonate induced by mixing T (molT -1 ), the calculation formula is,

[0075]

[0076] Where M T is the mass of calcium carbonate precipitation (M); M c is the molar mass of calcium carbonate mineral (Mmol -1 );t is the mixing time (T), and its calculation formula is,

[0077]

[0078] Where V T is the total injection volume of the solution (M); Q is the injection flow rate (L 3 T -1 ).

[0079] like Figure 6 As shown, the calcium carbonate precipitation rate at any time can be calculated.

[0080] S4 establishes a carbonate precipitation rate coefficient calculation model:

[0081] 1) Calculate the carbonate precipitation amount and precipitation rate at different times: Figure 5 and Figure 6 As shown, the carbonate precipitation amount M at different times is calculated T and the precipitation rate R T , the evolution curve of calcium carbonate precipitation amount and the evolution curve of calcium carbonate precipitation rate can be obtained;

[0082] 2) Calculate the carbonate precipitation rate coefficient: Based on the calculated carbonate precipitation rate, the precipitation rate coefficient is calculated in combination with the carbonate precipitation rate. The carbonate precipitation rate calculation is expressed as follows:

[0083]

[0084] That is, the precipitation rate constant k is calculated as:

[0085]

[0086] In the formula, R T is the precipitation rate (mol·L·T -1 ); k is the precipitation rate constant; ΔG is the change in Gibbs free energy of the entire reaction (J·mol -1 );R is the ideal gas constant (J·K -1 ·mol -1 ); T is the absolute temperature (K); m and n are empirical coefficients depending on the chemical kinetic process; Ω represents the saturation state, which can be calculated as,

[0087] Ω=IAP / K sp (7)

[0088] In the formula, K sp is the solubility product constant of carbonate; IAP is the ion activity product, which can be calculated as,

[0089] IAP=c + γ + c - γ - (8)

[0090]

[0091] In the formula, c - 、c + are the anion and cation concentrations of carbonate minerals (mol·L -3 ); γ i is the activity coefficient of ion i in the solution ( / ), where γ - , γ + is the corresponding anion and cation activity coefficient ( / ); I is the ionic strength of the solution (mol·L -3 );Z i is the charge number of ion i ( / ); c i is the concentration of ion i (mol·L -3 ); N is the total number of ions in the solution ( / ); A is a constant that depends on the dielectric constant, density, and temperature of water. In this example, the calculated calcium carbonate precipitation rate coefficient is 2.64×10 -17 .

[0092] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing, characterized in that: The method comprises the following steps: S1 conducts microscale mixing-induced carbonate precipitation experiments, uses a camera to record images of the carbonate precipitation process, and obtains a carbonate precipitation image dataset; S2 adjusts the position, size and brightness of the original image to a uniform style through image calibration, uses a preset grayscale threshold to identify carbonate precipitation, and continuously iterates the grayscale according to the recognition effect until the preset requirements are met, and identifies carbonate precipitation according to the iterated grayscale threshold; S3 counts the number of pixels occupied by carbonate precipitation in the calibrated image based on the carbonate precipitation recognition result, calculates the amount of carbonate precipitation induced by mixing according to the number of pixels, and calculates the carbonate precipitation rate in combination with the mixing time; S4 calculates the carbonate precipitation amount and precipitation rate at different times, and calculates the precipitation rate coefficient of carbonate induced by mixing.

2. The method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing according to claim 1, characterized in that: The step S1 specifically includes the following steps: 1) Conduct microscale mixing-induced carbonate precipitation experiments: prepare a two-dimensional microscale transparent medium according to the research objectives, and inject two or more miscible fluids that can produce carbonate precipitation reactions into the microscale transparent medium at a flow rate; 2) Establish a microscale mixing-induced carbonate precipitation image dataset: Use a CMOS camera to record images of the mixing-induced carbonate precipitation process in real time, and use all images of a single experiment as the image dataset of a single experiment, and use images of all experiments as the image dataset of all experiments.

3. The method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing according to claim 1, characterized in that: The step S2 specifically includes the following steps: 1) Carbonate precipitation image calibration: rotate the original image captured by the CMOS camera to an orthogonal direction, crop the original image to the target field of view, and then adjust the image brightness to the same value; 2) Iterative adjustment of grayscale threshold: carbonate precipitation is identified on the calibrated image according to the preset grayscale threshold. If the boundary overlap rate between the original image and the calibrated image is greater than 98%, the identification result is judged to be accurate. If not, the grayscale threshold is adjusted iteratively until the identification is accurate. 3) Carbonate precipitation identification: Carbonate precipitation is identified in the calibrated image according to the iterative grayscale threshold, and the pixels occupied by the precipitation are modified to black and the remaining positions are modified to white.

4. The method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing according to claim 1, characterized in that: The step S3 specifically includes the following steps: 1) Carbonate precipitation pixel statistics: According to the carbonate precipitation recognition results, the number of pixels N occupied by precipitation in the pixel matrix of the calibrated image is counted. p To conduct statistics; 2) Calculation of carbonate precipitation: Based on the number of pixels counted, calculate the mass M of carbonate precipitation induced by mixing T , and its calculation is shown as: M T =N p δhρ c (1) Where N p is the number of pixels occupied by carbonate precipitation; h is the depth of the two-dimensional microscale transparent medium flow channel (L); ρ c is the density of carbonate precipitation (ML -3 ); δ is the pixel resolution (L 2 ), and its calculation formula is: δ=S0 / N0 (2) Where S0 is the actual viewing area represented by the calibrated image (L 2 ); N0 is the total number of pixels in the calibrated image ( / ); 3) Calculation of carbonate precipitation rate: Based on the calculated carbonate precipitation amount, calculate the mixing-induced carbonate precipitation rate R T (molT -1 ), and its calculation formula is: Where M T is the mass of carbonate precipitation (M); M c is the molar mass of carbonate minerals (Mmol -1 );t is the mixing time (T), and its calculation formula is: Where V T is the total injection volume of the solution (M); Q is the injection flow rate (L 3 T -1 ).

5. The method for calculating the precipitation rate coefficient of carbonate induced by microscale mixing according to claim 4, characterized in that: The step S4 specifically includes the following steps: 1) Calculate the carbonate precipitation amount and precipitation rate at different times: Calculate the carbonate precipitation amount M at different times according to equations (1) and (3): T and the precipitation rate R T ; 2) Calculate the carbonate precipitation rate coefficient: Based on the calculated carbonate precipitation rate, the precipitation rate coefficient is calculated in combination with the carbonate precipitation rate as follows: The precipitation rate constant k is calculated as: In the formula, R T is the precipitation rate (mol·L·T -1 ); k is the precipitation rate constant (mol·L·T -1 );ΔG is the change in Gibbs free energy of the entire reaction (J·mol -1 );R is the ideal gas constant (J·K -1 ·mol -1 ); T is the absolute temperature (K); m and n are the coefficients of the chemical kinetic process; Ω represents the saturation state, which is calculated as: Ω=IAP / K sp (7) In the formula, K sp is the solubility product constant of carbonate, and IAP is the ion activity product, which are calculated as follows: IAP=c + c + c - c - (8) In the formula, c-, c + are the anion and cation concentrations of carbonate minerals (mol·L -3 ); γ i is the activity coefficient of ion i in the solution ( / ), where γ-, γ + is the corresponding anion and cation activity coefficient ( / ); I is the ionic strength of the solution (mol·L -3 );Z i is the charge number of ion i ( / ); c i is the concentration of ion i (mol·L -3 );N is the total number of ions in the solution ( / ); A is a constant.

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