A simulation generation method of a microbial-induced carbonate precipitation technology considering carbonate distribution patterns

Abstract

The present application relates to the technical field of microbial induced carbonate precipitation engineering, and discloses a simulation generation method of a microbial induced carbonate precipitation technology considering carbonate distribution patterns, which comprises the following steps: generating a skeleton particle model based on a discrete element platform, and obtaining three-dimensional position and radius information of the skeleton particle; based on the skeleton particle information, generating three-dimensional position and radius information of carbonate particles according to geometric constraint relationships of a target carbonate distribution pattern by using a MATLAB algorithm; importing the carbonate particle information into the discrete element platform to generate carbonate particles, and constructing a discrete element model containing the skeleton particles and the carbonate particles. The carbonate distribution pattern includes four micro-distribution patterns, namely, bridging, cemented contact, coating and pore filling. The present application can accurately construct a three-dimensional model of different carbonate micro-distribution patterns under the discrete element framework, and solves the problem that the prior art is difficult to simulate the influence of the micro-distribution pattern on the MICP reinforcement effect.

Description

Technical Field

[0001] This invention relates to the field of microbial induced carbonate deposition engineering technology, and in particular to a simulation generation method for microbial induced carbonate deposition technology that takes into account carbonate distribution patterns. Background Technology

[0002] Microbial-induced carbonate deposition (MICP) is a microbiological reinforcement method and an environmentally friendly technology. It involves injecting specific bacteria into sand, allowing them to combine with the nutrients required by the microorganisms and surrounding organic matter to form biocement with good cementing properties. Compared to traditional chemical grouting materials, MICP offers better durability and ensures that the reinforced soil has a similar porosity to the original sand while maintaining reinforcement strength. This technology is a biomineralization process driven by microbial metabolic activity, involving the crystallization and deposition of calcium carbonate. It is primarily achieved through metabolic pathways such as urea decomposition, amino acid ammoniation, denitrification, sulfate reduction, methane oxidation, and photosynthesis. This process utilizes the combination of carbonate ions produced by microorganisms with calcium ions in the environment to form calcium carbonate precipitates, while also fixing heavy metal ions. During biomineralization, carbonates typically exist in different distribution patterns, mainly including bridging, cementing contact, coating, and pore filling.

[0003] Although there are numerous studies on the overall reinforcement effect of MICP and the carbonate formation mechanism, research on the distribution pattern of carbonate at the microscopic level is still lacking. Questions such as whether the reinforcement mechanism of different distribution patterns is consistent and which reinforcement effect is stronger or weaker are still worth considering. Summary of the Invention

[0004] The purpose of this invention is to propose a simulation generation method for microbial-induced carbonate deposition technology that considers carbonate distribution patterns, thereby addressing the technical problem that existing technologies still lack sufficient research on the distribution patterns of carbonate at the microscopic level.

[0005] Specifically, this invention provides a method for simulating microbial-induced carbonate deposition technology that considers carbonate distribution patterns. The method includes the following steps: S1. Generate a skeleton particle model based on the discrete element platform and obtain the three-dimensional position and radius information of the skeleton particles; S2. Based on the three-dimensional position and radius information of the skeleton particles, the MATLAB algorithm is used to generate the three-dimensional position and radius information of the carbonate particles according to the geometric constraints of the target carbonate distribution pattern. S3. Import the three-dimensional position and radius information of the carbonate particles into the discrete element platform to generate carbonate particles and construct a discrete element model containing skeletal particles and carbonate particles.

[0006] A storage medium storing instructions and data for implementing a simulation generation method of microbial-induced carbonate deposition technology that takes into account carbonate distribution patterns.

[0007] A device for simulating microbial-induced carbonate deposition technology that considers carbonate distribution patterns includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a method for simulating microbial-induced carbonate deposition technology that considers carbonate distribution patterns.

[0008] The beneficial effects provided by this invention are: First, this invention is the first to achieve discrete element modeling of MICP that considers the microscopic distribution patterns of carbonates. Existing techniques typically simplify the carbonates generated by MICP as uniformly cemented or randomly distributed, neglecting the influence of different distribution patterns of carbonates at the microscale (bridging, cemented contact, coating, pore filling) on ​​the mechanical properties of soil. This invention, by defining the geometric constraints of four distribution patterns and using MATLAB algorithms to accurately calculate the three-dimensional positions of carbonate particles, can realistically reproduce the microscopic occurrence state of carbonates during MICP, providing a foundation for studying the intrinsic relationship between microscopic distribution patterns and macroscopic mechanical properties.

[0009] Secondly, this invention organically combines the discrete element method (DEM) platform with the MATLAB algorithm, fully leveraging the advantages of both. The PFC3D DEM platform excels at simulating the mechanical behavior of granular materials but lacks the ability to generate particles in batches based on complex geometric constraints; the MATLAB algorithm possesses powerful spatial computation and geometric search capabilities but lacks particle mechanics simulation functionality. This invention combines the two through data interaction, utilizing both the mature particle mechanics simulation framework of PFC3D and the MATLAB algorithm to achieve accurate generation of carbonate particles based on geometric constraints. The method is flexible, efficient, and highly scalable.

[0010] Third, this invention provides a parameterized modeling process, facilitating systematic comparative studies. In step S2, through the adjustability of parameters such as the target carbonate content, particle radius range, and distribution pattern selection, users can easily construct a series of models with different carbonate contents, particle sizes, and distribution patterns, and compare and analyze the influence of various factors on the MIP reinforcement effect, providing theoretical guidance for MIP formulation optimization and process parameter design.

[0011] Fourth, this invention has the advantages of low cost, high efficiency, and strong repeatability. Compared with physical experiments, numerical simulation methods do not require chemical reagents and bacterial culture materials, avoiding the uncertainties caused by fluctuations in experimental conditions, and can complete comparative studies of a large number of schemes in a short time. At the same time, the discrete element method can reveal the mechanical response mechanism at the microscopic particle level and obtain microscale information that is difficult to observe in physical experiments, thus providing a powerful supplement to physical experiments.

[0012] Fifth, this invention provides a numerical simulation tool for the engineering application of MICP technology. By constructing discrete element models with different carbonate distribution patterns, the mechanical response of MICP-reinforced soil under different load conditions can be simulated, the reinforcement effect can be evaluated, and construction parameters can be optimized, providing technical support for the application of MICP technology in engineering fields such as foundation reinforcement, slope stabilization, and seepage prevention. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the bridging distribution mode of the present invention; Figure 3 This is a schematic diagram of the adhesive contact distribution pattern of the present invention; Figure 4 This is a schematic diagram of the coating distribution pattern of the present invention; Figure 5 This is a schematic diagram of the pore filling distribution pattern of the present invention; Figure 6 This is a schematic diagram of the hardware device operation according to an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0015] Before formally describing the present invention, a general description of the solution of the present invention will be given first to facilitate understanding.

[0016] Example 1 Please refer to Figure 1 The present invention provides a method for simulating microbial-induced carbonate deposition technology that considers carbonate distribution patterns, comprising the following steps: S1. Generate a skeleton particle model based on the discrete element platform and obtain the three-dimensional position and radius information of the skeleton particles; As one embodiment, step S1 of the present invention specifically includes: First, a three-dimensional generation space is defined in the PFC3D 6.0 discrete element method platform, for example, a cubic region with a side length of 10 mm. Skeleton particles are generated according to the porosity and particle size distribution requirements of the target sample. For example, if the target porosity is set to 35% and the particle size range is 0.2 mm to 0.5 mm, the particle positions and radii are randomly generated using uniform distribution or a specified gradation curve (such as a cumulative distribution curve). During the generation process, various particle generation commands built into the PFC3D 6.0 discrete element method platform, such as "ball distribute," can be used to generate skeleton particles with different target porosities.

[0017] Subsequently, equilibrium calculations were performed on the generated skeletal particles: A loop calculation was run in PFC3D, setting the contact model between particles to a linear contact model, and assigning parameters such as particle density (e.g., 2650 kg / m³), stiffness, etc., until the ratio of the maximum unbalanced force to the average contact force within the system was less than a set threshold (e.g., 1 × 10⁻⁶). -5 When the model reaches mechanical equilibrium, it is considered to have achieved mechanical equilibrium.

[0018] After balancing, a script is written using the FISH language built into PFC3D to traverse all skeleton particles, export the 3D coordinates (x, y, z) and radius r of each particle, and save it as a text file (such as "skeleton_data.txt"), with one particle per line: "xyzr".

[0019] It should be noted that step S1 includes the following sub-steps: S11. Set the generation space in the discrete element platform and generate skeleton particles according to the target porosity and particle size distribution. S12. Perform balance calculations on the generated skeleton particles. After the model is balanced, export the three-dimensional position and radius information of the skeleton particles.

[0020] In specific implementation, step S11, "generating skeleton particles based on target porosity and particle size distribution," can be implemented as follows: The target porosity φ = 0.35 is set, and the particle size distribution uses sieve data from an actual sand sample, for example, a particle size distribution of 20% for 0.2-0.3mm, 50% for 0.3-0.4mm, and 30% for 0.4-0.5mm. A .dat file is created in PFC3D. First, a particle-free space is generated. Then, based on the "ball distribute" command, particles are generated in a certain space according to the preset gradation ratio until the entire sample reaches the target porosity (as described above, the target porosity φ = 0.35).

[0021] The "equilibrium calculation" mentioned in step S12 specifically involves: setting the gravitational acceleration to 9.81 m / s² (optional depending on the problem requirements), assigning initial stiffness to the particles, and running the PFC3D equilibrium calculation (solve command). When the unbalanced force / average contact force < 10... -5 When the equilibrium is maintained and stable, it is considered complete. A FISH script is written to iterate through all currently generated particles, using the keywords "ball.pos" and "ball.rad" to retrieve all particle information, and then the "file.write" command is used to export the data.

[0022] S2. Based on the three-dimensional position and radius information of the skeleton particles, the MATLAB algorithm is used to generate the three-dimensional position and radius information of the carbonate particles according to the geometric constraints of the target carbonate distribution pattern. It should be noted that the geometric constraint relationship of the target carbonate distribution pattern in step S2 includes one or more of the following: bridging mode, cemented contact mode, coating mode, and pore filling mode.

[0023] As one embodiment, step S2 of the present invention specifically includes: importing the "skeleton_data.txt" file exported in step S1 into the MATLAB environment.

[0024] First, set the target carbonate distribution mode, for example, select "Bridging Mode". Then, set the target mass percentage of carbonate particles. m c = 0.15 (i.e., the mass of carbonate accounts for 15% of the skeleton mass), the density of carbonate particles is set to 2710 kg / m³ (calcite density), according to the formula m c = m carbonas / m skeleton Calculate the total required mass of carbonate, then divide by the mass of a single particle (calculated based on a preset radius) to obtain the number of carbonate particles to be generated. For example, if the preset carbonate particle radius is 0.05mm~0.1mm, take the average value of 0.075mm to calculate the volume and mass of a single particle, estimating that approximately 5000 particles need to be generated.

[0025] Next, particle positions are generated based on geometric constraints. Taking the bridging mode as an example: traverse all skeleton particle pairs (i, j) and calculate the distance between the centers of the two particles. d ij sum of radius R i + R j .like d ij > R i+ R j (i.e., the two particles do not come into contact) and d ij <( R i + R j )+ D max ( D max If the maximum bridging distance is set (e.g., 0.3mm), then it is identified as a potential bridging location. On the line connecting the centers of the two particles, find a point P such that a distance of a preset radius... r c The generated carbonate particles are simultaneously tangent to both framework particles (i.e., the distance from point P to the surface of both framework particles is equal to the distance from point P to the surface of both framework particles). r c The coordinates of point P are determined through geometric solutions. The above calculations are then performed on all eligible particle pairs to generate a list of candidate locations. If the number of candidate locations exceeds the target number of particles, random selection is used; if the number is insufficient, the preset radius is adjusted or the search range is increased. After generating the three-dimensional positions and radii of all carbonate particles, they are also saved as a text file (e.g., "carbonate_data.txt"), in the same format as the skeleton particle file.

[0026] Please refer to Figures 2-5 , Figure 2 This is a schematic diagram of the bridging distribution mode of the present invention; Figure 3 This is a schematic diagram of the adhesive contact distribution pattern of the present invention; Figure 4 This is a schematic diagram of the coating distribution pattern of the present invention; Figure 5 This is a schematic diagram of the pore filling distribution mode of the present invention.

[0027] Specifically, the bridging mode refers to carbonate particles being generated between non-contact skeletal particles and in contact with those two skeletal particles. The cemented contact mode specifically refers to the formation of carbonate particles near the contact point of mutually contacting skeletal particles, and their contact with the two skeletal particles. The coating mode specifically refers to the formation of carbonate particles on the surface of a single skeleton particle, and their contact with the skeleton particle being limited to that single particle. The pore-filling mode specifically refers to the formation of carbonate particles in the pore space composed of framework particles, without contacting any framework particles.

[0028] In practical implementation, the logic for determining these constraints in the MATLAB algorithm is as follows: Bridge mode: Find pairs of skeletal particles that do not touch but are at a suitable distance, and generate carbonate particles on the connecting lines.

[0029] Cemented contact mode: Traversing all mutually contacting skeletal particle pairs (i.e. d ij ≤ R i + R j + δ , δ For a small tolerance (e.g., 0.01 mm), carbonate particles are generated near the contact point between the two particles. Specifically, the intersection of the line connecting the centers of the two particles and the surfaces of the two particles can be calculated. The midpoint of the line connecting the two intersection points can be taken as the center of the carbonate particle, and the radius can be adjusted to make it tangent to both skeletal particles.

[0030] Coating mode: Traverse each skeleton particle and randomly generate a point on its surface as the center of the carbonate particle. The generation direction can be randomly and uniformly distributed, and the generation distance is the radius of the skeleton particle plus the radius of the carbonate particle, ensuring that the carbonate particle only contacts the skeleton particle and does not overlap with other particles.

[0031] Pore-filling mode: Randomly generate points in the pore space enclosed by the skeleton particles. Spatial random sampling method can be used: Randomly generate points in the model space, check the distance between the point and all skeleton particles. If all distances are greater than the radius of the skeleton particle plus the radius of the carbonate particle (i.e., no contact), and the distance to the surface of the nearest skeleton particle does not exceed a set threshold (e.g., 0.3 mm, to ensure it is within the pores), then it is considered a valid location.

[0032] Specifically, the three-dimensional position and radius information of the carbonate particles generated in step S2 further includes: S21. Based on the target carbonate percentage Determine the total mass or total volume of the carbonate particles, where , For carbonate quality, For skeleton quality; S22. Set the radius range and quantity of carbonate particles; S23. Based on the geometric constraints, the MATLAB algorithm is used to iteratively search for three-dimensional positions that satisfy the constraints in the spatial domain of the skeleton particle model, thereby generating the three-dimensional position information of the carbonate particles.

[0033] As one embodiment, the target carbonate percentage in step S21 m c Conversion: For example, m c Set to 0.10, total mass of skeleton particles m skeleton The density of all skeletal particles is calculated by multiplying their volume by their density (assumed to be 1.2 × 10⁻⁶). -5 (kg), then the total mass of carbonate mcarbonas = 0.10 × 1.2 × 10 -5 = 1.2×10 -6 kg. Assume the average radius of carbonate particles. r avg =0.06mm, single particle volume V=4 / 3πr³, single particle mass m single = ρ × V =2710×4 / 3π(6×10 -5 )³ ≈ 2.45×10 - ¹¹ kg, then the required number of particles N = m carbonas / m single ≈ 49,000 pieces.

[0034] Step S22 sets the radius range to 0.05mm~0.07mm, using a normal or uniform distribution for random assignment.

[0035] The iterative search algorithm in step S23 can be specifically described as follows: using the Monte Carlo random drop method, candidate positions are randomly generated in each iteration step, and it is checked whether they meet the geometric constraints of the current pattern. If they do, a particle is added, and the remaining number of particles to be generated is updated, until the target number or the maximum number of iterations is reached.

[0036] S3. Import the three-dimensional position and radius information of the carbonate particles into the discrete element platform to generate carbonate particles and construct a discrete element model containing skeletal particles and carbonate particles.

[0037] As one embodiment, step S3 of the present invention specifically includes: in PFC3D 6.0, a data reading script is written using the FISH language to open the "carbonate_data.txt" file generated in step S2, and the coordinates and radii of the carbonate particles are read line by line. The "ball.create" command is used to generate particles of a specified radius at a specified location. After generating all carbonate particles, physical properties and a contact model need to be assigned to the entire model. For example, the density of the skeleton particles is set to 2650 kg / m³, and the density of the carbonate particles is set to 2710 kg / m³; the contact between particles is set to use a parallel bond model, and a normal stiffness of 1×10⁻⁶ is assigned. 8 N / m, tangential stiffness 1×10 8 N / m, bond strength 5×10 6Parameters such as Pa (which can be calibrated based on actual rock mechanics experiments) are then used. Finally, the equilibrium calculation is run again to bring the newly generated carbonate particles into mechanical equilibrium with the original framework particles, forming a stable discrete element model. At this point, a MICP-reinforced discrete element model with a bridging distribution pattern is complete.

[0038] It should be noted that step S3 includes the following sub-steps: S31. Import the three-dimensional position and radius information of the carbonate particles generated in step S2 into the discrete element platform; S32. Read the information imported in step S31 using the scripting language built into the discrete element platform to generate carbonate particles; S33. Assign physical property parameters to the skeleton particles and carbonate particles, and set the contact model between the particles to form a complete discrete element model.

[0039] As one example, in step S31, the information is imported as follows: In PFC3D, a script is written using the FISH language, "carbonate_data.txt" is opened using "file.open", the coordinates and radius are read line by line using "file.read", and then the four data points of three-dimensional coordinates and radius are obtained based on the "string.token" command.

[0040] After generating particles in step S32, the carbonate particles are grouped separately to facilitate the subsequent assignment of particle properties and contact model parameters. At the same time, a small number of cycles (such as cycle 100) need to be run to initially stabilize the particles.

[0041] The specific operations for assigning properties and setting the contact model in step S33 are as follows: To set properties for carbonate particles individually, you can first group the carbonate particles using "ball group carbonate range..." and then set properties for that group; for the contact model, use "contact model linearpbond install" to install the parallel bonding model and set parameters such as "contact property pb_ten 5e6 pb_coh 5e6 pb_fa 30.0".

[0042] The physical property parameters mentioned in step S33 include particle density, elastic modulus, and Poisson's ratio; the contact model includes a linear contact model or a parallel bonding model.

[0043] As one example, in specific implementations, the density of the skeleton particles is typically taken as 2650 kg / m³ for quartz sand and 2710 kg / m³ for carbonate particles. The elastic modulus can be calibrated based on actual sand sample mechanical experiments. For example, by simulating a uniaxial compression test and adjusting the microscopic parameters to make the macroscopic mechanical response consistent with the experiment, the final calibration result is an elastic modulus E = 30 GPa and a Poisson's ratio ν = 0.25. The selection principle for the contact model is as follows: if simulating uncemented or weakly cemented sand, a linear contact model can be used; if simulating MIP-cemented sand (with cementation strength between particles), a parallel bond model should be used to simulate the fracture and mechanical behavior of the cemented bonds. The parallel bond model can transmit forces and moments, which better reflects the mechanical properties of cemented granular materials.

[0044] As one embodiment, the discrete element platform is PFC3D 6.0; the MATLAB algorithm is implemented based on the MATLAB platform and is used to calculate the three-dimensional position of carbonate particles according to geometric constraints.

[0045] PFC3D 6.0, as a discrete element method platform, provides core functionalities for granular flow simulation; MATLAB uses its built-in functions. Data exchange between the two is achieved through text files: PFC exports skeleton data → MATLAB reads and runs the data to generate carbonate data → PFC reads the carbonate data and generates the model. This loosely coupled approach is flexible and easy to debug.

[0046] Example 2: Please see Figure 6 , Figure 6 This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: a simulation generation device 401 for microbial induced carbonate deposition technology that considers carbonate distribution patterns, a processor 402, and a storage medium 403.

[0047] A simulation generation device 401 for microbial-induced carbonate deposition technology considering carbonate distribution patterns: The simulation generation device 401 for microbial-induced carbonate deposition technology considering carbonate distribution patterns realizes the simulation generation method for microbial-induced carbonate deposition technology considering carbonate distribution patterns.

[0048] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the simulation generation method of microbial induced carbonate deposition technology that considers carbonate distribution patterns.

[0049] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the simulation generation method of microbial induced carbonate deposition technology that considers carbonate distribution patterns.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simulating microbial-induced carbonate deposition considering carbonate distribution patterns, characterized in that: Includes the following steps: S1. Generate a skeleton particle model based on the discrete element platform and obtain the three-dimensional position and radius information of the skeleton particles; S2. Based on the three-dimensional position and radius information of the skeleton particles, the MATLAB algorithm is used to generate the three-dimensional position and radius information of the carbonate particles according to the geometric constraints of the target carbonate distribution pattern. S3. Import the three-dimensional position and radius information of the carbonate particles into the discrete element platform to generate carbonate particles and construct a discrete element model containing skeletal particles and carbonate particles.

2. The method for simulating microbial-induced carbonate deposition considering carbonate distribution patterns as described in claim 1, characterized in that: Step S1 includes the following sub-steps: S11. Set the generation space in the discrete element platform and generate skeleton particles according to the target porosity and particle size distribution. S12. Perform balance calculations on the generated skeleton particles. After the model is balanced, export the three-dimensional position and radius information of the skeleton particles.

3. The method for simulating microbial-induced carbonate deposition considering carbonate distribution patterns as described in claim 1, characterized in that, The geometric constraint relationship of the target carbonate distribution pattern in step S2 includes one or more of the following: bridging mode, cemented contact mode, coating mode, and pore filling mode.

4. The method for simulating microbial-induced carbonate deposition considering carbonate distribution patterns as described in claim 3, characterized in that: The bridging mode specifically refers to carbonate particles being generated between non-contact skeletal particles and in contact with those two skeletal particles. The cemented contact mode specifically refers to the formation of carbonate particles near the contact point of mutually contacting skeletal particles, and their contact with the two skeletal particles. The coating mode specifically refers to the formation of carbonate particles on the surface of a single skeleton particle, and their contact with the skeleton particle being limited to that single particle. The pore-filling mode specifically refers to the formation of carbonate particles in the pore space composed of framework particles, without contacting any framework particles.

5. The method for simulating microbial-induced carbonate deposition considering carbonate distribution patterns as described in claim 1, characterized in that: The three-dimensional position and radius information of the carbonate particles generated in step S2 further includes: S21. Based on the target carbonate percentage Determine the total mass or total volume of the carbonate particles, where , For carbonate quality, For skeleton quality; S22. Set the radius range and quantity of carbonate particles; S23. Based on the geometric constraints, the MATLAB algorithm is used to iteratively search for three-dimensional positions that satisfy the constraints in the spatial domain of the skeleton particle model, thereby generating the three-dimensional position information of the carbonate particles.

6. The method for simulating microbial-induced carbonate deposition considering carbonate distribution patterns as described in claim 1, characterized in that: Step S3 includes the following sub-steps: S31. Import the three-dimensional position and radius information of the carbonate particles generated in step S2 into the discrete element platform; S32. Read the information imported in step S31 using the scripting language built into the discrete element platform to generate carbonate particles; S33. Assign physical property parameters to the skeleton particles and carbonate particles, and set the contact model between the particles to form a complete discrete element model.

7. The method for simulating microbial-induced carbonate deposition considering carbonate distribution patterns as described in claim 6, characterized in that, The physical property parameters mentioned in step S33 include particle density, elastic modulus, and Poisson's ratio; the contact model includes a linear contact model or a parallel bonding model.

8. The method for simulating microbial-induced carbonate deposition considering carbonate distribution patterns as described in claim 1, characterized in that: The discrete element platform is PFC3D 6.0; the MATLAB algorithm is implemented on the MATLAB platform and is used to calculate the three-dimensional position of carbonate particles based on geometric constraints.

9. A storage medium, characterized in that: The storage medium stores instructions and data to implement a simulation generation method for microbial-induced carbonate deposition technology that considers carbonate distribution patterns, as described in any one of claims 1 to 8.

10. A simulation device for microbial-induced carbonate deposition technology considering carbonate distribution patterns, characterized in that: include: A processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a simulated generation method for microbial-induced carbonate deposition technology considering carbonate distribution patterns, as described in any one of claims 1 to 8.

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