A method, device and equipment for constructing a mineral dissolution and precipitation simulation model

Abstract

The embodiment of the application provides a kind of mineral dissolution precipitation simulation model construction method, device and equipment, it is related to oil and gas field development field, the method is by obtaining shale mineral image and dividing boundary grid, the spatial position of solid-liquid reaction interface is clear, secondly, the calculation formula of net reaction flux is determined by combining reactant property parameter and preset rate reaction rule, the consumption of hydrogen ion of unit area solid-liquid interface is quantified, the two reactions of dissolution and precipitation are distinguished by the positive and negative of net reaction flux, then the volume source term representing the hydrogen ion consumption intensity of unit grid volume is derived based on net reaction flux, then the volume source term is integrated into the preset distribution function evolution equation to obtain the simulation model, the hydrogen ion migration process and the mineral dissolution and precipitation process caused by hydrogen ion are integrated into the same simulation system to operate synchronously, eliminate the simulation deviation caused by only single simulation mineral dissolution in prior art, improve the accuracy of mineral dissolution and precipitation simulation.

Description

Technical Field

[0001] This application relates to the field of oil and gas field development, and in particular to a method, apparatus and equipment for constructing a mineral dissolution and precipitation simulation model. Background Technology

[0002] Injecting carbon dioxide into shale reservoirs can achieve carbon sequestration and improve shale oil and gas recovery. The injected carbon dioxide dissolves in formation water to form an acidic fluid. This acidic fluid, upon contact with various minerals in the shale, triggers mineral dissolution and precipitation near the injection well. These two reactions collectively alter the reservoir's pore structure and connectivity, thereby affecting the injectability and sealing properties of carbon dioxide, as well as the production of shale oil and gas. Therefore, simulating the shale mineral dissolution and precipitation process is of great significance for studying the evolution of reservoir properties after carbon dioxide injection and optimizing injection and production strategies.

[0003] In existing technologies, fluid ion diffusion and mineral reaction kinetic parameters are input into the simulation model, fluid injection boundary conditions are set for the model, and the ion transport control equations are solved. The pore structure is iteratively updated to obtain pore structure evolution data, thereby completing the simulation of mineral dissolution and precipitation.

[0004] However, existing technologies suffer from low accuracy in simulating mineral dissolution and precipitation. While they simulate the processes of mineral dissolution and porosity increase, they fail to capture the complex response characteristics of reservoir properties during injection. This results in significant discrepancies between the simulation results and actual formation conditions, leading to low accuracy in simulating mineral dissolution and precipitation. Summary of the Invention

[0005] This application provides a method, apparatus, and equipment for constructing a mineral dissolution and precipitation simulation model, in order to solve the technical problem of low accuracy in mineral dissolution and precipitation simulation in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for constructing a mineral dissolution and precipitation simulation model, comprising:

[0007] Images of various minerals and multiple reactive physical property parameters are acquired, and the images are divided into multiple boundary grids; wherein, the boundary grids refer to the grids where mineral dissolution and precipitation occur, and the multiple reactive physical property parameters are used to represent the chemical reaction kinetics of the mineral surface and the migration ability of hydrogen ions in pore fluids;

[0008] Based on the multiple reaction property parameters and the preset rate reaction formula, the calculation formula for net reaction flux is determined; wherein, the net reaction flux is used to represent the rate of hydrogen ion consumption per unit area of ​​the solid-liquid interface within the boundary grid, the net reaction flux being greater than zero is used to indicate mineral dissolution, the net reaction flux being less than zero is used to indicate mineral precipitation, and the solid-liquid interface refers to the interface where minerals and pore fluids come into contact.

[0009] The calculation formula for the volumetric source term is derived based on the formula for calculating net reaction flux. A mineral dissolution and precipitation simulation model is generated based on the calculation formula for the volumetric source term and the preset distribution function evolution equation. The volumetric source term represents the consumption rate of hydrogen ions per unit volume within the boundary grid. The mineral dissolution and precipitation simulation model is used to simulate the hydrogen ion transport process in shale pores and the mineral dissolution and precipitation process caused by hydrogen ion transport.

[0010] In one possible implementation, acquiring images of multiple minerals and multiple reactive property parameters, and dividing the images into multiple boundary grids, includes:

[0011] Obtain a pre-defined shale core sample and classify the pre-defined shale core sample into the aforementioned multiple minerals; wherein, the multiple minerals include soluble minerals and insoluble minerals;

[0012] The images of the various minerals and the multiple reactive physical parameters are acquired, and the images are thresholded to obtain a numerical matrix. The numerical matrix is ​​then filtered by neighborhood to obtain the multiple boundary grids. The numerical matrix is ​​used as the computational domain for solving the mineral dissolution and precipitation simulation model.

[0013] In one possible implementation, deriving the formula for calculating the volumetric source term based on the formula for calculating net reaction flux includes:

[0014] Using the total area of ​​the solid-liquid interface as the independent variable, the formula for calculating the net reaction flux is integraled with respect to the area to obtain the formula for calculating the total reaction rate of the solid-liquid interface; wherein, the formula for calculating the total reaction rate is used to calculate the consumption rate of hydrogen ions at the solid-liquid interface within the boundary grid.

[0015] Divide both terms on both sides of the equation for calculating the total reaction rate by the preset volume of the boundary grid to obtain the calculation formula for the volume source term.

[0016] In one possible implementation, after deriving the calculation formula for the volumetric source term based on the calculation formula for net reaction flux, and generating a mineral dissolution and precipitation simulation model based on the calculation formula for the volumetric source term and a preset distribution function evolution equation, the method further includes:

[0017] The mineral dissolution and precipitation simulation model is verified multiple times. If the error of the mineral dissolution and precipitation simulation model is determined to be less than the preset error threshold based on the verification results, the multiple reactive physical property parameters are numerically adjusted multiple times, and the mineral dissolution and precipitation simulation model is solved after each numerical adjustment to obtain multiple mineral dissolution and precipitation evolution processes.

[0018] Based on the aforementioned multiple mineral dissolution and precipitation evolution processes, the porosity evolution curves and permeability evolution curves corresponding to various mineral dissolution and precipitation modes are determined; wherein, any numerical adjustment corresponds to a mineral dissolution and precipitation mode.

[0019] In one possible implementation, the multiple verifications include convection-diffusion verification and mineral dissolution-precipitation verification, wherein the multiple verifications of the mineral dissolution-precipitation simulation model include:

[0020] Convection-diffusion verification was performed based on the mineral dissolution and precipitation simulation model, and the verification results were obtained.

[0021] If the convection-diffusion error of the mineral dissolution-precipitation simulation model is determined to be less than a preset first threshold based on the convection-diffusion verification results, mineral dissolution-precipitation verification is performed according to the mineral dissolution-precipitation simulation model to obtain the verification results.

[0022] In one possible implementation, the step of performing multiple numerical adjustments on the plurality of reactive physical parameters and solving the mineral dissolution and precipitation simulation model after each numerical adjustment to obtain multiple mineral dissolution and precipitation evolution processes includes:

[0023] During any numerical adjustment, the multiple reactive property parameters are replaced with preset parameters to obtain the simulation model with updated parameters.

[0024] Carbon dioxide is injected into the preset shale core sample, and the updated simulation model is solved based on the computational domain to obtain the hydrogen ion transport process corresponding to any numerical adjustment.

[0025] The mineral boundary solute concentration of the boundary grid during the hydrogen ion transport process is obtained, and the mineral boundary solute concentration is input into the calculation formula of the net reaction flux to obtain the net reaction flux of the boundary grid.

[0026] The mineral phase state of the boundary grid is updated based on the net reaction flux to obtain the mineral dissolution and precipitation evolution process corresponding to any numerical adjustment.

[0027] In response to the completion of the multiple numerical adjustments, the mineral dissolution and precipitation evolution process corresponding to any one of the numerical adjustments is summarized to obtain the multiple mineral dissolution and precipitation evolution processes.

[0028] Secondly, embodiments of this application provide an apparatus for constructing a mineral dissolution and precipitation simulation model, comprising:

[0029] The first acquisition module is used to acquire images of various minerals and multiple reactive physical property parameters, and divide the images into multiple boundary grids; wherein, the boundary grids refer to the grids where mineral dissolution and precipitation occur, and the multiple reactive physical property parameters are used to represent the chemical reaction kinetics of the mineral surface and the migration ability of hydrogen ions in pore fluids;

[0030] The first determining module is used to determine the calculation formula of net reaction flux based on the multiple reaction property parameters and the preset rate reaction formula; wherein, the net reaction flux is used to represent the consumption rate of hydrogen ions per unit area of ​​the solid-liquid interface within the boundary grid, the net reaction flux being greater than zero is used to indicate that the mineral dissolves, the net reaction flux being less than zero is used to indicate that the mineral precipitates, and the solid-liquid interface refers to the interface where the mineral and the pore fluid come into contact.

[0031] The derivation module is used to derive the calculation formula of the volume source term based on the calculation formula of the net reaction flux, and generate a mineral dissolution and precipitation simulation model based on the calculation formula of the volume source term and the preset distribution function evolution equation. The volume source term is used to represent the consumption rate of hydrogen ions per unit volume within the boundary grid, and the mineral dissolution and precipitation simulation model is used to simulate the hydrogen ion transport process in shale pores and the mineral dissolution and precipitation process caused by hydrogen ion transport.

[0032] In one possible implementation, the first acquisition module includes:

[0033] The first acquisition unit is used to acquire a preset shale core sample and classify the preset shale core sample into the multiple minerals; wherein, the multiple minerals include soluble minerals and insoluble minerals;

[0034] A segmentation unit is used to acquire the images of the various minerals and the multiple reactive property parameters, and to perform threshold segmentation on the images to obtain a numerical matrix, and to perform neighborhood filtering on the numerical matrix to obtain the multiple boundary grids; wherein, the numerical matrix is ​​used as the computational domain for solving the mineral dissolution and precipitation simulation model.

[0035] In one possible implementation, the derivation module includes:

[0036] An integrator unit is used to perform an area integral on the formula for calculating the net reaction flux with the total area of ​​the solid-liquid interface as the independent variable, so as to obtain a formula for calculating the total reaction rate of the solid-liquid interface; wherein, the formula for calculating the total reaction rate is used to calculate the consumption rate of hydrogen ions at the solid-liquid interface within the boundary grid.

[0037] The derivation unit is used to divide both terms on both sides of the equation for calculating the total reaction rate by a preset volume of the boundary grid to obtain the calculation formula for the volume source term.

[0038] In one possible implementation, the apparatus for constructing the mineral dissolution and precipitation simulation model further includes:

[0039] The verification module is used to verify the mineral dissolution and precipitation simulation model multiple times. If the error of the mineral dissolution and precipitation simulation model is determined to be less than the preset error threshold based on the verification results, the multiple reactive physical property parameters are numerically adjusted multiple times, and the mineral dissolution and precipitation simulation model is solved after each numerical adjustment to obtain multiple mineral dissolution and precipitation evolution processes.

[0040] The determination module is used to determine the porosity evolution curve and permeability evolution curve corresponding to various mineral dissolution and precipitation modes based on the multiple mineral dissolution and precipitation evolution processes; wherein, any numerical adjustment corresponds to a mineral dissolution and precipitation mode.

[0041] In one possible implementation, the verification module includes:

[0042] The first verification unit is used to perform convection-diffusion verification based on the mineral dissolution and precipitation simulation model, and obtain the convection-diffusion verification results.

[0043] The second verification unit is used to perform mineral dissolution and precipitation verification based on the mineral dissolution and precipitation simulation model if the convection and diffusion error of the mineral dissolution and precipitation simulation model is determined to be less than a preset first threshold, and to obtain the verification result.

[0044] In one possible implementation, the verification module further includes:

[0045] The replacement unit is used to replace the plurality of reactive property parameters with preset parameters when making any numerical adjustment, so as to obtain a simulation model with updated parameters;

[0046] An injection unit is used to inject carbon dioxide into the preset shale core sample, and solve the updated simulation model based on the computational domain to obtain the hydrogen ion transport process corresponding to any numerical adjustment.

[0047] The input unit is used to obtain the mineral boundary solute concentration of the boundary grid during the hydrogen ion transport process, and input the mineral boundary solute concentration into the calculation formula of the net reaction flux to obtain the net reaction flux of the boundary grid.

[0048] The update unit is used to update the mineral phase state of the boundary grid according to the net reaction flux, so as to obtain the mineral dissolution and precipitation evolution process corresponding to any numerical adjustment.

[0049] The summarization unit is used to summarize the mineral dissolution and precipitation evolution process corresponding to any one of the multiple numerical adjustments in response to the end of the multiple numerical adjustments, so as to obtain the multiple mineral dissolution and precipitation evolution processes.

[0050] Thirdly, embodiments of this application provide an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0051] The memory stores computer-executed instructions;

[0052] When the processor executes the computer execution instructions stored in the memory, it is used to implement the method for constructing a mineral dissolution and precipitation simulation model as described in any of the first aspects.

[0053] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method for constructing a mineral dissolution and precipitation simulation model as described in any of the first aspects.

[0054] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, is used to implement the method for constructing a mineral dissolution and precipitation simulation model as described in any of the first aspects.

[0055] This application provides a method, apparatus, and device for constructing a mineral dissolution and precipitation simulation model. By acquiring shale mineral images and dividing the boundary grid, the spatial location of the solid-liquid reaction interface is clarified, providing an accurate geometric carrier for subsequent reaction calculations. Next, the calculation formula for net reaction flux is determined by combining reaction property parameters and preset rate reaction rules, quantifying the hydrogen ion consumption per unit area of ​​the solid-liquid interface. The positive and negative values ​​of the net reaction flux distinguish between dissolution and precipitation reactions, fully covering bidirectional reaction behavior and avoiding the one-sidedness of simulation caused by only representing mineral dissolution. Then, based on the net reaction flux, a volume source term representing the intensity of hydrogen ion consumption per unit grid volume is derived. This volume source term is then integrated into a preset distribution function evolution equation to obtain the simulation model. The hydrogen ion transport process and the hydrogen ion-induced mineral dissolution and precipitation process are integrated into the same simulation system for synchronous calculation, restoring the complex changes in reservoir properties caused by the alternating effects of dissolution and precipitation during fluid injection. This eliminates the simulation bias caused by only simulating mineral dissolution in existing technologies, improving the accuracy of mineral dissolution and precipitation simulation. Attached Figure Description

[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0057] Figure 1 A schematic diagram illustrating an application scenario of the method for constructing a mineral dissolution and precipitation simulation model provided in this application embodiment;

[0058] Figure 2 A flowchart illustrating the method for constructing a mineral dissolution and precipitation simulation model provided in this application embodiment. Figure 1 ;

[0059] Figure 3 A flowchart illustrating the construction process of a shale pore-scale multi-mineral component model provided in this application embodiment;

[0060] Figure 4 A flowchart illustrating the method for constructing a mineral dissolution and precipitation simulation model provided in this application embodiment. Figure 2 ;

[0061] Figure 5 This application provides a schematic diagram of the convection-diffusion verification of the mineral dissolution-precipitation process in its embodiments. Figure 1 ;

[0062] Figure 6 This is a schematic diagram illustrating the convection-diffusion verification of the mineral dissolution-precipitation process provided in an embodiment of the present invention. Figure 2 ;

[0063] Figure 7 This is a schematic diagram illustrating the convection-diffusion verification of the mineral dissolution-precipitation process provided in an embodiment of the present invention. Figure 3 ;

[0064] Figure 8 This is a schematic diagram illustrating the convection-diffusion verification of the mineral dissolution-precipitation process provided in an embodiment of the present invention. Figure 4 ;

[0065] Figure 9 Verification of the fitting of the mineral dissolution-precipitation process provided in the embodiments of this application. Figure 1 ;

[0066] Figure 10 Verification of the fitting of the mineral dissolution-precipitation process provided in the embodiments of this application. Figure 2 ;

[0067] Figure 11 A schematic diagram of the complete process of shale pore-scale mineral dissolution-precipitation simulation based on the lattice Boltzmann method provided in the embodiments of this application;

[0068] Figure 12 Schematic diagrams illustrating three dissolution and precipitation patterns occurring inside carbon dioxide-injected shale under different conditions, as provided in the embodiments of this application;

[0069] Figure 13 The evolution of physical properties resulting from three dissolution and precipitation modes after carbon dioxide injection in this embodiment of the invention. Figure 1 ;

[0070] Figure 14 The evolution of physical properties resulting from three dissolution and precipitation modes after carbon dioxide injection in this embodiment of the invention. Figure 2 ;

[0071] Figure 15 A schematic diagram of the structure of the apparatus for constructing a mineral dissolution and precipitation simulation model provided in the embodiments of this application;

[0072] Figure 16 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application.

[0073] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0074] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0075] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply difference. It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0076] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the method, apparatus, and equipment for constructing a mineral dissolution and precipitation simulation model provided in the embodiments of this application are merely examples; a method, apparatus, and equipment for constructing a mineral dissolution and precipitation simulation model may also include more or fewer elements.

[0077] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0078] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0079] To clearly understand the technical solution of this application, the existing technology is first described in detail. Injecting carbon dioxide into shale reservoirs can achieve carbon sequestration and improve shale oil and gas recovery. The injected carbon dioxide dissolves in formation water to form an acidic fluid. This acidic fluid, upon contact with various minerals in the shale, triggers mineral dissolution and precipitation near the injection well. These two reactions jointly alter the reservoir's pore structure and connectivity, thereby affecting the injectability and sealing properties of carbon dioxide, as well as the production of shale oil and gas. Therefore, simulating the shale mineral dissolution and precipitation process is of great significance for studying the evolution of reservoir properties after carbon dioxide injection and optimizing injection and production schemes.

[0080] In existing technologies, fluid ion diffusion and mineral reaction kinetic parameters are input into a simulation model. Fluid injection boundary conditions are set for the model, and the ion transport control equations are solved. The pore structure is then iteratively updated to obtain pore structure evolution data, thereby simulating mineral dissolution and precipitation. However, existing technologies simulate the processes of mineral dissolution and pore enlargement, failing to capture the complex response characteristics of reservoir properties during injection. This leads to significant discrepancies between the simulation results and actual formation conditions, ultimately resulting in low accuracy in existing mineral dissolution and precipitation simulations. Therefore, existing technologies suffer from the technical problem of low accuracy in simulating mineral dissolution and precipitation.

[0081] Therefore, to address the issue of low accuracy in simulating mineral dissolution and precipitation in existing technologies, this study found that the following solutions can be implemented: Optionally, images of various shale minerals can be acquired, and boundary grids representing only mineral dissolution and precipitation can be created based on these images. These boundary grids can accurately pinpoint the spatial location of the solid-liquid interface, and the resulting grid can then be used to carry subsequent calculations of reaction properties and fluxes. Optionally, multiple reaction properties characterizing the chemical reaction kinetics and hydrogen ion migration capabilities of the mineral surface can be extracted. Combined with a pre-defined rate reaction formula, a formula for calculating net reaction flux can be derived. The positive or negative net reaction flux can distinguish between mineral dissolution and precipitation, thus comprehensively depicting the bidirectional reaction process and overcoming the bias caused by simulating only a single dissolution process. Optionally, based on the net reaction flux corresponding to the hydrogen ion consumption rate per unit area of ​​the solid-liquid interface within the boundary grid, a formula for calculating the volumetric source term characterizing the hydrogen ion consumption rate per unit volume can be derived. This transforms the surface-scale interface reaction parameters into volumetric parameters that can be directly calculated by the grid, achieving a quantitative expression of solid-liquid interface mineral reactions within a numerical grid. Optionally, the derived volume source term calculation formula can be embedded into the preset distribution function evolution equation to obtain a mineral dissolution and precipitation simulation model that can simultaneously simulate hydrogen ion transport, mineral dissolution, and mineral precipitation. This enables the simultaneous solution of ion transport process and bidirectional mineral reaction, fully restoring the complex changes in reservoir properties under fluid injection and improving the overall simulation accuracy.

[0082] This application provides a method, apparatus, and device for constructing a mineral dissolution and precipitation simulation model. By acquiring shale mineral images and dividing the boundary grid, the spatial location of the solid-liquid reaction interface is clarified, providing an accurate geometric carrier for subsequent reaction calculations. Next, the calculation formula for net reaction flux is determined by combining reaction property parameters and preset rate reaction rules, quantifying the hydrogen ion consumption per unit area of ​​the solid-liquid interface. The positive and negative values ​​of the net reaction flux distinguish between dissolution and precipitation reactions, fully covering bidirectional reaction behavior and avoiding the one-sidedness of simulation caused by only representing mineral dissolution. Then, based on the net reaction flux, a volume source term representing the intensity of hydrogen ion consumption per unit grid volume is derived. This volume source term is then integrated into a preset distribution function evolution equation to obtain the simulation model. The hydrogen ion transport process and the hydrogen ion-induced mineral dissolution and precipitation process are integrated into the same simulation system for synchronous calculation, restoring the complex changes in reservoir properties caused by the alternating effects of dissolution and precipitation during fluid injection. This eliminates the simulation bias caused by only simulating mineral dissolution in existing technologies, improving the accuracy of mineral dissolution and precipitation simulation.

[0083] Based on the above-mentioned inventive discovery, the technical solution of this application is proposed.

[0084] The following describes the application scenarios of the method for constructing the mineral dissolution and precipitation simulation model provided in the embodiments of the present invention. Figure 1This diagram illustrates an application scenario of the method for constructing a mineral dissolution and precipitation simulation model provided in this application embodiment. For example... Figure 1 As shown, the application scenario includes a mobile terminal 101 and a server 102. The mobile terminal 101 collects images of various minerals and sends the images to the server 102. The server 102 acquires the images and multiple reaction property parameters, and divides the images into multiple boundary grids. Based on the multiple reaction property parameters and a preset rate reaction formula, the server 102 determines the calculation formula for the net reaction flux. Based on the calculation formula for the net reaction flux, the server 102 derives the calculation formula for the volume source term and couples the calculation formula for the volume source term to a preset distribution function evolution equation to obtain a mineral dissolution and precipitation simulation model.

[0085] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0086] Figure 2 A flowchart illustrating the method for constructing a mineral dissolution and precipitation simulation model provided in this application embodiment. Figure 1 .like Figure 2 As shown, in this embodiment, the execution entity of this invention is a server. The method for constructing the mineral dissolution and precipitation simulation model provided in this embodiment includes the following steps:

[0087] S201. Acquire images of various minerals and multiple reactive physical property parameters, and divide the images into multiple boundary grids; wherein, the boundary grid refers to the grid where mineral dissolution and precipitation occur, and the multiple reactive physical property parameters are used to represent the chemical reaction kinetics of the mineral surface and the migration ability of hydrogen ions in the pore fluid.

[0088] Specifically, small pieces can be cut and polished from shale core samples, and two-dimensional grayscale images can be obtained using a nanoscale scanning electron microscope. Simultaneously, energy dispersive spectroscopy (EDS) analysis is used to analyze the elemental composition of each pixel, and the mineral types and contents obtained from the EDS analysis are used as a reference for subsequent image segmentation. Among the multiple reactive physical parameters, the chemical reaction kinetic parameters, including the reaction rate constant and equilibrium concentration, are obtained through mineral dissolution experiments on shale samples. The hydrogen ion migration ability parameter in pore fluids, including the ion diffusion coefficient, is determined through solute diffusion experiments. Based on the differences in grayscale values ​​of different mineral components in scanning electron microscope images, a multi-threshold segmentation method is used to classify each pixel into its corresponding mineral or pore category, forming a digital core model. The lattice Boltzmann method is then applied to discretize the grid on this digital core model, and each grid point is assigned either solid or fluid properties. All grid points are traversed one by one, and the properties of each grid point and its adjacent grid points are checked. When a grid point itself has fluid properties and its adjacent grid points have solid properties, the grid point is determined to be a boundary grid. This step is used to provide the accurate spatial location and geometry of the solid-liquid reaction interface for subsequent mineral dissolution and precipitation simulation, and at the same time, it provides the area and orientation information of the solid-liquid interface within the boundary grid for the calculation of net reaction flux.

[0089] The digital core model is based on scanning electron microscope (SEM) images of shale core samples. A multi-threshold segmentation algorithm is used to classify each pixel in the image according to its grayscale value into a corresponding mineral or pore type, resulting in a numerical grid model where each grid point carries mineral or pore attribute properties. The model's input includes the grayscale image of the shale core sample from the SEM, mineral type and content comparison data obtained from energy dispersive spectroscopy (EDS), and parameters such as reaction rate constants, equilibrium concentrations, and ion diffusion coefficients measured through mineral dissolution and solute diffusion experiments. The output is a gridded data volume where each grid point is clearly identified as having a mineral or pore type after image segmentation and grid discretization. This model is not trained on a large number of samples; instead, it directly converts each pixel in the SEM image into the component attributes of the corresponding grid point for a specific shale core sample. Therefore, it reflects the true spatial distribution of minerals and pore structure of the sample itself, without involving a sample set-based learning process.

[0090] S202. Based on multiple reactant property parameters and a preset rate reaction formula, determine the calculation formula for net reaction flux; where net reaction flux is used to represent the rate of hydrogen ion consumption per unit area at the solid-liquid interface within the boundary grid, a net reaction flux greater than zero indicates mineral dissolution, and a net reaction flux less than zero indicates mineral precipitation, and the solid-liquid interface refers to the interface where minerals and pore fluids come into contact.

[0091] The preset rate reaction formula is a mathematical expression describing the balance between the hydrogen ion diffusion flux and the chemical reaction rate at the solid-liquid interface of a mineral surface. The preset rate reaction formula is Formula 1, which is:

[0092]

[0093] Where D is the diffusion coefficient of hydrogen ions in the porous fluid, and n is the unit normal vector pointing to the solid boundary of the fluid domain. This represents the current hydrogen ion concentration within the pore fluid. This represents the equilibrium concentration of hydrogen ions under the equilibrium state of the mineral reaction. The rate constant of the mineral surface reaction is... This represents the diffusion flux of hydrogen ions along the normal direction at the solid-liquid interface. This represents the rate at which chemical reactions on the mineral surface consume or generate hydrogen ions. Equal values ​​on both sides of the equation indicate that diffusion transport and chemical reactions at the solid-liquid interface have reached a continuous flux state.

[0094] Specifically, in each time step of the lattice Boltzmann method, the hydrogen ion concentration at each grid point can be obtained by summing the solute transport distribution function of each boundary grid. Based on the mineral category number marked by the boundary grid during the multi-mineral component partitioning stage, the reaction rate constant corresponding to the mineral category number is looked up in a pre-built parameter lookup table. and reaction equilibrium concentration This parameter lookup table is formed during the input phase by pairing various mineral names with their corresponding reactive physical properties measured experimentally. Then, the preset rate reaction formula is used... The formula for calculating net reaction flux is that net reaction flux equals the reaction rate constant multiplied by the difference between the hydrogen ion concentration at the boundary and the equilibrium concentration. This step is used to convert the chemical reaction rate at the mineral surface into the interface consumption that can be directly used in solute transport calculations in the lattice Boltzmann method, thereby quantifying the intensity and direction of dissolution or precipitation at each boundary grid.

[0095] S203. Derive the calculation formula for the volumetric source term based on the calculation formula for net reaction flux, and generate a mineral dissolution and precipitation simulation model based on the calculation formula for the volumetric source term and the preset distribution function evolution equation. The volumetric source term is used to represent the consumption rate of hydrogen ions per unit volume within the boundary grid, and the mineral dissolution and precipitation simulation model is used to simulate the hydrogen ion transport process in shale pores and the mineral dissolution and precipitation process caused by hydrogen ion transport.

[0096] Specifically, we can first consider the net reaction flux... Rewritten as gradient vector The dot product of the interface unit normal vector n can be rewritten as Formula 2, which is:

[0097]

[0098] Because the diffusion flux at the solid-liquid interface is essentially a projection component of a vector field onto the interface normal direction, i.e., the hydrogen ion concentration gradient vector. The projection along the unit normal vector n determines the net transport of ions into and out of the interface, while the normal derivative... It is precisely the gradient vector The dot product of the normal vector n and the dot product is mathematically equivalent to different expressions. Rewriting it in dot product form transforms the scalar operation, which was originally only applicable to a single direction (normal direction), into a dot product operation between vectors. This form facilitates subsequent surface integral operations on the solid-liquid interface, because the surface integral process requires the inner product of the normal vector direction and the concentration gradient direction at each infinitesimal element on the interface and then summing them up. The dot product form directly provides this computational framework.

[0099] Substituting Formula 2 into Formula 1, we get Formula 3, which is:

[0100]

[0101] Applying the area integral of both sides of Equation 3 over the area A of the solid-liquid interface within the boundary grid, the integral formula is Equation 4. Equation 4 is:

[0102]

[0103] Introducing a generalized reaction order *m* into the chemical reaction rate term on the right side of the equation can cover both linear and nonlinear reaction forms. In this case, Equation 4 becomes Equation 5, which is:

[0104]

[0105] Because actual mineral dissolution and precipitation reactions are not always linear, the dependence of reaction rates at the solid-liquid interface on the hydrogen ion concentration at the interface may differ for different mineral species. For some minerals, the reaction rate is linearly related to the concentration difference, while for others it is related to the square of the concentration difference or other exponents. By introducing a generalized reaction order *m* into the chemical reaction rate term on the right side of the equation, various different reaction kinetic scenarios can be covered. When *m*=1, it corresponds to a linear reaction; when *m* takes other values, it corresponds to a nonlinear reaction. This allows the same formula for calculating the volume source term to adapt to the actual reaction behavior of different minerals, eliminating the need to derive different formulas for each mineral. The value of *m* can be determined through dissolution experiments on the corresponding minerals in shale samples. After fitting the experimental data to obtain a specific exponent value, it can be directly substituted into the formula for calculation.

[0106] Dividing both sides of equation 5 by the volume V of the boundary mesh yields the volume source term. The calculation formula for volume source term The calculation formula is Formula 6, which is:

[0107]

[0108] Through the derivation of Equations 2 to 6, the calculation formula for the volumetric source term is derived from the calculation formula for net reaction flux. After completing the derivation of the volumetric source term calculation formula, it needs to be placed into a complete numerical simulation framework so that it can be calculated synchronously with the fluid flow and solute transport processes. The core of this simulation framework is the lattice Boltzmann method, which reproduces macroscopic fluid flow, solute transport, and mineral dissolution reactions by simulating the collision and migration processes of fluid particles on a fixed lattice. To simulate the transport process of hydrogen ions, this application introduces an independent distribution function to solve the convection-diffusion equation. This equation is expressed by Equation 7, which is:

[0109]

[0110] in, Let be the non-equilibrium distribution function of the i-th discrete velocity direction of hydrogen ions. For LBM discrete velocity vectors, For time step, Let be the updated solute distribution function in the x and i directions of the grid after one time step transition, where x is the abscissa of the hydrogen ion in the computational grid, representing the coordinates of a grid node in the lattice Boltzmann simulation, and i is the discrete velocity direction number. Let be the lattice Boltzmann distribution function of the fluid flow field, and eq represent the equilibrium state.

[0111] Equation 7 is the pre-defined distribution function evolution equation. The equilibrium distribution function of solute transport is expressed by Equation 8, which is:

[0112]

[0113] Where C represents the hydrogen ion concentration at the grid node. Let i be the discrete velocity vector in the i-th direction. Let be the lattice speed of sound, and u be the macroscopic velocity of the pore fluid. The current hydrogen ion concentration within the pore fluid. The summation is obtained by addition, and the summation process is expressed by Formula 9, which is:

[0114]

[0115] The diffusion coefficient D of the solute is directly related to the relaxation time. The relationship between the diffusion coefficient D of the solute and the relaxation time is expressed by Equation 10, which is:

[0116]

[0117] in, For relaxation time. Equations 8 to 10 are the supporting expressions required for the calculations in Equation 7, and together they constitute the complete mathematical foundation upon which Equation 7 can actually operate. Equation 8 is the equilibrium distribution function in Equation 7. The specific expansion involves substituting the macroscopic concentration C and macroscopic velocity u to calculate the local equilibrium value at the grid point, which is then used in Equation 7. The difference calculation uses Formula 9, which is the distribution function resulting from the combined action of the left and right sides of Formula 7. Extract macroscopic concentration The summation expression for this concentration value serves both as the current concentration calculated from the equilibrium state in Formula 8 and as the source of the concentration at the boundary in subsequent interface reaction formulas. Formula 10 determines the relaxation time in Formula 7. The conversion relationship is used to transform the physical diffusion coefficient D into the relaxation time in lattice units, which is then filled into the relaxation factor matrix in Formula 7. In this process, the evolution equations enable solute transport to achieve the expected macroscopic diffusion behavior. Therefore, Equations 8 to 10 are the equilibrium state definitions, physical quantity extraction methods, and parameter conversion rules upon which Equation 7 is based in its specific implementation.

[0118] Equation 6 is the calculation formula for the volumetric source term, and Equation 7 is the preset distribution function evolution equation. Based on Equations 6 and 7, a mineral dissolution and precipitation simulation model is generated. Based on Equation 6, for each boundary grid, the surface reaction flux at the solid-liquid interface is converted into a volumetric source term. Based on the sign and magnitude of the volume source term, the solid volume and porosity of the boundary grid are updated, and the grid properties are reassessed after the porosity update. According to Equation 7, the evolution equation of the preset distribution function of solute transport is used as the main framework for solving the hydrogen ion concentration problem. Combined with the equilibrium distribution function of solute transport and the macroscopic concentration summation formula, the hydrogen ion concentration distribution at each grid point is calculated in each time step. The concentration field solved by Equation 7 at each time step provides the hydrogen ion concentration at the boundary for Equation 6. Formula 6 calculates the volume source term at each time step, which drives the porosity update of the grid. The updated porosity changes the geometric distribution of the solid skeleton, which in turn affects the flow boundary conditions and velocity field input on which Formula 7 depends in the next time step. The two formulas are executed alternately between different time steps and transmit information to each other. Finally, they are combined to form a mineral dissolution and precipitation simulation model that can simultaneously calculate the evolution of concentration field and pore structure.

[0119] By incorporating the volumetric source term into the predefined distribution function evolution equation, the hydrogen ion concentration field and mineral phase evolution process are solved simultaneously through this equation. The distribution function evolution equation iteratively calculates the hydrogen ion concentration distribution at each time step, while updating the solid volume of the boundary mesh based on the volumetric source term. This allows hydrogen ion transport and mineral reactions to be solved alternately within a unified framework, avoiding simulation biases caused by the decoupling of reaction and transport processes in traditional methods.

[0120] The method for constructing a mineral dissolution and precipitation simulation model can be applied to the technical scenario of enhancing oil recovery through carbon dioxide injection in shale reservoirs. This model addresses the challenge of quantitatively predicting changes in reservoir properties caused by the dissolution and precipitation reactions between acidic fluids and minerals after carbon dioxide injection. In this scenario, the fractured shale reservoir contains a large amount of water. The injected carbon dioxide dissolves in the water to form an acidic fluid. This fluid induces mineral dissolution near the injection end, increasing porosity, while precipitation occurs further away from the injection end due to increased alkaline ion concentration, reducing porosity. The combined effect of these two processes results in complex changes in reservoir permeability that are difficult to accurately characterize using conventional experiments or empirical models. Using this model construction method, the evolution of hydrogen ion concentration over injection time can be simulated at the pore scale. Simultaneously, the changes in solid volume and porosity caused by dissolution or precipitation at each boundary grid point can be calculated, providing quantitative numerical data for parameter selection of the carbon dioxide injection scheme and prediction of reservoir stimulation effects.

[0121] The method for constructing a mineral dissolution and precipitation simulation model can also be directly applied to the shale oil extraction process, solving the industry problem of difficulty in quantitatively predicting the dynamic changes in reservoir porosity and fracture properties caused by water-rock reactions during CO2 flooding and fracturing fluid flowback. Shale oil reservoirs have well-developed natural porosity and microfractures. After hydraulic fracturing, a large amount of fracturing fluid remains in the formation. If CO2 huff and puff or CO2 displacement of shale oil is used, the injected CO2 dissolves in the formation water to form an acidic fluid, which will dissolve soluble minerals such as calcite and feldspar in the shale, widening the micropore throats and enhancing the crude oil seepage channels. As the fluid migrates deeper into the formation, the enrichment of calcium and magnesium ions in the system easily generates secondary carbonate precipitation, blocking small fractures and matrix pores, weakening the shale oil recovery rate. Traditional core experiments can only obtain average properties. The existing methods cannot distinguish the different dissolution and precipitation conditions between the matrix and fractures. However, this method can divide the solid-liquid boundary grid at the pore scale, simultaneously simulate hydrogen ion transport, two-way mineral reactions and dynamic updates of pore structure, quantitatively characterize the temporal evolution of porosity and permeability under different CO2 injection pressures, well-drainage durations and fracturing fluid salinity conditions, accurately predict the decay cycle of fracture conductivity, and provide refined quantitative simulation support for the optimization of CO2 huff and puff parameters and fracturing fluid system ratios in shale oil blocks, thereby improving the recoverable reserves and ultimate recovery rate of shale oil single wells.

[0122] This embodiment provides a method for constructing a mineral dissolution and precipitation simulation model. By acquiring shale mineral images and dividing the boundary grid, the spatial location of the solid-liquid reaction interface is clarified, providing an accurate geometric basis for subsequent reaction calculations. Secondly, the calculation formula for net reaction flux is determined by combining reaction property parameters and preset rate reaction rules, quantifying the hydrogen ion consumption per unit area of ​​the solid-liquid interface. The positive and negative values ​​of the net reaction flux distinguish between dissolution and precipitation reactions, fully covering bidirectional reaction behavior and avoiding the one-sidedness of simulation caused by only representing mineral dissolution. Then, based on the net reaction flux, a volume source term representing the intensity of hydrogen ion consumption per unit grid volume is derived. The volume source term is then integrated into a preset distribution function evolution equation to obtain the simulation model. The hydrogen ion transport process and the hydrogen ion-induced mineral dissolution and precipitation process are integrated into the same simulation system for synchronous calculation, restoring the complex changes in reservoir properties caused by the alternating effects of dissolution and precipitation during fluid injection, eliminating the simulation bias caused by only simulating mineral dissolution in existing technologies, and improving the accuracy of mineral dissolution and precipitation simulation.

[0123] In one possible design, S201, images of multiple minerals and multiple reactive property parameters are acquired, and the images are divided into multiple boundary grids, including:

[0124] S2011. Obtain a pre-defined shale core sample and classify the pre-defined shale core sample into multiple minerals; among which, multiple minerals include soluble minerals and insoluble minerals.

[0125] Specifically, a pre-defined shale core sample can be drilled from the target depth of the shale reservoir. The sample is cut into a standard cylinder with a diameter of approximately 25 mm and a length of approximately 50 mm, and its end face is polished with argon ion beam. A field emission scanning electron microscope (SEM) is used in backscatter mode to perform a row-by-row, column-by-column fixed-point scan of the polished end face to obtain a high-resolution two-dimensional grayscale image. Simultaneously with the SEM imaging, an energy dispersive spectroscopy (EDS) analyzer is used to perform elemental composition analysis on each pixel within the same field of view, recording the mass percentages of major elements such as oxygen, carbon, silicon, aluminum, calcium, magnesium, iron, and potassium for each pixel. The elemental composition data obtained from the EDS analysis is compared with the standard element ratio range of known minerals to determine the mineral type of each pixel. This process classifies the pre-defined shale core sample into multiple minerals. This step provides real mineral spatial distribution data of the shale sample for subsequent mineral dissolution and precipitation simulations, serving as a direct basis for assigning grid attributes in model construction and ensuring that the solid-liquid interface position and mineral type information relied upon by the simulation calculations are consistent with the actual core structure.

[0126] S2012. Obtain images of various minerals and multiple reactive physical parameters, and perform threshold segmentation on the images to obtain a numerical matrix. Then, perform neighborhood filtering on the numerical matrix to obtain multiple boundary grids. The numerical matrix is ​​used as the computational domain for solving the mineral dissolution and precipitation simulation model.

[0127] Specifically, multiple minerals can be placed in a field emission scanning electron microscope (FET) and scanned in backscatter mode to obtain two-dimensional grayscale images of the minerals. The reaction rate constant and equilibrium concentration among the multiple reactive physical property parameters are obtained by fitting the ion concentration change curves recorded at different times after acid dissolution experiments on various minerals in shale samples. The ion diffusion coefficient is determined by solute diffusion experiments. Based on the differences in grayscale values ​​of different mineral components in scanning electron microscope images, multiple grayscale threshold intervals are set. The grayscale value of each pixel is compared with each threshold interval, and the pixel is assigned to the corresponding mineral category or porosity category, resulting in a numerical matrix with a category number for each grid point. Then, each grid point in the numerical matrix is ​​traversed one by one, and its own category number and the category numbers of its surrounding adjacent grid points are read. When a grid point's own category number is porosity and there is a mineral category number among its adjacent grid points, the grid point is marked as a boundary grid. When a grid point's own category number is mineral and there is a porosity category number among its adjacent grid points, it is also marked as a boundary grid. Insoluble mineral grid points are not included in the boundary grid determination, ensuring that only soluble mineral interfaces are included in the reaction calculation. After marking all boundary grids, this step is used to provide information on the spatial location, interface area, and normal direction of the solid-liquid reaction interface for subsequent mineral dissolution and precipitation simulation.

[0128] Figure 3A schematic diagram of the process for simulating mineral dissolution and precipitation at the pore scale in shale based on the lattice Boltzmann method, as provided in this application embodiment, is shown below. Figure 3 As shown, the overall process is divided into three steps: data preprocessing and grid initialization, time-series bidirectional reaction numerical simulation, and quantitative analysis of reservoir property evolution.

[0129] The first part describes the preprocessing of the top microscopic core image and the initialization of the computational grid: First, import the shale microscopic scanning electron microscope core image and read the grayscale data. Based on the grayscale thresholds of 0 and 2, complete the phase division and identify the image region as three types of media: pores / fractures, insoluble minerals, and soluble minerals. Extract the digital matrix data of the pore mineral distribution in the core. Based on this matrix, complete the initialization of the computational grid and construct a discrete grid system containing fluids, soluble minerals, and insoluble minerals. At the same time, set the concentration criteria. With equilibrium concentration The comparison update logic, based on < , = , > The three operating conditions are respectively executed to perform the corresponding grid solid phase volume update operation for precipitation, no change, and dissolution. The updated grid data is cyclically substituted back to the iteration starting point to achieve closed-loop calculation.

[0130] The second part is the phased time-series numerical simulation cloud map display module: with time as the main evolution line, the entire process of the two-way reaction of minerals is fully presented in two sets of time-series cloud maps. The upper blue box sequence represents the precipitation evolution stage, showing the spatial distribution cloud map of solute concentration in sequence as precipitation begins, precipitation continues to increase, precipitation gradually decreases, and cracks are completely blocked. The lower red box sequence represents the dissolution evolution stage, showing the concentration field cloud map in sequence as dissolution begins, dissolution intensifies, dissolution is complete, and dissolution ends. The precipitation stage and the dissolution stage are connected and cycled, intuitively reproducing the dynamic process of water-rock reaction during acidic fluid migration, in which "mineral precipitation blocks cracks - fluid continues to dissolve and widens pores". Each cloud map is accompanied by a concentration color mark to represent the difference in hydrogen ion concentration distribution within the grid.

[0131] The third part is the quantitative curve analysis module for the evolution of bottom reservoir properties, which includes two sets of time-series curves. The left curve represents simulation time on the horizontal axis and reservoir permeability on the vertical axis. It plots the trend of permeability with reaction time under three working conditions: Mode 1, Mode 2, and Mode 3, clearly showing the three different evolution situations of permeability: initial decrease followed by increase, slight fluctuation, and continuous increase under different reaction systems. The right curve represents simulation time on the horizontal axis and solid volume on the vertical axis. It shows the change characteristics of solid mineral volume gradually decreasing with the reaction for the three modes. Through quantitative curves, the long-term effects of dissolution and precipitation on pore solid volume and reservoir permeability under different working conditions can be intuitively compared, providing quantitative data support for predicting reservoir stimulation effects.

[0132] The technical effect of this scheme in this embodiment is as follows: by dividing the shale core sample into mineral components, the spatial distribution of soluble and insoluble minerals is clarified, which defines the spatial region of reactants for subsequent reaction calculations. By thresholding the original image to obtain a numerical matrix, the true microstructure of the shale is discretized into a computer-recognizable digital model, providing a precise geometric calculation domain for fluid flow and solute transport. Furthermore, by spatial neighborhood filtering of this numerical matrix, the solid-liquid interface position is identified, thereby accurately separating the boundary grid where mineral dissolution or precipitation occurs from the entire grid. This allows the reaction calculation to focus on the local area where solid-liquid interaction actually occurs, avoiding the redundant overhead caused by indiscriminate calculations and ensuring the positioning accuracy of the reaction boundary.

[0133] In one possible design, the formula for calculating the volumetric source term in S203 is derived based on the formula for calculating net reaction flux, including:

[0134] S2031. Using the total area of ​​the solid-liquid interface as the independent variable, the formula for calculating the net reaction flux is integraled with an area to obtain the formula for calculating the total reaction rate of the solid-liquid interface; wherein, the formula for calculating the total reaction rate is used to calculate the consumption rate of hydrogen ions at the solid-liquid interface within the boundary grid.

[0135] Specifically, the grid points of the boundary grid can be traversed, and the category number of the grid point itself and the category numbers of the grid points in its four adjacent directions can be read. If the category number of the grid point itself is the porosity category and the category number of the grid point in a certain adjacent direction is the mineral category, then the common edge in that adjacent direction is determined to be part of the solid-liquid interface. In this way, all solid-liquid interface segments within the boundary grid are determined. This total area is used as the basic variable for area integration to ensure that the calculation of the total reaction rate covers all reaction interfaces and avoids underestimation of the reaction rate due to the omission of local interfaces. After determining the solid-liquid interface, the total number of common edges within the boundary grid that are determined to be solid-liquid interfaces is obtained. The total number is multiplied by the unit interface area to obtain the total area A of the solid-liquid interface within the boundary grid. Then, the formula for calculating the net reaction flux is used as the integrand and area integration is performed on the total area A to obtain Formula 4. Formula 4 is:

[0136]

[0137] Formula 4, left side of the equal sign The reaction rate at each micro-element on the total solid-liquid interface area A. Performing an area integral yields the total reaction rate across the entire solid-liquid interface within the boundary grid. This total reaction rate physically represents the total rate of hydrogen ion consumption at the solid-liquid interface within the boundary grid, expressed in moles per unit time. It represents the sum of reaction rates on all solid-liquid interface elements within the boundary grid. Although Equation 4 is given by integrating both sides of the equation, the integral expression on the right-hand side is itself the integral definition of the total reaction rate, directly providing the calculation formula for the total reaction rate. This step converts the reaction flux per unit area into the total reaction rate across the entire solid-liquid interface within the boundary grid, providing intermediate calculation results for subsequently dividing the total reaction rate by the grid volume to obtain the volume source term.

[0138] S2032. Divide both terms on both sides of the equation for calculating the total reaction rate by the preset volume of the boundary grid to obtain the calculation formula for the volume source term.

[0139] Specifically, within each marked boundary grid, the volume V of that grid can be obtained first. The volume V is obtained as follows: in grid units, the volume of each square grid is set to 1; in physical units, the actual area corresponding to each pixel is calculated based on the resolution of the scanning electron microscope image, and then multiplied by the unit thickness to obtain the actual volume value of the boundary grid. Then, the formula for calculating the total reaction rate, i.e., the chemical reaction rate term on the right side of Equation 4, is introduced into the generalized reaction order m to obtain Equation 5, which is:

[0140]

[0141] Dividing both sides of equation 5 by the volume V of the boundary mesh, we obtain the formula for calculating the volume source term, which is equation 6. Formula 6 is:

[0142]

[0143] The technical effect of this scheme in this embodiment is as follows: by integrating the net reaction flux with the total area of ​​the solid-liquid interface within the boundary grid as the benchmark, the hydrogen ion consumption rate per unit area is converted into the total hydrogen ion consumption rate of all solid-liquid interfaces within the entire grid. This comprehensively summarizes the total reaction amount of all reaction interfaces within a single boundary grid. Then, the total reaction rate is divided by the preset volume of the corresponding boundary grid, converting the reaction amount at the interface scale into the hydrogen ion consumption intensity at the grid volume scale. Since the distribution function evolution equation uses the grid unit volume as the calculation benchmark, the hydrogen ion consumption intensity at the volume scale after conversion is consistent with the calculation dimension of the equation. This yields a volume source term that can be directly adapted to the distribution function evolution equation, achieving a unified adaptation between the interface reaction scale and the grid numerical solution scale. This allows the mineral dissolution and precipitation reactions at the interface to directly act on the numerical solution framework for pore hydrogen ion transport, realizing the synchronous coupling calculation of ion transport and mineral bidirectional reaction. This solves the technical problem that the net reaction flux at the interface level cannot be directly adapted to the grid volume scale numerical model and that it is difficult to achieve synchronous coupling solution of reaction and ion transport.

[0144] Figure 4 A flowchart illustrating the method for constructing a mineral dissolution and precipitation simulation model provided in this application embodiment. Figure 2 In this embodiment, in Figure 2 Based on the provided embodiments, the method for constructing a mineral dissolution and precipitation simulation model is further explained. The method for constructing a mineral dissolution and precipitation simulation model includes:

[0145] S401. Acquire images of various minerals and multiple reactive physical property parameters, and divide the images into multiple boundary grids; wherein, the boundary grid refers to the grid where mineral dissolution and precipitation occur, and the multiple reactive physical property parameters are used to represent the chemical reaction kinetics of the mineral surface and the migration ability of hydrogen ions in the pore fluid.

[0146] S402. Based on multiple reactant property parameters and a preset rate reaction formula, determine the calculation formula for net reaction flux; where net reaction flux is used to represent the rate of hydrogen ion consumption per unit area at the solid-liquid interface within the boundary grid, a net reaction flux greater than zero indicates mineral dissolution, and a net reaction flux less than zero indicates mineral precipitation, and the solid-liquid interface refers to the interface where minerals and pore fluids come into contact.

[0147] S403. Derive the calculation formula for the volumetric source term based on the calculation formula for net reaction flux, and generate a mineral dissolution and precipitation simulation model based on the calculation formula for the volumetric source term and the preset distribution function evolution equation. The volumetric source term is used to represent the consumption rate of hydrogen ions per unit volume within the boundary grid, and the mineral dissolution and precipitation simulation model is used to simulate the hydrogen ion transport process in shale pores and the mineral dissolution and precipitation process caused by hydrogen ion transport.

[0148] S401-S403 are similar to S201-S203, and will not be described again in this embodiment.

[0149] S404. The mineral dissolution and precipitation simulation model is verified multiple times. If the error of the mineral dissolution and precipitation simulation model is determined to be less than the preset error threshold based on the verification results, multiple numerical adjustments are made to multiple reactive physical parameters, and the mineral dissolution and precipitation simulation model is solved after each numerical adjustment to obtain multiple mineral dissolution and precipitation evolution processes.

[0150] Specifically, after generating the mineral dissolution and precipitation simulation model, the upper left corner can be set as the fluid inlet and the lower right corner as the fluid outlet within a two-dimensional rectangular computational domain. Two source terms can be added to the mass transport equation to control the fluid concentration distribution. The concentration distribution values ​​obtained from the model solution can be compared point by point with the values ​​of the analytical solution under the same conditions. If the average relative error at each point is less than 5%, then the convection-diffusion verification is passed. The mass transport equation is expressed by Equation 11, which is:

[0151]

[0152] in, The value represents the rate of change of hydrogen ion concentration C at the grid node over time. For the spatial gradient operator of C, This is the first type of source term control coefficient, used to regulate the attenuation magnitude of the fluid solute along the lateral dimension; the square term... Constitutes a transverse attenuation source term. This is the second type of source term control coefficient, used to regulate the attenuation magnitude of the fluid solute along the longitudinal dimension; the square term... This constitutes the longitudinal attenuation source term. In this case, the governing equation for the boundary conditions is expressed by Equation 12, which is:

[0153]

[0154] Where x is the x-coordinate of the hydrogen ion in the computational grid, y is the y-coordinate of the hydrogen ion in the computational grid, L is the total horizontal length, H is the total vertical height, and sinh is the hyperbolic sine function. This represents a horizontal concentration gradient.

[0155] Figure 5This application provides a schematic diagram of the convection-diffusion verification of the mineral dissolution-precipitation process in its embodiments. Figure 1 ,like Figure 5 As shown in the figure, this is the lateral attenuation coefficient. =1, Longitudinal attenuation coefficient =1 operating condition hydrogen ion concentration distribution cloud map, with an independent colorimetric scale on the right side of the map, the scale unit is . The colors, from red to dark blue, correspond to hydrogen ion concentration values ​​ranging from... When the gradient is reduced to 0, multiple sets of parallel dashed lines are arranged in the figure as solute isolines. Under the conditions of a standard rectangular computational domain and fixed inlet and outlet boundaries, a complete reference concentration gradient field is output to obtain the reference morphology of hydrogen ion convection and diffusion distribution under standard conditions.

[0156] Figure 6 This is a schematic diagram illustrating the convection-diffusion verification of the mineral dissolution-precipitation process provided in an embodiment of the present invention. Figure 2 ,like Figure 6 As shown in the figure, this is the lateral attenuation coefficient. =1, Longitudinal attenuation coefficient =0.5 operating condition hydrogen ion concentration distribution cloud map, the right side of the figure is accompanied by Figure 5 Colorimetric scales with completely consistent specifications, covering a wide range of concentrations. Multiple sets of parallel dashed lines are drawn in the figure to represent solute isolines. Under this condition, the lateral attenuation coefficient remains unchanged, while the longitudinal attenuation coefficient is reduced. This allows for a direct comparison of the changes in the longitudinal diffusion amplitude on the hydrogen ion concentration field and the morphology of the isolines.

[0157] Figure 7 This is a schematic diagram illustrating the convection-diffusion verification of the mineral dissolution-precipitation process provided in an embodiment of the present invention. Figure 3 ,like Figure 7 As shown in the figure, this is the lateral attenuation coefficient. =0.5, longitudinal attenuation coefficient =1 condition hydrogen ion concentration distribution cloud map, the right side of the map is equipped with a uniform hydrogen ion concentration color scale, the gradient color is used to represent the hydrogen ion concentration gradient in the grid, the parallel dashed lines in the map are solute isolines, this condition reduces the lateral attenuation coefficient and keeps the longitudinal attenuation coefficient unchanged, to characterize the effect of the change in the lateral diffusion attenuation amplitude on the overall solute distribution.

[0158] Figure 8 This is a schematic diagram illustrating the convection-diffusion verification of the mineral dissolution-precipitation process provided in an embodiment of the present invention. Figure 4 ,like Figure 8 As shown in the figure, this is the lateral attenuation coefficient. =0.5, longitudinal attenuation coefficient The hydrogen ion concentration distribution cloud map under the condition of 0.5 is shown. A complete concentration colorimetric scale is provided on the right side of the map, with different hydrogen ion concentration values ​​distinguished by red, yellow, green, and blue gradients. The dashed lines in the map represent solute isopleths. This condition simultaneously reduces both the horizontal and vertical attenuation coefficients to observe the overall deformation characteristics of the hydrogen ion concentration field when the bidirectional diffusion amplitude changes synchronously. Under the above boundary conditions, the analytical solution of the mass transport equation is expressed by Equation 13, which is:

[0159]

[0160] in, The solute concentration is calculated for the mass transport equation. Then, a two-dimensional rectangular region composed of calcite is constructed in two parts. Figure 9 Verification of the fitting of the mineral dissolution-precipitation process provided in the embodiments of this application. Figure 1 ,like Figure 9 As shown, the overall structure is layered. The upper and lower gray areas represent calcite solid minerals, while the two middle blue fluid layers of equal thickness with a total thickness of H represent the reactive fluid region. The upper fluid layer is labeled... equilibrium concentration =1 and dissolution, used to characterize the fluid conditions under which calcite minerals undergo dissolution reactions; the lower fluid layer is labeled. equilibrium concentration =0 and precipitation are used to characterize the fluid conditions under which mineral ions undergo precipitation reactions. By constructing a simplified configuration of a symmetrical calcite solid phase sandwiching a stratified fluid, a standard physical model that simultaneously includes dissolution and precipitation of minerals is built. This model is used to carry out lattice Boltzmann mineral reaction simulation calculations. The numerical simulation results are compared with the analytical solution of the principle to verify the computational reliability of the model in simultaneously depicting the mineral dissolution and precipitation processes.

[0161] Figure 10 Verification of the fitting of the mineral dissolution-precipitation process provided in the embodiments of this application. Figure 2 ,like Figure 10As shown, the horizontal axis represents the reaction time Time (s), and the vertical axis represents the total thickness of the fluid layer H (m). The legend distinguishes four sets of curves: blue dots represent the dissolution simulation results of the model in this application, and red squares represent the precipitation simulation results of the model in this application. The solid blue and solid red lines correspond to the analytical solutions of the dissolution and precipitation processes, respectively. The two layered schematic diagrams in the figure illustrate the initial and evolved calcite-fluid layered structures, respectively. Mineral dissolution occurs in the upper fluid region, and mineral precipitation occurs in the lower fluid region. As the reaction time increases, the blue curve representing the dissolution process continuously decreases, reflecting the continuous reduction of the fluid channel thickness due to calcite erosion. The red curve representing the precipitation process continuously increases, reflecting the continuous increase of the fluid channel thickness due to ion precipitation. The numerical scatter plots highly coincide with the solid lines of the analytical solutions, proving that the constructed model can accurately and synchronously reproduce the bidirectional interface evolution of mineral dissolution and precipitation, effectively verifying the calculation accuracy of the lattice Boltzmann reaction simulation model.

[0162] After two successful validations, the reaction rate constants among several reactive physical property parameters in the model were adjusted five times sequentially: 0.5, 0.8, 1, 1.2, and 1.5 times their original values. After each adjustment, a flow boundary condition of carbon dioxide injection was applied to the shale digital core model, and the model was run to solve the problem. The porosity and permeability values ​​at each time step in each solution were recorded, resulting in five sets of mineral dissolution and precipitation evolution processes. This step was used to verify the correctness of the model's numerical solution to ensure that it could reliably reflect the actual dissolution and precipitation behavior. After the model validation was successful, reservoir physical property evolution data under different reaction conditions were obtained by systematically changing the reactive physical property parameters, providing a data foundation for subsequent analysis of the impact of different mineral dissolution and precipitation modes on shale porosity and permeability.

[0163] S405. Based on multiple mineral dissolution and precipitation evolution processes, determine the porosity evolution curve and permeability evolution curve corresponding to various mineral dissolution and precipitation modes; wherein, any numerical adjustment corresponds to a mineral dissolution and precipitation mode.

[0164] Specifically, after multiple numerical adjustments, for each mineral dissolution and precipitation evolution process corresponding to that adjustment, the porosity value at each time step can be read from the data file recorded for that evolution process. A porosity evolution curve corresponding to that adjustment can be plotted with time as the x-axis and porosity as the y-axis. Similarly, the permeability value at each time step can be read, and a permeability evolution curve corresponding to that adjustment can be plotted with time as the x-axis and permeability as the y-axis. Each numerical adjustment corresponds to a mineral dissolution and precipitation mode. Five numerical adjustments yield five porosity evolution curves and five permeability evolution curves. The porosity and permeability values ​​at each time step under the same reaction mode are arranged in chronological order, thus determining the porosity and permeability evolution curves corresponding to that mode. This step is used to organize the simulation outputs under different parameter conditions into intuitively comparable physical property evolution data, providing a graphical data basis for analyzing the changes in shale porosity and permeability with injection time under different dissolution and precipitation modes.

[0165] After obtaining the porosity and permeability evolution curves, practical applications can analyze the trends of these curves to deduce the physical property response of shale reservoirs after carbon dioxide injection, thereby guiding the optimization of injection parameters and the prediction of recovery rates in actual production. The porosity evolution curve reflects the dynamic changes in pore space over time during carbon dioxide injection in shale reservoirs, while the permeability evolution curve reflects the dynamic changes in fluid flow capacity within the reservoir over time. Both curves together reveal the true evolution trajectory of reservoir properties after the combined effects of mineral dissolution and precipitation. By comparing multiple curves obtained under different parameter conditions, possible patterns of change in reservoir properties over injection time can be identified, such as continuous increase, initial increase followed by decrease, or continuous decrease. This trend identification is crucial for determining the feasibility of an injection scheme: if the curve shows a continuous decrease in permeability, it indicates that precipitation is dominant, the reservoir is being blocked, and continued injection may lead to a sharp increase in injection pressure or even injection failure; if the curve shows that permeability initially increases and then tends to stabilize or decrease slowly, it indicates the existence of an injection window. The porosity and permeability evolution curves can be applied to the following scenarios:

[0166] First, the injection window is determined. The injection time point corresponding to the peak permeability or porosity is read from the curve; this time point is the injection duration under those conditions. In actual production, reservoir properties begin to deteriorate (permeability decreases) and injection efficiency declines after the injection time exceeds this window. For example, by reading the injection time at the peak porosity from the porosity evolution curve, if a well group has an injection rate of 0.5 times the pore volume and the porosity continuously increases from an initial value of 4.2% to a peak of 8.7% on day 42, then the injection time window for that block under those conditions is set at 42 days. In field operations, intermittent injection is switched on day 42 to avoid a decrease in permeability.

[0167] Second, the achievable range of reservoir stimulation effect is determined. The change in porosity and permeability relative to the initial values ​​is read from the final plateau segment of the curve. For example, after carbon dioxide dissolution, the porosity of shale can increase by 1 to 5 times, and the permeability can increase by 1 to 3 orders of magnitude. This change range is used to evaluate the upper limit of reservoir stimulation that can be achieved in this block under given injection conditions. Alternatively, the upper and lower limits of the stimulation effect can be read from the final plateau segment of the permeability evolution curve. If the permeability of a certain block increases from 0.05 millidarcy and stabilizes in the 0.20 to 0.24 millidarcy range after 100 days, then 0.20 millidarcy is taken as the lower limit of stimulation and 0.24 millidarcy as the upper limit. In field fracturing design, if the measured permeability increase is less than 0.20 millidarcy, the stimulation is considered insufficient, and the injection rate needs to be increased to pursue a higher upper limit value.

[0168] Third, differentiated injection strategies for reservoirs with different mineral compositions are determined. By comparing multiple sets of curves obtained under different reactive physical property parameters, the correspondence between mineral composition, injection parameters, and physical property evolution can be established. For example, cores with high oil saturation and cores with low oil saturation may exhibit completely opposite porosity-permeability evolution trends under the same injection conditions. This correspondence can be used to formulate differentiated injection pressure, injection rate, and total injection schemes for different blocks based on their mineral composition characteristics. By comparing the changing trends of multiple curves under different parameters, the correspondence between mineral composition and injection parameters is established. For example, in Block E with 15% calcite content, the permeability increases from 0.03 to 0.18 millidarcy at an injection rate of 0.4 times the pore volume per day. In Block F with 5% calcite content, the permeability fluctuates between 0.02 and 0.04 under the same conditions. Based on this, the total injection amount for Block E is set to 2.5 times the pore volume per day, and for Block F it is adjusted to 0.2 times the pore volume per day, with a reduction in injection pressure to suppress precipitation.

[0169] In actual production, operators can directly use the derived injection time window, reservoir stimulation effect upper limit, and differentiated injection strategy to set the injection rate, design the total injection volume, and determine the timing of injection termination for carbon dioxide injection operations in the block, thereby improving shale oil and gas recovery.

[0170] Figure 11 This is a simulation diagram of the mineral dissolution and precipitation process occurring in shale after carbon dioxide injection, provided in an embodiment of this application. Figure 11As shown, the overall structure is divided into two major cyclical evolution stages, with the timeline as the main axis. The upper blue box represents the precipitation stage, which sequentially displays solute concentration field cloud maps at three time points: the start of precipitation, the increase in precipitation, and the decrease in precipitation. Each main image is accompanied by a magnified detail image and a hydrogen ion concentration color scale, visually presenting the entire process of gradual deposition after mineral ion supersaturation, continuous crack blockage, and then a decrease in precipitation. This stage ultimately results in crack blockage. The lower pink box represents the dissolution stage, which sequentially displays concentration distribution cloud maps at the start of dissolution, the increase in dissolution, complete dissolution, and the cessation of dissolution. This corresponds to the evolution of acidic fluid dissolving minerals and gradually widening pore channels until the dissolution reaction stabilizes. This stage achieves crack restart. The arc-shaped arrows on the left and right sides of the image form a closed loop, representing the dynamic water-rock reaction process in the reservoir that alternates between "precipitation blocking cracks - continuous fluid migration triggering dissolution and channel widening." Through the color differences in the spatial distribution of hydrogen ion concentration at different times, the alternating evolution characteristics of dissolution and precipitation in the crack and matrix areas over the simulation time are clearly quantified.

[0171] The technical effect of this solution in this embodiment is as follows: After constructing the mineral dissolution and precipitation simulation model, the model is first verified multiple times to ensure that the model error meets the preset standard, avoiding the use of a model with excessive deviation for subsequent analysis. Then, the reaction property parameters are adjusted multiple times and the model is solved one by one to obtain multiple sets of differentiated mineral dissolution and precipitation evolution processes. Each set of parameter adjustment results corresponds to a unique mineral dissolution and precipitation mode. Based on multiple sets of evolution processes, the corresponding porosity and permeability evolution curves under different reaction modes are extracted. This allows for a direct quantification of the dynamic changes in reservoir porosity and permeability under different reaction conditions, solving the problem that a single simulation condition cannot distinguish multiple mineral dissolution and precipitation modes and that it is difficult to obtain the evolution of reservoir porosity and permeability under various modes in batches.

[0172] In one possible design, the mineral dissolution and precipitation simulation model in S404 is validated multiple times, including:

[0173] S4041. Convection diffusion verification was performed based on the mineral dissolution and precipitation simulation model, and the convection diffusion verification results were obtained.

[0174] Specifically, a two-dimensional rectangular region with a regular shape and simple boundary conditions can be selected as a verification example. A fluid inlet is set at the upper left corner and a fluid outlet at the lower right corner of this region. Two source terms are added to the mass transport equation to control the distribution of solute concentration in the fluid. Then, the mineral dissolution and precipitation simulation model is run to calculate the solute concentration at each grid point within this region. Simultaneously, for the same set of boundary conditions and source term parameters, the analytical solution of the mass transport equation is solved to obtain the principle concentration value for each grid point. Finally, the concentration value calculated by the model is subtracted from the principle value of the analytical solution point by point, and then divided by the principle value to obtain the relative error for each grid point. The average of the relative errors of all grid points is taken as the convection-diffusion verification result. This step is used to verify the numerical accuracy of the model in solving the solute convection-diffusion problem. By comparing the model output with the known analytical solution point by point, the accuracy and reliability of the model's concentration field calculation are confirmed, providing a basis for the numerical correctness of the subsequent application of the model to actual shale mineral dissolution and precipitation simulation.

[0175] S4042. If the convection-diffusion error of the mineral dissolution-precipitation simulation model is determined to be less than the preset first threshold based on the convection-diffusion verification results, mineral dissolution-precipitation verification is performed according to the mineral dissolution-precipitation simulation model to obtain the verification results.

[0176] Specifically, a rectangular region composed of calcite minerals in both its upper and lower parts can be constructed, with a horizontal fluid channel in the middle serving as the flow space. This fluid channel is divided into two halves along the flow direction. The upper half, near the upper wall, is designated as the precipitation zone, and the lower half, near the lower wall, is designated as the dissolution zone. An acidic fluid containing carbon dioxide is injected at a constant flow rate at the channel inlet, maintaining a constant hydrogen ion concentration at the inlet. The outlet is set as a free outflow boundary condition. A mineral dissolution and precipitation simulation model is run, recording the spatial positions of the solid-liquid interface on the upper and lower walls at different time steps. For the dissolution zone, the retreat distance of the lower wall interface calculated by the model is compared with the retreat distance given by the classical one-dimensional steady-state diffusion interface movement solution. For each time step, the difference between the two is calculated and the average is taken as the dissolution verification error. For the precipitation zone, the forward movement distance of the upper wall interface calculated by the model is compared with the forward movement distance given by the same analytical solution. The difference between the two is calculated at each time step and the average is taken as the precipitation verification error. This step is used to verify the model's tracking accuracy of the solid-liquid interface movement process under two scenarios: mineral dissolution reaction and precipitation reaction. By comparing the interface position changes of the dissolution zone and the precipitation zone with the known reference solution, it is confirmed that the model can accurately reflect the two interface evolution directions: solid phase retreat caused by mineral dissolution and solid phase growth caused by mineral precipitation. This provides a reliable basis for the model to handle both types of reactions simultaneously in subsequent actual injection simulations.

[0177] Optionally, convection-diffusion verification can be completed before mineral dissolution-precipitation verification. First, a fluid inlet and outlet are set in an idealized rectangular region, and the concentration distribution is controlled by the source term. The model calculation results are compared with the analytical solution. If the average relative error is less than a preset threshold, the verification is successful. After confirming the model's convection-diffusion numerical accuracy through this step, mineral dissolution-precipitation verification is then performed. This involves constructing a rectangular region composed of calcite at the top and bottom, and comparing the model calculation results with the analytical solution to verify the accuracy of the dissolution-precipitation interface movement, ensuring that the model meets accuracy requirements during both fluid flow and reaction processes.

[0178] The technical effect of this scheme in this embodiment is as follows: by first performing convection-diffusion verification separately and comparing the corresponding error with the first threshold, the accuracy of the hydrogen ion transport solution of the model is verified. Only when the convection-diffusion calculation meets the accuracy requirements is a special verification of mineral dissolution and precipitation carried out. The error problems of the two core calculation links of ion transport and interface reaction are investigated step by step. This avoids the situation where single-dimensional verification cannot locate the source of model error and inferior models directly enter the subsequent parameter analysis process. The layered verification method can accurately distinguish the calculation deviations brought by fluid transport and mineral reaction, ensuring that the model used for multi-condition solution has dual calculation accuracy guarantee. This solves the technical problem that it is difficult to distinguish the errors of convection-diffusion and mineral reaction and accurately locate calculation defects when the model is only verified as a whole.

[0179] In one possible design, multiple reactive physical parameters are numerically adjusted multiple times in S404, and the mineral dissolution and precipitation simulation model is solved after each adjustment to obtain multiple mineral dissolution and precipitation evolution processes, including:

[0180] S4043. When making any numerical adjustment, use preset parameters to replace multiple reactant property parameters to obtain the simulation model with updated parameters.

[0181] Specifically, a configuration list containing multiple sets of preset parameter combinations can be set up. Each row in this list corresponds to a set of preset reaction rate constant values. For example, the first set is 0.5 times the original experimentally measured value, the second set is 0.8 times, the third set is 1.0 times, the fourth set is 1.2 times, and the fifth set is 1.5 times. Each time the value is adjusted, the value corresponding to the current round is read from the configuration list, and the variable values ​​of the reaction rate constant stored in the model memory are replaced one by one with the corresponding values ​​in the current set. At the same time, other reaction property parameters in the model, such as ion diffusion coefficient and reaction equilibrium concentration, are kept unchanged. After the replacement is completed, the current parameter state is written to a new model configuration file for subsequent calculations. This step is used to generate multiple model instances with different reaction intensities by systematically changing the key property parameter of reaction rate constant while keeping the model geometry and numerical solution framework completely consistent. This allows subsequent calculations to cover a series of reaction scenarios from low to high reaction rates, providing different initial parameter inputs for finally obtaining reservoir property evolution data under various mineral dissolution and precipitation modes.

[0182] Optionally, a set of control values, different from the experimentally measured reaction property parameters, can be set as preset parameters. These preset parameters are derived from reaction rate constant data obtained through parallel dissolution experiments on core samples from adjacent wells in the same shale block, or from the range of reaction kinetic parameters of similar minerals recorded in geological literature published during the earlier exploration phase of the region. The original reaction rate constant obtained in the experiment is a certain value; for example, the reaction rate constant for calcite is 2.5 × 10⁻⁶. -6 The moles per square meter per second, while the preset parameter is 0.5 times that value (1.25 × 10⁻⁶). -6 ), 0.8 times (2.0×10 -6 ), 1.2 times (3.0×10 -6 ) and 1.5 times (3.75×10 -6 These multiples correspond to the common fluctuations in mineral reaction rates under different burial depths and temperatures in actual shale reservoirs. After replacing the original experimental values ​​with the aforementioned control values, the updated simulation model has a parameter configuration with the same geometric structure and numerical solution framework as the initial model, but with different reaction intensities. This step is used to enable the model to run under different reaction rate conditions in subsequent solutions, thereby obtaining process data on mineral dissolution and precipitation evolution under various reaction scenarios.

[0183] S4044. Inject carbon dioxide into a preset shale core sample, solve the updated simulation model based on the computational domain, and obtain the hydrogen ion transport process corresponding to any numerical adjustment.

[0184] Specifically, the locations of the inlet and outlet boundaries can be determined according to the spatial range of the digital core model. A constant hydrogen ion concentration is set as the injection concentration at all grid points on the inlet boundary, and a constant fluid velocity is set as the injection velocity. Free outflow conditions are set at all grid points on the outlet boundary. Then, the iterative solution loop of the updated simulation model is initiated. Within each time step, the model first calculates the hydrogen ion concentration distribution at each grid point for the next time step based on the current boundary conditions and the concentration and velocity fields from the previous time step, according to the preset distribution function evolution equation. Then, the velocity field at each grid point is updated based on the concentration gradient, completing one time step. This process is repeated until the set total injection duration is reached. The hydrogen ion concentration values ​​of each grid point in the entire computational domain calculated at each time step are recorded one by one in chronological order to form the hydrogen ion transport process data corresponding to that number of times. This step is used to simulate the process of acidic fluid formed by carbon dioxide dissolving in formation water entering the shale core from the inlet, gradually migrating forward, diffusing and filling each pore region in the pore space. By obtaining the hydrogen ion concentration distribution data of the computational domain at every moment during the injection, the current hydrogen ion concentration value at the solid-liquid interface is provided for the subsequent calculation of net reaction flux, so that the driving force of mineral dissolution or precipitation can be quantified at each boundary grid.

[0185] S4045. Obtain the mineral boundary solute concentration of the boundary grid during hydrogen ion transport, and input the mineral boundary solute concentration into the calculation formula of net reaction flux to obtain the net reaction flux of the boundary grid.

[0186] Specifically, all boundary grids within the computational domain can be traversed. For each boundary grid currently traversed, the hydrogen ion concentration value of that grid at the current time step is read from the hydrogen ion transport process, and this concentration value is used as the mineral boundary solute concentration at that boundary grid. Then, the mineral category number marked by that grid during the mineral division stage is read. Based on this number, the reaction rate constant and reaction equilibrium concentration corresponding to that mineral category are looked up in a pre-constructed reaction parameter lookup table. The three values ​​of mineral boundary solute concentration, reaction rate constant, and reaction equilibrium concentration are substituted together into the formula for calculating net reaction flux to calculate the net reaction flux value of that boundary grid at the current time step.

[0187] For example, for a boundary grid labeled as calcite, the hydrogen ion concentration at the current time step is read from the migration data as 0.8 mol / m. 3 From the table, the reaction rate constant of calcite is found to be 2.5 × 10⁻⁶. -6 mol / (m²·s), the equilibrium concentration for the reaction is 0.5 mol / m 3 Substituting these values ​​into the net reaction flux calculation formula, the net reaction flux is obtained as 2.5 × 10⁻⁶. -6 ×(0.8-0.5)=7.5×10-7 mol / (m 2 The value ·s) indicates that mineral dissolution has occurred at the boundary grid, while a negative value indicates that mineral precipitation has occurred. After performing the above values ​​and calculations on all boundary grids one by one, the net reaction flux data of all boundary grids in the entire computational domain at the current time step is obtained. This step is used to convert the concentration field calculated from the hydrogen ion transport process into the driving intensity of the chemical reaction at the solid-liquid interface. By extracting the interface concentration of each boundary grid and combining it with the inherent reaction parameters of the minerals in that grid, the dissolution or precipitation rate at each reaction interface position is quantified, providing direct numerical input for subsequent updates of the mineral phase state based on the reaction direction and intensity.

[0188] S4046. Update the mineral phase state of the boundary grid according to the net reaction flux to obtain the mineral dissolution and precipitation evolution process corresponding to any numerical adjustment.

[0189] Specifically, after calculating the net reaction flux, the net reaction flux value of each boundary grid is multiplied by the total area of ​​the solid-liquid interface within that grid, and then multiplied by the duration of the current time step to obtain the volume of mineral dissolution or precipitation at that boundary grid within that time step. If the net reaction flux is positive, it indicates that mineral dissolution has occurred. The current solid volume of the boundary grid is subtracted from this volume to obtain the updated solid volume. If the net reaction flux is negative, it indicates that mineral precipitation has occurred. The current solid volume of the boundary grid is added to the absolute value of this volume to obtain the updated solid volume. The updated solid volume is divided by the total volume of the boundary grid to obtain the updated solid volume fraction. When the solid volume fraction is lower than a preset minimum threshold, the phase of the grid is changed from the mineral phase to the porous phase. When the solid volume fraction increases from zero to exceed the same minimum threshold, the phase of the grid is changed from the porous phase to the mineral phase, and the new mineral category of the boundary grid is recorded.

[0190] After completing the volume update and phase discrimination for all boundary grids, the phase data and solid volume data of each grid after the current time step are saved as the mineral dissolution and precipitation state of that time step. This is done sequentially as time steps progress until the total injection duration is reached. The state records of all time steps are summarized to obtain the mineral dissolution and precipitation evolution process corresponding to that number of times. This step is used to convert the interfacial reaction rate represented by the net reaction flux into the increase or decrease of mineral solid volume in each boundary grid. By gradually accumulating the small volume changes in each time step, the movement trajectory of the pore boundary is tracked, so that the pore expansion caused by mineral dissolution and the pore shrinkage caused by mineral precipitation can be continuously recorded at the grid scale. This provides the original record of structural evolution for the subsequent extraction of porosity and permeability change data over time during the evolution process.

[0191] If the net reaction flux is greater than zero, the decrease in solid volume leads to an increase in porosity; if the net reaction flux is less than zero, the increase in solid volume leads to a decrease in porosity. Changes in porosity directly affect permeability calculations. By updating the porosity value in real time, the model can dynamically simulate the evolution of reservoir properties as the reaction progresses.

[0192] S4047. In response to the end of multiple numerical adjustments, the mineral dissolution and precipitation evolution process corresponding to any one numerical adjustment is summarized to obtain multiple mineral dissolution and precipitation evolution processes.

[0193] Specifically, after each numerical adjustment of the corresponding mineral dissolution and precipitation evolution process is completed, each set of mineral phase identifiers and solid volume data obtained in the solution process according to the time step is sequentially appended to an independent data file named after the adjustment round number. At the same time, the parameter multiplier value used in the adjustment is recorded at the beginning of the file. After all rounds of solutions are completed and all independent data files are confirmed to have been generated, the entire time series data is read from each independent data file, arranged in an overview table according to the time step, and an additional index table is generated to record the correspondence between the parameter multiplier corresponding to each round number and the file storage location. This step is used to summarize the pore structure change data generated under multiple sets of different parameter configurations into a unified data set. This allows any complete sequence to be directly called from the summary results when extracting the curves of porosity and permeability changes over time without re-executing the solution process, thereby ensuring the consistency of the comparison benchmark between the data sets and reducing redundant operations.

[0194] Figure 12 The following diagram illustrates three dissolution and precipitation patterns occurring inside carbon dioxide-injected shale under different conditions, as provided in the embodiments of this application. Figure 12 As shown, the overall vertical division is divided into three independent regions: Mode 1, Mode 2, and Mode 3. This was achieved by performing multiple numerical adjustments on various reaction property parameters and solving the corresponding simulation models. Each region is configured with a corresponding background border and multiple time-series hydrogen ion concentration cloud maps. A circular local magnification window is placed on the right side of each cloud map to indicate the reaction type of the region. Mode 1 sequentially displays three evolution states: simultaneous precipitation in two regions, precipitation and dissolution in one region, and crack blockage accompanied by dissolution. Mode 2 presents the changes in simultaneous precipitation in two regions, coexistence of precipitation and dissolution, and overall dissolution. Mode 3 fully demonstrates the evolution characteristics of continuous crack widening, further crack expansion, and no mineral precipitation throughout the process. Each cloud map uses color gradients to represent the differences in hydrogen ion concentration distribution inside the pores. By comparing the pore reaction evolution cloud maps under three different reaction property parameter conditions, the development paths of the three types of differentiated mineral dissolution and precipitation can be intuitively distinguished, clearly demonstrating the changes brought about by the adjustment of reaction property parameters to the precipitation and dissolution behavior and pore space morphology within the shale crack channels.

[0195] Figure 13 The evolution of physical properties resulting from three dissolution and precipitation modes after carbon dioxide injection in this embodiment of the invention. Figure 1 ,like Figure 13 As shown in the figure, the horizontal axis represents the simulation duration, and the vertical axis, in mD units, represents the reservoir permeability value. The figure contains three curves for differentiation: the black square line corresponds to the permeability of Mode 1, the red dotted line corresponds to the permeability of Mode 2, and the blue triangular line corresponds to the permeability of Mode 3. The Mode 1 curve initially drops rapidly to a low value and then slowly and continuously rises, corresponding to the initial blockage of fractures by mineral precipitation, followed by the gradual restoration of permeability through dissolution. The Mode 2 curve fluctuates slightly overall, with permeability maintaining a small fluctuation in the range of approximately 1.0 mD, corresponding to a continuous dynamic balance between precipitation and dissolution. The Mode 3 curve shows a stable upward trend throughout, corresponding to the absence of significant mineral precipitation and the continuous widening of fractures leading to a continuous increase in permeability. The three curves simultaneously compare the long-term changes in reservoir permeability under three different reaction property parameters, quantitatively presenting the differentiated impact of different dissolution and precipitation paths on the shale fluid flow capacity.

[0196] Figure 14 The evolution of physical properties resulting from three dissolution and precipitation modes after carbon dioxide injection in this embodiment of the invention. Figure 2 ,like Figure 14 As shown in the figure, the horizontal axis represents the simulation duration, and the vertical axis represents the proportion of solid mineral volume inside the shale. Three curves with different labels are set in the figure: the black triangular broken line corresponds to the solid volume of Mode 1, the red dotted broken line corresponds to the solid volume of Mode 2, and the blue square broken line corresponds to the solid volume of Mode 3. The curve of Mode 1 has a gentle overall decline, corresponding to a small difference between the volume of precipitated and dissolved minerals in the system and a low rate of solid mineral loss. The curve of Mode 2 has the largest decline and the decline rate continues to accelerate, corresponding to the continuous and large-scale dissolution of soluble minerals and a rapid reduction in solid volume. The decline of the curve of Mode 3 is between that of Mode 1 and Mode 2, corresponding to a moderate degree of mineral dissolution. By comparing the long-term decay of solid mineral volume under three different reaction property parameters, the figure quantifies the differentiated impact of different dissolution and precipitation paths on the amount of solid minerals remaining inside the shale.

[0197] The technical effect of this scheme in this embodiment is as follows: by batch replacing reaction property parameters with preset parameters to complete multiple sets of working condition configurations, the real scenario of injecting carbon dioxide into shale cores is simulated, and the hydrogen ion transport process is obtained by solving the model based on the established computational domain. The solute concentration at the mineral interface of the boundary grid is extracted and substituted into the calculation to obtain the corresponding net reaction flux. Then, the mineral phase state within the grid is updated synchronously according to the net reaction flux value. The entire process of mineral dissolution and precipitation driven by hydrogen ion transport under a single set of parameters is completely reproduced. After all parameter adjustment working conditions are completed, all evolution results are summarized to generate differentiated mineral dissolution and precipitation evolution processes. The complete chain change of ion transport, bidirectional mineral reaction, and dynamic update of mineral phase state after carbon dioxide injection into the formation under different property parameters is reproduced. This ensures that the calculation logic of each set of evolution processes is consistent with the real formation reaction chain, improves the authenticity of the multi-working condition evolution results, and solves the technical problem of lacking a standardized step-by-step solution process under multiple sets of reaction property parameter working conditions, making it difficult to completely reproduce the entire chain evolution process of ion transport to mineral phase state update after carbon dioxide injection.

[0198] In the prior art, the evolution of the fluid particle distribution function is described by the following formula 14:

[0199]

[0200] Where i and j are indices of the discrete velocity directions. Let i be the lattice velocity vector in the i-th discrete velocity direction. Let M be the time step, t be the current time, M be the transformation matrix, and S be the relaxation factor matrix. Let j be the equilibrium distribution function in the j-th direction. The calculation formula is Formula 15, which is:

[0201]

[0202] in, For macroscopic fluid pressure, For the speed of sound in a grid, Let be the weighting coefficient for the i-th discrete velocity direction. For macroscopic density, Here, u represents the macroscopic velocity. At each time step and each grid node, the macroscopic fluid parameters are obtained by summing the distribution function. This summation process can be expressed by Equation 16, which is:

[0203]

[0204] in, For the weighting coefficient in the stationary direction, Let be the particle distribution function in the i-th discrete velocity direction. Let v be the lattice velocity vector in the i-th discrete velocity direction, and v be the fluid kinematic viscosity. Let be the fluid relaxation time. The formula for calculating the macroscopic fluid velocity is Equation 17, which is:

[0205]

[0206] Where u is the macroscopic fluid velocity. For fluid reference density, Let be the particle distribution function in the i-th discrete velocity direction. Let be the grid velocity vector in the direction of the i-th discrete velocity. Using Equation 10, for each grid and each time step, the particle distribution function... We perform a weighted summation to obtain the macroscopic fluid velocity u.

[0207] The formula for converting fluid kinematic viscosity to relaxation time is Formula 18, which is:

[0208]

[0209] Based on the fluid particle distribution function evolution equation described in Formula 14, this application introduces an independent distribution function to address the transport characteristics of hydrogen ions as a solute in pore fluids. To construct a numerical solution framework for the solute convection-diffusion process, Equations 7 and 14 share the same basic collision-transfer structure in mathematical form, but they differ substantially in the following key aspects: the equilibrium distribution function in Equation 7... The relaxation factor matrix is ​​constructed based on solute concentration rather than fluid density. It is determined based on the solute diffusion coefficient rather than the fluid viscosity. More importantly, Formula 7 adds an additional source term to the right-hand side of the equation. The source term is constructed based on the volume source term calculation formula (i.e., Formula 6) derived above, and its specific form is determined by the chemical reaction rate at the solid-liquid interface within the boundary grid. Through this improvement, Formula 7 no longer simply describes the convection-diffusion equation of solute passive migration with the fluid, but embeds the concentration change caused by the consumption or generation of hydrogen ions on the mineral surface into the evolution of the distribution function. This ensures that the update of solute concentration in each time step naturally includes the feedback effect of chemical reactions on the concentration field. Ultimately, Formula 7 and Formula 14 are solved alternately within the same grid system and time-progression framework, achieving synchronous coupled calculation of the three physicochemical processes: fluid flow, solute transport, and mineral dissolution and precipitation. This solves the time-scale mismatch problem caused by the step-by-step updating of the concentration field and the interface reaction in traditional methods, providing a time-consistent numerical basis for subsequent pore structure and permeability evolution analysis.

[0210] Figure 15This is a schematic diagram of the structure of the apparatus for constructing a mineral dissolution and precipitation simulation model provided in an embodiment of this application. Figure 15 As shown, the apparatus for constructing the mineral dissolution and precipitation simulation model includes:

[0211] The first acquisition module 1501 is used to acquire images of various minerals and multiple reactive physical property parameters, and divide the images into multiple boundary grids; wherein, the boundary grids refer to the grids where mineral dissolution and precipitation occur, and the multiple reactive physical property parameters are used to represent the chemical reaction kinetics of the mineral surface and the migration ability of hydrogen ions in the pore fluid.

[0212] The first determining module 1502 is used to determine the calculation formula of net reaction flux based on multiple reaction property parameters and preset rate reaction formula; wherein, net reaction flux is used to represent the rate of hydrogen ion consumption per unit area of ​​the solid-liquid interface within the boundary grid, a net reaction flux greater than zero is used to indicate mineral dissolution, a net reaction flux less than zero is used to indicate mineral precipitation, and the solid-liquid interface refers to the interface where minerals and pore fluids come into contact.

[0213] The derivation module 1503 is used to derive the calculation formula of the volume source term based on the calculation formula of the net reaction flux, and to generate a mineral dissolution and precipitation simulation model based on the calculation formula of the volume source term and the preset distribution function evolution equation. The volume source term is used to represent the consumption rate of hydrogen ions per unit volume within the boundary grid, and the mineral dissolution and precipitation simulation model is used to simulate the hydrogen ion transport process in shale pores and the mineral dissolution and precipitation process caused by hydrogen ion transport.

[0214] In one possible implementation, the first acquisition module 1501 includes:

[0215] The first acquisition unit is used to acquire a preset shale core sample and classify the preset shale core sample into multiple minerals; among which, the multiple minerals include soluble minerals and insoluble minerals.

[0216] The segmentation unit is used to acquire images of various minerals and multiple reactive physical parameters, and to perform threshold segmentation on the images to obtain a numerical matrix. The numerical matrix is ​​then filtered by neighborhood to obtain multiple boundary grids. The numerical matrix is ​​used as the computational domain for solving the mineral dissolution and precipitation simulation model.

[0217] In one possible implementation, the derivation module 1503 includes:

[0218] The integrator is used to perform an area integral on the formula for calculating the net reaction flux with the total area of ​​the solid-liquid interface as the independent variable, so as to obtain the formula for calculating the total reaction rate of the solid-liquid interface; wherein, the formula for calculating the total reaction rate is used to calculate the consumption rate of hydrogen ions at the solid-liquid interface within the boundary grid.

[0219] The derivation unit is used to divide both sides of the equation for calculating the total reaction rate by the preset volume of the boundary grid to obtain the calculation formula for the volume source term.

[0220] In one possible implementation, the apparatus for constructing a mineral dissolution and precipitation simulation model further includes:

[0221] The verification module is used to verify the mineral dissolution and precipitation simulation model multiple times. If the error of the mineral dissolution and precipitation simulation model is determined to be less than the preset error threshold based on the verification results, multiple numerical adjustments are made to multiple reactive physical parameters, and the mineral dissolution and precipitation simulation model is solved after each numerical adjustment to obtain multiple mineral dissolution and precipitation evolution processes.

[0222] The determination module is used to determine the porosity evolution curve and permeability evolution curve corresponding to various mineral dissolution and precipitation modes based on multiple mineral dissolution and precipitation evolution processes; wherein, any numerical adjustment corresponds to a mineral dissolution and precipitation mode.

[0223] In one possible implementation, the verification module includes:

[0224] The first verification unit is used to perform convection-diffusion verification based on the mineral dissolution and precipitation simulation model, and obtain the convection-diffusion verification results.

[0225] The second verification unit is used to verify the mineral dissolution and precipitation simulation model if the convection and diffusion error of the model is determined to be less than a preset first threshold based on the convection and diffusion verification results, and to obtain the verification results.

[0226] In one possible implementation, the verification module further includes:

[0227] The replacement unit is used to replace multiple reactant property parameters with preset parameters during any numerical adjustment, thereby obtaining a simulation model with updated parameters.

[0228] The injection unit is used to inject carbon dioxide into a preset shale core sample. Based on the computational domain, the updated simulation model is solved to obtain the hydrogen ion transport process corresponding to any numerical adjustment.

[0229] The input unit is used to obtain the mineral boundary solute concentration of the boundary grid during hydrogen ion transport, and input the mineral boundary solute concentration into the calculation formula of net reaction flux to obtain the net reaction flux of the boundary grid.

[0230] The update unit is used to update the mineral phase state of the boundary grid according to the net reaction flux, so as to obtain the mineral dissolution and precipitation evolution process corresponding to any numerical adjustment.

[0231] The summarization unit is used to summarize the mineral dissolution and precipitation evolution process corresponding to any one of the multiple numerical adjustments after the adjustment is completed, so as to obtain multiple mineral dissolution and precipitation evolution processes.

[0232] The apparatus for constructing the mineral dissolution and precipitation simulation model provided in this embodiment can execute... Figure 2 and Figure 4 The technical solution of the embodiment of the method for constructing a mineral dissolution and precipitation simulation model shown herein, its implementation principle and technical effects are similar to those of... Figure 2 and Figure 4 The method for constructing a mineral dissolution and precipitation simulation model shown is similar to the example provided, and will not be described in detail here.

[0233] Figure 16 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application. Figure 16 As shown, the electronic device 160 includes at least one processor 1601 and a memory 1602. The electronic device 160 also includes a communication component 1603. The processor 1601, memory 1602, and communication component 1603 are connected via a bus 1604.

[0234] In the specific implementation process, at least one processor 1601 executes computer execution instructions stored in memory 1602, so that at least one processor 1601 is used to implement a method for constructing a mineral dissolution and precipitation simulation model according to the above embodiment.

[0235] The specific implementation process of processor 1601 can be found in the above method embodiments, and its implementation principle and technical effect are similar, so it will not be repeated here.

[0236] In the above embodiments, it should be understood that the processor 1601 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0237] The memory 1602 may include high-speed RAM memory, and may also include non-volatile memory NVM, such as at least one disk storage.

[0238] Bus 1604 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Bus 1604 can be divided into address bus, data bus, control bus, etc. For ease of illustration, the bus 1604 in the accompanying drawings of this application is not limited to only one bus or one type of bus.

[0239] The above description of the functions implemented by electronic devices and main control devices has introduced the solutions provided by the embodiments of the present invention. It is understood that, in order to implement the above functions, the electronic device or main control device includes hardware structures and / or software modules corresponding to the execution of each function. By combining the units and algorithm steps of the various examples described in the embodiments of the present invention, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present invention.

[0240] This application also provides a computer-readable storage medium storing computer-executable instructions. When executed by a processor, these instructions are used to implement a method for constructing a mineral dissolution and precipitation simulation model as described in the above embodiments. In the specific implementation of the aforementioned method for constructing a mineral dissolution and precipitation simulation model, each module can be implemented as a processor.

[0241] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0242] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and the readable storage medium can exist as discrete components in an electronic device or a host device.

[0243] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement a method for constructing a mineral dissolution and precipitation simulation model as described in the above embodiments.

[0244] The computer program is stored in a readable storage medium, and at least one processor can read the computer program from the readable storage medium and execute the computer program to perform the scheme provided in any of the above embodiments.

[0245] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0246] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for constructing a mineral dissolution and precipitation simulation model, characterized in that, include: Images of various minerals and multiple reactive physical property parameters are acquired, and the images are divided into multiple boundary grids; wherein, the boundary grids refer to the grids where mineral dissolution and precipitation occur, and the multiple reactive physical property parameters are used to represent the chemical reaction kinetics of the mineral surface and the migration ability of hydrogen ions in pore fluids; Based on the multiple reaction property parameters and the preset rate reaction formula, the calculation formula for net reaction flux is determined; wherein, the net reaction flux is used to represent the rate of hydrogen ion consumption per unit area of ​​the solid-liquid interface within the boundary grid, the net reaction flux being greater than zero is used to indicate mineral dissolution, the net reaction flux being less than zero is used to indicate mineral precipitation, and the solid-liquid interface refers to the interface where minerals and pore fluids come into contact. The calculation formula for the volumetric source term is derived based on the formula for calculating net reaction flux. A mineral dissolution and precipitation simulation model is generated based on the calculation formula for the volumetric source term and the preset distribution function evolution equation. The volumetric source term represents the consumption rate of hydrogen ions per unit volume within the boundary grid. The mineral dissolution and precipitation simulation model is used to simulate the hydrogen ion transport process in shale pores and the mineral dissolution and precipitation process caused by hydrogen ion transport.

2. The method according to claim 1, characterized in that, The process of acquiring images of multiple minerals and multiple reactive property parameters, and dividing the images into multiple boundary grids, includes: Obtain a pre-defined shale core sample and classify the pre-defined shale core sample into the aforementioned multiple minerals; wherein, the multiple minerals include soluble minerals and insoluble minerals; The images of the various minerals and the multiple reactive physical parameters are acquired, and the images are thresholded to obtain a numerical matrix. The numerical matrix is ​​then filtered by neighborhood to obtain the multiple boundary grids. The numerical matrix is ​​used as the computational domain for solving the mineral dissolution and precipitation simulation model.

3. The method according to claim 2, characterized in that, The derivation of the formula for calculating the volumetric source term based on the formula for calculating net reaction flux includes: Using the total area of ​​the solid-liquid interface as the independent variable, the formula for calculating the net reaction flux is integraled with respect to the area to obtain the formula for calculating the total reaction rate of the solid-liquid interface; wherein, the formula for calculating the total reaction rate is used to calculate the consumption rate of hydrogen ions at the solid-liquid interface within the boundary grid. Divide both terms on both sides of the equation for calculating the total reaction rate by the preset volume of the boundary grid to obtain the calculation formula for the volume source term.

4. The method according to claim 3, characterized in that, After deriving the calculation formula for the volumetric source term based on the calculation formula for net reaction flux, and generating a mineral dissolution and precipitation simulation model based on the calculation formula for the volumetric source term and the preset distribution function evolution equation, the process further includes: The mineral dissolution and precipitation simulation model is verified multiple times. If the error of the mineral dissolution and precipitation simulation model is determined to be less than the preset error threshold based on the verification results, the multiple reactive physical property parameters are numerically adjusted multiple times, and the mineral dissolution and precipitation simulation model is solved after each numerical adjustment to obtain multiple mineral dissolution and precipitation evolution processes. Based on the aforementioned multiple mineral dissolution and precipitation evolution processes, the porosity evolution curves and permeability evolution curves corresponding to various mineral dissolution and precipitation modes are determined; wherein, any numerical adjustment corresponds to a mineral dissolution and precipitation mode.

5. The method according to claim 4, characterized in that, The multiple verifications include convection-diffusion verification and mineral dissolution-precipitation verification. The multiple verifications of the mineral dissolution-precipitation simulation model include: Convection-diffusion verification was performed based on the aforementioned mineral dissolution and precipitation simulation model, and the results of the convection-diffusion verification were obtained. If the convection-diffusion error of the mineral dissolution-precipitation simulation model is determined to be less than a preset first threshold based on the convection-diffusion verification results, mineral dissolution-precipitation verification is performed according to the mineral dissolution-precipitation simulation model to obtain the verification results.

6. The method according to claim 4, characterized in that, The process involves performing multiple numerical adjustments to the various reactive physical parameters and solving the mineral dissolution and precipitation simulation model after each adjustment to obtain multiple mineral dissolution and precipitation evolution processes, including: During any numerical adjustment, the multiple reactive property parameters are replaced with preset parameters to obtain the simulation model with updated parameters. Carbon dioxide is injected into the preset shale core sample, and the updated simulation model is solved based on the computational domain to obtain the hydrogen ion transport process corresponding to any numerical adjustment. The mineral boundary solute concentration of the boundary grid during the hydrogen ion transport process is obtained, and the mineral boundary solute concentration is input into the calculation formula of the net reaction flux to obtain the net reaction flux of the boundary grid. The mineral phase state of the boundary grid is updated based on the net reaction flux to obtain the mineral dissolution and precipitation evolution process corresponding to any numerical adjustment. In response to the completion of the multiple numerical adjustments, the mineral dissolution and precipitation evolution process corresponding to any one of the numerical adjustments is summarized to obtain the multiple mineral dissolution and precipitation evolution processes.

7. A device for constructing a mineral dissolution and precipitation simulation model, characterized in that, include: The first acquisition module is used to acquire images of various minerals and multiple reactive physical property parameters, and divide the images into multiple boundary grids; wherein, the boundary grids refer to the grids where mineral dissolution and precipitation occur, and the multiple reactive physical property parameters are used to represent the chemical reaction kinetics of the mineral surface and the migration ability of hydrogen ions in pore fluids; The first determining module is used to determine the calculation formula of net reaction flux based on the multiple reaction property parameters and the preset rate reaction formula; wherein, the net reaction flux is used to represent the consumption rate of hydrogen ions per unit area of ​​the solid-liquid interface within the boundary grid, the net reaction flux being greater than zero is used to indicate that the mineral dissolves, the net reaction flux being less than zero is used to indicate that the mineral precipitates, and the solid-liquid interface refers to the interface where the mineral and the pore fluid come into contact. The derivation module is used to derive the calculation formula of the volume source term based on the calculation formula of the net reaction flux, and generate a mineral dissolution and precipitation simulation model based on the calculation formula of the volume source term and the preset distribution function evolution equation. The volume source term is used to represent the consumption rate of hydrogen ions per unit volume within the boundary grid, and the mineral dissolution and precipitation simulation model is used to simulate the hydrogen ion transport process in shale pores and the mineral dissolution and precipitation process caused by hydrogen ion transport.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement the method for constructing the mineral dissolution and precipitation simulation model as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for constructing a mineral dissolution and precipitation simulation model as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The system includes a computer program, which, when executed by a processor, is used to implement the method for constructing a mineral dissolution and precipitation simulation model as described in any one of claims 1 to 6.

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