High-salt mine water deep stratum reinjection chemical blockage prediction simulation method

Abstract

The invention provides a high-salt mine water deep stratum reinjection chemical blockage prediction simulation method, which comprises the following steps of: establishing a water-rock reaction mineral list by analyzing hydrochemical characteristics of reinjection water and underground water of a deep reinjection layer and mineral component content of the reinjection layer, and calculating a chemical blockage prediction result by combining mineral reaction kinetics and nonlinear influence of high TDS (Total Dissolved Solid) on a reaction rate; an underground water flow-chemical reaction coupling model is constructed, and the mineral precipitation process and the influence of the mineral precipitation process on the porosity are simulated. Finally, a risk index (CRI) early warning model based on the porosity change rate is established, quantitative evaluation and graded early warning of the blockage risk of the reinjection well are achieved, and a scientific basis is provided for guiding blockage dredging measures and prolonging the service life of a well hole.

Description

Technical Field

[0001] This invention belongs to the field of coal mine water protection and relates to a method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection. Background Technology

[0002] Deep well reinjection technology is widely used as a versatile, safe, economical and efficient method. It involves injecting high-salt mine water into specific deep underground strata that are isolated from shallow aquifers and surface water and have sufficient water storage space, in order to achieve safe disposal, avoid environmental pollution and protect shallow water resources.

[0003] It is worth noting that, unlike shallow formation reinjection, the high-temperature, high-pressure formation environment and high-salt, acidic groundwater environment of deep formations lead to complex chemical blockage problems during reinjection due to water-to-water and water-to-rock reactions. The reinjected water and the original groundwater differ in chemical phase and soluble salt concentration. The migration and transformation of ionic components during reinjection alters the deep formation groundwater environment, influencing temperature, pressure, and SO4 levels. 2- The accumulation of characteristic ions, such as calcium and magnesium in the water, leads to the formation of mineral precipitates due to supersaturation, which alters the permeability of the water-bearing medium, causing pore blockage and affecting reinjection efficiency.

[0004] Previous methods for predicting chemical blockage during reinjection have been mostly applied to areas such as groundwater over-extraction recharge and geothermal tailwater reinjection. However, no prediction method or early warning model has been proposed for deep formation reinjection of high-salt mine water. Furthermore, deep formations present challenges in sampling and monitoring. Therefore, there is an urgent need to establish a method for predicting and simulating chemical blockage and a risk early warning model for the reinjection of high-salt mine water into deep formations. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection, as well as a risk warning model. This method can identify the evolution of the chemical environment of deep formation water under the influence of reinjection, predict the main controlling components of chemical blockage and their spatiotemporal location, establish a risk warning model, and guide the implementation of blockage dredging schemes, thereby improving the service life of wells.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for predicting and simulating chemical blockage during deep formation reinjection of high-salinity mine water includes: S1 determines the water quality and reinjection layer parameters; S2 establishes a list of reacting minerals and calculates the kinetic rate constants; The S3 experiment calibrated the nonlinear effect coefficient of high TDS on the reaction rate; S4 uses the parameters determined by S1 to construct a numerical model of mine water reinjection and simulate the reinjection process; Based on the mineral list and reaction parameters obtained from S2 and S3, S5 couples chemical reactions into the model to identify the main clogging minerals. S6 calculates the rate of change in porosity caused by mineral precipitation; S7 establishes a CRI risk warning model based on the porosity change rate and outputs the risk level.

[0007] Optionally, S1 specifically includes: acquiring reinjected water quality parameters, injection parameters, reinjection layer hydrogeological parameters, physicochemical properties, and groundwater quality parameters. S101, Distributed fiber optic logging to determine the target layer for reinjection: Conduct wellhead construction and data acquisition using DTS fiber optics, analyze the water injection profile, identify water-absorbing sections, and determine the target layer for reinjection; S102, collect mine water and reinjection groundwater and perform simple water quality analysis to analyze the characteristic ions, water chemistry type and acid-base environment of deep formations in reinjection water and in-situ water. S103, calculate the temperature and pressure of the target layer for reinjection, and determine the layer thickness, reinjection flow rate and water temperature of the target layer for reinjection; S104, Digital Core Analysis: Porosity extraction analysis, seepage simulation analysis, and component analysis are carried out on the core of the reinjection target layer to determine the lithology, mineral component content and density, porosity, and permeability parameters of the reinjection layer.

[0008] Optionally, S2 specifically includes: establishing a mineral list and calculating kinetic rate constants: establishing a mineral list participating in the reaction based on water quality and core composition analysis results, and calculating the kinetic rate constants of the minerals under high temperature and high pressure environments in deep formations and under three mechanisms: neutral, acidic, and alkaline. k The calculation formula is as follows: ; In the formula, superscripts and subscripts nu、H、OH These represent the neutral mechanism, acidic mechanism, and basic mechanism, respectively. , ; These represent the kinetic rate constants at 25°C for the neutral, acidic, and basic mechanisms, respectively. , , denoted by , respectively, the surface activation energies of the mineral in neutral, acidic, and basic mechanisms, in kJ / mol; R is the gas constant, 8.314 J / (mol·K). T The absolute temperature of the deep geological environment is expressed in K. , These represent the activities of the relevant ions in the acidic and basic mechanisms, respectively. m This is an exponential term (usually a constant) related to a specific reaction.

[0009] Optionally, S3 specifically includes: experimentally calibrating the nonlinear influence coefficient of introducing high TDS on the reaction rate of sparingly soluble minerals. Gradient TDS solutions with concentrations ranging from 10,000 to 80,000 mg / L were prepared, and mineral precipitation experiments were conducted in a high-pressure reactor. The concentration decay of corresponding ions was monitored online using ICP-OES. The measured reaction rate r was calculated, and a fitting was performed. The ionic strength attenuation coefficient α is obtained. ; Calculated using the following formula: ; In the formula, The ideal mineral reaction rate is expressed in mol / s. k The kinetic rate constant is mol / (m 2 ·s); A The surface area of ​​the reacting mineral is represented by m. 2 ; Oh Indicates the mineral saturation index; i and or These are empirical constants; I This represents the ionic strength.

[0010] Optionally, S4 specifically includes: constructing a numerical model for mine water reinjection. S401, conduct regional hydrogeological surveys to determine the distribution of aquifers and impermeable layers above and below the reinjection layer, structural development conditions, boundary conditions, etc.

[0011] S402, establish a conceptual model, determine the depth and horizontal range of the reinjection layer, and mesh it in the vertical and horizontal directions (set the boundary mesh to have an infinite volume).

[0012] S403, given the boundary conditions and initial conditions, set parameters such as reinjection location, injection flow rate, injection water temperature, formation temperature and pressure, and porosity.

[0013] S404 uses the finite volume or finite integral difference numerical method to simulate the groundwater flow direction, temperature and pressure changes at different locations in the formation, and the mixing ratio of the two types of water during the reinjection process.

[0014] Optionally, S5 specifically includes: determining the main blockage mineral components and their locations: using the calculated mineral reaction rate constant and the nonlinear influence coefficient λ(I) of high TDS on the reaction rate of sparingly soluble minerals, based on the mine water reinjection numerical model, the simulation program is entered to activate the chemical reaction switch to couple the water chemical reaction, thereby determining the main blockage mineral components and their locations. Specific steps include: S501 sets the hydrochemical environmental parameters, including the main ionic components, secondary complexes, mineral components and their kinetic parameters, gas components, adsorption and decay effects, and cation exchange effects of reinjected water and deep groundwater. The kinetic parameters and nonlinear influence coefficients of deep formations and high-salinity environments are obtained from steps S2 and S3.

[0015] S502 sets up water chemistry calculations and output control, including simulation time, solution and convergence conditions, input and output files, output control, parameter partitioning, and sets up monitoring points in the blockage area to simulate water quality changes and the concentration of various ions at the monitoring points.

[0016] S503, calculate the ion activity product (IAP) of the relevant sparingly soluble minerals and the solubility product constant (K) based on the ion concentrations at the monitoring points. sp By comparing and identifying the main precipitating minerals causing the blockage, if IAP <K sp If IAP > K, then it is in an unsaturated state and no mineral precipitation occurs; sp If the mineral is in a supersaturated state, it tends to precipitate, and is therefore identified as the main precipitating mineral.

[0017] Optionally, the IAP is calculated using the following formula: ; ; ; ; In the formula, Indicates ion activity; It is a cation The activity level; It is anion The activity level; e It is the stoichiometric coefficient of the cation in the chemical formula; f It is the stoichiometric coefficient of the anion in the chemical formula; Indicates the activity coefficient; Indicates ion concentration. The concentration of ion i is mg / L, obtained from simulation; A is the Debye-Hückel constant, with a value of 0.51. Indicates the charge number of an ion. It represents the charge number of ion i; I represents the ionic strength of the solution, in mol / L; Optionally, the aforementioned The calculations include: The solubility product constant of minerals in deep formations was calculated using the van der Hoff equation. The calculation formula is as follows: ; In the formula, It is the absolute temperature of the deep geological environment, in K; It is the standard solubility product constant at 25℃, which can be obtained by looking up a table; The standard enthalpy change at 25℃ is expressed in J / mol and can be calculated from a table. The gas constant is 8.314 J / (mol·K).

[0018] Optionally, S6 specifically includes: simulating and calculating the porosity change rate: based on determining the type of secondary blockage minerals, simulating the change in the volume fraction of major precipitating minerals and the expansion of the precipitation change area, extracting mineral precipitation volume fraction data from different monitoring areas, obtaining the porosity distribution of the reinjection layer over time, and calculating the porosity change rate γ caused by the precipitation of each mineral based on the system's operating years and the magnitude of porosity change. i ; ; In the formula, i It is the i-th precipitated mineral; Df i The magnitude of the porosity change caused by the i-th precipitated mineral; Δt Let 'a' be the simulation time.

[0019] Optionally, S7 specifically includes: establishing a risk early warning model; determining the prescribed service life of reinjection wells; and proposing an early warning model based on the risk index CRI based on the simulated porosity change rate. ; In the formula, This represents the sum of porosity changes caused by each precipitated mineral during the operating time. n The average effective porosity of the reinjection layer; x To specify the service life 'a' of reinjection wells; When CRI < 1, the porosity reduction rate caused by chemical precipitation is considered to be within the normal range of variation within the specified service life and is judged as low risk; when 1 ≤ CRI < 1.5, it is easy to clog in the later stage of reinjection and certain anti-clogging measures need to be taken; when CRI ≥ 1.5, the porosity change rate is fast and the risk of clogging is high, and a full-cycle reinjection anti-clogging plan needs to be formulated and implemented.

[0020] The advantages of this invention are: This invention analyzes the hydrochemical characteristics of reinjected water and deep reinjection groundwater, as well as the mineral composition of the reinjection layer, to establish a water-rock reaction mineral inventory. Combining mineral reaction kinetics and the nonlinear influence of high TDS (total dissolved solids) on the reaction rate, a groundwater flow-chemical reaction coupled model is constructed to simulate the mineral precipitation process and its impact on porosity. Finally, a risk index (CRI) early warning model based on the porosity change rate is established to achieve quantitative assessment and graded early warning of reinjection well blockage risk, providing a scientific basis for guiding blockage removal measures and extending wellbore life. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to the present invention. Figure 2 This is a schematic diagram of the layout of reinjection monitoring points in Embodiment 1 of the present invention. Detailed Implementation

[0022] Following the above technical solutions, specific embodiments of the present invention are given below. It should be noted that the present invention is not limited to the following specific embodiments, and all equivalent modifications made based on the technical solutions of this application fall within the protection scope of the present invention.

[0023] It should be noted that, unless otherwise specified, all devices and methods in this invention employ those known in the prior art. Unless otherwise stated, the technical / scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0024] Those skilled in the art should understand that the simulation results of numerical simulation have certain deviations. It is recommended to set a deviation threshold in practical applications and to correct it in combination with on-site monitoring data. The risk index threshold can be adjusted according to specific engineering requirements and reinjection layer characteristics, and should not be regarded as an absolute standard.

[0025] The present invention provides a method for predicting and simulating chemical blockage in deep formations of high-salinity mine water and a risk warning model, comprising: S1, obtain the reinjection water quality parameters, injection parameters, hydrogeological parameters of the reinjection layer, physicochemical properties, and groundwater quality parameters: S101, Distributed Fiber Optic Logging to Determine the Target Layer for Reinjection: Conduct wellhead installation and data acquisition of DTS fiber optic cables, analyze the water injection profile, identify water-absorbing sections, and thus determine the target layer for reinjection.

[0026] S102, mine water and reinjection groundwater were collected and a simple water quality analysis was performed to analyze the characteristic ions, hydrochemical types, and acid-base environment of the deep formations in the reinjected and in-situ water.

[0027] S103, calculate the temperature and pressure of the reinjection layer, and determine the layer thickness, reinjection flow rate and water temperature.

[0028] S104, Digital Core Analysis: Porosity extraction analysis, seepage simulation analysis, and component analysis are carried out on the core of the reinjection target layer to determine the lithology, mineral component content and density, porosity, and permeability parameters of the reinjection layer.

[0029] S2. Establish a mineral list and calculate the kinetic rate constant: Based on water quality and core composition analysis results, establish a mineral list involved in the reaction and calculate the kinetic rate constants of the minerals under high temperature and high pressure environments in deep formations and under three mechanisms: neutral, acidic, and alkaline. k The calculation formula is as follows: In the formula, superscripts and subscripts nu、H、OH These represent the neutral mechanism, acidic mechanism, and basic mechanism, respectively. , ; These represent the kinetic rate constants at 25°C for the neutral, acidic, and basic mechanisms, respectively. , , denoted by , respectively, the surface activation energies of the mineral in neutral, acidic, and basic mechanisms, in kJ / mol; R is the gas constant, 8.314 J / (mol·K). T The absolute temperature of the deep geological environment is expressed in K. , These represent the activities of the relevant ions in the acidic and basic mechanisms, respectively. m This is an exponential term (usually a constant) related to a specific reaction.

[0030] S3. The nonlinear influence coefficient λ(I) of high TDS on the reaction rate of sparingly soluble minerals was experimentally determined. Gradient TDS solutions (10000-80000 mg / L) were prepared, and mineral precipitation experiments were conducted in a high-pressure reactor. The concentration decay of the corresponding ions was monitored online by ICP-OES, and the measured reaction rate r was calculated and fitted. The ionic strength attenuation coefficient α is obtained. . Calculated using the following formula: ; In the formula, The ideal mineral reaction rate is expressed in mol / s. k The kinetic rate constant is mol / (m2 ·s); A The surface area of ​​the reacting mineral is represented by m. 2 ; Oh Indicates the mineral saturation index; i and or These are empirical constants; I This represents the ionic strength.

[0031] S4, Constructing a numerical model for mine water reinjection: S401, conduct regional hydrogeological surveys to determine the distribution of aquifers and impermeable layers above and below the reinjection layer, structural development conditions, boundary conditions, etc.

[0032] S402, establish a conceptual model, determine the depth and horizontal range of the reinjection layer, and mesh it in the vertical and horizontal directions (set the boundary mesh to have an infinite volume).

[0033] S403, given the boundary conditions and initial conditions, set parameters such as reinjection location, injection flow rate, injection water temperature, formation temperature and pressure, and porosity.

[0034] S404 uses the finite volume or finite integral difference numerical method to simulate the groundwater flow direction, temperature and pressure changes at different locations in the formation, and the mixing ratio of the two types of water during the reinjection process.

[0035] S5, Determine the main blockage mineral components and their locations: Using the calculated mineral reaction rate constant and the nonlinear influence coefficient λ(I) of high TDS on the reaction rate of sparingly soluble minerals, based on the mine water reinjection numerical model, the simulation program is entered to activate the chemical reaction switch to couple the water chemical reaction, thereby determining the main blockage mineral components and their locations. Specific steps include: S501 sets the hydrochemical environmental parameters, including the main ionic components, secondary complexes, mineral components and their kinetic parameters, gas components, adsorption and decay effects, and cation exchange effects of reinjected water and deep groundwater. The kinetic parameters and nonlinear influence coefficients of deep formations and high-salinity environments are obtained from steps S2 and S3.

[0036] S502 sets up water chemistry calculations and output control, including simulation time, solution and convergence conditions, input and output files, output control, parameter partitioning, and sets up monitoring points in the blockage area to simulate water quality changes and the concentration of various ions at the monitoring points.

[0037] S503, calculate the ion activity product (IAP) of the relevant sparingly soluble minerals and the solubility product constant (K) based on the ion concentrations at the monitoring points. sp By comparing and identifying the main precipitating minerals causing the blockage, if IAP <K sp If IAP > K, then it is in an unsaturated state and no mineral precipitation occurs; spIf the concentration is high enough, the mineral is in a supersaturated state and tends to precipitate, thus it is identified as a major precipitating mineral. IAP is calculated using the following formula: ; ; ; ; In the formula, Indicates ion activity; It is a cation The activity level; It is anion The activity level; e It is the stoichiometric coefficient of the cation in the chemical formula; f It is the stoichiometric coefficient of the anion in the chemical formula; Indicates the activity coefficient; Indicates ion concentration. The concentration of ion i is mg / L, obtained from simulation; A is the Debye-Hückel constant, with a value of 0.51. Indicates the charge number of an ion. is the charge number of ion i; I represents the ionic strength of the solution, in mol / L.

[0038] The solubility product constant K of minerals in deep formations was calculated using the van der Hoff equation. sp The calculation formula is as follows: ; In the formula, It is the absolute temperature of the deep geological environment, in K; It is the standard solubility product constant at 25℃, which can be obtained by looking up a table; The standard enthalpy change at 25℃ is expressed in J / mol and can be calculated from a table. The gas constant is 8.314 J / (mol·K).

[0039] S6, Simulation Calculation of Porosity Change Rate: Based on the determination of the type of secondary blockage minerals, the simulation calculates the change in the volume fraction of major precipitating minerals and the expansion of the precipitation change area. Mineral precipitation volume fraction data from different monitoring areas are extracted to obtain the porosity distribution of the reinjection layer over time. The porosity change rate γ caused by each mineral precipitation is calculated based on the system's operating years and the magnitude of porosity change. i .

[0040] ; In the formula, i It is the i-th precipitated mineral; Df i The magnitude of the porosity change caused by the i-th precipitated mineral; Δt The simulation time is (a).

[0041] S7. Establish a risk early warning model. Determine the specified service life of the reinjection well, and propose an early warning model based on the risk index CRI based on the simulated porosity change rate. ; In the formula, This represents the sum of porosity changes caused by each precipitated mineral during the operating time. n The average effective porosity of the reinjection layer; x To specify the service life (a) of reinjection wells.

[0042] When CRI < 1, the porosity reduction rate caused by chemical precipitation is considered to be within the normal range of variation within the specified service life and is judged as low risk; when 1 ≤ CRI < 1.5, it is easy to clog in the later stage of reinjection and certain anti-clogging measures need to be taken; when CRI ≥ 1.5, the porosity change rate is fast and the risk of clogging is high, and a full-cycle reinjection anti-clogging plan needs to be formulated and implemented.

[0043] The present invention will be further described in detail below with reference to the embodiments.

[0044] Example 1: Combination Figure 1 and 2 The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection in this embodiment includes the following steps: Step 1: Obtain reinjection water quality parameters, injection parameters, hydrogeological parameters of the reinjection layer, physicochemical properties, and groundwater quality parameters: Step 1.1, Distributed fiber optic logging to determine the target layer for reinjection: Conduct wellhead installation and data acquisition of DTS fiber optics, analyze the water injection profile, identify the water-absorbing section, and thus determine that the target layer for reinjection is the Triassic Liujiagou Formation.

[0045] Step 1.2: Mine water and reinjection groundwater were collected and subjected to a simplified water quality analysis. The results are shown in Tables 1 and 2. The reinjection water was weakly alkaline, while the deep formation water was weakly acidic, and the water type was Cl-Ca·Na type.

[0046] Step 1.3: Calculate the reinjection layer temperature (70 ℃) and pressure (18 MPa), and determine the layer thickness (113 m) and reinjection flow rate (100 m³ / s). 3 / h) and water temperature (25 ℃).

[0047] Step 1.4, Digital Core Analysis: Porosity extraction analysis, seepage simulation analysis, and component analysis are performed on the core sample from the target reinjection layer to determine the reinjection layer density (2.45 g / cm³). 3The parameters of porosity (9.8%), permeability (0.8 mD) and mineral composition content of the reinjection layer are shown in Table 3.

[0048] Table 1 Reinjection Water Quality

[0049] Table 2 In-situ water quality

[0050] Table 3. Proportion of mineral components in the reinjection layer

[0051] Step 2: Based on the water quality and mineral composition analysis results, establish a list of minerals involved in the reaction, and find the parameters used to calculate the mineral kinetic rate constants, as shown in Table 4. Calculate the kinetic rate constants of minerals under high temperature and high pressure environments in deep formations and under three mechanisms: neutral, acidic, and alkaline. k The calculation formula is as follows: ; In the formula, superscripts and subscripts nu、H、OH These represent the neutral mechanism, acidic mechanism, and basic mechanism, respectively. , ; These represent the kinetic rate constants at 25°C for the neutral, acidic, and basic mechanisms, respectively. , , denoted by , respectively, the surface activation energies of the mineral in neutral, acidic, and basic mechanisms, in kJ / mol; R is the gas constant, 8.314 J / (mol·K). T The absolute temperature of the deep geological environment is expressed in K. , These represent the activities of the relevant ions in the acidic and basic mechanisms, respectively. m This is an exponential term (usually a constant) related to a specific reaction.

[0052] Table 4 Parameters for calculating mineral kinetic rate constants

[0053] Step 3: Experimentally calibrate the nonlinear influence coefficient λ(I) of introducing high TDS on the reaction rate of sparingly soluble minerals. Taking gypsum as an example, prepare gradient TDS solutions with concentration gradients of 10000, 30000, 50000, and 70000 mg / L, [Ca²⁺, Cr⁻¹ ... + The concentration of Ca²⁺ was 7000±50 mg / L. Mineral precipitation experiments were conducted in a high-pressure reactor, and the concentration was monitored online using ICP-OES. + Concentration decay, calculate the measured reaction rate r, r理想 Calculated using the following formula: ; In the formula, r 理想 The ideal mineral reaction rate is expressed in mol / s. k The kinetic rate constant is mol / (m 2 ·s); A The surface area of ​​the reacting mineral is represented by m. 2 ; Oh Indicates the mineral saturation index; i and or These are empirical constants; I This represents the ionic strength.

[0054] Fit λ(I)=r 实测 / r 理想 The ionic strength attenuation coefficient α was found to be 0.58. .

[0055] Step 4, construct a numerical model for mine water reinjection: Step 4.1: Conduct a regional hydrogeological survey to determine the distribution of aquifers and impermeable layers above and below the reinjection layer, as well as the structural development conditions and boundary conditions.

[0056] Step 4.2: Establish a conceptual model, determine the depth and horizontal range of the reinjection layer, and mesh it vertically and horizontally (the boundary mesh is set to infinite volume). The study area is vertically divided into 21 layers, with the top layer being a 2.5 m thick impermeable overburden. The aquifer is vertically divided into 20 layers, each 5.65 m thick. Each layer is further divided into 50 m cubes both longitudinally and laterally, resulting in a total of 8400 meshes.

[0057] Step 4.3: Given the boundary conditions, the stable aquitards above and below the reinjection layer are confined aquifers. The upper top plate and lower bottom plate are generalized as aquitard boundaries, and the lateral boundaries are set as Class I boundaries (constant head boundaries). Set parameters such as reinjection location, injection flow rate, injection water temperature, formation temperature and pressure, and porosity.

[0058] Step 4.4: Using the finite volume or finite integral difference numerical method, the direction of groundwater flow, temperature and pressure changes at different locations in the formation, and the mixing ratio of the two types of water during the reinjection process are simulated.

[0059] Step 5: Determine the main blockage mineral components and their locations. Using the calculated mineral reaction rate constant and the nonlinear influence coefficient λ(I) of high TDS on the reaction rate of sparingly soluble minerals, based on the mine water reinjection numerical model, the simulation program is entered to activate the chemical reaction switch to couple the water chemical reaction, thereby determining the main blockage mineral components and their locations. Specific steps include: Step 5.1: Set the hydrochemical environment parameters, including the main ionic components, secondary complexes, mineral components and their kinetic parameters, gas components, adsorption and decay effects, and cation exchange effects of reinjected water and deep groundwater. The kinetic parameters and nonlinear influence coefficients in deep formations and high-salinity environments are obtained from steps 2 and 3.

[0060] Step 5.2: Set up water chemistry calculation and output control, including simulation time, solution and convergence conditions, input and output files, output control, parameter partitioning, and set up monitoring points in the blockage area to simulate water quality changes and ion concentrations at the monitoring points.

[0061] Step 5.3: Calculate the ion activity product (IAP) and solubility product constant (K) of the relevant sparingly soluble minerals based on the ion concentrations at the monitoring points. sp By comparing and identifying the main precipitating minerals causing the blockage, if IAP <K sp If IAP > K, then it is in an unsaturated state and no mineral precipitation occurs; sp If the concentration is too high, the minerals will be in a supersaturated state and tend to precipitate, thus identifying them as the main precipitating minerals. The water quality changes at different monitoring points over three years of reinjection were obtained through simulation. The ion concentrations at monitoring point 2 are shown in Table 5. Calculations revealed that the ion activity product (IAP) of CaSO4·2H2O in the low-flow-rate zone is less than the solubility product constant (K) at 70 °C. sp Therefore, gypsum was determined to be the main precipitating mineral causing the blockage. The specific calculation process is as follows: Table 5 Monitoring point ion concentration

[0062] Substitute the molar concentration of the component into the formula The calculated ionic strength I is 1.70 mol / L. Substituting I into... The activity coefficient Ca² was calculated. + and SO4² - The activity coefficients of all were 0.769, and the Ca² values ​​were then calculated. + and SO4² - The activities were 0.2165 and 0.1093, respectively, and the ion activity product (IAP) of CaSO4 was further calculated to be 0.0237.

[0063] The solubility product constant K of gypsum in deep formations was calculated using the van der Hoff equation. sp Standard solubility product constant at 25℃ 4.93×10 -5 Standard enthalpy change of reaction at 25°C The value is -4.03 kJ / mol, and the absolute temperature T of the deep formation environment is 343.15 K. Substituting these values ​​into the formula: ; The calculated K sp is 3.98×10 -5 , K sp <IAP, so it is determined that gypsum is the main precipitation mineral.

[0064] Step 6, simulate and calculate the porosity change rate. On the basis of determining that gypsum is the secondary mineral causing plugging, simulate the change in its volume fraction and the expansion of the precipitation change area, extract the data of the gypsum precipitation volume fraction at monitoring point 2, i.e., the low-flow velocity area, at different operation times, obtain the distribution of the porosity of the reinjection layer over time, and calculate the porosity change rate γ caused by the precipitation of each mineral according to the operation life of the system and the magnitude of the porosity change i .

[0065] ; In the formula, i is the i-th precipitation mineral; Df i is the magnitude of the porosity change caused by the i-th precipitation mineral; [[ID=2B]] Δt is the operation simulation time (a).

[0066] [[ID=2B]]When the system operates for 5 a, there is no obvious change in the porosity of the low-flow velocity area on the formation profile. When it operates for 10 a, the magnitude of the porosity change is between 2.5 - 2.7, and the porosity change rate is between 0.025 - 0.027 / a.

[0067] Step 7, risk warning. Determine the specified service life of the reinjection well. According to the simulated porosity change rate, propose a warning model based on the risk index CRI: ; In the formula, [[ID=3B]] is the total magnitude of the porosity change caused by each precipitation mineral during the operation time; n is the average effective porosity of the reinjection layer; x is the specified service life of the reinjection well (a).

[0068] If the specified service life of the reinjection well is 3 years, the calculated CRI value is 0.80, and it is determined that there is no risk of chemical precipitation plugging; if the specified service life of the reinjection well is 5 years, the calculated CRI value is [1.33], and it is determined that there is a low risk of chemical precipitation plugging, and measures for removing and preventing plugging of the precipitation plugging minerals need to be taken in the later stage of reinjection. The risk warning ends.

[0069] Specifically, the deviation threshold set in this embodiment is 15%.

[0070] The above-described implementation process is merely an example to clearly illustrate this application and is not intended to limit the implementation methods. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementation methods here. However, obvious variations or modifications derived therefrom are still within the protection scope of this application.

Claims

1. A method for predicting and simulating chemical blockage during deep formation reinjection of high-salinity mine water, comprising: S1 determines the water quality and reinjection layer parameters; S2 establishes a list of reacting minerals and calculates the kinetic rate constants; The S3 experiment calibrated the nonlinear effect coefficient of high TDS on the reaction rate; S4 uses the parameters determined by S1 to construct a numerical model of mine water reinjection and simulate the reinjection process; Based on the mineral list and reaction parameters obtained from S2 and S3, S5 couples chemical reactions into the model to identify the main clogging minerals. S6 calculates the rate of change in porosity caused by mineral precipitation; S7 establishes a CRI risk warning model based on the porosity change rate and outputs the risk level.

2. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 1, characterized in that, S1 specifically includes: acquiring reinjected water quality parameters, injection parameters, hydrogeological parameters of the reinjection layer, physicochemical properties, and groundwater quality parameters. S101, Distributed fiber optic logging to determine the target layer for reinjection: Conduct wellhead construction and data acquisition using DTS fiber optics, analyze the water injection profile, identify water-absorbing sections, and determine the target layer for reinjection; S102, collect mine water and reinjection groundwater and perform simple water quality analysis to analyze the characteristic ions, water chemistry type and acid-base environment of deep formations in reinjection water and in-situ water. S103, calculate the temperature and pressure of the target layer for reinjection, and determine the layer thickness, reinjection flow rate and water temperature of the target layer for reinjection; S104, Digital Core Analysis: Porosity extraction analysis, seepage simulation analysis, and component analysis are carried out on the core of the reinjection target layer to determine the lithology, mineral component content and density, porosity, and permeability parameters of the reinjection layer.

3. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 1 or 2, characterized in that, The S2 specifically includes: establishing a mineral list and calculating kinetic rate constants: based on water quality and core composition analysis results, establishing a mineral list participating in the reaction, and calculating the kinetic rate constants of minerals under high temperature and high pressure environments in deep formations and under three mechanisms: neutral, acidic, and alkaline. k The calculation formula is as follows: ； In the formula, superscripts and subscripts nu, H, OH These represent the neutral mechanism, acidic mechanism, and basic mechanism, respectively. , ; These represent the kinetic rate constants at 25°C for the neutral, acidic, and basic mechanisms, respectively. , , denoted by , respectively, the surface activation energies of the mineral in neutral, acidic, and basic mechanisms, in kJ / mol; R is the gas constant, 8.314 J / (mol·K). T The absolute temperature of the deep geological environment is expressed in K. , These represent the activities of the relevant ions in the acidic and basic mechanisms, respectively. m This refers to the index term related to a specific reaction.

4. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 1, characterized in that, Specifically, S3 includes: experimentally calibrating the nonlinear influence coefficient of introducing high TDS on the reaction rate of sparingly soluble minerals. Gradient TDS solutions with concentrations ranging from 10,000 to 80,000 mg / L were prepared, and mineral precipitation experiments were conducted in a high-pressure reactor. The concentration decay of corresponding ions was monitored online using ICP-OES. The measured reaction rate r was calculated, and a fitting was performed. The ionic strength attenuation coefficient α is obtained. ; Calculated using the following formula: ； In the formula, The ideal mineral reaction rate is expressed in mol / s. k The kinetic rate constant is mol / (m 2 ·s); A The surface area of ​​the reacting mineral is represented by m. 2 ; Ω Indicates the mineral saturation index; θ and η These are empirical constants; I This represents the ionic strength.

5. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 1 or 2, characterized in that, S4 specifically includes: constructing a numerical model for mine water reinjection. S401, conduct regional hydrogeological surveys to determine the distribution of aquifers and aquitards above and below the reinjection layer, as well as the structural development conditions and boundary conditions; S402, Establish a conceptual model, determine the depth and horizontal range of the reinjection layer, and mesh it in the vertical and horizontal directions; S403, given boundary conditions and initial conditions, set parameters such as reinjection location, injection flow rate, injection water temperature, formation temperature and pressure, and porosity; S404 uses the finite volume or finite integral difference numerical method to simulate the groundwater flow direction, temperature and pressure changes at different locations in the formation, and the mixing ratio of the two types of water during the reinjection process.

6. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 1 or 2, characterized in that, S5 specifically includes: determining the main blockage mineral components and their locations: using the calculated mineral reaction rate constant and the nonlinear influence coefficient λ(I) of high TDS on the reaction rate of sparingly soluble minerals, based on the mine water reinjection numerical model, the simulation program is entered to activate the chemical reaction switch to couple the water chemical reaction, thereby determining the main blockage mineral components and their locations. Specific steps include: S501 sets the hydrochemical environmental parameters, including the main ionic components, secondary complexes, mineral components and their kinetic parameters, gas components, adsorption and decay effects and cation exchange effects of reinjected water and deep groundwater. The kinetic parameters and nonlinear influence coefficients of deep formations and high-salt environments are obtained from steps S2 and S3. S502 sets up water chemistry calculations and output control, including simulation time, solution and convergence conditions, input and output files, output control, parameter partitioning, and sets up monitoring points in the blockage area to simulate water quality changes and the concentration of various ions at the monitoring points. S503, calculate the ion activity product (IAP) of the relevant sparingly soluble minerals and the solubility product constant (K) based on the ion concentrations at the monitoring points. sp By comparing and identifying the main precipitating minerals causing the blockage, if IAP <K sp If IAP > K, then it is in an unsaturated state and no mineral precipitation occurs; sp If the mineral is in a supersaturated state, it tends to precipitate, and is therefore identified as the main precipitating mineral.

7. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 6, characterized in that, The IAP is calculated using the following formula: ； ； ； ； In the formula, Indicates ion activity; It is a cation The activity level; It is anion The activity level; e It is the stoichiometric coefficient of the cation in the chemical formula; f It is the stoichiometric coefficient of the anion in the chemical formula; Indicates the activity coefficient; Indicates ion concentration. The concentration of ion i is mg / L, obtained from simulation; A is the Debye-Hückel constant, with a value of 0.

51. Indicates the charge number of an ion. is the charge number of ion i; I represents the ionic strength of the solution, in mol / L.

8. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 6, characterized in that, The aforementioned The calculations include: The solubility product constant of minerals in deep formations was calculated using the van der Hoff equation. The calculation formula is as follows: ； In the formula, It is the absolute temperature of the deep geological environment, in K; It is the standard solubility product constant at 25℃, which can be obtained by looking up a table; The standard enthalpy change at 25℃ is expressed in J / mol and can be calculated from a table. The gas constant is 8.314 J / (mol·K).

9. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 1 or 2, characterized in that, S6 specifically includes: simulating and calculating the porosity change rate: Based on determining the type of secondary blockage minerals, simulating the change in the volume fraction of major precipitating minerals and the expansion of the precipitation change area, extracting mineral precipitation volume fraction data from different monitoring areas, obtaining the porosity distribution of the reinjection layer over time, and calculating the porosity change rate γ caused by the precipitation of each mineral based on the system's operating years and the magnitude of porosity change. i ; ； In the formula, i It is the i-th precipitated mineral; Δφ i The magnitude of the porosity change caused by the i-th precipitated mineral; Δt Let 'a' be the simulation time.

10. The method for predicting and simulating chemical blockage in deep formations of high-salinity mine water reinjection according to claim 1 or 2, characterized in that, Specifically, S7 includes: establishing a risk early warning model; determining the prescribed service life of reinjection wells; and proposing an early warning model based on the risk index CRI based on simulated porosity change rate. ； In the formula, This represents the sum of porosity changes caused by each precipitated mineral during the operating time. n The average effective porosity of the reinjection layer; x To specify the service life 'a' of reinjection wells; When CRI < 1, the porosity reduction rate caused by chemical precipitation is considered to be within the normal range of variation within the specified service life and is judged as low risk; when 1 ≤ CRI < 1.5, it is easy to clog in the later stage of reinjection and certain anti-clogging measures need to be taken; when CRI ≥ 1.5, the porosity change rate is fast and the risk of clogging is high, and a full-cycle reinjection anti-clogging plan needs to be formulated and implemented.

Images