Mine water deep well recharge multi-reaction field coupling numerical simulation and optimization method

Abstract

This invention discloses a method for multi-reaction field coupled numerical simulation and optimization of deep well reinjection of mine water. The method is as follows: First, the basic parameters of the reinjection reservoir and reinjection water are obtained, and a multi-reaction field coupled mathematical model including stress field, temperature field, etc., is established. Based on this model, a three-dimensional numerical model containing continuous medium and discrete fracture network characteristics is constructed, and boundary and initial conditions are set. Then, engineering evaluation indicators are obtained through multi-scheme simulation, and a multi-objective optimization function is constructed accordingly. A multi-objective optimization algorithm is used to find the optimal solution and output the Pareto optimal solution set. Finally, the selected scheme is substituted into the high-precision model for verification. After ensuring that indicators such as mass conservation error meet the standards, the final design scheme is output. This method realizes multi-field collaborative simulation and accurate parameter optimization, ensuring the safety and effectiveness of reinjection projects.

Description

Technical Field

[0001] This invention relates to the field of coal mine hydrogeology and resource environment engineering technology, and in particular to a method for numerical simulation and optimization of multi-reaction field coupling for deep well reinjection of mine water. Background Technology

[0002] With the increasing depth of coal mining, the issues of mine water quantity, quality, and environmental effects are becoming increasingly prominent, especially in areas where deep limestone aquifers are widely distributed, mine water often exhibits complex characteristics such as high mineralization, high hardness, and high temperature. The traditional "discharge-treatment" model is insufficient to meet the dual demands of ecological protection and resource utilization. Deep well reinjection of mine water, as a green governance technology for source reduction and resource preservation, is gradually being promoted and applied in coal mining areas.

[0003] In deep coal mining areas, the target layers for recharge are mostly fractured and highly heterogeneous limestone aquifers. Deep well recharge in such environments faces the following major technical challenges: Stress field disturbance: Water injection may induce rebalancing of the formation hydraulic-mechanical system, causing local rock mass fracturing, water inrush, or wellbore instability; Thermal-water interaction: Significant temperature differences between recharge water and surrounding rock can easily trigger thermal stress, rock mass thermal expansion, or fracture evolution; Water-rock chemical reactions: Solute migration may lead to reactions such as carbonate dissolution and metal ion precipitation, affecting permeability; Risk of microbial growth and blockage: Microorganisms in the recharge water may grow and multiply in pores or fractures, forming biofilms and causing channel blockage; Uncertainty of fracture seepage: Fracture channels have nonlinear transport characteristics, making it difficult to accurately predict flow field evolution using traditional continuous models.

[0004] Currently, most mine water reinjection designs rely on single-field hydraulic simulations or empirical formulas, neglecting the collaborative response mechanisms between multiple underground physical fields during the injection process. This often leads to problems such as low injection efficiency, frequent blockage of seepage channels, and difficulty in controlling operating costs. Summary of the Invention

[0005] To achieve the aforementioned objectives, the present invention employs the following technical solution: a method for numerical simulation and optimization of multi-reaction field coupling in deep mine water reinjection, comprising the following steps:

[0006] S1. Data Acquisition and Geological Modeling: Acquire the basic parameters of the reinjection reservoir and reinjection water; based on the basic parameters, establish a multi-reaction field coupled mathematical model including stress field, temperature field, water flow field, chemical field and microbial field;

[0007] S2. Construction of a three-dimensional numerical model for multi-reaction field coupling simulation: Based on the multi-reaction field coupling mathematical model, a three-dimensional numerical model for multi-reaction field coupling simulation, which includes the structural features of continuous medium and discrete fracture network, is constructed in the numerical simulation platform; and boundary conditions and initial conditions are set for the three-dimensional numerical model for multi-reaction field coupling simulation.

[0008] S3. Multi-scheme simulation and response analysis: Based on different reinjection schemes, a multi-field synergistic response simulation is performed using a multi-reaction field coupled simulation three-dimensional numerical model to obtain engineering evaluation indicators under each scheme.

[0009] S4. Multi-objective optimization and parameter optimization: Construct a multi-objective optimization function based on engineering evaluation indicators, use a multi-objective optimization algorithm to optimize parameters, and output the Pareto optimal solution set as the optimization scheme for the reinjection design parameters;

[0010] S5. Scheme Verification and Engineering Implementation: After outputting the optimized scheme, the selected optimized scheme parameters are substituted into the three-dimensional numerical model of multi-reaction field coupling simulation for high-precision verification simulation to ensure the mass conservation error, engineering constraint satisfaction and the consistency between the simulation results and the optimization target, and output the final reinjection design scheme.

[0011] In some embodiments, step S1 takes a deep limestone fractured aquifer as the research object, and the basic parameters include:

[0012] Reservoir porosity, fracture geometry parameters, matrix and fracture permeability, mineral composition, thermal conductivity, Young's modulus and Poisson's ratio were obtained through geological and core tests.

[0013] The ionic composition and concentration of the recharge water, TDS, temperature and microbial content were obtained by collecting water quality data of the recharge water.

[0014] Initial geothermal profile and initial geostress field obtained through geostress logging or estimation.

[0015] In some embodiments, in step S2, a five-field coupling model of stress field, temperature field, groundwater flow field, chemical field and microbial field is constructed in the COMSOL multiphysics platform; when constructing the three-dimensional numerical model of multi-reaction field coupling simulation, a discrete fracture network embedding strategy is used to explicitly model the fractures; and Darcy flow or non-Darcy flow, porous medium heat transfer, rare matter transfer and solid mechanics physical field modules are loaded to establish a mathematical model containing multi-reaction field coupling terms of flow, solid, heat, chemistry and biology;

[0016] The multi-reaction field coupling relationships include fluid-structure coupling, heat-fluid coupling, hydration coupling, thermochemical coupling, chemical-solid coupling, and biological coupling involving microbial fields.

[0017] In some embodiments, in step S1, the multi-reaction field coupled mathematical model should at least include the basic assumptions of the model, the set of governing equations, boundary conditions, initial conditions, discrete and numerical solution constraints, and output quantity and coupling term calibration.

[0018] In some embodiments, the multi-reaction field coupled mathematical model is based on the following basic assumptions: the fracture and matrix adopt a dual-porosity-dual-permeability model; the fluid is an incompressible Newtonian fluid and its flow obeys Darcy's law; heat transfer considers solid-liquid two-phase heat conduction and fluid heat convection; solute transport considers convection, diffusion, adsorption and chemical reaction; microbial activity considers its growth, transport, adsorption and metabolism in the aqueous phase of the fracture and matrix, and its growth rate is controlled by temperature, nutrient concentration and water flow conditions; rock mass deformation obeys linear elastic theory, and permeability varies with effective stress.

[0019] In some embodiments, the governing equations of the multi-reaction-field coupled mathematical model include: the water flow mass conservation equations include: the water flow mass conservation equation for the fracture system and the water flow mass conservation equation for the matrix system, wherein:

[0020] ;

[0021] ;

[0022] in: For fracture porosity, For matrix porosity; The density of water; Darcy velocity in fractured media; Darcy velocity in the matrix medium; Source and sink terms in fractured media; Source and sink terms in the matrix medium; This refers to the mass exchange term between the fracture and the matrix. For vector differential operators; t is time;

[0023] solute The transport and chemical reaction equations are expressed as follows:

[0024] ;

[0025] ;

[0026] ;

[0027] in: This refers to the solute concentration. The solute diffusion coefficient; For chemical reaction source terms; Stoichiometric coefficients The reaction rate is expressed in an Arrhenius-type temperature-dependent form. It is the reaction rate constant; Activation energy; This is the universal gas constant; Absolute temperature; It is the ion activity product; It is the equilibrium constant; It is a vector differential operator; ;

[0028] Microbial concentration The governing equations are expressed as follows:

[0029] ;

[0030] Among them, the microbial growth item Using the Monod equation and coupling it with temperature effects:

[0031] ;

[0032] in: Microbial concentration; The microbial diffusion coefficient; This refers to the microbial growth item; This is a microbial attenuation term; For microbial adsorption; This represents the maximum specific growth rate. It serves as the activation energy for microbial growth; The limiting substrate concentration; It is the half-saturation constant; Absolute temperature; For time; This is a reference temperature.

[0033] The equation for the conservation of heat energy is expressed as follows:

[0034] ;

[0035] in: For effective heat capacity; The specific heat capacity of water at constant pressure; For effective thermal conductivity; For heat source items; It is a vector differential operator; The density of water; Absolute temperature; For time; It is a vector differential operator; ;

[0036] The stress equilibrium equations, under which rock mass deformation follows the Navier equations, are expressed as follows:

[0037] ;

[0038] in: For stress tensor; It is a volume force; It is a vector differential operator;

[0039] The relationship between effective stress and pore pressure is as follows:

[0040] ;

[0041] in, The effective stress coefficient; Pore ​​pressure; Unit tensor; For stress tensor; ;

[0042] The permeability varies with effective stress using an exponential model:

[0043] ;

[0044] in, It is the current effective normal stress; It is the reference effective normal stress; It is the permeability under the current effective stress; Reference effective stress The initial permeability is given by β; β is an empirical constant representing the sensitivity coefficient of permeability to changes in effective stress.

[0045] In some embodiments, in step S3, the engineering evaluation indicators include: injection efficiency, a blockage risk indicator defined based on the local permeability decline rate, a water inrush risk indicator defined based on wellbore pressure and rock mass strength, and a cost estimation indicator; different reinjection schemes include at least different combinations of injection flow rate, wellbore structure, or injection temperature parameters.

[0046] In some embodiments, in step S4, the multi-objective optimization algorithm adopts a multi-objective genetic algorithm based on NSGA-II, the optimization variable is the combination of reinjection design parameters, the objective function value corresponding to each individual is obtained by calling the simulation results of the three-dimensional numerical model of multi-reaction field coupling simulation, and the algorithm finally outputs a set of Pareto optimal solutions.

[0047] In some embodiments, in step S4, the multi-objective optimization function includes: an objective function for maximizing injection efficiency, an objective function for minimizing congestion risk, an objective function for minimizing cost and energy consumption, and an objective function for maximizing the satisfaction of safety constraints;

[0048] The objective function for maximizing injection efficiency is defined as:

[0049] ;

[0050] in, To effectively inject traffic; The total cost includes well construction, operation, and maintenance.

[0051] The objective function for minimizing clogging risk is quantified by the rate of decrease in local permeability or the volume fraction of scale deposits, and is defined as follows:

[0052] ;

[0053] in, ; This refers to the volume fraction of the precipitate.

[0054] The objective function for minimizing cost and energy consumption includes well construction costs. Operating energy consumption Comprehensive economic indicators of maintenance costs Defined as:

[0055] ;

[0056] The objective function for maximizing the satisfaction of safety constraints, for the risk of sudden water inrush, is defined as the probability or safety margin of pressure exceeding the critical strength:

[0057] ;

[0058] Among them, critical water inrush pressure Maximum pressure around the well ;

[0059] The engineering constraints that the optimization process needs to meet include: water inrush safety constraints and maximum wellbore pressure. <Critical water inrush pressure The change in the concentration of key ions in the aquifer after injection does not exceed the environmental standard limits; total cost < The sleeve material and depth belong to a discrete set of options.

[0060] Compared with existing technologies, the beneficial effects of this invention are as follows: Addressing the issues of stress field disturbance, thermal-water interaction, and lack of multi-field coordination, this invention constructs a five-field coupling model, incorporating multi-element coupling relationships to accurately simulate formation hydraulic-mechanical rebalancing, thermal stress, and fracture evolution, thus avoiding potential risks of rock mass fracturing and wellbore instability. Addressing the uncertainties of water-rock chemical reactions, microbial blockage, and fracture seepage, this invention integrates relevant reaction and growth mechanisms, combined with a dual-porosity-dual-permeability model, to predict solute migration, precipitation, and blockage processes, overcoming the limitations of traditional models. Addressing the problems of design dependence on experience, low injection efficiency, and difficulty in cost control, this invention constructs an optimization framework based on the NSGA-II algorithm, outputting optimal parameters to improve injection efficiency and reservoir utilization, control engineering costs, and ensures the reliability of the scheme through high-precision verification, providing safe, economical, and efficient support for deep and complex reinjection. Attached Figure Description

[0061] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0062] Figure 1 The flowchart illustrates the numerical simulation and optimization method for multi-reaction field coupling in deep mine water reinjection, as provided in this embodiment of the invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0064] The present invention will now be described in further detail with reference to the accompanying drawings and specific preferred embodiments.

[0065] In the description of this invention, it should be understood that the terms "left side," "right side," "upper part," "lower part," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. "First," "second," etc., do not indicate the importance of the components, and therefore should not be construed as a limitation of this invention. The specific dimensions used in this embodiment are only for illustrating the technical solution and do not limit the scope of protection of this invention.

[0066] The embodiments of this application provide a method for numerical simulation and optimization of multi-reaction field coupling for deep well reinjection of mine water, the method comprising the following steps:

[0067] Step S1, Data Acquisition and Geological Modeling: Obtain the basic parameters of the reinjection reservoir and reinjection water. Based on the basic parameters, establish a multi-reaction field coupled mathematical model that includes stress field, temperature field, water flow field, chemical field, and microbial field.

[0068] The multi-reaction field coupling relationships include fluid-structure coupling, heat-fluid coupling, hydration coupling, thermochemical coupling, chemical-solid coupling, and biological coupling involving microbial fields.

[0069] Specifically, taking deep limestone fractured aquifers as the research object, the basic parameters include:

[0070] Reservoir porosity, fracture geometry, matrix and fracture permeability, mineral composition, thermal conductivity, Young's modulus and Poisson's ratio were obtained through geological and core tests.

[0071] The data on ionic composition and concentration, TDS, temperature, and microbial content of the recharge water were obtained by collecting water quality data.

[0072] Initial geothermal profile and initial geostress field obtained through geostress logging or estimation.

[0073] The multi-reaction field coupled mathematical model should at least include the basic assumptions of the model, the governing equations (representative expressions), the mathematical descriptions of the boundary and initial conditions, the discrete and numerical solution constraints, and the calibration of the output and coupling terms. Specifically:

[0074] The multi-reaction field coupled mathematical model is based on the following basic assumptions: the fracture and matrix adopt a dual-porosity-dual-permeability model; the fluid is an incompressible Newtonian fluid and its flow obeys Darcy's law; heat transfer considers solid-liquid two-phase heat conduction and fluid heat convection; solute transport considers convection, diffusion, adsorption and chemical reaction; microbial activity considers its growth, transport, adsorption and metabolism in the aqueous phase of the fracture and matrix, and its growth rate is controlled by temperature, nutrient concentration and water flow conditions; rock mass deformation obeys linear elastic theory, and permeability varies with effective stress.

[0075] Furthermore, water injection boundary conditions are applied at the reinjection well location, including flow rate, pressure, temperature, concentration, and displacement boundaries. Static pressure or no-flow boundaries are set on the periphery, and adiabatic or impermeable boundaries are applied to the top and bottom surfaces. In the numerical solution settings, time-dependent solution, strongly coupled or alternating coupling strategies, nonlinear solution control parameters, and adaptive mesh strategies are selected. The basic parameters obtained from the model boundary conditions are set; if basic parameter values ​​cannot be obtained under actual conditions, they are given by prior estimation.

[0076] Initial conditions include initial pore pressure, temperature field, stress field, and solute concentration field. Spatial discretization is performed using the finite element method, and implicit time progression is employed. Coupling strategies include full coupling or sequential coupling, with adaptive meshing and nonlinear iterative control implemented.

[0077] The model should mathematically include: water flow mass conservation equations for the fractured system and the matrix system respectively; chemical-biological field coupling equations considering solute transport, chemical reactions, and microbial growth kinetics, where the chemical reaction kinetics adopt an Arrhenius-type temperature-dependent form; thermal energy conservation equations considering solid-liquid two-phase heat conduction and fluid thermal convection; and stress field equilibrium equations based on linear elasticity theory. The relationship between permeability and effective stress is described using an exponential model.

[0078] Water-rock reactions include two types of reactions: precipitation and scaling, and dissolution and adsorption. The reaction kinetics are expressed using an Arrhenius-type temperature-dependent expression and are coupled with solute activity and ion balance.

[0079] The governing equations of the multi-reaction field coupled mathematical model include: the water flow mass conservation equation, the solute equation, and the solute equation. Transport and chemical reaction equations, microbial concentration The governing equations, thermal energy conservation equations, and stress field equilibrium equations are as follows:

[0080] The water flow mass conservation equations include: the water flow mass conservation equation for the fracture system and the water flow mass conservation equation for the matrix system, wherein:

[0081] ;

[0082] ;

[0083] in: For fracture porosity, For matrix porosity; The density of water; Darcy velocity in fractured media; Darcy velocity in the matrix medium; Source and sink terms in fractured media; Source and sink terms in the matrix medium; This refers to the mass exchange term between the fracture and the matrix. is a vector differential operator; t is time.

[0084] solute The transport and chemical reaction equations are expressed as follows:

[0085] ;

[0086] ;

[0087] ;

[0088] in: This refers to the solute concentration. The solute diffusion coefficient; For chemical reaction source terms; Stoichiometric coefficients The reaction rate is expressed in an Arrhenius-type temperature-dependent form. It is the reaction rate constant; Activation energy; This is the universal gas constant; Absolute temperature; It is the ion activity product; It is the equilibrium constant; It is a vector differential operator; .

[0089] Microbial concentration The governing equations are expressed as follows:

[0090] ;

[0091] in, Microbial concentration; The microbial diffusion coefficient; This refers to the microbial growth item; This is a microbial attenuation term; t represents the microbial adsorption term; t represents time.

[0092] Among them, the microbial growth item Using the Monod equation and coupling it with temperature effects:

[0093] ;

[0094] in: Microbial concentration; The microbial diffusion coefficient; This refers to the microbial growth item; This is a microbial attenuation term; For microbial adsorption; This represents the maximum specific growth rate. It serves as the activation energy for microbial growth; The limiting substrate concentration; It is the half-saturation constant; Absolute temperature; For time; This is a reference temperature.

[0095] The equation for the conservation of heat energy is expressed as follows:

[0096] ;

[0097] in: For effective heat capacity; The specific heat capacity of water at constant pressure; For effective thermal conductivity; For heat source items; It is a vector differential operator; The density of water; Absolute temperature; For time; It is a vector differential operator; ;

[0098] The stress equilibrium equations, under which rock mass deformation follows the Navier equations, are expressed as follows:

[0099] ;

[0100] in: For stress tensor; It is a volume force; It is a vector differential operator;

[0101] The relationship between effective stress and pore pressure is as follows:

[0102] ;

[0103] in, The effective stress coefficient; Pore ​​pressure; Unit tensor; For stress tensor; ;

[0104] The permeability varies with effective stress using an exponential model:

[0105] ;

[0106] in, It is the current effective normal stress; It is the reference effective normal stress; It is the permeability under the current effective stress; Reference effective stress The initial permeability is given by β; β is an empirical constant representing the sensitivity coefficient of permeability to changes in effective stress.

[0107] Step S2: Construction of a three-dimensional numerical model for multi-reaction field coupling simulation: Based on the multi-reaction field coupling mathematical model, a three-dimensional numerical model for multi-reaction field coupling simulation, which includes the structural features of continuous medium and discrete fracture network, is constructed in the numerical simulation platform; and boundary conditions and initial conditions are set for the three-dimensional numerical model for multi-reaction field coupling simulation.

[0108] Specifically, a five-field coupled model of stress field, temperature field, groundwater flow field, chemical field, and microbial field is constructed in the COMSOL multiphysics platform. When constructing the three-dimensional numerical model for multi-reaction field coupled simulation, a discrete fracture network embedding strategy is used to explicitly model the fractures. Darcy flow or non-Darcy flow, porous media heat transfer, rare matter transfer, and solid mechanics physical field modules are loaded to establish a mathematical model that includes multi-reaction field coupled terms involving flow, solid, heat, chemistry, and biology.

[0109] It should be explained that loading Darcy flow or non-Darcy flow refers to different flow patterns. The software offers two options; choose one based on the needs of the model.

[0110] Step S3, Multi-scheme simulation and response analysis: Based on the different reinjection schemes, a multi-field synergistic response simulation is performed using a multi-reaction field coupled simulation three-dimensional numerical model to obtain engineering evaluation indicators under each scheme.

[0111] Among them, the engineering evaluation indicators include: injection efficiency, blockage risk indicators defined based on the rate of decrease in local permeability, water inrush risk indicators defined based on wellbore pressure and rock mass strength, and cost estimation indicators; different reinjection schemes include at least different combinations of injection flow rate, wellbore structure or injection temperature parameters.

[0112] Under given constraints, perform parallel simulations of multiple scenarios (e.g., different injection flow rates, different well spacing and multi-branch well configurations, different injection temperatures and water quality); record the output results after simulation, including: pressure field and pressure sweep range, temperature distribution, main ion concentration profile, precipitation rate and precipitation volume distribution, permeability spatiotemporal evolution, fracture opening / closing and displacement field, and microbial growth trend.

[0113] S4. Multi-objective optimization and parameter optimization: Construct a multi-objective optimization function based on engineering evaluation indicators, use a multi-objective optimization algorithm to optimize parameters, and output the Pareto optimal solution set as the optimization scheme for reinjection design parameters.

[0114] The optimization framework of this application uses a multi-objective genetic algorithm based on NSGA-II (Nondominated Sorting Genetic Algorithm II) for optimization. This algorithm has many advantages: it can handle multiple conflicting objectives simultaneously and obtain Pareto front solution sets, which is convenient for engineering decision-making; it has good adaptability to nonlinear, discrete / continuous variables and black-box objective functions driven by numerical simulation, and can be adapted to the optimization requirements of this framework.

[0115] The elements of the optimization framework are as follows: the optimization variables are the combination of reinjection design parameters, with each individual corresponding to a set of specific parameters (including well depth, well diameter, injection flow rate, injection pressure, water temperature, etc.); the objective function is determined by the results of multi-reaction field coupled three-dimensional numerical simulation, covering four major categories of core engineering objectives: injection efficiency, blockage risk, cost and energy consumption, and safety constraints. The objective function values ​​of each individual are calculated by calling the simulation results of the above-mentioned multi-reaction field coupled three-dimensional numerical simulation model.

[0116] The specific algorithm flow is as follows: initialize the population and randomly generate an initial solution set; perform non-dominated sorting on the solution set to determine the quality level and calculate the crowding distance between individuals to maintain solution set diversity; generate offspring solutions through selection, crossover, and mutation operations; evaluate the objective function value of each solution by combining the results of multi-reaction field coupled numerical simulation; iteratively update the Pareto front until the algorithm converges.

[0117] The algorithm ultimately outputs a set of Pareto optimal solutions that achieve the best trade-offs for each optimization objective. Users can select the final solution based on their engineering preferences. After selection, it needs to be re-verified in the original high-precision coupled model to ensure that three core requirements are met: mass conservation error ≤1%, all engineering constraints are met, and simulation results are consistent with the optimization objectives.

[0118] The multi-objective optimization functions include: the objective function for maximizing injection efficiency, the objective function for minimizing congestion risk, the objective function for minimizing cost and energy consumption, and the objective function for maximizing the satisfaction of safety constraints.

[0119] The objective function F1 for maximizing injection efficiency is defined as follows:

[0120] ;

[0121] in, To effectively inject traffic; The total cost includes well construction, operation, and maintenance.

[0122] The objective function for minimizing clogging risk is quantified by the rate of decrease in local permeability or the volume fraction of scale deposits, and is defined as follows:

[0123] ;

[0124] in, ; This refers to the volume fraction of the precipitate.

[0125] The objective function for minimizing cost and energy consumption includes well construction costs. Operating energy consumption Comprehensive economic indicators of maintenance costs Defined as:

[0126] ;

[0127] The objective function for maximizing the satisfaction of safety constraints, for the risk of sudden water inrush, is defined as the probability or safety margin of pressure exceeding the critical strength:

[0128] ;

[0129] Among them, critical water inrush pressure Maximum pressure around the well ;

[0130] The engineering constraints that the optimization process needs to meet include: water inrush safety constraints and maximum wellbore pressure. <Critical water inrush pressure The change in the concentration of key ions in the aquifer after injection does not exceed the environmental standard limits; total cost < The casing material and depth are optional discrete sets; the well depth, well diameter, and water injection temperature are within feasible ranges.

[0131] Among them, the feasible range refers to the range of values ​​that a certain design parameter can actually achieve under the current level of industrial technology, engineering practice standards, safety regulations, and specific geological conditions.

[0132] Step S5, Scheme Verification and Engineering Implementation: After outputting the optimized scheme, the selected optimized scheme parameters are substituted into the three-dimensional numerical model of multi-reaction field coupling simulation for high-precision verification simulation to ensure the mass conservation error, engineering constraint satisfaction and the consistency between the simulation results and the optimization target, and output the final reinjection design scheme.

[0133] For each scheme, engineering evaluation indicators are calculated, including: injection efficiency, blockage risk index defined based on local permeability decline rate, water inrush risk index defined based on wellbore pressure and rock mass strength, and cost estimation index; different reinjection schemes include at least different combinations of injection flow rate, well structure or injection temperature parameters.

[0134] The reinjection scheme design should include at least 3 to 5 types of reinjection schemes as a comparison benchmark. Reinjection schemes may vary in low, medium, and high flow rates, as well as in parameters for single-well and multi-branch well systems.

[0135] In step S5, the output indicators are defined as follows: the blockage risk indicator is the volume where the permeability decrease rate per unit volume exceeds 30%; the water inrush indicator is defined as the probability or frequency of local pressure exceeding the critical strength of the rock mass; and the economic indicator is defined as the cost per unit of reinjection.

[0136] This invention achieves a detailed simulation of the multi-field synergistic response during deep well reinjection of mine water by constructing a five-field coupled model encompassing stress, temperature, water flow, chemical, and microbial fields. This overcomes the shortcomings of existing technologies that only consider hydraulic processes and neglect the multi-field interaction mechanisms in complex formations. Compared with existing technologies, this invention has the following advantages:

[0137] Improving injection efficiency and reservoir utilization: By considering the seepage and water storage characteristics of the fracture-matrix dual media, the rapid transport of reinjection water in the fracture channel and the slow release storage mechanism in the matrix pores can be accurately simulated, thereby effectively improving water injection efficiency and reservoir utilization.

[0138] Reduced risk of clogging and acid corrosion: The embodiments of this application introduce water-rock reaction processes such as precipitation / scaling and dissolution / adsorption into the model, and combine the effects of temperature field and microbial field to predict the risk of clogging and acid corrosion under different reinjection conditions, thereby avoiding crack clogging and chemical instability problems through optimized design.

[0139] Improving the realism and reliability of simulations: By introducing the coupling mechanism of multiple reaction fields such as stress, hydraulics, thermodynamics, chemistry and biology, the simulation can reflect the real process of fracture opening, closing and permeability evolution in reservoirs under reinjection disturbance, avoiding the neglect of complex coupling effects in traditional single-field simulations, thereby improving the realism and reliability of numerical simulation results.

[0140] Achieving intelligent optimization design: The embodiments of this application construct a multi-objective optimization function and adopt intelligent optimization methods such as genetic algorithms. Under constraints (such as water inrush coefficient limits, water quality standards, construction costs, etc.), the optimal combination of reinjection parameters can be obtained, and key parameters including well depth, well diameter, injection pressure, flow rate and casing material can be output to ensure the scientific nature and feasibility of the engineering plan.

[0141] Reduce engineering costs and risks: While achieving efficient water injection and avoiding blockages, it can reduce the risk of cost runaway during construction and operation, and reduce engineering risks such as water inrush and blockages, providing a stable, safe and economical design basis for mine water reinjection under complex geological conditions.

[0142] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A numerical simulation and optimization method for coupled multi-reaction fields in deep mine water reinjection, characterized in that, Includes the following steps: S1. Data Acquisition and Geological Modeling: Acquire the basic parameters of the reinjection reservoir and reinjection water, and based on the basic parameters, establish a multi-reaction field coupled mathematical model including stress field, temperature field, water flow field, chemical field and microbial field; S2. Construction of a three-dimensional numerical model for multi-reaction field coupling simulation: Based on the multi-reaction field coupling mathematical model, a three-dimensional numerical model for multi-reaction field coupling simulation, which includes the structural features of continuous medium and discrete fracture network, is constructed in the numerical simulation platform; and boundary conditions and initial conditions are set for the multi-reaction field coupling simulation three-dimensional numerical model. S3. Multi-scheme simulation and response analysis: Based on the different reinjection schemes, the multi-response field coupled simulation three-dimensional numerical model is used to perform multi-field synergistic response simulation and obtain the engineering evaluation indicators under each scheme. S4. Multi-objective optimization and parameter optimization: Construct a multi-objective optimization function based on the engineering evaluation index, use a multi-objective optimization algorithm to optimize the parameters, and output the Pareto optimal solution set as the optimization scheme for the reinjection design parameters. S5. Scheme Verification and Engineering Implementation: After outputting the optimized scheme, the selected optimized scheme parameters are substituted into the three-dimensional numerical model of the multi-reaction field coupling simulation for high-precision verification simulation to ensure the mass conservation error, the satisfaction of engineering constraints, and the consistency between the simulation results and the optimization objectives, and output the final reinjection design scheme.

2. The method for coupled numerical simulation and optimization of multiple reaction fields in deep mine water reinjection according to claim 1, characterized in that, In step S1, the deep limestone fractured aquifer is taken as the research object, and the basic parameters include: Reservoir porosity, fracture geometry parameters, matrix and fracture permeability, mineral composition, thermal conductivity, Young's modulus and Poisson's ratio were obtained through geological and core tests. The ionic composition and concentration of the recharge water, TDS, temperature and microbial content were obtained by collecting water quality data of the recharge water. Initial geothermal profile and initial geostress field obtained through geostress logging or estimation.

3. The method for coupled numerical simulation and optimization of multiple reaction fields in deep mine water reinjection according to claim 1, characterized in that, In step S2, a five-field coupling model including stress field, temperature field, groundwater flow field, chemical field and microbial field is constructed in the COMSOL multiphysics platform; when constructing the three-dimensional numerical model of the multi-reaction field coupling simulation, a discrete fracture network embedding strategy is used to explicitly model the fracture; and Darcy flow or non-Darcy flow, porous medium heat transfer, rare matter transfer and solid mechanics physical field modules are loaded to establish a mathematical model including flow, solid, heat, chemical and biological multi-reaction field coupling terms; The multi-reaction field coupling relationships include fluid-structure coupling, heat-fluid coupling, hydration coupling, thermochemical coupling, chemical-solid coupling, and biological coupling involving microbial fields.

4. The method for coupled numerical simulation and optimization of multi-reaction field in deep mine water reinjection according to claim 3, characterized in that, In step S2, the three-dimensional numerical model for multi-reaction field coupled simulation includes at least the basic assumptions of the model, governing equations, boundary conditions, initial conditions, discretization and numerical solution constraints, and output quantity and coupling term calibration.

5. The method for numerical simulation and optimization of multi-reaction field coupling for deep mine water reinjection according to claim 4, characterized in that, The multi-reaction-field coupling mathematical model is based on the following fundamental assumptions: The fractures and matrix are modeled using a dual-porosity-dual-permeability system; the fluid is an incompressible Newtonian fluid and its flow follows Darcy's law; heat transfer is considered in both solid-liquid phase heat conduction and fluid convection; solute transport is considered in terms of convection, diffusion, adsorption, and chemical reactions; microbial activity is considered in terms of growth, transport, adsorption, and metabolism in the aqueous phase of the fractures and matrix, with its growth rate controlled by temperature, nutrient concentration, and water flow conditions; rock mass deformation follows linear elasticity theory, and permeability varies with effective stress.

6. The method for coupled numerical simulation and optimization of multiple reaction fields in deep mine water reinjection according to claim 4, characterized in that, The governing equations of the multi-reaction-field coupled mathematical model include: the water flow mass conservation equation, the solute equation, and the solute equation. Transport and chemical reaction equations, microbial concentration The governing equations, thermal energy conservation equations, and stress field equilibrium equations.

7. The method for coupled numerical simulation and optimization of multi-reaction field in deep mine water reinjection according to claim 6, characterized in that, The water flow mass conservation equations include: the water flow mass conservation equation for the fracture system and the water flow mass conservation equation for the matrix system, wherein: ； ； in: For fracture porosity, For matrix porosity; The density of water; Darcy velocity in fractured media; Darcy velocity in the matrix medium; Source and sink terms in fractured media; Source and sink terms in the matrix medium; This refers to the mass exchange term between the fracture and the matrix. For vector differential operators; t is time; solute The transport and chemical reaction equations are expressed as follows: ； ； ； in: This refers to the solute concentration. The solute diffusion coefficient; For chemical reaction source terms; Stoichiometric coefficients The reaction rate is expressed in an Arrhenius-type temperature-dependent form. It is the reaction rate constant; Activation energy; This is the universal gas constant; Absolute temperature; It is the ion activity product; It is the equilibrium constant; It is a vector differential operator; ; Microbial concentration The governing equations are expressed as follows: ； in, Microbial concentration; The microbial diffusion coefficient; This refers to the microbial growth item; This is a microbial attenuation term; For microbial adsorption; For time; Microbial growth items Using the Monod equation and coupling it with temperature effects: ； in: Microbial concentration; This represents the maximum specific growth rate. It serves as the activation energy for microbial growth; The limiting substrate concentration; It is the half-saturation constant; Absolute temperature; For time; For reference temperature; The equation for the conservation of heat energy is expressed as follows: ； in: For effective heat capacity; The specific heat capacity of water at constant pressure; For effective thermal conductivity; For heat source items; It is a vector differential operator; The density of water; Absolute temperature; For time; It is a vector differential operator; ; The stress equilibrium equations, under which rock mass deformation follows the Navier equations, are expressed as follows: ； in: For stress tensor; It is a volume force; It is a vector differential operator; The relationship between effective stress and pore pressure is as follows: ； in, The effective stress coefficient; Pore ​​pressure; Unit tensor; For stress tensor; ; The permeability varies with effective stress using an exponential model: ； in, It is the current effective normal stress; It is the reference effective normal stress; It is the permeability under the current effective stress; Reference effective stress The initial permeability is given by β; β is an empirical constant representing the sensitivity coefficient of permeability to changes in effective stress.

8. The method for coupled numerical simulation and optimization of multiple reaction fields in deep mine water reinjection according to claim 1, characterized in that, In step S3, the engineering evaluation indicators include: injection efficiency, a blockage risk indicator defined based on the local permeability decline rate, a water inrush risk indicator defined based on wellbore pressure and rock mass strength, and a cost estimation indicator; the different reinjection schemes include at least different combinations of injection flow rate, wellbore structure, or injection temperature parameters.

9. The method for coupled numerical simulation and optimization of multiple reaction fields in deep mine water reinjection according to claim 1, characterized in that, In step S4, the multi-objective optimization algorithm adopts a multi-objective genetic algorithm based on NSGA-II. Its optimization variable is the combination of reinjection design parameters. The objective function value corresponding to each individual is obtained by calling the simulation results of the multi-reaction field coupled simulation three-dimensional numerical model. The algorithm finally outputs a set of Pareto optimal solutions.

10. The method for coupled numerical simulation and optimization of multiple reaction fields in deep mine water reinjection according to claim 1, characterized in that, In step S4, the multi-objective optimization function includes: an objective function to maximize injection efficiency, an objective function to minimize congestion risk, an objective function to minimize cost and energy consumption, and an objective function to maximize the satisfaction of safety constraints; The objective function for maximizing injection efficiency is defined as: ； in, To effectively inject traffic; The total cost includes well construction, operation, and maintenance. The objective function for minimizing clogging risk is quantified by the rate of decrease in local permeability or the volume fraction of scale deposits, and is defined as follows: ； in, ; This refers to the volume fraction of the precipitate. The objective function for minimizing cost and energy consumption includes well construction costs. Operating energy consumption Comprehensive economic indicators of maintenance costs Defined as: ； The objective function for maximizing the satisfaction of safety constraints, for the risk of sudden water inrush, is defined as the probability or safety margin of pressure exceeding the critical strength: ； Among them, critical water inrush pressure Maximum pressure around the well ; The engineering constraints that the optimization process needs to meet include: water inrush safety constraints and maximum wellbore pressure. <Critical water inrush pressure The change in the concentration of key ions in the aquifer after injection does not exceed the environmental standard limits; total cost < The sleeve material and depth belong to a discrete set of options.

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