Reservoir bedrock anti-seepage grouting method

Abstract

The invention relates to the technical field of reservoir bedrock anti-seepage grouting, and discloses a reservoir bedrock anti-seepage grouting method which comprises the following steps: acquiring fracture data through geological exploration, and constructing a reservoir bedrock fracture distribution model; simulating a flow path and permeability characteristics of the slurry in the fracture based on the fracture model; the grouting pressure and flow are determined through an optimization algorithm, and a grouting scheme is formulated; screening grouting materials according to fracture characteristics, and analyzing stress-strain and permeability of the grouting materials; a construction process is formulated, the grouting state is monitored in real time, and parameters are dynamically adjusted; and after grouting, carrying out permeation and pressure test to evaluate whether the curing effect reaches the standard or not. Based on the three-dimensional fracture structure modeling and flow field simulation technology, high-precision recognition and dynamic evolution tracking of the fracture permeation path in the complex rock mass of the reservoir bank are achieved, so that grouting hole layout is more accurate, the slurry flow direction is more controllable, and the method is different from a point-selecting grouting mode of a traditional empirical method.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir bedrock anti-seepage grouting, in particular to a reservoir bedrock anti-seepage grouting method. Background Art

[0002] As a key provider of peak-shaving and energy storage, the operational safety and energy efficiency of pumped-storage power stations are closely linked to the ability of the upstream reservoir to control leakage. Under high head conditions, the reservoir bank's complex bedrock structure, with its prevalence of heterogeneous geological units such as weathered fissures and fault fracture zones, easily creates potential leakage pathways. This is particularly true in mountainous areas where the Liufan Formation's diorite gneiss and Bengbuian diorite are combined, where strongly weathered areas and thin aquicludes are widespread and groundwater levels are high, making it difficult to establish an effective seepage control system using conventional measures.

[0003] Existing reservoir bedrock grouting projects usually adopt the "drilling-water pressure-grouting-sealing" process, with experience dominating the grouting parameter setting and grouting process scheduling. The design process relies on the interpretation of two-dimensional geological profiles or local exposed surfaces, and the fracture structure is only simplified. In terms of parameter setting, manual setting of pressure and flow boundary values ​​is used for pre-grouting and main grouting operations, and real-time adjustment relies on the operator's on-site experience and judgment.

[0004] However, in the existing reservoir bedrock anti-seepage grouting technology, the two-dimensional structural information is insufficient to support the effective modeling of the three-dimensional fracture connectivity network, resulting in low accuracy in the grouting area division; on the other hand, the diffusion behavior of the slurry in the fracture is significantly affected by the difference in opening and local pressure disturbances, and the fixed parameter configuration is difficult to adapt to the changes in the non-steady-state flow field. In addition, the effectiveness evaluation method after the grouting is completed is single, relying on single-point water pressure or backflow volume analysis, which makes it difficult to quantitatively describe the relationship between the grouting volume and the actual anti-seepage effect. Therefore, the present invention provides a reservoir bedrock anti-seepage grouting method to address the shortcomings of the existing technology. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a reservoir bedrock anti-seepage grouting method, which solves the problems of existing grouting design relying on two-dimensional geological simplification, parameter control relying on experience judgment, lack of real-time regulation during the construction process, and single means of effectiveness evaluation.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a reservoir bedrock anti-seepage grouting method, comprising the following steps: Collect reservoir bedrock fracture structure data through geological exploration technology and establish a fracture distribution model; Based on the fracture distribution model, the flow path of grouting fluid in the reservoir bedrock fractures was simulated using the principles of fluid mechanics to analyze the fracture permeability characteristics. Based on the simulation results of the flow path of the grouting fluid in the reservoir bedrock cracks, an optimization algorithm is used to determine the injection pressure and flow parameters of the grouting fluid to form a grouting injection plan; Based on the grouting injection plan, select grouting materials that match the permeability characteristics of the fractures, conduct mechanical property analysis, and establish the material's stress-strain relationship and permeability change model; Based on the grouting injection plan and grouting material properties, formulate the grouting construction process, monitor the fluid injection status in real time and dynamically adjust the grouting parameters; After grouting is completed, the curing effect is evaluated, and the anti-seepage effect is verified through penetration test and pressure test. Based on the evaluation results of the curing effect, it is decided whether to add grouting.

[0007] Preferably, the step of establishing the crack distribution model includes: Use drones equipped with digital cameras to conduct low-altitude digital close-range photogrammetry to reconstruct the three-dimensional topography of the reservoir bank slope; Identify fracture structural surfaces based on a three-dimensional interactive platform and complete automated structural surface cataloging; The spatial distribution model of the fracture system is constructed through the three-dimensional network simulation technology of rock mass structural surface.

[0008] Preferably, the step of simulating the flow path of the grouting fluid in the reservoir bedrock fissures using the principles of fluid mechanics includes: The extended Darcy's law is used to calculate the equivalent seepage rate of fluid in each fracture unit; The fracture permeability field is established using fractal geometry, and a heterogeneous permeability model of the bedrock fracture system is constructed. The influence of rock anisotropy parameters on fluid distribution is considered in the simulation process.

[0009] Preferably, the step of determining the injection pressure and flow parameters of the grouting fluid using an optimization algorithm includes: Genetic algorithm is used to conduct multi-objective optimization of grouting parameters, and the objective functions include grouting uniformity, material utilization and time cost; The encoding method of grouting parameters adopts real number encoding, which is suitable for nonlinear constrained optimization problems; The grouting control variables are iteratively optimized by performing evolutionary selection, crossover and mutation operations based on the simulation feedback data.

[0010] Preferably, the step of performing mechanical property analysis on the grouting material comprises: A nonlinear constitutive model of the material is established to describe the stress-strain evolution relationship during the slurry solidification process; The relationship between the permeability coefficient and time under different water-cement ratio conditions was determined through permeability tests; The coupling relationship between the diffusion radius of slurry in cracks and solidification time is analyzed.

[0011] Preferably, the step of real-time monitoring of the fluid injection status and dynamically adjusting the grouting parameters includes: The integrated intelligent pressure regulating device and centralized grouting system in the intelligent grouting equipment are used to collect pressure, flow, and concentration data. The collected data is transmitted to the grouting control coordination center in real time via a short-range wireless network and an on-site self-organizing network system. The control system dynamically adjusts the fluid injection pressure and ratio concentration according to the collected data to achieve intelligent closed-loop control.

[0012] Preferably, the step of evaluating the curing effect includes: Seepage changes are measured by piezometers and head meters arranged through monitoring holes within the reservoir bank curtain; Conduct a comparative test of the permeability coefficient before and after grouting of the curtain section to determine the degree of improvement in the anti-seepage capacity of the grouting section; A multi-index evaluation model for grouting effect is constructed to comprehensively evaluate the reliability and durability of anti-seepage performance.

[0013] Preferably, the extended Darcy's law calculation formula is: in, is the grouting fluid velocity vector; K is the permeability coefficient; μ is the slurry viscosity; is the pressure gradient.

[0014] Preferably, the objective function of the genetic algorithm includes the following expression: minF=α·σ+β·(1-η)+γ·T; Among them, F is the objective function value; σ is the standard deviation of slurry distribution in cracks; η is the material utilization rate; T is the grouting time; α, β, and γ are the weight coefficients of each performance index.

[0015] Preferably, the fluid injection pressure and the ratio concentration are adjusted using a parameter control method based on fuzzy control and prediction model, and the parameter control method includes the following formula: P t =P0+ΔP=P0+K p ·(Q d -Q t )+K c ·(C d -C t ); Among them, P t is the actual grouting pressure at time t; P0 is the initial set pressure; Q d , Q t are the target and real-time grouting flow rates respectively; C d 、Ct are the target and real-time collected slurry concentrations respectively; K p , K c is the pressure adjustment proportional coefficient; ΔP is the pressure correction amount.

[0016] The present invention provides a reservoir bedrock anti-seepage grouting method, which has the following beneficial effects: 1. Based on three-dimensional fracture structure modeling and flow field simulation technology, the present invention achieves high-precision identification and dynamic evolution tracking of fracture seepage paths in complex rock masses on the reservoir bank, thereby making the layout of grouting holes more accurate and the slurry flow more controllable. Different from the traditional empirical method of point selection grouting, it avoids the material waste and ineffective filling problems caused by blind construction.

[0017] 2. By constructing a grouting parameter optimization model and integrating a genetic algorithm with a simulation feedback mechanism, this invention achieves intelligent matching control of grouting flow and pressure. Ultimately, the result is a significant reduction in the risk of slurry seepage while ensuring injection efficiency. Compared to the previous practice of relying on empirically corrected grouting pressure, this approach solves engineering challenges such as insufficient control precision and uncontrollable fracture opening.

[0018] 3. The present invention adopts an intelligent grouting equipment system, integrating sensor networks, wireless communications, adaptive voltage regulation and on-site real-time data processing technology to form a complete information-based and automated grouting control chain. Compared with the traditional method that relies on manual inspections and manual adjustments, it overcomes the problems of high concealment of grouting operations and large feedback delays.

[0019] 4. This invention combines an inversion model with field monitoring data to dynamically assess changes in fracture permeability. Using image recognition technology, it infers the grout diffusion range, enabling quantitative evaluation and visual display of grouting effectiveness. Compared to conventional, crude assessments based solely on single-point water pressure testing, this significantly improves the accuracy of anti-seepage assessments and the reliability of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 The embodiment of the present invention provides a reservoir bedrock anti-seepage grouting method, comprising the following steps: S1. Collect reservoir bedrock fracture structure data through geological exploration technology and establish a fracture distribution model; S2. Based on the fracture distribution model, the flow path of the grouting fluid in the reservoir bedrock fractures is simulated using the principles of fluid mechanics to analyze the fracture permeability characteristics; S3. Based on the simulation results of the flow path of the grouting fluid in the reservoir bedrock fissures, an optimization algorithm is used to determine the injection pressure and flow parameters of the grouting fluid to form a grouting injection plan; S4. Based on the grouting injection plan, select grouting materials that match the permeability characteristics of the fractures, conduct mechanical property analysis, and establish the material's stress-strain relationship and permeability change model; S5. Based on the grouting injection plan and grouting material properties, formulate the grouting construction process, monitor the fluid injection status in real time and dynamically adjust the grouting parameters; S6. After grouting is completed, the curing effect is evaluated and the anti-seepage effect is verified through penetration test and pressure test. Based on the evaluation results of the curing effect, it is decided whether to add grouting.

[0023] For step S1, in this embodiment, in order to ensure the scientific implementation of the reservoir bedrock anti-seepage grouting project, before implementing the grouting treatment, it is necessary to conduct a detailed identification and analysis of the rock structure in the upper reservoir bank area to clarify its structural surface morphology, structural distribution and weathering characteristics, and provide basic input data for subsequent grouting design. For the areas near the dam on the east and west banks of the upper reservoir, multi-source geological information fusion technology is used to obtain regional three-dimensional terrain and geological structure information.

[0024] Specifically, small unmanned aerial vehicles (UAVs) equipped with high-resolution digital cameras can be used to conduct oblique photogrammetry of the reservoir bank slope area at low altitude. Through aerial surveying and calculation, orthophotos and point cloud data with spatial registration accuracy better than 10 cm are obtained.

[0025] As an option, a small number of ground control points (GCPs) can be deployed simultaneously to improve the overall modeling accuracy, especially in vegetation-covered or shadowed areas, which is of practical significance for the accurate restoration of the structural surface.

[0026] After acquiring the three-dimensional data, in one possible implementation, point cloud densification processing can be completed based on a multi-view stereo vision algorithm to generate a dense three-dimensional model of the reservoir bank slope.

[0027] The model was then imported into the 3D modeling and interactive platform to automatically identify and analyze rock mass structural surfaces. Specifically, initial identification was performed based on features such as changes in the normal orientation of the structural surface, sudden grayscale gradient changes, and localized concavity and convexity distribution. This was then combined with manual interpretation to make corrections to ensure complete annotation of structures such as joints, fault zones, and unloading fissures.

[0028] In some embodiments, image recognition algorithms (such as deep learning semantic segmentation networks) can be further used to assist in extracting typical structural lines to improve recognition efficiency and accuracy.

[0029] After identification, the present invention uses a proprietary 3D structural network modeling module to topologically reconstruct the identified structural surface data. When constructing the 3D fracture network model, complex geometric factors such as the interlocking relationships between structural surfaces, spatial ductility, undulating boundaries, and intersecting bedding planes must be considered.

[0030] Specifically, in the network modeling process, each structural surface is represented as a unit body, and its topological relationship is expressed in the form of a graph structure, which is defined as follows: G=(V,E); Where G represents the entire fracture structure network; V represents the set of structural surface unit nodes; and E represents the set of connected edges between structural surfaces. Node attributes include: Spatial position vector Normal vector Area A i ; Weathering grade mark W i , with a value range of 0 to 3, corresponding to weak, moderate, strong, and fully weathered edge properties, including geometric and geological indicators such as the angle between structural planes, the shortest distance, and the connection strength, and is defined as: E ij =(θ ij ,d ij ,S ij ); Among them, x i 、y i 、z i are the positions of coordinate i on the three coordinate axes; n xi 、n yi 、n zi are the normal components of the surface in the x, y, and z directions respectively; d ij Indicates the shortest spatial distance between structural surfaces; S ij Indicates the degree of extension coupling between two structural surfaces, defined by the empirical model, with a range of [0, 1]; θ ij It represents the normal vector angle, and the calculation formula is as follows: in, is the normal vector of the i-th and j-th structural surfaces; θ ij Represents the spatial angle between two surfaces.

[0031] In some embodiments, in order to accurately express the three-dimensional spatial form of the structural surface, Bezier surface fitting can be introduced to compensate for the curvature of the structural surface boundary, so that the structural network is more in line with the actual geological conditions.

[0032] The constructed 3D fracture structure model will serve as input for downstream seepage simulation modules and grouting parameter optimization algorithms. The model can be aligned with the water level and dam profile to determine the direction and corresponding depth of potential leakage paths.

[0033] It should be noted that the above-mentioned three-dimensional model can be visualized in the platform, and users can manually adjust the transparency and display scale of the structural surface to facilitate the precise selection of the grouting area.

[0034] In addition, during the model construction process, the initial permeability parameter k needs to be assigned to the structural surface. i The value of this parameter is set based on lithologic experience and can generally be calculated as follows: k i =k0·f(W i )·φ i ; Among them, k i is the permeability of the i-th fracture unit; k0 is the base fracture permeability, which depends on the main lithology of the region; f(W i ) is the weathering correction coefficient, defined as a nonlinear function; φ i is the factor affecting the crack opening, which can be estimated through on-site scanning. As a supplement, in some embodiments, a time factor can also be introduced to simulate the trend of structural surface permeability over time for subsequent grouting effect prediction.

[0035] In step S2, in this embodiment, all identified fracture networks are imported into the seepage simulation platform based on the established three-dimensional structural surface topology model. The fracture structure is abstracted in the platform as an irregular polygonal channel network, with each structural surface possessing independent hydraulic property parameters, including permeability, porosity, aperture, and extension length.

[0036] In general, the migration of slurry in fractured media can be regarded as a viscous seepage problem with low Reynolds number, so the generalized Darcy law can be used for modeling. It can be described by the following formula: in, is the seepage velocity vector of the slurry in the crack; k is the crack permeability, which comes from the structural surface properties and weathering classification; μ is the dynamic viscosity of the slurry; is the pressure gradient vector, and its direction is from the slurry injection point to the far end of the structural surface.

[0037] Specifically, in one embodiment, the permeability k is assigned a distributed value based on the location of different structural surfaces, weathering level, and original opening rather than a uniform constant. The permeability of the fracture unit is calculated as follows: Among them, k i is the permeability of the i-th fracture unit; w i is the crack opening, which is estimated based on the identification data in the 3D modeling stage; “12” is the theoretical constant of the parallel plate model under laminar flow conditions.

[0038] As an alternative, to more realistically simulate the time-dependent viscosity changes during slurry diffusion, in one possible implementation, the dynamic viscosity μ can be considered as a time-dependent variable. The empirical model is set as follows.

[0039] μ(t)=μ0·e λt ; Wherein, μ(t) is the viscosity of the slurry at time t; μ0 is the viscosity of the initial slurry; λ is the viscosity growth coefficient, which is affected by temperature, material composition, and chemical reaction rate; t is the time after the slurry is injected into the crack; e λt In the form of exponential change.

[0040] During the simulation process, the platform divides the fracture cells into a spatially discrete grid and uses the finite volume method for numerical solution. The simulation applies an equivalent pressure head boundary condition corresponding to the normal reservoir water level, while also setting an injection pressure boundary at the grouting hole location. Based on historical grouting projects, the pressure boundary value is set to fluctuate between 0.3 and 0.5 MPa, with the specific value adjusted based on the depth and lithology of the target area.

[0041] In some embodiments, to better reflect the nonlinear flow of slurry at the intersection of fracture branches, a local perturbation factor can be set at the intersection of the main fracture to fine-tune the velocity vector field. This can avoid unreasonable velocity changes or "dead space" errors in the simulation.

[0042] To enhance the decision-making value of the simulation results, the output includes not only the slurry path trajectory but also a time-evolution diagram of the diffusion range, a graph of pore pressure changes, and a cross-sectional diagram of the hyperpermeability path. These graphical data will be directly used as reverse input for grouting hole placement and parameter setting in subsequent steps.

[0043] In one possible implementation, cluster analysis can be performed on the simulation results. The slurry diffusion paths can be divided into several characteristic channel clusters based on spatial distribution similarity, and high-flow rate channels can be identified to determine key reinforcement areas.

[0044] In addition, this embodiment also introduces a dynamic response analysis of flow and pressure in the fracture system. Under the condition of continuous slurry injection, the injection response curve of the fracture unit is simulated to establish a basic function template for the subsequent intelligent grouting control algorithm.

[0045] Regarding step S3, in this embodiment, after completing the simulation analysis of the permeability characteristics of the fracture system, in order to further develop a scientific and effective grouting injection plan, it is necessary to precisely control the grouting parameters based on the simulation results. This control process is directly related to the uniformity of slurry injection, material utilization efficiency, and overall grouting construction time, and is a key step in ensuring grouting effectiveness and controlling upper reservoir leakage. Therefore, by introducing a multi-objective optimization algorithm, the injection pressure and flow rate parameters of the grouting fluid are systematically optimized to generate a parameter combination that meets the actual geological conditions and grouting objectives.

[0046] After obtaining the spatial distribution of fractures, the equivalent permeability field, and the grout migration path in the simulation platform, a grouting parameter optimization control module was constructed. This module uses a genetic algorithm as its core solver to optimize an objective function containing multiple performance indicators.

[0047] In general, the optimization objectives include the uniformity of grout distribution in the cracks, the utilization efficiency of the selected materials, and the grouting operation time. The multi-objective function constructed for these objectives is as follows: minF=α·σ+β·(1-η)+γ·T; Where F is the objective function value; σ represents the standard deviation of the grout distribution in the fracture system, reflecting the uniformity of grouting; η represents the material utilization rate, defined as the ratio of the effective filling volume to the total injection volume, ranging from [0, 1]; T is the time required to complete grouting in the target area; α, β, and γ are the weight coefficients of the three indicators, respectively, which are set according to the site conditions and project objectives.

[0048] To address the parameter optimization problem under nonlinear constraints, real number encoding is used to represent individual parameters as vectors, such as [P,Q], where P represents the grouting pressure and Q represents the grouting flow rate (L / min). During the optimization process, the initial solution of the population is set based on historical grouting cases, and a dynamic mutation rate is set to improve the globality of the search space.

[0049] Alternatively, a simulation feedback correction mechanism can be introduced into each optimization iteration. Specifically, the simulation module evaluates the slurry distribution results in the fracture field under the current parameter configuration in real time, extracts local response indicators, and feeds them back into the genetic algorithm to guide the next generation of parameter selection and enhance the physical adaptability of the results.

[0050] In one possible implementation, a constraint function can be incorporated into the optimization framework to control the slurry pressure not to exceed the dual thresholds of formation tensile strength and fracture opening pressure. The constraint is expressed as follows: P≤min{P tensile ,P open}; Wherein, P represents the grouting pressure; P tensile is the maximum allowable grouting pressure corresponding to the tensile strength of the rock mass; P open The upper limit of the fracture opening pressure is calculated based on the fault plane direction and fluid tensile stress.

[0051] This embodiment also combines the 3D model data to map the optimization parameters in the spatial coordinate system to the actual hole location grouting strategy. Independent optimization results are set for each grouting hole, and a parameter scheduling table is generated on the platform for subsequent grouting process program settings.

[0052] In addition, to ensure the stability of the optimization results in actual scenarios, sensitivity analysis can be performed to evaluate the changing trend of the objective function under parameter disturbances, thereby identifying key control variables and laying the foundation for subsequent adaptive control.

[0053] In step S4, in this embodiment, after the grouting parameters are determined, to ensure that the grouting fluid performs the intended filling and sealing function within the fractured rock mass, it is necessary to combine the simulation results and the fracture permeability characteristics to select a grouting material that matches the target grouting area. Furthermore, to achieve parameterized control and process prediction, a material mechanical response model and a permeability change model must be established to provide engineering support for subsequent grouting construction scheduling.

[0054] Considering the weathering zonation characteristics of the Liufan Formation diorite gneiss and the Bengbu Period diorite, cement-water glass two-liquid slurry, silicate rapid-setting slurry, and polymer emulsion materials suitable for strongly to weakly weathered zones were prioritized. Given the geological conditions of high groundwater levels and deep aquicludes near the reservoir bank, the slurry's setting rate, bond strength, and permeability regulation were examined.

[0055] Generally speaking, the slurry undergoes a three-stage mechanical behavior transition during the pouring process: flow-diffusion-solidification. To describe the response relationship of the entire process, a nonlinear constitutive model is established as follows: σ(ε)=E0·ε·e -λt ; Wherein, σ(ε) represents the equivalent stress after the slurry solidifies; ε is the strain rate; E0 is the initial elastic modulus, reflecting the initial stiffness of the slurry; λ is the solidification rate coefficient, which depends on the slurry composition and ambient temperature; t is the time; e -λt is an exponential decay term.

[0056] The model can be dynamically loaded in the grouting construction control system to evaluate the slurry solidification state in real time and correct the grouting strategy.

[0057] In one possible implementation, in order to reflect the evolution of slurry permeability characteristics over time during the grouting process, a time-permeability coupling model is introduced for parameter control. The model is fitted based on field test data and is expressed as follows: k(t) = k0·(1-e -βt ); Where k(t) is the effective permeability at time t; k0 is the initial permeability of the slurry, which depends on the particle size and dispersion; β is the permeability attenuation coefficient, which depends on the gel densification rate; t is the grouting time; and e is the base of the natural logarithm.

[0058] In some embodiments, this model is used to simulate and predict the failure diffusion boundary of grout at the end of a fracture, assisting in determining the grouting cutoff condition. This model can be particularly helpful in explaining excessive grout diffusion or translaminar leakage in highly weathered areas.

[0059] Specifically, to ensure the adaptability of material properties, the platform establishes a mapping matching mechanism between the predicted value of penetration radius and the actual rock mass aperture. i and slurry diffusion radius r i As a matching parameter, if it satisfies: r i ≥w i +δ, the slurry is considered to be suitable for fracture filling at this location, where δ is the safety redundancy margin.

[0060] As an option, this embodiment also introduces a multi-attribute decision-making method into the material screening system to perform normalized scoring on candidate materials in terms of compressive strength, bonding strength, initial permeability, setting time, etc., to achieve automated push of material optimization.

[0061] In addition, to facilitate subsequent construction control, this embodiment stores the mechanical parameters, permeability coefficient curve, and stress response function of each selected material in the grouting database as a calling template for the subsequent intelligent complete set of grouting equipment parameter adjustment module.

[0062] Regarding step S5, in this embodiment, after completing the grouting material screening and performance modeling, it is necessary to further formulate a matching grouting construction process, and during the actual grouting process, dynamically adjust the grouting parameters based on changes in on-site working conditions.

[0063] Based on the aforementioned optimized parameter and material matching results, an intelligent grouting system integrating perception, transmission, decision-making, and execution functions was constructed. This system primarily consists of three modules: an integrated intelligent pressure regulator, a mobile intelligent centralized grouting system, and a grouting site control and coordination center. This system utilizes a distributed architecture and is deployed across multiple grouting sites.

[0064] Generally, the system collects key physical quantities during the fluid injection process through front-end devices such as high-frequency pressure sensors, magnetic flowmeters, and concentration detection probes. The data includes grouting pressure, flow, concentration, and orifice return slurry rate, and the sampling period is controlled within the range of 1-3 seconds.

[0065] As an option, to enhance the system's adaptability in complex geological conditions, this embodiment utilizes a data transmission mechanism that combines short-range wireless communication with on-site ad hoc networking technology. Specifically, all grouting units form a grid-like communication architecture, supporting local network self-healing and self-identification capabilities to ensure complete data transmission in signal-restricted locations such as tunnel sections and deep curtain buried areas.

[0066] Based on data collection and feedback, the core control module adjusts the grouting status in real time according to the preset control strategy. The control logic is based on the fusion algorithm of fuzzy control and prediction model, and the grouting pressure output is corrected in real time. The adjustment formula is as follows: P t =P0+ΔP=P0+K p ·(Q d -Q t )+K c ·(C d -C t ); Among them, P t is the grouting pressure at the current moment t; P0 is the initial grouting setting pressure; Q d , Q t are the target and current flow values ​​respectively; C d 、C t are the target and current concentration values ​​respectively; K p , K c is the proportional adjustment coefficient, which is set according to the on-site slurry rheological characteristics; ΔP is the pressure correction.

[0067] Specifically, this formula can be used to assess the risk of diffusion blockage or abnormal leakage of slurry in the structural surface, and automatically increase or decrease the grouting pressure accordingly to achieve a real-time "closed-loop" control effect.

[0068] In one possible implementation, to enhance the robustness of the control strategy, the system also introduces a prediction module. This module uses a sliding window algorithm to perform trend fitting on the grout response curve, predicting changes in the grouting state within a certain period of time, and adjusting parameters in advance to form a feedforward control compensation mechanism. The prediction model is constructed based on the following response rate function: Where R(t) is the effective filling ratio at time t, reflecting the grouting efficiency; V in(t) is the injection volume; V return (t) is the orifice return volume. When R(t) continues to decrease and falls below the set threshold, the system issues an early warning signal and executes grouting suspension or concentration adjustment operations.

[0069] In some embodiments, the system also supports multi-hole coordinated control. This means that the grouting rate at a specific hole is adjusted based on the synchronized response of the seepage pressure in the entire grouting area, using multiple adjacent grouting holes as units. This allows for balanced grouting in the area and prevents rock splitting or unexpected crack penetration caused by local high pressure.

[0070] In addition, this embodiment also constructs a supporting grouting data management platform to achieve archiving, curve display and indicator comparison analysis of all real-time data, providing a data basis for subsequent grouting effectiveness evaluation and re-grouting strategy formulation.

[0071] For step S6, in this embodiment, on the basis of completing the real-time regulation of grouting, in order to accurately evaluate the permeability changes of the fracture body under the action of grouting, thereby providing a decision-making basis for subsequent construction and determining the grouting termination conditions or the necessity of re-grouting, it is necessary to carry out grouting effect evaluation and model inversion calculation. Through the previous construction data and monitoring feedback, combined with mechanical modeling and seepage field simulation, a multi-scale comprehensive analysis of the permeability changes of the fracture structure is achieved.

[0072] In this example, a permeability assessment method based on the joint inversion of displacement field response and seepage field evolution was developed. This method uses monitoring data as input and stress perturbations and flow field parameters before and after grouting as boundary conditions to invert the changes in equivalent permeability of fractures within the effective grouting area. The effectiveness of the grouting treatment was quantified by comparing the hydraulic pressure test results, strain gauge responses, and stress concentration area trends in the test borehole sections before and after grouting.

[0073] In general, the equivalent permeability of fractured rock mass decays exponentially with the grouting process. To describe this change, the following permeability evolution model is proposed: k e (t) = k0·e -α·Φ(t) ; Among them, k e (t) is the equivalent permeability of the fracture body after grouting is completed; k0 is the initial fracture permeability; α is the grouting effect coefficient, which reflects the slurry blocking ability; Φ(t) is the grouting amount per unit volume, which is defined as the ratio of the mass of slurry injected per unit time to the fracture volume; e is the base of the natural logarithm.

[0074] Specifically, the model normalizes the actual grouting volume and combines it with the fracture space structure parameters to achieve quantitative fitting between the grouting volume ratio and the permeability reduction trend.

[0075] As an option, this embodiment also uses multi-channel transient pulsating water pressure test data and an equivalent medium model to construct an inversion function to further improve the evaluation accuracy. The inversion function is expressed as follows: Among them, E r is the inversion error index, used to evaluate the model fitting accuracy; k i,obs is the measured permeability of the i-th monitoring point; k i,mod is the simulated value of the i-th monitoring point; n is the total number of monitoring points. r When the error is less than the preset threshold ∈, the model is considered to have converged and the inversion process is completed.

[0076] In some embodiments, post-grouting data can be compared and analyzed using BTV imaging technology, combined with pre-grouting fracture connectivity images to annotate the grout diffusion range and further calibrate the equivalent grout ratio in the simulated fracture grid. The system automatically identifies grout filling texture features in the image using machine learning technology, enhancing the intelligent assessment level.

[0077] In one possible implementation, the permeability assessment results of fractured bodies are mapped onto a 3D rock mass structural model, enabling spatial visualization of the permeability evolution in different regions. This serves as the input for the distribution of "grouting equivalent impermeability bodies" in the mapping platform. These visualization results can be linked to the grouting database to facilitate subsequent optimization of re-grouting and selection of sealing strategies.

[0078] In addition, to improve the adaptability of the system in complex geological areas, this embodiment also introduces an adaptive weight adjustment mechanism to automatically adjust the contribution weight of each evaluation index in the inversion process according to the heterogeneity of the rock structure, thereby improving the sensitivity of fracture identification.

[0079] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A reservoir bedrock anti-seepage grouting method, characterized in that: The following steps are involved: Collect reservoir bedrock fracture structure data through geological exploration technology and establish a fracture distribution model; Based on the fracture distribution model, the flow path of grouting fluid in the reservoir bedrock fractures was simulated using the principles of fluid mechanics to analyze the fracture permeability characteristics. Based on the simulation results of the flow path of the grouting fluid in the reservoir bedrock cracks, an optimization algorithm is used to determine the injection pressure and flow parameters of the grouting fluid to form a grouting injection plan; Based on the grouting injection plan, select grouting materials that match the permeability characteristics of the fractures, conduct mechanical property analysis, and establish the material's stress-strain relationship and permeability change model; Based on the grouting injection plan and grouting material properties, formulate the grouting construction process, monitor the fluid injection status in real time and dynamically adjust the grouting parameters; After grouting is completed, the curing effect is evaluated, and the anti-seepage effect is verified through penetration test and pressure test. Based on the evaluation results of the curing effect, it is decided whether to add grouting.

2. The reservoir bedrock anti-seepage grouting method according to claim 1, characterized in that: The steps of establishing the crack distribution model include: Use drones equipped with digital cameras to conduct low-altitude digital close-range photogrammetry to reconstruct the three-dimensional topography of the reservoir bank slope; Identify fracture structural surfaces based on a three-dimensional interactive platform and complete automated structural surface cataloging; The spatial distribution model of the fracture system is constructed through the three-dimensional network simulation technology of rock mass structural surface.

3. The reservoir bedrock anti-seepage grouting method according to claim 1, characterized in that: The step of simulating the flow path of the grouting fluid in the reservoir bedrock fissures by using the principles of fluid mechanics includes: The extended Darcy's law is used to calculate the equivalent seepage rate of fluid in each fracture unit; The fracture permeability field is established using fractal geometry, and a heterogeneous permeability model of the bedrock fracture system is constructed. The influence of rock anisotropy parameters on fluid distribution is considered in the simulation process.

4. The reservoir bedrock anti-seepage grouting method according to claim 1, characterized in that: The step of using the optimization algorithm to determine the injection pressure and flow parameters of the grouting fluid includes: Genetic algorithm is used to conduct multi-objective optimization of grouting parameters, and the objective functions include grouting uniformity, material utilization and time cost; The encoding method of grouting parameters adopts real number encoding, which is suitable for nonlinear constrained optimization problems; The grouting control variables are iteratively optimized by performing evolutionary selection, crossover and mutation operations based on the simulation feedback data.

5. The reservoir bedrock anti-seepage grouting method according to claim 1, characterized in that: The step of performing mechanical property analysis on the grouting material comprises: A nonlinear constitutive model of the material is established to describe the stress-strain evolution relationship during the slurry solidification process; The relationship between the permeability coefficient and time under different water-cement ratio conditions was determined through permeability tests; The coupling relationship between the diffusion radius of slurry in cracks and solidification time is analyzed.

6. The reservoir bedrock anti-seepage grouting method according to claim 1, characterized in that: The steps of real-time monitoring of fluid injection status and dynamic adjustment of grouting parameters include: The integrated intelligent pressure regulating device and centralized grouting system in the intelligent grouting equipment are used to collect pressure, flow, and concentration data. The collected data is transmitted to the grouting control coordination center in real time via a short-range wireless network and an on-site self-organizing network system. The control system dynamically adjusts the fluid injection pressure and ratio concentration according to the collected data to achieve intelligent closed-loop control.

7. The reservoir bedrock anti-seepage grouting method according to claim 1, characterized in that: The steps of evaluating the curing effect include: Seepage changes are measured by piezometers and head meters arranged through monitoring holes within the reservoir bank curtain; Conduct a comparative test of the permeability coefficient before and after grouting of the curtain section to determine the degree of improvement in the anti-seepage capacity of the grouting section; A multi-index evaluation model for grouting effect is constructed to comprehensively evaluate the reliability and durability of anti-seepage performance.

8. The reservoir bedrock anti-seepage grouting method according to claim 3, characterized in that: The extended Darcy's law calculation formula is: in, is the grouting fluid velocity vector; K is the permeability coefficient; μ is the slurry viscosity; is the pressure gradient.

9. The reservoir bedrock anti-seepage grouting method according to claim 4, characterized in that: The objective function of the genetic algorithm includes the following expression: minF=α·σ+β·(1-η)+γ·T; Among them, F is the objective function value; σ is the standard deviation of slurry distribution in cracks; η is the material utilization rate; T is the grouting time; α, β, and γ are the weight coefficients of each performance index.

10. The reservoir bedrock anti-seepage grouting method according to claim 6, characterized in that: The fluid injection pressure and ratio concentration are adjusted using a parameter control method based on fuzzy control and prediction model, and the parameter control method includes the following formula: P t =P0+ΔP=P0+K p ·(Q d -Q t )+K c ·(C d -C t ); Among them, P t is the actual grouting pressure at time t; P0 is the initial set pressure; Q d , Q t are the target and real-time grouting flow rates respectively; C d 、C t are the target and real-time collected slurry concentrations respectively; K p , K c is the pressure adjustment proportional coefficient; ΔP is the pressure correction amount.

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