Intelligent grouting water prevention and control system and method for narrow space in well

Abstract

The application provides an underground narrow space intelligent grouting water prevention and control system and method, and relates to the technical field of underground grouting water prevention and control, which comprises a slurry diffusion monitoring subsystem, a three-dimensional diffusion control subsystem, a coagulation time prediction subsystem and a water plugging effect evaluation subsystem.The slurry diffusion monitoring subsystem is provided with a distributed pressure monitoring network, and pressure monitoring points are arranged in multiple directions to obtain pressure distribution data, temperature distribution data and slurry proportioning data at different distances in each direction.The three-dimensional diffusion control subsystem is provided with a three-dimensional starting pressure gradient model which is constructed based on pressure distribution data and rheological parameters, and each direction pressure compensation is calculated to control double-liquid synchronous injection.The coagulation time prediction subsystem is provided with a spatial coagulation reaction rate model which is established based on temperature distribution data, pressure distribution data and slurry proportioning data, and the coagulation time of each position in the three-dimensional space is predicted.The water plugging effect evaluation subsystem is provided with diffusion-coagulation coupling calculation to predict and evaluate the water plugging effect.The application can solve the technical problems of uneven three-dimensional diffusion of slurry, lack of intelligent control and prediction ability in the prior art.

Description

Technical Field

[0001] This invention relates to the field of downhole grouting for water control technology, and in particular to an intelligent downhole grouting system and method for water control in confined spaces. Background Technology

[0002] Grouting technology for water control in coal mines is a core technology for mine water hazard prevention, playing a crucial role in controlling water inrush disasters, reinforcing fractured surrounding rock, and managing seepage in goaf areas. Current grouting technologies mainly employ a cement-water glass dual-liquid grouting method. A high-pressure pump injects the prepared grout into formation fissures, utilizing the grout's cementing and filling properties to form a seepage barrier. Traditional grouting processes typically rely on operator experience and judgment, manually adjusting parameters such as grouting pressure, flow rate, and mix ratio to control the grouting process, and using basic instruments such as mechanical pressure gauges and flow meters to monitor the grouting status. Under general geological conditions, these traditional methods can meet basic water control needs, providing important guarantees for safe mine production.

[0003] However, with the increasing depth of mining and the growing complexity of geological conditions, traditional grouting technology has revealed significant limitations in terms of precision control, intelligence, and adaptability. First, existing technologies lack a deep understanding and precise control of grout diffusion patterns, making it difficult to accurately predict grout diffusion behavior in complex formations, resulting in uneven spatial distribution of grouting effects. Second, traditional monitoring methods are relatively simple, relying mainly on basic parameters such as pressure and flow rate, lacking real-time perception and intelligent analysis capabilities for the grouting process. Third, the confined space underground places higher demands on equipment integration, making it difficult for traditionally distributed grouting equipment to adapt to limited operating spaces. Finally, existing control strategies are mostly passive response-based, unable to proactively optimize parameters and control processes based on real-time monitoring data, leading to difficulties in guaranteeing grouting quality and efficiency.

[0004] Chinese invention patent CN113153429A discloses a method and system for online monitoring and intelligent evaluation of grouting quality in coal mines. This patent integrates five core modules, including an underground grouting information input / output module, a data monitoring module, and a wireless communication module, to achieve real-time, high-precision monitoring of grout flow rate, grouting pressure, and water-cement ratio parameters for each grouting anchor. It also provides accurate diagnosis and alarm for abnormal grouting areas through preset thresholds and can automatically generate a cloud map of grouting information distribution for visual analysis. While this patent offers an effective technical solution for grouting parameter monitoring and quality evaluation, it still suffers from the technical problem of not being able to actively and intelligently control the grout diffusion process. Summary of the Invention

[0005] In view of this, the present invention provides an intelligent grouting system and method for water control in narrow downhole spaces, aiming to solve the technical problems of uneven three-dimensional diffusion of grout, lack of intelligent control and predictive capabilities in existing grouting technologies. By establishing a three-dimensional non-uniform starting pressure gradient model and intelligent control algorithm, the system achieves three-dimensional uniform diffusion and precise control of grout in complex formations, upgrading the traditional experience-based grouting mode to a predictive intelligent grouting mode, thereby improving grouting accuracy, water shut-off success rate, and material utilization efficiency.

[0006] The technical solution of this invention is implemented as follows:

[0007] On one hand, the present invention provides an intelligent grouting system for water control in narrow downhole spaces, comprising:

[0008] The slurry diffusion monitoring subsystem uses a distributed pressure monitoring network to arrange pressure monitoring points in multiple directions, simultaneously monitoring the flow rate of the two liquids, temperature field distribution, and slurry rheological parameters. Through data fusion processing, it obtains pressure distribution data, temperature data, and slurry mix ratio data at different distances in each direction.

[0009] The three-dimensional diffusion control subsystem constructs a three-dimensional start-up pressure gradient model based on the pressure distribution data and rheological parameters, predicts the diffusion resistance differences in each direction, and calculates the pressure compensation amount in each direction according to the diffusion resistance differences through an adaptive pressure distribution algorithm to control the synchronous injection of two liquids.

[0010] The setting time prediction subsystem establishes a spatialized setting reaction rate model based on the temperature distribution data, pressure distribution data, and slurry ratio data to predict the setting time at each location in three-dimensional space and manage the time window.

[0011] The water-blocking effect evaluation subsystem combines the three-dimensional starting pressure gradient model and the spatialized condensation reaction rate model to perform diffusion-condensation coupling calculations, establish a three-dimensional permeability evolution model, and predict and evaluate the water-blocking effect.

[0012] Preferably, the distributed pressure monitoring network arranges pressure monitoring points in three directions at 120-degree intervals around the grouting hole. Pressure sensors are installed at distances of 0.5 meters, 1.0 meters, and 1.5 meters from the grouting hole in each direction, with a measurement range of 0-15 MPa and an accuracy of ±0.1%FS. Pressure sensors are also arranged at the outlets of liquid A, liquid B, the mixer, and the grouting hole. The dual-liquid flow monitoring module uses a Coriolis mass flow meter to monitor the flow rate of liquid A and an electromagnetic flow meter to monitor the flow rate of liquid B, with a measurement accuracy of ±0.5%FS. The temperature field monitoring module arranges temperature sensors at the mixer outlet and the grouting hole, and ground temperature sensors in the main diffusion direction.

[0013] Preferably, the mathematical expression of the three-dimensional initiation pressure gradient model is:

[0014]

[0015] in, Let be the position vector from the grouting hole to the calculation point. The magnitude of the position vector. Radial angle, For axial angle, For grouting time, For yield stress, The hydraulic radius;

[0016] Here is the direction correction function, defined as:

[0017]

[0018] in, The radial drag enhancement factor is... This is the axial drag enhancement factor;

[0019] The range-direction coupling attenuation function is defined as follows:

[0020]

[0021] in, For characteristic attenuation length, This refers to the direction-distance coupling coefficient.

[0022] The time-space coupling enhancement function is defined as follows:

[0023]

[0024] in, The spatial distribution of the condensation intensification coefficient. Basic reinforcement coefficient, The spatial attenuation coefficient, This refers to the slurry setting time.

[0025] Preferably, the adaptive pressure distribution algorithm obtains pressure attenuation data in each direction in real time and calculates the current pressure gradient distribution:

[0026]

[0027] in, The pressure at the grouting orifice. Direction angle Pressure at monitoring points The modulus of the corresponding monitoring location vector;

[0028] Calculate the starting resistance in each direction using a three-dimensional starting pressure gradient model. ;

[0029] Calculate the effective driving pressure gradient: ,when Only in this direction will effective diffusion occur;

[0030] Identify the direction of greatest diffusion resistance and the direction of fastest spread. ;

[0031] Calculate pressure compensation ,in, This represents the current average diffusion radius;

[0032] The final output optimal grouting pressure is ,in Based on the grouting pressure, For safety margin.

[0033] Preferably, the spatialized condensation reaction rate model is based on the Arrhenius equation:

[0034]

[0035] in, For position At any time The reaction progress, For frequency factors, For activation energy, The gas constant is... For local temperature, The reaction order is... The concentration of water glass, The concentration is the reaction order. For position At any time Local pressure, This is the pressure correction function. The pressure sensitivity coefficient, Standard atmospheric pressure, For the proportion correction function, This refers to the volume ratio of the two liquids. For the optimal ratio, This is the ratio sensitivity index.

[0036] Preferably, the three-dimensional permeability evolution model is based on the modified Kozeny-Carman equation:

[0037]

[0038] in, and Positions The permeability tensor before and after grouting and The porosity before and after grouting is represented. and The specific surface area before and after grouting;

[0039] Porosity changes are calculated using filling efficiency:

[0040]

[0041] Among them, filling efficiency , For position The volume of grout filling at the location, For the original void volume, This represents the local filling efficiency coefficient.

[0042] Preferably, the rheological parameters in the slurry diffusion monitoring subsystem are obtained by measuring the apparent viscosity of the slurry with an online viscometer, and by combining the data with the Bingham fluid parameter database pre-set in the laboratory, and by correcting for temperature and proportion, the real-time plastic viscosity and yield stress parameters are obtained; the data fusion processing uses the extended Kalman filter algorithm to fuse the distributed sensor data, with a data acquisition frequency of 10Hz and a data fusion processing frequency of 1Hz.

[0043] Preferably, the local temperature in the spatialized condensation reaction rate model The temperature field equations were established based on three-dimensional temperature field calculations.

[0044]

[0045] in, For temperature field distribution, Where is the thermal diffusivity, For the Laplace operator, This is the term for the heat source of a chemical reaction;

[0046] The time window management dynamically optimizes grouting flow rate and pressure parameters by predicting the setting time distribution at various locations in three-dimensional space.

[0047] Preferably, the quantitative evaluation of the water-blocking effect in the water-blocking effect evaluation subsystem is performed by predicting the permeability coefficient distribution:

[0048]

[0049] in, The modulus of the permeability tensor The density of water, The dynamic viscosity of water;

[0050] The overall water-blocking effect was calculated using a weighted average:

[0051]

[0052] in, To assess the volume of the region, For importance weighting functions;

[0053] The evaluation results are then fed back to the three-dimensional diffusion control subsystem to establish a database linking three-dimensional grouting parameters with formation conditions.

[0054] On the other hand, the present invention also provides a method for intelligent grouting and water control in narrow downhole spaces, comprising:

[0055] S1. A distributed pressure monitoring network is used to arrange pressure monitoring points in multiple directions around the grouting hole to simultaneously monitor the flow rate of the two liquids, the temperature field distribution and the rheological parameters of the grout. The pressure distribution data, temperature distribution data and grout ratio data at different distances in each direction are obtained through data fusion processing.

[0056] S2. Based on the pressure distribution data and rheological parameters, a three-dimensional starting pressure gradient model is constructed to predict the difference in diffusion resistance in each direction. An adaptive pressure distribution algorithm is used to calculate the pressure compensation amount in each direction according to the difference in diffusion resistance, and the flow rate and pressure parameters of the dual-liquid synchronous injection are adjusted in real time.

[0057] S3. Based on the temperature distribution data, pressure distribution data and grout ratio data, establish a spatialized coagulation reaction rate model to predict the coagulation time distribution at each location in three-dimensional space, manage the time window, and dynamically optimize the grouting parameters to avoid premature coagulation in some areas.

[0058] S4. Combine the three-dimensional starting pressure gradient model and the spatialized condensation reaction rate model to perform diffusion-condensation coupling calculations, establish a three-dimensional permeability evolution model, and predict and quantify the water shut-off effect.

[0059] The present invention has the following advantages over the prior art:

[0060] (1) This invention realizes the transformation of downhole grouting operations from traditional experience-based to predictive intelligent control by constructing a collaborative working mechanism of four intelligent subsystems: slurry diffusion monitoring, three-dimensional diffusion control, setting time prediction, and water shut-off effect evaluation. The three-dimensional non-uniform starting pressure gradient model established by the system can accurately reflect the differences in diffusion resistance in various directions in complex formations. Combined with the adaptive pressure distribution algorithm, it realizes uniform control of slurry diffusion in various directions, effectively solving the problem of local weak links caused by uneven directional diffusion in traditional grouting technology, and improving the integrity and reliability of the water shut-off curtain.

[0061] (2) The multi-directional deployment strategy and high-precision sensor configuration of the distributed pressure monitoring network can acquire accurate distribution data of pressure field, temperature field and rheological parameters in three-dimensional space in real time. By using the extended Kalman filter algorithm to fuse multi-sensor data, the influence of environmental interference and measurement noise is effectively eliminated;

[0062] (3) The three-dimensional initiation pressure gradient model, by integrating three key functions—direction correction, distance decay, and time-varying enhancement—deeply couples the principles of fluid mechanics, solid mechanics, and chemical reaction kinetics, and accurately describes the diffusion law of Bingham fluid in complex three-dimensional fracture networks. This model can predict the differential distribution of diffusion resistance in each direction, providing a theoretical basis for adaptive pressure distribution algorithms;

[0063] (4) The spatialized condensation reaction rate model based on the Arrhenius equation comprehensively considers the coupled effects of multiple factors such as temperature, pressure, and mix ratio on the condensation process, and can accurately predict the condensation time distribution at various locations in three-dimensional space. The time window management mechanism dynamically optimizes the grouting parameters according to the condensation time prediction results, ensuring that the designed diffusion range is completed before the grout solidifies, thus avoiding the diffusion interruption problem caused by premature local condensation;

[0064] (5) The diffusion-coagulation coupled calculation model realizes the quantitative prediction and evaluation of the water shut-off effect by establishing a three-dimensional permeability evolution equation. The system can predict the three-dimensional distribution of the permeability coefficient after grouting, calculate the overall water shut-off effect by weighted averaging, and establish a database of the correlation between grouting parameters and formation conditions to provide a basis for parameter optimization for subsequent grouting projects. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a system module diagram of the present invention;

[0067] Figure 2 This is a diagram illustrating the technical implementation of the present invention;

[0068] Figure 3 This is a schematic diagram of the three-dimensional diffusion control principle of the present invention;

[0069] Figure 4 This is a flowchart of the method of the present invention. Detailed Implementation

[0070] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0071] like Figure 1 and Figure 2 As shown, the present invention provides an intelligent grouting system for water control in narrow downhole spaces, comprising:

[0072] The slurry diffusion monitoring subsystem uses a distributed pressure monitoring network to arrange pressure monitoring points in multiple directions, simultaneously monitoring the flow rate of the two liquids, temperature field distribution, and slurry rheological parameters. Through data fusion processing, it obtains pressure distribution data, temperature data, and slurry mix ratio data at different distances in each direction.

[0073] The three-dimensional diffusion control subsystem constructs a three-dimensional start-up pressure gradient model based on the pressure distribution data and rheological parameters, predicts the diffusion resistance differences in each direction, and calculates the pressure compensation amount in each direction according to the diffusion resistance differences through an adaptive pressure distribution algorithm to control the synchronous injection of two liquids.

[0074] The setting time prediction subsystem establishes a spatialized setting reaction rate model based on the temperature distribution data, pressure distribution data, and slurry ratio data to predict the setting time at each location in three-dimensional space and manage the time window.

[0075] The water-blocking effect evaluation subsystem combines the three-dimensional starting pressure gradient model and the spatialized condensation reaction rate model to perform diffusion-condensation coupling calculations, establish a three-dimensional permeability evolution model, and predict and evaluate the water-blocking effect.

[0076] Specifically, in one embodiment of the present invention, the slurry diffusion monitoring subsystem includes a distributed pressure monitoring network, a dual-liquid flow monitoring module, a temperature field monitoring module, a rheological parameter real-time acquisition module, and a multi-sensor data fusion processing module. The distributed pressure monitoring network is the core component of this subsystem, specifically deployed as follows: three main monitoring directions are arranged at 120-degree intervals around the grouting hole, corresponding to the main fracture development directions of the formation. In each direction, high-precision pressure sensors are installed at distances of 0.5 meters, 1.0 meters, and 1.5 meters from the grouting hole, made of corrosion-resistant and impact-resistant 316L stainless steel, with a measurement range of 0-15 MPa, an accuracy of ±0.1%FS, and a response time of less than 1 second. The system also deploys pressure monitoring points at key fluid nodes: the outlet pressure range of pump A (0-20 MPa), the outlet pressure range of pump B (0-20 MPa), the mixer outlet pressure range of 0-15 MPa, and the grouting hole pressure range of 0-12 MPa. Each pressure monitoring point transmits signals to the central control system via a 4-20mA signal, with a transmission distance of up to 500 meters and strong resistance to electromagnetic interference. The dual-liquid flow monitoring module uses a combination of high-precision mass flow meters and electromagnetic flow meters. For liquid A (cement slurry), a Coriolis mass flow meter is used, with a measurement range of 0-100 L / min and an accuracy of ±0.5%FS. It can simultaneously measure mass flow rate, volumetric flow rate, density, and temperature, providing accurate data for proportioning control. For liquid B (water glass), an electromagnetic flow meter is used, with a measurement range of 0-50 L / min and an accuracy of ±0.5%FS, exhibiting good corrosion resistance and stability. Both flow meters are equipped with explosion-proof housings with an explosion-proof rating of ExdⅠMb, meeting downhole explosion-proof safety requirements. The temperature field monitoring module places temperature sensors at multiple key locations. The mixer outlet temperature sensor uses a Pt100 platinum resistance thermometer, with a measurement range of 5-80℃, an accuracy of ±0.5℃, and a response time of less than 3 seconds. The grouting orifice temperature sensor also uses a Pt100 platinum resistance thermometer with the same technical specifications. In ground temperature monitoring along the main diffusion direction, a distributed fiber optic temperature measurement system is employed, with a monitoring distance of up to 200 meters, a spatial resolution of 1 meter, and a temperature resolution of ±0.1℃, enabling real-time monitoring of temperature changes during slurry diffusion. The real-time rheological parameter acquisition module uses an online viscometer to measure the apparent viscosity of the slurry, with a measurement range of 1-1000 mPa·s and an accuracy of ±2%. It employs a vibration-based measurement principle, unaffected by fluid density and temperature. The system has a pre-set Bingham fluid parameter database, containing plastic viscosity and yield stress parameters under different temperature and mix proportions. Accurate rheological parameters under current operating conditions are obtained through real-time temperature and mix proportion data correction.

[0077] The multi-sensor data fusion processing module employs an extended Kalman filter algorithm to fuse distributed sensor data. The data acquisition frequency is set to 10Hz to ensure the capture of transient changes during the grouting process. The data fusion processing frequency is 1Hz, effectively filtering out high-frequency noise while maintaining real-time performance. The algorithm fuses multi-source sensor data into a consistent monitoring result through three steps: state prediction, observation update, and error covariance calculation.

[0078] The system outputs a standardized three-dimensional diffusion data interface, providing real-time monitoring data packets to the control subsystem; the data includes pressure gradient vectors in each direction.

[0079]

[0080] in The pressure gradients are shown in the x, y, and z directions, respectively.

[0081] Traffic data group ,in Let A be the flow rate. For the flow rate of liquid B, Total flow;

[0082] Temperature data group ,in For mixer temperature, For orifice temperature, The average ground temperature in the diffusion area;

[0083] Two-component ratio Cumulative grouting volume and slurry rheological parameters including plastic viscosity and yield stress .

[0084] Specifically, such as Figure 3 As shown, in one embodiment of the present invention, the three-dimensional diffusion control subsystem achieves precise control of diffusion in all directions based on a three-dimensional non-uniform start-up pressure gradient model. The subsystem includes a three-dimensional start-up pressure gradient calculation module, a diffusion resistance distribution prediction module, an adaptive pressure distribution algorithm module, and a dual-liquid synchronous control execution module.

[0085] Initiation pressure gradient in traditional grouting theory Assuming it to be an isotropic constant, but in actual three-dimensional fracture networks there are significant differences in geometric constraints, wall friction characteristics and hydrodynamic boundary conditions in different directions;

[0086] This invention establishes a three-dimensional non-uniform starting pressure gradient model, which deeply integrates the direction-sensitive flow characteristics of non-Newtonian fluids in complex geometries in fluid mechanics, the spatial distribution law of frictional stress on fracture walls in solid mechanics, and the time-varying influence mechanism of condensation process on rheological properties in chemical reaction kinetics.

[0087] The mathematical expression for the three-dimensional non-uniform initiation pressure gradient is:

[0088]

[0089] in, Let be the position vector from the grouting hole to the calculation point. The magnitude of the position vector. Radial angle, For axial angle, For grouting time, For yield stress, The hydraulic radius;

[0090] The directional correction function reflects the differences in geometric resistance of the fracture network in different directions:

[0091]

[0092] in The radial resistance enhancement factor is 0.1-0.3, and the specific value is determined based on the angle between the borehole axis and the direction of the main formation fracture. The axial resistance enhancement coefficient, with a value of 0.05-0.2, mainly considers the degree of vertical crack development. The physical meaning of this function is that the flow resistance of horizontal cracks in the vertical direction is greater than that in the horizontal direction, and the resistance distribution of vertical cracks in the horizontal direction is uneven.

[0093] The distance decay function reflects the gradual decrease in starting resistance in the far-field region:

[0094]

[0095] in The characteristic attenuation length is determined based on the connectivity of the fracture network, with typical values ​​ranging from 2 to 8 meters. This function reflects the gradual weakening of the constraint effect of the fracture wall as the distance increases during slurry diffusion.

[0096] The time-varying intensification function reflects the enhancing effect of the condensation process on the initiation resistance:

[0097]

[0098] in This is the condensation strengthening coefficient, with typical values ​​ranging from 0.2 to 0.8. This represents the slurry setting time. The physical mechanism behind this function is that as the chemical reaction proceeds, the thixotropy and viscoelasticity of the slurry gradually increase, and the starting stress required to overcome static friction increases accordingly.

[0099] The three-dimensional non-uniform initiation pressure gradient model of this invention couples the correction functions in a product form, reflecting the interaction mechanism between multiple physics fields. The following coupling characteristics exist between the correction functions:

[0100] Directional-distance coupling effect: Differences in fracture connectivity in different directions lead to attenuation length. It exhibits directional dependence. The connectivity distance of a horizontal fracture network is typically greater than that of a vertical fracture, therefore the distance attenuation function can be modified as follows:

[0101]

[0102] in This is the direction-range coupling coefficient, typically ranging from 0.1 to 0.3.

[0103] Time-space coupling effect: The differences in the condensation process at different locations and directions are reflected in the spatial distribution characteristics of the condensation intensification coefficient γ. In the far-field region, due to differences in temperature and pressure conditions, the condensation rate is relatively slow, and the time-varying intensification function can be expressed as:

[0104]

[0105] in Spatial distribution of condensation strengthening coefficient The basic strengthening coefficient ranges from 0.2 to 0.8. This is the spatial attenuation coefficient;

[0106] Pressure-temperature-condensation ternary coupling: During slurry diffusion, the pressure field affects the temperature distribution, temperature changes alter the condensation rate, and the condensation process, in turn, affects the local pressure gradient. In engineering applications, this coupling effect is manifested through the following correction mechanism: when the local condensation degree... Exceeding the critical value At that time, the starting pressure gradient at the corresponding location increases proportionally, with a correction factor of [value missing]. ,in This is the condensation-resistance coupling coefficient, typically ranging from 1.5 to 3.0.

[0107] Based on a three-dimensional non-uniform starting pressure gradient model, the system establishes an adaptive pressure distribution algorithm to achieve uniform control of diffusion in all directions;

[0108] The algorithm first acquires pressure decay data in all directions through real-time monitoring and calculates the current pressure gradient distribution:

[0109]

[0110] in, The pressure at the grouting orifice. Direction angle Pressure at monitoring points The modulus of the corresponding monitoring location vector;

[0111] Then, the starting resistance in each direction is calculated using a three-dimensional starting pressure gradient model. ;

[0112] Next, determine the effective driving pressure gradient: ,when Only in this direction will effective diffusion occur;

[0113] The algorithm identifies the direction of greatest diffusion resistance. and the direction of fastest spread. ;

[0114] To achieve uniform diffusion velocity in all directions, the algorithm calculates the pressure compensation amount. ,in, This represents the current average diffusion radius;

[0115] The final output optimal grouting pressure is ,in Based on the grouting pressure, For safety margin;

[0116] The adaptive pressure allocation algorithm is implemented through five computational steps: first, it calculates the current pressure gradient distribution; then, it calculates the initiation resistance in each direction; next, it determines the effective driving pressure gradient, identifies the directions of maximum and minimum diffusion resistance, and finally, it calculates the pressure compensation. The algorithm executes at a frequency of 1 Hz, enabling it to respond quickly to changes in the diffusion state.

[0117] The dual-liquid synchronous control module employs a combination of a variable frequency speed-regulating pump and an electrically controlled regulating valve to achieve precise control of flow rate and pressure. Pump A is a screw pump with a maximum flow rate of 150 L / min and a maximum pressure of 20 MPa. Pump B is a plunger pump with a maximum flow rate of 75 L / min and a maximum pressure of 20 MPa. Both pumps are equipped with frequency converters, achieving a control accuracy of ±0.5%. The grouting pressure is adjusted via the electrically controlled regulating valve, with an adjustment range of 0-15 MPa, an adjustment accuracy of ±0.1 MPa, and a response time of less than 2 seconds.

[0118] Specifically, in one embodiment of the present invention, the condensation time prediction subsystem establishes a multi-factor coupled condensation time prediction model that considers three-dimensional diffusion characteristics, providing time constraints for pressure control; the subsystem includes a three-dimensional temperature field calculation module, a multi-factor condensation kinetics module, a dynamic time window management module, and an emergency time control module;

[0119] The three-dimensional temperature field calculation module considers the non-uniform temperature distribution during the slurry diffusion process and establishes the temperature field equation:

[0120]

[0121] in, For temperature field distribution, Where is the thermal diffusivity, For the Laplace operator, This is the heat source term for the chemical reaction; specifically, the thermal diffusivity. Based on the type of strata and rocks, 0.8 × 10⁻⁶ was used for sandstone. -6 m 2 / s, limestone 1.2×10 -6 m 2 / s, mudstone is taken as 0.6×10 -6 m 2 / s. Chemical reaction pyrogen term. The calculations take into account the enthalpy of the two-liquid reaction and the reaction progress;

[0122] The multi-factor condensation kinetics module establishes condensation reaction rate equations that consider spatial location based on the Arrhenius equation:

[0123]

[0124] in, For position At any time The reaction progress, For frequency factors, For activation energy, The gas constant is... For local temperature, The reaction order is... The concentration of water glass, The concentration is the reaction order. For position At any time Local pressure, This is the pressure correction function. The pressure sensitivity coefficient, Standard atmospheric pressure, For the proportion correction function, This refers to the volume ratio of the two liquids. For the optimal ratio, For the ratio sensitivity index;

[0125] Specifically, frequency factor Determined through laboratory calibration, the typical value is 10. 8 -10 12 s-1 .activation energy Based on the chemical composition of the two-component system, the typical value for the cement-water glass system is 40-60 kJ / mol. The reaction order n is taken as 1-2, and the reaction order m for the water glass concentration is taken as 0.5-1.5. Pressure sensitivity coefficient... The value ranges from 0.05 to 0.15, representing the sensitivity index. Values ​​range from 0.5 to 2.0;

[0126] The dynamic time window management module calculates the effective operating time window for each direction in real time based on the three-dimensional setting time distribution prediction. When the average setting degree in a certain direction exceeds 0.3, the system automatically reduces the grouting flow rate in that direction. When the average setting degree exceeds 0.5, the system suspends grouting in that direction and focuses on strengthening diffusion in other directions. When the overall average setting degree reaches 0.8, the system automatically ends the grouting operation.

[0127] The emergency time control module monitors for abnormal acceleration in the setting process. When the actual setting rate exceeds the predicted value by 50%, the system immediately increases the grouting flow rate to accelerate the diffusion speed. When setting acceleration occurs simultaneously in multiple directions, the system automatically initiates an emergency flushing procedure to clean the mixer and grouting pipeline.

[0128] Specifically, in one embodiment of the present invention, the water-blocking effect evaluation subsystem achieves quantitative prediction and evaluation of the water-blocking effect by establishing a permeability evolution model that considers three-dimensional diffusion non-uniformity. The subsystem includes a three-dimensional diffusion-condensation coupling calculation module, a spatial permeability evolution prediction module, a water-blocking effect quantitative evaluation module, and a quality control feedback optimization module;

[0129] This subsystem receives real-time pressure field data output from the preceding three-dimensional uniform diffusion intelligent control subsystem. The three-dimensional temperature field provided by the rapid condensation time dynamic prediction and management subsystem and the distribution of solidification progress By establishing a deep coupling calculation model of diffusion-coagulation multiphysics fields, the final distribution pattern of slurry in three-dimensional space can be accurately predicted, and the water plugging effect can be quantitatively evaluated based on the permeability evolution theory.

[0130] The three-dimensional diffusion-coagulation coupling calculation module establishes a set of coupled equations for the mutual influence of diffusion and coagulation of the slurry in three-dimensional space. The diffusion equation is as follows:

[0131]

[0132] in, Let be the integral of the slurry at time t at position r, with dimension 1, representing the proportion of slurry in a unit volume of fracture space; It is the slurry velocity vector, with dimensions in m / s, reflecting the motion state of the slurry in three-dimensional space; It is a divergence operator; The time partial derivative;

[0133] The physical meaning of this equation lies in describing the spatiotemporal evolution of slurry concentration: the first term on the left represents the rate of change of local concentration over time, and the second term represents the concentration flux divergence caused by convective transport. When When the outflow is greater than the inflow at a given location, the slurry concentration decreases; conversely, when the outflow is greater, the concentration increases. This equation is used for subsequent calculations of the slurry filling volume at each location. It provides a theoretical basis;

[0134] Traditional Darcy's law assumes the fluid is a Newtonian fluid and diffuses isotropically. However, cement-water glass two-component slurry is a typical Bingham fluid, exhibiting a distinct yield stress characteristic. The modified Darcy's law proposed in this invention fully considers the three-dimensional non-uniform starting pressure gradient and time-varying viscosity characteristics.

[0135]

[0136] in, Let r be the permeability tensor at position r, which reflects the geometric properties and connectivity of the fracture network; The effective viscosity, taking into account the effects of condensation, changes dynamically with the condensation process; The actual pressure gradient originates from the output of the previous pressure control subsystem; The three-dimensional non-uniform initiation pressure gradient reflects the yield characteristics of Bingham fluid. The core of this modified equation lies in introducing the concept of a direction-dependent initiation pressure gradient. Only when the effective driving pressure gradient... Only at that time will actual slurry flow occur at the corresponding location, accurately reflecting the flow characteristics of Bingham fluid. Meanwhile, The spatial non-uniformity of the space leads to differences in the initiation conditions in different directions and locations, providing a theoretical basis for the implementation of the adaptive pressure allocation algorithm;

[0137] The effective viscosity model establishes a quantitative relationship between the rheological properties of the slurry and the setting process, using an exponential-linear composite correction form:

[0138]

[0139] in This is the initial plastic viscosity, determined by the slurry ratio and temperature, with a typical value of 0.1-0.5 Pa·s; It is the viscosity growth coefficient with respect to condensation, with a dimension of 1, reflecting the sharp effect of molecular cross-linking on viscosity during condensation, with a typical value of 2.0-4.0; The condensation-rheology coupling coefficient is dimensionless and reflects the linear contribution of initial condensation, with a typical value of 0.5-1.5. The chemical reaction progress at time t at position r is dimensionless and ranges from 0 to 1, derived from the previous condensation time prediction subsystem;

[0140] The physical mechanism of this model is reflected on two levels: the exponential term This reflects the sharp increase in viscosity caused by molecular chain cross-linking during the chemical reaction, an effect that is particularly pronounced in the later stages of the reaction (α>0.5); the linear term This describes the gradual change in the thixotropy of the slurry in the initial stage of the reaction (α<0.3). The product of the two reflects the nonlinear composite characteristics of the coagulation process, causing the effective viscosity to increase slowly at first and then rapidly with the progress of the reaction;

[0141] The three core equations form a tightly coupled set of nonlinear equations, and numerical computation is achieved using a step-by-step solution strategy:

[0142] Step 1: Receive preliminary data input; obtain real-time pressure field distribution from the pressure control subsystem. and starting pressure gradient Temperature field obtained from condensation prediction subsystem and reaction progress Obtain the penetration tensor from the monitoring subsystem. Initial value; Second step: Effective viscosity calculation; Based on the reaction progress distribution at the current moment. The rheological parameters at each location were calculated using the effective viscosity model. The first step is to demonstrate the real-time impact of the condensation process on flow properties; the second step is to solve for the velocity field; the third step is to substitute the updated effective viscosity into the modified Darcy's law and, combined with the known pressure gradient distribution, solve for the three-dimensional velocity field. The fourth step involves obtaining the motion state of the slurry in various directions; substituting the velocity field into the diffusion equation and using the finite difference method to solve for the spatiotemporal evolution of the slurry integral number. The fifth step is to predict the three-dimensional distribution of the slurry. The new concentration distribution is fed back to the temperature field calculation (through the reaction heat source term), and the local reaction progress is updated at the same time, forming a ternary coupled cycle of diffusion-condensation-temperature.

[0143] The spatial permeability evolution prediction module establishes a three-dimensional permeability evolution model based on the modified Kozeny-Carman equation:

[0144]

[0145] in, and Positions The permeability tensor before and after grouting and The porosity before and after grouting is represented. and The specific surface area before and after grouting;

[0146] Porosity changes are calculated using filling efficiency:

[0147]

[0148] Among them, filling efficiency , For position The volume of grout filling at the location, For the original void volume, The local filling efficiency coefficient is determined based on the local pore structure characteristics: 1.2-1.5 for large pore regions, 0.8-1.2 for medium pore regions, and 0.5-0.8 for small pore regions. Specific surface area variation is calculated based on porosity variation and particle size distribution.

[0149] The water-blocking effect quantitative evaluation module establishes an evaluation index based on the three-dimensional permeability coefficient distribution, predicting the permeability coefficient distribution as follows:

[0150]

[0151] in, The magnitude of the permeability tensor is calculated using the Frobenius norm of the matrix: ; The specific weight of water, with a standard value of 9800 N / m³. 3 ; The dynamic viscosity of water is approximately 1.0 × 10⁻⁶ at room temperature. -3 Pa·s;

[0152] The overall water-blocking effect was calculated using a weighted average:

[0153]

[0154] in, To assess the volume of the region, The importance weighting function is determined based on hydrogeological conditions: near the main seepage channels In general areas In secondary areas The weighting function design fully considers the actual needs of the project: the sealing quality of the main seepage channels directly affects the water control effect, so it is given a higher weight; while areas far from the water source also need to be treated, but their importance is relatively low. Through this differentiated weighting design, the actual water control effect of the grouting project can be reflected more accurately.

[0155] The evaluation results are then fed back to the three-dimensional diffusion control subsystem to establish a database linking three-dimensional grouting parameters with formation conditions.

[0156] Specifically, the system establishes a model verification feedback mechanism based on measured data: the predicted three-dimensional permeability coefficient distribution is compared with the actual pressure test results, and the prediction error is calculated.

[0157]

[0158] in The number of test points. and The first Predicted and measured values ​​for each test point. When At that time, the system automatically starts the parameter correction program to adjust key coupling coefficients, such as... Etc., to ensure that the model's accuracy meets engineering requirements;

[0159] The system establishes a database linking the predicted results of each grouting project with the actual effects, recording the quantitative relationships between formation conditions (porosity, fracture development degree, and geostress state), grouting parameters (pressure, flow rate, and mix ratio), and water-blocking effects (permeability coefficient distribution and overall water control effect).

[0160] The entire intelligent control system of this invention is integrated into an explosion-proof control cabinet measuring 1600mm×900mm×700mm, employing a modular design. The control cabinet is divided into three functional areas: a data acquisition and processing area, a logic control calculation area, and an actuator control area. Electrical isolation between these areas is achieved through isolation transformers and opto-isolators, ensuring the safe and stable operation of the system.

[0161] The system operation process includes six main steps: system initialization checks the status and communication connection of each sensor, sets basic grouting parameters including initial values ​​of flow rate, pressure and mix ratio, starts the distributed monitoring network to begin data acquisition, runs the three-dimensional diffusion control algorithm to optimize grouting parameters in real time, performs setting time management to dynamically adjust the time window, and completes the water plugging effect evaluation to generate a quality report.

[0162] In addition, such as Figure 4 As shown, the present invention also provides a method for intelligent grouting and water control in narrow downhole spaces, comprising:

[0163] S1. After system startup, the system first performs sensor status checks and communication connection confirmation. The distributed pressure monitoring network then synchronously collects data from each monitoring point at a frequency of 10Hz, including pressure values ​​at nine pressure monitoring points in three main directions, flow data at four key locations, temperature field distribution at multiple temperature monitoring points, and rheological parameters from the online viscometer. During data acquisition, the system automatically performs sensor fault diagnosis, marking and removing abnormal data. The extended Kalman filter algorithm fuses the multi-source sensor data at a frequency of 1Hz, outputting standardized pressure distribution data sets, temperature distribution data sets, and real-time slurry ratio data.

[0164] S2. Based on the acquired pressure distribution data and rheological parameters, the system calls the three-dimensional starting pressure gradient model to calculate the diffusion resistance distribution in each direction under the current operating condition. The adaptive pressure distribution algorithm calculates the actual pressure gradient in each direction in real time, compares it with the starting pressure gradient, and determines the effective driving pressure gradient distribution. The algorithm identifies the directions with the maximum and minimum diffusion resistance and calculates the required pressure compensation. According to the calculation results, the control system adjusts the flow output of the dual liquid pump through the frequency converter and adjusts the grouting pressure through the electronically controlled regulating valve to achieve balanced control of diffusion in each direction.

[0165] S3. Based on real-time acquired temperature, pressure, and proportioning data, the spatialized condensation reaction rate model is invoked to predict the condensation process at various locations within three-dimensional space. By solving the reaction kinetic equations, the condensation degree distribution and condensation time prediction at each location are obtained. The time window management module calculates the effective operating time window for each direction in real time based on the prediction results. When the average condensation degree in a certain direction is detected to exceed a preset threshold, the system automatically adjusts the grouting parameters for that direction, reducing the flow rate or pausing grouting to avoid pipeline blockage.

[0166] S4. The diffusion-coagulation coupled calculation module is used to establish a three-dimensional permeability evolution model, comprehensively considering the interaction between grout diffusion patterns and coagulation processes. The modified Kozeny-Carman equation is used to calculate the changes in porosity and permeability at various locations after grouting, predicting the distribution of water-blocking effects. The system calculates the weighted average of the overall water-blocking effect to assess whether the grouting quality meets design requirements. The evaluation results are fed back to the diffusion control subsystem in real time to optimize subsequent grouting parameters. Key parameters and effect data from this grouting are stored in a database to establish the correlation between formation conditions and optimal grouting parameters.

[0167] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart grouting system for preventing water in confined spaces in wells, characterized in that, include: The slurry diffusion monitoring subsystem includes a distributed pressure monitoring network, a dual-liquid flow monitoring module, a temperature field monitoring module, a rheological parameter real-time acquisition module, and a multi-sensor data fusion processing module. The distributed pressure monitoring network is used to arrange pressure monitoring points in multiple directions to simultaneously monitor the dual-liquid flow, temperature field distribution, and slurry rheological parameters. Through data fusion processing, pressure distribution data, temperature distribution data, and slurry mix ratio data at different distances in each direction are obtained. The three-dimensional diffusion control subsystem constructs a three-dimensional start-up pressure gradient model based on the pressure distribution data and rheological parameters, predicts the diffusion resistance differences in each direction, and calculates the pressure compensation amount in each direction according to the diffusion resistance differences through an adaptive pressure distribution algorithm to control the synchronous injection of two liquids. The setting time prediction subsystem establishes a spatialized setting reaction rate model based on the temperature distribution data, pressure distribution data, and slurry ratio data to predict the setting time at each location in three-dimensional space and manage the time window. The water-blocking effect evaluation subsystem combines the three-dimensional starting pressure gradient model and the spatialized condensation reaction rate model to perform diffusion-condensation coupling calculations, establish a three-dimensional permeability evolution model, and predict and evaluate the water-blocking effect. The mathematical expression for the three-dimensional initiation pressure gradient model is: ； in, Let be the position vector from the grouting hole to the calculation point. Represents the magnitude of the position vector. Radial angle, It is the axial angle. For grouting time, For yield stress, The hydraulic radius; Here is the direction correction function, defined as: ； in, The radial drag enhancement factor is... This is the axial drag enhancement factor; The range-direction coupling attenuation function is defined as follows: ； in, For characteristic attenuation length, This refers to the direction-distance coupling coefficient. The time-space coupling enhancement function is defined as follows: ； in, The spatial distribution of the condensation strengthening coefficient. Basic reinforcement coefficient, The spatial attenuation coefficient, This refers to the slurry setting time; The spatialized condensation reaction rate model is based on the Arrhenius equation: ； in, For position At any time The reaction progress, For frequency factors, For activation energy, The gas constant is... For local temperature, Let be the reaction order. The concentration of water glass, The concentration is the reaction order. For position At any time Local pressure, This is the pressure correction function. The pressure sensitivity coefficient, Standard atmospheric pressure, For the proportion correction function, This refers to the volume ratio of the two liquids. For the optimal ratio, This is the ratio sensitivity index.

2. The intelligent grouting and water control system for narrow downhole spaces according to claim 1, characterized in that, The distributed pressure monitoring network arranges pressure monitoring points in three directions at 120-degree intervals around the grouting hole. Pressure sensors are installed at distances of 0.5 meters, 1.0 meters, and 1.5 meters from the grouting hole in each direction, with a measurement range of 0-15 MPa and an accuracy of ±0.1%FS. Pressure sensors are also arranged at the outlets of liquid A and liquid B, the mixer outlet, and the grouting hole. The dual-liquid flow monitoring module uses a Coriolis mass flow meter to monitor the flow rate of liquid A and an electromagnetic flow meter to monitor the flow rate of liquid B, with a measurement accuracy of ±0.5%FS. The temperature field monitoring module arranges temperature sensors at the mixer outlet and the grouting hole, and ground temperature sensors in the main diffusion direction.

3. The intelligent grouting and water control system for narrow downhole spaces according to claim 1, characterized in that, The adaptive pressure distribution algorithm obtains pressure attenuation data in each direction in real time and calculates the current pressure gradient distribution: ； in, The pressure at the grouting orifice. Direction angle Pressure at monitoring points The modulus of the corresponding monitoring location vector; Calculate the starting resistance in each direction using a three-dimensional starting pressure gradient model. ; Calculate the effective driving pressure gradient: ,when Only in this direction will effective diffusion occur; Identify the direction of greatest diffusion resistance and the direction of fastest spread. ; Calculate pressure compensation ,in, This represents the current average diffusion radius; The final output optimal grouting pressure is ,in Based on the grouting pressure, For safety margin.

4. The intelligent grouting and water control system for narrow downhole spaces according to claim 1, characterized in that, The three-dimensional permeability evolution model is based on the modified Kozeny-Carman equation: ； in, and Positions The permeability tensor before and after grouting and The porosity before and after grouting is represented. and The specific surface area before and after grouting; Porosity changes are calculated using filling efficiency: ； Among them, filling efficiency , For position The volume of grout filling at the location, For the original void volume, This represents the local filling efficiency coefficient.

5. The intelligent grouting and water control system for narrow downhole spaces according to claim 1, characterized in that, The rheological parameters in the slurry diffusion monitoring subsystem are acquired by measuring the apparent viscosity of the slurry with an online viscometer and combining it with the Bingham fluid parameter database pre-set in the laboratory. The real-time plastic viscosity and yield stress parameters are obtained through temperature and proportion correction. The data fusion processing uses an extended Kalman filter algorithm to fuse the distributed sensor data. The data acquisition frequency is 10Hz and the data fusion processing frequency is 1Hz.

6. The intelligent grouting and water control system for narrow downhole spaces according to claim 1, characterized in that, Local temperature in the spatialized condensation reaction rate model The temperature field equations were established based on three-dimensional temperature field calculations. ； in, For temperature field distribution, Where is the thermal diffusivity, For the Laplace operator, This is the heat source term for the chemical reaction; The time window management dynamically optimizes grouting flow rate and pressure parameters by predicting the setting time distribution at various locations in three-dimensional space.

7. The intelligent grouting and water control system for narrow downhole spaces according to claim 4, characterized in that, The quantitative evaluation of water-blocking effect in the water-blocking effect evaluation subsystem is conducted by predicting the permeability coefficient distribution: ； in, The modulus of the permeability tensor The density of water, The dynamic viscosity of water; The overall water-blocking effect was calculated using a weighted average: ； in, To assess the volume of the region, For importance weighting functions; The evaluation results are then fed back to the three-dimensional diffusion control subsystem to establish a database linking three-dimensional grouting parameters with formation conditions.

8. A method for intelligent grouting and water control in a narrow downhole space, characterized in that, The method is applied to the system as described in any one of claims 1-7, and the method includes: S1. A distributed pressure monitoring network is used to arrange pressure monitoring points in multiple directions around the grouting hole to simultaneously monitor the flow rate of the two liquids, the temperature field distribution and the rheological parameters of the grout. The pressure distribution data, temperature distribution data and grout ratio data at different distances in each direction are obtained through data fusion processing. S2. Based on the pressure distribution data and rheological parameters, a three-dimensional starting pressure gradient model is constructed to predict the difference in diffusion resistance in each direction. An adaptive pressure distribution algorithm is used to calculate the pressure compensation amount in each direction according to the difference in diffusion resistance, and the flow rate and pressure parameters of the dual-liquid synchronous injection are adjusted in real time. S3. Based on the temperature distribution data, pressure distribution data and grout ratio data, establish a spatialized coagulation reaction rate model to predict the coagulation time distribution at each location in three-dimensional space, manage the time window, and dynamically optimize the grouting parameters to avoid premature coagulation in some areas. S4. Combine the three-dimensional starting pressure gradient model and the spatialized condensation reaction rate model to perform diffusion-condensation coupling calculations, establish a three-dimensional permeability evolution model, and predict and quantify the water shut-off effect.

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