Settlement inversion prediction method based on water-soil coupling and well reservoir dynamic feedback

Abstract

The invention provides a sedimentation inversion prediction method based on water-soil coupling and well reservoir dynamic feedback, which comprises the following steps: by collecting geological, hydrological, deformation monitoring and engineering construction data, constructing a three-dimensional geologic model containing a shallow unconsolidated formation and an underlying bedrock layer, and setting physical boundary constraints; establishing a water-soil bidirectional coupling model, integrating a seepage model and a deformation model, and realizing bidirectional coupling through a dynamic feedback mechanism; calculating the leakage / extraction amount based on the dynamic water level in the well and the hydraulic long pipe model, dividing the layer leakage amount and feeding back the layer leakage amount to the seepage model; model parameters are inversed and calibrated in combination with actually measured data; and finally, predicting a settlement spatio-temporal evolution process based on the calibration model, and generating a risk prevention and control scheme in combination with building settlement indexes. The method has the advantages that comprehensive information is obtained through multi-source data fusion, the deformation mechanism model considering the water-soil bidirectional coupling and the well storage effect is established, and the defects in multi-factor coupling, boundary condition processing and parameter inversion in the prior art are overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of prediction, and particularly relates to a settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback. Background Art

[0002] Urban land subsidence is a major geological safety problem faced in the global urbanization process. Especially in coastal cities and areas with intensive underground resource development, due to inducing factors such as over-exploitation of groundwater, geothermal resource exploration, and precipitation during intensive engineering construction, ground subsidence disasters occur frequently. Local severe ground subsidence disasters will lead to serious consequences such as building inclination, underground pipeline rupture, and seawater intrusion.

[0003] With the progress of information technology and multi-source sensing and monitoring technology, settlement prediction models have gradually developed from single-factor analysis to multi-field coupling. However, the following technical problems still exist in the prior art:

[0004] Incomplete multi-factor coupling analysis: Existing models mostly focus on the unidirectional coupling of groundwater seepage or soil deformation, ignoring the two-way interaction between soil and water, such as the change of seepage channels caused by soil particle loss and the influence of seepage force on soil stability.

[0005] Absence of well storage effect and dynamic boundary conditions: In engineering scenarios such as geothermal wells and oil and gas wells, the wellbore is a key channel for soil-water leakage. The dynamic balance between the internal water level change in the wellbore and the formation seepage (i.e., the well storage effect) plays a dominant role in the settlement process. The prior art often simplifies the wellbore boundary as a constant head or constant flow condition, without considering the real-time coupling of the dynamic water level change in the well and the formation recharge, resulting in the model boundary conditions not conforming to the actual working conditions, especially in sudden leakage accidents, the prediction error increases significantly.

[0006] Formation heterogeneity and insufficient parameter inversion accuracy: The formation structure of urban underground space is complex, with alternating distributions of sand layers, clay layers, bedrock layers, etc. Parameters such as permeability coefficient and elastic modulus have strong heterogeneity. Existing parameter inversion methods are difficult to handle high-dimensional parameter spaces and do not fully utilize multi-source monitoring data for joint inversion, resulting in limited model prediction accuracy. Summary of the Invention

[0007] In view of this, the present invention aims to propose a settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback to solve at least one problem in the background art.

[0008] To achieve the above object, the technical solution of the present invention is realized as follows:

[0009] A settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback, comprising:

[0010] S1. Collect geological data, hydrological data, deformation monitoring data, and engineering construction data, and generate a standardized spatio-temporal dataset after initialization processing;

[0011] S2. Construct a three-dimensional geological model based on the standardized spatio-temporal dataset generated in S1, divide the strata in the three-dimensional geological model into shallow loose layers and underlying bedrock layers, and set boundary conditions to provide physical framework constraints for the coupled water-soil calculation;

[0012] S3. Construct a two-way water-soil coupling model, including the following interaction processes:

[0013] Establish a seepage model and simulate the formation seepage process using the wellbore-aquifer-formation coupling relationship;

[0014] Establish a three-dimensional deformation model and use the pore water pressure field of the seepage model as input;

[0015] Achieve two-way coupling between the seepage model and the three-dimensional deformation model by constructing a dynamic feedback mechanism;

[0016] S4. Real-time obtain the dynamic water level data in the well, determine the dynamic equilibrium water level based on the seepage model in S3, calculate the leakage or extraction volume according to the hydraulic long pipe model, divide the leakage volume of each layer and use it as a source-sink term to input into the seepage model in S3;

[0017] S5. Take the three-dimensional geological model with boundary conditions established in S2 as the inversion basic framework, combine the dynamic water level data in the well obtained in S4 and the measured deformation monitoring data, use the leveling measurement data and InSAR point cloud data in the measured deformation monitoring data as joint constraint conditions, and generate a calibrated two-way water-soil coupling model;

[0018] S6. Based on the calibrated two-way water-soil coupling model in S5, simulate the spatio-temporal evolution process of settlement under the boundary conditions set in S2, predict the distribution of settlement funnels and deformation trends, divide the risk levels according to the building settlement stability index, and generate an engineering prevention and control plan. [[ID=**]]

[0019] Further, in step S2, construct a two-way water-soil coupling model, specifically including the following interaction processes:

[0020] Establish a seepage model: Based on the wellbore-aquifer-formation coupling relationship, calculate the wellbore-reservoir exchange flow through the productivity index and bottom hole pressure, and characterize the formation seepage process in combination with Darcy's law;

[0021] Construct a three-dimensional deformation model based on Biot consolidation theory (Biot consolidation), and receive the pore water pressure field output by the seepage model as input data parameters;

[0022] Establish a dynamic feedback mechanism: Utilize the porosity changes calculated by the three-dimensional deformation model, update the permeability through the Kozeny-Carman equation, and transmit it back to the seepage model to achieve two-way coupling.

[0023] Furthermore, the initialization process includes unifying the coordinate systems of multi-source data using spatio-temporal registration technology, removing monitoring noise using a filtering algorithm, and filling in missing values through data interpolation methods to ensure data accuracy and integrity.

[0024] Furthermore, the deformation monitoring data includes leveling data, InSAR point cloud data, and automated sensor data.

[0025] Furthermore, the setting of boundary conditions specifically includes:

[0026] Set the lateral boundary as a constant pressure boundary with normal constraint to eliminate boundary effects, the bottom boundary as a fixed displacement boundary, and the top boundary as a free moving boundary to realistically simulate the free settlement state of the ground surface.

[0027] Furthermore, the construction of the seepage model includes:

[0028] Calculate the productivity index through wellbore geometric parameters and reservoir permeability, and determine the wellbore-reservoir exchange flow rate by combining the pressure difference between the bottom hole pressure and the reservoir adjacent pressure;

[0029] Based on Darcy's law, establish a linear relationship between the seepage velocity and the pressure gradient change to characterize the complete seepage process.

[0030] Furthermore, the three-dimensional deformation model is based on Biot consolidation theory, and calculates the displacement field using a three-dimensional equilibrium equation containing pore water pressure, temperature gradient, and soil gravity, where the pore water pressure field is driven by the output of the seepage model.

[0031] Furthermore, the dynamic feedback mechanism is realized through the dynamic correlation between porosity and permeability: Input the porosity change amount output by the deformation model into the permeability calculation function, generate an updated permeability value and feedback it to the seepage model to form a two-way closed-loop coupling of water and soil.

[0032] Furthermore, the hydraulic long pipe model calculates the leakage or extraction volume through the bottom hole acting head and pipeline attribute parameters. When calculating, the velocity head and local head loss are ignored, and only the flow scenario dominated by the head loss along the path is considered.

[0033] Furthermore, the parameter inversion performs the following operations:

[0034] Adopt an inversion algorithm based on gradient optimization to synchronously adjust the elastic modulus, Poisson's ratio, and permeability anisotropy ratio of the shallow loose layer and the underlying bedrock layer;

[0035] Quantify the impact of parameter uncertainty on the prediction results through random sampling statistics method.

[0036] Furthermore, this solution discloses an electronic device, including a processor and a memory communicatively connected to the processor and used to store the executable instructions of the processor, and the processor is used to execute the above-mentioned settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback.

[0037] Furthermore, this solution discloses a server, including at least one processor and a memory communicatively connected to the processor, the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to enable the at least one processor to execute the settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback.

[0038] Furthermore, this solution discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback.

[0039] Compared with the prior art, the settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback of the present invention has the following beneficial effects:

[0040] (1) For the settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback of the present invention, compared with the settlement prediction model based on one-way fluid-solid coupling, through the two-way soil-water coupling mechanism, the present invention can accurately capture the change of the seepage field caused by soil loss, effectively solve the problem that the one-way model underestimates the initial settlement, and greatly improve the prediction accuracy; after considering the well storage effect, it can truly reproduce the change of the dynamic water level in the well and the dynamic balance process, avoid the long-term prediction deviation caused by the simplification of boundary conditions, and make the model more in line with the actual working conditions;

[0041] (2) For the settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback of the present invention, compared with the settlement prediction method based on empirical formulas and single monitoring data, the inversion analysis based on the physical model of the present invention can deeply explain the settlement driving factors, has stronger generalization ability, and is applicable to extrapolation prediction in different engineering scenarios; through multi-source data fusion, combining geological prior knowledge and monitoring data, it can effectively process abnormal data, and significantly improve the parameter inversion accuracy and model robustness;

[0042] (3) The settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback according to the present invention realizes the dynamic assessment and prediction of the risk of local land subsidence caused by regional groundwater exploitation or geothermal well construction projects through the soil-water two-way coupling and well storage dynamic feedback mechanism, and provides technical support for the whole chain of "data acquisition - mechanism modeling - dynamic inversion - deformation prediction - early warning and prevention and control" for urban land subsidence monitoring and early warning and risk assessment, meeting the real-time risk assessment requirements of urban underground resource development. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0044] Figure 1 It is a schematic diagram of the settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0046] The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0047] As Figure 1 shown, the present invention proposes a settlement deformation inversion analysis and prediction method based on the soil-water two-way coupling and well storage dynamic feedback mechanism, and constructs a complete technical chain of "data acquisition - mechanism modeling - dynamic inversion - deformation prediction - early warning and prevention and control". Comprehensive information is obtained through multi-source data fusion, and a deformation mechanism model considering soil-water two-way coupling and well storage effect is established to solve the deficiencies of the existing technology in multi-factor coupling, boundary condition treatment and parameter inversion.

[0048] Specifically, it includes the following steps:

[0049] 1. Multi-source data collection and preprocessing (data layer)

[0050] Collect geological data (including formation structure, physical and mechanical parameters, etc.), hydrological data (aquifer structure, groundwater level monitoring), deformation monitoring data (level measurement data, InSAR point cloud data, automated sensor data, etc.) and engineering construction data.

[0051] Use spatio-temporal registration technology to unify the coordinate systems of multi-source data, use filtering algorithms to remove monitoring noise, and fill in missing values through data interpolation methods to ensure the accuracy and integrity of the data, providing a reliable basis for subsequent analysis.

[0052] 2. Three-dimensional geological modeling and horizon division (model layer)

[0053] Stratum generalization: Generalize based on stratum data according to certain rules, construct a three-dimensional geological model of the area, divide the stratum into different horizons such as shallow loose layer and underlying bedrock layer, and initially define the lithology parameters of each horizon.

[0054] Boundary condition definition: Reasonably set boundary conditions. The lateral boundary is set as a constant pressure boundary with normal constraint to eliminate boundary effects. The bottom boundary is a fixed displacement boundary, and the top boundary is a free movement boundary to truly simulate the free settlement state of the ground surface.

[0055] 3. Construction of soil-water two-way coupling model (mechanism layer)

[0056] 3.1 Construction of seepage model

[0057] Considering the factors inducing land subsidence in the area (such as groundwater pumping or geothermal well construction, generalized as shaft-like projects), establish a shaft-aquifer-stratum coupled seepage model, and fully consider the momentum conservation of soil-water mixed flow (flow in the shaft) and Darcy's law (stratum seepage) to describe the complete seepage process from the shaft to the aquifer and then to the stratum.

[0058] (1) Introduce the productivity model and coupling method in the petroleum engineering field to construct the flow coupling model of shaft-aquifer (well-reservoir):

[0059] The expression for calculating the exchange flow rate between the shaft and the reservoir in the productivity model through the bottom hole pressure and the productivity index PI is:

[0060] (3-1)

[0061] Where: is the mass flow rate; is the fluid density; is the relative permeability of the phase; is the fluid pressure of the grid at the bottom of the shaft, is the pressure of the adjacent grid in the reservoir;

[0062] The productivity index is calculated as follows:

[0063] (3-2)

[0064] Where: is the permeability; is the reservoir thickness; is the grid radius; is the well radius; is the skin factor.

[0065] The indirect coupling process involves two aspects: ① the mutual transfer of key parameters between the wellbore and the reservoir; ② the fluid exchange at the connection between the bottom of the wellbore and the reservoir grid. During reservoir simulation, the pressure and enthalpy values at the well grid are first calculated, and the water flow rate under a given wellhead pressure is calculated at each time step.

[0066] In Equation (3-1), the calculation formula for the bottom-hole pressure is as follows:

[0067] (3-3)

[0068] The bottom-hole pressure ( ) is controlled by the flow rate ( ), enthalpy value ( ), wellhead pressure ( ), the depth of the water intake section ( ), friction coefficient, and well diameter ( ), etc. This method can realize the correlation of fluids in different wellbores and reservoirs.

[0069] In the above equations, the mass conservation and energy conservation relationships of the wellbore and the reservoir are established in a unified control equation system. The seepage process in the wellbore and the reservoir is characterized by the exchange flow rate, and the solution of the wellbore-aquifer seepage coupling is realized within the same framework.

[0070] (2) Formation-aquifer coupling model. According to Darcy's law expression (3-4):

[0071] (3-4)

[0072] It can be obtained that the seepage velocity v is proportional to the hydraulic gradient:

[0073] (3-5)

[0074] Writing the above formula as , and at the same time introducing the permeability to characterize the inherent seepage performance of the formation rock skeleton, there is the following relationship between the permeability coefficient and the permeability :

[0075] (3-6)

[0076] Substituting Equation 3-6 into Equation 3-5, the relationship between the seepage velocity and the hydraulic gradient can be converted into the relationship between the seepage velocity and the pressure gradient:

[0077] (3-7)

[0078] In Equations (3-4) - (3-7): is the total seepage volume through the cross-section of the sand column (m3 / d), is the permeability coefficient (m / d), and are the water head values of the upstream and downstream water sections (m), is the distance between water-passing sections (m), is the cross-sectional area of water flow (m 2 ), Equivalence is the hydraulic gradient (dimensionless), is the average seepage velocity of the water-passing section (m / d), is the permeability (m 2 ), is the fluid density (kg / m 3 ), is the fluid dynamic viscosity (Pa·s), is the acceleration due to gravity (m / s 2 ), , is the pressure gradient (Pa / m). Replacing the hydraulic gradient with the pressure gradient facilitates the establishment of the aquifer-stratum fluid-solid coupling model.

[0079] The above equations express the wellbore-aquifer-stratum fluid-solid coupling model. and permeability As a key parameter between wellbore flow and aquifers and formations, it needs to be further coupled with the formation settlement and deformation model.

[0080] 3.2 Deformation model construction

[0081] Based on the elastic-plastic theory, a three-dimensional deformation equation coupling water-soil-temperature multiphase variables is constructed. Taking into account the effective stress principle and the drag force of water-soil interaction, an accurate characterization of soil deformation is achieved.

[0082] The BIOT consolidation equation is based on the assumption that the rock mass is saturated, the linear elastic deformation of the soil skeleton is minimal, and the seepage conforms to Darcy's law. Assuming that only gravity is considered for volume forces, a microelement (soil skeleton + pore water) is taken, with the z-coordinate set to be positive downward, stresses defined as pressure, and changes in momentum ignored (i.e., the rock mass is in static equilibrium). Considering the Terzaghi effective stress principle, and assuming that the soil skeleton is a linear elastic body that obeys generalized Hooke's law, the three-dimensional equilibrium differential equations representing displacement, pore water pressure, and temperature are obtained as follows:

[0083] (3-8)

[0084] In formula (3-8): Assume , Under small deformation conditions, the soil The displacement component in the direction, is the pore water pressure of the soil mass, is the temperature of the soil mass, is the coefficient of thermal expansion, is the coefficient of thermal conductivity, is the saturated unit weight of the soil mass, is the Poisson's ratio, and G is the shear modulus of the soil mass.

[0085] The above equation is the three-dimensional deformation calculation model of the soil mass, which couples the pore water pressure and the temperature of the rock and soil mass. The pressure, temperature, and mechanical variables at any point in the formation space can be solved in combination with the definite solution conditions (initial and boundary conditions).

[0086] Among them, the pore water pressure is a function of the porosity, and the two are interrelated through the soil mass deformation process.

[0087] 3.3 Construction of the bidirectional coupling mechanism

[0088] In order to establish a bidirectional coupling model for fluid exchange and solid deformation in the wellbore-aquifer-formation, it is necessary to establish a porosity-permeability correlation model to achieve the whole-process coupling of wellbore water level change → wellbore-aquifer exchange flow (permeability) → formation pore water pressure (porosity) → formation settlement deformation.

[0089] Based on the Kozeny-Carman equation, a porosity-permeability correlation model is established to achieve a bidirectional feedback mechanism between seepage and deformation. When the wellbore leaks or groundwater is exploited, resulting in changes in the groundwater level, it triggers the rearrangement of soil particles, changes in porosity, manifested as soil mass deformation, and further affects the permeability; changes in seepage force lead to redistribution of effective stress, promoting the migration of soil particles, forming a dynamic coupling relationship:

[0090] (3-9)

[0091] In Equation (3-9), is the permeability of the porous medium, which reflects the ability of the porous medium to allow fluid to pass through. The greater the permeability, the easier it is for the fluid to flow in the medium. d is the average particle size of the particles constituting the porous medium, which has a significant impact on the permeability. Generally speaking, the larger the particle size, the greater the permeability; : is the porosity of the porous medium, with a value range between 0 and 1, indicating the proportion of the pore volume in the total volume. The greater the porosity, the larger the space available for fluid flow in the medium.

[0092] 4. Construction of the well storage dynamic feedback mechanism (perception layer)

[0093] When there are ground settlement inducing factors in the area (such as geothermal wells, groundwater well pumping, or leakage during sudden geothermal well construction), the water level in the wellbore changes, resulting in the loss of formation groundwater in the field area, and then triggering ground settlement.

[0094] The development and dynamic duration of the above ground settlement deformation amount depend on two key factors: the dynamic water level in the well and the groundwater leakage amount (or extraction amount). Therefore, it is necessary to construct a well storage dynamic feedback mechanism. Through the dynamic perception of the dynamic water level in the well and the groundwater leakage amount (or extraction amount), and real-time feedback to the soil-water two-way coupling model, the dynamic inversion and prediction of the ground settlement deformation amount and deformation development trend based on the changes in water level and water volume are realized.

[0095] Perception of the dynamic water level in the well: Based on the well water level monitoring data and using the well-storage coupling model to simulate the dynamic balance process of the well water level, the equilibrium dynamic water level is determined by solving the numerical model of wellbore flow.

[0096] Calculation of the leakage amount (or extraction amount): Considering comprehensively the leakage flow rate caused by ground settlement inducing factors in the area, the dynamic change process and duration of the well water level, the wellbore is generalized as a hydraulic long pipe to calculate the leakage amount (or extraction amount).

[0097] The hydraulic long pipe model is an ideal model used in fluid mechanics to simplify the calculation of pipe flow. It is applicable to scenarios where the head loss along the way dominates and the velocity head and local head loss can be ignored. This can greatly simplify the calculation and does not affect the calculation accuracy:

[0098] (4-1)

[0099] In formula (4-1), Q is the leakage amount (or extraction amount), H is the acting head at the bottom of the well, l is the pipeline length, is the specific resistance.

[0100] After calculating the total leakage amount (or extraction amount), further divide the leakage amount of each layer to provide key input parameters for settlement inversion and achieve accurate simulation of the well storage effect.

[0101] 5. Parameter Inversion and Model Calibration (Inversion Layer)

[0102] Considering the uncertainty of the mechanical parameters of the shallow loose layer and the underlying bedrock with different lithologies, it is necessary to first determine the mechanical parameters of different lithologies through model optimization and historical data fitting under the condition that other model parameters are determined. To more accurately identify and correct the rock mechanical parameters in the shallow loose layer and the underlying bedrock, the PEST program is used for inverse numerical simulation, and relevant parameters are identified through an automated inversion method. The Gauss-Marquardt-Levenberg algorithm of the PEST program combines the advantages of the Newton algorithm and the gradient descent method, while avoiding the problem of excessive calculation when the global algorithm deals with complex problems.

[0103] Multi-objective optimization: The PEST program is used for multi-objective parameter inversion. With leveling measurement data, InSAR data, etc. as constraint conditions, geotechnical mechanical parameters (elastic modulus, Poisson's ratio, etc.) and seepage parameters (permeability anisotropy ratio, etc.) are optimized and adjusted. Algorithms such as Gauss-Marquardt-Levenberg are used to improve the efficiency and accuracy of parameter inversion, so that the simulated settlement curve is highly consistent with the measured data.

[0104] Uncertainty analysis: Monte Carlo simulation is introduced to evaluate the impact of parameter uncertainty on settlement prediction, determine key sensitive parameters, and provide a quantitative basis for the reliability of the model.

[0105] 6. Settlement prediction and early warning (application layer)

[0106] Spatio-temporal evolution simulation: Based on multi-source monitoring data such as water level monitoring, the calibrated model is used to predict the distribution of settlement funnels in the regional site at different times under inducing factors, and analyze the spatio-temporal evolution characteristics of settlement.

[0107] Settlement dynamic early warning: Based on the settlement model combined with the building settlement stability index, the risk level is evaluated in real time, and a targeted engineering treatment plan is output to achieve seamless connection from theoretical analysis to engineering application.

[0108] In summary, the innovation points of this solution are summarized as follows:

[0109] Two-way coupled modeling: Quantify the interaction between water and soil as a two-way feedback of seepage-deformation, and solve the distortion problem of traditional one-way models through the dynamic correlation of porosity-permeability.

[0110] Well storage dynamic boundary: Construct a well storage dynamic feedback mechanism to perceive and simulate the real-time balance between the water level change in the well and the formation recharge, and improve the prediction accuracy of settlement deformation in the scenarios of slow development or sudden leakage during the exploitation of groundwater and geothermal resources.

[0111] Multi-source data inversion: Integrate geological, hydrological, and monitoring data for parameter inversion, and use the PEST program to realize the optimization of high-dimensional parameter space, significantly improving the model calibration efficiency.

[0112] The advantages of this solution compared with the unidirectional coupling model in the prior art are as follows: Compared with the settlement prediction model based on unidirectional fluid-structure coupling, through the two-way water-soil coupling mechanism, this invention can accurately capture the change of the seepage field caused by soil loss, effectively solve the problem that the unidirectional model underestimates the initial settlement, and greatly improve the prediction accuracy; after considering the wellbore storage effect, it can truly reproduce the change of the dynamic water level in the well and the dynamic balance process, avoid the long-term prediction deviation caused by the simplification of boundary conditions, and make the model more conform to the actual working conditions.

[0113] The advantages of this solution compared with the empirical formula and data-driven model in the prior art are as follows:

[0114] Compared with the settlement prediction method based on empirical formula and single monitoring data, the inversion analysis based on physical model of this invention can deeply explain the settlement driving factors, has stronger generalization ability, and is applicable to extrapolation prediction in different engineering scenarios; through multi-source data fusion, combining geological prior knowledge and monitoring data, it can effectively process abnormal data and significantly improve the parameter inversion accuracy and model robustness.

[0115] Through the two-way water-soil coupling and wellbore storage dynamic feedback mechanism, this solution realizes the dynamic assessment and prediction of the risk of local land subsidence caused by regional groundwater exploitation or geothermal well construction projects, provides technical support for the whole chain of "data acquisition - mechanism modeling - dynamic inversion - deformation prediction - early warning and prevention and control" for urban land subsidence monitoring and early warning and risk assessment, and meets the real-time risk assessment requirements of urban underground resource development.

[0116] Those of ordinary skill in the art can realize that the units and method steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of this invention.

[0117] In several embodiments provided by the present application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The above units may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

[0119] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback, characterized in that Including: S1. Collect geological data, hydrological data, deformation monitoring data, and engineering construction data, and generate a standardized spatio-temporal dataset after initialization processing; S2. Construct a three-dimensional geological model based on the standardized spatio-temporal dataset generated in S1, divide the strata in the three-dimensional geological model into shallow loose layers and underlying bedrock layers, and set boundary conditions to provide a physical framework constraint for the coupled water and soil calculation; S3. Construct a two-way coupled water and soil model, including the following interaction processes: Establish a seepage model and simulate the stratum seepage process using the wellbore-aquifer-stratum coupling relationship; Establish a three-dimensional deformation model and use the pore water pressure field of the seepage model as input; Realize the two-way coupling between the seepage model and the three-dimensional deformation model through constructing a dynamic feedback mechanism; S4. Obtain the dynamic water level data in the well in real time, determine the dynamic equilibrium water level based on the seepage model in S3, calculate the leakage or extraction volume according to the hydraulic long pipe model, divide the leakage volume of each layer, and input it as a source-sink term into the seepage model in S3; S5. Take the three-dimensional geological model with boundary conditions established in S2 as the inversion basic framework, combine the dynamic water level data in the well obtained in S4 and the measured deformation monitoring data, and generate a calibrated two-way coupled water and soil model; S6. Based on the calibrated two-way coupled water and soil model in S5, simulate the spatio-temporal evolution process of settlement under the boundary conditions set in S2, predict the distribution of the settlement funnel and the deformation trend, divide the risk levels according to the building settlement stability index, and generate an engineering prevention and control plan.

2. The settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback according to claim 1, wherein The initialization processing includes using spatio-temporal registration technology to unify the coordinate systems of multi-source data, using filtering algorithms to remove monitoring noise, and filling missing values through data interpolation methods to ensure data accuracy and integrity.

3. The settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback according to claim 1, characterized in that The deformation monitoring data includes leveling measurement data, InSAR point cloud data, and automated sensor data.

4. The settlement inversion prediction method based on the coupling of soil and water and the dynamic feedback of well storage according to claim 1, wherein The setting of the boundary conditions specifically includes: Set the lateral boundary as a constant pressure boundary with normal constraint to eliminate the boundary effect, the bottom boundary as a fixed displacement boundary, and the top boundary as a free moving boundary to truly simulate the free settlement state of the ground surface.

5. The settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback according to claim 1, characterized in that, The construction of the seepage model includes: Calculate the productivity index through the wellbore geometric parameters and reservoir permeability, and determine the wellbore-reservoir exchange flow rate in combination with the pressure difference between the bottom hole pressure and the reservoir adjacent pressure; Based on Darcy's law, establish a linear relationship between the seepage velocity and the pressure gradient to characterize the complete seepage process.

6. The settlement inversion prediction method based on the coupling of soil and water and the dynamic feedback of well storage according to claim 1, wherein The three-dimensional deformation model is based on Biot consolidation theory and calculates the displacement field using a three-dimensional equilibrium equation containing pore water pressure, temperature gradient, and soil gravity, where the pore water pressure field is driven by the output of the seepage model.

7. The settlement inversion prediction method based on soil-water coupling and well storage dynamic feedback according to claim 1, wherein The dynamic feedback mechanism is realized through the dynamic correlation between porosity and permeability: input the change amount of porosity output by the deformation model into the permeability calculation function, generate an updated permeability value and feedback it to the seepage model to form a two-way closed-loop coupling of water and soil.

8. The settlement inversion prediction method based on the coupling of soil and water and the dynamic feedback of well storage according to claim 1, characterized in that The hydraulic long pipe model calculates the leakage or extraction volume through the bottom hole acting head and pipeline attribute parameters, and ignores the velocity head and local head loss during the calculation, only considering the flow scenario dominated by the head loss along the way.

9. The settlement inversion prediction method based on the coupling of soil and water and the dynamic feedback of well storage according to claim 1, characterized in that, Including parameter inversion steps, specifically perform the following operations: An inversion algorithm based on gradient optimization is used to synchronously adjust the elastic modulus, Poisson's ratio, and permeability anisotropy ratio of the shallow loose layer and the underlying bedrock layer. The influence of parameter uncertainty on the prediction results is quantified by a random sampling statistical method.

Images