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

Abstract

The present invention provides a settlement inversion prediction method based on water-soil coupling and well-storage dynamic feedback. By collecting geological, hydrological, deformation monitoring and engineering construction data, a three-dimensional geological model containing a shallow loose layer and an underlying bedrock layer is constructed and physical boundary constraints are set; a water-soil bidirectional coupling model is established, the seepage model and the deformation model are integrated, and bidirectional coupling is achieved through a dynamic feedback mechanism; the leakage volume / extraction volume is calculated based on the dynamic water level in the well and the hydraulic long pipe model, and the layer leakage volume is divided and fed back to the seepage model; the model parameters are inverted and calibrated based on the measured data; finally, the temporal and spatial evolution process of settlement is predicted based on the calibration model, and a risk prevention and control plan is generated based on the building settlement index. The present invention has the following beneficial effects: comprehensive information is obtained through multi-source data fusion, and a deformation mechanism model that considers water-soil bidirectional coupling and well-storage effects is established, which solves the shortcomings of the existing technology in multi-factor coupling, boundary condition processing and parameter inversion.

Description

Technical Field

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

[0002] Urban land subsidence is a major geological safety issue facing global urbanization. This is particularly true in coastal cities and areas with intensive underground resource development. Due to factors such as excessive groundwater exploitation, geothermal resource exploration, and precipitation from intensive engineering construction, land subsidence disasters are frequent. Severe localized land subsidence can lead to serious consequences such as building tilting, underground pipeline rupture, and seawater backflow.

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

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

[0005] Lack of well-reservoir effects and dynamic boundary conditions: In geothermal wells, oil and gas wells, and other engineering scenarios, the wellbore serves as a critical conduit for water and soil loss. The dynamic balance between internal water level changes and formation seepage (i.e., the well-reservoir effect) plays a dominant role in the settlement process. Existing technologies often simplify the wellbore boundary to constant head or constant flow conditions, failing to consider the real-time coupling of dynamic water level changes within the well and formation recharge. This results in model boundary conditions that are inconsistent with actual operating conditions, significantly increasing prediction errors, especially in the event of a sudden leakage incident.

[0006] Stratum heterogeneity and insufficient parameter inversion accuracy: Urban underground spaces have complex stratigraphic structures, with intersecting sand, clay, and bedrock layers. Parameters such as permeability and elastic modulus exhibit strong heterogeneity. Existing parameter inversion methods struggle to handle high-dimensional parameter spaces and fail to fully utilize multi-source monitoring data for joint inversion, limiting 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 water-soil coupling and well-reservoir dynamic feedback, so as to solve at least one problem in the background technology.

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

[0009] The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback includes:

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

[0011] S2. Construct a 3D geological model based on the standardized spatiotemporal dataset generated in S1. Divide the strata in the 3D geological model into a shallow loose layer and an underlying bedrock layer. Set boundary conditions to provide a physical framework constraint for the soil-water coupling calculation.

[0012] S3. Construct a water-soil bidirectional coupling model, including the following interactive processes:

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

[0014] A three-dimensional deformation model is established, and the pore water pressure field of the seepage model is used as input;

[0015] A dynamic feedback mechanism is constructed to achieve bidirectional coupling between the seepage model and the three-dimensional deformation model;

[0016] S4, real-time acquisition of dynamic water level data in the well, determination of dynamic equilibrium water level based on the seepage model of S3, calculation of leakage or extraction volume based on the hydraulic long-tube model, division of leakage volume in each layer and input into the seepage model of S3 as source and sink terms;

[0017] S5. Using the three-dimensional geological model with boundary conditions established in S2 as the inversion basic framework, combined with the dynamic water level data in the well and the measured deformation monitoring data obtained in S4, and using the leveling data and InSAR point cloud data in the measured deformation monitoring data as joint constraints, a calibrated water-soil bidirectional coupling model is generated;

[0018] S6. Based on the water-soil bidirectional coupling model calibrated in S5, simulate the spatiotemporal evolution 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.

[0019] Furthermore, in step S2, a water-soil bidirectional coupling model is constructed, which specifically includes the following interactive processes:

[0020] Establish a seepage model: Based on the wellbore-aquifer-formation coupling relationship, the wellbore-reservoir exchange flow is calculated using the production index and bottomhole pressure, and Darcy's law is used to characterize the formation seepage process;

[0021] A three-dimensional deformation model is constructed based on Biot consolidation theory, and the pore water pressure field output by the seepage model is received as the input data parameter;

[0022] A dynamic feedback mechanism is established: the porosity changes calculated by the 3D deformation model are used to update the permeability through the Korzeny-Kalman equation and then fed back to the seepage model to achieve bidirectional coupling.

[0023] Furthermore, the initialization process includes using spatiotemporal registration technology to unify the coordinate system of multi-source data, using a filtering algorithm to remove monitoring noise, and filling missing values ​​through a data interpolation method to ensure data accuracy and completeness.

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

[0025] Furthermore, the boundary condition setting specifically includes:

[0026] The lateral boundaries are set as normal-constrained constant-pressure boundaries to eliminate boundary effects, the bottom boundary is set as a fixed-displacement boundary, and the top boundary is set as a free-moving boundary to realistically simulate the free settlement state of the surface.

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

[0028] The production index is calculated based on the wellbore geometry and reservoir permeability, and the wellbore-reservoir exchange flow is determined by combining the pressure difference between the bottom hole pressure and the reservoir adjacent pressure;

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

[0030] Furthermore, the three-dimensional deformation model is based on Biot's consolidation theory and uses a three-dimensional equilibrium equation containing pore water pressure, temperature gradient and soil gravity to calculate the displacement field, wherein 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: the porosity change output by the deformation model is input into the permeability calculation function, and the updated permeability value is generated and fed back to the seepage model, forming a two-way closed-loop coupling between water and soil.

[0032] Furthermore, the hydraulic long pipe model calculates the leakage or extraction volume through the bottom hole acting head and pipeline property parameters, ignoring the velocity head and local head loss during the calculation, and only considering the flow scenario dominated by the head loss along the way.

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

[0034] The 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;

[0035] The impact of parameter uncertainty on prediction results is quantified through random sampling statistical methods.

[0036] Furthermore, the present solution discloses an electronic device, comprising a processor and a memory connected to the processor for storing instructions executable by the processor, wherein the processor is used to execute the above-mentioned settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback.

[0037] Furthermore, the present solution discloses a server comprising at least one processor and a memory communicatively connected to the processor, wherein 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 a settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback.

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

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

[0040] (1) Compared with the settlement prediction model based on unidirectional fluid-solid coupling, the settlement inversion prediction method based on water-soil coupling and well-storage dynamic feedback described in the present invention can accurately capture the changes in the seepage field caused by soil loss through the bidirectional water-soil coupling mechanism, effectively solve the problem of underestimation of initial settlement in the unidirectional model, and greatly improve the prediction accuracy. After considering the well-storage effect, it can truly reproduce the dynamic water level changes and dynamic equilibrium process in the well, avoid long-term prediction deviations caused by simplified boundary conditions, and make the model more in line with actual working conditions.

[0041] (2) Compared with the settlement prediction method based on empirical formulas and single monitoring data, the settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback described in the present invention can deeply explain the settlement driving factors through inversion analysis based on physical models, has stronger generalization ability, and is suitable for extrapolation prediction in different engineering scenarios. By integrating multi-source data, combining geological prior knowledge and monitoring data, it can effectively handle abnormal data and significantly improve parameter inversion accuracy and model robustness.

[0042] (3) The settlement inversion prediction method based on water-soil coupling and well-storage dynamic feedback described in the present invention can realize the dynamic assessment and prediction of local ground subsidence risks caused by regional groundwater extraction or geothermal well construction projects through the water-soil bidirectional coupling and well-storage dynamic feedback mechanism, and provide "data collection-mechanism modeling-dynamic inversion-deformation prediction-early warning and control" full-chain technical support for urban ground subsidence monitoring, early warning and risk assessment, thus meeting the real-time risk assessment needs of urban underground resource development. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 Schematic diagram of the settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

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

[0047] like Figure 1 As shown, this paper proposes a settlement deformation inversion analysis and prediction method based on the dynamic feedback mechanism of water-soil bidirectional coupling and well-reservoir storage. This method constructs a complete technical chain of "data acquisition - mechanism modeling - dynamic inversion - deformation prediction - early warning and control". By fusing multi-source data to obtain comprehensive information, a deformation mechanism model is established that considers water-soil bidirectional coupling and well-reservoir effects, addressing the shortcomings of existing technologies in multi-factor coupling, boundary condition processing, and parameter inversion.

[0048] The specific steps include:

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

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

[0051] Spatiotemporal registration technology is used to unify the coordinate system of multi-source data, filtering algorithms are used to remove monitoring noise, and data interpolation methods are used to fill missing values ​​to ensure the accuracy and completeness of the data, providing a reliable basis for subsequent analysis.

[0052] 2. 3D geological modeling and stratigraphic division (model layer)

[0053] Strata generalization: Based on the stratigraphic data, generalization is performed according to certain rules to construct a three-dimensional geological model of the region. The stratigraphic layers are divided into different layers, such as the shallow loose layer and the underlying bedrock layer, and the lithologic parameters of each layer are preliminarily defined.

[0054] Definition of boundary conditions: The boundary conditions are set reasonably. The lateral boundaries are set as constant pressure boundaries with normal constraints 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 surface.

[0055] 3. Construction of water-soil bidirectional coupling model (mechanistic layer)

[0056] 3.1 Seepage model construction

[0057] Taking into account the factors inducing ground subsidence in the region (such as groundwater extraction or geothermal well construction, which are generally referred to as wellbore projects), a wellbore-aquifer-formation coupled seepage model is established. The momentum conservation of water-soil mixed flow (flow in the wellbore) and Darcy's law (formation seepage) are fully considered to describe the complete seepage process from the wellbore to the aquifer and then to the formation.

[0058] (1) Introducing the productivity model and coupling method from the field of petroleum engineering to build a wellbore-aquifer (well-reservoir) flow coupling model:

[0059] In the productivity model, the wellbore-reservoir exchange flow is calculated using the bottom hole pressure and the production index PI as follows:

[0060] (3-1)

[0061] Where: is the mass flow rate; is the fluid density; yes relative permeabilities of phases; is the fluid viscosity, is the fluid pressure at the bottom grid of the wellbore, is the pressure of the adjacent grid of the reservoir;

[0062] The production 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; and 2) the fluid exchange at the junction of the wellbore bottom and the reservoir grid. Reservoir simulation begins by calculating the pressure and enthalpy at the well grid and, at each time step, the water flow rate for a given wellhead pressure.

[0066] The bottom hole pressure calculation formula in formula (3-1) is as follows:

[0067] (3-3)

[0068] Bottom hole pressure ( ) changes are controlled by the flow rate ( ), enthalpy ( ), wellhead pressure ( ), water inlet depth ( ), 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 reservoir are established in a unified system of governing equations. By exchanging flow rates, the seepage process in the wellbore and reservoir is characterized, and the wellbore-aquifer seepage coupling is solved within the same framework.

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

[0071] (3-4)

[0072] It is concluded that the infiltration velocity v is proportional to the hydraulic gradient:

[0073] (3-5)

[0074] Write the above formula , while introducing the permeability Characterizes the inherent permeability of the rock skeleton of the formation, the permeability coefficient and permeability The following relationship exists:

[0075] (3-6)

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

[0077] (3-7)

[0078] In formula (3-4) to formula (3-7): is the total seepage volume through the sand column section (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 soil pore water pressure, is the soil temperature, is the coefficient of thermal expansion, is the thermal conductivity, is the saturated density of soil, is Poisson's ratio, and G is the soil shear modulus.

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

[0086] Among them, pore water pressure is a function of porosity, and the two are related to each other through the soil deformation process.

[0087] 3.3 Construction of bidirectional coupling mechanism

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

[0089] A porosity-permeability correlation model is established based on the Kozeny-Carman equation to realize a two-way feedback mechanism between seepage and deformation. When wellbore leakage or groundwater extraction causes changes in groundwater levels, soil particles are rearranged, porosity changes, and soil deformation occurs, which in turn affects permeability. Changes in seepage force lead to a redistribution of effective stress, promoting the migration of soil particles and forming a dynamic coupling relationship:

[0090] (3-9)

[0091] In formula (3-9), is the permeability of the porous medium, reflecting its ability to allow fluid to pass through. The greater the permeability, the easier it is for fluid to flow through the medium. d is the average particle size of the particles that make up the porous medium, which has a significant impact on permeability. Generally speaking, the larger the particle size, the greater the permeability. : The porosity of a porous medium, ranging from 0 to 1, represents the ratio of pore volume to total volume. A higher porosity means more space available for fluid flow in the medium.

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

[0093] When there are ground subsidence-inducing factors in the area (such as geothermal wells, groundwater well extraction, or sudden leakage during geothermal well construction), the water level in the wellbore changes, causing groundwater loss in the site area, which in turn triggers ground subsidence.

[0094] The development and dynamic duration of these land subsidence deformations depend on two key factors: the dynamic water level within the well and the amount of groundwater loss (or extraction). To address this, a dynamic feedback mechanism for well storage is needed. This dynamic sensing of the dynamic water level within the well and the amount of groundwater loss (or extraction) is fed back to the water-soil bidirectional coupling model in real time. This allows for dynamic inversion and prediction of land subsidence deformations and deformation trends based on changes in water level and volume.

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

[0096] Solution for leakage (or extraction): Comprehensively consider the leakage velocity caused by ground subsidence in the area, the dynamic change process and duration of the water level in the well, and generalize the wellbore into a long hydraulic pipe to calculate the leakage (or extraction).

[0097] The hydraulic long pipe model is an ideal model for simplifying pipe flow calculations in fluid mechanics. It is suitable for scenarios where head losses along the pipe dominate and velocity head and local head losses are negligible. This greatly simplifies the calculation without affecting the accuracy.

[0098] (4-1)

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

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

[0101] 5. Parameter inversion and model calibration (inversion layer)

[0102] Given the uncertainty of the mechanical parameters of the different lithologies in the shallow unconsolidated layer and the underlying bedrock, it was necessary to first determine the mechanical parameters of these different lithologies through model optimization and historical data fitting, provided that other model parameters were determined. To more accurately identify and calibrate the rock mechanical parameters in the shallow unconsolidated layer and the underlying bedrock, the PEST program was used for reverse numerical simulation, identifying the relevant parameters through automated inversion methods. The PEST program's Gauss-Marquardt-Levenberg algorithm combines the advantages of the Newton algorithm and the gradient descent method, while avoiding the computational overhead associated with global algorithms for complex problems.

[0103] Multi-objective optimization: The PEST program was used for multi-objective parameter inversion. Using leveling data and InSAR data as constraints, geotechnical parameters (elastic modulus, Poisson's ratio, etc.) and seepage parameters (permeability anisotropy ratio, etc.) were optimized. Algorithms such as Gauss-Marquardt-Levenberg were used to improve the efficiency and accuracy of parameter inversion, ensuring a high degree of agreement between simulated settlement curves and 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 model reliability.

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

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

[0107] Dynamic settlement warning: Based on the settlement model and building settlement stability indicators, the risk level is assessed in real time, and targeted engineering disposal plans are output to achieve seamless connection from theoretical analysis to engineering application.

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

[0109] Bidirectional coupling modeling: Quantify the water-soil interaction as a bidirectional seepage-deformation feedback, and solve the distortion problem of traditional unidirectional models through the dynamic correlation of porosity and permeability.

[0110] Well-reservoir dynamic boundary: Build a well-reservoir dynamic feedback mechanism to sense and simulate the real-time balance between well water level changes and formation recharge, and improve the accuracy of settlement and deformation prediction in slow development or sudden leakage scenarios during groundwater and geothermal resource extraction.

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

[0112] The advantages of this solution over the one-way coupling model in the prior art are: compared with the settlement prediction model based on one-way fluid-solid coupling, the present invention can accurately capture the changes in the seepage field caused by soil loss through the two-way coupling mechanism of water and soil, effectively solve the problem of underestimation of initial settlement by the one-way model, and greatly improve the prediction accuracy; after considering the well storage effect, it can truly reproduce the dynamic water level changes and dynamic equilibrium process in the well, avoid long-term prediction deviations caused by simplified boundary conditions, and make the model more in line with actual working conditions.

[0113] Compared with the empirical formula and data-driven model in the existing technology, the advantages of this solution are:

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

[0115] This solution uses a two-way coupling of water and soil and a dynamic feedback mechanism between wells and reservoirs to dynamically assess and predict the risk of local ground subsidence caused by regional groundwater extraction or geothermal well construction projects. It provides full-chain technical support for urban ground subsidence monitoring, early warning, and risk assessment, encompassing "data acquisition – mechanism modeling – dynamic inversion – deformation prediction – early warning and prevention,” meeting the real-time risk assessment needs of urban underground resource development.

[0116] Those skilled in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the division of the units described above is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The above-mentioned units may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment 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, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

[0119] 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 in the scope of protection of the present invention.

Claims

1. The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback is characterized by: include: S1. Collect geological data, hydrological data, deformation monitoring data and engineering construction data, and generate a standardized spatiotemporal dataset after initialization processing; S2. Construct a 3D geological model based on the standardized spatiotemporal dataset generated in S1. Divide the strata in the 3D geological model into a shallow loose layer and an underlying bedrock layer. Set boundary conditions to provide a physical framework constraint for the soil-water coupling calculation. S3. Construct a water-soil bidirectional coupling model, including the following interactive processes: Establish a seepage model and use the wellbore-aquifer-formation coupling relationship to simulate the formation seepage process; A three-dimensional deformation model is established, and the pore water pressure field of the seepage model is used as input; A dynamic feedback mechanism is constructed to achieve bidirectional coupling between the seepage model and the three-dimensional deformation model; S4, real-time acquisition of dynamic water level data in the well, determination of dynamic equilibrium water level based on the seepage model of S3, calculation of leakage or extraction volume based on the hydraulic long-tube model, division of leakage volume in each layer and input into the seepage model of S3 as source and sink terms; S5: Using the three-dimensional geological model with boundary conditions established in S2 as the basic framework for inversion, combined with the dynamic water level data in the well and the measured deformation monitoring data obtained in S4, a calibrated water-soil two-way coupling model is generated; S6. Based on the water-soil bidirectional coupling model calibrated in S5, simulate the spatiotemporal evolution 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.

2. The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback according to claim 1 is characterized in that: The initialization process includes unifying the multi-source data coordinate system using a spatiotemporal registration technique, removing monitoring noise using a filtering algorithm, and filling in missing values ​​using a data interpolation method to ensure data accuracy and completeness.

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

4. The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback according to claim 1 is characterized in that: The boundary condition setting specifically includes: The lateral boundaries are set as normal-constrained constant-pressure boundaries to eliminate boundary effects, the bottom boundary is set as a fixed-displacement boundary, and the top boundary is set as a free-moving boundary to realistically simulate the free settlement state of the surface.

5. The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback according to claim 1 is characterized in that: The seepage model construction includes: The production index is calculated based on the wellbore geometry and reservoir permeability, and the wellbore-reservoir exchange flow is determined by combining the pressure difference between the bottom hole pressure and the reservoir adjacent pressure; Based on Darcy's law, a linear relationship between the seepage velocity and the pressure gradient is established to characterize the complete seepage process.

6. The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback according to claim 1 is characterized in that: The three-dimensional deformation model is based on Biot's consolidation theory and uses a three-dimensional equilibrium equation containing pore water pressure, temperature gradient and soil gravity to calculate the displacement field, where the pore water pressure field is driven by the output of the seepage model.

7. The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback according to claim 1 is characterized in that: The dynamic feedback mechanism is realized through the dynamic correlation between porosity and permeability: the porosity change output by the deformation model is input into the permeability calculation function, and the updated permeability value is generated and fed back to the seepage model, forming a two-way closed-loop coupling between water and soil.

8. The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback according to claim 1 is characterized in that: The hydraulic long pipe model calculates the leakage or extraction volume by using the bottom hole acting head and pipeline property parameters. The velocity head and local head loss are ignored during the calculation, and only the flow scenario dominated by the head loss along the pipeline is considered.

9. The settlement inversion prediction method based on water-soil coupling and well-reservoir dynamic feedback according to claim 1 is characterized in that: This includes the parameter inversion step, specifically the following operations: The 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 impact of parameter uncertainty on prediction results is quantified through random sampling statistical methods.

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