Method and system for analyzing sensitivity of anti-seepage drainage based on reservoir seepage data

Abstract

The application discloses a seepage prevention and drainage sensitivity analysis method and system based on reservoir area seepage amount data, relates to the technical field of reservoir area anomaly monitoring, and discloses obtaining structure information of a water body contact surface of a reservoir area, and drawing a stereogram; temperature measuring points are arranged by using distributed optical fiber sensing technology, temperature parameters are mapped, and temperature distribution blocks are divided; a flow velocity detection device is set, flow velocity, block characteristics, water level height and rock quality structure characteristics are associated, and a seepage block parameter group is formed; based on historical and approximate variation parameter groups, a finite element model is constructed, rock quality distortion sensitive parameters are analyzed, and real-time sensitive parameters are determined by comparing real-time and simulation parameter groups. Through fine data processing and simulation, the application improves seepage analysis precision and real-time performance, reduces reservoir area seepage risks, and is suitable for reservoir seepage prevention and drainage optimization in complex geological environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reservoir area anomaly monitoring, in particular to a seepage prevention and drainage sensitivity analysis method and system based on reservoir area seepage data. BACKGROUND

[0002] With the rapid development of reservoir projects, seepage monitoring and sensitivity analysis of the reservoir area seepage prevention and drainage system have become a key challenge to ensure the stability of the project and the surrounding ecological environment. The traditional seepage monitoring method mainly relies on discrete point sensors or manual inspection, which cannot realize continuous real-time monitoring, making it difficult to accurately evaluate the sensitivity of water level changes to rock mass distortion under complex geological conditions, thereby causing potential risks such as increased seepage and rock mass collapse, increasing the cost of project maintenance and safety hazards. In order to solve the defects in the prior art that the seepage data analysis is not accurate, the temperature distribution, flow rate and rock structure cannot be effectively associated, and the early warning of the increased seepage and even collapse danger of the rock mass distortion caused by different water level heights is insufficient, the present application provides a seepage prevention and drainage sensitivity analysis method and system based on reservoir area seepage data. SUMMARY

[0003] The purpose of the present application is to provide a method and system for providing seepage sensitivity analysis of the reservoir area seepage prevention and drainage system,

[0004] The present application discloses a seepage prevention and drainage sensitivity analysis method based on reservoir area seepage data, comprising:

[0005] The historical reservoir area seepage record data and the real-time reservoir area seepage record data are analyzed respectively, and the analysis method comprises:

[0006] Step S100, obtaining the structure information of the reservoir water body contact surface, including the underwater geographical structure information and the overwater edge structure information, and drawing a reservoir water body contact surface solid model;

[0007] Step S200, using distributed optical fiber sensing technology to preliminarily arrange distributed optical fibers on the reservoir water body contact surface, mapping the temperature measurement points on the optical fibers on the reservoir water body contact surface solid model to obtain a plurality of initial virtual temperature measurement points, and mapping the temperature parameters of the temperature measurement points on the optical fibers to the corresponding initial virtual temperature measurement points;

[0008] Step S300, performing position correlation analysis on the initial virtual temperature measurement points on the reservoir water body contact surface solid model and the corresponding temperature parameters, determining the correlation relationship between the position adjacent initial virtual temperature measurement points, and based on the correlation relationship between the initial virtual temperature measurement points, dividing the initial virtual temperature measurement points into temperature distribution blocks;

[0009] Step S400, a flow rate detection device is set at the center of the temperature distribution block corresponding to the three-dimensional graph of the reservoir water body contact surface, the representative water flow flow rate parameter of the temperature distribution block is determined, and the water flow flow rate parameter, the block characteristics of the temperature distribution block, the water level height and the rock mass structure characteristic parameter are associated to obtain a seepage block parameter group;

[0010] The seepage block parameter group is subjected to finite element simulation analysis, and the analysis method comprises:

[0011] Step S500, based on the seepage block parameter group generated by history and approximation, a finite element analysis model for rock mass distortion analysis is constructed, and the rock mass distortion sensitive parameter corresponding to each simulated seepage block parameter group is determined, wherein the seepage block parameter group generated by approximation is the reference historical seepage block parameter group, and the approximation parameter variation is performed.

[0012] The rock mass distortion sensitive parameter corresponding to the real-time seepage block parameter group is determined, and the method comprises:

[0013] Step S600, the real-time seepage block parameter group and the simulated seepage block parameter group are compared, and based on the comparison result, the rock mass distortion sensitive parameter corresponding to the real-time seepage block parameter group is determined.

[0014] In some embodiments disclosed in the application, the method for constructing a finite element analysis model for rock mass distortion analysis based on the seepage block parameter group generated by history and approximation comprises:

[0015] Step S501, the seepage block parameter group is intercepted on the block corresponding to the three-dimensional graph of the reservoir water body contact surface to obtain a three-dimensional seepage block surface, and the three-dimensional seepage block surface is gridded.

[0016] Step S502, the water flow flow rate parameter, the block characteristics of the temperature distribution block and the rock mass structure characteristic parameter in the seepage block parameter group are used to configure material properties of the three-dimensional seepage block surface by using the rock mass structure characteristic parameter, to determine the water flow direction performance of the three-dimensional seepage block surface by using the block characteristics of the temperature distribution block, to determine the water flow flow rate parameter of the nodes at different positions of the three-dimensional seepage block surface based on the water flow flow rate parameter of the block center, and to determine the water pressure parameter of the three-dimensional seepage block surface based on the water level height in the seepage block parameter group.

[0017] Step S503, the three-dimensional seepage block surface is subjected to finite element simulation based on the water flow direction performance, the water flow flow rate parameter and the water pressure parameter of the three-dimensional seepage block surface.

[0018] In some embodiments disclosed in the application, the method for determining the rock mass distortion sensitive parameter corresponding to each simulated seepage block parameter group comprises:

[0019] Step S504, the finite element simulation of the stereoscopic seepage block surface is analyzed, and the abnormal probability of the abnormality of the stereoscopic seepage block surface under different water level heights is determined, and the abnormal probability under different water level heights is determined as the rock distortion sensitive parameter.

[0020] In some embodiments of the application, the method for determining the abnormal probability of the abnormality of the stereoscopic seepage block surface under different water level heights comprises:

[0021] Step S505, a plurality of rock mass defect characteristics of the stereoscopic seepage block surface are randomly constructed, each rock mass defect characteristic meets the water flow direction performance and water flow velocity requirement when being constructed, and the strength contrast of the defect degree of different position nodes on the stereoscopic seepage block surface is determined based on the water flow direction performance.

[0022] Step S506, each rock mass defect characteristic is configured on the stereoscopic seepage block surface, and the finite element simulation is performed respectively, the ratio of the number of rock distortion to the number of rock mass defect characteristics is calculated, and is recorded as the abnormal probability.

[0023] In some embodiments of the application, the method for determining the abnormal probability of the abnormality of the stereoscopic seepage block surface under different water level heights comprises:

[0024] Step S507, each parameter in the seepage block parameter group is set with a parameter interval, and a parameter is randomly selected in the parameter interval corresponding to each type of parameter to form a new seepage block parameter group.

[0025] In some embodiments of the application, the method for performing position correlation analysis on the initial virtual temperature measurement point on the stereoscopic graph of the reservoir water body contact surface and the corresponding temperature parameter comprises:

[0026] Step S301, a space coordinate system is established on the stereoscopic graph of the reservoir water body structure surface, the temperature measurement point coordinates of each initial virtual temperature measurement point are determined, and the straight line distance between adjacent temperature measurement point coordinates is calculated respectively.

[0027] Step S302, a plurality of temperature parameter intervals are set, it is judged that the temperature parameter interval to which each initial virtual temperature measurement point belongs, and the initial virtual temperature measurement points in the same temperature parameter interval are connected in a straight line, and an initial virtual temperature measurement surrounding line is formed.

[0028] Step S303, the area enveloped by the initial virtual temperature measurement surrounding line is determined, and is recorded as an initial virtual temperature measurement block, and the initial virtual temperature measurement block is sequentially marked based on the high-low order of the temperature parameter interval, and the high-low direction between the initial virtual temperature measurement blocks that are mutually enveloped is determined.

[0029] Step S304, the initial virtual temperature measurement blocks are associated to form initial virtual temperature measurement blocks nested from large to small in area, and the initial virtual temperature measurement blocks are mutually enveloped and have the same high-low direction.

[0030] In some embodiments of the present application, the block characteristics of the temperature distribution block include:

[0031] Step S401, a block center point of the temperature distribution block is determined, and a plurality of radiation vector lines are emitted in an equi-angle manner with the block center point as a radiation center, and the heads of the radiation vector lines are terminated at the edge lines of the temperature distribution block to obtain a radiation vector line cluster for representing the temperature distribution block.

[0032] Step S402, the length of each radiation vector line in the radiation vector line cluster is determined and marked on the radiation vector line cluster, and the radiation vector line cluster is identified as the block characteristics of the temperature distribution block.

[0033] In some embodiments of the present application, the method for comparing the real-time seepage block parameter group and the simulated seepage block parameter group includes:

[0034] Step S601, the flow velocity parameter difference, the water level height difference and the rock structure characteristic parameter difference between the seepage block parameter groups are calculated, and it is judged whether each difference belongs to a preset difference interval, if all belong, the block difference between the block characteristics of the temperature distribution block is calculated, and if the block difference is less than or equal to a preset value, it is determined that the real-time seepage block parameter group and the simulated seepage block parameter group are consistent.

[0035] The method for calculating the block difference between the block characteristics of the temperature distribution block includes: rotating the radiation vector line clusters corresponding to the block characteristics one by one, screening the radiation vector line clusters with coarse angle consistency, and then calculating the cluster consistency degree between the radiation vector line clusters with coarse angle consistency, and the cluster consistency degree is identified as the block difference between the block characteristics.

[0036] The method for screening the radiation vector line clusters with coarse angle consistency includes: comparing the line length difference between each pair of opposite radiation vector lines, if the length difference is less than or equal to a preset value, the corresponding radiation vector line is identified as a coarse consistent radiation vector line, and if the ratio of the number of coarse consistent radiation vector lines to the number of all radiation vector lines is less than or equal to a preset value, the radiation vector line clusters are identified as having coarse angle consistency.

[0037] In some embodiments of the present application, the method for calculating the cluster consistency degree between the radiation vector line clusters includes:

[0038] Step S602, the line length difference amount between each relative radiation vector line is calculated, and the line coincidence parameter of the corresponding radiation vector line is determined based on the length difference interval to which each line length difference amount belongs;

[0039] Step S603, the first line number of the radiation vector line whose line coincidence parameter is less than or equal to the preset value is counted, and the ratio of the first line number to the total number of the radiation vector line is calculated, which is recorded as the first line number ratio;

[0040] Step S604, the sum of the line coincidence parameters of all the radiation vector lines is calculated, which is recorded as the line total coincidence parameter, and the line total coincidence parameter is corrected based on the first line number ratio to obtain the cluster coincidence degree;

[0041] Wherein, the expression for calculating the cluster coincidence degree is:

[0042] Wherein, J is the cluster coincidence degree, f is the first line number ratio, L is the first line number ratio influence adjustment coefficient, b is the first line number ratio influence adjustment constant, is the line coincidence parameter judgment function of the i th radiation vector line, and n is the total number of the radiation vector lines.

[0043] Some embodiments disclosed in the present application also disclose a seepage prevention and drainage sensitivity analysis system based on reservoir area seepage amount data, comprising:

[0044] The first module is used for acquiring the structural information of the reservoir water body contact surface, including underwater geographical structure information and water surface edge structure information, and drawing a reservoir water body contact surface stereogram;

[0045] The second module is used for using distributed optical fiber sensing technology to preliminarily arrange distributed optical fibers on the reservoir water body contact surface, mapping the temperature measurement points on the optical fibers on the reservoir water body contact surface stereogram to obtain a plurality of initial virtual temperature measurement points, and mapping the temperature parameters of the temperature measurement points on the optical fibers on the corresponding initial virtual temperature measurement points;

[0046] The third module is used for performing position correlation analysis on the initial virtual temperature measurement points on the reservoir water body contact surface stereogram and the corresponding temperature parameters, determining the correlation relationship between the initial virtual temperature measurement points adjacent in position, and delimiting temperature distribution blocks based on the correlation relationship between the initial virtual temperature measurement points;

[0047] The fourth module is used for setting a flow velocity detection device at the center of the corresponding temperature distribution block on the reservoir water body contact surface stereogram, determining the representative water flow flow velocity parameter of the temperature distribution block, and correlating the water flow flow velocity parameter, the block characteristics of the temperature distribution block, the water level height and the rock quality structure characteristic parameter to obtain a seepage block parameter group;

[0048] The fifth module is used for constructing a finite element analysis model of rock distortion analysis based on the history and the approximately generated seepage block parameter group, and determining the rock distortion sensitive parameter corresponding to each simulated seepage block parameter group, wherein the approximately generated seepage block parameter group is a reference history seepage block parameter group, and the approximate parameter variation is performed;

[0049] The sixth module is used for comparing the real-time seepage block parameter group with the simulated seepage block parameter group, and determining the rock distortion sensitive parameter corresponding to the real-time seepage block parameter group based on the comparison result.

[0050] The application discloses a reservoir seepage data-based anti-seepage drainage sensitivity analysis method and system, relates to the technical field of reservoir anomaly monitoring, and discloses obtaining reservoir water body contact surface structure information and drawing a stereogram; temperature points are arranged by using a distributed optical fiber sensing technology, temperature parameters are mapped, and temperature distribution blocks are divided; a flow rate detection device is set, flow rate, block characteristics, water level height, and rock structure characteristics are associated, and a seepage block parameter group is formed; a finite element model is constructed based on a history and an approximate variation parameter group, rock distortion sensitive parameters are analyzed, real-time and simulated parameter groups are compared, and real-time sensitive parameters are determined. The application improves seepage analysis precision and real-time performance, reduces reservoir seepage risks, and is suitable for reservoir anti-seepage drainage optimization in complex geological environments.

[0051] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The application discloses a reservoir seepage data-based anti-seepage drainage sensitivity analysis method and system, relates to the technical field of reservoir anomaly monitoring, and discloses obtaining reservoir water body contact surface structure information and drawing a stereogram; temperature points are arranged by using a distributed optical fiber sensing technology, temperature parameters are mapped, and temperature distribution blocks are divided; a flow rate detection device is set, flow rate, block characteristics, water level height, and rock structure characteristics are associated, and a seepage block parameter group is formed; a finite element model is constructed based on a history and an approximate variation parameter group, rock distortion sensitive parameters are analyzed, real-time and simulated parameter groups are compared, and real-time sensitive parameters are determined. The application improves seepage analysis precision and real-time performance, reduces reservoir seepage risks, and is suitable for reservoir anti-seepage drainage optimization in complex geological environments. DETAILED DESCRIPTION

[0053] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments.

[0054] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments.

[0055] Embodiment:

[0056] The application discloses a reservoir seepage data-based anti-seepage drainage sensitivity analysis method, which is referred toFigure 1 , comprising:

[0057] respectively on historical reservoir seepage record data and real-time reservoir seepage record data, the analysis method comprising:

[0058] Step S100, obtain the structure information of the reservoir water body contact surface, including underwater geographical structure information and water edge structure information, and draw a three-dimensional map of the reservoir water body contact surface.

[0059] Reservoir seepage is closely related to the geological structure of the water body contact surface. The underwater geological structure (such as rock layer distribution, fissure) and the water edge structure (such as bank slope shape) directly affect the seepage path and intensity. Through geological survey, remote sensing or sonar technology, three-dimensional spatial data (such as terrain coordinates, rock mass types) of the contact surface are obtained, and a three-dimensional map is generated using modeling software. This three-dimensional map provides a spatial basis for subsequent measurement point arrangement and seepage analysis, ensuring that the analysis covers the entire reservoir and captures the seepage characteristics under complex terrain.

[0060] Step S200, use distributed optical fiber sensing technology to preliminarily arrange distributed optical fibers on the reservoir water body contact surface, map the temperature measurement points on the optical fibers on the three-dimensional map of the reservoir water body contact surface, obtain a plurality of initial virtual temperature measurement points, and map the temperature parameters of the temperature measurement points on the optical fibers to the corresponding initial virtual temperature measurement points.

[0061] Distributed optical fiber sensing technology is based on the light scattering effect (such as Brillouin or Rayleigh scattering) in optical fibers, which can continuously monitor temperature changes along the optical fiber, and is suitable for large-scale, high-precision seepage detection. By arranging optical fibers on the reservoir water body contact surface (such as dam body, reservoir bottom), temperature data of temperature measurement points are obtained, and their coordinates and temperature values are mapped to the three-dimensional map of step S100 to form initial virtual temperature measurement points. These points reflect the temperature anomalies caused by seepage (such as local low temperature caused by cold water seepage), providing a data basis for subsequent block division.

[0062] Step S300, perform position correlation analysis on the initial virtual temperature measurement points on the three-dimensional map of the reservoir water body contact surface and the corresponding temperature parameters, determine the correlation relationship between the initial virtual temperature measurement points with adjacent positions, and based on the correlation relationship between the initial virtual temperature measurement points, divide the initial virtual temperature measurement points into temperature distribution blocks.

[0063] The temperature change caused by seepage has spatial continuity, and the temperature data of adjacent regions usually have correlation. Through spatial correlation analysis (such as clustering algorithm based on distance and temperature similarity) on the coordinates and temperature values of virtual temperature measurement points, the relationship (such as temperature gradient, distribution pattern) between adjacent temperature measurement points is determined. According to these relationships, the temperature measurement points are grouped into temperature distribution blocks, reflecting the temperature characteristics of the seepage area. This block division facilitates focusing on high-risk areas, reduces data processing complexity, and improves analysis efficiency.

[0064] In step S400, a flow velocity detection device is set at the center of the corresponding temperature distribution block on the three-dimensional diagram of the reservoir water body contact surface, the representative water flow velocity parameter of the temperature distribution block is determined, and the water flow velocity parameter, the block characteristics of the temperature distribution block, the water level height, and the rock structure characteristic parameter are associated to obtain a seepage block parameter group.

[0065] The center of the temperature distribution block is usually the key area of seepage activity, and the setting of the flow velocity detection device (such as a Doppler flow velocity meter) can directly measure the representative water flow velocity to supplement the temperature data. Combined with the block characteristics (such as shape, area), the water level height (which affects the seepage pressure), and the rock structure characteristics (such as porosity, fracture density), the seepage block parameter group is formed through parameter association. This group of parameters comprehensively describes the multi-dimensional characteristics of seepage, provides input for subsequent simulation, and ensures that multiple influencing factors are considered in the analysis.

[0066] The seepage block parameter group is subjected to finite element simulation analysis, and the analysis method includes:

[0067] In step S500, based on the seepage block parameter group generated by history and approximation, a finite element analysis model for rock distortion analysis is constructed, and the rock distortion sensitive parameters corresponding to each simulated seepage block parameter group are determined, wherein the seepage block parameter group generated by approximation is the reference historical seepage block parameter group, and the approximation parameter variation is performed.

[0068] Finite element analysis discretizes complex structures into grids to simulate the mechanical influence (such as stress and deformation) of seepage on rock mass. Based on the historical parameter group (reflecting the past seepage pattern) and the approximate variation parameter group (simulating possible scenarios through random disturbance), a finite element model is constructed to calculate the sensitive parameters (such as abnormal probability) of rock distortion (such as fracture expansion and rock mass instability). Approximate variation randomly selects values within the historical parameter range to increase the robustness of the model to uncertainty (such as geological heterogeneity), ensuring that the simulation results cover multiple working conditions.

[0069] The method for determining the rock distortion sensitive parameters corresponding to the real-time seepage block parameter group includes:

[0070] In step S600, the real-time seepage block parameter group and the simulated seepage block parameter group are compared, and based on the comparison result, the rock distortion sensitive parameters corresponding to the real-time seepage block parameter group are determined.

[0071] The real-time seepage parameter group (based on current monitoring data) and the simulation parameter group (based on history and variation) are compared in the parameter space (such as Euclidean distance, feature matching). By analyzing the differences in flow velocity, water level, temperature block characteristics and rock mass structure, it is determined which simulation scenario is closest to the real-time data, thereby mapping the corresponding rock mass distortion sensitive parameters (such as abnormal probability). This step uses the prior knowledge of simulation results to quickly assess real-time seepage risk, support dynamic monitoring and early warning, and is suitable for seepage management in complex environments.

[0072] In some embodiments disclosed by the application, the method for constructing a finite element analysis model for rock mass distortion analysis based on historical and approximate seepage block parameter groups includes:

[0073] In step S501, the seepage block parameter group is intercepted on the corresponding block of the three-dimensional diagram of the water body contact surface of the reservoir area, and a three-dimensional seepage block surface is obtained. The three-dimensional seepage block surface is meshed.

[0074] The seepage block parameter group corresponds to a specific area in the three-dimensional diagram of the water body contact surface of the reservoir area, and contains key geological structures of potential seepage. By intercepting these areas from the three-dimensional diagram, an independent three-dimensional seepage block surface is formed, focusing on the active seepage area. Meshing is the process of discretizing the block surface into finite elements (such as tetrahedral or hexahedral meshes) required for finite element analysis, each element has nodes and boundary conditions. Meshing ensures that complex geometries can be processed by numerical methods, and refining the mesh can improve simulation accuracy while balancing computational efficiency, laying the foundation for subsequent mechanical and fluid analysis.

[0075] In step S502, the water flow velocity parameter in the seepage block parameter group, the block characteristics of the temperature distribution block, and the rock mass structure characteristic parameter are used to configure the material properties of the three-dimensional seepage block surface using the rock mass structure characteristic parameter, determine the water flow direction performance of the three-dimensional seepage block surface using the block characteristics of the temperature distribution block, determine the water flow velocity parameter of the nodes at different positions of the three-dimensional seepage block surface based on the water flow velocity parameter of the block center, and determine the water pressure parameter of the three-dimensional seepage block surface based on the water level height in the seepage block parameter group.

[0076] The seepage block parameter group contains multi-dimensional data, which needs to be converted into the input of the finite element model. The rock mass structure characteristics (such as porosity, permeability coefficient) are used to configure the material properties (such as elastic modulus, Poisson's ratio) of the grid elements, reflecting the physical properties of the rock mass. The temperature distribution block characteristics (such as radiation vector line clusters) indicate the geometric distribution of the water flow path, and determine the water flow direction (such as along the fissure or pore). Based on the measured flow velocity at the block center, the water flow velocity at the grid nodes is calculated through interpolation or fluid dynamics models. The water level height determines the water pressure distribution, and the pressure parameters of each node are calculated through hydrostatic formulas (such as Bernoulli's equation). These parameters jointly construct the boundary conditions and initial conditions of the seepage field.

[0077] Step S503, based on the water flow direction performance, water flow velocity parameters and water pressure parameters of the three-dimensional seepage block surface, the three-dimensional seepage block surface is simulated by finite element simulation.

[0078] The finite element simulation simulates the movement of water flow in the grid block surface and its influence on the rock mass by solving the control equations of seepage field and rock mass mechanics (such as Darcy's law, stress-strain relationship). The water flow direction performance provides fluid path constraints, and the water flow velocity and water pressure parameters are used as inputs to drive fluid-solid coupling analysis. The simulation calculates the displacement, stress or seepage rate of the grid nodes, and outputs the possibility of rock mass distortion (such as crack propagation, rock mass instability). Through iterative solution, the seepage behavior under different working conditions is simulated, and the rock mass distortion risk is quantified to provide data support for sensitivity analysis.

[0079] In some embodiments of the present application, the method for determining the rock mass distortion sensitive parameters corresponding to each simulated seepage block parameter group comprises:

[0080] Step S504, analyzing the finite element simulation of the three-dimensional seepage block surface, determining the abnormal probability of the three-dimensional seepage block surface under different water level heights, and identifying the abnormal probability under different water level heights as the rock mass distortion sensitive parameters.

[0081] In some embodiments of the present application, the method for determining the abnormal probability of the three-dimensional seepage block surface under different water level heights comprises:

[0082] Step S505, constructing a plurality of rock mass defect features of the three-dimensional seepage block surface, each rock mass defect feature meets the water flow direction performance and water flow velocity requirements during construction, including determining the strength contrast of the defect degree of different position nodes on the three-dimensional seepage block surface based on the water flow direction performance.

[0083] Step S506, configuring each rock mass defect feature to the three-dimensional seepage block surface and respectively performing finite element simulation, calculating the ratio of the number of rock mass distortion to the number of rock mass defect features, and recording it as the abnormal probability.

[0084] In some embodiments of the present application, the method for approximating the variation of the parameters comprises:

[0085] Step S507, setting a parameter interval for each parameter in the seepage block parameter group, randomly selecting a parameter in the parameter interval corresponding to each type of parameter to form a new seepage block parameter group.

[0086] In some embodiments of the present application, the method for position correlation analysis of the initial virtual temperature measurement point on the three-dimensional graph of the reservoir water body contact surface and the corresponding temperature parameter comprises:

[0087] Step S301, a spatial coordinate system is established for the structure surface of the reservoir area water body, the temperature measurement point coordinates of each initial virtual temperature measurement point are determined, and the straight line distances between adjacent temperature measurement point coordinates are calculated respectively.

[0088] Step S302, a plurality of temperature parameter intervals are set, the temperature parameter interval to which each initial virtual temperature measurement point belongs is judged, and the initial virtual temperature measurement points belonging to the same temperature parameter interval are connected in a straight line if the straight line distance between the initial virtual temperature measurement points is less than or equal to a preset value, to form an initial virtual temperature measurement enclosing line.

[0089] Step S303, the area enclosed by the initial virtual temperature measurement enclosing line is determined, which is recorded as an initial virtual temperature measurement block, and the initial virtual temperature measurement block is sequentially marked based on the high-low order of the temperature parameter interval, and the high-low direction between the initial virtual temperature measurement blocks that enclose each other is determined.

[0090] Step S304, a plurality of initial virtual temperature measurement blocks that enclose each other and have the same high-low direction are associated to form initial virtual temperature measurement blocks nested in a descending order of area, which are recorded as temperature distribution blocks.

[0091] In some embodiments disclosed in the present application, the block characteristics of the temperature distribution block include:

[0092] Step S401, the block center point of the temperature distribution block is determined, and a plurality of radiation vector lines are emitted in an equiangular manner with the block center point as the radiation center, and the heads of the radiation vector lines are terminated at the edge lines of the temperature distribution block, to obtain a radiation vector line cluster for representing the temperature distribution block.

[0093] Step S402, the length of each radiation vector line in the radiation vector line cluster is determined and marked on the radiation vector line cluster, and the radiation vector line cluster is identified as the block characteristics of the temperature distribution block.

[0094] In some embodiments disclosed in the present application, the method for comparing the real-time seepage block parameter group and the simulated seepage block parameter group includes:

[0095] Step S601, the water flow velocity parameter difference, the water level height difference and the rock structure characteristic parameter difference between the seepage block parameter groups are calculated, and it is judged whether each difference belongs to a preset difference interval, if all belong, the block difference between the block characteristics of the temperature distribution block is calculated, and if the block difference is less than or equal to a preset value, it is determined that the real-time seepage block parameter group and the simulated seepage block parameter group are consistent.

[0096] The method for calculating the block difference amount between the block features of the temperature distribution block comprises: rotating the radiation vector line clusters corresponding to the block features in sequence, screening out radiation vector line clusters with coarse angle coincidence, and then calculating the cluster coincidence degree between the radiation vector line clusters with coarse angle coincidence, wherein the cluster coincidence degree is identified as the block difference amount between the block features.

[0097] The method for screening the radiation vector line clusters with coarse angle coincidence comprises: comparing the line length difference amount between each pair of opposite radiation vector lines, identifying the corresponding radiation vector line as a coarse coincidence radiation vector line if the length difference amount is less than or equal to a preset value, and identifying the radiation vector line clusters as coarse angle coincidence if the ratio of the number of coarse coincidence radiation vector lines to the number of all radiation vector lines is less than or equal to a preset value.

[0098] In some embodiments of the present application, the method for calculating the cluster coincidence degree between the radiation vector line clusters comprises:

[0099] Step S602: calculating the line length difference amount between each pair of opposite radiation vector lines, and determining the line coincidence parameter of the corresponding radiation vector line based on the length difference amount interval to which each line length difference amount belongs.

[0100] Step S603: counting the first line number of the radiation vector lines with a line coincidence parameter less than or equal to a preset value, and calculating the ratio of the first line number to the total number of radiation vector lines, denoted as the first line number ratio.

[0101] Step S604: calculating the total sum of the line coincidence parameters of all radiation vector lines, denoted as the total line coincidence parameter, and correcting the total line coincidence parameter based on the first line number ratio to obtain the cluster coincidence degree.

[0102] The expression for calculating the cluster coincidence degree is:

[0103] J is the cluster coincidence degree, f is the first line number ratio, L is the first line number ratio influence adjustment coefficient, and b is the first line number ratio influence adjustment constant. is the line coincidence parameter judgment function of the i-th radiation vector line, and n is the total number of radiation vector lines.

[0104] In some embodiments of the present application, a seepage prevention and drainage sensitivity analysis system based on reservoir area seepage amount data is also disclosed, comprising:

[0105] The first module is configured to obtain the structural information of the reservoir water body contact surface, including underwater geographical structure information and water surface edge structure information, and draw a three-dimensional diagram of the reservoir water body contact surface.

[0106] The second module is used for arranging the distributed optical fiber on the water body contact surface of the reservoir area by using the distributed optical fiber sensing technology, mapping the temperature measuring points on the optical fiber on the stereogram of the water body contact surface of the reservoir area to obtain a plurality of initial virtual temperature measuring points, and mapping the temperature parameters of the temperature measuring points on the optical fiber on the corresponding initial virtual temperature measuring points.

[0107] The third module is used for performing position correlation analysis on the initial virtual temperature measuring points and the corresponding temperature parameters on the stereogram of the water body contact surface of the reservoir area, determining the correlation relationship between the initial virtual temperature measuring points which are adjacent in position, and delimiting the initial virtual temperature measuring points based on the correlation relationship between the initial virtual temperature measuring points to obtain temperature distribution blocks.

[0108] The fourth module is used for setting a flow velocity detection device on the center of the corresponding temperature distribution block on the stereogram of the water body contact surface of the reservoir area, determining the representative water flow flow velocity parameter of the temperature distribution block, and correlating the water flow flow velocity parameter, the block characteristics of the temperature distribution block, the water level height and the rock mass structure characteristic parameter to obtain a seepage block parameter group.

[0109] The fifth module is used for constructing a finite element analysis model of rock mass distortion analysis based on the historical and approximately generated seepage block parameter group, and determining the rock mass distortion sensitive parameter corresponding to each simulated seepage block parameter group, wherein the approximately generated seepage block parameter group is a reference historical seepage block parameter group, and the approximately generated seepage block parameter group is a reference historical seepage block parameter group.

[0110] The sixth module is used for comparing the real-time seepage block parameter group with the simulated seepage block parameter group, and determining the rock mass distortion sensitive parameter corresponding to the real-time seepage block parameter group based on the comparison result.

[0111] The application discloses a seepage prevention and drainage sensitivity analysis method and system based on reservoir leakage data, relates to the technical field of reservoir anomaly monitoring, and discloses obtaining reservoir water body contact surface structure information and drawing a stereogram; arranging temperature measuring points by using a distributed optical fiber sensing technology, mapping temperature parameters, and dividing temperature distribution blocks; setting a flow velocity detection device, correlating flow velocity, block characteristics, water level height and rock mass structure characteristics, and forming a seepage block parameter group; constructing a finite element model based on historical and approximately varied parameter groups, analyzing rock mass distortion sensitive parameters; comparing real-time and simulated parameter groups to determine real-time sensitive parameters.

[0112] Those skilled in the art can clearly understand the present application through the description of the above embodiments. The present application can be realized by hardware or by means of software with a necessary general hardware platform. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.), and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method described in each embodiment scenario of the present application.

[0113] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application rather than limit them. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: the technical solutions of the present application can still be modified or replaced by equivalents, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for analyzing sensitivity of anti-seepage drainage based on data of seepage amount in a reservoir area, characterized in that, The application relates to a method for analyzing seepage of a reservoir area. The method comprises: Step S100, obtaining structure information of a water body contact surface of the reservoir area, including underwater geographical structure information and overwater edge structure information, and drawing a three-dimensional diagram of the water body contact surface of the reservoir area; Step S200, using a distributed optical fiber sensing technology to preliminarily arrange distributed optical fibers on the water body contact surface of the reservoir area, mapping temperature measuring points on the optical fibers on the three-dimensional diagram of the water body contact surface of the reservoir area to obtain a plurality of initial virtual temperature measuring points, and mapping temperature parameters of the temperature measuring points on the optical fibers to the corresponding initial virtual temperature measuring points; Step S300, performing position correlation analysis on the initial virtual temperature measuring points on the three-dimensional diagram of the water body contact surface of the reservoir area and the corresponding temperature parameters, determining the correlation between the initial virtual temperature measuring points which are adjacent in position, and delimiting temperature distribution blocks of the initial virtual temperature measuring points based on the correlation between the initial virtual temperature measuring points; Step S400, setting a flow velocity detection device at the center of the corresponding temperature distribution block on the three-dimensional diagram of the water body contact surface of the reservoir area, determining a representative water flow flow velocity parameter of the temperature distribution block, and correlating the water flow flow velocity parameter, block characteristics of the temperature distribution block, water level height and rock mass structure characteristic parameters to obtain a seepage block parameter group; The method comprises: Step S500, based on the seepage block parameter group generated by the history and the approximation, constructing a finite element analysis model for rock mass distortion analysis, and determining a rock mass distortion sensitive parameter corresponding to each simulated seepage block parameter group, wherein the seepage block parameter group generated by the approximation is a reference historical seepage block parameter group, and the approximation parameter variation is performed; The method comprises: Step S600, comparing the real-time seepage block parameter group with the simulated seepage block parameter group, and based on the comparison result, determining the rock mass distortion sensitive parameter corresponding to the real-time seepage block parameter group.

2. The method of analyzing the sensitivity of the anti-seepage drainage according to the data of the seepage amount of the warehouse area of claim 1, characterized in that, The method comprises: Step S501, intercepting the block corresponding to the seepage block parameter group on the three-dimensional diagram of the water body contact surface of the reservoir area to obtain a three-dimensional seepage block surface, and meshing the three-dimensional seepage block surface; Step S502, using the rock mass structure characteristic parameters, configuring material properties of the three-dimensional seepage block surface, determining water flow direction performance of the three-dimensional seepage block surface by using the block characteristics of the temperature distribution block, determining water flow flow velocity parameters of different position nodes of the three-dimensional seepage block surface based on the water flow flow velocity parameter of the block center, and determining water pressure parameters of the three-dimensional seepage block surface based on the water level height in the seepage block parameter group; Step S503, based on the water flow direction performance, the water flow flow velocity parameter and the water pressure parameter of the three-dimensional seepage block surface, performing finite element simulation on the three-dimensional seepage block surface.

3. The method of analyzing the sensitivity of the anti-seepage drainage according to the data of the seepage amount of the warehouse area as claimed in claim 2, characterized in that, The method for determining the rock mass distortion sensitive parameter corresponding to each simulated seepage block parameter set comprises: Step S504, analyzing the finite element simulation of the stereoscopic seepage block surface, determining the abnormal probability of the abnormality of the stereoscopic seepage block surface under different water levels, and identifying the abnormal probability under different water levels as the rock mass distortion sensitive parameter.

4. The method for analyzing the sensitivity of anti-seepage drainage according to the reservoir area seepage data of claim 3, wherein, The method for determining the abnormal probability of the abnormality of the stereoscopic seepage block surface under different water levels comprises: Step S505, randomly constructing a plurality of rock mass defect features of the stereoscopic seepage block surface, each rock mass defect feature meeting the water flow direction performance and water flow velocity requirements during construction, including determining the strength contrast of the defect degree of the nodes at different positions on the stereoscopic seepage block surface based on the water flow direction performance; Step S506, configuring each rock mass defect feature on the stereoscopic seepage block surface and respectively performing finite element simulation, calculating the ratio of the number of rock mass distortion to the number of rock mass defect features, and recording it as the abnormal probability.

5. The method for analyzing the sensitivity of anti-seepage drainage according to the reservoir area seepage data of claim 1, wherein, The method for approximating the parameter variation comprises: Step S507, setting a parameter interval for each parameter in the seepage block parameter set, randomly selecting a parameter in the parameter interval corresponding to each type of parameter to form a new seepage block parameter set.

6. The method for analyzing the sensitivity of anti-seepage drainage according to the data of the seepage amount of the reservoir area of claim 1, characterized in that, The method for position correlation analysis of the initial virtual temperature measurement points on the stereoscopic graph of the reservoir water body contact surface and the corresponding temperature parameters comprises: Step S301, establishing a spatial coordinate system for the stereoscopic graph of the reservoir water body structure surface, determining the temperature measurement point coordinates of each initial virtual temperature measurement point, and respectively calculating the straight line distance between adjacent temperature measurement point coordinates; Step S302, setting a plurality of temperature parameter intervals, determining the temperature parameter interval to which each initial virtual temperature measurement point belongs, and connecting the initial virtual temperature measurement points belonging to the same temperature parameter interval and having a straight line distance less than or equal to a preset value to form an initial virtual temperature measurement enclosing line; Step S303, determining the area enclosed by the initial virtual temperature measurement enclosing line, recording it as an initial virtual temperature measurement block, and based on the high-low order of the temperature parameter intervals, marking the order of the initial virtual temperature measurement block and determining the high-low direction between the initial virtual temperature measurement blocks that enclose each other; Step S304, correlating a plurality of initial virtual temperature measurement blocks that enclose each other and have the same high-low direction to form initial virtual temperature measurement blocks nested in descending order of area, recording them as temperature distribution blocks.

7. The method for analyzing the sensitivity of anti-seepage drainage according to the reservoir area seepage data of claim 6, wherein, The block features of the temperature distribution block comprise: Step S401, determining the block center point of the temperature distribution block, taking the block center point as the radiation center, emitting a plurality of radiation vector lines in an equiangular manner, and letting the heads of the radiation vector lines terminate at the edge lines of the temperature distribution block to obtain a radiation vector line cluster for representing the temperature distribution block; Step S402, determining the length of each radiation vector line in the radiation vector line cluster and marking it on the radiation vector line cluster, and identifying the radiation vector line cluster as the block features of the temperature distribution block.

8. The method for analyzing the sensitivity of anti-seepage drainage according to the reservoir area seepage data of claim 7, wherein, The method for comparing the real-time seepage block parameter set with the simulated seepage block parameter set comprises: In step S601, the flow velocity parameter difference, the water level difference and the rock structure characteristic parameter difference between the seepage block parameter groups are calculated, and it is determined whether each difference belongs to a preset difference interval. If all belong to the preset difference interval, the block difference between the block characteristics of the temperature distribution block is calculated. If the block difference is less than or equal to a preset value, it is determined that the real-time seepage block parameter group and the simulated seepage block parameter group are consistent. The method for calculating the block difference between the block characteristics of the temperature distribution block comprises: rotating and comparing the radiation vector line clusters corresponding to the block characteristics one by one, screening out the radiation vector line clusters with coarse angle consistency, and then calculating the cluster consistency degree between the radiation vector line clusters with coarse angle consistency, wherein the cluster consistency degree is determined as the block difference between the block characteristics. The method for screening the radiation vector line clusters with coarse angle consistency comprises: comparing the line length difference between each pair of opposite radiation vector lines. If the length difference is less than or equal to a preset value, the corresponding radiation vector line is determined as a coarse consistent radiation vector line. If the ratio of the number of coarse consistent radiation vector lines to the total number of radiation vector lines is less than or equal to a preset value, the radiation vector lines are determined to have coarse angle consistency.

9. The method for analyzing the sensitivity of anti-seepage drainage according to the reservoir area seepage data of claim 8, wherein, The method for calculating the cluster consistency degree between the radiation vector line clusters comprises: In step S602, the line length difference between each pair of opposite radiation vector lines is calculated, and the line consistency parameter of the corresponding radiation vector line is determined based on the length difference interval to which each line length difference belongs. In step S603, the first line number of the radiation vector lines with a line consistency parameter less than or equal to a preset value is counted, and the ratio of the first line number to the total number of radiation vector lines is calculated, which is denoted as the first line number ratio. In step S604, the sum of the line consistency parameters of all radiation vector lines is calculated, which is denoted as the total line consistency parameter. The total line consistency parameter is corrected based on the first line number ratio to obtain the cluster consistency degree. The expression for calculating the cluster consistency degree is: Wherein, J is the cluster degree of conformity, f is the first line quantity ratio, L is the first line quantity ratio influence adjustment coefficient, b is the first line quantity ratio influence adjustment constant, Is the line conformity parameter judgment function of the i th radiation vector line, n is the total number of radiation vector lines.

10. A seepage prevention and drainage sensitivity analysis system based on reservoir seepage data, for performing the seepage prevention and drainage sensitivity analysis method of any one of claims 7-8, characterized in that, The method comprises: The first module is configured to obtain the structural information of the reservoir water body contact surface, including the underwater geographical structure information and the overwater edge structure information, and draw a three-dimensional diagram of the reservoir water body contact surface. The second module is configured to use the distributed optical fiber sensing technology to preliminarily arrange the distributed optical fiber on the reservoir water body contact surface, map the temperature measurement points on the optical fiber on the three-dimensional diagram of the reservoir water body contact surface to obtain a plurality of initial virtual temperature measurement points, and map the temperature parameters of the temperature measurement points on the optical fiber to the corresponding initial virtual temperature measurement points. The third module is configured to perform position correlation analysis on the initial virtual temperature measurement points and the corresponding temperature parameters on the three-dimensional diagram of the reservoir water body contact surface, determine the correlation relationship between the initial virtual temperature measurement points with adjacent positions, and delimit the initial virtual temperature measurement points into temperature distribution blocks based on the correlation relationship between the initial virtual temperature measurement points. The fourth module is configured to set a flow rate detection device on the center of a corresponding temperature distribution block on the three-dimensional view of the water body contact surface of the reservoir area, determine a representative water flow rate parameter of the temperature distribution block, and associate the water flow rate parameter, a block feature of the temperature distribution block, a water level height, and a rock structure feature parameter to obtain a seepage block parameter group; The fifth module is configured to construct a finite element analysis model of rock distortion analysis based on the historical and approximately generated seepage block parameter groups, and determine a rock distortion sensitive parameter corresponding to each simulated seepage block parameter group, wherein the approximately generated seepage block parameter group is a reference historical seepage block parameter group, and the approximation parameter variation is performed; The sixth module is configured to compare the real-time seepage block parameter group with the simulated seepage block parameter group, and determine a rock distortion sensitive parameter corresponding to the real-time seepage block parameter group based on the comparison result.

Images