Anti-seepage drainage sensitivity analysis method and system based on reservoir area leakage data

Abstract

The invention discloses an anti-seepage drainage sensitivity analysis method and system based on reservoir area leakage amount data, relates to the technical field of reservoir area abnormity monitoring, and discloses the steps of obtaining reservoir area water body contact surface structure information, and drawing a stereogram; a distributed optical fiber sensing technology is used for arranging temperature measurement points, mapping temperature parameters and dividing temperature distribution blocks; setting a flow velocity detection device, and correlating the flow velocity, the block characteristics, the water level height and the rock structure characteristics to form a seepage block parameter group; constructing a finite element model based on historical and approximate variation parameter groups, and analyzing rock distortion sensitive parameters; and comparing the real-time parameter group with the simulation parameter group to determine a real-time sensitive parameter. According to the method, through refined data processing and simulation, the leakage analysis precision and real-time performance are improved, the reservoir leakage risk is reduced, and the method is suitable for reservoir anti-seepage drainage optimization in a complex geological environment.

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, leading to difficulty in accurately evaluating the sensitivity of water level changes to rock mass distortion under complex geological conditions, and further 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 risk of 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, The present application discloses a seepage prevention and drainage sensitivity analysis method based on reservoir area seepage data, comprising: The historical reservoir area seepage record data and the real-time reservoir area seepage record data are analyzed respectively, and the analysis method comprises: 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; 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; 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 initial virtual temperature measurement points adjacent in position, and based on the correlation relationship between the initial virtual temperature measurement points, dividing the initial virtual temperature measurement points into temperature distribution blocks; Step S400, setting a flow rate detection device at the center of the corresponding temperature distribution block on the reservoir water body contact surface solid model, determining the representative water flow flow rate parameter of the temperature distribution block, and correlating the water flow flow rate parameter, the block characteristics of the temperature distribution block, the water level height and the rock structure characteristic parameter to obtain a seepage block parameter group; The finite element simulation analysis is performed on the seepage block parameter group, and the analysis method comprises: In step S500, based on the historical and approximately generated seepage block parameter group, a finite element analysis model for rock distortion analysis is constructed, and the rock distortion sensitive parameter corresponding to each simulated seepage block parameter group is determined, wherein the approximately generated seepage block parameter group is a reference historical seepage block parameter group, and the approximate parameter variation is performed. The method for determining the rock distortion sensitive parameter corresponding to the real-time seepage block parameter group comprises: 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 parameter corresponding to the real-time seepage block parameter group is determined.

[0004] In some embodiments of the present application, the method for constructing the finite element analysis model for rock distortion analysis based on the historical and approximately generated seepage block parameter group comprises: In step S501, the seepage block corresponding to the block on the stereogram of the water body contact surface of the seepage block parameter group is intercepted to obtain a stereoscopic seepage block surface, and the stereoscopic seepage block surface is meshed. In step S502, the water flow velocity 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 the material properties of the stereoscopic seepage block surface by using the rock mass structure characteristic parameter, to determine the water flow direction performance of the stereoscopic seepage block surface by using the block characteristics of the temperature distribution block, to determine the water flow velocity parameters of the nodes at different positions of the stereoscopic seepage block surface based on the water flow velocity parameter of the block center, and to determine the water pressure parameter of the stereoscopic seepage block surface based on the water level height in the seepage block parameter group. In step S503, the stereoscopic seepage block surface is subjected to finite element simulation based on the water flow direction performance, the water flow velocity parameter and the water pressure parameter of the stereoscopic seepage block surface.

[0005] In some embodiments of the present application, the method for determining the rock distortion sensitive parameter corresponding to each simulated seepage block parameter group comprises: In step S504, the finite element simulation of the stereoscopic seepage block surface is analyzed to determine the abnormal probability of the stereoscopic seepage block surface under different water level heights, and the abnormal probability under different water level heights is determined as the rock distortion sensitive parameter.

[0006] In some embodiments of the present application, the method for determining the abnormal probability of the stereoscopic seepage block surface under different water level heights comprises: In step S505, the rock mass defect features of the stereoscopic seepage block surface are randomly constructed, and each rock mass defect feature meets the water flow direction performance and water flow velocity requirements during construction, including determining the defect degree of the nodes at different positions on the stereoscopic seepage block surface based on the water flow direction performance. In step S506, each rock mass defect feature is configured on the stereoscopic seepage block surface, and finite element simulation is performed respectively to calculate the ratio of the number of rock mass distortion to the number of rock mass defect features, which is recorded as an abnormal probability.

[0007] In some embodiments of the present application, the approximate parameter variation method includes: In step S507, a parameter interval is set for each parameter in the seepage block parameter group, and a parameter is randomly selected in the parameter interval corresponding to each type of parameter to form a new seepage block parameter group.

[0008] In some embodiments of the present application, the method for position correlation analysis of the initial virtual temperature measurement points on the stereogram of the reservoir water body contact surface and the corresponding temperature parameters includes: In step S301, a space coordinate system is established on the stereogram 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 distances between adjacent temperature measurement point coordinates are calculated respectively. In step S302, a plurality of temperature parameter intervals are set, the temperature parameter interval to which each initial virtual temperature measurement point belongs is determined, and the initial virtual temperature measurement points belonging to the same temperature parameter interval are connected by straight lines if the straight line distance between them is less than or equal to a preset value, thereby forming an initial virtual temperature measurement enclosing line. In 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 directions between the initial virtual temperature measurement blocks that enclose each other are determined. In 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 descending order of area, which are recorded as temperature distribution blocks.

[0009] In some embodiments of the present application, the block features of the temperature distribution block include: In 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 terminate at the edge lines of the temperature distribution block, thereby obtaining a radiation vector line cluster for representing the temperature distribution block. Step S402, determine the length of each radiation vector line in the radiation vector line cluster, mark the length on the radiation vector line cluster, and identify the radiation vector line cluster as a block feature of the temperature distribution block.

[0010] 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: Step S601, calculate the flow velocity parameter difference, water level difference and rock structure feature parameter difference between the seepage block parameter groups, and determine whether each difference belongs to a preset difference interval. If all belong, calculate the block difference between the block features of the temperature distribution block. If the block difference is less than or equal to a preset value, it is identified 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 features of the temperature distribution block includes: rotating the radiation vector line clusters corresponding to the block features in turn, 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. The cluster consistency degree is identified as the block difference between the block features. 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. 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, it is identified that the radiation vector line clusters have coarse angle consistency.

[0011] In some embodiments of the present application, the method for calculating the cluster consistency degree between the radiation vector line clusters includes: Step S602, calculate the line length difference between each pair of opposite radiation vector lines, and determine the line consistency parameter of the corresponding radiation vector line based on the length difference interval to which each line length difference belongs. Step S603, count the first line number of the radiation vector lines with a line consistency parameter less than or equal to a preset value, and calculate the ratio of the first line number to the total number of radiation vector lines, denoted as the first line number ratio. Step S604, calculate the sum of the line consistency parameters of all radiation vector lines, denoted as the total line consistency parameter, and correct the total line consistency parameter based on the first line number ratio to obtain the cluster consistency degree. The expression for calculating the cluster consistency degree is: Where J is the cluster consistency 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. A line coincidence parameter judgment function of the i-th radiation vector line, and n is the total number of radiation vector lines.

[0012] In some embodiments disclosed in the present application, a seepage prevention and drainage sensitivity analysis system based on reservoir area seepage amount data is also disclosed, comprising: The first module is configured to acquire structural information of a reservoir water body contact surface, including underwater geographical structure information and overwater edge structure information, and draw a three-dimensional diagram of the reservoir water body contact surface. The second module is configured to use distributed optical fiber sensing technology to preliminarily arrange distributed optical fibers on the reservoir water body contact surface, map temperature measurement points on the optical fibers on the three-dimensional diagram of the reservoir water body contact surface to obtain a plurality of initial virtual temperature measurement points, and map temperature parameters of the temperature measurement points on the optical fibers 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 position-adjacent initial virtual temperature measurement points, and demarcate temperature distribution blocks based on the correlation relationship between the initial virtual temperature measurement points. The fourth module is configured to set a flow velocity detection device at the center of the corresponding temperature distribution block on the three-dimensional diagram of the reservoir water body contact surface, determine a representative water flow flow velocity parameter of the temperature distribution block, and correlate the water flow flow velocity parameter, block characteristics of the temperature distribution block, water level height, and rock quality structure characteristic parameters to obtain a seepage block parameter group. The fifth module is configured to construct a finite element analysis model for rock quality distortion analysis based on the historical and approximately generated seepage block parameter groups, and determine a rock quality 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 a parameter variation is performed in an approximate manner. 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 quality distortion sensitive parameter corresponding to the real-time seepage block parameter group based on the comparison result.

[0013] 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, drawing a stereogram, arranging temperature measuring points by using 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 quality structure characteristics, forming a seepage block parameter group, constructing a finite element model based on historical and approximate variation parameter groups, analyzing rock quality distortion sensitive parameters, comparing real-time and simulation parameter groups, and determining real-time sensitive parameters. 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.

[0014] The technical scheme of the application will be further described below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A method step diagram of the seepage prevention and drainage sensitivity analysis method based on reservoir area seepage amount data disclosed in the embodiments of the application is shown in the figure. DETAILED DESCRIPTION

[0016] The technical scheme of the application will be further described below with reference to the drawings and embodiments.

[0017] The technical scheme of the application will be further described below with reference to the drawings and embodiments.

[0018] Embodiment: The application discloses a seepage prevention and drainage sensitivity analysis method based on reservoir area seepage amount data, which is shown in Figure 1 , and comprises the following steps. The historical reservoir area seepage record data and the real-time reservoir area seepage record data are analyzed respectively, and the analysis method comprises the following steps. Step S100, structure information of a water body contact surface of a reservoir area is obtained, including underwater geographical structure information and overwater edge structure information, and a stereogram of the water body contact surface of the reservoir area is drawn.

[0019] The seepage of the reservoir area is closely related to the geological structure of the water body contact surface. The underwater geological structure (such as rock layer distribution, fracture) and the edge structure above water (such as the shape of the bank slope) directly affect the seepage path and intensity. Through geological survey, remote sensing or sonar technology, three-dimensional spatial data (such as terrain coordinates, rock types) of the contact surface are obtained, and a three-dimensional graph is generated using modeling software. This three-dimensional graph provides a spatial basis for subsequent measurement point arrangement and seepage analysis, ensuring that the analysis covers the entire reservoir area and captures the seepage characteristics under complex terrain.

[0020] In step S200, a distributed optical fiber sensing technology is used to arrange a distributed optical fiber on the water body contact surface of the reservoir area. The temperature measurement points on the optical fiber are mapped on the three-dimensional graph of the water body contact surface of the reservoir area to obtain a plurality of initial virtual temperature measurement points. The temperature parameters of the temperature measurement points on the optical fiber are mapped to the corresponding initial virtual temperature measurement points.

[0021] The distributed optical fiber sensing technology is based on the light scattering effect (such as Brillouin or Rayleigh scattering) in the optical fiber, which can continuously monitor temperature changes along the optical fiber, and is suitable for large-scale and high-precision seepage detection. By arranging the optical fiber on the water body contact surface (such as the dam body and the reservoir bottom) of the reservoir area, the temperature data of the temperature measurement points are obtained, and the coordinates and temperature values are mapped to the three-dimensional graph 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.

[0022] In step S300, a position correlation analysis is performed on the initial virtual temperature measurement points on the three-dimensional graph of the water body contact surface of the reservoir area and the corresponding temperature parameters to determine the correlation between the initial virtual temperature measurement points adjacent in position. Based on the correlation between the initial virtual temperature measurement points, the initial virtual temperature measurement points are divided into temperature distribution blocks.

[0023] 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) of the coordinates and temperature values of the 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.

[0024] In step S400, a flow rate detection device is set at the center of the corresponding temperature distribution block on the three-dimensional graph of the water body contact surface of the reservoir area, a 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 correlated to obtain a seepage block parameter group.

[0025] The center of the temperature distribution block is usually the key area of seepage activity. The flow velocity detection device (such as a Doppler flowmeter) is arranged to directly measure the representative water flow velocity, and the temperature data are supplemented. In combination with the block characteristics (such as shape, area), water level height (affecting seepage pressure) and rock structure characteristics (such as porosity, fracture density), a seepage block parameter group is formed through parameter correlation. The 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.

[0026] The seepage block parameter group is simulated and analyzed by finite element simulation analysis, and the analysis method includes: In step S500, based on the historical and approximately generated seepage block parameter group, 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 approximately generated seepage block parameter group is the reference historical seepage block parameter group, and the approximate parameter variation is performed.

[0027] The finite element analysis discretizes the complex structure into a grid to simulate the mechanical influence (such as stress and deformation) of seepage on the rock mass. Based on the historical parameter group (reflecting the past seepage mode) and the approximately varied 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). The approximate variation is achieved by randomly selecting values within the historical parameter range, which increases the robustness of the model to uncertainties (such as geological heterogeneity) and ensures that the simulation results cover multiple working conditions.

[0028] The method for determining the rock distortion sensitive parameters corresponding to the real-time seepage block parameter group includes: 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.

[0029] 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 and feature matching). By analyzing the differences in flow velocity, water level, temperature block characteristics and rock structure, it is determined which simulation scenario is closest to the real-time data, so as to map the corresponding rock distortion sensitive parameters (such as abnormal probability). This step uses the prior knowledge of simulation results to quickly evaluate the real-time seepage risk, supports dynamic monitoring and early warning, and is suitable for seepage management in complex environments.

[0030] In some embodiments disclosed in the present application, the method for constructing a finite element analysis model for rock distortion analysis based on the historical and approximately generated seepage block parameter group includes: Step S501, intercept the corresponding block of the seepage block parameter group on the three-dimensional graph of the reservoir water body contact surface to obtain a three-dimensional seepage block surface, and grid the three-dimensional seepage block surface.

[0031] The seepage block parameter group corresponds to a specific area in the three-dimensional graph of the reservoir water body contact surface, and contains key geological structures that may seep. By intercepting these areas from the three-dimensional graph, an independent three-dimensional seepage block surface is formed, focusing on the active seepage area. Griding is to discretize the block surface into finite elements (such as tetrahedral or hexahedral grids) required for finite element analysis, each element has nodes and boundary conditions. Griding ensures that complex geometric structures can be processed by numerical methods, and refining the grid can improve the simulation accuracy while balancing the calculation efficiency, laying the foundation for subsequent mechanical and fluid analysis.

[0032] Step S502, for the flow velocity parameter, temperature distribution block feature of the seepage block parameter group, and rock mass structure feature parameter, use the rock mass structure feature parameter to configure the material properties of the three-dimensional seepage block surface, use the block feature of the temperature distribution block to determine the water flow direction of the three-dimensional seepage block surface, and based on the water flow velocity parameter of the block center, determine the water flow velocity parameter of the nodes at different positions of the three-dimensional seepage block surface, and based on the water level height in the seepage block parameter group, determine the water pressure parameter of the three-dimensional seepage block surface.

[0033] 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 features (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 feature (such as the radiation vector line cluster) indicates the geometric distribution of the water flow path, determines 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 model. The water level height determines the water pressure distribution, and the pressure parameters of each node are calculated through the hydrostatic formula (such as Bernoulli equation). These parameters together construct the boundary conditions and initial conditions of the seepage field.

[0034] Step S503, based on the water flow direction performance, water flow velocity parameter and water pressure parameter of the three-dimensional seepage block surface, perform finite element simulation on the three-dimensional seepage block surface.

[0035] The finite element simulation simulates the movement of water flow in the meshed 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 input to drive the 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.

[0036] 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: Step S504, analyze the finite element simulation of the three-dimensional seepage block surface, determine the abnormal probability of the abnormality of the three-dimensional seepage block surface under different water level heights, and identify the abnormal probability under different water level heights as the rock mass distortion sensitive parameters.

[0037] In some embodiments of the present application, the method for determining the abnormal probability of the abnormality of the three-dimensional seepage block surface under different water level heights comprises: Step S505, construct 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 when constructed, including determining the strength contrast of the defect degree of the nodes at different positions on the three-dimensional seepage block surface based on the water flow direction performance.

[0038] Step S506, configure each rock mass defect feature to the three-dimensional seepage block surface and perform finite element simulation respectively, calculate the ratio of the number of rock mass distortion to the number of rock mass defect features, and record it as the abnormal probability.

[0039] In some embodiments of the present application, the method for approximating the variation of the parameters comprises: Step S507, set a parameter interval for each parameter in the seepage block parameter group, randomly select a parameter in the parameter interval corresponding to each type of parameter to form a new seepage block parameter group.

[0040] 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: Step S301, establish a space coordinate system for the three-dimensional graph of the reservoir water body structure surface, determine the temperature measurement point coordinates of each initial virtual temperature measurement point, and calculate the straight line distance between adjacent temperature measurement point coordinates respectively.

[0041] 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 to form an initial virtual temperature measurement enclosing line, if the straight line distance between the initial virtual temperature measurement points is less than or equal to a preset value.

[0042] Step S303, the area enclosed by the initial virtual temperature measurement enclosing line is determined, denoted 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.

[0043] 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, denoted as a temperature distribution block.

[0044] In some embodiments of the present application, the block characteristics of the temperature distribution block include: Step S401, the 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 the radiation center, and the head of the radiation vector line is terminated at the edge line of the temperature distribution block, to obtain a radiation vector line cluster used to represent the temperature distribution block.

[0045] 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.

[0046] 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: 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.

[0047] The method for calculating the block difference between the block characteristics of the temperature distribution block includes, rotating the radiation vector line cluster corresponding to the block characteristics in sequence, screening out the radiation vector line clusters with the same coarse angle, and then calculating the cluster consistency between the radiation vector line clusters with the same coarse angle, and the cluster consistency is identified as the block difference between the block characteristics.

[0048] The method for screening the radiation vector line cluster with a coarse angle includes comparing the line length difference between each pair of opposite radiation vector lines, and 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, and if the number of coarse consistent radiation vector lines accounts for less than or equal to a preset value of the total number of radiation vector lines, the coarse angle of the radiation vector line cluster is determined to be consistent.

[0049] In some embodiments of the present application, the method for calculating the cluster consistency degree between the radiation vector line clusters includes: 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.

[0050] In step S603, the first line number of the radiation vector line 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.

[0051] 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, and the total line consistency parameter is modified based on the first line number ratio to obtain the cluster consistency degree.

[0052] 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, and the total line consistency parameter is modified based on the first line number ratio to obtain the cluster consistency degree. 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, and the total line consistency parameter is modified based on the first line number ratio to obtain the cluster consistency degree. is the line consistency parameter judgment function of the i-th radiation vector line, and n is the total number of radiation vector lines.

[0053] In some embodiments of the present application, a seepage prevention and drainage sensitivity analysis system based on reservoir area seepage data is also disclosed, which includes: The first module is used for acquiring the structural information of the reservoir water body contact surface, including the underwater geographical structure information and the overwater edge structure information, and drawing a three-dimensional diagram of the reservoir water body contact surface.

[0054] The second module is used for using the distributed optical fiber sensing technology to preliminarily arrange the distributed optical fiber on the reservoir water body contact surface, mapping 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 mapping the temperature parameters of the temperature measurement points on the optical fiber to the corresponding initial virtual temperature measurement points.

[0055] The third module is used for position correlation analysis on the initial virtual temperature measuring points and corresponding temperature parameters on the stereogram of the reservoir area water body contact surface, determining the correlation between the initial virtual temperature measuring points in position adjacency, and delimiting temperature distribution blocks based on the correlation between the initial virtual temperature measuring points.

[0056] 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 reservoir area water body contact surface, determining a representative water flow flow velocity parameter of the temperature distribution block, and correlating the water flow flow velocity parameter, the block feature of the temperature distribution block, the water level height and the rock quality structure feature parameter to obtain a seepage block parameter group.

[0057] The fifth module is used for constructing a finite element analysis model of rock quality distortion analysis based on the historical and approximately generated seepage block parameter groups, and determining the rock quality 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.

[0058] 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 quality distortion sensitive parameter corresponding to the real-time seepage block parameter group based on the comparison result.

[0059] The application discloses a seepage prevention and drainage sensitivity analysis method and system based on reservoir seepage amount data, relates to the technical field of reservoir anomaly monitoring, and discloses obtaining reservoir water body contact surface structure information 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 features, water level height and rock quality structure features are correlated, and a seepage block parameter group is formed; a finite element model is constructed based on historical and approximately varied parameter groups, rock quality distortion sensitive parameters are analyzed; real-time and simulated parameter groups are compared, and real-time sensitive parameters are determined.

[0060] Through the description of the above implementation manners, those skilled in the art can clearly understand that the application can be implemented by hardware, or by means of software and a necessary general hardware platform. Based on such understanding, the technical solutions of the application can be embodied in the form of a software product, which 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 methods described in various implementation scenarios of the application.

[0061] It should be pointed out finally that the above examples are only used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can still be modified or replaced equivalently, and these modifications or equivalent replacements should not 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 the following steps: In step S100, structure information of a water body contact surface of the reservoir area is acquired, including underwater geographical structure information and overwater edge structure information, and a three-dimensional diagram of the water body contact surface of the reservoir area is drawn; In step S200, a distributed optical fiber sensing technology is used 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; In step S300, position correlation analysis is performed 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, the correlation between the initial virtual temperature measuring points in adjacent positions is determined, and the initial virtual temperature measuring points are divided into temperature distribution blocks based on the correlation between the initial virtual temperature measuring points; In step S400, a flow velocity detection device is arranged at the center of each temperature distribution block on the three-dimensional diagram of the water body contact surface of the reservoir area, a representative water flow flow velocity parameter of the temperature distribution block is determined, and the water flow flow velocity parameter, block characteristics of the temperature distribution block, water level height and rock mass structure characteristic parameters are correlated to obtain a seepage block parameter group; The seepage block parameter group is subjected to finite element simulation analysis, and the analysis method comprises the following steps: In step S500, based on the historical and approximately generated seepage block parameter group, a finite element analysis model for rock mass distortion analysis is constructed, and rock mass distortion sensitive parameters corresponding to each simulated seepage block parameter group are determined, 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 subjected to approximate parameter variation; The method for determining the rock mass distortion sensitive parameters corresponding to the real-time seepage block parameter group comprises the following steps: 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 mass distortion sensitive parameters corresponding to the real-time seepage block parameter group are determined.

2. The method of analyzing the sensitivity of anti-seepage drainage according to the reservoir area leakage data according to claim 1, characterized in that, The method for constructing the finite element analysis model for rock mass distortion analysis based on the historical and approximately generated seepage block parameter group comprises the following steps: In step S501, the seepage block parameter group is intercepted on the block corresponding to the three-dimensional diagram of the water body contact surface of the reservoir area to obtain a three-dimensional seepage block surface, and the three-dimensional seepage block surface is meshed; In step S502, the water flow flow velocity parameter, the block characteristics of the temperature distribution block and the rock mass structure characteristic parameters 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 parameters, to determine water flow direction performance of the three-dimensional seepage block surface by using the block characteristics of the temperature distribution block, to determine 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 to determine water pressure parameters of the three-dimensional seepage block surface based on the water level height in the seepage block parameter group; In 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 velocity parameter and the water pressure parameter of 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 number of all 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.

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