Reservoir slope seepage management method and system based on intelligent sensor

Abstract

The present invention relates to the field of management technology and is a reservoir slope seepage management method and system based on intelligent sensors. The specific method comprises: obtaining reservoir water level change data, slope geological structure data, water flow velocity field and particle concentration distribution data; performing slope seepage dynamic anomaly analysis based on a three-dimensional model of the reservoir slope and the particle concentration distribution data to obtain a first seepage threat index; performing seepage environmental impact analysis based on historical reservoir water level changes, particle deposition data and slope area location to obtain a second seepage threat index; obtaining a comprehensive seepage threat index based on the first seepage threat index and the second seepage threat index, and performing risk ranking and early warning on the reservoir slope based on the comprehensive seepage threat index. The present invention solves the problem of delayed or misjudgment of slope instability risk assessment in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of management, and is a reservoir slope seepage management method and system based on intelligent sensors. Background Art

[0002] The seepage stability of reservoir slopes is a core issue concerning engineering safety. Existing monitoring and analysis methods have the following limitations. Existing methods primarily rely on single or limited monitoring data (such as fixed-point water levels and fracture water pressures) for static or quasi-static assessments, making it difficult to fully capture the complex dynamic seepage behavior and its hazard-causing mechanisms during the periodic storage and release of reservoir water. Specifically, existing technologies cannot effectively quantify the impact of dynamically changing reservoir water levels (especially the unsteady flow caused by rapid rise and fall) and the drag of water on the particle transport-deposition-scour cycle within slope fractures (such as the nonlinear amplification of shear stress and the release of particle clogging caused by the surge in flow velocity during the release period). They also lack the ability to accurately characterize the dynamic expansion of fracture networks due to hydraulic-stress coupling (such as accelerated expansion caused by tip stress concentration) and its synergistic effects (the formation of dominant seepage channels due to hydraulic interference between neighboring fractures). Furthermore, existing technologies exhibit biased predictions of equivalent seepage paths (modulated by both the dynamic fracture aperture and particle clogging) and equivalent hydraulic gradients (influenced by spatial attenuation factors). The current situation of incomplete capture of dynamic processes, insufficient characterization of multi-field coupling mechanisms, and shallow utilization of multi-source information makes it difficult for existing models to accurately warn of potential instability risks caused by the superposition of seepage dynamic anomalies (such as a sharp increase in local shear stress and the interconnection of fracture groups) and environmental factors (cyclical hydraulic loads and chemical corrosion), often leading to delayed risk assessment or misjudgment. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the problem of delayed or misjudgment of slope instability risk assessment in the prior art, and propose a reservoir slope seepage management method and system based on intelligent sensors.

[0004] In order to achieve the above-mentioned object, the technical solution of the reservoir slope seepage management method based on intelligent sensors of the present invention includes the following steps:

[0005] Step 1: Obtain reservoir water level change data, slope geological structure data, water velocity field and particle concentration distribution data, and calculate the equivalent seepage cross-sectional area of ​​the reservoir at different stages based on the collected data, and obtain the real-time dynamic seepage volume based on the equivalent seepage cross-sectional area;

[0006] Step 2: Extract slope geological structure data to establish a 3D model of the reservoir slope. Based on the 3D reservoir slope model and particle concentration distribution data, conduct slope seepage dynamic anomaly analysis, including regional threat assessment of individual fractures and identification and assessment of high-risk seepage channels.

[0007] The first seepage threat index is obtained based on the regional threat assessment results of individual fractures and the assessment results of high-risk seepage channels;

[0008] Step 3: Conduct seepage environmental impact analysis based on historical reservoir water level changes and particulate matter concentration distribution data, including: calculating the average seepage rate and the environmental correction factor by calculating the equivalent hydraulic gradient;

[0009] The second seepage threat index is obtained according to the real-time dynamic seepage rate, average seepage rate and environmental correction factor;

[0010] Step 4: Obtain a comprehensive seepage threat index based on the first seepage threat index and the second seepage threat index, and perform risk ranking and early warning on the reservoir slope based on the comprehensive seepage threat index. Preferably, step 1 includes:

[0011] A11: Acquire reservoir water level change data, slope geological structure data, water flow velocity field, and particulate matter concentration distribution data. The reservoir water level change data is initial fracture permeability data; the slope geological structure data includes a three-dimensional point cloud of the fracture network and optical fiber strain data; the water flow velocity field includes real-time water velocity data, real-time reservoir water storage rate data, and real-time water release rate data; and the particulate matter concentration distribution data is real-time particulate matter concentration data of the reservoir water body.

[0012] A12: Quantify dynamic crack opening based on optical fiber strain data, and obtain crack length and average width based on the three-dimensional point cloud of the crack network;

[0013] The equivalent seepage cross-sectional area of ​​each seepage zone during the reservoir filling stage is obtained based on the dynamic fissure opening, fissure length and average width;

[0014] At the same time, the clogging effect of particles is quantified based on the real-time particle concentration data of the reservoir water body, and the equivalent seepage cross-sectional area of ​​each seepage zone during the reservoir release stage is obtained;

[0015] A13: Collect the distances between all fracture measurement points and the drainage outlet, build a drainage outlet impact model, and output a spatial attenuation factor, which is used to predict the equivalent hydraulic gradient in C31.

[0016] A14: Extracts the reservoir water velocity field data and real-time particle concentration data collected in A11, and simultaneously extracts the equivalent seepage cross-sectional area of ​​each seepage zone during the reservoir's water storage and water release phases calculated in A12.

[0017] The real-time dynamic seepage rate is obtained by combining the reservoir water velocity field data, the equivalent seepage cross-sectional area of ​​each seepage area during the reservoir water storage and release stages, and the real-time particle concentration data of the reservoir water body. .

[0018] Preferably, step 2 includes:

[0019] B21: Extract the water velocity field, calculate the local shear stress of a single crack based on the single crack stress assessment strategy, continuously monitor the local shear stress of a single crack on the slope, and calculate the regional threat index corresponding to all single cracks on the slope based on the local shear stress of the single crack ;

[0020] B22: Collect the work logs of the reservoir and define the screening strategy for the starting point cracks. The screening strategy for the starting point cracks is as follows: when the reservoir is in the water storage stage, the single crack closest to the reservoir inlet is used as the starting point crack; when the reservoir is in the water discharge stage, the single crack closest to the reservoir outlet is used as the starting point crack;

[0021] Taking the starting crack on the slope as the central crack, the single cracks around the central crack are constructed as the neighboring crack groups of the central crack;

[0022] A coupling analysis is performed on the dynamic expansion of the neighboring fracture groups of the central fracture. Based on the coupling analysis results, it is determined whether individual fractures in the neighboring fracture groups should be merged with the central fracture. If they are merged, the merged fracture group is marked as a potential high-risk seepage channel. If they are not merged, several individual fractures that have not merged with other individual fractures on the slope are still marked as individual fractures.

[0023] The unmerged individual fractures are used as the targets for the next coupled analysis. Based on the screening strategy for the starting fractures, the starting fractures for the next coupled analysis are screened from the unmerged individual fractures.

[0024] Traverse all the individual cracks on the slope and execute the coupled analysis process in a loop;

[0025] Identify potential high-risk seepage channels and individual fractures marked on the slope and update the marked status to the reservoir slope 3D model;

[0026] The regional threat index of a single fissure does not need to be re-evaluated, and the regional threat index corresponding to the single fissure calculated in B21 is still used;

[0027] As for the channel threat index of potential high-risk seepage channels, it is necessary to re-evaluate and obtain it;

[0028] Channel threat index for evaluating potential high-risk seepage channels in three-dimensional reservoir slope models ;

[0029] B23: Extract the regional threat index of a single fracture output from B21 based on the updated 3D model of the reservoir slope and the channel threat index of potential high-risk seepage channels output by B22 , comprehensive assessment of the first seepage threat index of reservoir slopes ;

[0030] The calculation strategy of the first seepage threat index is:

[0031] ;

[0032] in, is the average value of the regional threat index of all individual cracks in the three-dimensional model of the reservoir slope; It is the average value of the channel threat index of all high-risk seepage channels in the three-dimensional model of the reservoir slope.

[0033] Preferably, step three includes:

[0034] C31: Collect the historical reservoir water level time series data for the past M years, the reservoir water level scheduling plan for the next three months, and the spatial attenuation factor calculated in step A13, construct a spectral characteristic function of water level changes, and extract the dominant frequency based on the spectral characteristic function to predict the water level fluctuation period;

[0035] The water level fluctuation cycle is predicted based on the dominant frequency to predict the future water level change curve of the reservoir, and the maximum water level change amplitude is intercepted in the predicted future water level change curve of the reservoir;

[0036] Combine the maximum water level fluctuation and the spatial attenuation factor calculated in step A13 to predict the equivalent hydraulic gradient ;

[0037] C32: Extract the equivalent hydraulic gradient predicted in C31 Based on the permeability data of the slope fracture network, a seepage rate-fracture aperture coupling equation was established to predict and analyze the dynamic seepage path evolution of the slope and obtain the average seepage rate. .

[0038] C33: Synchronously collect the rock mass-reservoir water temperature difference in each seepage area of ​​the slope And the groundwater dissolved oxygen concentration DO, calculate the environmental correction factor through the environmental coupling strategy, and output the environmental correction factor ;

[0039] C34: Extract the real-time dynamic seepage volume output by A14 in step 1 , the average seepage rate calculated by C32 Environmental correction factor calculated with C33 , establish a seepage threat prediction model, and generate the second seepage threat index through the seepage threat prediction model .

[0040] Preferably, step four includes:

[0041] D41: Extract the first seepage threat index output from step 2 and the second seepage threat index output in step 3 The first seepage threat index and the second seepage threat index are introduced into the comprehensive seepage threat assessment strategy to generate a comprehensive seepage threat index. , the comprehensive assessment strategy of seepage threat is specifically as follows:

[0042] ;

[0043] in, is the comprehensive seepage threat index, and e is the base of the natural logarithm.

[0044] D42: Compare the comprehensive seepage threat index with the seepage anomaly threshold. If the comprehensive seepage threat index is greater than or equal to the seepage anomaly threshold, it indicates that there is a seepage risk on the reservoir slope during the next reservoir operation cycle, and the slope needs to be reinforced according to the ranking results of the comprehensive seepage threat index. If the comprehensive seepage threat index is less than the seepage anomaly threshold, there is no seepage risk on the reservoir slope during the next reservoir operation cycle, and the reservoir slope should continue to be monitored.

[0045] In addition, the reservoir slope seepage management system based on intelligent sensors of the present invention includes the following modules:

[0046] Data acquisition module, seepage dynamics analysis module, seepage environment analysis module, seepage threat assessment module and early warning trigger module;

[0047] The data acquisition module is used to obtain reservoir water level change data, slope geological structure data, water flow velocity field and particle concentration distribution data, and calculate the equivalent seepage cross-sectional area of ​​the reservoir at different stages based on the collected data, and obtain real-time dynamic seepage volume based on the equivalent seepage cross-sectional area;

[0048] The seepage dynamics analysis module is used to extract slope geological structure data to establish a three-dimensional model of the reservoir slope. Based on the three-dimensional reservoir slope model and particle concentration distribution data, it performs slope seepage dynamics anomaly analysis, including regional threat assessment of individual fractures and channel identification and assessment of high-risk seepage channels.

[0049] The first seepage threat index is obtained based on the regional threat assessment results of individual fractures and the assessment results of high-risk seepage channels;

[0050] The seepage environment analysis module is used to analyze the seepage environment impact by combining the historical water level changes and particle concentration distribution data of the reservoir, including: obtaining the average seepage volume by calculating the equivalent hydraulic gradient prediction and calculating the environmental correction factor;

[0051] The second seepage threat index is obtained according to the real-time dynamic seepage rate, average seepage rate and environmental correction factor;

[0052] The seepage threat assessment module is used to obtain a comprehensive seepage threat index based on the first seepage threat index and the second seepage threat index;

[0053] The early warning trigger module performs risk ranking and early warning on the reservoir slope based on the comprehensive seepage threat index.

[0054] Compared with the prior art, the technical effects of the present invention are as follows:

[0055] The present invention constructs a comprehensive seepage risk perception system by integrating multi-source information such as reservoir water level dynamics, slope geological structure, water flow velocity field, and particle concentration. It quantifies the dynamic opening of cracks in the water storage stage and the scouring and clogging effect of particles in the water release stage, and realizes the dynamic and accurate calculation of the equivalent cross-sectional area of ​​the seepage channel. At the same time, the present invention introduces a spatial attenuation factor to effectively characterize the significant difference in the impact of water level fluctuations between the area near the drainage outlet and the far field area, thereby improving the accuracy of the spatial characteristic analysis of the seepage path. Based on the three-dimensional model of the reservoir slope, the local shear stress is calculated in real time and its nonlinear amplification effect with the surge in flow velocity is captured, thereby warning of the risk of crack wall scouring.

[0056] The present invention also combines the assessment of crack expansion rate and the analysis of the interference potential of adjacent cracks to dynamically identify and quantify the potential high-risk through-seepage channels formed by hydraulic-stress coupling and their threat level, and uses historical water level spectrum analysis to predict the dominant fluctuation period, drive the equivalent hydraulic gradient prediction and seepage path evolution simulation, and realize the prediction of seepage behavior under future scheduling scenarios. It effectively solves the problem of risk assessment lag or misjudgment caused by traditional methods due to information fragmentation, incomplete capture of dynamic processes, and insufficient characterization of multi-physical field coupling mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0058] Figure 1 Schematic diagram of the process of the reservoir slope seepage management method based on intelligent sensors of the present invention;

[0059] Figure 2 Schematic diagram of the process of step B22 of the present invention;

[0060] Figure 3It is a structural schematic diagram of the reservoir slope seepage management system based on intelligent sensors of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0064] Example 1:

[0065] like Figure 1 As shown, the reservoir slope seepage management method based on intelligent sensors according to an embodiment of the present invention is:

[0066] It should be noted that the reservoir slope is partially covered by the reservoir water, and the water flow exerts a drag force on the slope cracks. At the same time, the particles in the reservoir water affect the dynamic expansion of the cracks, exacerbating the risk of slope seepage.

[0067] like Figure 1 As shown, the specific steps are as follows:

[0068] In another embodiment, step one includes:

[0069] A11: Acquire reservoir water level change data, slope geological structure data, water flow velocity field, and particulate matter concentration distribution data. The reservoir water level change data is initial fracture permeability data; the slope geological structure data includes a three-dimensional point cloud of the fracture network and optical fiber strain data; the water flow velocity field includes real-time water velocity data, real-time reservoir water storage rate data, and real-time water release rate data; and the particulate matter concentration distribution data is real-time particulate matter concentration data of the reservoir water body.

[0070] A12: Quantify dynamic crack opening based on optical fiber strain data, and obtain crack length and average width based on the three-dimensional point cloud of the crack network;

[0071] The equivalent seepage cross-sectional area of ​​each seepage zone during the reservoir storage stage is obtained based on the dynamic fracture opening, fracture length and average width. ;

[0072] For example, in this embodiment, an equivalent seepage cross-sectional area of ​​each seepage zone in the reservoir water storage stage is provided. The calculation strategy is as follows:

[0073] ;

[0074] in, is the strain increment of the nth segment of optical fiber, reflecting the expansion degree of rock microcracks under stress; is the base strain; are the length and average width of the i-th crack respectively;

[0075] At the same time, the clogging effect of particles is quantified based on the real-time particle concentration data of the reservoir water body, and the equivalent seepage cross-sectional area of ​​each seepage area during the reservoir release stage is obtained. ;

[0076] For example, in this embodiment, an equivalent seepage cross-sectional area of ​​each seepage area in the reservoir release stage is provided. The calculation strategy is as follows:

[0077] ;

[0078] in, is the total cross-sectional area of ​​the seepage path;

[0079] G is the total number of real-time particulate matter types in the reservoir water body; is the average radius of the g-type particles, is the volume ratio of particles per unit volume.

[0080] It should be noted that particle deposition will block the seepage channel and reduce the effective flow space;

[0081] It should be noted that during the impoundment stage, particulate matter is redeposited and slope cracks have dynamic openings, which are quantified by optical fiber strain data;

[0082] When the reservoir releases water, the drag force of the water flow causes the sediment particles in the cracks to be washed away, and the effective hydraulic contact area increases;

[0083] A13: Collect the distances between all fracture measurement points and the drainage outlet, build a drainage outlet impact model, and output a spatial attenuation factor, which is used to predict the equivalent hydraulic gradient in C31.

[0084] For example, in this embodiment, an implementation method of influencing the model output spatial attenuation factor through a drain outlet is provided, specifically:

[0085] ;

[0086] in, is the spatial attenuation factor; is the distance between the i-th crack measuring point and the drainage outlet, =30m is the impact peak position, =15m is the standard deviation.

[0087] In this embodiment, the impact peak position and standard deviation are obtained through CFD simulation verification;

[0088] It should be noted that in the area near the drain outlet >0.8, the seepage threat index is significantly affected by water level fluctuations; in the far field area <0.2, mainly controlled by primary cracks in the rock mass.

[0089] A14: Extracts the reservoir water velocity field data and real-time particle concentration data collected in A11, and simultaneously extracts the equivalent seepage cross-sectional area of ​​each seepage zone during the reservoir's water storage and water release phases calculated in A12.

[0090] The real-time dynamic seepage rate is obtained by combining the reservoir water velocity field data, the equivalent seepage cross-sectional area of ​​each seepage area during the reservoir water storage and release stages, and the real-time particle concentration data of the reservoir water body. .

[0091] In this embodiment, the crack length and average width are obtained based on the crack three-dimensional point cloud data; the reservoir water velocity field data is obtained by ADCP measurement; the real-time particle concentration data of the reservoir water body is measured by the real-time turbidity sensor of the water body;

[0092] For example, in this embodiment, a real-time dynamic seepage rate is provided. The acquisition strategy is as follows:

[0093] ;

[0094] in, is the particle concentration uploaded by the real-time turbidity sensor of the water body closest to the i-th fissure; is the critical blocking concentration. For example, in this embodiment, =500mg / L; are the real-time water storage rate and water release rate of the reservoir respectively;

[0095] In another embodiment, step 2 includes:

[0096] B21: Extract the water velocity field, calculate the local shear stress of a single crack based on the single crack stress assessment strategy, continuously monitor the local shear stress of a single crack on the slope, and calculate the regional threat index corresponding to all single cracks on the slope based on the local shear stress of the single crack ;

[0097] For example, in this embodiment, a single crack stress assessment strategy is provided. The local shear stress of a single crack is calculated based on the single crack stress assessment strategy, specifically:

[0098] ;

[0099] in, is the local shear stress in the seepage area of ​​the i-th fracture;

[0100] is the water density in the reservoir; is the friction coefficient between all particles and cracks in the current water body; is the water flow velocity measured at the measuring point closest to the i-th fissure;

[0101] It should be noted that shear stress is proportional to the square of the flow velocity, and a small increase in the flow velocity of the reservoir water body will lead to a significant increase in erosion potential (non-linear effect). For example, when the flow velocity surges during the reservoir water release phase, It may grow exponentially, exacerbating fracture wall scour.

[0102] For example, in this embodiment, a regional threat index corresponding to all individual cracks on the slope is provided: The acquisition strategy is as follows:

[0103] ;

[0104] in, is the average level of real-time local shear stress of all cracks in the reservoir slope;

[0105] is the average level of historical local shear stress in all fractures of the current reservoir slope;

[0106] B22: Collect the work logs of the reservoir and define the screening strategy for the starting point cracks. The screening strategy for the starting point cracks is as follows: when the reservoir is in the water storage stage, the single crack closest to the reservoir inlet is used as the starting point crack; when the reservoir is in the water discharge stage, the single crack closest to the reservoir outlet is used as the starting point crack;

[0107] Taking the starting crack on the slope as the central crack, the single cracks around the central crack are constructed as the neighboring crack group of the central crack;

[0108] Conduct a coupling analysis on the dynamic expansion of the neighboring fracture groups of the central fracture (the first coupling analysis corresponds to the starting fracture, and the second coupling analysis corresponds to the starting fracture of the second screening). Based on the coupling analysis results, determine whether a single fracture in the neighboring fracture group should merge with the central fracture.

[0109] If merged, the merged fracture group will be marked as a potential high-risk seepage channel. If not merged, several single fractures that have not been merged with other single fractures on the slope will still be marked as single fractures.

[0110] The unmerged individual fractures are used as the targets for the next coupled analysis. Based on the screening strategy for the starting fractures, the starting fractures for the next coupled analysis are screened from the unmerged individual fractures.

[0111] Traverse all the individual cracks on the slope and execute the coupled analysis process in a loop;

[0112] Identify potential high-risk seepage channels and individual fractures marked on the slope and update the marked status to the reservoir slope 3D model;

[0113] It should be noted that the regional threat index of a single fissure does not need to be re-evaluated, and the regional threat index corresponding to the single fissure calculated in B21 is still used. ;

[0114] The channel threat index of the identified potential high-risk seepage channels (i.e., potential high-risk seepage channels composed of several single fractures) needs to be re-evaluated.

[0115] Channel threat index for evaluating potential high-risk seepage channels in three-dimensional reservoir slope models ;

[0116] B23: Extract the regional threat index of a single fracture output from B21 based on the updated 3D model of the reservoir slope and the channel threat index of potential high-risk seepage channels output by B22 , comprehensive assessment of the first seepage threat index of reservoir slopes ;

[0117] For example, in this embodiment, the calculation strategy of the first seepage threat index is:

[0118] ;

[0119] in, is the average value of the regional threat index of all individual cracks in the three-dimensional model of the reservoir slope; is the average value of the channel threat index of all high-risk seepage channels in the three-dimensional model of the reservoir slope;

[0120] In another embodiment, step three includes:

[0121] C31: Collect the historical reservoir water level time series data for the past M years, the reservoir water level scheduling plan for the next three months, and the spatial attenuation factor calculated in step A13, construct a spectral characteristic function of water level changes, and extract the dominant frequency based on the spectral characteristic function to predict the water level fluctuation period;

[0122] The water level fluctuation cycle is predicted based on the dominant frequency to predict the future water level change curve of the reservoir, and the maximum water level change amplitude is intercepted in the predicted future water level change curve of the reservoir;

[0123] Combine the maximum water level fluctuation and the spatial attenuation factor calculated in step A13 to predict the equivalent hydraulic gradient ;

[0124] Exemplarily, in this embodiment, the reservoir historical water level time series data includes: water storage rate, water storage duration, water release rate and water release duration;

[0125] For example, in this embodiment, a strategy for constructing a spectral characteristic function H(f) of water level change is provided, specifically:

[0126] ;

[0127] in, is the Fourier transform result of the reservoir water level time series data, is the reservoir water level at time time; f is the frequency, which represents the inverse of the water level change cycle; TIME is the total number of time data points; is the imaginary unit, used for complex number operations of Fourier transform;

[0128] It should be noted that the water level time series data is converted from the time domain to the frequency domain through Fourier transform, and the analysis (spectral amplitude), extract the dominant frequency (the frequency with the largest amplitude), identify the water level fluctuation period (the inverse of the dominant frequency), and use the water level fluctuation period and the water storage rate and water release rate in the reservoir water level scheduling plan for the next three months to predict the future water level change curve of the reservoir;

[0129] In the predicted reservoir water level change curve, the maximum water level change amplitude is intercepted. ;

[0130] For example, in this embodiment, combined with the maximum water level fluctuation The equivalent hydraulic gradient is predicted by the spatial attenuation factor calculated in step A13, specifically:

[0131] ;

[0132] in, is the predicted equivalent hydraulic gradient of the jth seepage area; The maximum water level fluctuation amplitude of the predicted future water level;

[0133] is the seepage path length of the jth seepage area; is the spatial attenuation factor of the jth seepage area;

[0134] It should be noted that when the jth seepage area is a potential high-risk seepage channel, that is, when there are several single cracks, Take the average crack length of all cracks in the jth seepage zone; Take the average value of the spatial attenuation factors of all cracks in the jth seepage zone;

[0135] C32: Extract the equivalent hydraulic gradient predicted in C31 Based on the permeability data of the slope fracture network, a seepage rate-fracture aperture coupling equation was established to predict and analyze the dynamic seepage path evolution of the slope and obtain the average seepage rate. .

[0136] For example, in this embodiment, a strategy for obtaining the average seepage rate is provided, specifically:

[0137] ;

[0138] in, is the average seepage rate; is the equivalent permeability of the j-th seepage area output in step B225; is the equivalent seepage cross-sectional area of ​​the jth seepage region; is the fluid dynamic viscosity of the reservoir water; is the reservoir water density; is the gravitational acceleration, that is, the gravitational factor that drives the fluid motion;

[0139] is the time attenuation coefficient; in this embodiment, ;

[0140] C33: Synchronously collect the rock mass-reservoir water temperature difference in each seepage area of ​​the slope And the groundwater dissolved oxygen concentration DO, calculate the environmental correction factor through the environmental coupling strategy, and output the environmental correction factor ;

[0141] For example, in this embodiment, the environmental correction factor The calculation strategy is as follows:

[0142] ;

[0143] in, is the benchmark seepage volume. In this embodiment, the average seepage volume in the dry season of the past three years is taken as the benchmark seepage volume;

[0144] It should be noted that the thermodynamic term Used to characterize the effect of temperature difference accelerating or inhibiting seepage; chemical terms Used to characterize the effect of dissolved oxygen concentration on the corrosion rate of slope rock mass; hydraulic term Used to characterize the environment's ability to transmit water flow.

[0145] C34: Extract the real-time dynamic seepage volume output by A14 in step 1 , the average seepage rate calculated by C32 Environmental correction factor calculated with C33 , establish a seepage threat prediction model, and generate the second seepage threat index through the seepage threat prediction model .

[0146] For example, in this embodiment, an implementation strategy for generating a second seepage threat index through a seepage threat prediction model is provided, specifically:

[0147] ;

[0148] in, is the second seepage threat index, I is the total number of single cracks on the slope;

[0149] In another embodiment, step four includes:

[0150] D41: Extract the first seepage threat index output from step 2 and the second seepage threat index output in step 3 The first seepage threat index and the second seepage threat index are introduced into the comprehensive seepage threat assessment strategy to generate a comprehensive seepage threat index. , the comprehensive assessment strategy of seepage threat is specifically as follows:

[0151] ;

[0152] in, is the comprehensive seepage threat index, and e is the base of the natural logarithm.

[0153] In another embodiment, step four also includes: comparing the comprehensive seepage threat index with the seepage anomaly threshold; if the comprehensive seepage threat index is greater than or equal to the seepage anomaly threshold, it indicates that there is a seepage risk on the reservoir slope during the next reservoir operation cycle, and the slope needs to be reinforced according to the ranking result of the comprehensive seepage threat index; if the comprehensive seepage threat index is less than the seepage anomaly threshold, there is no seepage risk on the reservoir slope during the next reservoir operation cycle, and the reservoir slope continues to be continuously monitored.

[0154] Example 2:

[0155] like Figure 2 As shown, in this embodiment, a specific implementation of step B22 is provided, specifically:

[0156] B221: Evaluate the stress concentration at the tip of a single crack and output the crack growth rate;

[0157] For example, in this embodiment, a single crack expansion rate is provided. The calculation strategy is as follows:

[0158] ;

[0159] in, is the current stress intensity factor, measured by distributed optical fiber; is the critical stress intensity factor, i.e., the ability of the reservoir slope to resist crack growth; is the nominal crack growth rate, calibrated by triaxial test; is the brittleness index of the slope rock mass. For example, in this embodiment, m=3.2;

[0160] It should be noted that the crack propagation rate is used to evaluate the mechanical stability of the crack tip. The higher the rate, the higher the risk of crack propagation.

[0161] B222: Collect the work logs of the reservoir and define the screening strategy for the starting point cracks. The screening strategy for the starting point cracks is as follows: when the reservoir is in the water storage stage, the single crack closest to the reservoir water inlet is used as the starting point crack; when the reservoir is in the water discharge stage, the single crack closest to the reservoir outlet is used as the starting point crack;

[0162] Taking the starting crack on the slope as the central crack, the central crack as the center of the circle, and the preset maximum collaborative distance as the radius, search for nearby single cracks around the crack center, and construct the single cracks around the central crack into a neighboring crack group of the central crack; illustratively, in this embodiment, the maximum collaborative distance is the crack length of the central crack;

[0163] Extracting the three-dimensional spatial parameters of all individual fractures in the neighboring fracture group, importing the three-dimensional spatial parameters into the interference potential index calculation strategy, and evaluating the synergistic potential of all individual fractures in the neighboring fracture group and the central fracture;

[0164] Exemplarily, in this embodiment, the three-dimensional spatial parameters include: strike angle and crack length;

[0165] For example, in this embodiment, a strategy for calculating the interference potential index is provided, specifically:

[0166] ;

[0167] in, is the interference potential index of the bth single fracture in the neighboring fracture group;

[0168] are the crack length and strike angle of the central crack, respectively; is the length and strike angle of the bth single fracture in the adjacent fracture group; d is the average distance between the central fracture and the fracture center of all single fractures in the adjacent fracture group;

[0169] B223: Preset the neighboring fracture interference threshold, screen out individual fractures in the neighboring fracture group whose interference potential index is greater than the neighboring fracture interference threshold, merge the screened individual fractures with the central fracture, and mark them as potential high-risk seepage channels (i.e., potential high-risk seepage channels composed of several individual fractures);

[0170] If a single fissure in a neighboring fissure group has an interference potential index less than or equal to the neighboring fissure interference threshold, it will not be merged. Several single fissures that have not been merged with other single fissures on the slope are still marked as single fissures.

[0171] Identify potential high-risk seepage channels and individual fractures marked on the slope and update the marked status to the reservoir slope 3D model;

[0172] B224: Evaluate the synergy effect coefficient of potential high-risk seepage channels based on the spacing between individual fractures and the average fracture length in the potential high-risk seepage channels ;

[0173] For example, in this embodiment, a calculation strategy for the synergistic effect coefficient of a potential high-risk seepage channel is provided, specifically:

[0174] ;

[0175] in, is the synergistic effect coefficient of the potential high-risk seepage channel, reflecting the hydraulic interference intensity between adjacent fractures;

[0176] is the average value of the spacing between all adjacent fractures in the potential high-risk seepage channel, that is, the smaller the spatial distance, the higher the possibility of interference;

[0177] is the average fracture length of all adjacent fractures in the potential high-risk seepage channel;

[0178] B225: Synergy coefficient based on the output of B224 Correcting the equivalent permeability to obtain a corrected equivalent permeability value;

[0179] For example, in this embodiment, a correction implementation of equivalent permeability is provided, specifically:

[0180] ;

[0181] in, are the initial permeability of the fracture and the corrected equivalent permeability, respectively. It should be noted that the corrected equivalent permeability is used to reflect the seepage capacity under the action of potential high-risk seepage channels;

[0182] B226: Extract the single crack expansion rate output from step B221 Equivalent permeability correction value output by B225 , assessing the channel threat index of potential high-risk seepage channels .

[0183] For example, in this embodiment, a strategy for evaluating the channel threat index of a potential high-risk seepage channel is provided, specifically:

[0184] ;

[0185] in, is the channel threat index of the potential high-risk seepage channel;

[0186] is the critical crack expansion rate, that is, the threshold rate that causes rock mass instability, is the critical permeability, which is the threshold permeability that characterizes the rock mass failure caused by seepage, and is obtained by experimental fitting.

[0187] Example 3:

[0188] like Figure 3 As shown, the reservoir slope seepage management system based on intelligent sensors according to an embodiment of the present invention is as follows: Figure 3 As shown, it includes the following modules:

[0189] Data acquisition module, seepage dynamics analysis module, seepage environment analysis module, seepage threat assessment module and early warning trigger module;

[0190] The data acquisition module is used to obtain reservoir water level change data, slope geological structure data, water flow velocity field and particle concentration distribution data, and calculate the equivalent seepage cross-sectional area of ​​the reservoir at different stages based on the collected data, and obtain real-time dynamic seepage volume based on the equivalent seepage cross-sectional area;

[0191] The seepage dynamics analysis module is used to extract slope geological structure data to establish a three-dimensional model of the reservoir slope. Based on the three-dimensional reservoir slope model and particle concentration distribution data, it performs slope seepage dynamics anomaly analysis, including regional threat assessment of individual fractures and channel identification and assessment of high-risk seepage channels.

[0192] The first seepage threat index is obtained based on the regional threat assessment results of individual fractures and the assessment results of high-risk seepage channels;

[0193] The seepage environment analysis module is used to analyze the seepage environment impact by combining the historical water level changes and particle concentration distribution data of the reservoir, including: obtaining the average seepage volume by calculating the equivalent hydraulic gradient prediction and calculating the environmental correction factor;

[0194] The second seepage threat index is obtained according to the real-time dynamic seepage rate, average seepage rate and environmental correction factor;

[0195] The seepage threat assessment module is used to obtain a comprehensive seepage threat index based on the first seepage threat index and the second seepage threat index;

[0196] The early warning trigger module performs risk ranking and early warning on the reservoir slope based on the comprehensive seepage threat index.

[0197] Example 4:

[0198] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0199] The processor executes the above-mentioned reservoir slope seepage management method based on intelligent sensors by calling the computer program stored in the memory.

[0200] This electronic device can vary significantly depending on its configuration or performance. It can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the smart sensor-based reservoir slope seepage management method provided in the above-mentioned method embodiment. The electronic device can also include other components for implementing the device's functions. For example, the electronic device can also include components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0201] Embodiment 5:

[0202] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0203] When the computer program is run on a computer device, the computer device is enabled to execute the above-mentioned reservoir slope seepage management method based on intelligent sensors.

[0204] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0205] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0206] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0207] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0208] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0209] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0210] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0211] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A reservoir slope seepage management method based on intelligent sensors, characterized in that: The method comprises: Step 1: Obtain reservoir water level change data, slope geological structure data, water velocity field and particle concentration distribution data, and calculate the equivalent seepage cross-sectional area of ​​the reservoir at different stages based on the collected data, and obtain the real-time dynamic seepage volume based on the equivalent seepage cross-sectional area; Step 2: Extract slope geological structure data to establish a 3D model of the reservoir slope. Based on the 3D reservoir slope model and particle concentration distribution data, conduct slope seepage dynamic anomaly analysis, including regional threat assessment of individual fractures and identification and assessment of high-risk seepage channels. The first seepage threat index is obtained based on the regional threat assessment results of individual fractures and the assessment results of high-risk seepage channels; Step 3: Conduct seepage environmental impact analysis based on historical reservoir water level changes and particulate matter concentration distribution data, including: calculating the average seepage rate and the environmental correction factor by calculating the equivalent hydraulic gradient; The second seepage threat index is obtained according to the real-time dynamic seepage rate, average seepage rate and environmental correction factor; Step 4: Obtain a comprehensive seepage threat index based on the first seepage threat index and the second seepage threat index, and perform risk ranking and early warning on the reservoir slope based on the comprehensive seepage threat index.

2. The reservoir slope seepage management method based on intelligent sensors according to claim 1 is characterized in that: Step 1 includes: A11: Acquire reservoir water level change data, slope geological structure data, water flow velocity field, and particulate matter concentration distribution data. The reservoir water level change data is initial fracture permeability data; the slope geological structure data includes a three-dimensional point cloud of the fracture network and optical fiber strain data; the water flow velocity field includes real-time water velocity data, real-time reservoir water storage rate data, and real-time water release rate data; and the particulate matter concentration distribution data is real-time particulate matter concentration data of the reservoir water body. A12: Quantify dynamic crack opening based on optical fiber strain data, and obtain crack length and average width based on the three-dimensional point cloud of the crack network; The equivalent seepage cross-sectional area of ​​each seepage zone during the reservoir filling stage is obtained based on the dynamic fissure opening, fissure length and average width; At the same time, the clogging effect of particles is quantified based on the real-time particle concentration data of the reservoir water body, and the equivalent seepage cross-sectional area of ​​each seepage zone during the reservoir release stage is obtained; A13: Collect the distances from all fracture measurement points to the drainage outlet, build a drainage outlet impact model, and output a spatial attenuation factor, which is used to predict the equivalent hydraulic gradient; A14: Extracts the reservoir water velocity field data and real-time particle concentration data collected in A11, and simultaneously extracts the equivalent seepage cross-sectional area of ​​each seepage zone during the reservoir's water storage and water release phases calculated in A12. The real-time dynamic seepage rate is obtained by combining the reservoir water velocity field data, the equivalent seepage cross-sectional area of ​​each seepage area during the reservoir water storage and release stages, and the real-time particle concentration data of the reservoir water body. .

3. The reservoir slope seepage management method based on intelligent sensors according to claim 2 is characterized in that: Step 2 includes: B21: Extract the water velocity field, calculate the local shear stress of a single crack based on the single crack stress assessment strategy, continuously monitor the local shear stress of a single crack on the slope, and calculate the regional threat index corresponding to all single cracks on the slope based on the local shear stress of the single crack ; B22: Collect the work logs of the reservoir and define the screening strategy for the starting point cracks. The screening strategy for the starting point cracks is as follows: when the reservoir is in the water storage stage, the single crack closest to the reservoir inlet is used as the starting point crack; when the reservoir is in the water discharge stage, the single crack closest to the reservoir outlet is used as the starting point crack; Taking the starting crack on the slope as the central crack, the single cracks around the central crack are constructed as the neighboring crack groups of the central crack; Conduct a coupling analysis on the dynamic expansion of the neighboring fracture groups of the central fracture, and determine whether a single fracture in the neighboring fracture group will merge with the central fracture based on the coupling analysis results; If merged, the merged fracture group will be marked as a potential high-risk seepage channel. If not merged, several single fractures that have not been merged with other single fractures on the slope will still be marked as single fractures. The unmerged individual fractures are used as the targets for the next coupled analysis. Based on the screening strategy for the starting fractures, the starting fractures for the next coupled analysis are screened from the unmerged individual fractures. Traverse all the individual cracks on the slope and execute the coupled analysis process in a loop; Identify potential high-risk seepage channels and individual fractures marked on the slope and update the marked status to the reservoir slope 3D model; The regional threat index of a single fissure does not need to be re-evaluated, and the regional threat index corresponding to the single fissure calculated in B21 is still used; As for the channel threat index of potential high-risk seepage channels, it is necessary to re-evaluate and obtain it; Channel threat index for evaluating potential high-risk seepage channels in three-dimensional reservoir slope models ; B23: Extract the regional threat index of a single fracture output from B21 based on the updated 3D model of the reservoir slope and the channel threat index of potential high-risk seepage channels output by B22 , comprehensive assessment of the first seepage threat index of reservoir slopes ; The calculation strategy of the first seepage threat index is: ； in, is the average value of the regional threat index of all individual cracks in the three-dimensional model of the reservoir slope; It is the average value of the channel threat index of all high-risk seepage channels in the three-dimensional model of the reservoir slope.

4. The reservoir slope seepage management method based on intelligent sensors according to claim 3 is characterized in that: Step three includes: C31: Collect the historical reservoir water level time series data for the past M years, the reservoir water level scheduling plan for the next three months, and the spatial attenuation factor calculated in step A13, construct a spectral characteristic function of water level changes, and extract the dominant frequency based on the spectral characteristic function to predict the water level fluctuation period; The water level fluctuation cycle is predicted based on the dominant frequency to predict the future water level change curve of the reservoir, and the maximum water level change amplitude is intercepted in the predicted future water level change curve of the reservoir; Combine the maximum water level fluctuation and the spatial attenuation factor calculated in step A13 to predict the equivalent hydraulic gradient ; C32: Extract the equivalent hydraulic gradient predicted in C31 Based on the permeability data of the slope fracture network, a seepage rate-fracture aperture coupling equation was established to predict and analyze the dynamic seepage path evolution of the slope and obtain the average seepage rate. .

5. The reservoir slope seepage management method based on intelligent sensors according to claim 4 is characterized in that: Step three also includes: C33: Synchronously collect the rock mass-reservoir water temperature difference in each seepage area of ​​the slope And the groundwater dissolved oxygen concentration DO, calculate the environmental correction factor through the environmental coupling strategy, and output the environmental correction factor ; C34: Extract the real-time dynamic seepage volume output by A14 in step 1 , the average seepage rate calculated by C32 Environmental correction factor calculated with C33 , establish a seepage threat prediction model, and generate the second seepage threat index through the seepage threat prediction model .

6. The reservoir slope seepage management method based on intelligent sensors according to claim 5 is characterized in that: Step 4 includes: D41: Extract the first seepage threat index output from step 2 and the second seepage threat index output in step 3 The first seepage threat index and the second seepage threat index are introduced into the comprehensive seepage threat assessment strategy to generate a comprehensive seepage threat index. , the comprehensive assessment strategy of seepage threat is specifically as follows: ； in, is the comprehensive seepage threat index, and e is the base of the natural logarithm.

7. The reservoir slope seepage management method based on intelligent sensors according to claim 6 is characterized in that: Step 4 also includes: D42: Compare the comprehensive seepage threat index with the seepage anomaly threshold. If the comprehensive seepage threat index is greater than or equal to the seepage anomaly threshold, it indicates that there is a seepage risk on the reservoir slope during the next reservoir operation cycle, and the slope needs to be reinforced according to the ranking results of the comprehensive seepage threat index. If the comprehensive seepage threat index is less than the seepage anomaly threshold, there is no seepage risk on the reservoir slope during the next reservoir operation cycle, and the reservoir slope should continue to be monitored.

8. A reservoir slope seepage management system based on an intelligent sensor, used to implement the reservoir slope seepage management method based on an intelligent sensor according to any one of claims 1 to 7, characterized in that: The system includes the following modules: Data acquisition module, seepage dynamics analysis module, seepage environment analysis module, seepage threat assessment module and early warning trigger module; The data acquisition module is used to obtain reservoir water level change data, slope geological structure data, water flow velocity field and particle concentration distribution data, and calculate the equivalent seepage cross-sectional area of ​​the reservoir at different stages based on the collected data, and obtain real-time dynamic seepage volume based on the equivalent seepage cross-sectional area; The seepage dynamics analysis module is used to extract slope geological structure data to establish a three-dimensional model of the reservoir slope. Based on the three-dimensional reservoir slope model and particle concentration distribution data, it performs slope seepage dynamics anomaly analysis, including regional threat assessment of individual fractures and channel identification and assessment of high-risk seepage channels. The first seepage threat index is obtained based on the regional threat assessment results of individual fractures and the assessment results of high-risk seepage channels; The seepage environment analysis module is used to analyze the seepage environment impact by combining the historical water level changes and particle concentration distribution data of the reservoir, including: obtaining the average seepage volume by calculating the equivalent hydraulic gradient prediction and calculating the environmental correction factor; The second seepage threat index is obtained according to the real-time dynamic seepage rate, average seepage rate and environmental correction factor; The seepage threat assessment module is used to obtain a comprehensive seepage threat index based on the first seepage threat index and the second seepage threat index; The early warning trigger module performs risk ranking and early warning on the reservoir slope based on the comprehensive seepage threat index.

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