Real-time prediction method, device and equipment for gas storage surrounding rock fracture and medium

Abstract

The invention provides a real-time prediction method and device for gas storage surrounding rock fracture, equipment and a medium. Comprising the following steps: acquiring an acoustic emission signal and a stress-strain parameter generated in a fracture process of a specified type of rock in surrounding rock of the gas storage; screening out target acoustic emission signals meeting training conditions based on acoustic emission characteristic parameters corresponding to the acoustic emission signals; determining a rock state stage label of the target acoustic emission signal based on the stress-strain curve of the stress-strain parameter corresponding to the target acoustic emission signal; based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label of the target acoustic emission signal, constructing a sample data set; training based on the sample data set to obtain a rock fracture prediction model; the rock fracture prediction model is adopted to process to-be-detected acoustic emission signals, collected in real time, of the target rock to obtain a rock state stage, and the to-be-detected acoustic emission signals are signals, collected by the piezoelectric ceramic sensor, of the target rock around the reinforced structure of the gas storage.

Description

Technical Field

[0001] The present application relates to the technical field of rock fracture prediction, and in particular to a real-time prediction method, device, equipment and medium for surrounding rock fracture of a gas storage reservoir. Background Art

[0002] Compressed air energy storage power station is a new type of energy storage power station. It uses the excess electricity of the power system at low capacity load to compress air and store it in underground caves. It is released when needed and generates electricity through generator sets to meet the needs of peak loads. Underground caves usually include natural salt caverns and artificial underground energy storage chambers. However, natural salt cavern gas storage is difficult to locate due to the distribution of salt cavern resources. The gas storage device of large-scale compressed air energy storage power station generally requires a larger gas storage capacity. Artificial excavation of underground gas storage is considered to be a popularizable type of gas storage. For the artificial excavation and construction of underground gas storage in hard rock, the current mainstream design idea is that the sealing layer only ensures air tightness, and the high-pressure gas acts on the inner wall of the gas storage and is transmitted through the lining, and the surrounding rock mainly bears the high internal pressure. In the underground gas storage scenario, rock fracture prediction technology is crucial.

[0003] Rock fracture prediction technology is not only related to the safety and economy of gas storage, but also can prevent geological disasters caused by rock fracture. Traditional rock fracture monitoring methods mainly rely on direct observation and deformation monitoring, but these methods have problems such as poor real-time performance, insufficient precursors and low success rate. Summary of the invention

[0004] The embodiments of the present application provide a real-time prediction method, device, equipment and medium for the fracture of surrounding rocks of a gas storage reservoir, so as to solve the problems of poor real-time performance, insufficient precursors and low success rate of traditional rock fracture monitoring methods.

[0005] In order to solve the above technical problems, the embodiments of the present application are implemented as follows: In a first aspect, an embodiment of the present application provides a real-time prediction method for surrounding rock fracture of a gas storage reservoir, the method comprising: Acquiring acoustic emission signals generated by a specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and stress and strain parameters of the rock, wherein the acoustic emission signals are signals collected by a piezoelectric ceramic sensor arranged on the surface of the rock during the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock during the process of continuously applying external force to the rock; Based on the acoustic emission characteristic parameters corresponding to the acoustic emission signals, a target acoustic emission signal meeting the training conditions is screened out from the acoustic emission signals; Determine a rock state stage label corresponding to the target acoustic emission signal based on a stress-strain curve of stress-strain parameters corresponding to the target acoustic emission signal; Constructing a sample data set based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal; A rock fracture prediction model for predicting the rock state stage is obtained by training based on the sample data set; The rock fracture prediction model is used to process the acoustic emission signals to be measured of the target rock of the specified type in the surrounding rock of the gas storage reservoir collected in real time to obtain the rock state stage corresponding to the target rock. The acoustic emission signals to be measured are signals of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor.

[0006] Optionally, the acoustic emission characteristic parameters include: acoustic emission impact number, acoustic emission energy, acoustic emission RA value, acoustic emission b value, signal duration, ringing count and acoustic emission rate.

[0007] Optionally, the filtering out a target acoustic emission signal that meets a training condition from the acoustic emission signal based on the acoustic emission characteristic parameters corresponding to the acoustic emission signal includes: The target acoustic emission signal is obtained by eliminating the acoustic emission signals whose signal duration is less than a time threshold and / or whose ring count is less than a count threshold.

[0008] Optionally, the determining the rock state stage label corresponding to the target acoustic emission signal based on the stress-strain curve of the stress-strain parameter corresponding to the target acoustic emission signal includes: Determining a stress-strain curve corresponding to the target acoustic emission signal based on the stress-strain parameter corresponding to the target acoustic emission signal; determining a rock elastic modulus of the rock based on the slope of the stress-strain curve; Based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter and the peak strain parameter of the rock, a rock state stage label corresponding to the target acoustic emission signal is determined.

[0009] Optionally, determining the rock elastic modulus of the rock based on the slope of the stress-strain curve includes: Obtaining a target stress-strain curve having a linear relationship on the stress-strain curve; The rock elastic modulus of the rock is calculated based on the stress parameters and strain parameters corresponding to the starting point and the end point of the target stress-strain curve, respectively.

[0010] Optionally, determining the rock state stage label corresponding to the target acoustic emission signal based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter and the peak strain parameter of the rock includes: Based on the stress parameter and strain parameter of each point on the stress-strain curve, the instantaneous secant modulus corresponding to each point is calculated; When the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the first point on the stress-strain curve is greater than a first threshold value, and the strain parameter of the first point is less than the peak strain parameter, it is determined that the rock state stage label of the target acoustic emission signal corresponding to the first point is an elastic deformation stage label, and the first threshold value is a value greater than 0 and less than 1; When the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the second point on the stress-strain curve is greater than 0, and the strain parameter of the second point is less than a set multiple of the peak strain parameter, it is determined that the rock state stage label of the target acoustic emission signal corresponding to the second point is a compaction stage label, and the set multiple is a value greater than 0 and less than 1; When the ratio of the instantaneous secant modulus corresponding to the third point on the stress-strain curve to the rock elastic modulus is less than 0, determining that the rock state stage label of the target acoustic emission signal corresponding to the third point is a post-peak failure stage label; The rock state stage labels of the remaining acoustic emission signals in the target acoustic emission signal are determined as plastic stage labels, and the remaining acoustic emission signals refer to the acoustic emission signals in the target acoustic emission signal that are not the elastic deformation stage label, the compaction stage label, and the post-peak failure stage label.

[0011] Optionally, the rock fracture prediction model for predicting the rock state stage obtained by training based on the sample data set includes: Inputting the acoustic emission characteristic parameters of the target acoustic emission signal in the sample data set into the rock fracture prediction model to be trained, and processing the acoustic emission characteristic parameters of the target acoustic emission signal by the rock fracture prediction model to be trained; Acquiring a predicted rock state stage corresponding to the target acoustic emission signal output by the rock fracture prediction model to be trained; Based on the rock state stage label and the predicted rock state stage, a loss value of the rock fracture prediction model to be trained is calculated; When the loss value is within a preset range, the trained rock fracture prediction model to be trained is used as the final rock fracture prediction model.

[0012] In a second aspect, an embodiment of the present application provides a real-time prediction device for surrounding rock fracture of a gas storage reservoir, the device comprising: An acoustic emission signal acquisition module, used to acquire acoustic emission signals generated by a specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and stress and strain parameters of the rock, wherein the acoustic emission signals are signals collected by a piezoelectric ceramic sensor arranged on the surface of the rock during the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock during the process of continuously applying external force to the rock; A target signal screening module, used for screening target acoustic emission signals that meet training conditions from the acoustic emission signals based on acoustic emission characteristic parameters corresponding to the acoustic emission signals; A state stage label determination module, used to determine the rock state stage label corresponding to the target acoustic emission signal based on a stress-strain curve of stress-strain parameters corresponding to the target acoustic emission signal; A sample data set construction module, used to construct a sample data set based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal; A rock fracture prediction model training module, used for training a rock fracture prediction model for predicting a rock state stage based on the sample data set; The rock state real-time prediction module is used to process the acoustic emission signals to be measured of the target rock of the specified type in the surrounding rock of the gas storage reservoir collected in real time by using the rock fracture prediction model to obtain the rock state stage corresponding to the target rock, wherein the acoustic emission signals to be measured are signals of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor.

[0013] Optionally, the acoustic emission characteristic parameters include: acoustic emission impact number, acoustic emission energy, acoustic emission RA value, acoustic emission b value, signal duration, ringing count and acoustic emission rate.

[0014] Optionally, the target signal screening module includes: The target signal acquisition unit is used to eliminate the acoustic emission signals whose signal duration is less than a time threshold and / or whose ring count is less than a count threshold in the acoustic emission signals, so as to obtain the target acoustic emission signal.

[0015] Optionally, the state stage label determination module includes: A stress-strain curve determining unit, configured to determine a stress-strain curve corresponding to the target acoustic emission signal based on the stress-strain parameter corresponding to the target acoustic emission signal; A rock elastic modulus determination unit, used to determine the rock elastic modulus of the rock based on the slope of the stress-strain curve; The state stage label determination unit is used to determine the rock state stage label corresponding to the target acoustic emission signal based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter and the peak strain parameter of the rock.

[0016] Optionally, the rock elastic modulus determination unit includes: A target curve acquisition subunit, used to acquire a target stress-strain curve having a linear relationship on the stress-strain curve; The rock elastic modulus calculation subunit is used to calculate the rock elastic modulus of the rock based on the stress parameters and strain parameters corresponding to the starting point and the end point of the target stress-strain curve respectively.

[0017] Optionally, the state stage label determination unit includes: An instantaneous secant modulus calculation subunit, used to calculate the instantaneous secant modulus corresponding to each point on the stress-strain curve based on the stress parameter and the strain parameter of each point; An elastic stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the first point on the stress-strain curve is an elastic deformation stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the first point on the stress-strain curve is greater than a first threshold value and the strain parameter of the first point is less than the peak strain parameter, and the first threshold value is a value greater than 0 and less than 1; A compaction stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the second point on the stress-strain curve is a compaction stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the second point on the stress-strain curve is greater than 0 and the strain parameter of the second point is less than a set multiple of the peak strain parameter, and the set multiple is a value greater than 0 and less than 1; A post-peak stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the third point on the stress-strain curve is a post-peak failure stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the third point on the stress-strain curve is less than 0; The plastic stage label determination subunit is used to determine the rock state stage labels of the remaining acoustic emission signals in the target acoustic emission signal as plastic stage labels, and the remaining acoustic emission signals refer to the acoustic emission signals in the target acoustic emission signal that are not the elastic deformation stage label, the compaction stage label, and the post-peak failure stage label.

[0018] Optionally, the rock fracture prediction model training module includes: An acoustic emission characteristic parameter input unit, used to input the acoustic emission characteristic parameters of the target acoustic emission signal in the sample data set into the rock fracture prediction model to be trained, and the rock fracture prediction model to be trained processes the acoustic emission characteristic parameters of the target acoustic emission signal; A rock state stage prediction unit, used to obtain the predicted rock state stage corresponding to the target acoustic emission signal output by the rock fracture prediction model to be trained; A loss value calculation unit, used for calculating the loss value of the rock fracture prediction model to be trained based on the rock state stage label and the predicted rock state stage; The rock fracture prediction model acquisition unit is used to use the trained rock fracture prediction model to be trained as the final rock fracture prediction model when the loss value is within a preset range.

[0019] In a third aspect, an embodiment of the present application provides an electronic device, including: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements any of the above-mentioned methods for real-time prediction of surrounding rock fractures of a gas storage reservoir.

[0020] In a fourth aspect, an embodiment of the present application provides a readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute any of the above-mentioned methods for real-time prediction of gas storage surrounding rock fractures.

[0021] In the embodiment of the present application, by obtaining the acoustic emission signal and the stress and strain parameters of the rock generated by the specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process, the acoustic emission signal is a signal collected by a piezoelectric ceramic sensor arranged on the surface of the rock in the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock in the process of continuously applying external force to the rock. Based on the acoustic emission characteristic parameters corresponding to the acoustic emission signal, the target acoustic emission signal that meets the training conditions is screened out from the acoustic emission signal. Based on the stress-strain curve of the stress-strain parameters corresponding to the target acoustic emission signal, the rock state stage label corresponding to the target acoustic emission signal is determined. Based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal, a sample data set is constructed. Based on the sample data set training, a rock fracture prediction model for predicting the rock state stage is obtained, and the rock fracture prediction model is used to process the acoustic emission signal to be measured of the specified type of target rock in the surrounding rock of the gas storage reservoir collected in real time, and the rock state stage corresponding to the target rock is obtained. The acoustic emission signal to be measured is the signal of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor. The embodiment of the present application uses acoustic emission technology to monitor the acoustic emission characteristic parameters of rock fracture at different stages to train a rock fracture prediction model, so that the trained model can predict the stage of rock fracture, improve the real-time monitoring capability of the rock fracture process, quickly capture and process micro-fracture signals, achieve timely warning, and improve the timeliness of rock fracture monitoring. At the same time, the use of model prediction can greatly improve the accuracy of acoustic emission signal feature extraction and state stage classification prediction, and can more accurately identify the precursor signals and fracture modes of rock fracture.

[0022] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A flowchart of the steps of a real-time prediction method for surrounding rock fracture of a gas storage reservoir provided in an embodiment of the present application; Figure 2 A schematic diagram of a label generation process provided in an embodiment of the present application; Figure 3 A schematic diagram of a model training process provided in an embodiment of the present application; Figure 4 A schematic structural diagram of a real-time prediction device for surrounding rock fracture of a gas storage reservoir provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In this embodiment, a compressed air energy storage power station (CAES) is an electric power storage technology that uses compressed air to store energy. It mainly compresses air to a high-pressure state by using an electric motor during the low-demand period of the power grid and stores it in an underground cave, releases the stored compressed air during the peak demand period of the power grid, and drives the generator to generate electricity through a turbine or expander, thereby realizing the storage and release of electricity. Underground caves usually include natural salt caverns and artificial underground energy storage chambers, etc. However, natural salt cavern gas storage is difficult to site due to the distribution of salt cavern resources. The gas storage device of a large-scale compressed gas energy storage power station generally requires a large gas storage capacity. Artificial excavation of underground gas storage is considered to be a popularizable gas storage type. For the artificial excavation and construction of underground gas storage in hard rock, the current mainstream design idea is that the sealing layer only ensures air tightness, and the high-pressure gas acts on the inner wall of the gas storage and is transmitted through the lining, and the surrounding rock mainly bears the high internal pressure.

[0027] However, underground rock is a material with defects such as joints, microcracks and pores. Under the influence of external loads and environmental factors, the cracks inside the rock will expand, eventually leading to rock failure. Rock failure may cause geological disasters such as karst collapse and ground subsidence. Rock failure may cause the gas storage to be impacted or squeezed, causing damage or failure of the gas storage, greatly reducing the operational stability of the gas storage.

[0028] Based on the above technical problems, an embodiment of the present application is proposed. Please see the following description of each method step corresponding to the embodiment of the present application for details.

[0029] Reference Figure 1 , showing a flow chart of the steps of a real-time prediction method for gas storage surrounding rock fracture provided by an embodiment of the present application, such as Figure 1 As shown, the real-time prediction method for surrounding rock fracture of a gas storage reservoir may include: step 101, step 102, step 103, step 104, step 105 and step 106.

[0030] Step 101: Acquire the acoustic emission signal generated by the specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and the stress and strain parameters of the rock, wherein the acoustic emission signal is a signal collected by a piezoelectric ceramic sensor arranged on the surface of the rock during the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock during the process of continuously applying external force to the rock.

[0031] The embodiments of the present application can be applied to the scenario of training a rock fracture prediction model through acoustic emission signal parameters at different rock state stages.

[0032] In underground gas storage projects, surrounding rock refers to the rock mass surrounding the gas storage cavern. During the construction and operation of the gas storage, these rock masses will be affected by multiple factors such as excavation, gas pressure, groundwater, etc., resulting in changes in stress state.

[0033] The designated type refers to the rock type selected when acquiring characteristic parameters of the acoustic emission signal during rock fracturing. In this example, the designated type of rock may be, but is not limited to, granite or the like.

[0034] In this example, the specified type of rock can be one type of rock or multiple types of rock, and this embodiment does not limit this. It can be understood that the type of rock forming the surrounding rock of the gas storage reservoir can be one type or multiple types, and specifically, it can be determined according to actual conditions, and this embodiment does not limit this.

[0035] When training a model for predicting the rock state stage of a specified type of rock, the acoustic emission signal and the stress-strain parameters of the rock generated by the specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process can be obtained. Among them, the acoustic emission signal refers to the transient elastic wave (i.e., the acoustic emission signal) generated by the rock during the fracture process. The stress-strain parameters include stress parameters and strain parameters, among which the stress parameter refers to the force per unit area, which is used to describe the ability of the rock to resist deformation. The stress parameter refers to the ratio of the deformation amount generated by the rock after being subjected to force to the original size, which is used to describe the degree of rock deformation.

[0036] In this embodiment, the acoustic emission signal can be a signal collected by a piezoelectric ceramic sensor arranged on the surface of the rock in the process of continuously applying external force to the rock, and the stress-strain parameter is a parameter monitored by a displacement monitoring sensor arranged on the side of the rock in the process of continuously applying external force to the rock.

[0037] In this embodiment, the acoustic emission signals and stress-strain parameters of different rock state stages are collected in combination with specific experiments. During the experiment, granite can be selected as a sample. During the experiment, two piezoelectric ceramic sensors are arranged on the surface of the sample (the size and resonance frequency of the sensor can be determined according to the experimental requirements). The acoustic emission characteristic parameters during the rock damage and destruction process can be received through the piezoelectric ceramic sensor. In addition, linear displacement differential transformers and circumferential displacement sensors are arranged on both sides of the rock to monitor the axial strain and radial strain of the rock during the deformation process.

[0038] The experiment can be carried out under a uniaxial compression device and a multi-channel acoustic emission waveform acquisition system. The system mainly consists of three parts: a loading system, a stress-strain monitoring system and an acoustic emission monitoring system. The loading system uses a servo hydraulic controller for uniaxial loading. The waveform signal received by the piezoelectric ceramic sensor is amplified by a 40dB amplifier and then transmitted to the monitoring equipment, which can record all micro-fracture signals of the entire loading process. During the loading process, acoustic emission signals and stress-strain monitoring are carried out simultaneously to study the mesoscopic fracture law of rocks.

[0039] After obtaining the acoustic emission signals generated by the specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and the stress and strain parameters of the rock, step 102 is performed.

[0040] Step 102: based on the acoustic emission characteristic parameters corresponding to the acoustic emission signals, select target acoustic emission signals that meet the training conditions from the acoustic emission signals.

[0041] Acoustic emission characteristic parameters refer to characteristic parameters extracted from the acoustic emission waveform and described in a series of numerical forms. In this example, the acoustic emission characteristic parameters may include: acoustic emission impact number, acoustic emission energy, acoustic emission RA value, acoustic emission b value, signal duration, ring count, acoustic emission rate and other parameters.

[0042] Among them, the acoustic emission impact number refers to the number of signals that pass the threshold value and cause a certain channel to obtain data, which can be used to reflect the number of micro-cracks generated during the rock destruction process.

[0043] Acoustic emission energy refers to the energy of an acoustic emission event, which is proportional to the square of the amplitude of the waveform where the acoustic emission event is observed, and can be used to reflect the strength of the acoustic emission event. In specific implementation, a local change in rock that produces acoustic emission is called an acoustic emission event, which can be divided into total counts and count rates.

[0044] The RA value of acoustic emission refers to the ratio of the rise time of the acoustic emission impact to the amplitude. The size of the RA value of acoustic emission reflects the nature of the crack. A higher RA value corresponds to a tensile crack, and a lower RA value corresponds to a shear crack. The formula for the RA value of acoustic emission is shown in the following formula (1): (1) In the above formula (1), is the rise time of the AE event, is the amplitude of the acoustic emission event.

[0045] In acoustic emission monitoring, the number of acoustic emission impacts is proportional to the number of crack extensions inside the rock, and the AE amplitude (the maximum voltage peak of the acoustic emission signal waveform) is proportional to the length of the crack extension increment in the rock. Therefore, the brittle deformation and crack extension mechanism of the rock can be inferred based on the Gutenberg-Richter relationship: (2) In the above formula (2), Indicates magnitude ≥ The number of acoustic emission impacts, is a constant, In acoustic emission monitoring, the magnitude of the earthquake source can be expressed by the AE amplitude. , is the acoustic emission amplitude monitored during the experiment, The magnitude of the earthquake.

[0046] As a parameter introduced from seismology, the acoustic emission b value can quantitatively analyze the relationship between the number of cracks in the rock and the size of the cracks. Generally speaking, the larger the b value, the more small fractures there are, and vice versa, it means that large fractures are the main ones.

[0047] That is, the above formula (2) can be used to obtain the acoustic emission b value during the experiment using the least squares rule: (3) In the above formula (2), is the acoustic emission b value, is the total number of acoustic emission impacts in the entire experiment, For the The median magnitude of the earthquake, For the The number of acoustic emission impacts of magnitude.

[0048] In this embodiment, in order to analyze the changes in the acoustic emission b-values ​​at different times, a sliding sampling method (also called a sliding window method) can be adopted. The calculation window can be set to 1000, and the sliding step size can be set to 1 / 4 of the calculation window, so as to slide on the data sequence through a fixed-size window and perform calculations or analysis at each window position.

[0049] Signal duration refers to the time interval from when the signal first crosses the threshold to when it finally drops to the threshold.

[0050] The ring count refers to the number of times the waveform crosses a threshold (a manually set value). In this embodiment, the ring count is used to indicate the number of times the waveform corresponding to the acoustic emission signal is greater than the set threshold.

[0051] Acoustic emission rate refers to the frequency or number of acoustic emission events per unit time used to indicate the target acoustic emission signal. The calculation of acoustic emission rate is usually based on the number of acoustic emission events accumulated within a certain time interval. For example, within a one-hour measurement time, a total of 1,000 acoustic emission events were detected, then the acoustic emission rate for this time period is 1,000 times / hour.

[0052] The calculation formula of acoustic emission rate is as follows: Introducing event interval time function , the function reflects the average frequency of acoustic emission impacts in the time window of N acoustic emission impacts. In order to derive the time function , the time interval between events can be defined as: (4) In the formula, Represents the time interval between two adjacent events. Representative The moment of the impact event, Represents the Then, the average time taken for N consecutive impacts to occur is defined as the event interval time at this moment. , as shown in the following formula: ,i=2,3,…(5) For the first time interval , the above formula can be corrected as follows: (6) exist and Within a given time window between The definition can be The reciprocal of: (7) After obtaining the acoustic emission signals collected during the fracturing process of the specified type of rock, the target acoustic emission signals meeting the training conditions can be screened out from the acoustic emission signals based on the acoustic emission characteristic parameters corresponding to the acoustic emission signals.

[0053] In this embodiment, the target acoustic emission signal can be screened in combination with the signal duration and the ring count. Specifically, the acoustic emission signals whose signal duration is less than the time threshold and / or whose ring count is less than the count threshold can be eliminated, and the remaining acoustic emission signals are used as the target acoustic emission signals. It can be understood that the specific values ​​of the time threshold and the count threshold can be determined according to business requirements, and this embodiment does not limit this.

[0054] After the target acoustic emission signals meeting the training conditions are screened out from the acoustic emission signals based on the acoustic emission characteristic parameters corresponding to the acoustic emission signals, step 103 is performed.

[0055] Step 103: Determine the rock state stage label corresponding to the target acoustic emission signal based on the stress-strain curve of the stress-strain parameter corresponding to the target acoustic emission signal.

[0056] Through experiments, the rock state stage can be divided into four stages, namely: elastic deformation stage, compaction stage, post-peak failure stage and plastic stage. Among them, the elastic deformation stage refers to the stage when the rock undergoes elastic deformation. The compaction stage refers to the stage when the primary cracks inside the rock gradually close. The post-peak failure stage refers to the stage when the internal structure of the rock is destroyed. The plastic stage refers to the stage when the deformation characteristics of the rock change significantly.

[0057] After the target acoustic emission signal is screened, the rock state stage label corresponding to the target acoustic emission signal can be determined based on the stress-strain curve of the stress-strain parameter corresponding to the target acoustic emission signal. The implementation process can be described in detail in conjunction with the following specific implementation method.

[0058] In a specific implementation of the present application, the above step 103 may include: Sub-step A1: Based on the stress-strain parameters corresponding to the target acoustic emission signal, determine the stress-strain curve corresponding to the target acoustic emission signal.

[0059] In this embodiment, the stress-strain curve corresponding to the target acoustic emission signal may be determined based on the stress-strain parameters corresponding to the target acoustic emission signal.

[0060] After determining the stress-strain curve corresponding to the target acoustic emission signal based on the stress-strain parameters corresponding to the target acoustic emission signal, sub-step A2 is performed.

[0061] Sub-step A2: Determine the rock elastic modulus of the rock based on the slope of the stress-strain curve.

[0062] After determining the stress-strain curve corresponding to the target acoustic emission signal based on the stress-strain parameters corresponding to the target acoustic emission signal, the rock elastic modulus of the rock can be determined based on the slope of the stress-strain curve. In a specific implementation, a target stress-strain curve with a linear relationship on the stress-strain curve can be obtained. And based on the stress parameters and strain parameters corresponding to the starting point and the end point of the target stress-strain curve, the rock elastic modulus of the rock is calculated.

[0063] Sub-step A3: Determine the rock state stage label corresponding to the target acoustic emission signal based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter and the peak strain parameter of the rock.

[0064] After calculating the rock elastic modulus of the rock, the rock state stage label corresponding to the target acoustic emission signal can be determined based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter of the rock, and the peak strain parameter. The implementation process can be described in detail in conjunction with the following specific implementation method.

[0065] In a specific implementation of the present application, the above sub-step A3 may include: Sub-step B1: Based on the stress parameter and strain parameter of each point on the stress-strain curve, calculate the instantaneous secant modulus corresponding to each point.

[0066] In this embodiment, after the stress-strain curve corresponding to the target acoustic emission signal is obtained, the instantaneous secant modulus corresponding to each point on the stress-strain curve can be calculated based on the stress parameter and strain parameter of each point.

[0067] In the specific implementation, if you want to calculate the instantaneous secant modulus corresponding to each point on the stress-strain curve, you need to calculate the difference between stress and strain for each point on the curve (except the starting point) and its previous point. Since you are calculating the instantaneous secant modulus of each point, you are actually calculating the slope of the secant between two adjacent points.

[0068] Sub-step B2: When the ratio of the instantaneous secant modulus corresponding to the first point on the stress-strain curve to the rock elastic modulus is greater than a first threshold value, and the strain parameter of the first point is less than the peak strain parameter, determine that the rock state stage label of the target acoustic emission signal corresponding to the first point is an elastic deformation stage label, and the first threshold value is a value greater than 0 and less than 1.

[0069] When the ratio of the instantaneous secant modulus corresponding to the first point on the stress-strain curve to the rock elastic modulus is greater than the first threshold, and the strain parameter of the first point is less than the peak strain parameter, it can be determined that the rock state stage label of the target acoustic emission signal corresponding to the first point is an elastic deformation stage label, wherein the first threshold is a value greater than 0 and less than 1.

[0070] Sub-step B3: When the ratio of the instantaneous secant modulus corresponding to the second point on the stress-strain curve to the rock elastic modulus is greater than 0, and the strain parameter of the second point is less than a set multiple of the peak strain parameter, determine that the rock state stage label of the target acoustic emission signal corresponding to the second point is a compaction stage label, and the set multiple is a value greater than 0 and less than 1.

[0071] When the ratio of the instantaneous secant modulus corresponding to the second point on the stress-strain curve to the rock elastic modulus is greater than 0, and the strain parameter of the second point is less than the set multiple of the peak strain parameter, it can be determined that the rock state stage label of the target acoustic emission signal corresponding to the second point is a compaction stage label, wherein the set multiple is a value greater than 0 and less than 1.

[0072] Sub-step B4: When the ratio of the instantaneous secant modulus corresponding to the third point on the stress-strain curve to the rock elastic modulus is less than 0, determining that the rock state stage label of the target acoustic emission signal corresponding to the third point is a post-peak failure stage label.

[0073] When the ratio of the instantaneous secant modulus corresponding to the third point on the stress-strain curve to the rock elastic modulus is less than 0, it can be determined that the rock state stage label of the target acoustic emission signal corresponding to the third point is a post-peak failure stage label.

[0074] Sub-step B5: Determine the rock state stage labels of the remaining acoustic emission signals in the target acoustic emission signal as plastic stage labels, wherein the remaining acoustic emission signals refer to the acoustic emission signals in the target acoustic emission signal that are not the elastic deformation stage label, the compaction stage label, and the post-peak failure stage label.

[0075] At the same time, the rock state stage labels of the remaining acoustic emission signals in the target acoustic emission signal can be determined as plastic stage labels, wherein the remaining acoustic emission signals refer to the acoustic emission signals of the inelastic deformation stage label, the compaction stage label and the post-peak failure stage label in the target acoustic emission signal. That is, the labels of the other acoustic emission signals in the target acoustic emission signal other than the above three labels are set as plastic stage labels.

[0076] The above implementation process can be combined with Figure 2 The following example is given to illustrate this.

[0077] Classification labels are the goal of supervised machine learning. Labels are usually measured data or classification criteria defined by the data. In an embodiment, the uniaxial compression process can be divided into four stages by the instantaneous secant modulus and the axial strain value. Figure 2 Shown is a method for generating four types of labels. Figure 2 middle is the elastic modulus of rock is the peak strain.

[0078] When labeling the target acoustic emission signal, you can enter the instantaneous secant modulus With axial strain .

[0079] exist and When , the label of the corresponding target acoustic emission signal is set to label 2, that is, the elastic stage label. Among them, 0.95 is the first threshold in this embodiment.

[0080] exist and When , the label of the corresponding target acoustic emission signal is set to label 1, that is, the label of the compaction stage. Among them, 0.6 is the setting multiple in this embodiment.

[0081] exist When , the label of the corresponding target acoustic emission signal is set to label 4, that is, the post-peak stage label.

[0082] At the same time, the label of the target acoustic emission signal that does not meet the above three conditions is set to label 3, that is, the plastic stage label.

[0083] Understandably, Figure 2 The label setting process is only described in combination with the axial strain. In practical applications, the label setting can also be performed in combination with the radial strain. Specifically, the label setting conditions can be set according to actual conditions, and this embodiment does not limit this.

[0084] After the rock state stage label corresponding to the target acoustic emission signal is determined based on the stress-strain curve of the stress-strain parameters corresponding to the target acoustic emission signal, step 104 is performed.

[0085] Step 104: construct a sample data set based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal.

[0086] Furthermore, a sample data set can be constructed based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage labels corresponding to the target acoustic emission signal. That is, the acoustic emission characteristic parameters of the target acoustic emission signal and the corresponding rock state stage labels are used as sample data for model training. A large number of sample data constitute the sample data set.

[0087] After the sample data set is constructed based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal, step 105 is performed.

[0088] Step 105: A rock fracture prediction model for predicting the rock state stage is obtained by training based on the sample data set.

[0089] After constructing a sample data set based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal, a rock fracture prediction model for predicting the rock state stage can be trained based on the sample data set to make a real-time prediction of the rock state stage of the surrounding rock of the gas storage reservoir according to the rock fracture prediction model.

[0090] In this embodiment, the rock fracture prediction model to be trained can use XGBoost (eXtreme GradientBoosting, extreme gradient boosting tree). XGBoost is a boosting method that uses a set of decision trees to estimate the logistic function to determine the expected category of the input data. The boosting model uses an iterative method to build multiple estimators and uses the previous estimator residuals as data to generate new predictions. The output of the XGBoost logistic classifier is a linear combination of the weighted average of the output probabilities of all trees. Similar to random forests, XGBoost uses random subsamples of columns and rows for each tree to reduce bias, and the maximum depth of the tree is specified by the user.

[0091] In classification problems, the imbalance of data samples is an extremely common problem, and unbalanced data samples have a great impact on the training and testing of the model. During the experiment, the acoustic emission signal value generated by the rock in the plastic stage was 44070, while it was only 11364 in the elastic stage, which means that in order to maintain high accuracy, the model will only predict the plastic stage. In order to balance this, the SMOTE "Synthetic Minority Oversampling Technology" method is combined with the machine learning algorithm in this embodiment to optimize the generalization of the model. The SMOTE method is an improved solution for the random oversampling algorithm. Since random oversampling adopts a simple sample duplication strategy to increase minority class samples, this can easily lead to the problem of model overfitting, that is, the information learned by the model is too specific and not generalized enough. The basic idea of ​​the SMOTE algorithm is to analyze minority class samples and artificially synthesize new samples based on minority class samples and add them to the data set. The algorithm flow is shown below (1) For each sample in the class with a smaller number of samples , calculate its distance to all samples in the minority class sample set using the Euclidean distance as the standard, and obtain its k nearest neighbors.

[0092] (2) Set a sampling ratio according to the sample imbalance ratio to determine the sampling rate N. For each minority class sample , randomly select several samples from its k nearest neighbors, assuming that the selected neighbors are .

[0093] (3) For each randomly selected neighbor , and construct new samples with the original samples according to the following formulas.

[0094] (8) In the above formula, For the new samples generated, For the The original samples, For the randomly selected The neighboring samples of the original samples, is a random number between 0 and 1.

[0095] The model training process can be described in detail in combination with the following specific implementation methods.

[0096] In a specific implementation of the present application, the above step 105 may include: Sub-step C1: inputting the acoustic emission characteristic parameters of the target acoustic emission signal in the sample data set into the rock fracture prediction model to be trained, and processing the acoustic emission characteristic parameters of the target acoustic emission signal by the rock fracture prediction model to be trained.

[0097] In this embodiment, after obtaining the sample data set, the acoustic emission characteristic parameters of the target acoustic emission signal in the sample data set can be input into the rock fracture prediction model to be trained, and the rock fracture prediction model to be trained processes the acoustic emission characteristic parameters of the target acoustic emission signal. That is, the rock fracture prediction model to be trained learns the acoustic emission characteristic parameters of the target acoustic emission signal.

[0098] Sub-step C2: obtaining the predicted rock state stage corresponding to the target acoustic emission signal output by the rock fracture prediction model to be trained.

[0099] After the acoustic emission characteristic parameters of the target acoustic emission signal are processed by the rock fracture prediction model to be trained, the predicted rock state stage corresponding to the target acoustic emission signal output by the rock fracture prediction model to be trained can be obtained.

[0100] Sub-step C3: Based on the rock state stage label and the predicted rock state stage, the loss value of the rock fracture prediction model to be trained is calculated.

[0101] Furthermore, the loss value of the rock fracture prediction model to be trained can be calculated based on the rock state stage label and the predicted rock state stage. In this embodiment, the loss value can be a cross entropy loss value, a mean square error loss value, a category cross entropy loss value, etc. Specifically, the specific type of the loss value can be determined according to business needs, and this embodiment does not limit this.

[0102] After the loss value of the rock fracture prediction model to be trained is calculated based on the rock state stage label and the predicted rock state stage, sub-step C4 is executed.

[0103] Sub-step C4: when the loss value is within a preset range, the trained rock fracture prediction model to be trained is used as the final rock fracture prediction model.

[0104] After the loss value of the rock fracture prediction model to be trained is calculated based on the rock state stage label and the predicted rock state stage, it can be determined whether the loss value is within a preset range.

[0105] If the loss value is not within the preset range, it means that the rock fracture prediction model to be trained has not converged. At this time, the rock fracture prediction model to be trained can be further trained in combination with more sample data sets until the model converges.

[0106] If the loss value is within a preset range, it indicates that the rock fracture prediction model to be trained has converged. At this time, the trained rock fracture prediction model to be trained can be used as the final rock fracture prediction model.

[0107] For the model training process, it can be combined Figure 3 The following is a description. Figure 3 As shown, the model training process may include: 1. Monitor acoustic emission signals and stress-strain values.

[0108] 2. Data preprocessing: Data is screened through the acoustic emission characteristic parameters of the acoustic emission signal (such as duration and ring count) to construct a data set for model training.

[0109] 3. Label generation: The classification label of the acoustic emission signal is determined by the stress-strain value, that is, the stress-strain value is used to draw the stress-strain curve, and the stress-strain curve is used to determine the classification label, thereby generating a label reflecting the rock state.

[0110] 4. Combine the dataset and the labels reflecting the rock state to train a model for predicting the rock state stage.

[0111] 5. Model evaluation: The predicted labels are compared with the observed labels to determine the model accuracy and feature importance. Specifically, the importance of each feature can be evaluated using the trained model.

[0112] This embodiment focuses on the relationship between rock microfractures and acoustic emission signals, uses acoustic emission technology to analyze the evolution process of rock microfractures and the accumulation of damage, and uses machine learning technology in combination with multivariate acoustic emission signals to classify and predict the different stages of rock. The classification model for rock stages established based on mesoscopic acoustic emission signals can accurately predict the different stages of granite with an accuracy of 78.6%, which is higher than the previous prediction accuracy using X-ray technology.

[0113] After the training of the rock fracture prediction model is completed, step 106 is executed.

[0114] Step 106: The rock fracture prediction model is used to process the acoustic emission signals to be measured of the target rock of the specified type in the surrounding rock of the gas storage reservoir collected in real time to obtain the rock state stage corresponding to the target rock, wherein the acoustic emission signals to be measured are signals of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor.

[0115] In this embodiment, when predicting the rock state stage of a specified type of target rock in the surrounding rock of an underground gas storage reservoir, the acoustic emission signal to be measured of the specified type of target rock in the surrounding rock of the gas storage reservoir can be collected. In a specific implementation, the acoustic emission signal to be measured of the target rock around the reinforced structure of the gas storage reservoir can be collected by a piezoelectric ceramic sensor.

[0116] In specific implementation, the installation of the reinforcement structure of the surrounding rock of the gas storage reservoir is a complex and critical engineering process, which is essential to ensure the stability and safety of the gas storage reservoir. The reinforcement structure of the surrounding rock of the gas storage reservoir can be, but is not limited to, anchor rods. The method of installing the reinforcement structure on the surface of the surrounding rock (taking anchor rods as an example) can be: first drill a hole in the surrounding rock, and then insert and fix the anchor rod. Install the sensor in a pre-designed fixed position, and fix the sensor to the surface of the surrounding rock using adhesives, bolts or other fixing devices.

[0117] In this example, the piezoelectric ceramic sensor can be pre-installed at the peripheral position of the reinforced structure of the gas storage reservoir to collect the acoustic emission signal of the rock around the reinforced structure of the gas storage reservoir. The piezoelectric ceramic sensor can be installed at the peripheral position of the reinforced structure of the gas storage reservoir in the following ways: 1. Epoxy resin glue bonding: Select epoxy resin glue with corrosion resistance, avoid using substances that may produce corrosive reactions, ensure that the glue is evenly applied and thin, so as to improve the bonding quality, and during the bonding process, cure at room temperature or in a slightly heated environment, and ensure that the curing time is greater than 30 minutes. 2. Mechanical clamping fixation: For piezoelectric ceramic sensors of certain shapes, mechanical clamping can be used for fixation. Such as the reinforced structure pre-installed in the gas storage reservoir. Taking the anchor rod as an example, a fixing fixture or bracket can be used to connect firmly with the reinforced structure, and then the sensor can be installed on the fixture or bracket to fix the piezoelectric ceramic sensor through the reinforced structure of the gas storage reservoir, so as to avoid the sensor falling off and causing the inability to collect the acoustic emission signal of the surrounding rock of the gas storage reservoir.

[0118] It can be understood that the above-mentioned installation methods of the reinforced structure and the piezoelectric ceramic sensor are only examples listed for a better understanding of the technical solution of the embodiment of the present application. In actual applications, any method that can install the reinforced structure and the piezoelectric ceramic sensor on the surrounding rock of the gas storage reservoir can be applied to the present embodiment, and the present embodiment does not impose any restrictions on this.

[0119] After collecting the acoustic emission signal to be measured of the specified type of target rock in the surrounding rock of the gas storage reservoir, the acoustic emission characteristic parameters of the acoustic emission signal to be measured can be input into the rock fracture prediction model, so that the rock fracture prediction model can process the acoustic emission characteristic parameters of the acoustic emission signal to be measured. Then, the rock state stage corresponding to the target rock output by the rock fracture prediction model can be obtained. It can be understood that after the training of the rock fracture prediction model is completed, it is only necessary to arrange piezoelectric ceramic sensors on the outside of the specified type of target rock in the surrounding rock of the gas storage reservoir to monitor the acoustic emission signal to be measured, and identify the acoustic emission characteristic parameters of the acoustic emission signal to be measured and input them into the model to predict the rock state stage of the target rock. This process does not require stress-strain parameters.

[0120] The embodiment of the present application significantly improves the real-time monitoring capability of the rock fracture process by optimizing the collection and analysis system of the acoustic emission signal. It is able to quickly capture and process micro-fracture signals, achieve timely warning, and effectively prevent the occurrence of geological disasters. In addition, the embodiment of the present application can monitor the reinforcement effect of the reinforced structure on the surrounding rock in real time by collecting the rock fracture acoustic emission signal at the location of the reinforced structure. If the reinforced structure can effectively disperse and withstand the stress inside the surrounding rock, then the rock fracture acoustic emission signal at this location may be relatively weak or less. The rock fracture acoustic emission signal at the location of the reinforced structure can also be used as an important indicator for warning of potential damage. When the signal changes abnormally, it may mean that the reinforced structure has been subjected to excessive stress or damage, so as to remind relevant personnel to take timely measures to repair or reinforce it.

[0121] Of course, in a specific implementation, piezoelectric ceramic sensors can also be installed at the non-reinforced structure position of the gas storage surrounding rock to collect the acoustic emission signals to be measured of the rock at the non-reinforced structure position to predict the rock fracture state stage.

[0122] The real-time prediction method for the fracture of the surrounding rock of the gas storage reservoir provided in the embodiment of the present application is to obtain the acoustic emission signal generated by the specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and the stress and strain parameters of the rock, wherein the acoustic emission signal is a signal collected by a piezoelectric ceramic sensor arranged on the surface of the rock in the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock in the process of continuously applying external force to the rock. Based on the acoustic emission characteristic parameters corresponding to the acoustic emission signal, the target acoustic emission signal that meets the training conditions is screened out from the acoustic emission signal. Based on the stress-strain curve of the stress-strain parameters corresponding to the target acoustic emission signal, the rock state stage label corresponding to the target acoustic emission signal is determined. Based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal, a sample data set is constructed. A rock fracture prediction model for predicting the rock state stage is obtained based on the training of the sample data set, and the rock fracture prediction model is used to process the acoustic emission signal to be measured of the specified type of target rock in the surrounding rock of the gas storage reservoir collected in real time to obtain the rock state stage corresponding to the target rock, and the acoustic emission signal to be measured is the signal of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor. The embodiment of the present application uses acoustic emission technology to monitor the acoustic emission characteristic parameters of rock fracture at different stages to train a rock fracture prediction model, so that the trained model can predict the stage of rock fracture, improve the real-time monitoring capability of the rock fracture process, quickly capture and process micro-fracture signals, achieve timely warning, and improve the timeliness of rock fracture monitoring. At the same time, the use of model prediction can greatly improve the accuracy of acoustic emission signal feature extraction and state stage classification prediction, and can more accurately identify the precursor signals and fracture modes of rock fracture.

[0123] Reference Figure 4 , shows a schematic diagram of the structure of a real-time prediction device for gas storage surrounding rock fracture provided by an embodiment of the present application. Figure 4 As shown, the real-time prediction device 400 for surrounding rock fracture of a gas storage reservoir may include the following modules: The acoustic emission signal acquisition module 410 is used to acquire the acoustic emission signal generated by the specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and the stress and strain parameters of the rock, wherein the acoustic emission signal is a signal collected by a piezoelectric ceramic sensor arranged on the surface of the rock in the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock in the process of continuously applying external force to the rock; A target signal screening module 420 is used to screen out target acoustic emission signals that meet training conditions from the acoustic emission signals based on acoustic emission characteristic parameters corresponding to the acoustic emission signals; A state stage label determination module 430 is used to determine the rock state stage label corresponding to the target acoustic emission signal based on the stress-strain curve of the stress-strain parameter corresponding to the target acoustic emission signal; A sample data set construction module 440 is used to construct a sample data set based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal; A rock fracture prediction model training module 450 is used to train a rock fracture prediction model for predicting a rock state stage based on the sample data set; The rock state real-time prediction module 460 is used to use the rock fracture prediction model to process the real-time collected acoustic emission signals of the target rock of the specified type in the surrounding rock of the gas storage reservoir to obtain the rock state stage corresponding to the target rock. The acoustic emission signals to be measured are signals of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor.

[0124] Optionally, the acoustic emission characteristic parameters include: acoustic emission impact number, acoustic emission energy, acoustic emission RA value, acoustic emission b value, signal duration, ringing count and acoustic emission rate.

[0125] Optionally, the target signal screening module includes: The target signal acquisition unit is used to eliminate the acoustic emission signals whose signal duration is less than a time threshold and / or whose ring count is less than a count threshold in the acoustic emission signals, so as to obtain the target acoustic emission signal.

[0126] Optionally, the state stage label determination module includes: A stress-strain curve determining unit, configured to determine a stress-strain curve corresponding to the target acoustic emission signal based on the stress-strain parameter corresponding to the target acoustic emission signal; A rock elastic modulus determination unit, used to determine the rock elastic modulus of the rock based on the slope of the stress-strain curve; The state stage label determination unit is used to determine the rock state stage label corresponding to the target acoustic emission signal based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter and the peak strain parameter of the rock.

[0127] Optionally, the rock elastic modulus determination unit includes: A target curve acquisition subunit, used to acquire a target stress-strain curve having a linear relationship on the stress-strain curve; The rock elastic modulus calculation subunit is used to calculate the rock elastic modulus of the rock based on the stress parameters and strain parameters corresponding to the starting point and the end point of the target stress-strain curve respectively.

[0128] Optionally, the state stage label determination unit includes: An instantaneous secant modulus calculation subunit, used to calculate the instantaneous secant modulus corresponding to each point on the stress-strain curve based on the stress parameter and the strain parameter of each point; An elastic stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the first point on the stress-strain curve is an elastic deformation stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the first point on the stress-strain curve is greater than a first threshold value and the strain parameter of the first point is less than the peak strain parameter, and the first threshold value is a value greater than 0 and less than 1; A compaction stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the second point on the stress-strain curve is a compaction stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the second point on the stress-strain curve is greater than 0 and the strain parameter of the second point is less than a set multiple of the peak strain parameter, and the set multiple is a value greater than 0 and less than 1; A post-peak stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the third point on the stress-strain curve is a post-peak failure stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the third point on the stress-strain curve is less than 0; The plastic stage label determination subunit is used to determine the rock state stage labels of the remaining acoustic emission signals in the target acoustic emission signal as plastic stage labels, and the remaining acoustic emission signals refer to the acoustic emission signals in the target acoustic emission signal that are not the elastic deformation stage label, the compaction stage label, and the post-peak failure stage label.

[0129] Optionally, the rock fracture prediction model training module includes: An acoustic emission characteristic parameter input unit, used to input the acoustic emission characteristic parameters of the target acoustic emission signal in the sample data set into the rock fracture prediction model to be trained, and the rock fracture prediction model to be trained processes the acoustic emission characteristic parameters of the target acoustic emission signal; A rock state stage prediction unit, used to obtain the predicted rock state stage corresponding to the target acoustic emission signal output by the rock fracture prediction model to be trained; A loss value calculation unit, used for calculating the loss value of the rock fracture prediction model to be trained based on the rock state stage label and the predicted rock state stage; The rock fracture prediction model acquisition unit is used to use the trained rock fracture prediction model to be trained as the final rock fracture prediction model when the loss value is within a preset range.

[0130] The real-time prediction device for the fracture of the surrounding rock of the gas storage reservoir provided in the embodiment of the present application obtains the acoustic emission signal generated by the specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and the stress and strain parameters of the rock. The acoustic emission signal is a signal collected by a piezoelectric ceramic sensor arranged on the surface of the rock in the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock in the process of continuously applying external force to the rock. Based on the acoustic emission characteristic parameters corresponding to the acoustic emission signal, the target acoustic emission signal that meets the training conditions is screened out from the acoustic emission signal. Based on the stress-strain curve of the stress-strain parameters corresponding to the target acoustic emission signal, the rock state stage label corresponding to the target acoustic emission signal is determined. Based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal, a sample data set is constructed. Based on the sample data set, a rock fracture prediction model for predicting the rock state stage is obtained by training, and the rock fracture prediction model is used to process the acoustic emission signal to be measured of the specified type of target rock in the surrounding rock of the gas storage reservoir collected in real time to obtain the rock state stage corresponding to the target rock. The acoustic emission signal to be measured is the signal of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor. The embodiment of the present application uses acoustic emission technology to monitor the acoustic emission characteristic parameters of rock fracture at different stages to train a rock fracture prediction model, so that the trained model can predict the stage of rock fracture, improve the real-time monitoring capability of the rock fracture process, quickly capture and process micro-fracture signals, achieve timely warning, and improve the timeliness of rock fracture monitoring. At the same time, the use of model prediction can greatly improve the accuracy of acoustic emission signal feature extraction and state stage classification prediction, and can more accurately identify the precursor signals and fracture modes of rock fracture.

[0131] In addition, an embodiment of the present application also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the above-mentioned real-time prediction method for gas storage surrounding rock fracture.

[0132] Figure 5 FIG. 5 is a schematic diagram showing the structure of an electronic device 500 according to an embodiment of the present invention. Figure 5As shown, the electronic device 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 502 or computer program instructions loaded from a storage unit 508 to a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0133] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, a microphone, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0134] The various processes and processing described above may be performed by the processing unit 501. For example, the method of any of the above embodiments may be implemented as a computer software program, which is tangibly contained in a computer-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the CPU 501, one or more actions in the method described above may be performed.

[0135] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above-mentioned gas storage reservoir surrounding rock fracture real-time prediction method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0136] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0137] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A real-time prediction method for surrounding rock fracture of a gas storage reservoir, characterized in that: The method comprises: Acquiring acoustic emission signals generated by a specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and stress and strain parameters of the rock, wherein the acoustic emission signals are signals collected by a piezoelectric ceramic sensor arranged on the surface of the rock during the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock during the process of continuously applying external force to the rock; Based on the acoustic emission characteristic parameters corresponding to the acoustic emission signals, a target acoustic emission signal meeting the training conditions is screened out from the acoustic emission signals; Determine a rock state stage label corresponding to the target acoustic emission signal based on a stress-strain curve of stress-strain parameters corresponding to the target acoustic emission signal; Constructing a sample data set based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal; A rock fracture prediction model for predicting the rock state stage is obtained by training based on the sample data set; The rock fracture prediction model is used to process the acoustic emission signals to be measured of the target rock of the specified type in the surrounding rock of the gas storage reservoir collected in real time to obtain the rock state stage corresponding to the target rock. The acoustic emission signals to be measured are signals of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor.

2. The method according to claim 1, characterized in that The acoustic emission characteristic parameters include: acoustic emission impact number, acoustic emission energy, acoustic emission RA value, acoustic emission b value, signal duration, ringing count and acoustic emission rate.

3. The method according to claim 2, characterized in that The step of selecting a target acoustic emission signal that meets a training condition from the acoustic emission signal based on the acoustic emission characteristic parameters corresponding to the acoustic emission signal comprises: The target acoustic emission signal is obtained by eliminating the acoustic emission signals whose signal duration is less than a time threshold and / or whose ring count is less than a count threshold.

4. The method according to claim 1, characterized in that The step of determining the rock state stage label corresponding to the target acoustic emission signal based on the stress-strain curve of the stress-strain parameter corresponding to the target acoustic emission signal comprises: Determining a stress-strain curve corresponding to the target acoustic emission signal based on the stress-strain parameter corresponding to the target acoustic emission signal; determining a rock elastic modulus of the rock based on the slope of the stress-strain curve; Based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter and the peak strain parameter of the rock, a rock state stage label corresponding to the target acoustic emission signal is determined.

5. The method according to claim 4, characterized in that Determining the rock elastic modulus of the rock based on the slope of the stress-strain curve includes: Obtaining a target stress-strain curve having a linear relationship on the stress-strain curve; The rock elastic modulus of the rock is calculated based on the stress parameters and strain parameters corresponding to the starting point and the end point of the target stress-strain curve, respectively.

6. The method according to claim 4, characterized in that The step of determining the rock state stage label corresponding to the target acoustic emission signal based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter and the peak strain parameter of the rock comprises: Based on the stress parameter and strain parameter of each point on the stress-strain curve, the instantaneous secant modulus corresponding to each point is calculated; When the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the first point on the stress-strain curve is greater than a first threshold value, and the strain parameter of the first point is less than the peak strain parameter, it is determined that the rock state stage label of the target acoustic emission signal corresponding to the first point is an elastic deformation stage label, and the first threshold value is a value greater than 0 and less than 1; When the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the second point on the stress-strain curve is greater than 0, and the strain parameter of the second point is less than a set multiple of the peak strain parameter, it is determined that the rock state stage label of the target acoustic emission signal corresponding to the second point is a compaction stage label, and the set multiple is a value greater than 0 and less than 1; When the ratio of the instantaneous secant modulus corresponding to the third point on the stress-strain curve to the rock elastic modulus is less than 0, determining that the rock state stage label of the target acoustic emission signal corresponding to the third point is a post-peak failure stage label; The rock state stage labels of the remaining acoustic emission signals in the target acoustic emission signal are determined as plastic stage labels, and the remaining acoustic emission signals refer to the acoustic emission signals in the target acoustic emission signal that are not the elastic deformation stage label, the compaction stage label, and the post-peak failure stage label.

7. The method according to claim 1, characterized in that The rock fracture prediction model for predicting the rock state stage obtained by training based on the sample data set includes: Inputting the acoustic emission characteristic parameters of the target acoustic emission signal in the sample data set into the rock fracture prediction model to be trained, and processing the acoustic emission characteristic parameters of the target acoustic emission signal by the rock fracture prediction model to be trained; Acquiring a predicted rock state stage corresponding to the target acoustic emission signal output by the rock fracture prediction model to be trained; Based on the rock state stage label and the predicted rock state stage, a loss value of the rock fracture prediction model to be trained is calculated; When the loss value is within a preset range, the trained rock fracture prediction model to be trained is used as the final rock fracture prediction model.

8. A real-time prediction device for gas storage surrounding rock fracture, characterized in that: The device comprises: An acoustic emission signal acquisition module, used to acquire acoustic emission signals generated by a specified type of rock in the surrounding rock of the gas storage reservoir during the fracture process and stress and strain parameters of the rock, wherein the acoustic emission signals are signals collected by a piezoelectric ceramic sensor arranged on the surface of the rock during the process of continuously applying external force to the rock, and the stress and strain parameters are parameters monitored by a displacement monitoring sensor arranged on the side of the rock during the process of continuously applying external force to the rock; A target signal screening module, used for screening target acoustic emission signals that meet training conditions from the acoustic emission signals based on acoustic emission characteristic parameters corresponding to the acoustic emission signals; A state stage label determination module, used to determine the rock state stage label corresponding to the target acoustic emission signal based on a stress-strain curve of stress-strain parameters corresponding to the target acoustic emission signal; A sample data set construction module, used to construct a sample data set based on the acoustic emission characteristic parameters of the target acoustic emission signal and the rock state stage label corresponding to the target acoustic emission signal; A rock fracture prediction model training module, used for training a rock fracture prediction model for predicting a rock state stage based on the sample data set; The rock state real-time prediction module is used to process the acoustic emission signals to be measured of the target rock of the specified type in the surrounding rock of the gas storage reservoir collected in real time by using the rock fracture prediction model to obtain the rock state stage corresponding to the target rock, wherein the acoustic emission signals to be measured are signals of the target rock around the reinforced structure of the gas storage reservoir collected by the piezoelectric ceramic sensor.

9. The device according to claim 8, characterized in that The acoustic emission characteristic parameters include: acoustic emission impact number, acoustic emission energy, acoustic emission RA value, acoustic emission b value, signal duration, ringing count and acoustic emission rate.

10. The device according to claim 9, characterized in that The target signal screening module comprises: The target signal acquisition unit is used to eliminate the acoustic emission signals whose signal duration is less than a time threshold and / or whose ring count is less than a count threshold in the acoustic emission signals, so as to obtain the target acoustic emission signal.

11. The device according to claim 8, characterized in that The state stage label determination module includes: A stress-strain curve determining unit, configured to determine a stress-strain curve corresponding to the target acoustic emission signal based on the stress-strain parameter corresponding to the target acoustic emission signal; A rock elastic modulus determination unit, used to determine the rock elastic modulus of the rock based on the slope of the stress-strain curve; The state stage label determination unit is used to determine the rock state stage label corresponding to the target acoustic emission signal based on the rock elastic modulus, the instantaneous secant modulus corresponding to the stress-strain curve, the strain parameter and the peak strain parameter of the rock.

12. The device according to claim 11, characterized in that The rock elastic modulus determination unit comprises: A target curve acquisition subunit, used to acquire a target stress-strain curve having a linear relationship on the stress-strain curve; The rock elastic modulus calculation subunit is used to calculate the rock elastic modulus of the rock based on the stress parameters and strain parameters corresponding to the starting point and the end point of the target stress-strain curve respectively.

13. The device according to claim 11, characterized in that The state stage label determination unit comprises: An instantaneous secant modulus calculation subunit, used to calculate the instantaneous secant modulus corresponding to each point on the stress-strain curve based on the stress parameter and the strain parameter of each point; An elastic stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the first point on the stress-strain curve is an elastic deformation stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the first point on the stress-strain curve is greater than a first threshold value and the strain parameter of the first point is less than the peak strain parameter, and the first threshold value is a value greater than 0 and less than 1; A compaction stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the second point on the stress-strain curve is a compaction stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the second point on the stress-strain curve is greater than 0 and the strain parameter of the second point is less than a set multiple of the peak strain parameter, and the set multiple is a value greater than 0 and less than 1; A post-peak stage label determination subunit is used to determine that the rock state stage label of the target acoustic emission signal corresponding to the third point on the stress-strain curve is a post-peak failure stage label when the ratio of the instantaneous secant modulus to the rock elastic modulus corresponding to the third point on the stress-strain curve is less than 0; The plastic stage label determination subunit is used to determine the rock state stage labels of the remaining acoustic emission signals in the target acoustic emission signal as plastic stage labels, and the remaining acoustic emission signals refer to the acoustic emission signals in the target acoustic emission signal that are not the elastic deformation stage label, the compaction stage label, and the post-peak failure stage label.

14. The device according to claim 8, characterized in that The rock fracture prediction model training module includes: An acoustic emission characteristic parameter input unit, used to input the acoustic emission characteristic parameters of the target acoustic emission signal in the sample data set into the rock fracture prediction model to be trained, and the rock fracture prediction model to be trained processes the acoustic emission characteristic parameters of the target acoustic emission signal; A rock state stage prediction unit, used to obtain the predicted rock state stage corresponding to the target acoustic emission signal output by the rock fracture prediction model to be trained; A loss value calculation unit, used for calculating the loss value of the rock fracture prediction model to be trained based on the rock state stage label and the predicted rock state stage; The rock fracture prediction model acquisition unit is used to use the trained rock fracture prediction model to be trained as the final rock fracture prediction model when the loss value is within a preset range.

15. An electronic device, characterized in that: include: The memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the real-time prediction method for gas storage surrounding rock rupture according to any one of claims 1 to 7 by executing corresponding computer instructions.

16. A readable storage medium, characterized in that: Computer instructions are stored, which are used to enable a computer to execute the real-time prediction method for gas storage surrounding rock rupture according to any one of claims 1 to 7.

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