Hydrofracture simulation method and system based on acoustic emission monitoring

Abstract

The embodiment of the invention discloses a hydrofracture simulation method and system based on acoustic emission monitoring, and the method comprises the steps: collecting the cuttable data of a sample rock mass based on a triaxial fracturing device and an acoustic emission monitoring system, and building a joint development scale simulation model; the method comprises the following steps: collecting integrity indexes and strength parameters of a sample rock mass, constructing a dynamic characteristic evolution system, and establishing a rock mass strength deterioration prediction model; checking and optimizing the model based on historical case data; a multi-scale fracturing crack propagation prediction algorithm is constructed, a hydraulic fracturing construction simulation model is established, and the hydraulic fracturing effect of a hydraulic fracturing planning scheme is simulated and generated according to a simulation instruction; and / or, according to the planning instruction, generating a hydraulic fracturing simulation scheme. According to the embodiment of the invention, the spatial distribution characteristics and development laws of the joints in the rock mass can be accurately identified, dynamic prediction of rock mass strength and permeability changes is realized, the controllability and safety of fracturing construction are improved, and the influence of human factors on construction quality is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geotechnical engineering and mine safety, and particularly relates to a hydraulic fracturing simulation method and system based on acoustic emission monitoring. BACKGROUND

[0002] Traditional roadway hydraulic fracturing construction scheme design mainly relies on experience judgment and simplified geological model, lacks accurate identification of complex joint structure and stress state inside the rock mass, and thus leads to difficulty in predicting and controlling fracturing effect.

[0003] The prior art has problems of low rock mass joint network identification accuracy and single fracturing crack propagation path prediction algorithm, and these technical defects seriously restrict the application effect of long-distance hydraulic fracturing technology in roadway engineering.

[0004] In view of the above problems, a method capable of overcoming the above problems, realizing long-distance hydraulic fracturing construction scheme effect simulation and construction scheme simulation generation, accurately identifying the spatial distribution characteristics and development law of joints inside the rock mass, and realizing dynamic prediction of rock mass strength and permeability change is urgently needed. The hydraulic fracturing simulation method and system based on acoustic emission monitoring provided by the present application is exactly to solve these problems in the prior art. SUMMARY

[0005] The present application aims to provide a hydraulic fracturing simulation method and system based on acoustic emission monitoring, which can accurately identify the spatial distribution characteristics and development law of joints inside the rock mass by joint application of the acoustic emission monitoring system and the triaxial fracturing device, realize dynamic prediction of rock mass strength and permeability change under different working conditions, and improve the controllability and safety of fracturing construction.

[0006] In a first aspect, the embodiments of the present disclosure provide a hydraulic fracturing simulation method based on acoustic emission monitoring, which adopts the following technical scheme: based on a triaxial fracturing device and an acoustic emission monitoring system, cuttability data of a sample rock mass are collected, the correlation between acoustic emission signal characteristics and rock mass joint distribution is analyzed, and a joint development scale simulation model is established; wherein the cuttability data includes the development degree of rock mass joints; the integrity index and strength parameters of the sample rock mass are collected, a dynamic state characteristic evolution system is constructed based on a rock mass permeability tensor evolution model and a stress-seepage-damage coupling mechanism, and a rock mass strength degradation prediction model based on damage accumulation is established; the joint development scale simulation model, the dynamic state characteristic evolution system and the rock mass strength degradation prediction model are verified and optimized based on historical case data; a multi-scale fracturing fracture propagation prediction algorithm is constructed, a hydraulic fracturing construction simulation model is established by combining the interaction mechanism of natural fractures and artificial fractures, and the redistribution law of the in-situ stress field and the connectivity evaluation of the fracture network are integrated; according to the simulation instruction, the hydraulic fracturing construction simulation model simulates the hydraulic fracturing effect of the hydraulic fracturing planning scheme, and / or generates a hydraulic fracturing simulation scheme according to the planning instruction.

[0007] Preferably, the hydraulic fracturing simulation method based on acoustic emission monitoring further comprises: acquiring real-time rock mass characteristic parameters of the construction area by using the acoustic emission monitoring system, inputting the real-time rock mass characteristic parameters into the hydraulic fracturing construction simulation model, correcting the hydraulic fracturing effect according to the simulation instruction, and / or correcting the hydraulic fracturing simulation scheme according to the planning instruction, wherein the hydraulic fracturing simulation scheme includes borehole trajectory design, staged fracturing parameters and fracturing fluid formulation.

[0008] Preferably, the hydraulic fracturing simulation method based on acoustic emission monitoring further comprises: adopting a crack network imaging technology combined with microseismic monitoring and the acoustic emission monitoring system to verify the effectiveness of the hydraulic fracturing construction scheme by comparison; wherein the hydraulic fracturing construction scheme is one of the hydraulic fracturing planning scheme, the hydraulic fracturing simulation scheme and a hydraulic fracturing actual scheme; a fracturing effect quantitative evaluation system based on crack connectivity rate and stress release efficiency is established to evaluate the improvement degree of the long-distance hydraulic fracturing of the hydraulic fracturing construction scheme on the prevention and control effect of coal rock dynamic disaster.

[0009] Preferably, the hydraulic fracturing simulation method based on acoustic emission monitoring further comprises: based on the effectiveness and the improvement degree, a fracturing parameter self-adaptive adjustment algorithm based on Bayesian optimization is used to optimize the hydraulic fracturing construction simulation model.

[0010] Preferably, the tri-axial fracturing device and acoustic emission monitoring system collect the sample rock mass's cuttability data, analyze the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establish a joint development scale simulation model, including: obtaining the acoustic wave signals in the sample rock mass's breaking process through the acoustic emission monitoring system; wherein the acoustic emission monitoring system includes an acoustic emission sensor array, and the acoustic wave signals include the cuttability data; statistically analyzing the characteristic parameters of the acoustic wave signals; wherein the characteristic parameters include amplitude, frequency and duration; identifying the characteristic modes related to joint density and joint length, analyzing the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establishing the joint development scale simulation model; wherein the joint development scale simulation model is a mathematical model of the correlation between acoustic emission parameters and joint development scale.

[0011] Preferably, after the establishment of the joint development scale simulation model, it further includes: based on the anisotropy feature recognition technology of rock mass, calculating the wave velocity ratio by comparing the propagation velocity differences of acoustic emission waves in different directions; identifying the spatial geometric parameters of the sample rock mass; wherein the spatial geometric parameters include the inclination and dip angle of the internal bedding surface and joint surface of the sample rock mass; performing three-dimensional visual reconstruction on the spatial geometric parameters to form a joint network model.

[0012] Preferably, the tri-axial fracturing device and acoustic emission monitoring system collect the sample rock mass's cuttability data, analyze the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establish a joint development scale simulation model, including: obtaining the acoustic wave signals in the sample rock mass's breaking process through the acoustic emission monitoring system; wherein the acoustic emission monitoring system includes an acoustic emission sensor array, and the acoustic wave signals include the cuttability data; statistically analyzing the characteristic parameters of the acoustic wave signals; wherein the characteristic parameters include amplitude, frequency and duration; identifying the characteristic modes related to joint density and joint length, analyzing the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establishing the joint development scale simulation model; wherein the joint development scale simulation model is a mathematical model of the correlation between acoustic emission parameters and joint development scale.

[0013] Preferably, the multi-scale fracturing fracture propagation prediction algorithm is constructed, the interaction mechanism of natural fractures and artificial fractures is combined, a hydraulic fracturing construction simulation model integrating the redistribution law of the in-situ stress field and the fracture network connectivity evaluation is established, including: constructing the multi-scale fracturing fracture propagation prediction algorithm; combining the interaction mechanism of natural fractures and artificial fractures, determining the fracture propagation path; wherein the interaction mechanism includes deflection, penetration or termination when natural fractures and artificial fractures meet; constructing a geological parameter, process parameter and fracturing effect knowledge graph; establishing the hydraulic fracturing construction simulation model integrating the redistribution law of the in-situ stress field and the fracture network connectivity evaluation.

[0014] Preferably, the hydraulic fracturing simulation scheme is corrected according to the planning instruction, and the hydraulic fracturing simulation scheme includes drilling trajectory design, staged fracturing parameters and fracturing fluid formula, including: determining the spatial position of acoustic emission events through the acoustic emission monitoring system, recording the direction and speed of fracture propagation, and obtaining fracture propagation path information; using a fracturing fluid filtration loss dynamic monitoring method, comparing the difference between the injection volume and the flowback volume, determining the real-time change of rock mass permeability, and generating a filtration loss change law; based on the real-time rock mass characteristic parameters, the fracture propagation path information and the filtration loss change law as input parameters, input into the hydraulic fracturing construction simulation model; the hydraulic fracturing construction simulation model corrects the hydraulic fracturing simulation scheme according to the planning instruction, and outputs.

[0015] In a second aspect, the embodiments of the present disclosure further provide a hydraulic fracturing simulation system based on acoustic emission monitoring, including: A joint development scale simulation module is used to collect sample rock mass cuttability data based on a triaxial fracturing device and an acoustic emission monitoring system, analyze the correlation between acoustic emission signal characteristics and rock joint distribution, and establish a joint development scale simulation model; wherein the cuttability data includes the development degree of rock joint; A rock mass strength degradation prediction module is used to collect sample rock mass integrity indicators and strength parameters, construct a dynamic state characteristic evolution system based on a rock mass permeability tensor evolution model and a stress-seepage-damage coupling mechanism, and establish a rock mass strength degradation prediction model based on damage accumulation; A model verification and optimization module is used to verify and optimize the joint development scale simulation model, the dynamic state characteristic evolution system and the rock mass strength degradation prediction model based on historical case data; A hydraulic fracturing construction simulation module is used to construct a multi-scale fracturing fracture propagation prediction algorithm, combine the interaction mechanism of natural fractures and artificial fractures, and establish a hydraulic fracturing construction simulation model integrating the redistribution law of the in-situ stress field and the fracture network connectivity evaluation; The simulation execution module is configured to simulate the hydraulic fracturing effect of the hydraulic fracturing planning scheme according to simulation instructions, and generate a hydraulic fracturing simulation scheme according to planning instructions.

[0016] In a third aspect, the embodiments of the present disclosure further provide a computer device, which adopts the technical scheme as follows: The computer device comprises: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the hydraulic fracturing simulation method based on acoustic emission monitoring as described above.

[0017] In a fourth aspect, the embodiments of the present disclosure further provide a computer readable storage medium storing computer instructions for causing a computer to execute the hydraulic fracturing simulation method based on acoustic emission monitoring as described above.

[0018] In a fifth aspect, the embodiments of the present disclosure further provide a computer program product comprising computer programs / instructions, which, when executed by a processor, implement the steps of the method as described above.

[0019] The hydraulic fracturing simulation method and system based on acoustic emission monitoring provided by the embodiments of the present disclosure have the following beneficial effects: 1. Through the joint application of the acoustic emission monitoring system and the triaxial fracturing device, the spatial distribution characteristics and development rules of the joints inside the rock mass can be accurately identified, the prediction accuracy is effectively improved, and reliable geological structure data support is provided for subsequent fracturing path design; 2. The established rock mass behavior evolution system can realize dynamic prediction of the strength and permeability changes of the rock mass under different working conditions, improve the prediction accuracy, effectively reduce the safety risks caused by sudden changes in rock mass properties during construction, and improve the controllability and safety of fracturing construction; 3. The hydraulic fracturing construction simulation model can automatically optimize the drilling arrangement and fracturing parameter configuration, and compared with the traditional manual design method, the scheme generation efficiency is improved, the consistency and repeatability of the fracturing effect are significantly improved, and the influence of human factors on the construction quality is reduced.

[0020] The above description is only a summary of the technical solutions of the present disclosure, in order to more clearly understand the technical means of the present disclosure, the preferred embodiments can be implemented according to the content of the specification, and in order for the above and other purposes, features and advantages of the present disclosure to be more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0021] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of a hydraulic fracturing simulation method based on acoustic emission monitoring provided in this disclosure embodiment; Figure 2 A flowchart of another hydraulic fracturing simulation method based on acoustic emission monitoring provided in this disclosure embodiment; Figure 3 A schematic diagram of the structure of a hydraulic fracturing simulation system based on acoustic emission monitoring provided in this embodiment of the disclosure; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. Detailed Implementation

[0023] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0024] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0025] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0026] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0027] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0028] like Figure 1 As shown, this embodiment provides a hydraulic fracturing simulation method based on acoustic emission monitoring, including the following steps: Step S1: Collect cutability data of sample rock mass based on triaxial fracturing device and acoustic emission monitoring system, analyze the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establish a joint development scale simulation model, wherein the cutability data includes the degree of rock mass joint development.

[0029] In this embodiment, step S1 includes: Step S11: Acquire acoustic signals during the fracture process of the sample rock mass through the acoustic emission monitoring system; wherein, the acoustic emission monitoring system includes an acoustic emission sensor array, and the acoustic signals include the cutability data; wherein, the cutability data includes the degree of joint development in the rock mass.

[0030] Specifically, a multi-channel acoustic emission sensor array is installed on the surface of the sample rock mass or inside the borehole according to a preset spatial layout. When the rock mass undergoes micro-fractures under the action of a triaxial fracturing device, the elastic wave energy released by the fracture is captured by the sensor array in real time and converted into an electrical signal. Then, the acoustic wave signal is synchronously sampled and digitally processed by a high-frequency data acquisition system to extract the original acoustic emission data containing information about the internal structure of the rock mass.

[0031] The acoustic emission monitoring system includes an acoustic emission sensor array arranged according to three-dimensional spatial geometry, a signal conditioning and amplification circuit, a multi-channel data acquisition card, and a real-time data processing unit. The acoustic signals include cutability data reflecting the rock mass joint density, crack development degree, and fracture mode. By analyzing the time and frequency domain characteristics of the acoustic signals, the location and fracture mechanism of different types of fracture events inside the rock mass can be identified. The cutability data includes the rock mass joint development degree.

[0032] In other embodiments, cutability data also include the density of microcracks within the rock mass and the connectivity of structural surfaces.

[0033] Furthermore, other technologies such as borehole parameter analysis, well logging data analysis, image recognition technology, and principal strain cloud map analysis can be used to monitor the cutability data and physical and mechanical parameters of the sample rock mass, such as rock hardness, compressive strength, tensile strength, elastic modulus, Poisson's ratio, joint surface roughness, joint infill properties, rock mass integrity coefficient, drillability index, and abrasiveness index, which reflect the ease with which the rock mass can be cut. These physical and mechanical parameters are then verified against the cutability data obtained by the acoustic emission monitoring system in this embodiment to further ensure the accuracy of the monitoring data.

[0034] Step S12: Perform statistical analysis on the characteristic parameters of the acoustic signal; wherein the characteristic parameters include amplitude, frequency and duration.

[0035] Specifically, firstly, amplitude parameters reflecting fracture intensity (e.g., the amplitude reaches 80-120 dB when sandstone samples fracture under uniaxial compression) and duration parameters reflecting the fracture duration process are extracted through time-domain analysis (e.g., the duration of shear fracture on typical joint surfaces is 50-200 μs). Secondly, parameters reflecting the dominant frequency, frequency range, and bandwidth of the fracture mode are extracted through frequency-domain analysis. The differences between shear fracture and tensile fracture are identified by calculating the correlation coefficient between amplitude and frequency (e.g., the amplitude-frequency correlation coefficient for shear fracture in a coal-rock tunnel test is 0.65, while that for tensile fracture is 0.42). Thirdly, the scale of the fracture event is determined by analyzing the ratio of duration to amplitude (e.g., a ratio less than 0.8 μs / dB indicates micro-fracture, 0.8-2.0 μs / dB indicates moderate fracture, and greater than 2.0 μs / dB indicates large-scale fracture). Finally, different frequencies are statistically analyzed... The energy distribution ratio of segments identifies the response characteristics of different types of structural surfaces within the rock mass (for example, the energy of bedding plane fracture is mainly concentrated in the 50-150kHz frequency band, accounting for more than 70% of the total energy, while the energy of joint plane fracture is concentrated in the 150-300kHz frequency band, accounting for more than 60%). Based on this, a framework for analyzing the spatiotemporal evolution of characteristic parameters is established. By tracking the changing trends of each parameter with the loading process, the stage of rock mass fracture development is predicted (for example, in a limestone tunnel project, the amplitude gradually increases from the initial 30dB to 120dB before failure, indicating that it has entered the critical failure stage). By comparing the parameter differences of the signals received by sensors at different spatial locations, the propagation path and attenuation law of the fracture event are determined (for example, the amplitude attenuates to 60% of the original value at 1m from the fracture source and to 35% at 3m), providing quantitative acoustic emission characteristic data support for subsequent joint network reconstruction and rock mass property assessment.

[0036] Step S13: Identify the characteristic patterns related to joint density and joint length, analyze the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establish the joint development scale simulation model; wherein, the joint development scale simulation model is a mathematical model of the correlation between acoustic emission parameters and joint development scale.

[0037] Specifically, statistical analysis is used to establish the correspondence between acoustic emission signal amplitude distribution and joint density (e.g., high-density joint areas correspond to a significantly increased frequency of acoustic emission events with a multi-peaked amplitude distribution, while low-density joint areas have relatively few acoustic emission events with a relatively concentrated amplitude distribution). The correlation between acoustic emission signal duration and joint length is analyzed (e.g., acoustic emission signals from long joint fractures have a significantly longer duration than those from short joints, and the signal attenuation process exhibits a specific exponential decay pattern), resulting in characteristic patterns related to joint density and length. Based on this, the correlation between acoustic emission signal characteristics and rock mass joint distribution is analyzed, and acoustic emission events are established... The mapping relationship between spatial aggregation and joint network connectivity is established, enabling the spatial distribution density of acoustic emission events to reflect the development level and connectivity characteristics of the joint network. The spectral characteristics of acoustic emission signals can distinguish the fracture response of joint surfaces with different attitudes. Finally, a joint development scale simulation model is established, using the time-domain characteristic parameters, frequency-domain characteristic parameters, and spatial distribution characteristics of acoustic emission signals as input variables. Through multiple regression analysis and machine learning algorithms, a nonlinear mapping relationship is established between acoustic emission parameters and joint development scale parameters such as joint density, joint length, and joint connectivity, forming a correlational mathematical model that can quantitatively predict the joint development characteristics of rock mass based on acoustic emission monitoring data.

[0038] In this embodiment, after step S13, the method further includes: Step S141: Based on the rock mass anisotropy feature identification technology, the wave velocity ratio is calculated by comparing the differences in the propagation speed of acoustic emission waves in different directions.

[0039] Specifically, an array of acoustic emission sensors is first arranged on the surface of the sample rock mass in three orthogonal directions. Based on the anisotropic feature recognition technology of the rock mass, acoustic wave propagation signals in each direction are collected synchronously when the sample rock mass fractures. Then, by calculating the difference in propagation speed of the sound wave in the three main directions of X, Y, and Z, the wave velocity value in each direction is obtained, and then the wave velocity ratio parameter between different directions is calculated.

[0040] For example, sensors are installed on the surface of the surrounding rock in a coal mine roadway along the horizontal direction (X-axis), vertical direction (Y-axis), and roadway axis (Z-axis), with a spacing of 2-3 meters. When the rock mass fractures, the sound wave propagation signals in each direction are collected simultaneously. Then, by calculating the difference in the propagation time of the sound waves in the three main directions of X, Y, and Z, the wave velocity values ​​in each direction are obtained (e.g., in a sandstone roadway, the wave velocity in the X direction is 3200 m / s, in the Y direction it is 2800 m / s, and in the Z direction it is 3500 m / s). Then, the wave velocity ratio parameters between different directions are calculated (e.g., Vx / Vy=1.14, Vz / Vx=1.09).

[0041] Step S142: Identify the spatial geometric parameters of the sample rock mass; wherein, the spatial geometric parameters include the dip and dip angle of the internal bedding planes and joint planes of the sample rock mass.

[0042] By analyzing the numerical distribution of acoustic emission wave velocity ratios in different directions—for example, when the wave velocity in one direction is significantly lower than in other directions—it is determined that a dominant structural plane exists in that direction, and the magnitude of the wave velocity ratio directly reflects the strength of the rock mass's anisotropy. Then, combining spectral analysis techniques, the differences in the dominant frequency distribution of acoustic emission signals in different directions are compared. Utilizing the characteristic that the dominant frequency of bedding plane fracture signals is usually lower than that of joint fracture signals, the types of bedding planes and joints are distinguished. Next, by establishing a three-dimensional mapping relationship model of wave velocity ratio, frequency characteristics, and joint orientation, the wave velocity ratio data and spectral characteristic data are correlated and analyzed, and the spatial orientation parameters of each structural plane within the sample rock mass are systematically calculated. Finally, through the spatial geometric relationship of the multi-sensor array and the analysis of the acoustic propagation path, the dip angle and tilt direction of the bedding planes and joints within the sample rock mass are accurately determined, forming complete structural plane orientation data. The spatial geometric parameters include the dip and dip angle of the bedding planes and joints within the sample rock mass, as well as the strike and spacing parameters reflecting the spatial distribution characteristics of the structural planes.

[0043] For example, in the monitoring of the surrounding rock in the main haulage roadway of a coal mine, the wave velocity in the X direction (horizontal) is 3200 m / s, the wave velocity in the Y direction (vertical) is 2650 m / s, and the wave velocity in the Z direction (roadway axis) is 3450 m / s. The calculated values ​​of Vx / Vy = 1.21 and Vz / Vy = 1.30 indicate the presence of a dominant joint surface in the Y direction. If the dominant frequency of the signal in the Y direction of this roadway is 180 kHz, in the X direction it is 280 kHz, and in the Z direction it is 320 kHz, based on the dominant frequency distribution characteristics, the Y direction is determined to be bedding. The direction of surface development was determined; then, by establishing a three-dimensional mapping relationship model of wave velocity ratio-frequency characteristics-joint orientation, combined with the geological data of the tunnel, the main bedding plane dipped at 45° northeast and had a dip angle of 15°. Three main structural planes were identified in the tunnel: the first group of bedding planes dipped at 45° / 15° with a spacing of 0.8-1.2m; the second group of joint planes dipped at 135° / 75° with a spacing of 1.5-2.0m; and the third group of joint planes dipped at 280° / 60° with a spacing of 2.0-3.0m.

[0044] Step S143: The spatial geometric parameters are reconstructed using three-dimensional visualization to form a joint network model.

[0045] Specifically, firstly, based on the dip, dip angle, strike, and spacing parameters of the identified bedding planes and joint surfaces, a mathematical description system of joint geometric elements is established by converting the spatial attitude data of each structural plane into plane equations and boundary conditions in a three-dimensional coordinate system. Then, by calculating the spatial intersection relationships and connectivity paths between different joint surfaces, joint combinations with hydraulic connectivity potential are identified, and the topological connectivity of the joint network is established. Next, the identified joint network is modeled in layers according to different scales, constructing an overall joint network framework at the macro scale and accurately describing the geometric details and connectivity features of key joints at the micro scale. Finally, through three-dimensional visualization rendering technology, the spatial distribution, connectivity state, and geometric features of the joint network are presented in an intuitive three-dimensional image form, forming a joint network model that can accurately reflect the internal structural characteristics of the rock mass, providing reliable geological structural data support for subsequent hydraulic fracturing path prediction and construction parameter optimization.

[0046] Step S2: Collect the integrity index and strength parameters of the sample rock mass, construct a dynamic behavior characteristic evolution system based on the rock mass permeability tensor evolution model and stress-seepage-damage coupling mechanism, and establish a rock mass strength deterioration prediction model based on damage accumulation.

[0047] In this embodiment, step S2 includes: Step S21: Establish a multi-condition test database and collect the response characteristics of the sample rock mass under different combinations of conditions. The response characteristics of the sample rock mass include the integrity index and strength parameters.

[0048] Specifically, an orthogonal test scheme (i.e., a multi-condition test database) covering different geological conditions (such as lithology, degree of bedding development, and joint density), different stress states (such as uniaxial compression, triaxial compression, and cyclic loading), and different water content conditions (such as dry, saturated, and partially saturated) was designed to ensure comprehensive coverage of all influencing factors. Then, under each combination of conditions, the integrity indicators of the sample rock mass were systematically measured, including structural characteristic parameters such as rock quality index RQD, joint spacing, joint roughness coefficient, and joint filling condition. At the same time, strength parameters reflecting the mechanical properties of the rock mass were measured, including mechanical characteristic parameters such as uniaxial compressive strength, triaxial compressive strength, tensile strength, cohesion, and internal friction angle.

[0049] Furthermore, the influence of different working conditions on various parameters was analyzed, and key influencing mechanisms such as how stress state changes rock mass integrity, how water content affects strength decay, and how geological conditions control the magnitude of parameter changes were identified. Finally, a correlation database between working conditions and sample rock mass response characteristics was established. By statistically analyzing the changing trends and distribution patterns of various parameters under different conditions, reliable basic data support was provided for the subsequent construction of permeability tensor evolution models and strength degradation prediction models.

[0050] Among them, the response characteristics of the sample rock mass include integrity indicators that reflect the integrity of the rock mass structure and strength parameters that reflect the bearing capacity of the rock mass. Together, they constitute the core parameter system for describing the engineering properties of the rock mass.

[0051] Step S22: Based on the rock mass permeability tensor evolution model, monitor the change law of permeability in three main directions, and construct the dynamic behavior characteristic evolution system by combining the stress-seepage-damage coupling mechanism.

[0052] Specifically, based on the rock mass permeability tensor evolution model, by analyzing the variation law of pore structure of sample rock mass under different stress states, the influence mechanism of stress loading on permeability caused by pore compression and joint closure is identified. At the same time, the changes in pore pressure caused by seepage are considered in turn to affect the effective stress distribution. Then, a three-dimensional coupling feedback mechanism is introduced to monitor the variation law of permeability in three main directions, analyze how stress changes change pore geometry and thus affect seepage path, how seepage pressure accelerates microcrack propagation and accumulates damage, and how damage development reduces the rock mass bearing capacity and changes the stress distribution state, forming a dynamic evolution process in which stress, seepage and damage promote each other.

[0053] Step S23: Construct the diffusion equation of pore pressure with time and space, and combine it with the creep deformation law of the sample rock mass to establish a rock mass strength deterioration prediction model based on damage accumulation; wherein, the rock mass strength deterioration prediction model can quantify the correlation between damage accumulation rate and strength decay.

[0054] Specifically, a diffusion equation for pore pressure over time and space is constructed. By analyzing the propagation mechanism of pore pressure within the rock mass, considering the influence of rock mass heterogeneity on the pressure diffusion path, and combining the anisotropic characteristics of the permeability tensor, a correlation between pressure diffusion rate and rock mass structural characteristics is established. By identifying the development law of creep deformation under different stress levels, the study analyzes how long-term loading leads to the gradual expansion and connection of microcracks, establishing a quantitative mapping relationship between creep strain and damage accumulation, and forming the creep deformation law of the sample rock mass. Then, the effective stress change caused by pore pressure diffusion, the structural deterioration caused by creep deformation, and the fatigue damage caused by cyclic loading are comprehensively considered. By establishing a time evolution function of damage variables, the contribution of different damage mechanisms to rock mass strength attenuation is quantified. Finally, a rock mass strength deterioration prediction model based on damage accumulation is established, using the damage accumulation rate as the core control parameter. Combining the initial strength characteristics of the rock mass and environmental conditions, a nonlinear correlation relationship between the damage accumulation rate and the strength attenuation amplitude is established, achieving accurate prediction of the dynamic change trend of rock mass strength over time.

[0055] The rock mass strength degradation prediction model innovatively integrates the pore pressure diffusion effect, creep deformation mechanism and damage accumulation process. It quantifies the correlation between damage accumulation rate and strength decay through a multi-physics field coupling analysis framework, forming a dynamic prediction system that can reflect the long-term performance evolution law of rock mass.

[0056] Step S3: Verify and optimize the joint development scale simulation model, the dynamic behavior characteristic evolution system, and the rock mass strength deterioration prediction model based on historical case data.

[0057] Specifically, a large amount of historical construction case data is collected to establish a case database, recording the correspondence between the fracturing effect and rock mass characteristic parameters under different geological conditions. By comparing the predicted results with the actual effects, the model parameters are adjusted to verify and optimize the joint development scale simulation model, the dynamic behavior characteristic evolution system, and the rock mass strength deterioration prediction model.

[0058] Step S4: Construct a multi-scale hydraulic fracturing fracture propagation prediction algorithm, combine the interaction mechanism between natural and artificial fractures, and establish a hydraulic fracturing construction simulation model that integrates the redistribution law of the geostress field and the evaluation of fracture network connectivity.

[0059] In this embodiment, step S4 includes: Step S41: Construct the multi-scale hydraulic fracturing fracture propagation prediction algorithm.

[0060] Specifically, a microscale single-fracture propagation mechanical model is established. By analyzing the stress concentration state at the fracture tip and the local failure criteria of the rock mass, the initiation conditions, propagation direction, and propagation rate of a single fracture are determined, while considering the control effect of the stress field distribution around the fracture on the propagation path. Then, a mesoscale fracture interaction analysis framework is constructed. By calculating the stress interference effect and pressure transmission mechanism between multiple fractures, the propagation of adjacent fractures is analyzed to determine how it changes the local stress field distribution and thus affects the propagation behavior of other fractures, establishing the correlation between fracture spacing and interaction intensity. Next, a macroscale overall fracture network evolution model is established. By spatially integrating and temporally evolving the analysis results at the micro and mesoscale levels, the formation process and final distribution morphology of the fracture network within the entire fracturing region are predicted, with a focus on analyzing the development sequence and connectivity patterns of primary and secondary fractures. Finally, based on a cross-scale information transmission mechanism, a mapping relationship between micro-fracture events and the macro-fracture network is established to achieve dynamic coupling and mutual verification of prediction results at different scales, ensuring the consistency and accuracy of the prediction algorithm at all scales, thus forming a multi-scale fracturing fracture propagation prediction algorithm that can comprehensively describe the entire process from single-point fracture to network formation.

[0061] Step S42: Determine the crack propagation path by combining the interaction mechanism between natural cracks and artificial cracks; wherein, the interaction mechanism includes deflection, crossing or termination when natural cracks and artificial cracks meet.

[0062] Specifically, firstly, a mechanical discrimination criterion is established when natural and artificial cracks meet. By analyzing the stress state, crack angle, and rock mass strength characteristics at the meeting point, the mechanical conditions for crack interaction are determined. When an artificial crack extends to the vicinity of a natural crack, the influence of the dip angle, roughness, and filling state of the natural crack on the extension path of the artificial crack is calculated to determine whether deflection behavior occurs. When the angle between the natural and artificial cracks is small and the strength of the natural crack is low, the artificial crack tends to deflect and extend along the direction of the natural crack. Next, the conditions for crossing behavior are analyzed. When the extension stress of the artificial crack is sufficiently large and the cementation degree of the natural crack is high, the artificial crack can directly cross the natural crack and continue to extend in the original direction. By establishing a mechanical balance equation for crossing resistance and extension dynamics, the critical conditions for crossing behavior are quantified. Finally, a discrimination mechanism for termination behavior is established. When the natural crack is large and intersects the artificial crack approximately perpendicularly, the extension energy of the artificial crack is absorbed by the natural crack, leading to termination of extension. By analyzing the stress concentration release and energy dissipation mechanisms, the boundary conditions for crack extension termination are determined.

[0063] Among them, the interaction mechanism comprehensively considers the geometric characteristics, mechanical properties and stress environment of the fracture. By establishing a deflection angle prediction model, a crossing probability assessment model and a termination position determination model, it can accurately predict the propagation path of artificial fractures in complex fracture networks, providing a scientific basis for optimizing fracturing design and improving the connectivity of fracture networks.

[0064] Step S43: Construct a knowledge graph of geological parameters, process parameters and fracturing effects.

[0065] Specifically, this study analyzes the causal relationships between geological parameters (lithology, joint density, geostress magnitude, water content, etc.), technological parameters (injection pressure, injection rate, fracturing fluid viscosity, segment length, etc.), and fracturing effects (fracture length, fracture width, connectivity, stress release degree, etc.), identifying key parameter combinations and mechanisms affecting fracturing performance. Then, direct correlations between parameters are designated as first-level knowledge nodes, synergistic effects of parameter combinations as second-level knowledge nodes, and comprehensive influence patterns under complex conditions as third-level knowledge nodes. This hierarchical knowledge network structure comprehensively expresses the effects from single-parameter influence to multi-parameter coupling effects. Next, a dynamic knowledge update mechanism is established. By analyzing the correspondence between parameter configurations and fracturing effects in newly added construction cases, abnormal patterns inconsistent with the existing knowledge graph are automatically identified, and verified new knowledge is integrated into the graph structure, achieving continuous improvement and expansion of the knowledge graph. Finally, a comprehensive knowledge system (knowledge graph) integrating expert experience rules and data-driven discovery is formed.

[0066] By organically combining the qualitative judgment rules in traditional construction experience with the quantitative correlation patterns derived from big data analysis, a knowledge graph is established that includes both physical mechanism explanations and statistical regularities, providing comprehensive knowledge support and decision-making basis for the generation of subsequent intelligent construction schemes.

[0067] The knowledge graph innovatively adopts multi-dimensional parameter space mapping technology. By establishing a parameter similarity calculation model and an effect prediction reasoning mechanism, it realizes the intelligent recommendation function of quickly retrieving the optimal parameter configuration under given geological conditions and process constraints.

[0068] Step S44: Establish the hydraulic fracturing construction simulation model that integrates the redistribution law of the geostress field and the evaluation of fracture network connectivity.

[0069] Specifically, by establishing a spatiotemporal evolution equation for stress field redistribution during fracturing, this study analyzes how a single fracturing operation alters the stress state of the surrounding rock mass, how the cumulative effect of multiple fracturing operations influences the regional stress field distribution, and how stress field changes, in turn, affect the initiation direction and propagation path of subsequent fractures, thus forming a two-way coupled feedback mechanism between the stress field and fracture development. Then, it analyzes geometric characteristic parameters such as fracture length, fracture density, fracture intersection angle, and fracture aperture, and establishes a connectivity index calculation model combining two dimensions: seepage connectivity and stress transfer connectivity. Seepage connectivity reflects the conductivity of the fracture network, while stress transfer connectivity reflects the stress release effect of the fracture network. Finally, it analyzes stress field changes such as... This study investigates how fracture aperture and connectivity affect stress transmission paths and stress concentration, establishes the interaction mechanism between these two factors, and forms a predictive framework capable of forecasting the dynamic changes in fracturing effects as the stress field evolves. Ultimately, it integrates three core modules: geostress field evolution prediction, fracture propagation simulation, and connectivity evaluation. This results in the establishment of a hydraulic fracturing construction simulation model that combines geostress field redistribution patterns and fracture network connectivity evaluation. This model enables dynamic simulation of the entire fracturing process, predicting fracture distribution morphology, stress release degree, and overall connectivity effect after fracturing based on initial geological conditions and design parameters. This provides a reliable theoretical basis and technical support for the scientific formulation and real-time adjustment of construction plans.

[0070] Step S5: The hydraulic fracturing construction simulation model, according to the simulation instructions, simulates and generates the hydraulic fracturing effect of the hydraulic fracturing planning scheme, and / or, according to the planning instructions, generates a hydraulic fracturing simulation scheme.

[0071] Specifically, the hydraulic fracturing construction simulation model, according to simulation instructions, first calls the geostress field evolution prediction module to analyze the current stress state, then starts the fracture propagation simulation module to predict the fracture development path, then calculates the connectivity effect through the connectivity evaluation module, and finally integrates the calculation results of the three modules to simulate and generate the hydraulic fracturing effect of the hydraulic fracturing planning scheme, including the predicted results of fracture distribution morphology, stress release degree and connectivity index.

[0072] In another embodiment, the hydraulic fracturing construction simulation model, according to the planning instructions, calls the knowledge graph retrieval module to match historical successful cases of similar geological conditions, combines the decision-making logic of the expert experience system to perform initial parameter selection, and uses a multi-objective constraint optimization algorithm to find the optimal parameter combination under the conditions of meeting construction cost and technical feasibility constraints. At the same time, it considers the influence of the interaction mechanism between natural and artificial fractures on the expansion path, and generates a complete hydraulic fracturing simulation scheme including borehole layout coordinates, segmented fracturing parameters and fracturing fluid formulation.

[0073] It should be noted that in this embodiment, data is collected and analyzed using sample rocks. When simulating the effect of the planning scheme or generating a simulation scheme, it is necessary to input the rock information of the target area corresponding to the scheme for simulation and prediction.

[0074] like Figure 2 As shown, this embodiment provides another hydraulic fracturing simulation method based on acoustic emission monitoring, including the following steps: Step S1: Collect cutability data of sample rock mass based on triaxial fracturing device and acoustic emission monitoring system, analyze the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establish a joint development scale simulation model.

[0075] Step S2: Collect the integrity index and strength parameters of the sample rock mass, construct a dynamic behavior characteristic evolution system based on the rock mass permeability tensor evolution model and stress-seepage-damage coupling mechanism, and establish a rock mass strength deterioration prediction model based on damage accumulation.

[0076] Step S3: Verify and optimize the joint development scale simulation model, the dynamic behavior characteristic evolution system, and the rock mass strength deterioration prediction model based on historical case data.

[0077] Step S4: Construct a multi-scale hydraulic fracturing fracture propagation prediction algorithm, combine the interaction mechanism between natural and artificial fractures, and establish a hydraulic fracturing construction simulation model that integrates the redistribution law of the geostress field and the evaluation of fracture network connectivity.

[0078] Step S5: The hydraulic fracturing construction simulation model, according to the simulation instructions, simulates and generates the hydraulic fracturing effect of the hydraulic fracturing planning scheme, and / or, according to the planning instructions, generates a hydraulic fracturing simulation scheme.

[0079] Steps S1-S5 in this embodiment are the same as the corresponding steps in the previous embodiments, and will not be repeated here.

[0080] Step S6: Use the acoustic emission monitoring system to obtain real-time rock mass characteristic parameters of the construction area, input the real-time rock mass characteristic parameters into the hydraulic fracturing construction simulation model, and correct the hydraulic fracturing effect according to the simulation instructions, and / or correct the hydraulic fracturing simulation scheme according to the planning instructions. The hydraulic fracturing simulation scheme includes borehole trajectory design, segmented fracturing parameters and fracturing fluid formulation.

[0081] In this embodiment, step S6 includes: Step S61: Determine the spatial location of the acoustic emission event using the acoustic emission monitoring system, record the direction and speed of crack propagation, and obtain crack propagation path information.

[0082] Specifically, utilizing the time-difference positioning principle of the acoustic emission monitoring system (multi-sensor array), the spatial location (spatial coordinates) of each acoustic emission event is accurately determined by calculating the time difference in arrival of the acoustic emission signal at different sensors, combined with the sensor spatial coordinates and the sound wave propagation speed, using a three-dimensional positioning algorithm. Then, by continuously monitoring the positional changes of acoustic emission events within adjacent time periods, a spatiotemporal evolution sequence of the crack propagation trajectory is established. The dominant and branching directions of crack propagation are determined by analyzing the spatial vector relationships between adjacent event points. Finally, by calculating the change in crack propagation distance per unit time, combined with the time-varying frequency of acoustic emission events and the energy release intensity... The study analyzes the distribution characteristics of acoustic emission events to quantify the dynamic changes in fracture propagation rate, identify the transition between rapid and slow propagation stages, and record the direction and speed of fracture propagation. Simultaneously, it analyzes the angle between the current propagation direction and the geostress field direction, and, combined with the distribution characteristics of the rock mass joint network, predicts possible subsequent propagation paths and termination locations of the fracture. Finally, it comprehensively processes the spatial location sequence of acoustic emission events, propagation direction vectors, and propagation rate variation curves to form complete fracture propagation path information, including the fracture initiation point, propagation path, branch structure, and propagation rate. This provides accurate fracture development status data support for subsequent evaluation of fracturing effects and real-time adjustment of construction parameters.

[0083] Step S62: Using the dynamic monitoring method of fracturing fluid filtration loss, compare the changes in the difference between the injection volume and the return volume to determine the real-time changes in rock mass permeability and generate the filtration loss change pattern.

[0084] Specifically, a dynamic monitoring method for fracturing fluid loss is adopted to establish a real-time monitoring system for the injection and return flow of fracturing fluid. The injection rate and cumulative injection volume of fracturing fluid are continuously recorded using a high-precision flow meter. At the same time, a flow monitoring device is installed in the return pipeline to measure the flow rate change of the return fluid in real time. By comparing the difference and trend between the injection volume and the return flow, the filtration pattern of fracturing fluid in the rock mass is identified. Then, a multi-stage filtration characteristic analysis mechanism is established, dividing the fracturing process into an initial filtration stage, a stable filtration stage, and a late filtration stage. By analyzing the differences in the rate of change of filtration volume in each stage, the real-time changes in rock mass permeability are judged, and the dynamic evolution law of rock mass permeability (i.e., the filtration volume change law) is identified.

[0085] The rapid increase in filtration loss in the initial stage reflects the initial permeability of the rock mass, the gradual change in filtration loss in the stable stage indicates the stable propagation of fractures, and the fluctuation in filtration loss in the later stage reflects the change in the connectivity of the fracture network.

[0086] The variation law of filtration loss uses the difference between injection volume and return volume as the core monitoring indicator. Through multi-stage characteristic analysis and permeability correlation modeling, it can accurately identify and quantitatively evaluate the dynamic changes in rock mass permeability.

[0087] Step S63: Input the real-time rock mass characteristic parameters, the fracture propagation path information, and the filtration loss variation law into the hydraulic fracturing construction simulation model.

[0088] Step S64: The hydraulic fracturing construction simulation model corrects the hydraulic fracturing simulation scheme according to the planning instructions and outputs the results.

[0089] Specifically, the real-time rock mass characteristic parameters such as rock mass strength, joint development degree, and stress state are standardized. At the same time, the geometric characteristic data such as spatial coordinates, propagation direction vector, and propagation rate in the crack propagation path information are aligned with coordinate system and time sequence. The seepage characteristic data such as permeability evolution trend and filtration rate change in the filtration loss variation law are normalized.

[0090] The hydraulic fracturing construction simulation model, based on planning instructions, first comprehensively analyzes the processed real-time rock mass characteristic parameters, fracture propagation path information, and filtration loss variation patterns to identify the degree of deviation between the current state of the area and the state during the previous simulation. Then, it activates an intelligent decision engine to determine whether spatial adjustments to the borehole layout, pressure and rate corrections to the segmented fracturing parameters, and viscosity and additive ratio optimizations to the fracturing fluid formulation are necessary, by comparing and analyzing the differences between real-time monitoring data and the original design parameters. Based on multi-objective constrained optimization algorithms, such as setting the goal of maximizing fracture connectivity and optimizing cost control while ensuring construction safety and technical feasibility, it automatically searches for the optimal parameter combination using genetic algorithms or particle swarm optimization, while considering the impact of the interaction between natural and artificial fractures on the modified scheme. Finally, the scheme consistency verification module verifies the internal logical rationality and engineering feasibility of the modified scheme, ensuring coordination and matching between various modified parameters, and outputs the modified hydraulic fracturing simulation scheme in a standardized format, including updated borehole coordinates, adjusted fracturing parameters, and optimized fracturing fluid formulation.

[0091] It should be noted that the above embodiments compare and analyze the early simulation predictions with the latest rock mass condition before construction to correct the hydraulic fracturing simulation scheme. In another embodiment, the hydraulic fracturing construction scheme can also be corrected by comparing and analyzing the data during the construction process with the data at the beginning of construction.

[0092] Step S7: The effectiveness of the hydraulic fracturing construction scheme is verified by comparing the fracture network imaging technology combining microseismic monitoring and the acoustic emission monitoring system; wherein, the hydraulic fracturing construction scheme is one of the hydraulic fracturing planning scheme, the hydraulic fracturing simulation scheme, and the actual hydraulic fracturing scheme.

[0093] Specifically, a microseismic monitoring system captures the macroscopic distribution characteristics of large-scale rock mass fracturing events, while an acoustic emission monitoring system obtains microscopic details of localized fracture propagation. The two monitoring systems are synchronized and calibrated in time series and spatial coordinates, respectively. A data fusion algorithm eliminates systematic errors and noise interference between different monitoring systems. Then, the spatial clustering pattern of the microseismic monitoring is used as the basis for identifying macroscopic fracture zones, and the continuous distribution trajectory of the acoustic emission monitoring system is used as the basis for precise location of individual fractures. A hierarchical imaging system from microscopic fractures to macroscopic networks is established through a fracture network imaging technique combining cross-scale microseismic monitoring and acoustic emission monitoring systems, forming a high-precision three-dimensional fracture network model that includes fracture geometry, spatial distribution, and connectivity. Next, a quantitative comparative analysis is performed between the fracture network distribution obtained from actual monitoring and the expected fracture development pattern in the hydraulic fracturing construction plan. By calculating key indicators such as fracture length deviation rate, fracture direction consistency, and fracture density matching degree, the effectiveness of the hydraulic fracturing construction plan, including construction results and prediction accuracy, is evaluated.

[0094] The hydraulic fracturing construction scheme includes one of the following: hydraulic fracturing planning scheme, hydraulic fracturing simulation scheme, and hydraulic fracturing actual scheme. The hydraulic fracturing actual scheme refers to the hydraulic fracturing scheme actually implemented in the target area. It can be one of the hydraulic fracturing planning scheme, hydraulic fracturing simulation scheme, or a scheme modified or artificially optimized from both. It can also be a historically successful scheme or a scheme designed by the construction personnel. There are no restrictions here.

[0095] Furthermore, when the actual crack development effect deviates from the expected target by more than a set threshold, the scheme correction mechanism is automatically triggered.

[0096] Step S8: Establish a quantitative evaluation system for fracturing effect based on fracture connectivity and stress release efficiency, and evaluate the extent to which the hydraulic fracturing construction scheme improves the effectiveness of long-distance hydraulic fracturing in preventing and controlling coal and rock dynamic disasters.

[0097] Specifically, by analyzing the geometric characteristics of the fracture network formed after hydraulic fracturing, the ratio of the effective connected fracture length to the total fracture length is calculated to determine the fracture connectivity rate. Simultaneously, the stress release efficiency is quantified by comparing the changes in surrounding rock stress monitoring data before and after fracturing. These two core indicators serve as quantitative standards for evaluating the fracturing effect. Then, an evaluation framework for the prevention and control of coal and rock dynamic disasters is established. By statistically analyzing the changes in the frequency and intensity of dynamic disaster events such as rockbursts and coal and gas outbursts within the fracturing area, and combining the results of surrounding rock deformation monitoring and stability evaluation, the specific improvement of the long-distance hydraulic fracturing implementation on the prevention and control of coal and rock dynamic disasters is quantitatively assessed, forming a complete evaluation chain from technical indicators to engineering effects.

[0098] Step S9: Based on the effectiveness and the degree of improvement, optimize the hydraulic fracturing construction simulation model using a Bayesian-optimized adaptive adjustment algorithm for fracturing parameters.

[0099] Specifically, firstly, a priori model of the probability distribution of fracturing parameters is established, using key parameters such as injection pressure, injection rate, and fracturing fluid viscosity as optimization variables. The initial probability distribution of each parameter is determined using historical engineering data. Then, a Gaussian process regression surrogate model is constructed with fracture connectivity and stress release efficiency as objectives. The mapping relationship between parameters and fracturing effects is learned through a small amount of experimental data, and the posterior probability of the parameter distribution is continuously updated using Bayesian inference. Next, the expected improvement criterion is used as the acquisition function to intelligently select the next set of experimental parameters in the parameter space, balancing global exploration and local development to maximize the probability of obtaining effective information. At the same time, the effectiveness verification results of the scheme and the degree of improvement in the prevention and control of coal and rock dynamic disasters are used as constraints to ensure that the optimized parameter configuration meets the minimum effectiveness threshold and the expected improvement standard. Finally, through an iterative optimization process, the Gaussian process model and parameter probability distribution are updated each time based on new experimental results. Under the premise of satisfying the effectiveness and improvement constraints, the model gradually converges to the optimal parameter configuration. When the improvement of multiple consecutive iterations is less than the preset threshold and the constraints are satisfied, the optimization is terminated, and the optimal fracturing parameter combination is output, thereby realizing the optimization of the hydraulic fracturing construction simulation model.

[0100] Furthermore, parameter combinations corresponding to construction schemes whose effectiveness and improvement reach the high satisfaction threshold can be used as the default parameter combinations for the initial iteration. The optimal fracturing parameter combination can also be compared with existing construction scheme parameter combinations. If a construction scheme with similar parameter combinations (e.g., the difference between each parameter is less than 10%) has an effectiveness and improvement lower than the low satisfaction threshold, then the iteration is restarted, and that parameter combination is blacklisted and no longer participates in iteration. The high and low satisfaction thresholds can be set according to the construction scenario and purpose; no restrictions are imposed here.

[0101] It should be noted that steps S6, S7, and S8 can be performed independently, sequentially, or simultaneously; there are no restrictions on this.

[0102] like Figure 3 As shown, this embodiment also provides a hydraulic fracturing simulation system based on acoustic emission monitoring, including: The joint development scale simulation module is used to collect sample rock mass cutability data based on a triaxial fracturing device and an acoustic emission monitoring system, analyze the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establish a joint development scale simulation model; wherein, the cutability data includes the degree of rock mass joint development; The rock mass strength deterioration prediction module is used to collect sample rock mass integrity indicators and strength parameters. Based on the rock mass permeability tensor evolution model and stress-seepage-damage coupling mechanism, a dynamic behavior characteristic evolution system is constructed, and a rock mass strength deterioration prediction model based on damage accumulation is established. The model validation and optimization module is used to validate and optimize the joint development scale simulation model, the dynamic behavior characteristic evolution system, and the rock mass strength deterioration prediction model based on historical case data. The hydraulic fracturing construction simulation module is used to construct a multi-scale fracturing fracture propagation prediction algorithm. It combines the interaction mechanism between natural and artificial fractures to establish a hydraulic fracturing construction simulation model that integrates the redistribution law of the geostress field and the evaluation of fracture network connectivity. The simulation execution module is used to simulate and generate the hydraulic fracturing effect of the hydraulic fracturing planning scheme according to the simulation instructions, and to generate the hydraulic fracturing simulation scheme according to the planning instructions.

[0103] In another embodiment, a hydraulic fracturing simulation system based on acoustic emission monitoring further includes: The correction module is used to acquire real-time rock mass characteristic parameters of the construction area using the acoustic emission monitoring system, input the real-time rock mass characteristic parameters into the hydraulic fracturing construction simulation model, correct the hydraulic fracturing effect according to the simulation instructions, and / or correct the hydraulic fracturing simulation scheme according to the planning instructions. The hydraulic fracturing simulation scheme includes borehole trajectory design, segmented fracturing parameters and fracturing fluid formulation.

[0104] The evaluation module is used to verify the effectiveness of the hydraulic fracturing construction scheme by comparing and using fracture network imaging technology that combines microseismic monitoring and the acoustic emission monitoring system; wherein the hydraulic fracturing construction scheme is one of the hydraulic fracturing planning scheme, the hydraulic fracturing simulation scheme, and the actual hydraulic fracturing scheme.

[0105] The evaluation module is also used to establish a quantitative evaluation system for fracturing effects based on fracture connectivity and stress release efficiency, and to assess the extent to which the hydraulic fracturing construction scheme improves the effectiveness of long-distance hydraulic fracturing in preventing and controlling coal and rock dynamic disasters.

[0106] Furthermore, the evaluation module is also used to optimize the hydraulic fracturing construction simulation model based on a Bayesian-optimized adaptive adjustment algorithm for fracturing parameters, according to the effectiveness and the degree of improvement.

[0107] The functions and implementation methods of the above modules are the same as the corresponding steps in the aforementioned method embodiments, and will not be repeated here.

[0108] The hydraulic fracturing simulation method and system based on acoustic emission monitoring provided by this invention, through the combined application of an acoustic emission monitoring system and a triaxial fracturing device, can accurately identify the spatial distribution characteristics and development patterns of joints within the rock mass, effectively improving prediction accuracy and providing reliable geological structural data support for subsequent fracturing path design. The established rock mass property evolution system can achieve dynamic prediction of changes in rock mass strength and permeability under different working conditions, improving prediction accuracy and effectively reducing safety risks caused by abrupt changes in rock mass properties during construction, thereby improving the controllability and safety of fracturing construction. The hydraulic fracturing construction simulation model can automatically optimize borehole layout and fracturing parameter configuration. Compared with traditional manual design methods, the efficiency of scheme generation is improved, while the consistency and repeatability of fracturing effects are significantly improved, reducing the impact of human factors on construction quality.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0110] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0111] like Figure 4 This is a schematic diagram of a computer device provided for an embodiment of the present disclosure. It illustrates a structural schematic diagram suitable for implementing the computer device in the embodiments of the present disclosure. Figure 4 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0112] A computer device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0113] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory, causing the computer device to perform all or part of the steps of the multi-source data fusion database security situation awareness method of the foregoing embodiments of this disclosure.

[0114] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0115] like Figure 4 As shown, a computer device may include a processor (such as a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) or programs loaded from storage devices into random access memory (RAM). The RAM also stores various programs and data required for the operation of the computer device. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0116] Typically, the following devices can be connected to the I / O interface: input devices, such as sensors or visual information acquisition devices; output devices, such as displays; storage devices, such as magnetic tapes or hard drives; and communication devices. Communication devices allow the computer device to communicate wirelessly or wiredly with other devices (such as edge computing devices) to exchange data. Although Figure 4 A computer apparatus with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or included alternatively.

[0117] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the hydraulic fracturing simulation method based on acoustic emission monitoring according to embodiments of this disclosure are performed.

[0118] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0119] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the hydraulic fracturing simulation method based on acoustic emission monitoring described in the foregoing embodiments of the present disclosure are performed.

[0120] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0121] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0122] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0123] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0124] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0125] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0126] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0127] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0128] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A hydraulic fracturing simulation method based on acoustic emission monitoring, characterized in that, include: Based on the triaxial fracturing device and acoustic emission monitoring system, the shearability data of sample rock masses were collected, the correlation between acoustic emission signal characteristics and rock mass joint distribution was analyzed, and a joint development scale simulation model was established; wherein, the shearability data includes the degree of rock mass joint development; The integrity index and strength parameters of the sample rock mass were collected. Based on the rock mass permeability tensor evolution model and stress-seepage-damage coupling mechanism, a dynamic behavior characteristic evolution system was constructed, and a rock mass strength deterioration prediction model based on damage accumulation was established. Based on historical case data, the joint development scale simulation model, the dynamic behavior evolution system, and the rock mass strength deterioration prediction model were validated and optimized. A multi-scale fracturing fracture propagation prediction algorithm was constructed, and a hydraulic fracturing construction simulation model was established by combining the interaction mechanism between natural and artificial fractures and integrating the redistribution law of the geostress field and the evaluation of fracture network connectivity. The hydraulic fracturing construction simulation model, according to simulation instructions, simulates and generates the hydraulic fracturing effect of the hydraulic fracturing planning scheme, and / or, according to the planning instructions, generates a hydraulic fracturing simulation scheme.

2. The method according to claim 1, characterized in that, Also includes: The acoustic emission monitoring system is used to obtain real-time rock mass characteristic parameters of the construction area. The real-time rock mass characteristic parameters are input into the hydraulic fracturing construction simulation model. The hydraulic fracturing effect is corrected according to the simulation instructions, and / or the hydraulic fracturing simulation scheme is corrected according to the planning instructions. The hydraulic fracturing simulation scheme includes borehole trajectory design, segmented fracturing parameters and fracturing fluid formulation.

3. The method according to claim 1, characterized in that, Also includes: The effectiveness of the hydraulic fracturing construction scheme was verified by comparing the fracture network imaging technology combining microseismic monitoring and the acoustic emission monitoring system. The hydraulic fracturing construction scheme is one of the hydraulic fracturing planning scheme, the hydraulic fracturing simulation scheme, and the actual hydraulic fracturing scheme. Establish a quantitative evaluation system for fracturing effect based on fracture connectivity and stress release efficiency, and assess the extent to which the aforementioned hydraulic fracturing construction scheme improves the prevention and control of coal and rock dynamic disasters through long-distance hydraulic fracturing.

4. The method according to claim 3, characterized in that, Also includes: Based on the effectiveness and the degree of improvement, the hydraulic fracturing construction simulation model is optimized using a Bayesian-optimized adaptive adjustment algorithm for fracturing parameters.

5. The method according to claim 1, characterized in that, The method involves collecting cutability data of sample rock masses using a triaxial fracturing device and an acoustic emission monitoring system, analyzing the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establishing a joint development scale simulation model, including: The acoustic emission monitoring system acquires acoustic signals during the fracture process of the sample rock mass; wherein, the acoustic emission monitoring system includes an acoustic emission sensor array, and the acoustic signals include the cutability data; Statistical analysis is performed on the characteristic parameters of the acoustic signal; wherein the characteristic parameters include amplitude, frequency, and duration. Characteristic patterns related to joint density and joint length are identified, the correlation between acoustic emission signal characteristics and rock mass joint distribution is analyzed, and a joint development scale simulation model is established; wherein, the joint development scale simulation model is a mathematical model of the correlation between acoustic emission parameters and joint development scale.

6. The method according to claim 1, characterized in that, After establishing the joint development scale simulation model, the following is also included: Based on the rock mass anisotropy feature identification technology, the wave velocity ratio is calculated by comparing the differences in the propagation speed of acoustic emission waves in different directions; Identify the spatial geometric parameters of the sample rock mass; wherein, the spatial geometric parameters include the dip and dip angle of the internal bedding planes and joint planes of the sample rock mass; The spatial geometric parameters are reconstructed using three-dimensional visualization to form a joint network model.

7. The method according to claim 1, characterized in that, The integrity indices and strength parameters of the sampled rock mass are collected. Based on the rock mass permeability tensor evolution model and the stress-seepage-damage coupling mechanism, a dynamic performance characteristic evolution system is constructed, and a rock mass strength deterioration prediction model based on damage accumulation is established, including: A multi-condition test database was established, and the response characteristics of the sample rock mass under different combinations of conditions were collected. The response characteristics of the sample rock mass include the integrity index and strength parameters. Based on the rock mass permeability tensor evolution model, the variation law of permeability in three main directions is monitored, and the dynamic behavior characteristic evolution system is constructed by combining the stress-seepage-damage coupling mechanism. A diffusion equation for pore pressure over time and space is constructed. Combined with the creep deformation law of the sample rock mass, a rock mass strength deterioration prediction model based on damage accumulation is established. The rock mass strength deterioration prediction model can quantify the correlation between damage accumulation rate and strength decay.

8. The method according to claim 1, characterized in that, The proposed multi-scale fracturing fracture propagation prediction algorithm, combined with the interaction mechanism between natural and artificial fractures, establishes a hydraulic fracturing construction simulation model that integrates the redistribution law of the geostress field and the evaluation of fracture network connectivity, including: Construct the multi-scale hydraulic fracturing fracture propagation prediction algorithm; Based on the interaction mechanism between natural and artificial cracks, the crack propagation path is determined; wherein, the interaction mechanism includes deflection, crossing, or termination when natural and artificial cracks meet; Construct a knowledge graph of geological parameters, technological parameters, and fracturing effects; A hydraulic fracturing construction simulation model was established that integrates the redistribution law of the geostress field and the evaluation of fracture network connectivity.

9. The method according to claim 2, characterized in that, The hydraulic fracturing simulation scheme is modified according to the planning instructions. The hydraulic fracturing simulation scheme includes borehole trajectory design, staged fracturing parameters, and fracturing fluid formulation, including: The acoustic emission monitoring system determines the spatial location of acoustic emission events, records the direction and velocity of crack propagation, and obtains crack propagation path information. A dynamic monitoring method for fracturing fluid filtration loss was adopted to compare the changes in the difference between the injection volume and the return volume, thereby determining the real-time changes in rock mass permeability and generating the filtration loss change pattern. The real-time rock mass characteristic parameters, the fracture propagation path information, and the filtration loss variation law are used as input parameters and input into the hydraulic fracturing construction simulation model. The hydraulic fracturing construction simulation model modifies the hydraulic fracturing simulation scheme according to the planning instructions and outputs the results.

10. A hydraulic fracturing simulation system based on acoustic emission monitoring, comprising: The joint development scale simulation module is used to collect sample rock mass cutability data based on a triaxial fracturing device and an acoustic emission monitoring system, analyze the correlation between acoustic emission signal characteristics and rock mass joint distribution, and establish a joint development scale simulation model; wherein, the cutability data includes the degree of rock mass joint development; The rock mass strength deterioration prediction module is used to collect sample rock mass integrity indicators and strength parameters. Based on the rock mass permeability tensor evolution model and stress-seepage-damage coupling mechanism, a dynamic behavior characteristic evolution system is constructed, and a rock mass strength deterioration prediction model based on damage accumulation is established. The model validation and optimization module is used to validate and optimize the joint development scale simulation model, the dynamic behavior characteristic evolution system, and the rock mass strength deterioration prediction model based on historical case data. The hydraulic fracturing construction simulation module is used to construct a multi-scale fracturing fracture propagation prediction algorithm. It combines the interaction mechanism between natural and artificial fractures to establish a hydraulic fracturing construction simulation model that integrates the redistribution law of the geostress field and the evaluation of fracture network connectivity. The simulation execution module is used to simulate and generate the hydraulic fracturing effect of the hydraulic fracturing planning scheme according to the simulation instructions, and to generate the hydraulic fracturing simulation scheme according to the planning instructions.

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