Hydraulic fracturing test crack propagation monitoring system and method

Abstract

The invention relates to the technical field of data acquisition and data analysis, in particular to a hydraulic fracturing test crack propagation monitoring system which comprises a monitoring system and is used for monitoring crack propagation behaviors in the hydraulic fracturing process in real time. Constant or dynamically changing confining pressure and axial pressure are provided; the pore pressure dynamic regulation and control unit is used for accurately controlling the injection pressure, flow and saturation of fracturing fluid and simulating fracture propagation behaviors under different seepage conditions; the multi-physics field sensing monitoring network unit is used for collecting strain, sound wave signals and a surface displacement field in the crack expansion process in real time; full-scale monitoring of rock fracture propagation is realized, an influence mechanism of pore pressure and time effect on fracture evolution is disclosed, an accurate prediction means is provided for shale gas development and deep engineering, and the fracturing effect and engineering safety are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition and data analysis, and in particular to a hydraulic fracturing test crack expansion monitoring system and method. Background Art

[0002] The hydraulic fracturing test fracture expansion monitoring system is a key technology in the development of unconventional oil and gas resources such as shale gas and tight oil. It is mainly used to track and evaluate the morphology, extension direction and spatial distribution characteristics of artificial fractures during the fracturing process in real time.

[0003] According to the publication number: CN114737967A, a real-time monitoring system and method for hydraulic fracturing cracks is disclosed. This technology discloses "a real-time monitoring method, system and storage medium for hydraulic fracturing cracks, which can send collection instructions to a water pressure sensor, an oil pressure sensor, and a flow sensor based on a preset collection interval, obtain the water pressure signal, oil pressure signal, and flow signal sent by the water pressure sensor, the oil pressure sensor, and the flow sensor respectively, convert the obtained signals into data formats, obtain corresponding water pressure data, oil pressure data, and flow data, and display the data in real time. It also uses built-in evaluation conditions to determine whether an abnormal working condition has occurred. When no abnormal working condition has occurred, the fracturing effect is evaluated based on the water pressure data, oil pressure data, and flow data, and the evaluation result is output." The technical solution has "realized real-time monitoring of downhole hydraulic fracturing, and can also obtain the crack expansion shape and range under different fracturing pressures and flows, so that users can make timely adjustments to avoid failures" and other technical effects;

[0004] Existing technologies for studying multiphase porous rock and soil media suffer from three key deficiencies: First, they cannot fully simulate the coupling between pore fluids and the solid matrix, making it difficult to accurately reveal the patterns of crack initiation and propagation under complex seepage conditions. Second, they lack the ability to dynamically control pore pressure and simultaneously monitor multiple fields, preventing systematic analysis of the mechanisms by which pressure fluctuations influence crack evolution. Third, they lack long-term monitoring capabilities, making it impossible to quantitatively assess time-dependent properties such as rock creep and fatigue damage, and to provide early warning of the resulting risk of crack instability. These deficiencies severely hinder the accurate prediction and safe control of crack propagation in shale gas extraction and geothermal engineering. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a hydraulic fracturing test crack expansion monitoring system and method, which can realize full-scale monitoring of rock crack expansion, reveal the influence mechanism of pore pressure and time effect on crack evolution, provide accurate prediction means for shale gas development and deep engineering, and significantly improve fracturing effect and engineering safety.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a hydraulic fracturing test crack expansion monitoring system, including a monitoring system and used for real-time monitoring of crack expansion behavior during hydraulic fracturing, the monitoring system comprising:

[0007] Triaxial stress loading unit, used to simulate the in-situ stress state of the formation, providing constant or dynamically changing confining pressure and axial pressure;

[0008] Pore ​​pressure dynamic control unit, used to accurately control the injection pressure, flow rate and saturation of fracturing fluid, and simulate the fracture propagation behavior under different seepage conditions;

[0009] Multi-physics field sensing monitoring network unit, used to collect strain, acoustic wave signals and surface displacement fields during crack propagation in real time;

[0010] X-ray / micro-CT real-time scanning unit for high-resolution three-dimensional imaging and dynamic capture of the internal structural evolution of the crack;

[0011] Data fusion and intelligent analysis platform, used for synchronous processing of multi-source data, 3D reconstruction of cracks and prediction of expansion trends.

[0012] Preferably, the triaxial stress loading unit uses a servo-controlled hydraulic system to simulate a non-uniform stress field, and supports cyclic loading, step loading and creep tests to study the long-term mechanical behavior of rocks.

[0013] Preferably, the pore pressure dynamic control unit includes:

[0014] High-precision fluid injection pump, used for constant pressure, constant flow and pulsed fracturing fluid injection;

[0015] Multi-channel pressure sensor to monitor the pore pressure distribution inside the rock sample in real time;

[0016] The chemical-permeability coupling module is used to study the effect of the chemical interaction between fracturing fluid and rock on fracture propagation.

[0017] Preferably, the multi-physical field sensing monitoring network unit includes:

[0018] Distributed fiber optic sensing modules are deployed along the surface and inside the rock sample for high-precision strain monitoring;

[0019] Broadband acoustic emission sensor array module, used to capture microfracture events and locate crack initiation locations;

[0020] Ultrasonic transmission / reflection detection module, used to assess the overall damage degree of rock;

[0021] High-speed optical camera + DIC module is used to record the deformation field of the rock sample surface in real time.

[0022] Preferably, the X-ray / micro CT real-time scanning unit adopts a dynamic scanning mode of synchrotron radiation / industrial CT technology to continuously obtain the three-dimensional structure of the fracture during the fracturing process and realize quantitative analysis of the fracture width, branching morphology and expansion path.

[0023] Preferably, the data fusion and intelligent analysis platform includes:

[0024] Multi-source data synchronous acquisition and storage module for spatiotemporal alignment of mechanical, acoustic, optical, and CT data;

[0025] The crack 3D reconstruction algorithm module reconstructs the crack network model based on CT scan data and acoustic emission positioning information;

[0026] The machine learning prediction module uses deep neural networks to analyze historical data and predict future crack expansion trends.

[0027] The present invention also discloses a method for monitoring crack expansion in a hydraulic fracturing test, which is characterized by comprising the following steps:

[0028] S1, rock sample preparation and initial state scanning: prepare rock samples with natural cracks or artificial prefabricated cracks, and perform CT scanning to obtain the initial structure;

[0029] S2, multi-physics sensor deployment: installation of optical fiber, acoustic emission sensors, and optical observation equipment;

[0030] S3, stress and pore pressure loading: applying simulated ground stress and injecting fracturing fluid according to the preset scheme;

[0031] S4, real-time data acquisition and synchronization: synchronously record mechanical response, acoustic emission signals, strain field and CT scan data;

[0032] S5, dynamic reconstruction and evolution analysis of cracks: based on CT and acoustic emission data, the three-dimensional crack model is updated in real time;

[0033] S6, Intelligent Prediction and Early Warning: Use machine learning models to analyze crack expansion trends and trigger early warnings in critical states.

[0034] Preferably, the threshold of the critical state in the S6 intelligent prediction and early warning is calculated by rock fracture toughness and ground stress field, and the specific formula is:

[0035]

[0036] Among them, K IC is the rock fracture toughness, E is the elastic modulus, σ h is the horizontal stress, and d is the characteristic crack spacing.

[0037] The present invention provides a hydraulic fracturing test crack expansion monitoring system and method. Compared with the existing technology, it has the following advantages:

[0038] 1. Through the synergistic effect of the triaxial stress loading unit and the pore pressure dynamic control unit, a full-scale simulation of the mechanical behavior of multiphase and porous rock and soil media is achieved. The system uses distributed fiber optic sensing and X-ray / micro-CT real-time scanning technology to accurately capture the coupling effect between pore fluid and solid skeleton, revealing the crack initiation and expansion mechanism under complex seepage conditions. Through multi-physics field data fusion, the system can quantitatively analyze the impact of pore pressure gradient on rock strength degradation and the migration patterns of multiphase fluids (water / gas / oil) in the fracture network. This multi-field coupling analysis method provides a theoretical basis for shale gas reservoir fracturing design and significantly improves the accuracy of crack expansion prediction.

[0039] 2. By integrating a chemical-permeability coupling module with a high-precision fluid injection system, the dynamic regulation of pore pressure and the simultaneous monitoring of rock mechanical responses are achieved. The spatial distribution of pore pressure is tracked in real time by a multi-channel pressure sensor, and micro-fracture events are located in combination with an acoustic emission array, revealing the regulatory mechanism of pore pressure fluctuations on rock fracture toughness and crack propagation rate. In addition, the pulsed injection mode can simulate the intermittent pressurization process of on-site fracturing construction, providing an experimental means to study the impact of pore pressure transients on rock damage accumulation.

[0040] 3. The time effect of rock deformation and failure was systematically studied through cyclic loading, step loading, and creep test modes. The distributed fiber optic sensing network and ultrasonic detection module can monitor the spatiotemporal evolution of rock damage factors over a long period of time, while the machine learning prediction module established a relationship model between crack propagation rate and time by analyzing historical data. In addition, it can provide early warning of the risk of crack instability caused by rock creep or fatigue damage, providing an innovative solution for the surrounding rock stability assessment of long-term projects such as deep resource mining and nuclear waste disposal. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a block diagram of the monitoring system of the present invention;

[0042] Figure 2 is a block diagram of the pore pressure dynamic control unit in the present invention;

[0043] Figure 3 A block diagram of a multi-physics field sensing monitoring network unit in the present invention;

[0044] Figure 4 This is a block diagram of the data fusion and intelligent analysis platform of the present invention;

[0045] Figure 5 This is a block diagram of the monitoring method of the present invention.

[0046] In the figure: 1. Monitoring system; 11. Triaxial stress loading unit; 12. Pore pressure dynamic control unit; 121. High-precision fluid injection pump; 122. Multi-channel pressure sensor; 123. Chemical-seepage coupling module; 13. Multi-physics field sensing and monitoring network unit; 131. Distributed fiber optic sensing module; 132. Broadband acoustic emission sensor array module; 133. Ultrasonic transmission / reflection detection module; 134. High-speed optical camera + DIC module; 14. X-ray / micro-CT real-time scanning unit; 15. Data fusion and intelligent analysis platform; 151. Multi-source data synchronous acquisition and storage module; 152. Fracture three-dimensional reconstruction algorithm module; 153. Machine learning prediction module. DETAILED DESCRIPTION

[0047] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] See also Figure 1 - Figure 5 The present invention provides a technical solution: a hydraulic fracturing test crack expansion monitoring system, including a monitoring system 1 and used for real-time monitoring of crack expansion behavior during hydraulic fracturing. The monitoring system 1 includes:

[0049] A triaxial stress loading unit 11 is used to simulate the in-situ stress state of the formation and provide constant or dynamically changing confining pressure and axial pressure;

[0050] The pore pressure dynamic control unit 12 is used to accurately control the injection pressure, flow rate and saturation of the fracturing fluid and simulate the fracture expansion behavior under different seepage conditions;

[0051] The multi-physics field sensing monitoring network unit 13 is used to collect strain, acoustic wave signals and surface displacement fields in the crack propagation process in real time;

[0052] X-ray / micro-CT real-time scanning unit 14, used for high-resolution three-dimensional imaging to dynamically capture the internal structural evolution of the crack;

[0053] Data fusion and intelligent analysis platform 15 is used for synchronous processing of multi-source data, three-dimensional reconstruction of cracks, and prediction of expansion trends.

[0054] In this embodiment, the monitoring system 1 realizes the whole life cycle monitoring of cracks through the cooperation of multiple units. The triaxial stress loading unit 11 reproduces the real stress environment of the stratum through a precision hydraulic servo system to provide basic mechanical boundary conditions for the experiment. The pore pressure dynamic regulation unit 12 adopts an intelligent fluid injection system to accurately simulate the seepage process of fracturing fluid under different construction processes. The multi-physical field sensing monitoring network unit 13 constructs a stereoscopic monitoring system, which captures the sound-light-force multi-physical field signals in the crack evolution process through a distributed sensor array. The X-ray / micro-CT real-time scanning unit 14 adopts a dynamic imaging technology to realize the time sequence observation of the three-dimensional structure of the crack. The data fusion and intelligent analysis platform 15 establishes a full-scale evolution model from micro-cracking to macro-cracking through multi-source information fusion and machine learning algorithms. The units realize data synchronization through a unified time sequence control system to form a complete technical chain of “stress loading-fluid injection-signal acquisition-image reconstruction-intelligent analysis”, which provides an advanced experimental research platform for studying the crack initiation criterion, expansion law and control method. Through the combination of simulation accuracy of real geological conditions and multi-scale observation ability, the demand of basic theory research can be met, and scientific basis can be provided for the optimization of field fracturing construction parameters.

[0055] Specifically, the triaxial stress loading unit 11 simulates a non-uniform stress field by using a servo-controlled hydraulic system, and supports cyclic loading, step loading and creep testing to study the long-term mechanical behavior of rock.

[0056] In this embodiment, the triaxial stress loading unit 11 realizes accurate simulation of complex stress conditions through a multi-axis independent servo control system. A high-rigidity frame structure and a precision hydraulic actuator are combined to independently adjust the axial and radial loading pressures through a closed-loop feedback control algorithm, which can reproduce the real non-uniform stress state of the underground rock stratum. Multiple loading modes are integrated. In the cyclic loading mode, the sinusoidal or triangular wave load is used to simulate the periodic stress change of the stratum. In the step loading mode, the hierarchical load holding method is used to study the cumulative damage effect of rock. In the creep test mode, the time-dependent deformation characteristics of rock are observed by constant stress loading for a long time. During the loading process, the system monitors the stress-strain response curve in real time, and identifies the mechanical behavior transition of rock in different deformation stages in combination with the acoustic emission signal characteristics.

[0057] Specifically, the pore pressure dynamic regulation unit 12 includes:

[0058] A high-precision fluid injection pump 121 is used for constant pressure, constant flow and pulse fracturing fluid injection.

[0059] A multi-channel pressure sensor 122 is used to monitor the pore pressure distribution inside the rock sample in real time.

[0060] A chemical-seepage coupling module 123 is used to study the influence of the chemical action of fracturing fluid on rock on crack propagation.

[0061] In this embodiment, the pore pressure dynamic control unit 12 simulates fracture propagation under complex seepage conditions through precise multi-parameter control. The high-precision fluid injection pump 121 utilizes a closed-loop feedback control system, intelligently switching between constant pressure mode, constant flow mode, and pulse oscillation mode according to experimental requirements. By adjusting the injection rate and pressure waveform, it accurately simulates the fracturing fluid injection process under different construction techniques. The multi-channel pressure sensor 122 is deployed in a star topology at key locations on the rock sample. It uses dynamic pressure field reconstruction technology to invert the spatial gradient distribution of pore pressure in real time and calculates fluid migration paths in conjunction with Darcy's seepage theory. The chemical-permeability coupling module 123 integrates an electrochemical workstation and an online fluid composition monitoring device. It uses ion concentration sensors and pH detection units to track the reaction kinetics of the fracturing fluid-rock interface and combines micro-CT scanning to analyze the effects of mineral dissolution and precipitation on pore structure. This unit, through coordinated control with the triaxial stress loading unit 11, conducts fracture propagation experiments under the multi-field coupling conditions of mechanics, chemistry, and permeability, providing an important experimental tool for studying the optimization of fracturing fluids in unconventional reservoirs.

[0062] Specifically, the multi-physics field sensing monitoring network unit 13 includes:

[0063] Distributed fiber optic sensing modules 131 are deployed along the surface and inside the rock sample for high-precision strain monitoring;

[0064] A broadband acoustic emission sensor array module 132 is used to capture microfracture events and locate crack initiation locations;

[0065] Ultrasonic transmission / reflection detection module 133, used to assess the overall damage degree of the rock;

[0066] A high-speed optical camera + DIC module 134 is used to record the deformation field of the rock sample surface in real time.

[0067] In this embodiment, the multi-physics field sensing monitoring network unit 13 realizes the capture of the entire process of crack evolution through multi-dimensional collaborative perception; the distributed optical fiber sensing module 131 adopts a topological structure combining spiral winding and axial layout, and constructs a three-dimensional strain monitoring network covering the surface and interior of the sample through Brillouin scattering and fiber Bragg grating technology, which can accurately identify the strain concentration area and its evolution process; the broadband acoustic emission sensor array module 132 adopts a spatial optimization layout strategy, based on the time difference positioning principle and waveform inversion algorithm, to achieve spatial positioning of micro-fracture events and fracture mode recognition, and combines moment tensor analysis to determine the mechanical properties of micro-cracks. The ultrasonic transmission / reflection detection module 133 uses the optimized configuration of the transmitting-receiving probe group and the velocity variation and attenuation characteristics of longitudinal and shear waves to invert the spatial distribution of damage factors inside the rock. The high-speed optical camera + DIC module 134 adopts a multi-camera stereo vision system to reconstruct the three-dimensional deformation field of the sample surface through speckle pattern matching and displacement field calculation. Each sensor module achieves data synchronization through a unified time base system and transmits the multi-physical field monitoring data to the central processing platform in real time, forming a crack diagnosis system that integrates multiple parameters of sound, light and force, providing a complete experimental observation basis for the study of crack initiation criteria and expansion laws.

[0068] Specifically, the X-ray / micro CT real-time scanning unit 14 adopts a dynamic scanning mode of synchrotron radiation / industrial CT technology to continuously obtain the three-dimensional structure of the fracture during the fracturing process and realize quantitative analysis of the fracture width, branching morphology and expansion path.

[0069] In this embodiment, the X-ray / micro-CT real-time scanning unit 14 realizes four-dimensional monitoring of crack evolution through dynamic tomography technology. During the fracturing process, a working mode combining rotational scanning and continuous acquisition is adopted. The X-ray source and the detector array move synchronously around the rock sample. Through the rapid acquisition of multi-angle projection data, the three-dimensional volume data of the internal structure of the rock mass is reconstructed in real time. In view of the dynamic expansion characteristics of the crack, an adaptive scanning strategy is adopted. In the stable stage of the crack, the conventional scanning mode is used to ensure image quality. When the rapid expansion of the crack is detected, it automatically switches to the high-speed scanning mode to improve the time resolution. The acquired tomographic image sequence is subjected to noise reduction and artifact removal. After preprocessing such as shadow correction, a deep learning-based image segmentation algorithm is used to accurately extract crack voxels. Combined with morphological operations and skeleton extraction technology, the system can achieve quantitative characterization of crack aperture, branching angle and three-dimensional connectivity. Through time series alignment and displacement field calculation, the system can track the expansion trajectory of the crack front and establish a dynamic correlation model between crack volume and injection parameters. This unit fuses data with the acoustic emission positioning system to compare and verify the spatial distribution of microfracture events with the macroscopic crack morphology displayed by CT, and jointly construct a full-scale evolution map from micro-destruction to macro-fracture, providing a complete experimental observation data chain for the study of crack propagation mechanism.

[0070] Specifically, the data fusion and intelligent analysis platform 15 comprises:

[0071] A multi-source data synchronous acquisition and storage module 151 for spatiotemporal alignment of mechanical, acoustic, optical and CT data;

[0072] A crack three-dimensional reconstruction algorithm module 152 for reconstructing a crack network model based on CT scanning data and acoustic emission positioning information;

[0073] A machine learning prediction module 153 for predicting future crack expansion trends by analyzing historical data using a deep neural network.

[0074] In this embodiment, the data fusion and intelligent analysis platform 15 realizes intelligent monitoring and prediction of crack expansion through the collaborative work of multiple modules; the multi-source data synchronous acquisition and storage module 151 uses a unified time reference and spatial coordinate system to perform spatiotemporal registration of multi-modal data from mechanical sensors, acoustic emission arrays, optical observation equipment and CT scanners, and establishes a multi-dimensional database with strict temporal correlation; the crack three-dimensional reconstruction algorithm module 152 first performs noise reduction and image enhancement processing on the CT scanning data, extracts crack features through edge detection and region growing algorithms, simultaneously combines spatiotemporal positioning information of acoustic emission events, generates a three-dimensional geometric model of the crack surface using Delaunay triangulation and surface reconstruction techniques, and realizes visual expression of the crack network using volume rendering techniques; the machine learning prediction module 153 constructs a hybrid model architecture comprising a convolutional neural network and a long short-term memory network, the convolutional neural network branch processes CT image sequences to extract spatial features of crack morphology, the long short-term memory network branch analyzes the temporal evolution law of acoustic emission energy, frequency and mechanical parameters, the spatial and temporal features are jointly modeled through a feature fusion layer, and finally the prediction results of crack expansion direction, speed and branch probability are output; the data fusion and intelligent analysis platform 15 dynamically evaluates the crack stability state by comparing the prediction results with the critical threshold calculated based on fracture mechanics theory in real time, automatically triggers a graded warning mechanism when abnormal expansion behavior is detected, and feeds back the analysis results to the fracturing control system to form a closed-loop management.

[0075] The application further discloses a hydraulic fracturing test crack expansion monitoring method, characterized by comprising the following steps:

[0076] S1, rock sample preparation and initial state scanning: preparing a rock sample containing natural fissures or artificial pre-prepared cracks, and performing CT scanning to obtain an initial structure;

[0077] S2, multi-physical field sensor layout: installing optical fibers, acoustic emission sensors and optical observation equipment;

[0078] S3, stress and pore pressure loading: applying simulated ground stress, and injecting fracturing fluid according to a preset scheme;

[0079] S4, real-time data acquisition and synchronization: synchronously record mechanical response, acoustic emission signals, strain field and CT scan data;

[0080] S5, dynamic reconstruction and evolution analysis of cracks: based on CT and acoustic emission data, the three-dimensional crack model is updated in real time;

[0081] S6, Intelligent Prediction and Early Warning: Use machine learning models to analyze crack expansion trends and trigger early warnings in critical states.

[0082] In this embodiment, first, a rock sample with a representative fracture network is prepared according to the geological characteristics of the target rock formation, and an initial structural digital twin model is established through micro-CT scanning; a multi-type sensor array is arranged in three dimensions on the surface and inside of the sample to construct a three-dimensional monitoring network covering the strain field, acoustic emission signal, and ultrasonic propagation characteristics; then, graded fracturing fluid injection is carried out in a simulated real-world stress environment, and mechanical response, seepage field change and fracture dynamic expansion data are simultaneously collected; based on CT tomography and acoustic emission source positioning technology, image segmentation algorithm and three-dimensional reconstruction method are used to update the fracture geometry in real time; finally, the multi-source time series data are integrated through a trained deep learning model to establish a mapping relationship between the fracture expansion rate and the stress field and seepage field. When the monitoring parameters exceed the critical threshold calculated based on fracture mechanics theory, the system automatically triggers an early warning and generates a fracturing parameter optimization plan, realizing a closed-loop control from "monitoring-analysis-early warning-control".

[0083] Specifically, the critical state threshold in S6 intelligent prediction and early warning is calculated through rock fracture toughness and ground stress field. The specific formula is:

[0084]

[0085] Among them, K IC is the rock fracture toughness, E is the elastic modulus, σ h is the horizontal stress, and d is the characteristic crack spacing.

[0086] In this embodiment, when the strain energy released during the crack expansion process overcomes the resistance formed by the fracture toughness of the rock, the crack enters the unstable expansion stage; specifically, by establishing a constitutive relationship between fracture toughness and the ground stress field, a characteristic crack spacing parameter is introduced to characterize the crack interaction effect, and a critical expansion speed calculation model is constructed. The model comprehensively considers the inherent properties of the rock mass (fracture toughness, elastic modulus) and engineering environmental factors (horizontal stress, crack spatial distribution); first, the fracture toughness parameters of the target rock layer are obtained through laboratory testing or field data inversion, and the horizontal stress component is determined in combination with the ground stress measurement data. Then, based on the real-time monitored crack network distribution characteristics, representative crack spacing parameters are extracted. Finally, the above parameters are substituted into the critical speed calculation formula to dynamically update the warning threshold; when the real-time monitored crack expansion speed exceeds the threshold, the system determines that it has entered a critical instability state and triggers a graded warning, while automatically adjusting the fracturing parameters to control the crack expansion morphology.

[0087] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0088] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A hydraulic fracturing test crack expansion monitoring system, characterized by: The invention comprises a monitoring system (1) and is used for real-time monitoring of crack expansion behavior during hydraulic fracturing. The monitoring system (1) comprises: A triaxial stress loading unit (11) is used to simulate the in-situ stress state of the formation and provide constant or dynamically changing confining pressure and axial pressure; A pore pressure dynamic control unit (12) is used to precisely control the injection pressure, flow rate and saturation of the fracturing fluid and simulate the fracture expansion behavior under different seepage conditions; A multi-physics field sensing monitoring network unit (13) is used for real-time acquisition of strain, acoustic wave signals and surface displacement fields during crack expansion; X-ray / micro-CT real-time scanning unit (14) for high-resolution three-dimensional imaging and dynamic capture of the internal structural evolution of the crack; Data fusion and intelligent analysis platform (15) is used for synchronous processing of multi-source data, three-dimensional reconstruction of cracks and prediction of expansion trends.

2. A hydraulic fracturing test crack expansion monitoring system according to claim 1, characterized in that: The triaxial stress loading unit (11) uses a servo-controlled hydraulic system to simulate a non-uniform stress field and supports cyclic loading, step loading and creep tests to study the long-term mechanical behavior of rocks.

3. A hydraulic fracturing test crack expansion monitoring system according to claim 1, characterized in that: The pore pressure dynamic control unit (12) comprises: A high-precision fluid injection pump (121) for constant pressure, constant flow and pulsed fracturing fluid injection; A multi-channel pressure sensor (122) monitors the pore pressure distribution inside the rock sample in real time; The chemical-permeability coupling module (123) is used to study the effect of the chemical interaction between fracturing fluid and rock on fracture propagation.

4. A hydraulic fracturing test crack expansion monitoring system according to claim 1, characterized in that: The multi-physics field sensing monitoring network unit (13) comprises: Distributed optical fiber sensing modules (131) are arranged along the surface and inside of the rock sample for high-precision strain monitoring; A broadband acoustic emission sensor array module (132) for capturing microfracture events and locating crack initiation locations; Ultrasonic transmission / reflection detection module (133), used to assess the overall damage degree of the rock; A high-speed optical camera + DIC module (134) is used to record the deformation field of the rock sample surface in real time.

5. The hydraulic fracturing test crack expansion monitoring system according to claim 1, characterized in that: The X-ray / micro CT real-time scanning unit (14) adopts a synchrotron radiation / industrial CT technology dynamic scanning mode, and is used to continuously obtain the three-dimensional structure of the crack during the fracturing process, and to achieve quantitative analysis of the crack width, branching morphology and expansion path.

6. A hydraulic fracturing test crack expansion monitoring system according to claim 1, characterized in that: The data fusion and intelligent analysis platform (15) includes: Multi-source data synchronous acquisition and storage module (151) for the spatiotemporal alignment of mechanical, acoustic, optical and CT data; A three-dimensional fracture reconstruction algorithm module (152) reconstructs a fracture network model based on CT scan data and acoustic emission positioning information; The machine learning prediction module (153) uses a deep neural network to analyze historical data and predict the future expansion trend of cracks.

7. A method for monitoring crack expansion in a hydraulic fracturing test according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1, rock sample preparation and initial state scanning: prepare rock samples with natural cracks or artificial prefabricated cracks, and perform CT scanning to obtain the initial structure; S2, multi-physics sensor deployment: installation of optical fiber, acoustic emission sensors, and optical observation equipment; S3, stress and pore pressure loading: applying simulated ground stress and injecting fracturing fluid according to the preset scheme; S4, real-time data acquisition and synchronization: synchronously record mechanical response, acoustic emission signals, strain field and CT scan data; S5, dynamic reconstruction and evolution analysis of cracks: based on CT and acoustic emission data, the three-dimensional crack model is updated in real time; S6, Intelligent Prediction and Early Warning: Use machine learning models to analyze crack expansion trends and trigger early warnings in critical states.

8. A method for monitoring crack expansion in a hydraulic fracturing test according to claim 7, characterized in that: The threshold of the critical state in the S6 intelligent prediction and early warning is calculated by the rock fracture toughness and the ground stress field. The specific formula is: Among them, K IC is the rock fracture toughness, E is the elastic modulus, σ h is the horizontal stress, and d is the characteristic crack spacing.

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