Experimental Method and System for Studying the Process of Hydraulic Fracture Propagation

Abstract

The present application provides an experimental method and system for studying the process of hydraulic fracturing crack propagation. The method includes: using a true triaxial stress simulation module to apply stresses in the X, Y, and Z directions of the specimen to simulate the in-situ stress state; using a fluid injection control module to simulate the fluid injection process in actual fracturing construction for the specimen; acquiring the pressure data collected by a pressure sensor array and the acoustic emission data collected by an acoustic emission probe; synchronizing the time stamps of the pressure data and the acoustic emission data, and respectively performing feature extraction and spatio-temporal correlation on the pressure data and the acoustic emission data with synchronized time stamps to obtain pressure dynamic response feature information and acoustic emission feature information that meet the spatio-temporal correlation conditions; based on the pressure dynamic response feature information and the acoustic emission feature information that meet the spatio-temporal correlation conditions, determining the crack propagation process of the specimen. The present application can solve the problem of synchronous correlation between pressure fluctuations and crack behavior under multi-field coupling conditions.

Description

Technical Field

[0001] The present application relates to the technical field of hydraulic fracturing, and in particular to an experimental method and system for studying the hydraulic fracturing crack expansion process. Background Art

[0002] Hydraulic fracturing is a commonly used method of underground hydrological structural transformation. Its principle is to use high-pressure water to expand rock cracks, use groundwater pressure to expand the cracks and pump water into them. Hydraulic fracturing can expand rock cracks, making originally impermeable formations permeable. Therefore, hydraulic fracturing is widely used in fields such as oil development, coal mining and geothermal energy development. Summary of the invention

[0003] The embodiments of the present application provide an experimental method and system for studying the hydraulic fracturing crack propagation process, which can solve the problem of synchronous correlation between pressure fluctuations and crack behaviors under multi-field coupling conditions.

[0004] According to a first aspect of an embodiment of the present application, an experimental method for studying a hydraulic fracturing crack expansion process is provided, comprising:

[0005] A true triaxial stress simulation module is used to load stress in the X, Y, and Z directions of the sample to simulate the in-situ stress state; wherein the sample has a built-in pressure sensor array, the pressure sensor array is used to monitor the pressure change of the fluid at the crack mouth, and at least a plurality of acoustic emission probes are arranged on each of the multiple surfaces of the sample, and the acoustic emission probes are used to monitor the acoustic emission data of the crack extension;

[0006] Using a fluid injection control module to simulate the fluid injection process in actual fracturing construction for the sample;

[0007] Acquiring pressure data collected by the pressure sensor array and acoustic emission data collected by the acoustic emission probe;

[0008] The pressure data and the acoustic emission data are time-stamp synchronized, and feature extraction is performed on the pressure data and the acoustic emission data that have been time-stamp synchronized, respectively, to obtain pressure dynamic response feature information and acoustic emission feature information;

[0009] Performing spatiotemporal correlation on the pressure dynamic response characteristic information and the acoustic emission characteristic information to obtain the pressure dynamic response characteristic information and the acoustic emission characteristic information that satisfy the spatiotemporal correlation conditions;

[0010] Based on the pressure dynamic response characteristic information and acoustic emission characteristic information that meet the time-space correlation conditions, the crack extension process of the sample is identified.

[0011] According to a second aspect of an embodiment of the present application, an experimental system for studying a hydraulic fracturing crack expansion process is provided, comprising:

[0012] A true triaxial stress simulation module is used to apply stress in the X, Y, and Z directions of the sample to simulate the in-situ stress state; wherein the sample has a built-in pressure sensor array, the pressure sensor array is used to monitor the pressure change of the fluid at the crack mouth, and at least a plurality of acoustic emission probes are arranged on each of the multiple surfaces of the sample, and the acoustic emission probes are used to monitor the acoustic emission data of the crack extension;

[0013] A fluid injection control module, used to simulate the fluid injection process in actual fracturing construction for the sample;

[0014] A data processing module is used to obtain the pressure data collected by the pressure sensor array and the acoustic emission data collected by the acoustic emission probe, and synchronize the pressure data and the acoustic emission data with timestamps, and respectively extract features from the pressure data and the acoustic emission data that have been synchronized with timestamps to obtain pressure dynamic response characteristic information and acoustic emission characteristic information, perform spatiotemporal correlation on the pressure dynamic response characteristic information and the acoustic emission characteristic information to obtain pressure dynamic response characteristic information and acoustic emission characteristic information that meet spatiotemporal correlation conditions, and identify the crack extension process of the sample based on the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the spatiotemporal correlation conditions.

[0015] According to a third aspect of an embodiment of the present application, a storage medium is provided, wherein the storage medium stores instructions, and when the instructions are executed by a processor, the processor executes the method described in the first aspect above.

[0016] According to the technical solution of this application, the hydraulic fracturing crack expansion process can be simulated with high precision, and the dynamic response characteristics of fluid pressure can be monitored in real time. By synchronizing the timestamps of pressure data and acoustic emission data, and spatially and temporally correlating the pressure dynamic response characteristic information and acoustic emission characteristic information, the problem of synchronous correlation between pressure fluctuations and crack behavior under multi-field coupling conditions can be solved, and the accuracy and reliability of the hydraulic fracturing crack expansion process can be improved. In addition, the modular analysis process can be adapted to different reservoir types and fracturing processes, which can significantly improve the engineering conversion value of experimental data.

[0017] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 Schematic diagram of an experimental system provided by an embodiment of the present application for studying the process of hydraulic fracturing crack propagation;

[0020] Figure 2 Example diagram of pressure monitoring and acoustic emission monitoring provided by an embodiment of the present application;

[0021] Figure 3 Flow schematic diagram of an experimental method provided by an embodiment of the present application for studying the process of hydraulic fracturing crack propagation. Detailed implementation manners

[0022] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0023] An experimental method and system for studying the process of hydraulic fracturing crack propagation according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0024] Figure 1 Schematic diagram of an experimental system provided by an embodiment of the present application for studying the process of hydraulic fracturing crack propagation. As Figure 1 shown, the experimental system for studying the process of hydraulic fracturing crack propagation may include, but is not limited to: a true triaxial stress simulation module 101, a fluid injection control module 102, and a data processing module 103.

[0025] Among them, the true triaxial stress simulation module 101 is used to apply stresses in the X, Y, and Z directions of the specimen (for example, the value range of the stress can be 0 to 100 MPa, but is not limited thereto) to simulate the in-situ stress state; among them, the specimen A may be internally provided with a pressure sensor array, and the pressure sensor array may be used to monitor the change of fluid pressure at the crack mouth. At least a plurality of acoustic emission probes may be arranged on each of the multiple surfaces of the specimen A, and the acoustic emission probes may be used to monitor the acoustic emission data of crack propagation. The fluid injection control module 102 may be used to simulate the fluid injection process in actual fracturing construction for the specimen. A hydraulic fracturing experiment is carried out on the specimen through the true triaxial stress simulation module 101 and the fluid injection control module 102.

[0026] In some embodiments, the specimen may be a rock specimen. For example, it may be a coal mine rock specimen. The experimental system of the embodiments of the present application can be applied to the field of coal mine mining, but is not limited thereto. Exemplarily, the size of the specimen may be 400 mm × 400 mm × 400 mm, but is not limited thereto. In some embodiments, as Figure 1As shown, an experimental pressure pillow B can be arranged between the true triaxial stress simulation module 101 and the surface of the sample A. The experimental pressure pillow B can be a variable device filled with gas inside. The experimental pressure pillow B can be used to ensure that the stress loaded on the surface of the sample A is uniform stress, avoiding stress loading errors caused by the non-absolute plane of the sample surface.

[0027] In some embodiments, Figure 2 As shown, a perforation channel can be preset in the center of sample A, and a micro pressure sensor array C (for example, a sampling frequency greater than or equal to 1000 Hz) can be built in to monitor the changes in fluid pressure at the crack mouth in real time. The built-in micro pressure sensor array can ensure that the monitored fluid pressure is the crack mouth pressure, avoiding errors such as pipeline friction in the fluid pressure data monitored by the external sensor.

[0028] Exemplarily, the pressure sensor array can use a miniature high-frequency piezoresistive or optical fiber pressure sensor with a range of 0 to 100 MPa, a sampling frequency of ≥1000 Hz, a resolution of ≤0.1 MPa, and a size of ≤1 mm×1 mm×0.5 mm (miniaturized design). The sample can be processed into a 400×400×400 mm cube, and a perforation channel with a diameter of 2 mm is pre-drilled in the center to simulate the actual wellbore perforation structure. The pressure sensor array can be arranged in the sample based on a preset radial arrangement and a layered distribution array; wherein the radial arrangement is designed as follows: with the perforation channel as the center, arranged along the preset fracture extension direction, with a spacing of the first length, covering the first area; the layered distribution is designed as follows: buried in layers around the perforation channel, with a spacing of the second length between each layer; the pressure sensor array is embedded in the sample through precision drilling and epoxy resin encapsulation technology.

[0029] For example, the array arrangement of the pressure sensor array can be as follows: (1) Radial arrangement: With the perforation channel as the center, arranged along the preset crack extension direction (usually the direction of the minimum horizontal principal stress), with a spacing of 2 mm, covering an area with a radius of 10 mm. (2) Layered distribution: buried in layers around the perforation channel (such as 3 layers × 5 columns), with a spacing of 3 mm between each layer, to achieve three-dimensional pressure monitoring. Through precision drilling and epoxy resin encapsulation technology, the pressure sensor is embedded in the sample to ensure that the sensor surface is flush with the inner wall of the sample, which can avoid stress concentration interference.

[0030] In some embodiments, the monitoring data of the pressure sensor array can be collected synchronously by multi-channel. Exemplarily, a high-speed data acquisition card is used to support 32-channel parallel acquisition, and the sampling rate synchronization accuracy is ≤1μs. The original signal can be processed by a low-noise amplifier and an anti-aliasing filter (such as a cutoff frequency of 500Hz) to eliminate high-frequency noise.

[0031] In some embodiments, the acoustic emission probe can be a broadband acoustic emission sensor with a frequency response range of 50 kHz to 1 MHz, a sensitivity greater than or equal to 80 dB, and support for three-dimensional positioning. The acoustic emission probe can be fixed to the surface of the specimen by magnetic attraction or adhesive, and a coupling agent is applied to the contact surface with the specimen.

[0032] Exemplarily, as Figure 2 shown, acoustic emission probes D can be arranged on 6 surfaces of the specimen, with at least 2 acoustic emission probes set on each surface to form a spatial three-dimensional monitoring network. The coordinates of the crack event (accuracy ±1 mm) can be calculated based on the time difference of the acoustic emission signals reaching each acoustic emission probe and combined with the geometric parameters of the specimen.

[0033] It should be noted that in the embodiments of the present application, the fluid injection control module 102 is the core component of the experimental system for studying the process of hydraulic fracture propagation, and can be used to accurately simulate the fluid injection process in actual fracturing construction, support various injection modes (such as constant rate, pulse, stepped pressure increase, etc.) and real-time regulation of fluid viscosity to study the dynamic response of fluid pressure during fracture propagation under different working conditions. Exemplarily, the core functions of the fluid injection control module 102 can include:

[0034] ① Multi-mode fluid injection: accurately control the flow rate, pressure, and injection timing.

[0035] ② Dynamic viscosity adjustment: adjust the rheological properties of the fracturing fluid in real time according to experimental requirements.

[0036] ③ Data synchronization and feedback: work in coordination with the pressure sensor and the acoustic emission system to achieve closed-loop control.

[0037] In some embodiments, the fluid injection control module 102 can adopt a constant rate injection mode to simulate the fluid injection process in actual fracturing construction for the specimen. Among them, the control logic of this constant rate injection mode can be: set the target flow rate (such as 10 mL / min), and the servo motor adjusts the plunger displacement speed through closed-loop feedback to compensate for the pressure fluctuation caused by crack propagation in real time. Combine the feedback of the flow meter with this target flow rate to adjust the motor speed to correct the flow rate error.

[0038] In an embodiment of the present application, the data processing module 103 may be configured to: acquire pressure data collected by a pressure sensor array and acoustic emission data collected by an acoustic emission probe; synchronize the timestamps of the pressure data and the acoustic emission data, and respectively extract features from the pressure data and the acoustic emission data with synchronized timestamps to obtain pressure dynamic response feature information and acoustic emission feature information; perform spatio-temporal association on the pressure dynamic response feature information and the acoustic emission feature information to obtain pressure dynamic response feature information and acoustic emission feature information that meet the spatio-temporal association conditions; and identify the crack propagation process of the specimen based on the pressure dynamic response feature information and the acoustic emission feature information that meet the spatio-temporal association conditions.

[0039] That is to say, a hydraulic fracturing experiment can be performed on the specimen through the true triaxial stress simulation module and the fluid injection control module. During this hydraulic fracturing experiment, the data processing module 103 can acquire the pressure data collected by the pressure sensor array in the specimen and the acoustic emission data collected by the acoustic emission probe, and perform data preprocessing and time synchronization on the pressure data and the acoustic emission data. Then, features are respectively extracted from the pressure data and the acoustic emission data and spatio-temporal association is performed. The pressure dynamic response feature information and the acoustic emission feature information that meet the spatio-temporal association conditions can be obtained. Furthermore, based on the pressure dynamic response feature information and the acoustic emission feature information that meet the spatio-temporal association conditions, the crack propagation process and the behavior pattern of the crack propagation process of the specimen in the future time can be predicted.

[0040] In some embodiments, an optional implementation manner for performing data preprocessing on the pressure data is as follows: perform denoising processing and baseline correction on the pressure data. Exemplarily, wavelet transform (such as Daubechies 5) can be used to perform denoising processing on the pressure data to filter out high-frequency noise and retain the effective pressure fluctuation signal. The purpose of performing baseline correction on the pressure data can be to eliminate the pressure baseline offset caused by temperature drift or system error and ensure the accuracy of the absolute pressure value.

[0041] In some embodiments, an optional implementation manner for performing data preprocessing on the acoustic emission data is as follows: perform event screening on the acoustic emission data based on energy threshold, duration, and frequency characteristics, and perform positioning calibration on the acoustic emission data. Exemplarily, acoustic emission data with energy greater than or equal to the energy threshold (such as 40 dB), duration within a preset time range (such as 10 μs to 1 ms), and frequency characteristics within a preset frequency range (such as the main frequency being 50 to 500 kHz) can be retained, so as to eliminate environmental noise interference. Exemplarily, the optional implementation manner for performing positioning calibration on the acoustic emission data is as follows: the positioning accuracy of the acoustic emission event can be verified through an artificial lead break test at a known position (error ≤ ±1 mm).

[0042] In some embodiments, the optional implementation of timestamp synchronization for the pressure data and acoustic emission data is as follows: The global clock synchronization of the pressure data and acoustic emission data can be performed using GPS or the IEEE 1588 protocol (PTP) to ensure that the time error between the pressure sensor and the acoustic emission system is ≤1 ms. Based on the fluid injection start signal as a reference, the start times of all data acquisition devices are synchronized to achieve the purpose of trigger alignment.

[0043] In some embodiments, the above-mentioned pressure dynamic response characteristic information may include the sudden drop amplitude at the pressure sudden drop point, the peak pressure gradient, and the fluctuation frequency. Among them, the pressure sudden drop point may refer to a pressure drop ≥5 MPa within a continuous time window (the time window Δt ≤ 10 ms), which can be defined as a crack tip breakthrough or bifurcation event. The peak pressure gradient may refer to the maximum value of the pressure difference between adjacent sensors (such as ΔP / Δx ≥ 10 MPa / mm, where ΔP is the pressure difference and Δx is the displacement), which can be defined as the crack propagation direction and rate. The fluctuation frequency can be determined based on the periodic fluctuation data in the pressure data. By analyzing the main frequency (such as 1 - 10 Hz) and the amplitude spectrum energy distribution through FFT, the periodic fluctuation can be identified, and this periodic fluctuation can be defined as the response to pulse injection or intermittent crack propagation.

[0044] In some embodiments, the above-mentioned acoustic emission characteristic information may include the event density, the energy accumulation rate, and the main frequency distribution. Among them, the event density can be determined based on the event coordinates (X, Y, Z) and the occurrence time in the acoustic emission data. Exemplarily, the acoustic emission data of each acoustic emission event may include event coordinates, occurrence time (t), energy (E), and main frequency (f). The acoustic emission events can be clustered based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to identify microcrack clusters. The DBSCAN algorithm can define a cluster as the largest set of density-connected points, which can divide the region with sufficient high density into clusters and can discover clusters of any shape in the spatial database with noise, thereby distinguishing the main crack and branch crack events. That is to say, the event density can represent the event coordinates, the occurrence time, and the specific crack event (such as the main crack event or the branch crack event). The energy accumulation rate can be determined based on the occurrence time and energy of the events in the acoustic emission data. The main frequency distribution can be determined based on the main frequency of the acoustic emission events in the acoustic emission data.

[0045] In an embodiment of the present application, the optional implementation manner of performing spatio-temporal association on the pressure dynamic response characteristic information and the acoustic emission characteristic information to obtain the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the spatio-temporal association conditions is as follows: Set a sliding time window, associate the pressure sudden drop point with the acoustic emission events within the sliding time window, and calculate the correlation coefficient between the pressure dynamic response characteristic information and the acoustic emission characteristic information; Project the acoustic emission event coordinates to the pressure gradient peak region to verify the spatial consistency of the crack propagation path; Based on the correlation coefficient and the verification result of the spatial consistency, obtain the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the spatio-temporal association conditions; where, meeting the spatio-temporal association conditions includes that the correlation coefficient is greater than or equal to a preset value, and the verification result of the spatial consistency is consistent.

[0046] Exemplarily, a sliding time window can be set (such as the sliding time window Δt = 20 ms), the pressure sudden drop point is associated with the acoustic emission events within the window, the correlation coefficient between the pressure dynamic response characteristic information and the acoustic emission characteristic information is calculated through the Pearson correlation coefficient, and the acoustic emission event coordinates are projected to the pressure gradient peak region to verify the spatial consistency of the crack propagation path, so as to realize the spatio-temporal association of pressure-acoustic emission and break through the limitations of a single data source. The pressure dynamic response characteristic information and the acoustic emission characteristic information with a correlation coefficient greater than or equal to a preset value (such as 0.7, determined as a strong association) and a verification result of consistent spatial consistency are determined as the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the spatio-temporal association conditions, that is, they can be used for crack propagation process identification.

[0047] In some embodiments, an intelligent dynamic criterion can be performed based on a machine learning-based crack propagation prediction model to provide real-time decision support for the fracturing construction. Exemplarily, the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the spatio-temporal association conditions can be input into a pre-trained crack propagation prediction model to obtain the crack propagation process (such as main crack events or branch crack events) within a future time and the behavior patterns of the crack propagation process (such as crack branching, turning, or stable propagation patterns, etc.). Among them, the crack propagation prediction model can be a joint model based on an LSTM network and a random forest classifier, and the classification accuracy of the random forest classifier can be greater than or equal to 88%. Optionally, when training the crack propagation prediction model, 100 sets of experimental data can be used (70% of which is used for training and 30% is used for testing), and cross-validation is performed to prevent overfitting. The crack propagation prediction model can be verified based on the energy release rate and combined with the acoustic emission cumulative energy (ΣE). Among them, the calculation formula of the energy release rate can be as follows: , where, is the crack volume, with the unit of mm 3 ; is the crack area, with the unit of mm2 ; is the pressure change value in MPa. For example, the energy release rate can be calculated based on the pressure change value, crack volume and crack area at the previous moment, and the crack extension process at the next moment (main crack or branch crack) can be calculated based on the energy release rate combined with the acoustic emission accumulated energy. The result is compared with the prediction result of the crack extension prediction model to verify the accuracy of the crack extension prediction model.

[0048] In the above embodiment, the hydraulic fracturing crack expansion process can be simulated with high precision, and the dynamic response characteristics of fluid pressure can be monitored in real time. By synchronizing the timestamps of pressure data and acoustic emission data, and spatially correlating the pressure dynamic response characteristic information and acoustic emission characteristic information, the problem of synchronous correlation between pressure fluctuations and crack behavior under multi-field coupling conditions can be solved, and the accuracy and reliability of the hydraulic fracturing crack expansion process can be improved. In addition, the modular analysis process can be adapted to different reservoir types and fracturing processes, which can significantly improve the engineering conversion value of experimental data.

[0049] Figure 3 The flowchart of the experimental method for studying the hydraulic fracturing crack expansion process provided in the embodiment of the present application is shown in FIG. It should be noted that the execution subject of the experimental method in the embodiment of the present application can be the data processing module in the experimental system involved in any of the above embodiments. Figure 3 As shown, the experimental method for studying the hydraulic fracturing crack extension process may include but is not limited to the following steps.

[0050] In step 301, a true triaxial stress simulation module is used to apply stress to the sample in the X, Y, and Z directions to simulate an in-situ stress state.

[0051] In an embodiment of the present application, a pressure sensor array is built into the sample, and the pressure sensor array is used to monitor the pressure change of the fluid at the crack mouth. At least a plurality of acoustic emission probes are arranged on each of the multiple surfaces of the sample, and the acoustic emission probes are used to monitor the acoustic emission data of the crack expansion.

[0052] In some embodiments, the sample may be a rock sample. For example, the sample size may be 400 mm×400 mm×400 mm, but is not limited thereto. Figure 1 As shown, an experimental pressure pillow B can be arranged between the true triaxial stress simulation module 101 and the surface of the sample A. The experimental pressure pillow B can be a variable device filled with gas inside. The experimental pressure pillow B can be used to ensure that the stress loaded on the surface of the sample A is uniform stress, avoiding stress loading errors caused by the non-absolute plane of the sample surface.

[0053] In some embodiments, Figure 2As shown, a perforation channel can be preset in the center of sample A, and a micro pressure sensor array C (for example, a sampling frequency greater than or equal to 1000 Hz) can be built in to monitor the changes in fluid pressure at the crack mouth in real time. The built-in micro pressure sensor array can ensure that the monitored fluid pressure is the crack mouth pressure, avoiding errors such as pipeline friction in the fluid pressure data monitored by the external sensor.

[0054] Exemplarily, the pressure sensor array can use a miniature high-frequency piezoresistive or optical fiber pressure sensor with a range of 0 to 100 MPa, a sampling frequency of ≥1000 Hz, a resolution of ≤0.1 MPa, and a size of ≤1 mm×1 mm×0.5 mm (miniaturized design). The sample can be processed into a 400×400×400 mm cube, and a perforation channel with a diameter of 2 mm is pre-drilled in the center to simulate the actual wellbore perforation structure. The pressure sensor array can be arranged in the sample based on a preset radial arrangement and a layered distribution array; wherein the radial arrangement is designed as follows: with the perforation channel as the center, arranged along the preset fracture extension direction, with a spacing of the first length, covering the first area; the layered distribution is designed as follows: buried in layers around the perforation channel, with a spacing of the second length between each layer; the pressure sensor array is embedded in the sample through precision drilling and epoxy resin encapsulation technology.

[0055] For example, the array arrangement of the pressure sensor array can be as follows: (1) Radial arrangement: With the perforation channel as the center, arranged along the preset crack extension direction (usually the direction of the minimum horizontal principal stress), with a spacing of 2 mm, covering an area with a radius of 10 mm. (2) Layered distribution: buried in layers around the perforation channel (such as 3 layers × 5 columns), with a spacing of 3 mm between each layer, to achieve three-dimensional pressure monitoring. Through precision drilling and epoxy resin encapsulation technology, the pressure sensor is embedded in the sample to ensure that the sensor surface is flush with the inner wall of the sample, which can avoid stress concentration interference.

[0056] In some embodiments, the monitoring data of the pressure sensor array can be collected synchronously by multi-channel. Exemplarily, a high-speed data acquisition card is used to support 32-channel parallel acquisition, and the sampling rate synchronization accuracy is ≤1μs. The original signal can be processed by a low-noise amplifier and an anti-aliasing filter (such as a cutoff frequency of 500Hz) to eliminate high-frequency noise.

[0057] In some embodiments, the acoustic emission probe can be a wide-band acoustic emission sensor with a frequency response range of 50 kHz to 1 MHz, a sensitivity greater than or equal to 80 dB, and support for three-dimensional positioning; the acoustic emission probe can be fixed to the surface of the sample by magnetism or adhesion, and a coupling agent is applied to the contact surface with the sample.

[0058] For example, Figure 2As shown, acoustic emission probes can be arranged on the six surfaces of the specimen, with at least two acoustic emission probes on each surface, forming a spatial three-dimensional monitoring network. Based on the time difference of the acoustic emission signals arriving at each acoustic emission probe and combined with the geometric parameters of the specimen, the coordinates of the fracture events can be calculated (accuracy ±1 mm).

[0059] In step 302, a fluid injection control module is used to simulate the fluid injection process in actual fracturing construction for the specimen.

[0060] It should be noted that in the embodiments of the present application, the fluid injection control module is the core component of the experimental system for studying the process of hydraulic fracture propagation, and can be used to accurately simulate the fluid injection process in actual fracturing construction, support various injection modes (such as constant rate, pulse, stepped pressure increase, etc.) and real-time regulation of fluid viscosity to study the dynamic response of fluid pressure during fracture propagation under different working conditions. Exemplarily, the core functions of the fluid injection control module can include:

[0061] ① Multi-mode fluid injection: accurately control the flow rate, pressure and injection timing.

[0062] ② Dynamic viscosity adjustment: adjust the rheological properties of the fracturing fluid in real time according to experimental requirements.

[0063] ③ Data synchronization and feedback: work together with the pressure sensor and the acoustic emission system to achieve closed-loop control.

[0064] In some embodiments, the fluid injection control module can use the constant rate injection mode to simulate the fluid injection process in actual fracturing construction for the specimen. Among them, the control logic of this constant rate injection mode can be: set the target flow rate (such as 10 mL / min), and the servo motor adjusts the plunger displacement speed through closed-loop feedback, compensating in real time for the pressure fluctuations caused by fracture propagation. Combining the feedback of the flowmeter with this target flow rate, the motor speed is adjusted to correct the flow error.

[0065] In step 303, the pressure data collected by the pressure sensor array and the acoustic emission data collected by the acoustic emission probes are obtained.

[0066] In step 304, the pressure data and the acoustic emission data are synchronized with timestamps, and the feature extraction is respectively performed on the pressure data and the acoustic emission data that have been synchronized with timestamps to obtain the pressure dynamic response feature information and the acoustic emission feature information.

[0067] In some embodiments, before synchronizing the timestamps of the pressure data and the acoustic emission data, data preprocessing can be performed on the pressure data and the acoustic emission data. In some embodiments, optional implementation manners of performing data preprocessing on the pressure data are as follows: denoising the pressure data and performing baseline correction. Exemplarily, wavelet transform (such as Daubechies 5) can be used to denoise the pressure data to filter out high-frequency noise and retain the effective pressure fluctuation signal. The purpose of performing baseline correction on the pressure data can be to eliminate the pressure baseline offset caused by temperature drift or system error and ensure the accuracy of the absolute pressure value.

[0068] In some embodiments, optional implementation manners of performing data preprocessing on the acoustic emission data are as follows: performing event screening on the acoustic emission data based on energy threshold, duration, and frequency characteristics, and performing positioning calibration on the acoustic emission data. Exemplarily, acoustic emission data with energy greater than or equal to the energy threshold (such as 40 dB), duration within a preset time range (such as 10 μs to 1 ms), and frequency characteristics within a preset frequency range (such as the main frequency being 50 to 500 kHz) can be retained, so as to eliminate environmental noise interference. Exemplarily, the optional implementation manner of performing positioning calibration on the acoustic emission data is as follows: the positioning accuracy of the acoustic emission event can be verified through an artificial lead break test at a known position (error ≤ ±1 mm).

[0069] In some embodiments, the optional implementation manner of synchronizing the timestamps of the pressure data and the acoustic emission data is as follows: GPS or IEEE 1588 protocol (PTP) can be used to perform global clock synchronization on the pressure data and the acoustic emission data to ensure that the time error between the pressure sensor and the acoustic emission system is ≤1 ms. Based on the fluid injection start signal as a reference, synchronize the start times of all data acquisition devices to achieve the purpose of trigger alignment.

[0070] In some embodiments, the above-mentioned pressure dynamic response characteristic information can include the sudden drop amplitude of the pressure sudden drop point, the peak pressure gradient, and the fluctuation frequency. Among them, the pressure sudden drop point can refer to the pressure drop ≥5 MPa within a continuous time window (time window Δt ≤ 10 ms), which can be defined as a crack tip breakthrough or bifurcation event. The peak pressure gradient can refer to the maximum value of the pressure difference between adjacent sensors (such as ΔP / Δx ≥ 10 MPa / mm, ΔP is the pressure difference, and Δx is the displacement), which can be defined as the crack propagation direction and rate. The fluctuation frequency can be determined based on the periodic fluctuation data in the pressure data. By analyzing the main frequency (such as 1 - 10 Hz) and the amplitude spectrum energy distribution through FFT, periodic fluctuations can be identified, and this periodic fluctuation can be defined as the response of pulse injection or crack intermittent expansion.

[0071] In some embodiments, the above acoustic emission characteristic information may include event density, energy accumulation rate, and dominant frequency distribution. Among them, the event density may be determined based on the event coordinates (X, Y, Z) and occurrence time in the acoustic emission data. Exemplarily, the acoustic emission data of each acoustic emission event may include event coordinates, occurrence time (t), energy (E), and dominant frequency (f). The acoustic emission events may be clustered based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to identify microcrack clusters. The DBSCAN algorithm may define a cluster as the largest set of density-connected points, capable of dividing regions with sufficient high density into clusters and discovering clusters of any shape in a spatial database with noise, thereby distinguishing between main crack events and branch crack events. That is to say, the event density can represent the event coordinates, occurrence time, and specific crack events (such as main crack events or branch crack events). The energy accumulation rate may be determined based on the occurrence time and energy of the events in the acoustic emission data. The dominant frequency distribution may be determined based on the dominant frequency of the acoustic emission events in the acoustic emission data.

[0072] In step 305, the pressure dynamic response characteristic information and the acoustic emission characteristic information are spatiotemporally correlated to obtain the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the spatiotemporal correlation conditions.

[0073] In some embodiments, a sliding time window is set, the pressure drop points are associated with the acoustic emission events within the sliding time window, and the correlation coefficient between the pressure dynamic response characteristic information and the acoustic emission characteristic information is calculated; the acoustic emission event coordinates are projected onto the pressure gradient peak region to verify the spatial consistency of the crack propagation path; based on the correlation coefficient and the verification result of the spatial consistency, the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the spatiotemporal correlation conditions are obtained; among them, meeting the spatiotemporal correlation conditions includes that the correlation coefficient is greater than or equal to a preset value, and the verification result of the spatial consistency is consistent.

[0074] Exemplarily, a sliding time window may be set (such as the sliding time window Δt = 20 ms), the pressure drop points are associated with the acoustic emission events within the window, the correlation coefficient between the pressure dynamic response characteristic information and the acoustic emission characteristic information is calculated through the Pearson correlation coefficient, and the acoustic emission event coordinates are projected onto the pressure gradient peak region to verify the spatial consistency of the crack propagation path, thereby realizing the spatiotemporal correlation of pressure-acoustic emission and breaking through the limitations of a single data source. The pressure dynamic response characteristic information and the acoustic emission characteristic information with a correlation coefficient greater than or equal to a preset value (such as 0.7, determined as a strong correlation) and a verification result of consistent spatial consistency are determined as the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the spatiotemporal correlation conditions, that is, they can be used for identifying the crack propagation process.

[0075] In step 306, based on the pressure dynamic response characteristic information and acoustic emission characteristic information that meet the spatio-temporal correlation conditions, the crack propagation process of the specimen is identified.

[0076] In some embodiments, an intelligent dynamic criterion can be performed based on a machine learning-based crack propagation prediction model to provide real-time decision support for the fracturing construction. Exemplarily, the pressure dynamic response characteristic information and acoustic emission characteristic information that meet the spatio-temporal correlation conditions can be input into a pre-trained crack propagation prediction model to obtain the crack propagation process (such as main crack events or branch crack events) within a future time and the behavior pattern of the crack propagation process (such as crack branching, turning, or stable propagation mode, etc.). Among them, the crack propagation prediction model can be a joint model based on an LSTM network and a random forest classifier, and the classification accuracy of the random forest classifier can be greater than or equal to 88%. Optionally, when training the crack propagation prediction model, 100 sets of experimental data can be used (70% for training and 30% for testing), and cross-validation can be performed to prevent overfitting. The crack propagation prediction model can be verified based on the energy release rate and combined with the acoustic emission cumulative energy (ΣE). The calculation formula of the energy release rate can be as follows: , where is the crack volume, with the unit of mm 3 ; is the crack area, with the unit of mm 2 ; is the pressure change value, with the unit of MPa. For example, the energy release rate can be calculated based on the pressure change value, crack volume, and crack area at the previous moment, and the crack propagation process (main crack or branch crack) at the next moment can be calculated based on the energy release rate combined with the acoustic emission cumulative energy. The result can be compared with the prediction result of the crack propagation prediction model to verify the accuracy of the crack propagation prediction model.

[0077] According to the experimental method of the embodiments of the present application, the hydraulic fracturing crack propagation process can be simulated with high precision, the dynamic response characteristics of the fluid pressure can be monitored in real time. By synchronizing the pressure data and acoustic emission data with timestamps and correlating the pressure dynamic response characteristic information and acoustic emission characteristic information spatio-temporally, the synchronous correlation problem between pressure fluctuations and crack behavior under multi-field coupling conditions can be solved, and the accuracy and reliability of the identification of the hydraulic fracturing crack propagation process can be improved. In addition, different reservoir types and fracturing processes can be adapted through a modular analysis process, which can significantly enhance the engineering conversion value of experimental data.

[0078] To implement the above embodiments, the present application further provides a storage medium, which can store instructions. When the instructions are executed by a processor (such as the processor in the data processing module), the processor is caused to execute the experimental method provided by the present application for studying the process of hydraulic fracturing crack propagation. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to execute the experimental method provided by the present application for studying the process of hydraulic fracturing crack propagation.

[0079] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0080] In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0081] Any process or method description in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0082] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a predefined list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection unit (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0083] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application specific integrated circuit having appropriate combinational logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0084] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0085] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0086] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. An experimental method for studying the process of hydraulic fracturing crack propagation, characterized in that, Including: Adopting a true triaxial stress simulation module to apply stresses in the X, Y, and Z directions of the specimen to simulate the in-situ stress state; wherein, a pressure sensor array is built in the specimen, and the pressure sensor array is used to monitor the fluid pressure change at the crack opening. At least a plurality of acoustic emission probes are arranged on each of the multiple surfaces of the specimen, and the acoustic emission probes are used to monitor the acoustic emission data of crack propagation; Adopting a fluid injection control module to simulate the fluid injection process in actual fracturing construction for the specimen; Obtaining the pressure data collected by the pressure sensor array and the acoustic emission data collected by the acoustic emission probes; Performing timestamp synchronization on the pressure data and the acoustic emission data, and respectively performing feature extraction on the pressure data and the acoustic emission data after timestamp synchronization to obtain pressure dynamic response feature information and acoustic emission feature information; Performing spatio-temporal correlation on the pressure dynamic response feature information and the acoustic emission feature information to obtain pressure dynamic response feature information and acoustic emission feature information that meet the spatio-temporal correlation conditions; the pressure dynamic response feature information includes the sudden drop amplitude at the pressure sudden drop point, the peak pressure gradient, and the fluctuation frequency, and the acoustic emission feature information includes the event density, the energy accumulation rate, and the main frequency distribution; wherein, the performing spatio-temporal correlation on the pressure dynamic response feature information and the acoustic emission feature information to obtain pressure dynamic response feature information and acoustic emission feature information that meet the spatio-temporal correlation conditions includes: setting a sliding time window, correlating the pressure sudden drop point with the acoustic emission events within the sliding time window, and calculating the correlation coefficient between the pressure dynamic response feature information and the acoustic emission feature information; projecting the acoustic emission event coordinates to the peak pressure gradient region to verify the spatial consistency of the crack propagation path; based on the correlation coefficient and the verification result of the spatial consistency, obtaining pressure dynamic response feature information and acoustic emission feature information that meet the spatio-temporal correlation conditions; wherein, the meeting the spatio-temporal correlation conditions includes that the correlation coefficient is greater than or equal to a preset value, and the verification result of the spatial consistency is consistent; Based on the pressure dynamic response feature information and the acoustic emission feature information that meet the spatio-temporal correlation conditions, judging the crack propagation process of the specimen.

2. The method according to claim 1, wherein The adopting a fluid injection control module to simulate the fluid injection process in actual fracturing construction for the specimen includes: Controlling the fluid injection control module to simulate the fluid injection process in actual fracturing construction for the specimen in a constant speed injection mode; wherein, the control logic of the constant speed injection mode includes: the servo motor adjusts the plunger displacement speed through closed-loop feedback, compensates the pressure fluctuation caused by crack propagation in real time, and adjusts the motor speed by combining the feedback of the flowmeter with the set target flow rate to correct the flow error.

3. The method according to claim 1, wherein Before performing timestamp synchronization on the pressure data and the acoustic emission data, the method further includes: Performing denoising processing and baseline correction on the pressure data; and / or Performing event screening on the acoustic emission data based on the energy threshold, duration, and frequency characteristics, and performing positioning calibration on the acoustic emission data.

4. The method according to any one of claims 1 to 3, characterized in that The method of identifying the crack extension process of the sample based on the pressure dynamic response characteristic information and the acoustic emission characteristic information that satisfy the time-space correlation condition includes: Inputting the pressure dynamic response characteristic information and acoustic emission characteristic information satisfying the time-space correlation conditions into a pre-trained crack extension prediction model to obtain the crack extension process in the future and the behavior pattern of the crack extension process; Among them, the crack extension prediction model is a joint model based on LSTM network and random forest classifier.

5. An experimental system for studying the process of hydraulic fracture propagation, characterized in that, include: A true triaxial stress simulation module is used to apply stress in the X, Y, and Z directions of the sample to simulate the in-situ stress state; wherein the sample has a built-in pressure sensor array, the pressure sensor array is used to monitor the pressure change of the fluid at the crack mouth, and at least a plurality of acoustic emission probes are arranged on each of the multiple surfaces of the sample, and the acoustic emission probes are used to monitor the acoustic emission data of the crack extension; A fluid injection control module, used to simulate the fluid injection process in actual fracturing construction for the sample; a data processing module, for acquiring the pressure data collected by the pressure sensor array and the acoustic emission data collected by the acoustic emission probe, and performing time stamp synchronization on the pressure data and the acoustic emission data, and performing feature extraction on the pressure data and the acoustic emission data that have been time stamp synchronized, respectively, to obtain pressure dynamic response characteristic information and acoustic emission characteristic information, and performing spatiotemporal correlation on the pressure dynamic response characteristic information and the acoustic emission characteristic information to obtain pressure dynamic response characteristic information and acoustic emission characteristic information that satisfy spatiotemporal correlation conditions, and based on the pressure dynamic response characteristic information and the acoustic emission characteristic information that satisfy spatiotemporal correlation conditions, performing crack extension process identification on the sample; The pressure dynamic response characteristic information includes the pressure drop amplitude, pressure gradient peak value and fluctuation frequency of the pressure drop point, and the acoustic emission characteristic information includes event density, energy accumulation rate and main frequency distribution; wherein the pressure dynamic response characteristic information and the acoustic emission characteristic information are temporally and spatially associated to obtain the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the temporal and spatial association conditions, including: setting a sliding time window, associating the pressure drop point with the acoustic emission event within the sliding time window, and calculating the correlation coefficient between the pressure dynamic response characteristic information and the acoustic emission characteristic information; projecting the acoustic emission event coordinates to the pressure gradient peak area to verify the spatial consistency of the crack extension path; based on the verification results of the correlation coefficient and spatial consistency, obtaining the pressure dynamic response characteristic information and the acoustic emission characteristic information that meet the temporal and spatial association conditions; wherein the temporal and spatial association conditions are satisfied, including that the correlation coefficient is greater than or equal to a preset value, and the verification result of the spatial consistency is consistent.

6. The system according to claim 5, wherein An experimental pressure pillow is arranged between the true triaxial stress simulation module and the surface of the sample. The experimental pressure pillow is a variable device filled with gas. The experimental pressure pillow is used to ensure that the stress loaded on the surface of the sample is uniform stress.

7. The system according to claim 5, wherein A perforation channel is preset at the center of the specimen, and the pressure sensor array is arranged in the specimen based on a preset array with radial arrangement and layered distribution; wherein, the design of the radial arrangement is as follows: centered on the perforation channel, arranged along the preset crack propagation direction, with a spacing of a first length, covering a first area; the design of the layered distribution is as follows: buried in layers around the perforation channel, with a spacing of a second length between each layer; The pressure sensor array is embedded inside the specimen through precision drilling and epoxy resin encapsulation technology.

8. The system according to claim 5, wherein The acoustic emission probe is a broadband acoustic emission sensor with a frequency response range of 50 kHz to 1 MHz, a sensitivity greater than or equal to 80 dB, and supports three-dimensional positioning; the acoustic emission probe is fixed on the surface of the specimen by magnetic attraction or adhesives, and a coupling agent is applied to the contact surface with the specimen.

9. A storage medium, the storage medium stores instructions, characterized in that, When the instruction is executed by the processor, the processor is caused to execute the method according to any one of claims 1-4.

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