Method for monitoring pulse fracturing active fracture based on seismic-electric coupling

Abstract

The present application relates to the technical field of fracture monitoring, and is a pulse fracturing active fracture monitoring method based on seismic-electric coupling, which comprises using a fracturing nipple excitation source as an active elastic wave source, receiving the electric signal generated by seismic-electric coupling by using a multi-channel electrode array arranged, adopting time-frequency analysis and fracture inversion based on a frequency domain template to realize inversion and visualized output of the existence, spatial position, trend and scale of the fracture. The present application excites a pulse by using a fracturing nipple, generates an elastic wave signal, emits an excitation current, enhances the intensity of the seismic-electric effect electric signal, analyzes the change of the electric field around the fracturing nipple, and thus realizes real-time active monitoring of the development and distribution of fracturing cracks and improves the accuracy and real-time performance of crack detection.

Description

Technical Field

[0001] This invention relates to the field of fracturing crack monitoring technology, and is a pulse fracturing active crack monitoring method based on seismoelectric coupling. Background Technology

[0002] In the exploration and development of oil and gas fields and the fracturing stimulation of unconventional reservoirs, how to monitor and evaluate fracture networks in real time and accurately has always been a technical challenge that needs to be overcome. Although existing microseismic monitoring, tracer methods and distributed fiber optic sensing technologies are widely used, they still have inherent limitations such as weak signals, susceptibility to interference, difficulty in reflecting fracture conductivity, being post-event monitoring, and multiple interpretations.

[0003] Patent application CN119021656A discloses a device and method for monitoring fracture propagation height using pre-positioned downhole pressure gauges. The device includes several thermobarometers connected by optical fibers and fixed to the casing using ring clips. The casing has openings at corresponding positions where the thermobarometers are installed, filled with a soluble solid material. At the same depth for each thermobarometer, perforations are formed on the casing wall along the opposite direction of the optical fiber. Bridge plugs are sealed inside the casing and positioned between adjacent perforations to isolate them. A remote sensing coil is fitted onto the casing and optical fiber. A cement sheath is placed on the outside of the casing, providing pressure / temperature isolation between the thermobarometers. This document monitors the vertical propagation height of fractures during fracturing by tracking real-time changes in temperature and pressure measured by thermobarometers at each depth in the well.

[0004] Patent application CN112240189B discloses a hydraulic fracturing fracture monitoring simulation experimental device based on distributed optical fiber acoustic monitoring, including a hydraulic fracturing fracture simulation system, a distributed optical fiber acoustic monitoring system, a working fluid supply system, and a produced fluid collection system. This document utilizes the distributed optical fiber acoustic monitoring system to monitor the location of hydraulic fracturing fractures and the volume of proppant entering the fracture-forming section, thereby determining fracture parameters. Summary of the Invention

[0005] This invention provides a pulse fracturing active fracture monitoring method based on seismoelectric coupling, which can obtain the spatial location and size of the fracture, thereby monitoring the fracture propagation and development.

[0006] The technical solution of this invention is achieved through the following measures: a pulse fracturing active fracture monitoring method based on seismoelectric coupling, comprising:

[0007] Electrode pair layout: According to the requirements of potential difference measurement points in the corresponding well section of the fracturing reservoir, the required electrode pairs are laid on the inner wall of the fracturing sub. A pulse nozzle is fixedly installed on the outside of the fracturing sub where the electrode pairs are laid. A pulse generator is fixedly installed on the fracturing sub above the electrode pairs. The fracturing sub is then lowered into the corresponding well section of the fracturing reservoir.

[0008] Excitation and loading: A pulse generator outputs high-frequency pulses, which are excited through a fracturing sub. The excited pulses form a stress wave field through a pulse nozzle. The stress wave field excites the target fracture to generate a pulse jet, which induces elastic wave vibration in the target fracture. When the elastic wave passes through a fluid-containing porous medium, it is synchronously converted into an electrical signal characterizing the fracture properties based on the electro-optic coupling effect. The electrical signal propagates from the wellbore to the formation.

[0009] Data acquisition: Electrode pairs arranged on the inner wall of the fracturing sub section form a multi-channel electrode array, and electrical signals are acquired synchronously through the multi-channel electrode array;

[0010] Signal preprocessing: The acquired electrical signal is filtered in multiple stages, and wavelet threshold noise reduction is used to suppress background noise that overlaps with the effective signal frequency band, thereby improving the signal-to-noise ratio; then, through normalization and phase correction, preprocessed time-frequency data is obtained.

[0011] Time-frequency analysis: The preprocessed time-frequency data is converted to the frequency domain to obtain the spectrum. The characteristic peaks of energy anomalies in the spectrum are identified. The characteristic peaks of energy anomalies correspond to the crack initiation and seismoelectric coupling response. The characteristic peaks of energy anomalies are the crack response characteristic peaks. The center frequency f0, amplitude A0 and phase delay Δφ of the crack response characteristic peaks are extracted.

[0012] Fracture inversion: Using a theoretical spectrum response library of fractures at different scales under various geological conditions as a feature template library, the center frequency f0, amplitude A0, and phase delay Δφ of the fracture response characteristic peaks are input into the feature template library for search and matching to determine the optimal matching template. The corresponding fracture scale is obtained based on the optimal matching template. The electrical signals of each potential difference measurement point are input with a precision to the time difference Δt. The spatial location of the fracture is obtained by inversion using the double-difference positioning method.

[0013] Output results: A three-dimensional crack distribution image is generated based on the spatial location and scale of the crack obtained from the inversion.

[0014] Based on the 3D crack distribution image, the crack propagation direction is highly consistent with the predicted direction of the maximum principal stress in the geostress field. With a refresh time of 1 second, near real-time updates are achieved, enabling dynamic identification and monitoring of cracks during the fracturing process.

[0015] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0016] Furthermore, in the above-mentioned electrode pair arrangement, when the required electrode pairs are arranged on the inner wall of the fracturing sub, the spacing between each electrode pair is 5m to 10m, and the number of electrode pairs is 8 to 16 pairs. Each electrode pair includes an excitation electrode and a receiving electrode.

[0017] Furthermore, during excitation and loading, when a pulse generator is used to output high-frequency pulses, the pressure fluctuation range of the high-frequency pulses is 30MPa to 80MPa.

[0018] Furthermore, in the above time-frequency analysis, for complex stress wave fields, based on the preprocessed signal conversion to the frequency domain, continuous wavelet transform is used to obtain the local energy concentration area in time and frequency, and to confirm the frequency band of crack activity.

[0019] Furthermore, the spatial location of the crack is obtained by inverting the electrical signals of each input potential difference measuring point to the time difference Δt using the double-difference positioning method, specifically including:

[0020] The spatial location of the crack is obtained by combining the accurate time difference Δt of the electrical signals from each potential difference measuring point, and the travel time difference of several adjacent crack events arriving at different potential difference measuring points.

[0021] Furthermore, in the above crack inversion, the crack scale obtained based on the optimal matching template is verified by the crack scale value predicted by the convolutional neural network prediction model. If the difference between the crack scale and the crack scale value predicted by the convolutional neural network prediction model is less than a threshold, then the crack scale is reasonable; otherwise, it is unreasonable.

[0022] Furthermore, the construction of the above convolutional neural network prediction model includes:

[0023] The theoretical spectral response data and corresponding fracture scales under different geological conditions were used as the training sample set.

[0024] The convolutional neural network model is trained using a training sample set to obtain a convolutional neural network prediction model;

[0025] When using a convolutional neural network prediction model for prediction, the input is the spectral response data, and the output is the corresponding crack scale. The spectral response data includes the center frequency, amplitude, and phase delay of the crack response characteristic peak.

[0026] The beneficial effects of this invention are:

[0027] This invention uses fracturing subspindle as a standardized and controllable excitation source, effectively coupling elastic waves with the electromagnetic response generated by the fracture to achieve rapid signal acquisition and precise location. In terms of signal processing, a time-frequency domain analysis inversion method is proposed, which significantly improves fracture identification capabilities. Combined with a machine learning model (i.e., a convolutional neural network), it enables intelligent quantitative assessment of fracture size. Under complex reservoir conditions, it can not only acquire high signal-to-noise ratio seismoelectric signals but also achieve high-resolution inversion of fracture location and size, outputting real-time three-dimensional fracture distribution images and guiding fracturing parameter adjustments, thereby effectively reducing construction risks and improving the accuracy and efficiency of fracture detection. Attached Figure Description

[0028] Appendix Figure 1 This is a schematic diagram illustrating the application of the method described in this invention.

[0029] Appendix Figure 2 This is a controllable pulse excitation waveform.

[0030] Appendix Figure 3 The raw electrical signal of the seismoelectric response acquired by the electrode array.

[0031] Appendix Figure 4 The waveform is the signal waveform after one bandpass filtering by an adaptive bandpass filter from 20Hz to 2500Hz.

[0032] Appendix Figure 5 The waveform is the signal waveform after wavelet denoising and normalization.

[0033] Appendix Figure 6 This is a flowchart of spatial positioning based on the double-difference positioning method and crack scale calculation based on the optimal matching template.

[0034] The codes in the attached diagram are as follows: 1 is fracturing sub, 2 is pulse generator, 3 is pulse nozzle, 4 is excitation electrode, 5 is receiving electrode, 6 is pulse jet, 7 is elastic wave, 8 is hydraulic fracture, 9 is packer, and 10 is wellbore. Detailed Implementation

[0035] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0036] For ease of description, the relative positions of the components are described based on the appendix to the instruction manual. Figure 1 The description is based on a layout method, such as the layout of drilling tool views in this field, as shown in the appendix of the specification. Figure 1 The layout direction is: left is top, right is bottom.

[0037] The seismoelectric effect, also known as the coupling effect between elastic waves and electromagnetic fields, occurs when elastic waves propagate in porous media. The relative motion between the fluid and the solid framework disturbs the electric double layer (charge distribution at the solid-liquid interface), resulting in the accumulation and flow of net residual charge, forming a measurable electric field or current. Pulse fracturing induces the seismoelectric effect in reservoir rocks. By monitoring the electrical signals generated by this effect, the propagation and development of fractures can be monitored.

[0038] The working principle of this invention is as follows: Based on the seismoelectric coupling effect, the reservoir fracture network is stimulated by pulse, and the pressure fluctuation excites the fracture front to generate an electrical signal. Combined with the excitation current, the intensity of the seismoelectric effect electrical signal is amplified, and the changes in the downhole electrical signal are captured, thereby inverting the location and characteristics of the underground fracture (such as fracture size).

[0039] The present invention will be further described below with reference to embodiments:

[0040] Example 1: As Figure 1 As shown, this pulse fracturing active fracture monitoring method based on seismoelectric coupling includes:

[0041] Step 1, Electrode pair deployment: According to the requirements for deploying potential difference measuring points in the corresponding well section of the fracturing reservoir, the required electrode pairs are deployed on the inner wall of the fracturing sub 1. A pulse nozzle 3 is fixedly installed on the outside of the fracturing sub 1 where the electrode pairs are deployed. A pulse generator 2 is fixedly installed on the fracturing sub 1 above the electrode pairs. The fracturing sub 1 is then lowered into the corresponding well section of the fracturing reservoir.

[0042] Step 2, Excitation and Loading: A pulse generator 2 outputs a high-frequency pulse, which is excited by the fracturing sub 1. The excited pulse forms a stress wave field through the pulse nozzle 3. The stress wave field excites the target fracture (e.g., a hydraulic fracture 8) to generate a pulse jet 6, which induces the target fracture to generate an elastic wave 7. When the elastic wave 7 passes through a fluid-containing porous medium, it is synchronously converted into an electrical signal (i.e., a seismoelectric signal) that characterizes the fracture properties based on the seismoelectric coupling effect. The electrical signal propagates from the wellbore 10 to the formation.

[0043] The pulse generator 2 works in conjunction with the pulse nozzle 3 to achieve controllable periodic pulse excitation.

[0044] Step 3, Data Acquisition: The electrode pairs arranged on the inner wall of the fracturing sub section 1 form a multi-channel electrode array. The electrical signals are synchronously acquired through the multi-channel electrode array. The highest sampling frequency of the electrode pairs is 50kHz, and the dynamic range is better than 120dB. The signal waveform and timestamp corresponding to each excitation are recorded. Based on the oversampling principle, the synchronization error of the multi-channel electrode array is not less than 0.5µs, and the resolution of the acquired signal reaches 0.1µV.

[0045] Step 4, Signal preprocessing: The acquired electrical signal is first filtered in multiple stages using an adaptive bandpass filter from 10Hz to 2500Hz to effectively retain the main frequency band signal excited by pulse fracturing. At the same time, wavelet threshold noise reduction is used to suppress background noise that overlaps with the effective signal frequency band and improve the signal-to-noise ratio. Then, through normalization and phase correction, the preprocessed time-frequency data is obtained.

[0046] Because the diameter and length of the downhole tubing vary, there are certain errors in the excitation and reception signals. These differences can be eliminated through normalization and phase correction.

[0047] Step 5, Time-Frequency Analysis: The preprocessed time-frequency data is converted to the frequency domain using Fourier Transform or Short-Time Fourier Transform (STFT) to obtain the spectrum. The characteristic peaks of energy anomalies in the spectrum are identified. The characteristic peaks of energy anomalies correspond to the crack initiation and seismoelectric coupling response. The characteristic peaks of energy anomalies are the crack response characteristic peaks. The core parameters such as the center frequency f0, amplitude A0, and phase delay Δφ of the crack response characteristic peaks are extracted.

[0048] Step 6, Crack Inversion: Using the theoretical spectral response library of cracks at different scales under various geological conditions as a feature template library, the center frequency f0, amplitude A0, and phase delay Δφ of the crack response characteristic peaks are input into the feature template library for search and matching to determine the optimal matching template. The corresponding crack scale is obtained based on the optimal matching template. The electrical signals of each potential difference measurement point are input with a time difference Δt, and the spatial location of the crack is obtained by using the double-difference positioning method. With a time difference detection accuracy of µs, sub-meter-level positioning accuracy can be achieved.

[0049] Theoretical Spectral Response Library: This library collects the center frequency f0, amplitude A0, phase delay Δφ, and corresponding crack scales of different crack response characteristic peaks. These data were obtained through indoor experiments and numerical simulations.

[0050] Step 7, Output Results: Based on the spatial location and scale of the cracks obtained from the inversion, generate a three-dimensional crack distribution image.

[0051] Based on the 3D crack distribution image, the crack propagation direction is highly consistent with the predicted direction of the maximum principal stress in the geostress field. With a refresh time of 1 second, near real-time updates are achieved, enabling dynamic identification and monitoring of cracks during the fracturing process.

[0052] This invention uses a fracturing sub-section as an excitation source as an active elastic wave source, and utilizes a multi-channel electrode array to receive the electrical signals generated by seismoelectricity. By employing time-frequency analysis and crack inversion based on frequency domain templates, it achieves the inversion and visualization output of the existence, spatial location, orientation, and scale of cracks.

[0053] Example 2: As an optimization of the above examples, such as Figure 1As shown, in the electrode pair arrangement, when arranging the required electrode pairs on the inner wall of the fracturing sub 1, the spacing between each electrode pair is 5m to 10m, and the number of electrode pairs is 8 to 16 pairs. Each electrode pair includes an excitation electrode 4 and a receiving electrode 5. The electrode pairs are symmetrically distributed on the inner wall of the fracturing sub 1. The symmetrical distribution can be left-right symmetrical, top-bottom symmetrical, etc., aiming to construct a multi-channel electrode array.

[0054] Example 3: As an optimization of the above example, during excitation and loading, when the pulse generator 2 outputs high-frequency pulses, the pressure fluctuation range of the high-frequency pulses is 30MPa to 80MPa, the pressure peak of the high-frequency pulses is 70% to 80% of the formation fracturing pressure, and the pulse frequency range is 1Hz to 100Hz.

[0055] Example 4: As an optimization of the above examples, in time-frequency analysis, for complex stress wave fields, after the preprocessed signal is converted to the frequency domain, continuous wavelet transform (CWT) is used to obtain the local energy concentration area in time and frequency, confirm the frequency band of crack activity, and improve the accuracy of special signal identification in the spectrum.

[0056] Example 5: As an optimization of the above examples, the electrical signals of each potential difference measurement point are input with an accuracy of time difference Δt. The spatial location of the crack is obtained by inversion using the double-difference positioning method, specifically including:

[0057] The electrical signals of each potential difference measuring point are input with a time difference Δt accurate to ensure that the detection error of Δt does not exceed 2µs. The travel time of several adjacent crack events arriving at different potential difference measuring points (more precisely, the position of receiving electrode 5) is combined to invert the spatial location of the crack.

[0058] Example 6: As an optimization of the above examples, in the crack inversion, the crack scale obtained according to the optimal matching template is verified by the crack scale value predicted by the convolutional neural network prediction model. If the difference between the crack scale and the crack scale value predicted by the convolutional neural network prediction model is less than a threshold, then the crack scale is reasonable; otherwise, it is unreasonable.

[0059] Example 7: As an optimization of the above examples, the construction of the convolutional neural network prediction model includes:

[0060] The theoretical spectral response data and corresponding fracture scales under different geological conditions were used as the training sample set.

[0061] The convolutional neural network model is trained using a training sample set to obtain a convolutional neural network prediction model;

[0062] When using a convolutional neural network prediction model for prediction, the input is the spectral response data, and the output is the corresponding crack scale. The spectral response data includes the center frequency, amplitude, and phase delay of the crack response characteristic peak.

[0063] Example 8: As Figure 1 As shown, this pulse fracturing active fracture monitoring method based on seismoelectric coupling was tested in a field test in a tight sandstone reservoir. The test well was approximately 2850m deep, the reservoir thickness was 18m, and the permeability was approximately 0.05mD. The method included the following steps:

[0064] Step 1, Electrode pair layout: Eight pairs of electrodes are laid out on the inner wall of the fracturing sub 1. The spacing between each electrode pair is 10m. Each electrode pair includes an excitation electrode 4 and a receiving electrode 5. A pulse nozzle 3 is fixedly installed on the outside of the fracturing sub 1 where the electrode pairs are laid out. A pulse generator 2 is fixedly installed on the fracturing sub 1 above the electrode pairs. The fracturing sub 1 is then lowered into the corresponding well section of the fracturing reservoir.

[0065] Step 2, Excitation and Loading: A pulse generator 2 outputs a high-frequency pulse, which is excited by the fracturing sub 1. The excited pulse forms a stress wave field through the pulse nozzle 3. The stress wave field excites the target fracture to generate a pulse jet 6, which induces the target fracture to generate an elastic wave 7. When the elastic wave 7 passes through a fluid-containing porous medium, it is synchronously converted into an electrical signal characterizing the fracture properties based on the seismoelectric coupling effect. The electrical signal propagates from the wellbore 10 to the formation.

[0066] The high-frequency pulse has a peak pressure of 75 MPa, a pulse width of 5 ms, a pulse frequency of 50 Hz, and a sweep frequency range of 50 Hz to 2000 Hz.

[0067] Step 3, Data Acquisition: Electrode pairs arranged on the inner wall of fracturing sub section 1 form a multi-channel electrode array. Electrical signals are synchronously acquired through this array. The highest sampling frequency of the electrode pairs is 50kHz, with a dynamic range better than 120dB. The signal waveform and timestamp corresponding to each excitation are recorded. Based on the oversampling principle, the synchronization error of the multi-channel electrode array is no less than 0.5µs, and the acquired signal resolution reaches 0.1µV. Figure 2 As shown, it is a controllable pulse excitation waveform, demonstrating a periodic high-pressure pulse with a peak pressure of 75 MPa and a pulse width of 5 ms. Figure 3 As shown, it displays the raw electrical signal of the seismoelectric response acquired by the electrode array. The figure fully contains the crack generated synchronously by pulse excitation, including the effective crack response, 50 Hz power frequency interference, random spike noise, and background noise.

[0068] Step 4, Signal preprocessing: The acquired electrical signal is first filtered in multiple stages using an adaptive bandpass filter from 10Hz to 2500Hz to effectively retain the main frequency band signal excited by pulse fracturing. At the same time, wavelet threshold noise reduction is used to suppress background noise that overlaps with the effective signal frequency band and improve the signal-to-noise ratio. Then, normalization and phase correction are used to eliminate equipment differences and obtain preprocessed time-frequency data.

[0069] like Figure 4 As shown, it displays the signal waveform after one bandpass filtering by an adaptive bandpass filter from 20Hz to 2500Hz, and compares it with the waveform of the signal after one bandpass filtering. Figure 3 As shown in the original electrical signal, power frequency interference and high-frequency random noise are effectively suppressed, the signal waveform is smoother, the crack response component is effectively highlighted, and the signal-to-noise ratio is initially improved. Further wavelet threshold denoising further suppresses residual interference in the passband, such as... Figure 5 As shown, it displays the final signal after wavelet denoising (i.e., wavelet thresholding) and normalization. After a complete preprocessing process, the residual interference in the signal is further suppressed, the waveform features are clearer and more stable, and the normalization process achieves the consistency of amplitude and alignment in the time domain, providing high-quality input data for subsequent time-frequency analysis and feature extraction.

[0070] Step 5, Time-Frequency Analysis: For the transient signal excited by the pulse, the preprocessed time-frequency data with a window length of 256 points and an overlap rate of 50% is preferred. The data is converted to the frequency domain using Short Time Fourier Transform (STFT) to obtain the spectrum. In the frequency domain range of 200Hz to 500Hz, the characteristic peaks of energy anomalies in the spectrum are identified. The characteristic peaks of energy anomalies correspond to the crack initiation and seismoelectric coupling response. The characteristic peaks of energy anomalies are the crack response characteristic peaks. The core parameters of the crack response characteristic peaks, such as the center frequency f0, amplitude A0, and phase delay Δφ, are extracted.

[0071] Step Six, Crack Inversion: Using the theoretical spectral response library of cracks at different scales under various geological conditions as a feature template library, the center frequency f0, amplitude A0, and phase delay Δφ of the crack response characteristic peaks are input into the feature template library for search and matching to determine the optimal matching template. The corresponding crack scale is obtained based on the optimal matching template, with the calculation error controlled within 5%. The accurate time difference Δt of the electrical signal from each potential difference measuring point is input (i.e., the arrival time difference of the electrical signal generated by the seismoelectric effect to different receiving electrodes 5). Combined with the travel time differences (i.e., travel time data) of several adjacent crack events arriving at different potential difference measuring points, the spatial location of the crack (i.e., the three-dimensional coordinates of the crack) is inverted. See [link to relevant documentation]. Figure 6 ;

[0072] In crack inversion, the crack scale obtained based on the optimal matching template is verified by the crack scale value predicted by the convolutional neural network prediction model. If the difference between the crack scale and the crack scale value predicted by the convolutional neural network prediction model is less than a threshold, then the crack scale is reasonable; otherwise, it is unreasonable.

[0073] Step 7, Output Results: Based on the spatial location and scale of the cracks obtained from the inversion, generate a three-dimensional crack distribution image.

[0074] Based on the 3D fracture distribution image, the fracture propagation direction is highly consistent with the predicted direction of the maximum principal stress in the geostress field. With a refresh cycle of approximately 10 seconds, near real-time updates are achieved, enabling dynamic identification and monitoring of fractures during the fracturing process.

[0075] The above technical features constitute various embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A method for actively monitoring fractures in pulse fracturing based on seismoelectric coupling, characterized in that, The method comprises the following steps: Electrode pair arrangement: according to the requirement of potential difference measuring point of fractured reservoir corresponding well section, the required electrode pairs are arranged on the inner wall of the fracturing nipple, the pulse nozzle is fixedly installed outside the fracturing nipple where the electrode pairs are arranged, and the pulse generator is fixedly installed on the fracturing nipple above the electrode pairs, and then the fracturing nipple is lowered into the fractured reservoir corresponding well section; Excitation and loading: the pulse generator outputs high-frequency pulse, the pulse is excited by the fracturing nipple, the excited pulse forms a stress wave field through the pulse nozzle, after the stress wave field excites the target crack to generate a pulsed jet, the target crack is induced to generate elastic wave vibration, and when the elastic wave passes through the fluid-containing pore medium, the elastic wave is converted into an electrical signal representing the crack attribute based on the seismic-electric coupling effect, and the electrical signal is propagated from the wellbore to the formation; Data acquisition: the electrode pairs arranged on the inner wall of the fracturing nipple form a multi-channel electrode array, and the electrical signal is synchronously collected through the multi-channel electrode array; Signal preprocessing: the collected electrical signal is subjected to multi-stage filtering, and at the same time, the wavelet threshold denoising is used to suppress the background noise overlapping with the effective signal frequency band and improve the signal-to-noise ratio; then, the normalized and phase-corrected preprocessed time-frequency data are obtained; Time-frequency analysis: the preprocessed time-frequency data are converted to the frequency domain to obtain a frequency spectrum, a characteristic peak of energy anomaly in the frequency spectrum is identified, the characteristic peak of energy anomaly corresponds to the crack initiation and seismic-electric coupling response, the characteristic peak of energy anomaly is a crack response characteristic peak, and the center frequency, amplitude and phase delay of the crack response characteristic peak are extracted; Crack inversion: a theoretical frequency spectrum response library of different crack scales under various geological conditions is used as a feature template library, the center frequency, amplitude and phase delay of the crack response characteristic peak are input into the feature template library for searching and matching to determine an optimal matching template, and the corresponding crack scale is obtained according to the optimal matching template; the accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack through inversion; Result output: a three-dimensional crack distribution image is generated according to the spatial position of the crack obtained through inversion and the crack scale.

2. The method of claim 1, wherein the method is a method of actively monitoring a fracture in a pulse fracturing based on a seismic coupling. In the electrode pair arrangement, the spacing between adjacent electrode pairs is 5m to 10m, and the number of electrode pairs arranged is 8 to 16 pairs, each electrode pair comprising an excitation electrode and a receiving electrode.

3. The method of claim 1 or 2, wherein, In the excitation and loading, when the pulse generator outputs high-frequency pulse, the pressure fluctuation range of the high-frequency pulse is 30MPa to 80MPa.

4. The method of claim 1 or 2, wherein, In the time-frequency analysis, for a complex stress wave field, on the basis of converting the preprocessed signal to the frequency domain, the continuous wavelet transform is used to obtain a time-frequency local energy concentration area to confirm the crack activity frequency band.

5. The method of claim 3, wherein the method is a method of actively monitoring a fracture in a pulse fracturing based on a seismic coupling. In the time-frequency analysis, for a complex stress wave field, on the basis of converting the preprocessed signal to the frequency domain, the continuous wavelet transform is used to obtain a time-frequency local energy concentration area to confirm the crack activity frequency band.

6. The method of claim 1 or 2 or 5, wherein, The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The accurate time difference Δt of the electrical signal of each potential difference measuring point is input, and a double-difference positioning method is used to obtain the spatial position of the crack, specifically including: The 7. The method of claim 1 or 2 or 5, wherein, In the fracture inversion, the fracture size obtained according to the optimal matching template is verified by the fracture size value predicted by the convolutional neural network prediction model, and if the difference between the fracture size and the fracture size value predicted by the convolutional neural network prediction model is less than a threshold value, the fracture size is reasonable, otherwise, it is not reasonable.

8. The method of claim 6, wherein the method is a method of actively monitoring a fracture in a pulse fracturing based on a seismic coupling. In the fracture inversion, the fracture size obtained according to the optimal matching template is verified by the fracture size value predicted by the convolutional neural network prediction model, and if the difference between the fracture size and the fracture size value predicted by the convolutional neural network prediction model is less than a threshold value, the fracture size is reasonable, otherwise, it is not reasonable.

9. The method of claim 7, wherein the method is a method of actively monitoring a fracture in a pulse fracturing based on a seismic coupling. The construction of the convolutional neural network prediction model comprises: The theoretical frequency spectrum response data under different geological conditions and the corresponding fracture size are taken as a training sample set; The convolutional neural network model is trained using the training sample set to obtain a convolutional neural network prediction model; When the convolutional neural network prediction model is used for prediction, the frequency spectrum response data is input, and the corresponding fracture size is output, wherein the frequency spectrum response data comprises the center frequency, amplitude and phase delay of the fracture response characteristic peak.

10. The method of claim 8, wherein the method is a method of actively monitoring a fracture in a pulse fracturing based on a seismic coupling. The construction of the convolutional neural network prediction model comprises: The theoretical frequency spectrum response data under different geological conditions and the corresponding fracture size are taken as a training sample set; The convolutional neural network model is trained using the training sample set to obtain a convolutional neural network prediction model; When the convolutional neural network prediction model is used for prediction, the frequency spectrum response data is input, and the corresponding fracture size is output, wherein the frequency spectrum response data comprises the center frequency, amplitude and phase delay of the fracture response characteristic peak.

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