A hydraulic fracturing monitoring method based on Krauklis wave resonance

Abstract

This application belongs to the technical field of oil and gas exploration, and specifically relates to a hydraulic fracturing monitoring method based on Krauklis wave resonance, aiming to solve the problem of poor monitoring effect during the hydraulic fracturing process. It includes: obtaining the vibration signal of the target well during the hydraulic fracturing process; extracting multiple sub-signal segments of the vibration signal according to time sequence, and performing time-frequency analysis on each sub-signal segment using the short-time Fourier transform to obtain the time-frequency spectrogram of the vibration signal; obtaining the resonance energy amplitude statistical chart of the vibration signal; when the resonance energy amplitude statistical chart meets the preset conditions, it is determined that the hydraulic fracturing is successful, where the preset conditions include: after the average value of the resonance energy amplitude within the set duration changes from the first amplitude to the second amplitude, it remains within the set range centered on the third amplitude, and the second amplitude is higher than the first amplitude and the third amplitude. This application can intuitively and quickly evaluate the hydraulic fracturing process and realize the dynamic monitoring of the hydraulic fracturing process.

Description

Background Art

[0002] With the continuous development of unconventional oil and gas exploration and development, hydraulic fracturing technology, as one of the core technologies for unconventional oil and gas exploration and development, has been widely applied in recent years. Hydraulic fracturing technology injects high-pressure fluid (fracturing fluid composed of water, mud, etc.) into the wellbore, causing the reservoir rock to fracture and form fractures. Subsequently, proppants are injected to keep the fractures open, thereby increasing the permeability of the formation, enhancing the connectivity of the reservoir, and increasing the production of a single well, so as to achieve the purpose of increasing oil and gas production.

[0003] During the hydraulic fracturing process, a Krauklis wave will be generated in the fractures filled with fluid (such as fracturing fluid). The Krauklis wave propagates back and forth in the fractures and exhibits unique resonance characteristics, which can be used to evaluate hydraulic fracturing fractures. However, the current research on the resonance of the Krauklis wave still remains in the stage of numerical simulation and physical simulation, and the observation method of recording the Krauklis wave inside the fractures is impossible to achieve in practical applications. Summary of the Invention

[0004] In order to solve the above problems in the prior art, that is, the problem of poor monitoring effect during the hydraulic fracturing process, the present application proposes a hydraulic fracturing monitoring method based on the resonance of the Krauklis wave, including:

[0005] Step S10, obtaining the vibration signal during the hydraulic fracturing process of the target well, wherein the vibration signal is converted from the Krauklis wave, and the Krauklis wave is generated during the hydraulic fracturing process;

[0006] Step S20, extracting multiple sub-signal segments of the vibration signal according to time sequence, and performing time-frequency analysis on each sub-signal segment by using the short-time Fourier transform to obtain the time-frequency spectrogram of the vibration signal;

[0007] Step S30, obtaining the resonance energy amplitude statistical chart of the vibration signal by extracting the spectral energy amplitude of the time-frequency spectrogram, wherein the resonance energy amplitude statistical chart is used to characterize the intensity of the resonance energy at different time periods;

[0008] Step S40, determining that the hydraulic fracturing is successful when the resonance energy amplitude statistical chart meets the preset conditions, wherein the preset conditions include: after the average value of the resonance energy amplitude within the set time period changes from the first amplitude to the second amplitude, it remains within the set range centered on the third amplitude, and the second amplitude is higher than the first amplitude and the third amplitude.

[0009] As a possible implementation manner, extracting multiple sub-signal segments of the vibration signal according to time sequence, and performing time-frequency analysis on each sub-signal segment by using the short-time Fourier transform, includes:

[0010] Perform windowed short-time Fourier transform on the vibration signal, including:

[0011]

[0012] Among them, represents the transformation result of the windowed short-time Fourier transform of the vibration signal, represents the window function, represents the vibration signal, t represents the current moment, represents the conjugate of, represents that the Fourier window function is shifted at time , e represents the natural constant, j represents the imaginary unit, represents the angular frequency;

[0013] Convert the windowed short-time Fourier transform into the discrete form of the windowed short-time Fourier transform, and set the discrete signal of the vibration signal as , including:

[0014]

[0015] Among them, represents the discrete form of, N represents the number of points of the Fourier transform, represents the window function of the discrete signal, n represents the number of time sampling points, represents the Fourier window function, centered at the time point m , k represents the discrete frequency index, represents the frequency.

[0016] As a possible implementation, by extracting the spectral energy amplitude of the time-frequency spectrogram, obtain the statistical chart of the resonance energy amplitude of the vibration signal, including:

[0017] Extract the spectral energy amplitude at different moments in the time-frequency spectrogram;

[0018] Within the same time period, superimpose the spectral energy amplitudes within the preset frequency band range to generate the resonance energy amplitude;

[0019] Generate a statistical chart of the resonance energy amplitude based on the resonance energy amplitude and the corresponding time period.

[0020] As a possible implementation, when the statistical chart of the resonance energy amplitude meets the preset conditions, determine that the hydraulic fracturing is successful, including:

[0021] Input the statistical chart of resonance energy amplitude into the trained neural network for hydraulic fracturing monitoring, and output the monitoring results of hydraulic fracturing. Among them, the training data of the neural network for hydraulic fracturing monitoring are the statistical charts of historical resonance energy amplitudes of multiple historical vibration signals during the historical fracturing process.

[0022] As a possible implementation manner, the training process of the neural network includes:

[0023] Obtain the historical vibration signals generated during the historical hydraulic fracturing process;

[0024] Extract the spectral energy amplitude from the historical time-frequency spectrogram of any historical vibration signal to obtain the statistical chart of historical resonance energy amplitude of any historical vibration signal;

[0025] Use multiple statistical charts of historical resonance energy amplitudes as the training set, and use the hydraulic fracturing evaluation results of the statistical charts of historical resonance energy amplitudes as labels to train the residual neural network to obtain the trained neural network for hydraulic fracturing monitoring. Among them, the loss function of the residual neural network is the cross-entropy loss function.

[0026] As a possible implementation manner, before obtaining the historical vibration signals generated during the historical hydraulic fracturing process, it includes:

[0027] Perform staged fracturing operations on the target well, and record the start time and end time of each staged fracturing operation.

[0028] As a possible implementation manner, after obtaining the historical vibration signals generated during the historical hydraulic fracturing process, it includes:

[0029] Align the start time and end time of the historical vibration signal acquisition with the start time and end time of the staged fracturing operation respectively;

[0030] Perform filtering and denoising on the historical vibration signals.

[0031] As a possible implementation manner, the method further includes:

[0032] In response to the end of the hydraulic fracturing stage, when the resonance energy amplitude exceeds the threshold, it is determined that the hydraulic fracturing is successful, where the threshold is determined by statistically analyzing the background noise of the time-frequency spectrogram.

[0033] As a possible implementation manner, when the vibration signal is a tube wave signal, after obtaining the vibration signal of the target well during the hydraulic fracturing process, it includes:

[0034] Decode the vibration signal from the waveform signal and store it as a SEGY format file.

[0035] Advantages of this application:

[0036] (1) Obtain the vibration signals during the hydraulic fracturing process of the target well, which can collect the relevant signals of the Krauklis waves generated during the hydraulic fracturing process, avoiding the problem that the direct collection of Krauklis waves is affected by underground media; extract multiple sub-signal segments of the vibration signals according to time sequence, and perform time-frequency analysis on each sub-signal segment using the short-time Fourier transform to obtain the time-frequency spectrogram of the vibration signals. By extracting the sub-signal segments, the target signal segments to be analyzed can be analyzed separately, and the spectral characteristics at different times can be captured through the short-time Fourier transform; by extracting the spectral energy amplitude of the time-frequency spectrogram, obtain the resonance energy amplitude statistical chart of the vibration signals. Among them, the resonance energy amplitude statistical chart is used to characterize the intensity of the resonance energy at different time periods, which can intuitively reflect the strength of the resonance energy and is convenient for signal analysis; when the resonance energy amplitude statistical chart meets the preset conditions, it is determined that the hydraulic fracturing is successful. Among them, the preset conditions include: after the average value of the resonance energy amplitude within the set duration changes from the first amplitude to the second amplitude, it remains within the set range centered on the third amplitude, and the second amplitude is higher than the first amplitude and the third amplitude. By statistically analyzing the change of the resonance energy, the hydraulic fracturing process can be intuitively and quickly evaluated based on the resonance characteristics of the Krauklis waves, realizing the dynamic monitoring of the hydraulic fracturing process, improving the monitoring effect of the hydraulic fracturing process, and providing a guiding technology for the hydraulic fracturing operation. Description of the Drawings

[0037] Other features, objects, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0038] Figure 1 is a schematic flow chart of a hydraulic fracturing monitoring method based on the resonance of Krauklis waves provided by an embodiment of the present application;

[0039] Figure 2 is a schematic diagram of a "wellbore - fracture" model provided by an embodiment of the present application;

[0040] Figure 3 is a visualization example diagram of a numerical simulation trace gather provided by an embodiment of the present application;

[0041] Figure 4 is a single-trace waveform data example diagram provided by an embodiment of the present application;

[0042] Figure 5 is a windowed signal data example diagram provided by an embodiment of the present application;

[0043] Figure 6 is a time-frequency spectrogram before hydraulic fracturing without fractures provided by an embodiment of the present application;

[0044] Figure 7 It is the time-frequency spectrogram in the case of a single fracture after hydraulic fracturing provided by an embodiment of the present application;

[0045] Figure 8 It is the time-frequency spectrogram in the case of double fractures after hydraulic fracturing provided by an embodiment of the present application;

[0046] Figure 9 It is the time-frequency spectrogram in the case of triple fractures after hydraulic fracturing provided by an embodiment of the present application;

[0047] Figure 10 It is a well section design model diagram provided by an embodiment of the present application;

[0048] Figure 11 It is an example diagram of a time-frequency spectrogram provided by an embodiment of the present application;

[0049] Figure 12 It is provided by an embodiment of the present application Figure 11 The corresponding resonance energy amplitude statistical chart;

[0050] Figure 13 It is the system block diagram of a hydraulic fracturing monitoring system based on Krauklis wave resonance provided by an embodiment of the present application. Detailed implementation manners

[0051] The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. Additionally, it should be noted that for the convenience of description, only the parts related to the relevant invention are shown in the drawings.

[0052] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0053] To more clearly illustrate a hydraulic fracturing monitoring method based on Krauklis wave resonance of the present application, the following combines Figure 1 Each step in the embodiments of the present application will be described in detail.

[0054] A hydraulic fracturing monitoring method based on Krauklis wave resonance in the first embodiment of the present application includes steps S10 - S40, and each step is described in detail as follows:

[0055] Step S10, obtain the vibration signal during the hydraulic fracturing process of the target well, where the vibration signal is converted from Krauklis waves generated during the hydraulic fracturing process.

[0056] Optionally, the target well is an oil and gas well for hydraulic fracturing. By performing hydraulic fracturing on the target well, vibration signals are generated and collected by signal acquisition equipment.

[0057] In the embodiments of the present application, the staged fracturing technology is adopted to perform the hydraulic fracturing operation, and a horizontal well is selected as the target well for hydraulic fracturing. In other embodiments, other oil and gas wells can also be selected for hydraulic fracturing, such as extended reach wells, multilateral wells, etc.

[0058] In the embodiments of the present application, the ball-drop sliding sleeve staged fracturing technology is adopted to achieve staged fracturing, and the mechanical operation of downhole tools is used to achieve precise staged fracturing of different formations, thereby significantly improving the production efficiency of oil and gas wells.

[0059] It should be noted that the main stages of staged fracturing are perforation, fracturing fluid injection, fracture generation by fracturing, and proppant injection. The ball-drop sliding sleeve staged fracturing is achieved by dropping balls of different diameters. First-stage fracturing: It is carried out inside the pressure sliding sleeve at the end of the fracturing pipe. The pressure is increased to open the sliding sleeve for perforation, the fracturing fluid is injected into the fracturing formation, the proppant is injected, the sliding sleeve is closed, and the proppant is flushed to complete the operation. Staged fracturing: Balls of different diameters are dropped to separate each fracturing stage. The pressure is increased to open the ball sliding sleeve and perforate, the fracturing fluid is injected to form fractures, the proppant is injected, and then flushed. The operation is repeated to achieve staged fracturing. By simply dropping balls, the fracturing stages are separated, realizing high-efficiency production of fracturing construction.

[0060] Optionally, a signal acquisition device is installed at the wellhead of the target well to collect vibration signals during the hydraulic fracturing process of the target well. For example, a single-component geophone or a three-component geophone.

[0061] In the embodiments of the present application, by installing a signal acquisition device at the wellhead and only receiving vibration signals at the wellhead, the signal reception cost can be reduced.

[0062] It should be noted that only the vertical component data needs to be analyzed in the present application. If a three-component geophone is selected as the data acquisition device, only the vertical component data needs to be analyzed during data analysis.

[0063] Optionally, the collected vibration signal is a waveform signal, mainly dominated by pipe waves.

[0064] It should be noted that during the process of hydraulic fracturing, due to the action of the fracturing pressure, Krauklis waves will be generated in the cracks. When the Krauklis waves are converted into pipe waves at the connection between the cracks and the well, that is, when the propagation medium of the Krauklis waves changes from the cracks to the wellbore wall, vibration signals can be generated. The vibration signals carry the resonance information of the Krauklis waves, propagate along the wellbore wall of the target well, and are transmitted to the geophone at the wellhead.

[0065] Receiving signals through the wellbore can obtain vibration signals more directly. Because if the signals are directly transmitted to the ground through the underground medium, the underground medium will have a greater impact on the waves, resulting in the inability to identify Krauklis waves.

[0066] It should be noted that during the staged fracturing process, the geophones at the wellhead need to record the waveform data of each fracturing stage throughout the process.

[0067] In the embodiment of the present application, before collecting vibration signals, it is necessary to deploy the signal collection instrument. In order to better receive the wellhead data, geophones are selected to be deployed at the wellhead. During the deployment process, the receiving end (bottom end) of the instrument is in close contact with the pipe wall to reduce signal loss. The instrument itself needs to be fixed with tape or rope, and it is not allowed for the instrument to shake during the measurement. At the same time, considering the environmental factors around the wellhead, such as environmental noise, temperature, humidity, and potential electromagnetic interference, corresponding measures need to be taken to protect the geophones to ensure the accuracy of the data. For environmental noise, it is necessary to avoid the movement of personnel and the driving of vehicles near the wellhead during the fracturing monitoring process. For temperature, the working temperature range of commonly used geophones on the market (taking the SmartSolo seismograph as an example) is -40°C to 70°C, and the specific temperature range depends on the instrument model. It is necessary to ensure that the measurement is carried out within the specified temperature range during the monitoring process. For humidity, since the instrument wires and interfaces are exposed, they cannot come into contact with liquids such as water to avoid damaging the instrument. Therefore, it is necessary to avoid measuring during rainy days. For potential electromagnetic interference, the detection construction location needs to avoid high-voltage transmission lines, mains power networks, telluric currents, solar panels, etc. to avoid electromagnetic crosstalk.

[0068] Furthermore, to ensure the integrity and reliability of the data during the experiment, it is necessary to prepare spare geophones. The specific quantity is determined according to the actual monitoring time and the instrument power. The quantity should ensure that there are more than 3 spare geophones available for replacement during each monitoring. So that during the experiment, if a certain geophone fails or runs out of power (battery level < 5%), it can be quickly replaced to ensure the continuity of the experiment. All geophones need to be clock-synchronized so that when recording data, they can be accurately corresponding to the same time point. For example, clock synchronization can be carried out through GPS.

[0069] Furthermore, it is also necessary to conduct performance tests on the geophones to ensure the consistency of the instruments when recording the same seismic event: Place the geophones at equal intervals along a 5m-radius ring. Strike the ground with a heavy hammer in the center of the ring. Export the records of all geophones, check the waveform amplitude and sensitivity response of each geophone, ensure that the amplitude changes are consistent after normalization, and at the same time, the time difference between the first recorded seismic events of adjacent instruments is less than 3ms. If it exceeds this range, the accuracy is unqualified.

[0070] Furthermore, it is also necessary to store the data. To ensure the integrity of the data, the embodiments of the present application use a large-capacity data storage device and perform regular backups to prevent data loss.

[0071] Step S20: Extract multiple sub-signal segments of the vibration signal according to the time sequence, and perform time-frequency analysis on each sub-signal segment by using the short-time Fourier transform to obtain the time-frequency spectrogram of the vibration signal.

[0072] Optionally, by setting a window function to multiply with the vibration signal, individual sub-signal segments can be extracted for efficient analysis of the effective data. Furthermore, by translating this window function on the time axis, multiple sub-signal segments can be extracted according to the time sequence.

[0073] Optionally, perform short-time Fourier transform and discretization processing on each sub-signal segment to achieve time-frequency analysis of any sub-signal segment.

[0074] In the embodiments of the present application, a window function is added on the basis of the short-time Fourier transform. The time-frequency analysis of the vibration signal is performed by using the windowed short-time Fourier transform, which can be implemented by the following formula:

[0075]

[0076] Among them, represents the transformation result of the windowed short-time Fourier transform of the vibration signal, represents the window function, represents the vibration signal, t represents the current moment, represents the conjugate of, represents that the Fourier window function is shifted at time , e represents the natural constant, j represents the imaginary unit, represents the angular frequency.

[0077] In the embodiments of the present application, the window function is a Hanning window function. In other embodiments, it can also be other window functions such as a Gaussian window function, which can further narrow the window extracted by the short-time Fourier transform to obtain a more accurate and interference-free sub-signal segment. By performing the short-time Fourier transform on the vibration signal within the local area covered by the Hanning window function and continuously translating the Hanning window on the time axis, the spectral characteristics of the signal at different moments can be captured, thereby realizing the time-frequency analysis of the vibration signal.

[0078] Since the vibration signal is composed of signals at continuous time sampling points and is essentially a discrete signal, it is necessary to convert the windowed short-time Fourier transform into a discrete form of the windowed short-time Fourier transform. Set the vibration signal The discrete signal is , then the discrete form of the windowed short-time Fourier transform is:

[0079]

[0080] Wherein, represents the discrete form of N represents the number of points of the Fourier transform, represents the window function of the discrete signal, n represents the number of time sampling points, represents the Fourier window function, centered at the time point m , k represents the discrete frequency index, represents the frequency.

[0081] In the embodiment of the present application, taking the waveform data of numerical simulation as an example, the above time-frequency analysis is carried out to show the process and results of data processing. Among them, the numerical simulation method adopts the elastic wave staggered grid finite difference method. According to the actual fracturing mechanism, a "wellbore-fracture" model is set up, as Figure 2 shown. The model size is 20m×20m. It is assumed that the underground medium is uniform, the longitudinal wave velocity is set to 2300m / s, the transverse wave velocity is set to 1550m / s, the density is set to 2500kg / m3, the pipeline and the fracture are filled with fluid. It is assumed that the filled fluid is water, the longitudinal wave velocity of water is 1490m / s, the transverse wave velocity is 0m / s, and the density is 1000kg / m3. As the hydraulic fracturing process progresses, more fractures may be generated. Therefore, the situation where the number of fractures gradually increases with the fracturing process is further simulated in the model.

[0082] During the simulation process of the embodiment of the present application, a water hammer source is excited multiple times at the wellhead. The source function adopts the Ricker wavelet function commonly used in elastic wave numerical simulation. The source function is:

[0083]

[0084] Wherein, s ( t ) represents the source function, f 0 represents the source dominant frequency, t 0 represents the initial excitation time, π represents the pi, and e represents the natural constant.

[0085] Obtain the shot gather records received from the surface to the depth in the entire well, and obtain the numerical simulation gather as shown in Figure 3 . The arrow in the figure represents the response generated when the Krauklis wave generated in the fracture reaches the connection between the fracture and the wellbore.

[0086] In the embodiment of the present application, the main frequency of numerical simulation is set to 1000 Hz.

[0087] It should be noted that the shot gather record refers to the seismic wave data record received by multiple geophones after being excited by a single seismic source.

[0088] Further, select Figure 3 the first waveform data in Figure 4 as the simulated single-channel waveform data collected at the wellhead. As Figure 3 shown, select the waveform record with a simulated duration of 0.2 s for windowed short-time Fourier transform for time-frequency analysis. Based on Figure 5 the record, it can be seen that the response of the Krauklis wave will only appear after 0.04 s. Before 0.04 s, the spectra are all the spectra of the seismic source or its reflection waveforms at the fracture. Therefore, only analyze the data from 0.04 s to 0.2 s. The time range of the window function is from 0.04 s to 0.2 s. The windowed signal is as

[0089] shown. Figures 6 to 9 As the number of fractures increases synchronously with the progress of hydraulic fracturing, in the embodiment of the present application, the time-frequency response characteristics under different numbers of fractures are analyzed, and the time-frequency spectrogram as Figure 6 shown is obtained. Among them, Figure 7 is the time-frequency spectrogram without fractures before hydraulic fracturing, Figure 8 is the time-frequency spectrogram under the condition of a single fracture after hydraulic fracturing, Figure 9 is the time-frequency spectrogram under the condition of double fractures after hydraulic fracturing,

[0090] is the time-frequency spectrogram under the condition of triple fractures after hydraulic fracturing.

[0091] In step S30, by extracting the spectral energy amplitude of the time-frequency spectrogram, a resonance energy amplitude statistical chart of the vibration signal is obtained, where the resonance energy amplitude statistical chart is used to characterize the intensity of resonance energy at different time periods.

[0092] Optionally, the time-frequency spectrogram shows the energy distribution of the vibration signal at different times and frequencies. The intensity of the resonance energy is obtained by extracting the energy amplitude, and a statistical chart of the resonance energy amplitude is generated.

[0093] As a possible implementation, the spectral energy amplitudes at different times are extracted from the time-frequency spectrogram; within the same time period, the spectral energy amplitudes within the preset frequency band range are superimposed to generate the resonance energy amplitude; based on the resonance energy amplitude and the corresponding time period, a statistical chart of the resonance energy amplitude is generated.

[0094] Among them, the extraction of the spectral energy amplitude can be: calculating the amplitude spectrum of the windowed short-time Fourier result, and calculating the energy spectrum based on the amplitude spectrum, that is, calculating the square of the amplitude as the energy. The specific calculation formula is prior art and will not be elaborated here.

[0095] Furthermore, within the same time period, the spectral energy amplitudes within the preset frequency band range are superimposed to generate the resonance energy amplitude.

[0096] As a possible implementation, the resonance energy amplitude can be determined by the following formula:

[0097]

[0098] Among them, represents the resonance energy amplitude when the time parameter is , f 1 represents the lowest frequency of the preset frequency band range, f 2 represents the highest frequency of the preset frequency band range, represents the spectral energy amplitude when the time parameter is .

[0099] As an example, the preset frequency band range ( f 1 , f 2 ) can be (0, 3000 Hz). In other embodiments, the preset frequency band range can be determined according to the actual hydraulic fracturing situation.

[0100] Furthermore, with time and the resonance energy amplitude as the coordinate axes, a statistical chart of the resonance energy amplitude is constructed.

[0101] Step S40, when the statistical chart of the resonance energy amplitude meets the preset conditions, it is determined that the hydraulic fracturing is successful. Among them, the preset conditions include: after the average value of the resonance energy amplitude within the set duration changes from the first amplitude to the second amplitude, it remains within the set range centered on the third amplitude, and the second amplitude is higher than the first amplitude and the third amplitude.

[0102] It should be noted that during the staged fracturing process, in the perforation stage, natural fractures dominate in the formation and the effective fracture energy is relatively low; in the fracturing stage, as the pressure increases, the scale of the induced fractures in the formation gradually expands and the resonance energy increases; as the injection mud rate stabilizes, the fracturing pressure decreases, proppants are gradually injected, and the relative scale of the effective fractures tends to stabilize, and the resonance energy tends to stabilize. Therefore, the curve change trend of the resonance energy first increases, then decreases, and finally tends to be stable.

[0103] Optionally, evaluate the change trend of the resonance energy amplitude statistical chart. If it conforms to the change trend of the staged fracturing process, it is determined that the hydraulic fracturing is successful; otherwise, the hydraulic fracturing fails and the hydraulic fracturing operation needs to be carried out again.

[0104] As a possible implementation manner, the change of the resonance energy amplitude statistical chart is reflected by monitoring the amplitude average value of the resonance energy amplitude statistical chart within a preset duration, that is, the amplitude average value of the resonance energy within the set duration is statistically calculated. After the energy amplitude average value changes from the first amplitude to the second amplitude and then remains within the set range centered on the third amplitude, it indicates that the resonance energy change process conforms to the change trend of the staged fracturing, where the second amplitude is higher than the first amplitude and the third amplitude.

[0105] It can be understood that during the normal staged fracturing process, the change trend of the resonance energy is first to increase, then to decrease, and finally to tend to be stable. The change of the resonance energy is reflected by statistically calculating the energy amplitude average value. If the energy amplitude average value increases from the first amplitude to the second amplitude, it indicates that the resonance energy is increasing. Until the energy amplitude average value decreases from the second amplitude to the third amplitude and remains within a certain fluctuation range of the third amplitude, it indicates that the resonance energy tends to be stable, and this change conforms to the change trend of the resonance energy.

[0106] Among them, the set range is (P3 - P0, P3 + P0), P3 is the third amplitude, and P0 is the allowable fluctuation value. The energy amplitude average value fluctuates within the set range, indicating that the resonance energy has tended to be stable.

[0107] As a possible implementation manner, the resonance energy amplitude statistical chart is input into the trained hydraulic fracturing monitoring neural network, and the monitoring result of the hydraulic fracturing is output. Among them, the training data of the hydraulic fracturing monitoring neural network are the historical resonance energy amplitude statistical charts of multiple historical vibration signals during the historical fracturing process.

[0108] In the embodiments of the present application, the monitoring results of hydraulic fracturing are either successful or failed. According to the model prediction results, real-time decision support is provided for on-site fracturing operations. For example, if the prediction is "successful", it can be considered that the fracturing of this fracturing stage is completed, and the fracturing operation of the subsequent fracturing stage can be carried out; if the prediction is "failed", the fracturing parameters are adjusted in a timely manner (such as increasing the injection volume of fracturing fluid, adjusting the fracturing pressure, etc.), and fracturing is carried out again to improve the fracturing effect.

[0109] Among them, the training process of the neural network includes:

[0110] Obtain the historical vibration signals generated during the historical hydraulic fracturing process; by extracting the spectral energy amplitude of the historical time-frequency spectrogram of any historical vibration signal, obtain the historical resonance energy amplitude statistical chart of any historical vibration signal; use multiple historical resonance energy amplitude statistical charts as the training set, and use the hydraulic fracturing evaluation results of the historical resonance energy amplitude statistical chart as the labels to train the residual neural network, and obtain the trained hydraulic fracturing monitoring neural network. Among them, the loss function of the residual neural network is the cross-entropy loss function.

[0111] Optionally, the historical vibration signals are collected by geophones during the historical hydraulic fracturing process, and also include the resonance information of Krauklis waves. The number of historical vibration signals is multiple.

[0112] In the embodiments of the present application, simulated vibration signals can also be obtained by simulating the "wellbore-fracture" model as historical vibration signals to expand the data volume.

[0113] It should be noted that the specific steps for obtaining the historical resonance energy amplitude statistical chart based on the historical vibration signals refer to the process of steps S10 to S30, which will not be elaborated here.

[0114] In the embodiments of the present application, the residual neural network selects the ResNet-18 network, which is implemented using the PyTorch architecture, and replaces the last fully connected layer to adapt to the classification task.

[0115] Furthermore, data annotation is performed on any historical resonance energy amplitude statistical chart, and the labels are divided into two categories: "successful" and "failed". For example, if the oil and gas production increases significantly after fracturing, and the energy statistical curve meets the expectations (such as the average energy amplitude increases from the first amplitude to the second amplitude, and then decreases from the second amplitude to the third amplitude and remains within a certain fluctuation range of the third amplitude), it is labeled as "successful"; otherwise, if the production after fracturing fails to meet the expectations and the energy curve is abnormal (such as the energy does not increase significantly or fluctuates greatly and is unstable), it is labeled as "failed".

[0116] Optionally, using multiple historical resonance energy amplitude statistical graphs as the training set and the data annotation results of the historical resonance energy amplitude statistical graphs as labels, with the label data divided into two categories: "success" and "failure", use the cross-entropy loss function and the Adam optimizer to train the model until the loss value of the loss function is less than 0.01 to obtain a trained hydraulic fracturing monitoring neural network.

[0117] Optionally, before obtaining the historical vibration signals generated during the historical hydraulic fracturing process, perform staged fracturing operations on the target well and record the start time and end time of each staged fracturing operation. After obtaining the historical vibration signals generated during the historical hydraulic fracturing process, align the start time and end time of the collected historical vibration signals with the start time and end time of the staged fracturing operations respectively.

[0118] Optionally, filter and denoise the historical vibration signals.

[0119] As another possible implementation, in response to the end of the hydraulic fracturing stage, when the resonance energy amplitude exceeds the threshold, determine that the hydraulic fracturing is successful, where the threshold is determined by statistically analyzing the background noise of the time-frequency spectrogram.

[0120] In the embodiments of the present application, the threshold is 5 times the average value of the background noise amplitude of the time-frequency spectrogram. When the resonance energy amplitude exceeds the threshold, it indicates that a fracture propagation signal with sufficient intensity is generated during the fracturing process, indicating good fracturing effect. Otherwise, it means that the fracturing is incomplete and subsequent re-fracturing needs to be considered.

[0121] It should be noted that the method of the present application has also been applied in actual fracturing scenarios and achieved good results. The experimental site is a fracturing well in a desert and gobi area in Xinjiang. The depth of the fracturing well is 1300m - 1400m, the horizontal length is 841.4m, and the fracturing process is recorded sequentially in stages. There are a total of 26 fracturing stages, and the average length of each fracturing stage is 38.4m. The well section design model is as Figure 10 shown.

[0122] Select the data of the 15th fracturing stage as an example for analysis. After preprocessing the vibration signals, input them into the windowed short-time Fourier transform program for time-frequency analysis, output the time-frequency spectrogram, perform resonance energy statistics, and obtain the time-frequency spectrogram example diagram as shown in Figure 11 and the resonance energy amplitude statistical graph as shown in Figure 12 . Input the resonance energy amplitude statistical graph as shown in Figure 12 into the trained hydraulic fracturing monitoring neural network, and the output prediction result is "success".

[0123] It should be noted that from Figure 11It can be seen that the energy of the A-B section is relatively low, which is very likely the perforation stage. The energy of the B-C section gradually increases, which is very likely the fracturing stage. The energy of the C-D section tends to be stable, which is very likely the stage of proppant injection.

[0124] Furthermore, a visualization interface can be developed to display in real time the resonance energy amplitude statistical chart, the model prediction results, and other key parameters during the fracturing process (such as fracturing pressure, proppant injection volume, etc.), facilitating on-site engineers to intuitively understand the hydraulic fracturing process.

[0125] Although the various steps are described in the above order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are all within the protection scope of this application.

[0126] The embodiment of this application acquires the vibration signal during the hydraulic fracturing process of the target well, can collect the relevant signals of the Krauklis wave generated during the hydraulic fracturing process, and avoids the problem that the direct acquisition of the Krauklis wave is affected by underground media; extracts multiple sub-signal segments of the vibration signal according to time sequence, and performs time-frequency analysis on each sub-signal segment using the short-time Fourier transform to obtain the time-frequency spectrogram of the vibration signal. By extracting the sub-signal segments, the target signal segment to be analyzed is analyzed separately, and the spectral characteristics at different moments are captured through the short-time Fourier transform; by extracting the spectral energy amplitude of the time-frequency spectrogram, the resonance energy amplitude statistical chart of the vibration signal is obtained. Among them, the resonance energy amplitude statistical chart is used to characterize the intensity of the resonance energy in different time periods, can intuitively reflect the strength of the resonance energy, and is convenient for signal analysis; when the resonance energy amplitude statistical chart meets the preset conditions, it is determined that the hydraulic fracturing is successful. Among them, the preset conditions include: after the average value of the resonance energy amplitude within the set duration changes from the first amplitude to the second amplitude, it remains within the set range centered on the third amplitude, and the second amplitude is higher than the first amplitude and the third amplitude. By statistically analyzing the change of the resonance energy, it is possible to intuitively and quickly evaluate the hydraulic fracturing process based on the resonance characteristics of the Krauklis wave, realize the dynamic monitoring of the hydraulic fracturing process, improve the monitoring effect of the hydraulic fracturing process, and provide a guiding technology for the hydraulic fracturing operation.

[0127] Please refer to Figure 13 , a hydraulic fracturing monitoring system based on the resonance of Krauklis waves in the second embodiment of this application includes: a vibration signal acquisition module 100, a time-frequency analysis module 200, a resonance energy amplitude statistical chart acquisition module 300, and a hydraulic fracturing monitoring module 400.

[0128] The vibration signal acquisition module 100 is configured to acquire the vibration signal during the hydraulic fracturing of the target well, wherein the vibration signal is converted from the Krauklis wave, and the Krauklis wave is generated during the hydraulic fracturing process;

[0129] The time-frequency analysis module 200 is configured to extract multiple sub-signal segments of the vibration signal according to the time sequence, and perform time-frequency analysis on each sub-signal segment by using the short-time Fourier transform to obtain the time-frequency spectrogram of the vibration signal;

[0130] The resonance energy amplitude statistical chart acquisition module 300 is configured to obtain the resonance energy amplitude statistical chart of the vibration signal by extracting the spectral energy amplitude of the time-frequency spectrogram, wherein the resonance energy amplitude statistical chart is used to characterize the intensity of the resonance energy at different time periods;

[0131] The hydraulic fracturing monitoring module 400 is configured to determine that the hydraulic fracturing is successful when the resonance energy amplitude statistical chart meets the preset conditions, wherein the preset conditions include: after the average value of the resonance energy amplitude within the set duration changes from the first amplitude to the second amplitude, it remains within the set range centered on the third amplitude, and the second amplitude is higher than the first amplitude and the third amplitude.

[0132] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related descriptions of the above-described system can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0133] It should be noted that the above-described hydraulic fracturing monitoring system based on Krauklis wave resonance provided by the above embodiments is only illustrated by dividing the above functional modules. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the modules or steps in the embodiments of the present application can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present application are only for distinguishing each module or step, and are not regarded as an improper limitation of the present application.

[0134] An electronic device according to the third embodiment of the present application includes:

[0135] At least one processor; and

[0136] A memory communicatively connected to at least one of the processors; wherein,

[0137] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-described hydraulic fracturing monitoring method based on Krauklis wave resonance.

[0138] A computer-readable storage medium according to the fourth embodiment of the present application, wherein the computer-readable storage medium stores computer instructions for being executed by a computer to implement the above-mentioned hydraulic fracturing monitoring method based on Krauklis wave resonance.

[0139] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes and related descriptions of the above-described electronic devices and computer-readable storage media can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0140] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in the form of electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0141] Computer program code for performing the operations of the present application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages - such as Java, Smalltalk, C++, and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0143] The terms "first", "second", etc. are used to distinguish similar objects and not to describe or indicate a particular order or sequence.

[0144] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or apparatus / device.

[0145] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principle of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present application.

Claims

1. A hydraulic fracturing monitoring method based on Krauklis wave resonance, characterized in that It includes the following steps: Step S10, obtaining the vibration signal during the hydraulic fracturing of the target well, where the vibration signal is obtained by converting the Krauklis wave, and the Krauklis wave is generated during the hydraulic fracturing process; Step S20, extracting multiple sub-signal segments of the vibration signal in sequence and performing time-frequency analysis on each sub-signal segment using the short-time Fourier transform to obtain the time-frequency spectrogram of the vibration signal; Step S30, obtaining the resonance energy amplitude statistical chart of the vibration signal by extracting the spectral energy amplitude of the time-frequency spectrogram, where the resonance energy amplitude statistical chart is used to characterize the intensity of the resonance energy at different time periods; Step S40, determining that the hydraulic fracturing is successful when the resonance energy amplitude statistical chart meets the preset conditions, where the preset conditions include: after the average value of the resonance energy amplitude within the set time period changes from the first amplitude to the second amplitude, it remains within the set range centered on the third amplitude, and the second amplitude is higher than the first amplitude and the third amplitude; Among them, the obtaining the resonance energy amplitude statistical chart of the vibration signal by extracting the spectral energy amplitude of the time-frequency spectrogram includes: extracting the spectral energy amplitude at different moments in the time-frequency spectrogram; within the same time period, superimposing the spectral energy amplitudes within the preset frequency band range to generate the resonance energy amplitude; generating the resonance energy amplitude statistical chart based on the resonance energy amplitude and the corresponding time period.

2. The hydraulic fracturing monitoring method based on Krauklis wave resonance according to claim 1, wherein The extracting multiple sub-signal segments of the vibration signal in sequence and performing time-frequency analysis on each sub-signal segment using the short-time Fourier transform includes: performing windowed short-time Fourier transform on the vibration signal, including: ； Among them, represents the transformation result of performing windowed short-time Fourier transform on the vibration signal, represents the window function, represents the vibration signal, t represents the current moment, represents the conjugate of represents that the Fourier window function is translated at time t, e represents the natural constant, j represents the imaginary unit, represents the angular frequency; Convert the windowed short-time Fourier transform into its discrete form, and set the discrete signal of the vibration signal to be , including: ； wherein, represents in discrete form, N represents the number of points of the Fourier transform, represents the window function of the discrete signal, n represents the number of time sampling points, represents the Fourier window function, centered at the time point m, k represents the discrete frequency index, represents the frequency.

3. The hydraulic fracturing monitoring method based on Krauklis wave resonance according to claim 1, characterized in that, The determining that the hydraulic fracturing is successful when the resonance energy amplitude statistical chart meets the preset conditions includes: inputting the resonance energy amplitude statistical chart into the trained hydraulic fracturing monitoring neural network and outputting the monitoring result of the hydraulic fracturing, where the training data of the hydraulic fracturing monitoring neural network is the historical resonance energy amplitude statistical charts of multiple historical vibration signals during the historical fracturing process.

4. The hydraulic fracturing monitoring method based on Krauklis wave resonance according to claim 3, wherein The training process of the neural network includes: obtaining the historical vibration signals generated during the historical hydraulic fracturing process; obtaining the historical resonance energy amplitude statistical chart of any historical vibration signal by extracting the spectral energy amplitude of the historical time-frequency spectrogram of the historical vibration signal; using multiple historical resonance energy amplitude statistical charts as the training set and using the hydraulic fracturing evaluation result of the historical resonance energy amplitude statistical chart as the label to train the residual neural network to obtain the trained hydraulic fracturing monitoring neural network, where the loss function of the residual neural network is the cross-entropy loss function.

5. A hydraulic fracturing monitoring method based on Krauklis wave resonance according to claim 4, characterized in that, Before the obtaining the historical vibration signals generated during the historical hydraulic fracturing process, it includes: performing segmented fracturing operations on the target well and recording the start time and end time of each segmented fracturing operation.

6. The hydraulic fracturing monitoring method based on Krauklis wave resonance according to claim 5, wherein After the obtaining the historical vibration signals generated during the historical hydraulic fracturing process, it includes: aligning the start time and end time of the collection of the historical vibration signals with the start time and end time of the segmented fracturing operations respectively; Filter and denoise the historical vibration signal.

7. A hydraulic fracturing monitoring method based on Krauklis wave resonance according to claim 1, characterized in that The method further includes: In response to the end of the hydraulic fracturing stage, when the resonance energy amplitude exceeds a threshold, determine that the hydraulic fracturing is successful, where the threshold is determined by statistically analyzing the background noise of the time-frequency spectrogram.

8. A hydraulic fracturing monitoring method based on Krauklis wave resonance according to claim 1, characterized in that The vibration signal is a pipe wave signal. After obtaining the vibration signal of the target well during hydraulic fracturing, it includes: Decode the vibration signal from a waveform signal and store it as a SEGY format file.

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