A method of seismic exploration in a well

Abstract

This invention relates to a wellbore seismic wave detection method, belonging to the fields of well logging technology and wellbore geophysics technology. It solves the problems of limited detection range in existing well logging technology and limited accuracy in wellbore geophysics technology. The method involves acquiring wellbore seismic wave data along the well depth direction, extracting Stoneley waves and seismic scattered waves from the acquired data, obtaining Stoneley wave profiles after equal-offset gather extraction of the Stoneley waves, and unifying these profiles with the scattered wave profiles plotted using the extracted seismic scattered wave amplitudes, all within the same coordinate system. The corresponding geological state is determined based on the rock mass state displayed by the two profiles for that layer. This method expands the detection range of mature well logging technology and fully utilizes various seismic wave information to identify longitudinal and transverse fractures, resulting in a wellbore seismic wave detection method.

Description

Technical Field

[0001] This invention relates to the fields of well logging technology and well geophysics, and particularly to a method for well seismic wave detection. Background Technology

[0002] Borehole geophysical techniques refer to geophysical techniques conducted within wells, primarily including seismic, electrical, electromagnetic, and gravity methods. Their detection range falls between well logging and surface geophysical techniques, with a typical detection depth of less than 100 meters. Borehole seismic techniques offer excellent resolution and accuracy, making them widely applicable. Reflected and scattered waves are crucial components of borehole seismic techniques, each with distinct characteristics. Reflected waves offer high resolution and good quantitative capabilities, and are sensitive to absorption and attenuation effects; however, wavefield separation is required, which is challenging, and they cannot identify radial fractures. Scattered waves excel in detecting inhomogeneous media, have a wider range of applications, and can solve problems that reflected wave methods can also solve, effectively identifying non-radial fractures.

[0003] Well logging technology offers higher accuracy than wellbore geophysical techniques, enabling precise identification of radial fractures. However, its detection range is limited. For example, sonic logging commonly uses Stoneley waves to identify fractures, but its detection range is limited to 1-2 meters around the wellbore and cannot identify longitudinal fractures. The limited detection range of well logging technology and the limited accuracy of wellbore geophysical techniques are the respective shortcomings of both. Expanding the detection range of mature well logging technologies and fully utilizing various seismic wave information to identify longitudinal and transverse fractures, thereby improving identification accuracy and precision, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide a well seismic wave detection method to solve the problems of limited detection range of existing well logging technology and limited accuracy of well geophysical technology.

[0005] This invention provides a well seismic wave detection method, comprising:

[0006] Collect seismic wave data in the well along the well depth direction;

[0007] Stoneley waves and seismic scattered waves were extracted from the collected seismic wave data;

[0008] Stoneley wave profiles were obtained by extracting Stoneley waves from the equidistant gathers. These profiles were then unified with the scattered wave profiles drawn from the extracted seismic scattered wave amplitudes, all within the same coordinate system, where the horizontal axis represents well depth and the vertical axis represents time.

[0009] Within a defined area around the well, stratigraphic information was obtained based on Stoneley wave profile data.

[0010] Outside the well perimeter, when the Stoneley wave offset is within the threshold, the geological state of the rock mass in that layer is determined by combining the Stoneley wave average velocity, energy amplitude, the rock mass state of that layer shown by the Stoneley wave profile, and the scattering arc of the scattered wave profile.

[0011] Furthermore, outside the wellbore setting range, when the Stoneley wave offset is within a threshold, the geological state of the rock mass in that layer is determined based on the Stoneley wave average velocity, energy amplitude, the rock mass condition shown in the Stoneley wave profile, and the scattering arc of the scattered wave profile, including:

[0012] In the Stoneley wave profile and the scattered wave profile, continuous waveforms with amplitude variations higher than those of fractures and faults correspond to interfaces; discontinuous waveforms correspond to fractures or faults; and distorted or delayed waveforms correspond to bent or tilted strata.

[0013] The location of longitudinal and transverse fractures in the strata can be identified by combining the position of the V-shaped apex in the Stoneley wave profile.

[0014] Furthermore, seismic wave data was acquired along the well depth direction, including:

[0015] An observation system comprising one transmitting sensor and multiple receiving sensors is vertically placed into the well. The channel spacing between adjacent receiving sensors is a first distance, and the measurement point spacing is a second distance. The transmitting sensor is located within a set distance range directly below all receiving sensors. A communication base station and a data acquisition host are provided on the ground to collect the seismic wave data in the well. The measurement point spacing is the downward translation distance of the entire observation system during each repeated test when collecting seismic wave data in the well.

[0016] Furthermore, before acquiring seismic wave data along the well depth direction, the process also includes: determining the observation system parameters; and acquiring test data based on the determined observation system parameters.

[0017] The observation system parameters include excitation parameters and acquisition parameters; the excitation parameters are the power supply time to the excitation source; the acquisition parameters include the number of sampling points, the number of leading points, and the sampling frequency.

[0018] Furthermore, before acquiring wellbore seismic wave data along the well depth direction, the following steps are also included:

[0019] The quality of the test data is evaluated by using the time and frequency domain signals of the full wave train. After the evaluation is passed, the formal data is collected.

[0020] Furthermore, Stoneley waves and seismic scattered waves are extracted from the acquired seismic wave data, including data processing such as data export, data spectrum analysis, data filtering, static correction, and equal offset gather extraction.

[0021] Furthermore, the data spectrum analysis includes:

[0022] After the test is passed, the raw time-domain seismic wave data collected is subjected to Fourier transform or wavelet transform to the frequency domain; the amplitude spectrum of the frequency-domain seismic wave data is obtained, and the main frequency components, frequency range and energy distribution in the seismic signal are analyzed.

[0023] Furthermore, the data filtering and static correction include:

[0024] Filter out signal interference components that are irrelevant to stratigraphic information from the original seismic data transformed to the frequency domain;

[0025] Measure the velocity of the signal corresponding to different formations after removing interference components;

[0026] Calculate the corresponding time delay for each data point in the seismic record based on the velocity;

[0027] The statically corrected data is obtained by correcting the corresponding time delay of each data point.

[0028] The frequency domain data is obtained by performing a Fourier transform on the statically corrected data;

[0029] The Stoneley wave signal was obtained by extracting the statically corrected frequency domain data using a low-pass filtering method.

[0030] Median filtering was used to separate the reflected Stoneley wave from the direct wave.

[0031] The extracted Stoneley wave reflected wave signal was separated into uplink and downlink reflected waves using a two-dimensional frequency filtering method.

[0032] Furthermore, unifying the scattered wave profile plot drawn from the extracted seismic scattered wave amplitude to the same coordinate system includes:

[0033] Select a high-pass filter to extract seismic scattered waves from the statically corrected frequency domain data;

[0034] Perform an inverse transform on the filtered frequency domain data to convert the data back to the time domain;

[0035] By utilizing the diffusion compensation law of seismic signals, the seismic scattered wave signal is enhanced to obtain the separated seismic scattered wave with restored amplitude;

[0036] The amplitude of the scattered wave is extracted and plotted on a coordinate system to obtain a cross-sectional view of the scattered wave.

[0037] Furthermore, the process of obtaining a Stoneley wave profile by performing equal-offset gather extraction on the Stoneley wave includes:

[0038] By moving the transmitting and receiving sensors as a whole, and shifting the distance between the measuring points downwards each time, multiple source excitations are performed. The acquired Stoneley wave data is extracted into equal-offset gathers. The time-depth conversion method is used to convert time into distance to form a Stoneley wave profile.

[0039] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0040] 1. Combining Stoneley wave radial identification with longitudinal identification of seismic scattered waves makes fracture identification more precise and improves the accuracy and precision of seismic scattered wave detection results.

[0041] 2. By matching the responses of Stoneley waves and seismic scattered waves to the interface, that is, the Stoneley wave velocity, energy, and the rock mass state of the section shown by the Stoneley wave profile correspond to the position and number of scattered arcs of the scattered waves, the interface location is more accurately verified and the accuracy is higher.

[0042] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0043] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0044] Figure 1 This is a schematic diagram of an observation system for a well seismic wave detection method according to an embodiment of the present invention;

[0045] Figure 2 This is a data processing flowchart of a well seismic wave detection method according to an embodiment of the present invention;

[0046] Figure 3 This is a Stoneley wave profile of a well seismic wave detection method according to an embodiment of the present invention;

[0047] Figure 4 This is a cross-sectional view illustrating the comprehensive interpretation of a well seismic wave detection method according to an embodiment of the present invention. Detailed Implementation

[0048] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0049] A specific embodiment of the present invention discloses a wellbore seismic wave detection method, aiming to improve the detection range and accuracy of existing wellbore seismic wave detection methods. The method includes the following steps:

[0050] Step S1: Establish the observation system and determine its parameters. This includes:

[0051] Step S11, as follows Figure 1 As shown, an observation system for well seismic wave detection is established.

[0052] If there is too much gravel in the borehole during testing, flushing should be performed first.

[0053] An observation system consisting of one transmitting sensor and multiple receiving sensors is vertically placed inside the well. The spacing between the receiving sensor arrays is 0.4 meters, and the spacing between measuring points is 0.3-2.2 meters. The transmitting sensor is located 0.3-2.2 meters directly below all the sensors. The array spacing is the distance between two adjacent receiving sensors; the measuring point spacing is the downward displacement of the entire sensor array during each repeated test. A ground-based communication base station and a data acquisition host are used for signal transmission with the sensors and for acquiring and processing the seismic data. In this specific example, six receiving sensors are used, employing a one-transmitter-six-receiver configuration.

[0054] Step S12: Determine the parameters of the observation system through testing based on the geological task; the observation system parameters include the source excitation parameters and acquisition parameters.

[0055] Before formal data acquisition, tests are conducted to verify the source excitation parameters, followed by a full-wavelength acoustic test. The source excitation parameters refer to the power supply time for exciting the source; for example, it is 100ms.

[0056] The acquisition parameters include the number of sampling points, the number of leading points, and the sampling frequency. For example: the number of sampling points is 1K, the number of leading points is 256, and the sampling frequency is 10KHz.

[0057] Step S13: Evaluate the test data quality from the perspectives of the time and frequency domains. After confirmation, proceed to step S2 for formal data acquisition. This includes:

[0058] Conduct full-wavelength acoustic tests and evaluate the test data from the perspectives of the time and frequency domains.

[0059] Evaluate the quality of test data from both time and frequency domain perspectives, observing whether the arrival time of the direct wave matches the spatial and wave velocity background, whether the frequency band and frequency meet the detection requirements, and whether the full wave train signal morphology is complete. If the test meets the requirements, proceed with formal data acquisition; otherwise, readjust the sensor equipment.

[0060] Step S2: Use the observation system to collect data based on the determined observation system parameters to obtain raw data; the raw data is the seismic wave data collected by the sensor.

[0061] Step S3: Perform data processing on the raw data, including data export, data spectrum analysis, data filtering, and static correction, to obtain Stoneley waves and seismic scattered waves. This includes steps S31-S33.

[0062] Step S31: Export the multichannel raw data collected by the sensor according to the specified data format based on the model of the acquisition device. Common geophysical data formats include SEG-Y and ASCII. Select the data range to be exported as needed; this can be the entire dataset, a specific time window, spatial region, or measurement gather. Set the export parameters, such as sampling rate, data format, file naming rules, and compression options.

[0063] Step S32: Perform spectral analysis on each raw data channel to obtain the amplitude spectrum of the seismic data.

[0064] Spectrum analysis involves transforming the raw time-domain data acquired after successful testing into the frequency domain using Fourier transform or wavelet transform. Fourier transform decomposes the seismic waveform into components of different frequencies to analyze the spectral content of the seismic signal. By extracting the amplitude spectrum from the Fourier transform results, the amplitude spectrum of the seismic data is obtained, representing the amplitude distribution of the seismic signal at different frequencies and reflecting the frequency characteristics of the signal. The horizontal axis represents frequency, and the vertical axis represents amplitude. By observing the spectrum, the main frequency components, frequency range, and energy distribution of the seismic signal can be analyzed.

[0065] Step S33: Perform data filtering on the frequency domain data obtained in step S32 to obtain Stoneley wave and seismic scattered wave data; wherein, the obtained Stoneley wave includes Stoneley wave up-reflection wave and down-transmission wave; including steps S331-S336.

[0066] Step S331: Filter out signal interference components that are unrelated to the formation information in the frequency domain data obtained in step S32 to obtain the spectrum data after interference removal.

[0067] Interference components unrelated to formation information, such as abnormal peaks or frequency bands, are identified from the original signal and spectrum analysis results. For example, excessively strong amplitudes within certain frequency ranges indicate potential noise or interference of a specific type, which are interference components unrelated to formation information. An amplitude threshold is set, and portions of the spectrum with amplitudes below this threshold are considered interference components unrelated to formation information. In this embodiment, the amplitude threshold is set to 5% of the average amplitude.

[0068] Step S332: After filtering and removing noise from the data in step S331, the signal is converted back to the time domain and statically corrected to suppress irrelevant signals and eliminate the signal delay phenomenon that may be caused by the triggering of the seismic source.

[0069] Static correction compensates for time delays caused by variations in formation velocity by measuring and correcting the velocity differences of signals from different layers after noise removal through data filtering in step S331. By accurately estimating the velocities of different formations, the corresponding time delays can be calculated and applied to each data point in the seismic record to align the records. This eliminates the time delays caused by different formations, aligning the arrival times of the seismic records. The data is then transformed back to the frequency domain.

[0070] Step S333: Extract the Stoneley wave signal from the frequency domain data that has undergone static correction in step 332 using a low-pass filter method.

[0071] Design a low-pass filter with a cutoff frequency of 90% of the main energy range of the Stoneley wave signal to filter out high-frequency components and retain low-frequency components. Perform an inverse transform on the filtered frequency domain data to convert the data back to the time domain. The data after the inverse transform is the Stoneley wave signal.

[0072] Step S334: The extracted Stoneley wave signal is processed using the median filtering method to separate the Stoneley wave reflected wave from the direct wave.

[0073] A median filter is designed with the period of the Stoneley wave reflected wave as the window size. The period of the reflected wave is obtained from the time-domain signal. By sorting the data in each time window and selecting the median value as the output of the median filter, high-frequency noise and non-stationary signal components are suppressed. This eliminates high-frequency noise and other non-stationary components in the Stoneley wave reflected wave while retaining the energy of the direct wave. By subtracting the median-filtered data from the Stoneley wave signal extracted in step S333, the Stoneley wave reflected wave signal can be obtained, thus completing the separation of the reflected wave and the direct wave.

[0074] Step S335: Use a two-dimensional frequency filtering method to separate the uplink and downlink reflected waves from the Stoneley wave reflected wave signal extracted in step S334.

[0075] In the frequency domain, the uplink and downlink waves are separated by selecting appropriate filtering functions or bandpass filters. Specifically, the passband range of the filtering function or bandpass filter can be set according to the dominant frequency range of the uplink and downlink waves to filter out the remaining frequency data. An inverse Fourier transform is then performed on the filtered frequency domain data to convert the data back to the time domain. The data after the inverse transform is the separated uplink and downlink waves.

[0076] Step S336: Extract the frequency domain data after step S332 using a high-pass filter to obtain the seismic scattered wave.

[0077] Scattered waves typically exhibit strong energy in the high-frequency band and are filtered using a high-pass filter. An inverse transform is then performed on the filtered data to convert it back to the time domain; the data after the inverse transform is the separated scattered wave. The diffusion compensation law of seismic signals is utilized to obtain seismic scattered waves over a relatively long distance around the well.

[0078] It should be noted that steps S336 and steps S333-S335 do not have a sequential relationship in execution.

[0079] Step S4: Extract equal-offset gathers from the Stoneley wave data processed in Step 335 to obtain the Stoneley wave profile; unify it with the scattered wave profile plotted from the seismic scattered wave amplitude obtained in Step 336 in the same coordinate system. This includes:

[0080] By moving the transmitting and receiving sensors as a whole, and shifting them downwards by a distance 'a' (i.e., the distance between measuring points) each time, multiple seismic source excitations are performed from different locations to collect a series of seismic records. Stoneley wave profiles are obtained by extracting equal-offset gathers. The shift distance 'a' ranges from 0.3 to 2.2 m in this embodiment.

[0081] The time-depth conversion method is used to convert time into distance to form the Stoneley wave profile.

[0082] Each seismic record generated by a seismic source is called a ray trace, containing information about the seismic waves passing through the subsurface medium. The distance between the vibration receiving sensor and the seismic source is called the offset. Migration algorithms are used to process the data. For each offset, a pair of ray traces with the same offset are superimposed to obtain an equioffset trace, resulting in an equioffset trace profile, as shown below. Figure 3 As shown, the horizontal axis represents depth, and the vertical axis represents time. Time can be converted to radial distance using the following methods: Obtain subsurface velocity model data, such as formation velocity, from well logging data, geological exploration data, etc. Based on the data, derive the functional relationship of sound wave velocity at different distances to form a velocity model. Based on the velocity model and the arrival time of statically corrected seismic wave data, convert the time values ​​on the seismic profile into the time it takes for the seismic wave to propagate underground. Integrate the velocity values ​​in the velocity model with this time to convert the time on the seismic profile into distance.

[0083] The Stoneley wave profile and the scattered wave profile drawn from the seismic scattered wave amplitude extracted in step S336 are unified into the same coordinate system, where the horizontal axis of the coordinate system is the well depth and the vertical axis is time.

[0084] Step S5: Within the set range around the well, obtain the detected stratigraphic structure information based on the Stoneley wave profile information.

[0085] Stoneley waves are more accurate than scattered waves and can precisely identify radial fractures. Therefore, based on Stoneley wave profiles, detected geological structures such as interfaces, fractures, faults, and bent or tilted strata can be obtained. On both scattered and Stoneley wave profiles, amplitude values ​​are represented by the color of short horizontal lines; small amplitude values ​​are represented by white lines, and large amplitude values ​​by black lines. Interfaces correspond to continuous waveforms with greater amplitude variation than fractures and faults; fractures and faults correspond to discontinuous waveforms; bent or tilted strata correspond to distorted or delayed waveforms. Figure 3 As shown, the location of the fracture is determined based on the "V"-shaped anomaly of the Stoneley wave reflection. In this embodiment, the well perimeter range is set at 5 meters.

[0086] Outside the wellbore perimeter, the geological condition of the rock mass in that section is primarily determined by the scattering arc of the scattered wave profile. While the Stoneley wave profile shows weak signals at greater distances, insufficient to accurately identify fractures in the vicinity of the wellbore, it can serve as an auxiliary means to constrain the scattered wave results. The location of fractures is determined based on the "V"-shaped anomaly of the Stoneley wave reflected waves. For Stoneley wave signals with smaller offsets, typically within 2.2 meters, the Stoneley wave profile can be understood to some extent as a self-excited and self-received seismic wave profile, directly reflecting the development of rock fractures in the probed area.

[0087] Therefore, outside the well perimeter setting range, when the Stoneley wave offset is within the threshold, the geological state of the rock mass in that layer is determined by combining the Stoneley wave average velocity, energy amplitude, the rock mass state shown in the Stoneley wave profile, and the scattering arc of the scattered wave profile. For example, the location of the fracture can be determined based on the "V"-shaped anomaly position in the Stoneley wave profile. In this embodiment, the offset threshold is set to 2.2 meters. When the scattered wave encounters an inhomogeneous medium or interface, it will scatter. The scattered energy propagates in different directions, presenting the shape of a scattering arc. The scattering arc of the scattered wave profile can determine the longitudinal fracture; the combination of the two can improve the accuracy of the judgment.

[0088] One specific embodiment of the present invention is as follows: Figure 4 As shown.

[0089] Figure 4 The waterless area shows that the average velocity of the Stoneley wave is below 1000 m / s, the standard deviation of the normalized energy amplitude is less than 0.4, and the vertex of the Stoneley wave “V”-shaped reflection is the boundary. Combined with the scattering arc in the scattering wave profile, the location of the water surface in the well is corresponding to the location of the water surface above 458 meters above the ground in the figure, and the area below 458 meters is the waterless area.

[0090] Figure 4The interbedded sandstone and mudstone shows that the responses of Stoneley waves and scattered waves are basically consistent. The standard deviation of the Stoneley wave energy amplitude is greater than 0.4, and the velocity is greater than 1100 m / s with some fluctuation. Its profile shows that the rock mass in this section is relatively intact, but also contains small fractures, mostly in the range of 458–463 m, which is consistent with the characteristics of interbedded sandstone and mudstone and well-developed bedding. The scattered wave profile shows no obvious scattering arcs in the 6–10 ms range, while a small number of scattering arcs appear in the range of 10–14 ms at a depth of 470–480 m. Based on a shear wave velocity of 2000 m / s, a small number of fractures are developed beyond 10 m around the well in this section, but they are not obvious overall. The mudstone's water absorption and… Figure 4 The judgment that there is water above 458 meters is consistent with this. In this judgment, since there are cracks in the range of 458-463m of Stoneley waves, it is inferred that there are also cracks in the range of 463-480m. Stoneley waves become less obvious due to the distance, and this inference is confirmed by the scattered wave profile.

[0091] Figure 4 The fracture zone shows that the standard deviation of the Stoneley wave energy amplitude is greater than 0.4 in the radial range of more than 5 meters around the well, and the amplitude shows a pattern of decreasing from high to low. The velocity is generally high, greater than 1100 m / s, and also shows a trend of fluctuating from high to low. The waveform of the upper half of the rock mass in this section is relatively complete, while the waveform of the lower half shows varying degrees of loss. The scattered wave profile shows that the upper half has no obvious scattered arc to the appearance of obvious scattered arc, while the lower half has obvious scattered arc. The responses of the two are consistent, which is judged to be a fracture zone.

[0092] Figure 4 The fractured zone shows that the responses of Stoneley waves and scattered waves are consistent at well depths of 506–522 m. Among them, the standard deviation of the Stoneley wave energy amplitude is less than 0.4 and the normalized average amplitude is less than 0.3, with an average velocity of 1100 m / s. Its profile shows that the waveform of the upper half (506-516 m) of the rock mass has leakage, while the waveform of the lower half (516-522 m) is complete. It is inferred that the upper half of the rock mass is severely damaged and has developed fractures, while the lower half of the rock mass should have been more severely damaged and had more developed fractures, but no large fractures appeared. Therefore, it is inferred that the lower half of the rock mass was filled with grout. The scattering wave profile shows scattering arcs in the upper half (506-516 m) within the range of 6-14 ms, while the lower half (516-522 m) only has 1-2 scattering arcs beyond 10 ms. This indicates that the upper half of the rock mass has developed fractures around the well, while the lower half of the rock mass has been grouted but is still not very dense, especially with fractures still existing beyond 10 m around the well.

[0093] Compared with existing technologies, the well seismic wave detection method provided in this embodiment,

[0094] Combining Stoneley wave radial identification with longitudinal identification of seismic scattered waves makes fracture identification more precise and improves the accuracy and precision of seismic scattered wave detection results.

[0095] By matching the responses of Stoneley waves and seismic scattered waves to the interface, that is, the Stoneley wave velocity, energy, and the rock mass state of the section shown by the Stoneley wave profile correspond to the position and number of scattered arcs of the scattered waves, the interface location is more accurately verified and the accuracy is higher.

[0096] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0097] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A well seismic wave detection method, characterized in that, include: Collect seismic wave data in the well along the well depth direction; Stoneley waves and seismic scattered waves were extracted from the collected seismic wave data; Stoneley wave profiles were obtained by extracting the Stoneley waves using equal-offset gathers; these profiles were then unified with the scattered wave profiles drawn from the extracted seismic scattered wave amplitudes in the same coordinate system; where the horizontal axis of the coordinate system represents well depth and the vertical axis represents time. Within a defined area around the well, stratigraphic information was obtained based on Stoneley wave profile data. Outside the well perimeter, when the Stoneley wave offset is within the threshold, the geological state of the rock mass in that section is determined by combining the Stoneley wave average velocity, energy amplitude, the rock mass state of the section shown in the Stoneley wave profile, and the scattering arc of the scattered wave profile.

2. The well seismic wave detection method according to claim 1, characterized in that, Outside the wellbore setting range, when the Stoneley wave offset is within a threshold, the geological state of the rock mass in that layer is determined based on the Stoneley wave average velocity, energy amplitude, the rock mass condition shown in the Stoneley wave profile, and the scattering arc of the scattered wave profile, including: In the Stoneley wave profile and the scattered wave profile, continuous waveforms with amplitude variations higher than those of fractures and faults correspond to interfaces; discontinuous waveforms correspond to fractures or faults; and distorted or delayed waveforms correspond to bent or tilted strata. The location of longitudinal and transverse fractures in the strata can be identified by combining the position of the V-shaped apex in the Stoneley wave profile.

3. The well seismic wave detection method according to claim 1, characterized in that, Acquire seismic wave data along the well depth direction, including: An observation system comprising one transmitting sensor and multiple receiving sensors is vertically placed into the well. The channel spacing between adjacent receiving sensors is a first distance, and the measurement point spacing is a second distance. The transmitting sensor is located within a set distance range directly below all receiving sensors. A communication base station and a data acquisition host are provided on the ground to collect the seismic wave data in the well. The measurement point spacing is the downward translation distance of the entire observation system during each repeated test when collecting seismic wave data in the well.

4. The well seismic wave detection method according to claim 3, characterized in that, Before acquiring seismic wave data along the well depth direction, the process also includes: determining the observation system parameters; and acquiring test data based on the determined observation system parameters. The observation system parameters include excitation parameters and acquisition parameters; the excitation parameters are the power supply time to the excitation source; the acquisition parameters include the number of sampling points, the number of leading points, and the sampling frequency.

5. The well seismic wave detection method according to claim 3, characterized in that, Before acquiring wellbore seismic wave data along the well depth direction, the following steps are also included: The quality of the test data is evaluated using the time and frequency domain signals of the full-wavelength acoustic spectrum. Once the evaluation is passed, formal data collection is carried out.

6. The well seismic wave detection method according to claim 5, characterized in that, Stoneley waves and seismic scattered waves are extracted from the acquired seismic wave data, including data processing such as data export, data spectrum analysis, data filtering, static correction, and equal offset gather extraction.

7. The well seismic wave detection method according to claim 6, characterized in that, The data spectrum analysis includes: After the test is passed, the raw time-domain seismic wave data collected is subjected to Fourier transform or wavelet transform to the frequency domain; the amplitude spectrum of the frequency-domain seismic wave data is obtained, and the main frequency components, frequency range and energy distribution in the seismic signal are analyzed.

8. A well seismic wave detection method according to claim 6 or 7, characterized in that, The data filtering and static correction include: Filter out signal interference components that are irrelevant to stratigraphic information from the original seismic data transformed to the frequency domain; Measure the velocity of the signal corresponding to different formations after removing interference components; Calculate the corresponding time delay for each data point in the seismic record based on the velocity; The statically corrected data is obtained by correcting the corresponding time delay of each data point. The frequency domain data is obtained by performing a Fourier transform on the statically corrected data; The Stoneley wave signal was obtained by extracting the statically corrected frequency domain data using a low-pass filtering method. Median filtering was used to separate the reflected Stoneley wave from the direct wave. The extracted Stoneley wave reflected wave signal was separated into uplink and downlink reflected waves using a two-dimensional frequency filtering method.

9. A well seismic wave detection method according to claim 7, characterized in that, The process of unifying the scattered wave profile plot drawn from the extracted seismic scattered wave amplitude into the same coordinate system includes: Select a high-pass filter to extract seismic scattered waves from the statically corrected frequency domain data; Perform an inverse transform on the filtered frequency domain data to convert the data back to the time domain; By utilizing the diffusion compensation law of seismic signals, the seismic scattered wave signal is enhanced to obtain the separated seismic scattered wave with restored amplitude; The amplitude of the scattered wave is extracted and plotted on a coordinate system to obtain a cross-sectional view of the scattered wave.

10. A well seismic wave detection method according to claim 6, characterized in that, The Stoneley wave profile obtained after performing equal-offset gather extraction on the Stoneley wave includes: By moving the transmitting and receiving sensors as a whole, and shifting the distance between the measuring points downwards each time, multiple source excitations are performed. The acquired Stoneley wave data is extracted into equal-offset gathers. The time-depth conversion method is used to convert time into distance to form a Stoneley wave profile.

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