A reservoir natural frequency downhole in-situ measurement method based on neural network filtering

Abstract

The application provides a reservoir inherent frequency downhole in-situ measurement method based on neural network filtering, which comprises the following steps: (1) generating a pulse shock wave to induce reservoir rock vibration through a continuous high-voltage pulse discharge device; (2) obtaining self-vibration noise signal data of the continuous high-voltage pulse discharge device at the wellhead when the pulse discharge is performed; (3) obtaining background noise signal and feedback vibration signal data of the continuous high-voltage pulse discharge device in a static state in the wellbore; (4) filtering and denoising the high-noise background data; (5) performing vibration signal analysis; (6) obtaining a Hilbert marginal spectrum and determining the correlation coefficient of each eigenmode and the original vibration signal; and (7) determining the target reservoir inherent frequency. The application can directly measure the target reservoir inherent frequency downhole, effectively solves the problem of difficulty in downhole inherent frequency measurement or inaccuracy in laboratory inherent frequency test, and realizes efficient in-situ acquisition of the reservoir inherent frequency downhole.

Description

Technical Field

[0001] The invention belongs to the technical field of rock physics and well logging, and particularly relates to a downhole in-situ measurement method of reservoir natural frequency based on neural network filtering. Background Art

[0002] Unconventional oil and gas, such as shale oil and gas, are currently a key area for increasing reserves and production. Due to the dense, low-porosity, and low-permeability characteristics of these reservoirs, reservoir transformation is crucial for boosting productivity. To overcome the complex processes, high costs, and significant environmental pressures associated with traditional hydraulic fracturing reservoir transformation techniques, innovative waterless reservoir transformation technologies are constantly being developed. A reservoir resonance transformation technology based on pulse vibration and resonance has been proposed. This technology is highly efficient, low-cost, and environmentally friendly, with broad application prospects. For example, shale reservoirs exhibit the highest displacement response amplitude when in resonance. By continuously applying a pulse force of a certain frequency to the formation surrounding the wellbore and stimulating the reservoir into resonance, the tensile stress on the rock can overcome the in-situ stress and tensile strength with minimal single-shot excitation energy or force. This allows existing fractures within the formation to expand or new fractures to form, increasing the permeability of the shale reservoir and, consequently, the productivity of individual wells.

[0003] In reservoir resonance transformation technology, an external excitation frequency must be set based on the natural frequency of the target reservoir to stimulate the reservoir to resonate. Therefore, accurately measuring the natural frequency of the rock mass within the target transformation range is the key to implementing reservoir resonance transformation. Currently, the main methods for testing the natural frequency of an object are the "hammering method" and the "sweeping frequency method." Among them, the "hammering method" has the advantages of high efficiency and universality. It is often used to analyze the vibration characteristics of buildings, bridges, and mechanical structures. By monitoring the surface vibration signal of the object under excitation, the structural response data is obtained, and the natural frequency of the measured object is extracted using signal processing methods. The "sweeping frequency method" is intuitive and highly accurate. By collecting the response changes of the measured object under different excitation frequencies, the resonance frequency with a more obvious displacement response is directly determined based on the amplitude-frequency curve. It can also intuitively obtain the vibration characteristics of the object under the natural frequency.

[0004] The natural frequency of an object is determined by the size and properties of the material. Small-scale rock samples differ greatly from the original reservoir rock mass in terms of size and physical properties. Therefore, the natural frequency test results of small-scale rock samples cannot be used as the natural frequency of the reservoir rock mass. The latter measurement should be carried out in situ underground to accurately obtain the natural frequency of the rock mass in a specific range under original conditions.

[0005] The current downhole in-situ measurement method of reservoir natural frequency is similar to the "hammer method". It induces reservoir vibration by applying pulse excitation, and analyzes the monitored reservoir vibration signal to extract the characteristic frequency. However, due to the significant influence of structures such as casing, perforation, and cement ring on vibration propagation, coupled with the heterogeneity of the formation and liquid interference in the wellbore, the vibration signals collected downhole are very complex, and data analysis is difficult. It is difficult to quickly and accurately extract the reservoir natural frequency. Therefore, it is necessary to innovate the measurement and analysis methods of reservoir natural frequency to achieve efficient acquisition of natural frequency. Summary of the Invention

[0006] In response to the technical problems existing in the above-mentioned background technology, the present invention proposes a downhole in-situ measurement method of reservoir natural frequency based on neural network filtering. The method has a reasonable concept and can directly carry out the target reservoir natural frequency measurement downhole, effectively solving the problem of difficulty in downhole natural frequency measurement or inaccurate indoor natural frequency testing, and realizing efficient in-situ acquisition of reservoir natural frequency downhole.

[0007] To solve the above technical problems, the present invention provides a downhole in-situ measurement method for reservoir natural frequency based on neural network filtering, which mainly includes the following steps:

[0008] (1) First, a pulse shock wave is generated by a continuous high-voltage pulse discharge device to induce reservoir rock vibration;

[0009] (2) Based on distributed fiber optic acoustic wave sensing, the self-vibration noise signal data of the continuous high-voltage pulse discharge device during pulse discharge at the wellhead is obtained;

[0010] (3) Based on distributed fiber optic acoustic wave sensing, the background noise signal of the continuous high-voltage pulse discharge device in the wellbore at rest and the feedback vibration signal data during pulse discharge are obtained;

[0011] (4) Filter and reduce noise on the self-vibration noise signal data, background noise signal and feedback vibration signal data during pulse discharge based on the deep residual shrinkage neural network;

[0012] (5) Use the improved adaptive complete empirical mode decomposition method and Fourier transform method to perform vibration signal analysis;

[0013] (6) Perform Hilbert transform to obtain the Hilbert marginal spectrum reflecting the change trend of the eigenmode, and combine the Pearson correlation coefficient method to determine the correlation coefficient between each eigenmode and the original vibration signal;

[0014] (7) Finally, the target reservoir natural frequency is determined based on the Hilbert marginal spectrum and correlation coefficient.

[0015] The downhole in-situ measurement method of reservoir natural frequency based on neural network filtering, wherein the specific process of step (1) is:

[0016] (1.1) Locate the perforation position of the oil and gas well using the logging device, and record the perforation boundary position, target reservoir position, and non-perforation section position;

[0017] (1.2) Bundling the armored optical fiber with the cable of the continuous high-voltage pulse discharge device to achieve synchronous retraction and extension of the optical fiber and the cable of the continuous high-voltage pulse discharge device;

[0018] (1.3) Use a winch to lower the continuous high-voltage pulse discharge device to the desired location at the wellhead and downhole, and perform single or frequency-increasing continuous pulse discharges to generate pulse vibrations and stimulate the vibration of the formation around the well.

[0019] The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering, wherein: the optical fiber used in the step (1.2) is a distributed acoustic wave sensing optical fiber.

[0020] The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering, wherein: the cable of the continuous high-voltage pulse discharge device in step (1.2) can also use an optoelectronic composite cable containing optical fiber.

[0021] The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering, wherein the specific process of step (2) is: using a winch to lift the continuous high-voltage pulse discharge device and suspend it at the wellhead, controlling the continuous high-voltage pulse discharge device to perform single discharge and continuous discharge with increasing frequency, and collecting the continuous high-voltage pulse discharge device's own vibration signal under this condition through optical fiber, and this signal is called the continuous high-voltage pulse discharge device's self-vibration noise.

[0022] The downhole in-situ measurement method of reservoir natural frequency based on neural network filtering, wherein the specific process of step (3) is as follows:

[0023] (3.1) A continuous high-voltage pulse discharge device is lowered using a winch. The optical fiber is lowered synchronously with the cable of the continuous high-voltage pulse discharge device. When the continuous high-voltage pulse discharge device reaches the perforation section, it is stopped. The vibration signal detected by the optical fiber determines that the continuous high-voltage pulse discharge device has stopped. The optical fiber is used to monitor and record the downhole background vibration signal data, which is named the perforation section background noise. The continuous high-voltage pulse discharge device is then controlled to perform a single discharge and continuous discharge with increasing frequency at this location. The generated pulse shock wave excites the rock mass in the formation around the wellbore. During the discharge process, the downhole vibration signal is collected through the optical fiber.

[0024] (3.2) Continue lowering the continuous high-voltage pulse discharge device. When the continuous high-voltage pulse discharge device reaches the target reservoir location, stop lowering the continuous high-voltage pulse discharge device. Monitor the downhole environment using optical fiber. After the continuous high-voltage pulse discharge device stops, start recording data and name it as the target reservoir background noise. Then control the continuous high-voltage pulse discharge device to perform a single discharge and continuous discharge with increasing frequency at the location. During the discharge process, collect vibration signals through optical fiber.

[0025] (3.3) Continue lowering the continuous high-voltage pulse discharge device. When the continuous high-voltage pulse discharge device reaches the non-perforation section, stop lowering the continuous high-voltage pulse discharge device. Monitor the downhole environment based on the optical fiber. After the continuous high-voltage pulse discharge device stops, start recording data and name it as the non-perforation section background noise. Then control the continuous high-voltage pulse discharge device to perform a single discharge and continuous discharge with increasing frequency at this location. During the discharge process, collect vibration signals through the optical fiber.

[0026] The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering, wherein the specific process of step (4) is: based on the deep residual shrinkage neural network, the vibration signal background noise is filtered out, the self-vibration noise of the continuous high-voltage pulse discharge device, the perforation section background noise, the target reservoir background noise, and the non-perforation section background noise are used as training samples, and the vibration signals collected during the single discharge and the continuous discharge with increasing frequency at the corresponding position are subjected to noise reduction filtering processing based on the deep residual shrinkage neural network.

[0027] The downhole in-situ measurement method of reservoir natural frequency based on neural network filtering, wherein the specific process of step (5) is:

[0028] (5.1) Perform modal analysis on the denoised and filtered vibration signal using an improved adaptive complete empirical mode decomposition method. Perform Fourier transform on the eigenmode components obtained from the signal decomposition to extract the eigenmodes within the desired frequency range. The extracted eigenmodes are then reassembled into a reconstructed vibration signal.

[0029] (5.2) Perform modal analysis on the reconstructed vibration signal based on an improved adaptive complete empirical mode decomposition method. Perform Fourier transform on the eigenmode components obtained from the signal decomposition. Perform Hilbert transform on the eigenmodes whose dominant frequencies are below the required frequency range. Analyze the occurrence time of the dominant frequencies of each mode to obtain the Hilbert marginal spectrum reflecting the variation trend of the eigenmodes.

[0030] (5.3) The shock wave velocity is calculated based on the difference in the starting time of the shock response between different optical fiber channels and the distance between each channel. Then, the time it takes for the shock wave to reach the wall of each structure is calculated based on the size of each foundation structure. The time required for the stress wave caused by the vibration of each foundation structure and the target reservoir rock mass to be transmitted to the optical fiber is inverted based on the stress wave velocity in the medium of each foundation structure.

[0031] The downhole in-situ measurement method of reservoir natural frequency based on neural network filtering, wherein the specific process of step (6) is: statistically analyzing and dividing the main frequencies of the eigenmodes extracted from all vibration signals into intervals, and comparing the main frequency occurrence time in the Hilbert marginal spectrum of the samples in each interval with the theoretical transmission time, providing a reference for the identification of the natural frequency of each structure, and then extracting the natural frequency of the target reservoir rock mass.

[0032] The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering, wherein the specific process of step (7) is: estimating the data points in the natural frequency interval corresponding to the target reservoir based on the strong tracking filter to obtain the universal natural frequency of the target reservoir.

[0033] By adopting the above technical solution, the present invention has the following beneficial effects:

[0034] The in-situ downhole measurement method of reservoir natural frequency based on neural network filtering in the present invention is rationally conceived and can directly carry out target reservoir natural frequency measurement downhole, effectively solving the problems of difficult downhole natural frequency measurement or inaccurate indoor natural frequency testing, and realizing efficient in-situ acquisition of reservoir natural frequency downhole.

[0035] The present invention also has the following characteristics and advantages:

[0036] (1) The present invention is the first in-situ measurement method of reservoir natural frequency that combines a "tapping method" with a machine learning algorithm. The present invention uses field optical fiber monitoring to collect in-situ measurement data of natural frequency for the first time, extracts characteristic frequencies from complex vibration signals based on measured data sets and deep learning methods, and identifies the natural frequency of reservoir rock mass based on empirical values ​​and theoretical solutions, thereby realizing in-situ, efficient and accurate measurement of reservoir natural frequency.

[0037] (2) The reservoir vibration signal is complex, including different structural vibration modes. At the same time, the structural vibration modes induced by different shock wave excitation positions are different. The present invention can construct a database based on deep learning theory to realize dynamic monitoring and synchronous measurement of the reservoir natural frequency in reservoir transformation projects, which is more in line with actual application needs.

[0038] (3) This method can be applied to various reservoir downhole continuous dynamic load transformation technologies and devices, and has the advantages of wide applicability, strong timeliness and high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 This is a flow chart of the downhole in-situ measurement method of reservoir natural frequency based on neural network filtering of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] The present invention will be further explained below with reference to specific embodiments.

[0043] Example 1

[0044] like Figure 1 As shown, the present embodiment 1 provides an in-situ downhole measurement method for reservoir natural frequency based on neural network filtering, which first generates a pulse shock wave through a continuous high-voltage pulse discharge device to induce reservoir rock vibration, then obtains feedback vibration signal data in the wellbore based on distributed optical fiber acoustic wave sensing, and filters and reduces noise on the high-noise background data based on a deep residual shrinkage neural network. Then, an improved adaptive complete empirical mode decomposition-Fourier transform method is used to perform vibration signal analysis, and then a Hilbert transform is performed to obtain a Hilbert marginal spectrum reflecting the change trend of the intrinsic mode. The correlation coefficient between each intrinsic mode and the original vibration signal is determined by combining the Pearson correlation coefficient method, and finally the target reservoir natural frequency is determined based on the Hilbert marginal spectrum and the correlation coefficient.

[0045] The present invention provides a downhole in-situ measurement method for reservoir natural frequency based on neural network filtering, which specifically includes the following steps:

[0046] S010. Using a gamma-ray logging device, the wellbore depth is calibrated, and the target drilling perforation section is located. The perforation boundary, target reservoir, and non-perforation section locations are recorded. The armored optical fiber is bundled with the cable of a continuous high-voltage pulse discharge device (hereinafter referred to as the discharge device) to achieve synchronous retraction and extension of the optical fiber and the discharge device cable. The optical fiber used is a distributed acoustic sensor fiber.

[0047] S020, use the winch to lift the discharge device and hang it at the wellhead, control the discharge device to perform single discharge and continuous discharge with increasing frequency, collect the device's own vibration signal under this condition through the optical fiber, and name this signal as the device's own vibration noise.

[0048] S030, use the winch to lower the discharge device, and lower the optical fiber along with the discharge device cable, stop lowering when the discharge device reaches the perforation section position, judge the device to be stationary according to the vibration signal monitored by the optical fiber, use the optical fiber to monitor and record the background vibration signal data in the well, and name this signal as the background noise of the perforation section; then control the discharge device to perform single discharge and continuous discharge with increasing frequency at this position, and collect the vibration signal through the optical fiber.

[0049] S040, continue to lower the discharge device, stop the device when it reaches the target reservoir position, monitor the downhole environment according to the optical fiber, and record the data after the device is stationary and name it as the background noise of the target reservoir; then control the discharge device to perform single discharge and continuous discharge with increasing frequency at this position, and collect the vibration signal through the optical fiber.

[0050] S050, continue to lower the discharge device, stop the device when it reaches the non-perforation section position, monitor the downhole environment according to the optical fiber, and record the data after the device is stationary and name it as the background noise of the non-perforation section; then control the discharge device to perform single discharge and continuous discharge with increasing frequency at this position, and collect the vibration signal through the optical fiber.

[0051] S060, based on the deep residual shrinkage neural network, filter and denoise the high-noise background data (i.e. the self-vibration noise signal data, the background noise signal and the feedback vibration signal data during pulse discharge), use the device's own vibration noise, the background noise of the perforation section, the background noise of the target reservoir and the background noise of the non-perforation section as training samples, and perform denoising and filtering processing on the vibration signals collected during single discharge and continuous discharge with increasing frequency at the corresponding position according to the deep residual shrinkage neural network.

[0052] S070, based on the improved adaptive complete empirical mode decomposition method, perform modal analysis on the denoised vibration signal, perform Fourier transform on the intrinsic mode components obtained by signal decomposition, extract intrinsic modes with main frequency lower than 50Hz, and reconstitute the extracted intrinsic modes into a reconstructed vibration signal.

[0053] S080, based on the improved adaptive complete empirical mode decomposition method, perform modal analysis on the reconstructed vibration signal, perform Fourier transform on the intrinsic mode components obtained by signal decomposition, perform Hilbert transform on the intrinsic modes with main frequency lower than 50Hz, and analyze the occurrence time of each modal main frequency.

[0054] S090. The shock wave velocity is calculated based on the difference in the starting time of the shock response between different optical fiber channels and the distance between each channel. The time it takes for the shock wave to reach the wall of each structure is then calculated based on the dimensions of each foundation structure. The time required for the stress wave caused by the vibration of each foundation structure and the target reservoir rock mass to be transmitted to the optical fiber is inverted based on the stress wave velocity in the medium of each foundation structure.

[0055] S100, statistically analyze and divide the main frequencies of the eigenmodes extracted from all vibration signals into intervals, and compare the main frequency occurrence time in the Hilbert spectrum of the samples in each interval with the theoretical transmission time, so as to provide a reference for the identification of the natural frequency of each structure, and then extract the natural frequency of the target reservoir rock mass.

[0056] S110 , estimating data points within a natural frequency interval corresponding to the target reservoir based on a strong tracking filter to obtain a universal natural frequency of the target reservoir.

[0057] Example 2

[0058] The structure of this embodiment is basically the same as that of embodiment 1, except that it is not limited to performing Hilbert transform on the eigenmodes with a main frequency lower than 50 Hz, and the occurrence time of the main frequency of each mode is analyzed, and the specific frequency is adjusted according to the actual situation.

[0059] The present invention has a reasonable concept and can directly carry out target reservoir natural frequency measurement underground, effectively solving the problems of difficult underground natural frequency measurement or inaccurate indoor natural frequency testing, and realizing efficient in-situ acquisition of reservoir natural frequency underground.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A downhole in-situ measurement method for reservoir natural frequency based on neural network filtering, characterized in that: The main steps include: (1) First, a pulse shock wave is generated by a continuous high-voltage pulse discharge device to induce reservoir rock vibration; (2) Based on distributed fiber optic acoustic wave sensing, the self-vibration noise signal data of the continuous high-voltage pulse discharge device during pulse discharge at the wellhead is obtained; (3) Based on distributed fiber optic acoustic wave sensing, the background noise signal of the continuous high-voltage pulse discharge device in the static state in the wellbore and the feedback vibration signal data during pulse discharge are obtained; (4) Based on the deep residual shrinkage neural network, the self-vibration noise signal data, background noise signal and feedback vibration signal data during pulse discharge are filtered and denoised; (5) Use the improved adaptive complete empirical mode decomposition method and Fourier transform method to perform vibration signal analysis; (6) Perform Hilbert transform to obtain the Hilbert marginal spectrum that reflects the change trend of the eigenmode, and combine it with the Pearson correlation coefficient method to determine the correlation coefficient between each eigenmode and the original vibration signal; (7) Finally, the target reservoir natural frequency is determined based on the Hilbert marginal spectrum and correlation coefficient.

2. The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering according to claim 1, characterized in that: The specific process of step (1) is as follows: (1.1) Locate the perforation positions of oil and gas wells using logging equipment, and record the perforation boundary positions, target reservoir positions, and non-perforated interval positions; (1.2) Bundling the armored optical fiber and the cable of the continuous high-voltage pulse discharge device to achieve synchronous retraction and extension of the optical fiber and the cable of the continuous high-voltage pulse discharge device; (1.3) Use a winch to lower the continuous high-voltage pulse discharge device to the wellhead and the required position underground and perform single or continuous pulse discharge with increasing frequency to generate pulse vibration and stimulate the vibration of the formation around the well.

3. The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering according to claim 2, characterized in that: The optical fiber used in the step (1.2) is a distributed acoustic wave sensing optical fiber.

4. The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering according to claim 3, characterized in that: The cable of the continuous high-voltage pulse discharge device in the step (1.2) can also be a photoelectric composite cable containing optical fiber.

5. The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering according to claim 1, characterized in that: The specific process of step (2) is: use a winch to lift the continuous high-voltage pulse discharge device and suspend it at the wellhead, control the continuous high-voltage pulse discharge device to perform single discharge and continuous discharge with increasing frequency, and collect the vibration signal of the continuous high-voltage pulse discharge device under this condition through optical fiber. This signal is called the self-vibration noise of the continuous high-voltage pulse discharge device.

6. The downhole in-situ measurement method of reservoir natural frequency based on neural network filtering according to claim 1, characterized in that: The specific process of step (3) is as follows: (3.1) A continuous high-voltage pulse discharge device is lowered using a winch. The optical fiber is lowered synchronously with the cable of the continuous high-voltage pulse discharge device. When the continuous high-voltage pulse discharge device reaches the perforation section, it is stopped. The vibration signal detected by the optical fiber determines that the continuous high-voltage pulse discharge device has stopped. The optical fiber is then used to monitor and record the downhole background vibration signal data, which is named the perforation section background noise. The continuous high-voltage pulse discharge device is then controlled to perform a single discharge at this location and a continuous discharge with increasing frequency. The generated pulse shock wave excites the surrounding formation rock mass to vibrate. During the discharge process, the downhole vibration signal is collected through the optical fiber. (3.2) Continue lowering the continuous high-voltage pulse discharge device. When the continuous high-voltage pulse discharge device reaches the target reservoir location, stop lowering the continuous high-voltage pulse discharge device. Monitor the downhole environment using optical fiber. After the continuous high-voltage pulse discharge device stops, start recording data and name it as the target reservoir background noise. Then control the continuous high-voltage pulse discharge device to perform a single discharge and continuous discharge with increasing frequency at the target reservoir location. During the discharge process, collect vibration signals through optical fiber. (3.3) Continue lowering the continuous high-voltage pulse discharge device. Stop lowering the device when it reaches the non-perforation section. Monitor the downhole environment using optical fiber. After the device comes to a stop, begin recording data and name it the non-perforation section background noise. Then, control the device to perform a single discharge and continuous discharge with increasing frequency at that location. During the discharge process, collect vibration signals through optical fiber.

7. The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering according to claim 6, characterized in that: The specific process of step (4) is as follows: the background noise of the vibration signal is filtered out based on the deep residual shrinkage neural network, the self-vibration noise of the continuous high-voltage pulse discharge device, the background noise of the perforation section, the background noise of the target reservoir, and the background noise of the non-perforation section are used as training samples, and the vibration signals collected during the single discharge and the continuous discharge with increasing frequency at the corresponding position are subjected to noise reduction filtering processing based on the deep residual shrinkage neural network.

8. The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering according to claim 1, characterized in that: The specific process of step (5) is as follows: (5.1) Perform modal analysis on the denoised and filtered vibration signal using an improved adaptive complete empirical mode decomposition method. Perform Fourier transform on the eigenmode components obtained from the signal decomposition to extract the eigenmodes within the desired frequency range. Reconstruct the vibration signal from these extracted eigenmodes. (5.2) Perform modal analysis on the reconstructed vibration signal using an improved adaptive complete empirical mode decomposition method. Perform Fourier transforms on the eigenmode components obtained from the signal decomposition. Perform Hilbert transforms on the eigenmodes whose dominant frequencies are below the desired frequency range. Analyze the occurrence time of the dominant frequencies of each mode to obtain the Hilbert marginal spectrum reflecting the variation trend of the eigenmodes. (5.3) The shock wave velocity is calculated based on the difference in the starting time of the shock response between different optical fiber channels and the distance between each channel. The time it takes for the shock wave to reach the wall of each structure is then calculated based on the dimensions of each foundation structure. The time required for the stress wave caused by the vibration of each foundation structure and the target reservoir rock mass to be transmitted to the optical fiber is inverted based on the stress wave velocity in the medium of each foundation structure.

9. The method for downhole in-situ measurement of reservoir natural frequency based on neural network filtering according to claim 8, characterized in that: The specific process of step (6) is: statistically analyze and divide the main frequencies of the eigenmodes extracted from all vibration signals into intervals, and compare the main frequency occurrence time in the Hilbert marginal spectrum of the samples in each interval with the theoretical transmission time, so as to provide a reference for the identification of the natural frequencies of each structure, and then extract the natural frequencies of the target reservoir rock mass.

10. The downhole in-situ measurement method of reservoir natural frequency based on neural network filtering according to claim 1, characterized in that: The specific process of step (7) is: estimating the data points in the natural frequency interval corresponding to the target reservoir based on the strong tracking filter to obtain the universal natural frequency of the target reservoir.

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