Gas storage wellbore leakage sound wave signal extraction method and system based on distributed optical fibers

Abstract

The invention belongs to but is not limited to the technical field of signal extraction, and particularly relates to a gas storage wellbore leakage sound wave signal extraction method and system based on distributed optical fibers, and the method comprises the steps: S1, hardware platform construction: constructing a hardware platform based on a DAS technology; s2, data demodulation: a digital orthogonal demodulation technology is adopted in the demodulation process, and the collected data is decomposed into two mutually orthogonal components, so that phase information is extracted; and S3, shaft leakage sound wave signal extraction: performing algorithm processing according to the demodulated phase information. Compared with the originally collected wellbore leakage sound wave signal, the similarity between the extracted signal and the actual wellbore leakage sound wave signal is higher, and the similarity is mainly characterized in that the difference value of the average amplitude is reduced from 0.21 rad to 0.03 rad, the mean square error is reduced from 0.55 rad2 to 0.17 rad2, and the signal-to-noise ratio is increased by 16.9 dB.

Description

Technical Field

[0001] The present invention belongs to, but is not limited to, the technical fields of signal extraction and underground gas storage and safety monitoring of oil and gas, and particularly relates to a method and system for extracting acoustic wave signals of wellbore leakage in a gas storage reservoir based on distributed optical fiber. Background Art

[0002] During high-intensity operations, the wellbore of a gas storage reservoir is exposed to temperature, pressure changes, and stress, which can easily lead to stress fatigue, wear, and corrosion of the casing within the wellbore, increasing the risk of wellbore leakage. Distributed acoustic sensing (DAS) technology, with its high sensitivity, high temperature and high pressure resistance, corrosion resistance, and electromagnetic interference resistance, can effectively and real-time detect the acoustic vibrations of wellbore leakage and prevent potential safety hazards.

[0003] In recent years, the application research of DAS technology in wellbore leakage detection has been increasing. For example, Li Haitao et al. explored the application potential of DAS technology in downhole dynamic intelligent monitoring and interpretation methods; Juun van der Horst et al. analyzed the sensing capabilities of DAS technology during the production monitoring of oil and gas wells, and its high-efficiency data transmission capabilities are highly practical in wellbores; Wang Wenquan et al. showed through indoor simulation experiments that DAS technology can quickly respond to the vibrations caused by leakage during the leakage monitoring of the wellbore in a salt cavern gas storage reservoir; Zou Xianjian et al. confirmed through simulated wellbore leakage point tests that DAS technology can accurately capture the acoustic wave signals generated by wellbore leakage in an ideal environment. These experiments have strongly confirmed the feasibility of applying DAS technology to wellbore leakage monitoring. However, these experiments mainly focused on the monitoring of vibrations and did not deeply explore the essential characteristics of leakage signals, so it is easy to confuse the vibrations caused by leakage signals with those caused by environmental interference, resulting in misjudgment of leakage signals. Therefore, effectively extracting the acoustic wave signals generated by leakage is a key step in distinguishing between on-site environmental interference and real leakage.

[0004] In view of the above analysis, the technical problems that urgently need to be solved in the existing technology are as follows: In practical engineering applications, extracting acoustic wave signals faces many challenges. Especially in the initial stage of leakage, due to the extremely small leakage aperture, the energy of the generated acoustic wave signals is extremely weak and is easily masked by a large amount of noise. Although DAS technology can accurately capture these weak vibration signals due to its high sensitivity to small vibrations, this characteristic also makes it vulnerable to noise interference, which poses a huge obstacle to its application. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a method and system for extracting acoustic wave signals of wellbore leakage in a gas storage reservoir based on distributed optical fiber.

[0006] The present invention is implemented as follows. A method for extracting acoustic wave signals of wellbore leakage in a gas storage based on distributed optical fiber includes:

[0007] S1, Hardware platform construction: Construct a hardware platform based on DAS technology;

[0008] S2, Data demodulation: The digital quadrature demodulation technology is used in the demodulation process to decompose the collected data into two mutually orthogonal components, thereby extracting the phase information;

[0009] S3, Extraction of wellbore leakage acoustic wave signals: Algorithm processing is performed according to the demodulated phase information.

[0010] Further, the working process of the hardware platform of DAS technology in S1 is as follows: First, the signal processing and controller send an instruction to the laser, and the laser responds to this instruction and emits a narrow linewidth optical pulse with a specific frequency. When the optical pulse enters the first optical fiber coupler, it is divided into two optical pulses. One of the optical pulses is modulated by an acousto-optic modulator to be converted into a detection pulse light. In order to ensure the long-distance stable transmission of the detection pulse light, it also needs to be further processed by an erbium-doped fiber amplifier. The processed detection pulse light is sent into the armored optical cable through a circulator. In the optical cable, the detection pulse light generates Rayleigh scattering, and part of the scattered light returns along the original path, passes through the circulator again, and finally enters the second optical fiber coupler. The other unmodulated optical pulse directly enters the second optical fiber coupler as a local oscillator light source. In the second optical fiber coupler, the returned Rayleigh scattered light is mixed with the optical pulse of the local oscillator light source. The mixed optical signal is converted into an electrical signal through a photodetector. Subsequently, the acquisition card captures these electrical signals and transmits them to the signal processing and controller for data demodulation.

[0011] Further, the process of S2 is to decompose the mixed-frequency signal collected by the acquisition card into two orthogonal components, then perform low-pass filtering processing on the two components, and finally extract the phase value. The mixed-frequency signal collected is as shown in formula (1).

[0012]

[0013] A(t) = A s (t)A l (t) (2)

[0014] In the formula, U(t) is the collected electrical signal, A s (t) is the Rayleigh scattered light signal, A l (t) is the local oscillator light signal, A(t) is the coupled light signal, t is time, Δf is the frequency shift generated after the acousto-optic modulator processes, is the phase information of the optical signal. Subsequently, these signals are multiplied by the locally generated quadrature signals. Through this step, the original signal is decomposed into two quadrature components, which are represented by Equations (3) and (4) respectively:

[0015]

[0016] Furthermore, after the collected electrical signal is multiplied by the quadrature signal, sum-frequency components and difference-frequency components are generated. The frequency of the sum-frequency component is 4πΔf. The high-frequency part in the difference-frequency component has been cancelled out and only contains phase information. After filtering out the sum-frequency signal through a low-pass filter, only the useful phase information remains, and then the phase can be obtained by performing the arctangent function operation Its process is shown in Equations (5) to (8):

[0017]

[0018] where I(t) and Q(t) are the two signals after being processed by the low-pass filter respectively, is the finally demodulated phase signal, providing the original data for subsequent algorithm processing.

[0019] Furthermore, S3 specifically includes:

[0020] First, the CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is processed, and its steps are as follows: First, noise is added and EMD decomposition is performed. The corresponding expressions are as follows:

[0021] x i (t) = x(t) + β 0 n i (t), i = 1, 2,..., n (9)

[0022]

[0023] where x i (t) is the signal after adding noise, x(t) is the phase signal to be demodulated, β 0 is the standard deviation coefficient of the noise, and i is the number of times of adding noise. Then, x i (t) is subjected to EMD decomposition to obtain the first IMF component IMF 1i , because noise is introduced, the components obtained each time will be different. According to the central limit theorem, averaging these variables can obtain the final component Then, the subsequent IMF components are calculated, and the corresponding expressions are as follows:

[0024]

[0025] r 1j r(t) = 1 r(t) + β 1 n m r(t), m = 1, 2, ..., M (12)

[0026]

[0027] In the above formula, r 1 (t) is the first residual. Add new white noise n m to the residual to obtain the processed residual r 1m (t), and then perform EMD decomposition on it to obtain IMF 2m , thus obtaining a new IMF component In the formula, β 1 is the standard deviation coefficient of the noise, and M is the number of times of adding noise in this round. Finally, iterate the remaining IMF components according to this method until the residual becomes a monotonic function or the number of its extreme points is less than or equal to 2, and the decomposition process ends. The corresponding expression is as follows:

[0028]

[0029]

[0030] In the formula represents the k-th component, W is the corresponding number of times of adding noise, and r c (t) is the final residual signal. It can be seen that the original signal x(t) is composed of IMF components and residuals.

[0031] However, the original signal x(t) contains a large amount of noise. At this time, it is necessary to screen out the components with extremely low correlation with the original signal through the correlation coefficient. The corresponding expression for the correlation coefficient used for screening is as follows:

[0032]

[0033] In the formula, r represents the correlation coefficient, n represents the number of samples, x i represents the i-th observation value of the original signal, y i represents the i-th observation value of the IMF component, represents the sample mean of the original signal, represents the sample mean of the IMF component. To prevent screening out the noisy components containing useful information, the threshold setting of the correlation coefficient cannot be too high.

[0034] ​Furthermore, wavelet packet denoising is performed on the remaining IMF components one by one. Through wavelet packet denoising, these components can be more finely divided, so as to more accurately separate noise and effective signals. Finally, the effective signals are recombined to achieve maximum noise reduction and thus maximum extraction of effective signals. In the wavelet packet denoising process, wavelet packet decomposition is first carried out, the purpose of which is to decompose the original signal into a series of wavelet coefficients, and its corresponding expression is as follows:

[0035] W j,k =∫s(t)ψ j,k (t)dt (17)

[0036] ψ j,k (t)=2 -j / 2 ψ(2 -j t-k) (18)

[0037] In the above formula, s(t) is the noisy IMF component after correlation coefficient screening, W j,k is the wavelet packet coefficient, ψ j,k (t) is the form of the wavelet basis function at a specific scale and translation, j represents the decomposition level, and k represents the position of the wavelet function relative to the original signal. Subsequently, threshold processing is carried out, and its expression is as follows:

[0038]

[0039] In the above formula is the wavelet packet coefficient after threshold processing, and λ is the threshold determined according to the noise level. Finally, wavelet packet reconstruction is carried out, and its expression is as follows:

[0040]

[0041] In the above formula is the reconstructed signal, that is, the IMF component after denoising. Assuming that the number of IMF components after correlation coefficient screening is a, the final result after algorithm processing is:

[0042]

[0043] In the above formula is the i-th component after wavelet packet denoising, r c (t) is the residual signal, and z(t) is the final reconstructed signal.

[0044] Another object of the present invention is to provide a distributed fiber-based gas storage wellbore leakage acoustic signal extraction system for implementing the above-mentioned distributed fiber-based gas storage wellbore leakage acoustic signal extraction method, including:

[0045] Hardware platform building module: Build a hardware platform based on DAS technology;

[0046] Data demodulation module: The digital orthogonal demodulation technology is adopted in the demodulation process to decompose the collected data into two mutually orthogonal components, thereby extracting the phase information;

[0047] Wellbore leakage acoustic wave signal extraction module: Perform algorithm processing according to the demodulated phase information.

[0048] Another object of the present invention is to provide a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for extracting the wellbore leakage acoustic wave signal based on distributed optical fiber.

[0049] Another object of the present invention is to provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for extracting the wellbore leakage acoustic wave signal based on distributed optical fiber.

[0050] Another object of the present invention is to provide an information data processing terminal, which includes the wellbore leakage acoustic wave signal extraction system based on distributed optical fiber.

[0051] Combined with the above technical solutions and the solved technical problems, the advantages and positive effects of the technical solution to be protected by the present invention are as follows:

[0052] First, the present invention first builds a data acquisition system based on DAS technology and integrates the CEEMDAN algorithm, correlation coefficient screening and wavelet packet denoising algorithm. The research on the simulation experiment of wellbore leakage in the gas storage reservoir shows that:

[0053] (1) Compared with the original wellbore leakage acoustic wave signal collected, the extracted signal has a higher similarity with the actual wellbore leakage acoustic wave signal, which is mainly manifested in that the difference in the average amplitude is reduced from 0.21rad to 0.03rad, and the mean square error is reduced from 0.55rad 2 to 0.17rad 2 , and the signal-to-noise ratio is increased by 16.9dB.

[0054] (2) The signal extracted by this method retains the original frequency components of the actual wellbore leakage acoustic wave signal, and their frequency domain distributions are all concentrated in 20Hz - 1000Hz.

[0055] (3) This method can clearly distinguish whether the wellbore is in a leakage state, effectively reduce the external interference in the state of no leakage of the simulated wellbore, and provide an accurate basis for judging the wellbore leakage monitoring in a complex environment.

[0056] In summary, the present invention provides certain reference value for the application of distributed fiber optic acoustic sensing technology in the leakage monitoring of gas storage wellbores.

[0057] Second, the technical solution of the present invention has significant expected benefits and commercial value. At present, the fiber optic monitoring technology for gas storage wellbore leakage has not been deployed in domestic underground gas storage facilities, and relevant engineering applications are still blank. The technical solution of the present invention can effectively fill this gap in the field. It is estimated that the single application cost is only about 300,000 yuan. Compared with the mature technology introduced from abroad, it can save more than 1 million yuan, greatly reducing the economic burden of project implementation and having a broad market prospect.

[0058] The present invention has achieved significant technological breakthroughs through the integration and innovation of signal processing algorithms. By adopting a three-stage method of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), correlation coefficient screening, and wavelet packet denoising, the problems of mode mixing, noise interference, and insufficient signal accuracy of traditional methods are overcome. The optimized algorithm effectively improves the signal-to-noise ratio (by 16.9 dB) and reduces the mean square error (by 0.38 rad 2 ), achieving high-precision extraction and recognition of wellbore leakage acoustic signals.

[0059] The optimization of the distributed fiber optic sensing data acquisition system is another important innovation point of the present invention. The system designed based on the Φ-OTDR principle significantly improves the perception ability of weak acoustic signals by introducing a high-sensitivity laser, an acousto-optic modulator, and an erbium-doped fiber amplifier. Experiments show that the system can accurately capture leakage signals under the condition of only a 2-mm leakage aperture, providing technical guarantee for the real-time performance and reliability of wellbore leakage monitoring.

[0060] In response to the complex engineering environment, the present invention constructs a simulated wellbore experimental device to comprehensively verify the technical solution by adjusting the pressure difference between the inner casing and the annulus and introducing background noise. The experimental results show that the method of the present invention can maintain good signal extraction performance under high noise interference, solving the problem that the effectiveness of the algorithm cannot be verified by existing technologies in complex environments and providing strong support for practical engineering applications.

[0061] Compared with existing technologies, the innovation of the present invention is reflected in multiple aspects. The first existing technology only has the function of fatigue monitoring and cannot achieve fatigue regulation; although the second existing technology combines kinesiology taping technology, its effect in improving knee joint dynamic performance and fatigue regulation is limited. Through the optimization of signal processing algorithms, the improvement of fiber optic sensing systems, and the verification of simulated wellbore experiments, the present invention has reached a new technical level in fatigue regulation and application adaptability.

[0062] To further optimize the technical solution, the present invention can be extended in the following directions: First, combine machine learning or deep learning methods with the current signal processing algorithm to further improve the efficiency and accuracy of signal extraction through training the model; Second, improve the hardware design to achieve miniaturization and integration of the data acquisition system, and enhance the portability and on-site adaptability of the device. These optimization measures will lay a solid foundation for the popularization and application of the present invention in the field of gas storage wellbore leakage monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a schematic diagram of the hardware platform structure provided by an embodiment of the present invention;

[0064] Figure 2 is a schematic diagram of the data verification method provided by an embodiment of the present invention;

[0065] Figure 3 is a curve graph showing the relationship between the amplitude of the modulation signal and the change in optical phase provided by an embodiment of the present invention;

[0066] Figure 4 is a flowchart of the algorithm processing provided by an embodiment of the present invention;

[0067] Figure 5 is a schematic diagram of the experimental platform provided by an embodiment of the present invention;

[0068] Figure 6 is a schematic diagram of the simulated wellbore structure provided by an embodiment of the present invention;

[0069] Figure 7 is a schematic diagram of the leakage signals under three different conditions provided by an embodiment of the present invention; among them, (a) is the leakage signal in the wellbore exhaust state with noise added, (b) is the leakage signal after algorithm processing, and (c) is the leakage signal in the non-interference state;

[0070] Figure 8 is a spectrogram of the leakage signals under two different conditions provided by an embodiment of the present invention; among them, (a) is the spectrogram of the leakage signal in the non-interference state, and (b) is the spectrogram of the leakage signal after algorithm processing;

[0071] Figure 9 is a schematic diagram of the signal in the non-leakage state provided by an embodiment of the present invention; among them, (a) is the non-leakage signal of the wellbore without processing, and (b) is the non-leakage signal of the wellbore after algorithm processing;

[0072] Figure 10 is a schematic diagram of the DAS acoustic wave vibration signal automatic recognition and intelligent analysis software provided by an embodiment of the present invention;

[0073] Figure 11 is a software interface diagram of the DAS acoustic wave vibration signal automatic recognition and intelligent analysis software provided by an embodiment of the present invention;

[0074] Figure 12 It is a schematic diagram of the main comparative test results of the DAS device provided by the embodiments of the present invention;

[0075] Figure 13 It is a diagram of the main comparative test results of the DAS device provided by the embodiments of the present invention;

[0076] Figure 14 It is an effect diagram of the DAS device test provided by the embodiments of the present invention;

[0077] Figure 15 It is a schematic diagram of Well JK4-3 provided by the embodiments of the present invention;

[0078] Figure 16 It is a schematic diagram of Analysis Result 1 of the interference noise signal monitored by the DAS device provided by the embodiments of the present invention;

[0079] Figure 17 It is a schematic diagram of Analysis Result 2 of the interference noise signal monitored by the DAS device provided by the embodiments of the present invention;

[0080] Figure 18 It is a schematic diagram of Analysis Result 3 of the interference noise signal monitored by the DAS device provided by the embodiments of the present invention;

[0081] Figure 19 It is a schematic diagram of Analysis Result 4 of the interference noise signal monitored by the DAS device provided by the embodiments of the present invention. Detailed implementation manners

[0082] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0083] In order to improve the accuracy of wellbore leakage monitoring, it is first necessary to construct a high-quality wellbore leakage signal dataset. The construction of the dataset is based on a simulation experimental device. By adjusting the pressure difference inside and outside the wellbore and creating leakage apertures of different sizes, real leakage acoustic signals are collected. At the same time, background noise signals in the non-leakage state are added to ensure that the dataset contains acoustic characteristics under different working conditions and environments. The data is preprocessed, including denoising, normalization and feature extraction, to form standardized data suitable for convolutional neural network training.

[0084] Based on the dataset, a convolutional neural network model suitable for wellbore leakage monitoring is designed. The input of the model is the processed acoustic signal features, represented by a two-dimensional spectrogram, in order to utilize the convolutional layer to extract frequency-domain and time-domain features. The network structure consists of multiple convolutional layers, pooling layers, and fully connected layers. Among them, the convolutional layer is used to extract local features, the pooling layer is used for dimensionality reduction and reducing overfitting, and the fully connected layer is used to synthesize features and output the classification result of the leakage state.

[0085] The dataset is divided into a training set, a validation set, and a test set. The training set is used to train the model, and the optimization objective is to minimize the classification error function (such as cross-entropy loss). During the training process, the model performance is optimized by adjusting hyperparameters (such as learning rate, batch size, and the number of convolutional kernels). The validation set is used to monitor the training effect of the model in real time to prevent overfitting. Finally, the test set is used to evaluate the classification accuracy and robustness of the model to ensure that the model can accurately distinguish between leakage and non-leakage states.

[0086] After training, the convolutional neural network model is deployed into the wellbore leakage monitoring system to detect leakage by analyzing the collected acoustic signals in real time. The results output by the model are analyzed to evaluate its actual application performance in a complex environment, including the accuracy rate, false alarm rate, and missed alarm rate of signal detection. Through the analysis results, the network structure or training strategy is further adjusted to ensure that the model has strong adaptability and can provide a high-precision judgment basis for wellbore leakage monitoring.

[0087] The present invention proposes a method for extracting acoustic signals of wellbore leakage in a gas storage reservoir based on distributed optical fiber. By combining the distributed acoustic sensing technology (DAS) of optical fiber with the digital quadrature demodulation technology, the efficient extraction and analysis of acoustic signals of wellbore leakage in the gas storage reservoir are realized. The following will be described in detail from three main steps: hardware construction, data demodulation, and signal extraction.

[0088] First, a hardware platform based on DAS technology is constructed. This platform includes an optical fiber sensor, an optical pulse excitation system, an optoelectronic conversion module, and a data acquisition unit. The optical fiber sensor is arranged along the wellbore. By sensing the acoustic vibration signals in the environment, it converts the physical characteristics of the acoustic wave into a phase change in the optical fiber. The optical pulse excitation system generates high-frequency optical pulse signals and transmits them in the optical fiber to detect environmental changes. The optoelectronic conversion module converts the returned signals transmitted through the optical fiber into electrical signals to provide input for subsequent data processing.

[0089] The collected optical fiber return signal is subjected to digital quadrature demodulation processing. Digital quadrature demodulation technology decomposes the return signal into mutually orthogonal real and imaginary components, extracting the phase information related to acoustic wave vibration. This process eliminates the interference that the signal may be subject to during transmission, improving the accuracy and robustness of demodulation. At the same time, the demodulated phase information can directly reflect the acoustic wave vibration characteristics in the wellbore, providing basic data for accurately identifying leakage acoustic signals.

[0090] Using the phase information obtained by demodulation, the data is processed through a specially designed signal processing algorithm. The algorithm includes three main steps: filtering, noise suppression, and feature extraction.

[0091] 1. Filtering processing: Adopt frequency-domain or time-domain filtering techniques to remove background noise and irrelevant frequency components, retaining the acoustic signals related to leakage.

[0092] 2. Noise suppression: Further enhance the clarity and stability of the signal by removing random noise and multipath interference.

[0093] 3. Feature extraction: Based on the time, frequency, and phase characteristics of the leakage acoustic signal, extract relevant feature parameters to identify the specific location and intensity of the leakage event.

[0094] After the leakage acoustic signal is extracted, utilize the spatial resolution ability of fiber optic distributed sensing and combine the phase information to accurately locate the leakage position. By analyzing the amplitude change and spectral characteristics of the leakage signal, the severity of the leakage can also be evaluated, providing a scientific basis for the safety monitoring and fault troubleshooting of the gas storage reservoir.

[0095] In summary, the present invention effectively solves problems such as the difficulty in extracting leakage acoustic signals in the wellbore and the low positioning accuracy by building a DAS hardware platform and combining digital quadrature demodulation technology and specific algorithms, and has significant engineering application value.

[0096] 1. Data acquisition system based on DAS technology

[0097] 1.1 Hardware platform construction

[0098] The DAS technology relies on the phase-sensitive optical time domain reflectometry principle (Φ-OTDR), which utilizes the Rayleigh scattering phenomenon that naturally occurs inside the optical fiber. When a laser pulse is injected into the optical fiber, some photons collide with the molecules in the optical fiber material, resulting in scattering. After a certain time delay, these scattered photons will return to the starting end of the optical fiber. When the optical fiber is affected by external environmental vibrations, its refractive index and length will undergo slight changes, which will further affect the phase magnitude of the scattered light. It has been found that the change amplitude of the optical phase is linearly related to the vibration amplitude of the external signal. Therefore, the vibration law of the external signal can be reflected by the change of the scattered light phase. Based on this technical principle, the present invention designs and constructs a hardware platform based on the DAS technology. Figure 1 The schematic diagram of the hardware platform structure is shown as follows.

[0099] Its working process is as follows: First, the signal processing and controller send an instruction to the laser, and the laser responds to this instruction and emits a narrow linewidth optical pulse with a specific frequency. When the optical pulse enters the first optical fiber coupler, it is divided into two optical pulses. One of the optical pulses is modulated by an acousto-optic modulator and transformed into a detection pulse light. To ensure the long-distance stable transmission of the detection pulse light, it also needs to be further processed by an erbium-doped fiber amplifier. The processed detection pulse light is sent into the armored optical cable through a circulator. In the optical cable, the detection pulse light will generate Rayleigh scattering, and part of the scattered light will return along the original path, pass through the circulator again, and finally enter the second optical fiber coupler. The other unmodulated optical pulse directly enters the second optical fiber coupler as a local oscillator light source. In the second optical fiber coupler, the returned Rayleigh scattered light is mixed with the optical pulse of the local oscillator light source. The mixed optical signal is converted into an electrical signal by a photodetector. Subsequently, the acquisition card captures these electrical signals and transmits them to the signal processing and controller for data demodulation.

[0100] 1.2 Data demodulation process

[0101] According to the basic principle of the hardware architecture design, it can be known that the data collected from the hardware platform cannot be directly applied and needs to be demodulated. The demodulation process uses digital quadrature demodulation technology, whose function is to decompose the collected data into two mutually orthogonal components, thereby extracting the phase information. The main process is to decompose the mixed-frequency signal collected by the acquisition card into two orthogonal components, then perform low-pass filtering on the two components, and finally extract the phase value. The mixed-frequency signal collected is shown in formula (1).

[0102]

[0103] A(t) = A s (t)A l (t) (2)

[0104] Where U(t) is the collected electrical signal, and A s (t) is the Rayleigh scattered optical signal, and A l (t) is the local oscillator optical signal, A(t) is the coupled optical signal, t is time, and Δf is the frequency shift generated after the acousto-optic modulator processes. is the phase information of the optical signal. Subsequently, these signals are multiplied by the quadrature signals generated locally. Through this step, the original signal is decomposed into two quadrature components, which are represented by Equations (3) and (4) respectively:

[0105]

[0106] After the collected electrical signal is multiplied by the quadrature signal, sum-frequency components and difference-frequency components are generated. The frequency of the sum-frequency component is 4πΔf. The high-frequency part in the difference-frequency component has been cancelled out and only contains phase information. After filtering out the sum-frequency signal through a low-pass filter, only the useful phase information remains, and then the arctangent function operation is performed to obtain the phase Its process is as shown in Equations (5) to (8):

[0107]

[0108] Where I(t) and Q(t) are the two signals after passing through the low-pass filter respectively, is the finally demodulated phase signal, providing the original data for subsequent algorithm processing.

[0109] 1.3 System Verification

[0110] To ensure the accuracy of the collected data, verification is carried out based on the principle conclusion that the change amplitude of the optical phase is linearly related to the vibration amplitude of the external signal. The verification method is as Figure 2 shown:

[0111] In the figure, the PZT fiber optic vibrator is connected to a certain position of the optical cable. The vibration signal is set through a signal generator, and the hardware platform transmits the demodulated data to the display to show the corresponding parameters. The waveform of the modulation signal generated by the signal generator is set as a 200 Hz sine wave, and 10 different amplitude conditions are set. These amplitudes start from 0.5 V and increase in steps of 0.5 V to 5 V. Each group of experiments is carried out in the same experimental environment, and the corresponding optical phase change values are recorded. The results are as follows Figure 3 shown.

[0112] The results show that the vibration amplitude of the external signal and the change of the optical phase are basically linearly related, meeting the theoretical expectations, and verifying that the data collected and demodulated by this system meet the requirements.

[0113] 2 Wellbore Leakage Acoustic Signal Extraction Algorithm

[0114] According to the demodulated phase information, algorithm processing is carried out. First, the CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is processed, and its steps are as follows: First, noise is added and EMD decomposition is performed. The corresponding expression is as follows:

[0115] x i (t) = x(t) + β 0 n i (t), i = 1, 2,..., n (9)

[0116]

[0117] In the formula, x i (t) is the signal after adding noise, x(t) is the phase signal to be demodulated, β 0 is the standard deviation coefficient of the noise, and i is the number of times of adding noise. Then, EMD decomposition is performed on x i (t) to obtain the first IMF component IMF 1i , because noise is introduced, the components obtained each time will be different. According to the central limit theorem, these variables can be averaged to obtain the final component Then, the subsequent IMF components are calculated. The corresponding expression is as follows:

[0118]

[0119] r 1j (t) = r 1 (t) + β 1 n m (t), m = 1, 2,..., M (12)

[0120]

[0121] In the above formula, r 1 (t) is the first residual. New white noise n m (t) is added to the residual to obtain the processed residual r 1m (t), and then EMD decomposition is performed on it to obtain IMF 2m , so as to obtain a new IMF component In the formula, β 1 is the standard deviation coefficient of the noise, and M is the number of times of adding noise in this round. Finally, the remaining IMF components are iterated according to this method until the residual becomes a monotonic function or the number of its extreme

[0122] value points is less than or equal to 2, and the decomposition process ends. The corresponding expression is as follows:

[0123]

[0124] wherein represents the k-th component, W is the number of times of adding corresponding noise, and r c (t) is the final residual signal. It can be seen that the original signal x(t) is composed of IMF components and residuals.

[0125] However, there is a large amount of noise in the original signal x(t). At this time, it is necessary to screen out the components with extremely low correlation with the original signal through the correlation coefficient. The corresponding expression for the correlation coefficient used for screening is as follows: The corresponding expression for the correlation coefficient used for screening is as follows:

[0126]

[0127] where r represents the correlation coefficient, n represents the number of samples, x i represents the i-th observation value of the original signal, and y i represents the i-th observation value of the IMF component. represents the sample mean of the original signal. represents the sample mean of the IMF component. To prevent screening out the noisy components containing useful information, the threshold setting of the correlation coefficient cannot be too high.

[0128] However, the CEEMDAN algorithm has certain limitations. During its processing, the remaining IMF components obtained by screening often contain white noise. At the same time, it is also difficult to completely remove the noise originally contained in the original signal only by processing with the correlation coefficient. In view of this, wavelet packet denoising processing is carried out one by one for the remaining IMF components. Through wavelet packet denoising processing, these components can be more finely divided, so as to more accurately separate the noise and the effective signal. Finally, the effective signals are recombined to achieve maximum denoising and thus maximum extraction of the effective signal. In the wavelet packet denoising processing flow, wavelet packet decomposition is first performed, and its purpose is to decompose the original signal into a series of wavelet coefficients. The corresponding expression is as follows:

[0129] W j,k = ∫s(t)ψ j,k (t)dt (17)

[0130] ψ j,k (t) = 2 -j / 2 ψ(2 -j t - k) (18)

[0131] In the above formula, s(t) is the noisy IMF component after screening by the correlation coefficient, W j,k is the wavelet packet coefficient, and ψ j,k(t) is the form of the wavelet basis function at a specific scale and translation. j represents the decomposition level, and k represents the position of the wavelet function relative to the original signal. Subsequently, threshold processing is performed, and its expression is as follows:

[0132]

[0133] In the above formula are the wavelet packet coefficients after threshold processing, and λ is the threshold determined according to the noise level. Finally, wavelet packet reconstruction is performed, and its expression is as follows:

[0134]

[0135] In the above formula is the reconstructed signal, that is, the IMF component after denoising. Assuming that the number of IMF components after correlation coefficient screening is a, then the final result after algorithm processing is:

[0136]

[0137] In the above formula is the i-th component after wavelet packet denoising processing, r c (t) is the residual signal, and z(t) is the final reconstructed signal. Figure 4 The total process of algorithm processing is shown as follows.

[0138] 3 Wellbore Leakage Simulation Experiment

[0139] 3.1 Experimental Scheme Design

[0140] To comprehensively verify the effectiveness of this method in extracting acoustic signals when detecting wellbore leakage in gas storage caverns, an experimental device as shown in Figure 5 is built.

[0141] This device mainly consists of the following key components: gas cylinder, simulated wellbore, armored optical cable, hardware platform, and computer. Among them, the gas cylinder is responsible for providing air pressure for the inner casing in the simulated wellbore, thereby forming a pressure difference of 0.8 MPa between the casing and the annulus.

[0142] At a certain position of the inner

[0143] casing of the simulated wellbore, a leakage hole with a diameter of 2 mm is set through a control valve to simulate the leakage point. The armored optical cable is skillfully passed through the annulus and fixed at a specific position on the surface of the inner casing, which is exactly above the leakage hole. One end of the optical cable is led out and connected to the hardware platform, and the hardware platform is responsible for receiving and transmitting the data collected by the optical cable to the computer.

[0144] To more accurately simulate the measurement environment of the actual wellbore, PZT was connected to the optical fiber position near the leakage point to introduce white noise. At the same time, the exhaust port of the simulated wellbore was opened so that the gas in the wellbore could flow out at a certain flow rate, and this process simulated the flow phenomenon in the injection and production process of the gas storage wellbore. The specific structure of the simulated wellbore is as Figure 6 shown.

[0145] 3.2 Analysis of experimental results

[0146] To better observe the experimental effect, the experiment first collected the data when the exhaust port of the wellbore was in the open state and noise addition processing was carried out, and its signal diagram is as Figure 7 (a) shown. The signal after being processed by the algorithm is as Figure 7 (b) shown. To facilitate the comparison of the results after the algorithm processing, the experiment closed the exhaust port of the wellbore, cancelled the noise addition processing, and kept the other experimental conditions unchanged, and collected again without any interference, and the obtained signal is as Figure 7 (c) shown.

[0147] It can be intuitively found through the three results that the leakage signal under the exhaust state of the wellbore and after noise addition processing, that is, Figure 7 (a), shows an obvious situation of being interfered by noise. And the signal after being processed by the extraction algorithm, that is, Figure 7 (b), has successfully filtered out most of the interference. The leakage signal in the interference-free state for reference is as Figure 7 (c) shown. By making a preliminary comparison of these three, it can be clearly observed that the characteristics of the signal after being processed by the algorithm are more similar to the characteristics of the signal in the interference-free state. For a more in-depth analysis, it was fully demonstrated mainly from four aspects: the average amplitude of the signal, the mean square error, the signal-to-noise ratio, and the frequency domain distribution. Through calculation, it was found that the difference in the average amplitude of the signal after being processed by the algorithm decreased from 0.21 rad to 0.03 rad; the mean square error decreased from 0.55 rad 2 to 0.17 rad 2 ; the signal-to-noise ratio increased from 6.02 dB to 22.92 dB. To analyze the frequency domain distribution of the signal, the Fourier transform processing was respectively carried out on the leakage signal after being processed by the algorithm and the leakage signal in the interference-free state, and the results are as Figure 8 shown.

[0148] Comparing Figure 8 (a) and Figure 8From the results in (b), it can be intuitively found that after the leakage signal in the wellbore exhaust state and processed by noise addition is processed by the algorithm, its frequency-domain energy distribution shows a high degree of consistency with the frequency-domain energy distribution of the leakage signal in the interference-free state. Specifically, the energy of both is mainly concentrated in the frequency range of 20 Hz to 1000 Hz, and the energy is stronger in the range of 50 Hz to 200 Hz. Through comprehensive analysis in the time domain and frequency domain, it can be obtained that the wellbore leakage acoustic wave signal extraction algorithm can effectively filter out external interference while well retaining the original characteristics of the leakage signal.

[0149] To further verify the effectiveness of this method, the differences before and after the algorithm processing in the non-leakage state were compared, and the results are as Figure 9 shown.

[0150] In the ideal non-leakage state, the optical phase change should approach 0. The experimental results show that after being processed by the algorithm, the average optical phase change amount in the non-leakage case is only 0.013 rad, which is significantly closer to 0 compared with the unprocessed signal. This result not only proves again the advantage of the anti-interference ability of the method proposed in the present invention, but also can effectively determine whether the wellbore is in a leakage state.

[0151] The gas storage wellbore leakage acoustic wave signal extraction system based on distributed optical fiber provided by the embodiment of the present invention includes:

[0152] Hardware platform construction module: Construct a hardware platform based on DAS technology;

[0153] Data demodulation module: The digital quadrature demodulation technology is used in the demodulation process to decompose the collected data into two mutually orthogonal components, thereby extracting the phase information;

[0154] Wellbore leakage acoustic wave signal extraction module: Perform algorithm processing according to the demodulated phase information.

[0155] The application embodiment of the present invention provides a computer device, which includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the method for extracting the gas storage wellbore leakage acoustic wave signal based on distributed optical fiber.

[0156] The application embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for extracting the gas storage wellbore leakage acoustic wave signal based on distributed optical fiber.

[0157] The application embodiment of the present invention provides an information data processing terminal, which includes a gas storage wellbore leakage acoustic wave signal extraction system based on distributed optical fiber.

[0158] Specific application field of the present invention: the technical field of underground oil and gas storage and safety monitoring. Similar products exist both at home and abroad, but all of them are for on-site engineering application tests in salt cavern gas storage reservoirs.

[0159] By truly simulating the high-temperature and high-pressure environment inside and outside the wellbore of the salt cavern gas storage reservoir, analyzing and processing the acoustic vibration signals detected during the simulation test of wellbore leakage in the gas storage reservoir, and using the method described in the present invention for signal processing and analysis.

[0160] DAS acoustic vibration signal analysis method and its test results:

[0161] Wellbore leakage simulation test device. In Experiment 1, the needle valve of the nitrogen cylinder was opened at 1.4 MPa to inject gas into the pipe. The differential pressure between the tubing and the casing was about 1.4 MPa. The measurement start time was 9:22. After stabilizing the pressure for 18 minutes, the flowmeter showed a drainage volume of 9 L, and the calculated average displacement was 0.75 L / min. As shown in the figure, the DAS test effect was poor. The waterfall plot showed that the abnormal points were unstable, there were many peaks in the amplitude curve, but the overall amplitude energy was low. The noise meter showed that the measurement result was poor, the overall energy was low and distributed disorderly, and there was no obvious concentrated band. It was preliminarily judged that the leakage volume was too small and the instrument accuracy was not sufficient to accurately monitor.

[0162] Since the DAS detection technology is only related to the vibration magnitude generated by the leakage volume, the test only focused on the relationship between the leakage volume and the DAS detection accuracy. A fixed leakage point was set, and the leakage volume was adjusted by continuously increasing the differential pressure, supplemented by noise logging to observe the accuracy of DAS detection. The differential pressure between the inner and outer pipes under actual downhole working conditions generally ranges from 0 to 12 MPa. According to the actual conditions, the size of the experimental leakage volume and the corresponding differential pressure were set. The nitrogen cylinders used in the test also need to meet the corresponding pressure requirements. To ensure the measurement accuracy, the pressure gauge used in the test had a range of 0 - 700 bar and a high precision, and the flowmeter had a range of 2.5 - 30 m3 / h and an accuracy of ±1.5; an armored fiber optic sensor with a sufficient length was used as much as possible to simulate the real well conditions; to ensure the sound collection effect of the noise meter to verify the distributed optical fiber monitoring technology, the test environment was required to be as free from external noise interference as possible.

[0163] To verify the timeliness of distributed optical fiber vibration monitoring for natural gas pipeline leakage, a real physical test was carried out on natural gas pipeline leakage. After the experimental device was processed, a hydraulic test was carried out, and the test results met the test requirements. Finally, an automatic recognition and intelligent analysis software for DAS acoustic vibration signals was formed to determine the leakage depth and area range and give an early warning, as Figure 10 、 11 shown.

[0164] For the wellbore leakage conditions under different air pressures and different leakage rates, the latest DAS equipment developed by the Beijing Institute of Semiconductors was used to conduct leakage and non-leakage tests respectively under the condition that the pressure difference between the inner and outer tanks was 0.1 MPa; small leakage and large leakage tests were conducted under 1.7 MPa; over-range leakage and right-side leakage tests were conducted under 2.7 MPa; and a leakage test was conducted under 3.2 MPa. The main comparison test results of the latest DAS equipment developed by the Beijing Institute of Semiconductors are as Figure 12 , 13 shown:

[0165] For the wellbore leakage conditions under different air pressures and different leakage rates, the latest DAS equipment developed by the Shanghai Institute of Optics and Fine Mechanics was used to conduct tests under the background noise of no leakage (0 L / min), and comparative tests under the conditions of tiny leakage flow rate of about 0.5 L / min, small leakage flow rate of about 2.5 L / min, medium leakage flow rate of about 5.5 L / min, and large leakage flow rate of about 9.5 L / min respectively when the pressure difference between the inner and outer tanks was 3 MPa, 5 MPa, and 7 MPa. The main comparison test results are as follows Figure 14 shown:

[0166] It can be seen from Figure 14 that the DAS equipment has a good test effect, and extremely weak leakage acoustic vibration signals can be detected in the wellbore simulation test device, which basically verifies the feasibility and reliability of the equipment in engineering applications.

[0167] II. Evidence related to the technical effects obtained in the embodiments of the present invention.

[0168] To study the application of distributed optical fiber sensing technology in the integrity monitoring of salt cavern gas storage wellbores and verify the feasibility of this technology in wellbore integrity monitoring, it is necessary to conduct application tests for specific wellheads. Well JK4-3 is located near Xiaoyanqiao, Chenjiazhuang Salt Mine, Jintan District, Changzhou City, Jiangsu Province, and is structurally located in the Zhixi Bridge Sag of the Jintan Basin. For the new well JK4-3, the top of the salt rock in the fourth member of the Lower Tertiary Funing Formation, the target layer for cavity formation, is as Figure 15 , before the production and gas injection operation, there is no gas leakage in the wellbore casing, so this technology mainly conducts leakage monitoring on whether the integrity of the salt cavern gas storage wellbore is damaged.

[0169] First, use a DTS monitoring device to monitor the temperature around the underground wellbore of the salt cavern gas storage. Based on the monitoring data, judge whether the temperature change along the line from the wellhead to the bottom of the well is reasonable, and make a preliminary judgment on the wellbore integrity. Secondly, use a DAS device to analyze the acoustic wave signals around the wellbore of the salt cavern gas storage. Considering the weak acoustic wave vibrations caused by external knocking and other interferences along the optical fiber, further determine whether the integrity of the wellbore of the salt cavern gas storage is intact. That is: if the DTS monitors that the temperature change trend of the composite formation around the wellbore (the temperature gradually increases from top to bottom) and the wellhead and bottom hole temperatures basically conform to the actual situation, it can be preliminarily determined that the integrity of the wellbore of the salt cavern gas storage is intact; if the DAS monitors that the signals along the wellbore are normal, can sense the knocking operation at the wellhead and the vibration of the external well site vibration source vehicle, and there is no signal anomaly, it can be determined that the integrity of the wellbore of the salt cavern gas storage is intact. The well depth is 987.0 m, the salt bottom depth is 1095.0 m, and the thickness is 108 m; the well was drilled to completion on December 14, 2015, with a completion well depth of 1095 m and a casing setting depth of 1005.85 m; the dissolution cavity volume is 77102 m3, and the designed operating pressure of the cavity is 7 - 17 MPa. Therefore, this study monitors the integrity of the underground wellbore of the salt cavern gas storage for JK4-3.

[0170] DAS test results

[0171] During the monitoring of the integrity of the wellbore of the salt cavern gas storage, the DAS acoustic wave vibration signal monitoring of the single-mode optical fiber inside the optical cable was carried out synchronously. Start the laser light source and the photodetector, and use the pulsed laser source to inject short-time optical pulses into the optical fiber. When the optical pulse propagates in the optical fiber, it will generate a phase shift due to external influences. Subsequently, the monitoring system starts to continuously detect and record the phase or amplitude change signals at various positions of the optical fiber. The system analyzes and locates the signals through digital signal processing to judge whether there are abnormal change points, that is, whether the integrity of the wellbore of the salt cavern gas storage may be damaged.

[0172] The distributed fiber optic acoustic wave monitoring technology uses the optical fiber to transmit optical signals. When the optical fiber is affected by external acoustic waves or deformation, the optical signal will change in phase shift. Therefore, through the detection of the phase shift of the optical signal, the DAS system may monitor the influence of the noise vibration around the well site and the acoustic wave vibration influence after the integrity of the wellbore is damaged.

[0173] When conducting distributed fiber optic acoustic wave monitoring of the wellbore in a salt cavern gas storage, it is necessary to consider the impact of ground environmental noise on the monitoring structure. Since when using DAS equipment for the integrity monitoring of the wellbore in a salt cavern gas storage, there are various interference sources and noise sources in the environment, such as the large noise and vibration of the generator in the ground machine room, the hoisting operation of the crane, and the knocking, handling, and bumping of ground construction workers. As a result, the weak acoustic wave vibrations inside the wellbore and the external noise vibrations are mixed together, and finally, the acoustic wave vibration signals monitored by the DAS equipment are interfered by noise. When the external interfering acoustic wave signal is too large, it will have a greater impact on the distributed fiber optic acoustic wave monitoring results. If the intensity of the external interfering acoustic wave is large, it may cover up the real acoustic wave signal, making it difficult to accurately locate the sound source position. Strong external acoustic waves may be misjudged as abnormal events and trigger alarms, thus increasing the possibility of false alarms. In addition, the phase shift signals generated by external interfering acoustic waves will be mixed with the real acoustic wave signals, making the signal processing and analysis more complex. Therefore, to ensure the stability of the integrity monitoring means, corresponding noise reduction measures need to be taken in system design and signal processing, and the filtering ability of the system against external interfering acoustic waves needs to be improved to ensure the monitoring of the target acoustic waves.

[0174] This study focuses on monitoring the acoustic wave signals around the wellbore of a salt cavern gas storage during the process of annulus brine drainage. By analyzing the monitoring data graph of the DAS equipment, it is judged whether there are abnormal leakage points to complete the integrity monitoring of the wellbore of the salt cavern gas storage. Part of the analysis results of the interference noise signals monitored by the DAS equipment are as Figures 16 - 19 shown.

[0175] As Figure 16 can be seen, during the integrity monitoring of the wellbore of a salt cavern gas storage, before the annulus brine drainage is carried out, the main acoustic wave vibration sources come from the noise generated by the ground machine room and the crane hoisting. A large amount of ground interference noise signals penetrate into the wellbore at the exposed position of the multimode fiber fusion of the wellhead optical cable ( Figure 16 where the depth of the optical cable is about 120m), and also enter the optical fiber cable through the steel casing at the positions where the casing is in close contact ( Figure 16 where the depth of the optical cable is about 380m, 670m, 980m, etc.) and are sensed and monitored by the DAS equipment at these positions. During the internal software analysis of the DAS equipment, due to the influence of the built-in high signal-to-noise ratio automatic optimization processing measures in the equipment, Figure 16 the interference noise penetrating into the positions where the depth of the optical cable is about 380m, 670m, 980m, etc. is misjudged as a useful signal source, and thus targeted signal amplification and optimization processing are carried out at these positions. At the same time, the DAS built-in software system determines other positions as interference sources and synchronously performs noise suppression and filtering optimization, and finally forms the monitoring waterfall diagram as Figures 8 - 10 shown.

[0176] Figure 17 and Figure 18Shows the DAS monitoring signals during the process of injecting liquid and discharging brine in the annulus inside the casing. Among them, Figure 17 is the effect diagram of the acoustic wave signals around the underground wellbore monitored by the DAS device 10 minutes after injecting liquid and discharging brine in the annulus inside the casing; Figure 18 is the effect diagram of the acoustic wave signals around the underground wellbore monitored by the DAS device after the injection of liquid and discharge of brine in the annulus inside the casing are basically completed. In addition, the DAS device can also perform frequency band energy spectrum analysis for the depth position, and perform targeted frequency band and energy analysis of the acoustic wave vibration signals for a certain position monitored by the DAS device, as Figure 19 shown. Figure 19 is the frequency band energy spectrum analysis diagram monitored by DAS at a depth of 250 meters of the optical cable inside the casing.

[0177] Through the analysis of Figures 16 - 19 and the analysis of the entire monitoring process, it can be known that the distributed fiber optic DAS device can obtain the distribution of the acoustic wave vibration signals along the wellbore perimeter. The real-time monitoring results of DAS are consistent with the test results obtained in the relatively quiet and airtight underground environment.

[0178] After careful analysis, it can be seen that in individual areas, due to the influence of the acoustic waves generated by the ground and wellhead construction knocking at the well site, the vibrations of ground vehicles and the sounds of personnel construction operations are transmitted along the casing wall of the wellbore, and relatively large interfering acoustic wave vibration signals are generated at the places where the armored optical cable is in close contact with the casing wall, and the signal strength changes to a certain extent with the magnitude of the ground construction operation sounds. The characteristics of the signal strength monitored by this DAS basically conform to the actual situation of the ground construction at the well site. In addition, the regular "beeping" sound in the casing during the injection of water and discharge of brine at a tightly combined place of the wellbore casing inside the casing is also restored. Therefore, it is verified that the distributed DAS device can monitor the acoustic wave vibrations around the wellbore of the salt cavern gas storage, and the real-time monitoring results of the DAS device this time also verify that the integrity of the wellbore of the salt cavern gas storage has not been damaged.

[0179] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated designed hardware. Those of ordinary skill in the art can understand that the above devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and their modules of the present invention can be implemented by hardware circuits of programmable hardware devices such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above hardware circuits and software such as firmware.

[0180] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for extracting acoustic wave signals of gas storage wellbore leakage based on distributed optical fiber, characterized in that: The following steps are involved: Step 1: Build a hardware platform based on distributed optical sensing (DAS) technology; Step 2: Data demodulation, using digital orthogonal demodulation technology to decompose the collected data into two mutually orthogonal components to extract phase information; Step three: extract the wellbore leakage acoustic wave signal and perform algorithm processing based on the phase information obtained by demodulation.

2. The method for extracting acoustic wave signals of gas storage wellbore leakage based on distributed optical fiber according to claim 1, characterized in that: In step 1, the hardware platform workflow based on DAS technology includes the following steps: The signal processing and controller sends instructions to the laser, which responds and emits narrow linewidth light pulses with a specific frequency; After entering the first optical fiber coupler, the optical pulse is divided into two optical pulses. One optical pulse is modulated into a detection pulse light by an acousto-optic modulator and then sent into the armored optical cable after being processed by an erbium-doped fiber amplifier. Another unmodulated optical pulse enters the second optical fiber coupler as a local oscillator light source; In the second fiber coupler, the returned Rayleigh scattered light is mixed with the light pulse of the local oscillator light source. The mixed optical signal is converted into an electrical signal by the photodetector, which is captured by the acquisition card and transmitted to the signal processing and controller for data demodulation.

3. The method for extracting acoustic wave signals of gas storage wellbore leakage based on distributed optical fiber according to claim 1, characterized in that: The process of step 2 includes: Decompose the mixed signal collected by the acquisition card into two orthogonal components; Perform low-pass filtering on the two components; Extract the phase value.

4. The method for extracting acoustic wave signals of gas storage wellbore leakage based on distributed optical fiber according to claim 3, characterized in that: In the step 2, the collected electrical signal is multiplied by the orthogonal signal to generate a sum frequency component and a difference frequency component, the frequency of the sum frequency component is the sum of the frequencies of the two signals, the high frequency part of the difference frequency component is offset, and only the phase information is retained. The sum frequency signal is then filtered out by a low-pass filter, the phase signal is retained, and the phase information is obtained by an inverse tangent function operation.

5. The method for extracting acoustic wave signals of gas storage wellbore leakage based on distributed optical fiber according to claim 1, characterized in that: The step three comprises the following steps: The demodulated phase signal is processed by CEEMDAN algorithm. First, noise is added and empirical mode decomposition is performed to obtain multiple intrinsic mode function (IMF) components and residual signals. Calculate the correlation coefficient between each IMF component and the original signal, and select components with high correlation; The filtered IMF components are subjected to wavelet packet denoising processing one by one, and signal denoising and effective signal extraction are achieved through wavelet packet decomposition, threshold processing and wavelet packet reconstruction. The denoised IMF component is recombined with the residual signal to obtain the denoised effective signal.

6. The method for extracting acoustic wave signals of gas storage wellbore leakage based on distributed optical fiber according to claim 5, characterized in that: In the step three, for the remaining IMF components, wavelet packet decomposition is used to decompose the signal into wavelet packet coefficients of different frequency bands, and the signal is reconstructed after threshold processing to remove noise to the maximum extent and extract effective signals.

7. A distributed optical fiber-based gas storage wellbore leakage acoustic wave signal extraction system for realizing the distributed optical fiber-based gas storage wellbore leakage acoustic wave signal extraction method as claimed in any one of claims 1 to 6, characterized in that: include: Hardware platform building module: building a hardware platform based on DAS technology; Data demodulation module: The demodulation process uses digital orthogonal demodulation technology to decompose the collected data into two mutually orthogonal components, thereby extracting phase information; Wellbore leakage acoustic wave signal extraction module: performs algorithm processing based on the demodulated phase information.

8. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for extracting acoustic wave signals of gas storage wellbore leakage based on distributed optical fiber as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the steps of the method for extracting acoustic wave signals of gas storage wellbore leakage based on distributed optical fiber as described in any one of claims 1 to 6.

10. An information data processing terminal, comprising the distributed optical fiber-based gas storage wellbore leakage acoustic wave signal extraction system according to claim 7.

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