Sound wave signal identification method and device, storage medium and computer program

Abstract

The invention discloses a sound wave signal identification method and device, a storage medium and a computer program. The method comprises the steps that a sound wave logging signal and frequency spectrum information corresponding to the sound wave logging signal are acquired; based on the frequency spectrum information, performing wave field separation on the acoustic logging signal to obtain a separated stratum wave signal; calculating the total standard deviation of the waveform amplitude of each depth point of the stratum wave signal relative to the average waveform amplitude; judging whether the overall standard deviation of the waveform amplitude of the depth point relative to the average waveform amplitude is greater than a preset threshold value or not; and when the total standard deviation of the waveform amplitude of the depth point relative to the average waveform amplitude is greater than a preset threshold value, determining that the waveform of the depth point is a bad track signal. The bad track signal is identified based on the total standard deviation of the waveform amplitude of each depth point of the separated formation wave signal relative to the waveform average amplitude, and the horizontal well acoustic logging bad track identification and logging quality automatic evaluation technology is realized.

Description

Technical Field

[0001] This disclosure relates to the technical field of oil and gas exploration, and in particular to a method, device, storage medium and computer program for acoustic signal identification. Background Technology

[0002] In horizontal wells, dipole acoustic logging is susceptible to interference, leading to signal distortion and complicating subsequent data processing and interpretation. During dipole acoustic logging, the instrument may be affected by wellbore impacts, data loss at individual depth points, or momentary instrument malfunctions, resulting in minor distortion of the acoustic signal. This data is typically considered invalid or "bad path" signals. Typical characteristics of bad path signals include: an uneven baseline, abnormal waveform amplitude, difficulty in effectively identifying formation wave arrival times and valid formation signals from the waveform, significant phase differences compared to dipole acoustic logging signals from upper and lower formations, and scattered frequency distribution and unconcentrated energy in the frequency domain. These bad path signals negatively impact acoustic time difference extraction, anisotropy calculation, and acoustic reflection imaging. Therefore, it is necessary to identify and effectively correct bad path signals in dipole acoustic logging data.

[0003] Currently, the identification of dipole acoustic bad passage signals mainly relies on the amplitude method. This method uses waveform amplitude values ​​and preset thresholds to identify bad passages at various depth points. If the waveform amplitude value at a certain depth point exceeds the threshold, the waveform at that depth point is considered a bad passage. For correction of dipole acoustic bad passage signals, the waveform in the bad passage is replaced with a waveform from an adjacent non-bad passage. Typically, a non-bad passage adjacent to the upper part of the bad passage is selected to replace the bad passage waveform. If multiple bad passages appear consecutively, the non-bad passage adjacent to the uppermost bad passage completely replaces the bad passage. The problem with the above-mentioned waveform amplitude method for identifying dipole acoustic bad passage signals is that the waveform amplitude of dipole acoustic waves alternates between positive and negative over time, and the change in the average amplitude (or the average of the absolute values ​​of the amplitude) after superposition cannot objectively reflect the characteristics of the bad passage. The problem with the dipole acoustic bad passage signal correction method using the adjacent non-bad passage signal replacement method is that it cannot truly and accurately reflect the true wavefield information of the formation, such as the acoustic velocity and energy within the depth range of the bad passage signal, which will introduce significant errors into the quality of dipole acoustic logging data and formation evaluation. Summary of the Invention

[0004] To address the aforementioned technical issues, this disclosure provides a method, apparatus, storage medium, and computer program for acoustic signal recognition.

[0005] In a first aspect, this disclosure provides a method for identifying acoustic signals, including:

[0006] Acquire acoustic logging signals and the corresponding spectral information of the acoustic logging signals;

[0007] Based on the spectral information, wave field separation is performed on the acoustic logging signal to obtain the separated formation wave signal;

[0008] Calculate the overall standard deviation of the waveform amplitude at each depth point of the formation wave signal relative to the average waveform amplitude;

[0009] For each depth point, determine whether the overall standard deviation of the waveform amplitude at that depth point relative to the average waveform amplitude is greater than a preset threshold.

[0010] When the overall standard deviation of the waveform amplitude at the depth point relative to the average waveform amplitude is greater than a preset threshold, the waveform at that depth point is determined to be a bad channel signal.

[0011] In some embodiments, the step of performing wavefield separation on the acoustic logging signal based on the spectral information to obtain the separated formation wave signal includes:

[0012] The acoustic logging signal is subjected to discrete wavelet transform to obtain a multi-level decomposed signal in the wavelet domain;

[0013] Based on the spectrum information, the noise threshold of the interference signal in the acoustic logging signal is determined;

[0014] Based on the noise threshold, random interference signals in each level of the decomposed signal are removed to obtain the separated formation wave signals.

[0015] In some embodiments, the step of determining the noise threshold of the interference signal in the acoustic logging signal based on the spectrum information includes:

[0016] Based on the spectral information, the intensity and frequency distribution characteristics of formation wave signals and interference signals in the acoustic logging signal are determined;

[0017] The noise threshold of the interference signal is determined based on the intensity and frequency distribution characteristics of the formation wave signal and the interference signal.

[0018] In some embodiments, the population standard deviation is expressed as follows:

[0019]

[0020] Among them, X i For a given moment in a wave train, n is the number of data points in the wave train, QC_Ave is the average amplitude, and QC_Std is the population standard deviation.

[0021] In some embodiments, the method further includes: correcting the bad sector signal based on a non-bad sector signal adjacent to the bad sector signal.

[0022] In some embodiments, the step of correcting the bad sector signal based on a non-bad sector signal adjacent to the bad sector signal includes:

[0023] Determine the non-bad sector signal adjacent to the bad sector signal, and determine the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal;

[0024] Based on the matching position of the in-phase axis, the bad sector signal is corrected using bilinear interpolation.

[0025] In some embodiments, the step of determining a non-bad sector signal adjacent to the bad sector signal and determining the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal includes:

[0026] Calculate the cross-correlation coefficient of the non-bad sector signals adjacent to the bad sector signal;

[0027] The in-phase axis matching position of the non-bad sector signal and the bad sector signal is determined based on the largest cross-correlation coefficient among the cross-correlation coefficients.

[0028] In a second aspect, this disclosure provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0029] Thirdly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described above.

[0030] Fourthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0031] This disclosure provides a method, device, storage medium, and computer program for acoustic signal identification. By determining the separated formation wave signals, bad channel signals are identified based on the overall standard deviation of the waveform amplitude at each depth point of the separated formation wave signals compared to the average waveform amplitude. This enables the identification of bad channels in horizontal well acoustic logging and automatic evaluation of logging quality. It effectively solves the problem of automatic evaluation of acoustic logging data and correction of bad channel signals under complex horizontal well conditions, ensuring the reliability of acoustic logging data in applications such as acoustic time difference extraction, anisotropy calculation, and acoustic reflection imaging. Attached Figure Description

[0032] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0033] Figure 1 A schematic flowchart of an acoustic signal recognition method provided in an embodiment of this disclosure;

[0034] Figure 2 A schematic flowchart of an acoustic signal recognition method provided in an embodiment of this disclosure;

[0035] Figure 3 A simplified flowchart of an acoustic signal recognition method provided in this embodiment of the present disclosure;

[0036] Figure 4 Acoustic logging signals and spectrum diagrams provided in embodiments of this disclosure;

[0037] Figure 5 A time-frequency analysis result diagram of a single-pole sonic logging signal provided in an embodiment of this disclosure;

[0038] Figure 6 A time-frequency analysis result diagram of the acoustic logging dipole signal provided in the embodiments of this disclosure;

[0039] Figure 7 A comparison diagram of acoustic logging signals and separated formation wave signals provided in the embodiments of this disclosure;

[0040] Figure 8 Bad sector signal identification diagram provided in this embodiment of the disclosure;

[0041] Figure 9 A schematic diagram of bilinear interpolation provided in this embodiment of the disclosure;

[0042] Figure 10 A diagram showing the result of bad sector signal correction provided in an embodiment of this disclosure.

[0043] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0045] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0046] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0047] Example 1

[0048] Figure 1 This is a schematic flowchart illustrating a sound wave signal recognition method provided in an embodiment of this disclosure. Figure 1 As shown, a method for identifying acoustic signals includes:

[0049] Step 110: Obtain the acoustic logging signal and the corresponding spectrum information of the acoustic logging signal.

[0050] In one embodiment, a suitable acoustic logging instrument is selected to acquire acoustic logging signals based on the exploration target and formation characteristics. Common acoustic logging instruments include single-transmitter dual-receiver, dual-transmitter dual-receiver, or multi-array acoustic logging instruments.

[0051] In one embodiment, after acquiring the acoustic logging signal, mathematical methods such as Fourier transform can be used to convert the acoustic logging signal from the time domain to the frequency domain, thereby obtaining spectral information. Analyzing the spectral information corresponding to the acoustic logging signal can reveal the intensity and frequency distribution characteristics of the formation wave signal and interference signal in the acoustic signal.

[0052] In one embodiment, the dipole acoustic logging signal is converted to a standard depth, laying the data foundation for subsequent logging quality evaluation and correction.

[0053] Step 120: Based on the spectrum information, perform wave field separation on the acoustic logging signal to obtain the separated formation wave signal.

[0054] In this embodiment, based on the spectrum information, the noise threshold in the acoustic logging signal is determined, and wave field separation is performed on the acoustic logging signal according to the noise threshold to obtain the separated formation wave signal.

[0055] Wavefield separation refers to the process of effectively separating different types of wavefield signals in seismic records or acoustic wave propagation. In this embodiment, wavefield separation of acoustic logging signals, and the separation of formation wave signals and noise signals, facilitate the accurate identification of bad channel signals in the subsequent process.

[0056] In one embodiment, noise and formation wave signals in acoustic logging signals can be separated by wavelet transform, thereby achieving wavefield separation of horizontal well dipole acoustic logging signals, and realizing denoising processing of dipole logging data and accurate extraction of formation signals.

[0057] Step 130: Calculate the overall standard deviation of the waveform amplitude at each depth point of the formation wave signal relative to the average waveform amplitude.

[0058] In this embodiment, the average amplitude of the waveform is first determined, and then the overall standard deviation of the waveform amplitude at each depth point of the formation wave signal relative to the average amplitude of the waveform is determined. The difference between the waveform amplitude at each depth point and the average amplitude is determined by the standard deviation. When the overall standard deviation is larger, it indicates that the data dispersion is also greater, that is, the data points are more dispersed. When the standard deviation is smaller, it indicates that the data dispersion is smaller, that is, the data points are more concentrated.

[0059] For example, dipole acoustic waves can be simplified as XX = Asin(ωt + φ). As time increases, the waveform amplitude exhibits alternating positive and negative characteristics. The change in the average amplitude (or the average of the absolute values ​​of the amplitude) after superposition cannot objectively reflect the characteristics of bad passages. In this embodiment, the overall standard deviation method based on the average amplitude is used to identify bad passage signals in horizontal well dipole acoustic logging data, thereby improving the accuracy of bad passage signal identification.

[0060] Step 140: For each depth point, determine whether the overall standard deviation of the waveform amplitude at the depth point relative to the average waveform amplitude is greater than a preset threshold.

[0061] In this embodiment, for each depth point, the overall standard deviation of the waveform amplitude of the formation wave signal at each depth point relative to the average waveform amplitude is compared with a preset threshold. That is, the overall standard deviation of the average waveform amplitude of the horizontal well even-order acoustic logging waveform is quantitatively evaluated using the standard deviation calculation formula based on the quantitative result of the average waveform amplitude, thus providing a data basis for the identification of bad channels in horizontal well even-order acoustic logging.

[0062] Step 150: When the overall standard deviation of the waveform amplitude at the depth point relative to the average waveform amplitude is greater than a preset threshold, the waveform at the depth point is determined to be a bad channel signal.

[0063] In this embodiment, a preset threshold is set according to the actual situation. When the overall standard deviation of the waveform amplitude of the depth point relative to the average waveform amplitude is greater than the preset threshold, the waveform of the depth point is determined to be a bad channel signal; when the overall standard deviation of the waveform amplitude of the depth point relative to the average waveform amplitude is less than the preset threshold, the waveform of the depth point is determined to be a non-bad channel signal.

[0064] In this embodiment, by determining the separated formation wave signals, bad passage signals are identified based on the overall standard deviation of the waveform amplitude at each depth point of the separated formation wave signals compared to the average waveform amplitude. This enables the identification of bad passages and automatic evaluation of logging quality in horizontal wells using acoustic logging. It effectively solves the problem of automatic evaluation of even-order acoustic logging data and correction of bad passage signals under complex horizontal well conditions, ensuring the reliability of acoustic logging data in applications such as acoustic time difference extraction, anisotropy calculation, and acoustic reflection imaging.

[0065] Example 2

[0066] Based on the above embodiments, the step of performing wavefield separation on the acoustic logging signal based on the spectral information to obtain the separated formation wave signal includes:

[0067] The acoustic logging signal is subjected to discrete wavelet transform to obtain a multi-level decomposed signal in the wavelet domain;

[0068] Based on the spectrum information, the noise threshold of the interference signal in the acoustic logging signal is determined;

[0069] Based on the noise threshold, random interference signals in each level of the decomposed signal are removed to obtain the separated formation wave signals.

[0070] In this embodiment, discrete wavelet transform is used to separate the wave field of the acoustic logging signal. By setting different noise thresholds, the noise signal and the formation wave signal are separated, and then the signal is reconstructed to obtain the separated formation wave signal.

[0071] Specifically, taking the sonic logging signal as a horizontal formation dipole sonic logging signal as an example, the continuous wavelet transform (CWT) of the one-dimensional signal is as follows:

[0072]

[0073] In the formula, a is the scale parameter, b is the time shift parameter, t is time, and x(t) is the time series signal of the sonic logging while drilling. These are wavelet basis functions.

[0074] The continuous wavelet is discretized to obtain the discrete wavelet (DWT). The scale parameter 'a' is discretized according to a power series, where a = 2.m The time shift parameter b is discretized using binary methods, and its value is b = n.2. m Where m and n are integers. This invention extends the original data length of dipole acoustic logging to a = 256, satisfying the Nyquist sampling theorem.

[0075] The wavelet transform (DWT) after discretization is:

[0076]

[0077] Therefore, by performing discrete wavelet transform on the raw data of horizontal well dipole acoustic logging, m-level decomposed signals in the wavelet domain are obtained. These signals consist of two parts: detail components and smoothing components. Although wavelet domain signals are not equivalent to frequency domain signals, the detail components can approximate the high-frequency band of the raw acoustic logging signal, and the smoothing components approximate the low-frequency band. After multi-level wavelet transform decomposition, thresholds are set for each level of detail components, random interference signals are removed, and then the signals are reconstructed to obtain the formation wave signals.

[0078] Noise thresholds are typically set based on Gaussian distributions, such as soft thresholding and hard thresholding. The theoretical premise is that the wavelet detail components of the useful signal are greater than the noise detail components. However, the random interference signals at different depths during drilling acoustic logging are strong, which does not fully meet this premise. Spectral analysis has clearly shown that drilling acoustic logging is dominated by low-frequency interference, with the main noise energy concentrated in the wavelet detail components. Therefore, the inverse wavelet transform is improved by setting the wavelet weight coefficients K for each stage between 0 and 1 based on time-frequency analysis results. The K value for the low-frequency band is set to approach 0, and the K value for the high-frequency band is set to 1.

[0079]

[0080] In this embodiment, after wavelet transform wave field separation, the formation wave signal is separated, and low-frequency noise interference signal is effectively removed.

[0081] Example 3

[0082] Based on the above embodiments, the step of determining the noise threshold of the interference signal in the acoustic logging signal based on the spectrum information includes:

[0083] Based on the spectral information, the intensity and frequency distribution characteristics of formation wave signals and interference signals in the acoustic logging signal are determined;

[0084] The noise threshold of the interference signal is determined based on the intensity and frequency distribution characteristics of the formation wave signal and the interference signal.

[0085] In this embodiment, based on the spectral information, the intensity and frequency distribution characteristics of the formation wave signal and the interference signal in the acoustic logging signal are determined, and the noise threshold of the interference signal can be determined according to the intensity and frequency distribution characteristics of the formation wave signal and the interference signal.

[0086] In one embodiment, the acoustic logging signal is taken as an example of a horizontal formation dipole acoustic logging signal. The acoustic frequencies of dipole acoustic monopole logging (MP) in the horizontal formation are also divided into several distinct bands, but the amplitude in the low-frequency range is very high, indicating significant low-frequency interference. Specifically, the dipole XX waveform exhibits distortion in local well sections, with its spectrum showing a typical bimodal characteristic. The first peak corresponds to low-frequency interference, and its high amplitude indicates significant interference. The second peak corresponds to the formation signal, and its amplitude is even higher than the first peak, indicating that the formation signal measured by dipole acoustic logging in the horizontal well is stronger than the interference signal. These two can be identified through spectral analysis.

[0087] To further analyze the characteristics of the dipole acoustic signal, time-frequency analysis was conducted. The analysis of the time-frequency characteristics of both monopole and dipole acoustic logging signals showed that the original dipole acoustic logging signal in horizontal wells contained significant interference. Spectral analysis revealed high amplitude interference in the low-frequency band, indicating a large number of interference signals with low-frequency characteristics. The mid-to-high frequency bands exhibited relatively concentrated and high-amplitude characteristics, suggesting that dipole acoustic logging can still receive strong formation signals even under conditions of significant interference. Furthermore, the dominant frequency of the monopole signal was 8-10 kHz, while the interference signal was typically below 3 kHz; the dominant frequency of the dipole signal was between 2-3.5 kHz, while the interference signal was typically below 2 kHz.

[0088] Example 4

[0089] Based on the above embodiments, the formula for calculating the population standard deviation is as follows:

[0090]

[0091] Among them, X i For a given moment in a wave train, n is the number of data points in the wave train, QC_Ave is the average amplitude, and QC_Std is the population standard deviation.

[0092] In this embodiment, based on the average amplitude of the waveform, the overall standard deviation of the waveform amplitude at each depth point of the formation wave signal relative to the average amplitude of the waveform can be determined.

[0093] Specifically, the formula for calculating the average amplitude of the waveform is:

[0094]

[0095] Among them, X i This represents the data at a specific moment in a wave train, where n is the number of data points in the wave train, and QC_Ave is the average amplitude.

[0096] In existing technologies, the main method for identifying anomalies in dipole acoustic logging data is the waveform amplitude method. This method uses the waveform amplitude value and a preset threshold to identify bad channels in the waveform at each depth point. If the waveform amplitude value at a certain depth point is greater than the threshold value, then the waveform at that depth point is determined to be a bad channel. In fact, dipole acoustic waves can be simplified as XX = Asin(ωt + φ). As time increases, the waveform amplitude exhibits alternating positive and negative characteristics. The change in the average amplitude (or the average of the absolute values ​​of the amplitude) after superposition cannot objectively reflect the characteristics of bad channels. Therefore, in this embodiment, a method based on the overall standard deviation of the average amplitude is used to identify bad channel signals in horizontal well dipole acoustic logging data, reducing the possibility of misjudgment in bad channel signal identification and improving the accuracy of bad channel identification.

[0097] Example 5

[0098] Based on the above embodiments, the method further includes: correcting the bad sector signal according to a non-bad sector signal adjacent to the bad sector signal.

[0099] In this embodiment, after identifying the bad passage signal, it is also necessary to correct it. Specifically, the bad passage signal is corrected based on the non-bad passage signals adjacent to it. By correcting the bad passage signal, reliable and high-quality sonic logging data can be ensured for reservoir evaluation.

[0100] Example 6

[0101] Based on the above embodiments, the step of correcting the bad sector signal according to the non-bad sector signal adjacent to the bad sector signal includes:

[0102] Determine the non-bad sector signal adjacent to the bad sector signal, and determine the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal;

[0103] Based on the matching position of the in-phase axis, the bad sector signal is corrected using bilinear interpolation.

[0104] Because the acoustic signal wave train is continuously sampled and the propagation speed of acoustic waves varies in different formations, there may be differences in the effective waveform phase above and below the interpolation point. This leads to inconsistencies in the phase axis of the interpolated acoustic waveform, failing to accurately reproduce the formation acoustic logging information. In this embodiment, based on the non-bad passage signal adjacent to the bad passage signal, the phase axis matching position of the bad passage signal adjacent to it is first determined. Then, the bilinear interpolation method is used to correct the bad passage signal, ensuring a better interpolation correction effect.

[0105] Example 7

[0106] Based on the above embodiments, the step of determining the non-bad sector signal adjacent to the bad sector signal and determining the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal includes:

[0107] Calculate the cross-correlation coefficient of the non-bad sector signals adjacent to the bad sector signal;

[0108] The in-phase axis matching position of the non-bad sector signal and the bad sector signal is determined based on the largest cross-correlation coefficient among the cross-correlation coefficients.

[0109] In this embodiment, a bilinear interpolation method based on cross-correlation coefficients is used to correct acoustic bad channel signals. Specifically, firstly, the effective acoustic signals above and below the bad channel signal are matched in phase axis position according to the cross-correlation principle, and then tilted bilinear interpolation is performed to obtain a better interpolation correction effect.

[0110] The basic formula for calculating the cross-correlation coefficient of adjacent effective acoustic signals to the bad channel signal is as follows:

[0111] Xi = Wave(ki × step)

[0112] Yi = Wave(ki × step)

[0113]

[0114] Rmax = Max(Ri) where Xi and Yi are the effective acoustic signals within a given window length above and below the bad channel, respectively. Let Xi and Yi be the average values, respectively; n be the number of data points within a given window length; Ri be the cross-correlation coefficient; Rmax be the maximum cross-correlation coefficient within a given window length; step be the step size; and ki be the i-th step size.

[0115] In one embodiment, firstly, based on the separation of the original dipole acoustic data wave field to eliminate the influence of interference signals, a window length of 25 is given. The window is moved according to a certain step size to obtain a set of cross-correlation coefficients. Then, the maximum value of the cross-correlation coefficients is compared to obtain the maximum correlation coefficient. Finally, the optimal matching position of the effective acoustic signals above and below the corresponding bad channel signal is determined.

[0116] Based on the optimal matching position of the effective acoustic signals above and below the bad sector signal, select the known point Q. 11 (x1, y1), Q 12 (x1, y2), Q 21 (x2, y1), Q 22 (x2, y2), through the principle of bilinear interpolation ( Figure 6 The calculation formula is used to obtain the interpolation value of point P(x,y): First, linear interpolation is performed in the x direction to obtain R1 and R2. Then, the interpolation value of point P(x,y) is obtained by interpolating the calculated R1 and R2 in the y direction.

[0117] The basic formula for bilinear interpolation is:

[0118]

[0119] In this way, the static correction of the horizontal well dipole acoustic logging signal is achieved by using bilinear interpolation method at known points on the phase axis matching position, laying a reliable and high-quality dipole acoustic logging data foundation for reservoir evaluation.

[0120] Example 8

[0121] Based on the above embodiments, this embodiment provides an application example. For example... Figures 2 to 10 As shown, this embodiment is applied to the dipole sonic logging process in horizontal wells. Dipole sonic logging technology plays an important role in identifying lithology, assessing the degree and direction of formation anisotropy, and assisting in judging fracture development, fluid saturation, and rock stress loading. However, due to the complex well conditions of horizontal wells, bad channel signals in dipole sonic logging can adversely affect the application of dipole sonic data in sonic transit time extraction, anisotropy calculation, and sonic reflection imaging. This embodiment provides a sonic signal identification method to accurately identify and effectively correct bad channel signals in dipole sonic logging data.

[0122] like Figures 2 to 3 As shown, the acoustic signal recognition method includes:

[0123] Step 1: Loading horizontal well dipole sonic logging data.

[0124] In this embodiment, the horizontal well dipole acoustic logging data is converted to a standard depth to lay a data foundation for subsequent logging quality evaluation and correction.

[0125] Step 2: Time-frequency analysis of dipole acoustic logging signals in horizontal wells.

[0126] like Figure 4 As shown, the spectrum of dipole acoustic logging is obtained by Fourier transform, and the intensity and frequency distribution characteristics of formation wave signals and interference signals are analyzed and judged.

[0127] Among them, such as Figures 5 to 6 As shown, the acoustic frequencies of dipole acoustic waves and monopole MP (MP) measurements in the horizontal well section are divided into several distinct bands, but the amplitude in the low-frequency range is very high, indicating significant low-frequency interference. The dipole XX waveform exhibits distortion in local well sections, with its spectrum showing a typical double-peak characteristic. The first peak corresponds to low-frequency interference, and its amplitude is very high, indicating significant interference. The second peak corresponds to the formation signal, and its amplitude is even higher than the first peak, indicating that the formation signal measured by dipole acoustic waves in the horizontal well is stronger than the interference signal. The two can be identified through spectrum analysis.

[0128] To further analyze the characteristics of the dipole acoustic signal, a time-frequency analysis was performed. Based on the time-frequency analysis of monopole and dipole logging signals of the dipole acoustic wave, such as... Figure 5 and Figure 6 As shown, the original signals from horizontal well dipole acoustic logging contain a large amount of interference. Spectral analysis reveals that the low-frequency band has high amplitude and contains numerous interference signals with low-frequency characteristics. The mid-to-high frequency bands exhibit relatively concentrated and high-amplitude characteristics, indicating that dipole acoustic logging can still receive strong formation signals even under conditions of significant interference. The dominant frequency of the monopole signal is 8-10 kHz, while the interference signal is typically below 3 kHz; the dominant frequency of the dipole signal is between 2-3.5 kHz, while the interference signal is typically below 2 kHz.

[0129] In summary, both the frequency and time domains exhibit partial overlap, making it difficult to completely separate the effective signal from the noise using a single threshold. The above analysis demonstrates that employing a time-frequency combined approach in the wavelet domain can effectively determine the threshold range for both signal and noise, laying the foundation for the separation and extraction of dipole acoustic logging wavefield signals.

[0130] Step 3: Separation of the original signal wave field from the horizontal well dipole acoustic logging.

[0131] In this embodiment, discrete wavelet transform is used to separate the wave field of the original signal of horizontal well dipole acoustic logging. By setting different weight coefficients k, noise and formation signals are separated, and then the signal is reconstructed.

[0132] In this embodiment, since the interference signal is mainly low frequency and the Fourier transform in the frequency domain denoising method has problems such as the Gibbs effect, the present invention uses an improved wavelet transform to separate the original signal wave field of horizontal well dipole acoustic logging, and performs multi-resolution analysis, signal decomposition and reconstruction of the signal.

[0133] The continuous wavelet transform (CWT) of a one-dimensional signal is:

[0134]

[0135] In the formula, a is the scale parameter, b is the time shift parameter, t is time, and x(t) is the time series signal of the sonic logging while drilling. These are wavelet basis functions.

[0136] The continuous wavelet is discretized to obtain the discrete wavelet (DWT). The scale parameter 'a' is discretized according to a power series, where a = 2. m The time shift parameter b is discretized using binary methods, and its value is b = n.2. m Where m and n are integers. This invention extends the original data length of dipole acoustic logging to a = 256, satisfying the Nyquist sampling theorem.

[0137] The wavelet transform (DWT) after discretization is:

[0138]

[0139] Therefore, by performing discrete wavelet transform on the raw data of horizontal well dipole acoustic logging, m-level decomposed signals in the wavelet domain are obtained. These signals consist of two parts: detail components and smoothing components. Although wavelet domain signals are not equivalent to frequency domain signals, the detail components can approximate the high-frequency band of the raw acoustic logging signal, and the smoothing components approximate the low-frequency band. After multi-level wavelet transform decomposition, thresholds are set for each level of detail components, random interference signals are removed, and then the signals are reconstructed to obtain the formation wave signals.

[0140] Noise thresholds are typically set based on Gaussian distributions, such as soft thresholding and hard thresholding. The theoretical premise is that the wavelet detail components of the useful signal are greater than the noise detail components. However, the random interference signals at different depths during drilling acoustic logging are strong, which does not fully meet this premise. Spectral analysis has clearly shown that drilling acoustic logging is dominated by low-frequency interference, with the main noise energy concentrated in the wavelet detail components. Therefore, the inverse wavelet transform is improved by setting the wavelet weight coefficients K for each stage between 0 and 1 based on time-frequency analysis results. The K value for the low-frequency band is set to approach 0, and the K value for the high-frequency band is set to 1.

[0141]

[0142] like Figure 7 As shown, the fourth channel is the waveform after wavelet transform filtering. After wavelet transform wave field separation, the formation wave signal is separated, and the low-frequency noise interference signal is effectively removed.

[0143] Step 4: Calculate the average amplitude of the dipole acoustic logging waveform in the horizontal well.

[0144] In this embodiment, the average amplitude of the waveform in horizontal well dipole acoustic logging is quantitatively evaluated using the waveform average amplitude calculation formula, providing a data basis for using the overall standard deviation of the waveform average amplitude.

[0145] In this study, dipole acoustic waves can be simplified as XX = Asin(ωt + φ). As time increases, the waveform amplitude exhibits alternating positive and negative characteristics. The change in the average amplitude (or the average of the absolute values ​​of the amplitudes) after superposition cannot objectively reflect the characteristics of bad passages. Therefore, this embodiment uses the overall standard deviation method based on the average amplitude to identify bad passage signals in horizontal well dipole acoustic logging data, thereby achieving data quality control.

[0146] Step 5: Calculate the overall standard deviation of the average amplitude of the horizontal well dipole acoustic logging waveform.

[0147] In this embodiment, the average amplitude of the horizontal well dipole acoustic logging waveform is quantitatively evaluated using the standard deviation calculation formula, thus providing a data basis for the identification of bad channel signals in horizontal well dipole acoustic logging.

[0148] Specifically, the formula for calculating the population standard deviation based on the mean amplitude is:

[0149]

[0150] In the formula, Xi is the data at a certain moment in a wave train, n is the number of data in the wave train, QC_Ave is the average amplitude, and QC_Std is the population standard deviation.

[0151] Step 6: Identification of bad passage signals in horizontal well dipole acoustic logging.

[0152] In this embodiment, the identification threshold σ is set according to the actual situation. When QC_Std>σ, it is regarded as a bad sector signal.

[0153] like Figure 8 As shown, the average amplitude QC_Ave at depths of 5973.5m, 5979m, and 5994m did not change significantly, indicating that QC_Ave cannot effectively indicate bad passage signals. However, QC_Std showed obvious finger-like spikes in the above well sections, indicating waveform anomalies, which were judged as bad passage signals. Based on the waveform and frequency analysis results ( Figure 5The presence of dipole acoustic logging anomalies at the aforementioned depths has been confirmed in the third and fourth channels.

[0154] In this embodiment, an identification threshold σ is set according to the actual situation. When the overall standard deviation of the average amplitude of the waveform is greater than the threshold σ, it is regarded as a bad channel signal, and steps 7-8 are performed to correct the bad channel signal. When the overall standard deviation of the average amplitude of the waveform is less than the threshold σ, it is regarded as a non-bad channel signal, and step 9 is performed to evaluate the reservoir.

[0155] Step 7: Calculate the cross-correlation coefficient of the effective acoustic signals above and below the bad channel signal in the horizontal well dipole acoustic logging.

[0156] In this embodiment, based on the effective acoustic signals within a given window length above and below the bad passage signal of the horizontal well dipole acoustic logging, the cross-correlation coefficient of the effective acoustic signals is calculated using the formula for calculating the cross-correlation coefficient of the effective acoustic signals to obtain the maximum cross-correlation coefficient, and finally the optimal matching position of the effective acoustic signals above and below the corresponding bad passage signal is determined.

[0157] In existing technologies, for the correction of a small number of bad track signals, bilinear interpolation can be used to interpolate the intermediate failed data using the upper and lower effective waveforms. However, since the wave train is continuously sampled and the propagation speed of acoustic waves is different in different formations, there may be different phases of the upper and lower effective waveforms at the interpolation points, resulting in inconsistent phase axes of the acoustic waveforms in the interpolation correction, which cannot achieve true restoration of formation dipole acoustic logging information.

[0158] Therefore, in this embodiment, a bilinear interpolation method based on cross-correlation coefficients is used to correct the bad channel signal of the horizontal well dipole acoustic wave. First, the effective acoustic signals above and below the bad channel signal are matched with the same phase axis position according to the cross-correlation principle. Then, inclined bilinear interpolation is performed to obtain a better interpolation correction effect.

[0159] The basic formula for calculating the cross-correlation coefficient of effective acoustic signals above and below the bad sector signal is:

[0160] Xi = Wave(ki × step)

[0161] Yi = Wave(ki × step)

[0162]

[0163] Rmax = Max(Ri)

[0164] In the formula, Xi and Yi represent the effective acoustic signals within a given window length above and below the bad channel, respectively. Let Xi and Yi be the average values, respectively; n be the number of data points within a given window length; Ri be the cross-correlation coefficient; Rmax be the maximum cross-correlation coefficient within a given window length; step be the step size; and ki be the i-th step size.

[0165] In the actual calculation process, firstly, based on the separation of the original dipole acoustic data wave field to eliminate the influence of interference signals, a window length of 25 is given. The window is moved according to a certain step size to obtain a set of cross-correlation coefficients. Then, the maximum value of the cross-correlation coefficients is compared to obtain the maximum correlation coefficient. Finally, the optimal matching position of the effective acoustic signals above and below the corresponding bad channel signal is determined.

[0166] Step 8: Correction of bad passage signals in horizontal well dipole acoustic logging.

[0167] In this embodiment, the optimal matching position of the effective acoustic signals above and below the bad passage signal is determined according to step 7. Known points are selected and bilinear interpolation method is used to correct the bad passage signal of horizontal well dipole acoustic logging, laying a reliable and high-quality dipole acoustic logging data foundation for reservoir evaluation.

[0168] Specifically, based on the optimal matching position of the effective acoustic signals above and below the bad sector signal, a known point Q is selected. 11 (x1, y1), Q 12 (x1, y2), Q 21 (x2, y1), Q 22 (x2, y2), through the principle of bilinear interpolation ( Figure 9 The calculation formula is used to obtain the interpolation value of point P(x,y): First, linear interpolation is performed in the x direction to obtain R1 and R2. Then, the interpolation value of point P(x,y) is obtained by interpolating the calculated R1 and R2 in the y direction.

[0169] The basic formula for bilinear interpolation is:

[0170]

[0171] like Figure 10 As shown, the horizontal well dipole acoustic logging signal is corrected by using bilinear interpolation at known points on the phase axis matching position, resulting in a bad passage signal correction map. In this way, by identifying and correcting the bad passage signal, reliable and high-quality dipole acoustic logging data are provided for reservoir evaluation.

[0172] Step 9: Horizontal well dipole acoustic logging reservoir evaluation.

[0173] In this embodiment, steps 1-8 are completed to evaluate and correct the quality of horizontal well dipole sonic logging data, and to process and interpret horizontal well reservoir dipole sonic logging data.

[0174] In this embodiment, a bad passage identification method based on the overall standard deviation of the waveform average amplitude and a bad passage correction method based on bilinear interpolation of cross-correlation are used to realize bad passage identification and automatic logging quality evaluation technology and accurate bad passage signal correction technology in horizontal well dipole sonic logging. This provides high-quality raw data and preparatory interpretation results for the geological application of horizontal well dipole sonic logging data, effectively solving the problem of automatic quality evaluation and bad passage signal correction of dipole sonic logging data under complex horizontal well conditions. It ensures the reliability of dipole sonic logging data in applications such as sonic time difference extraction, anisotropy calculation, and sonic reflection imaging. This method has broad application prospects in the acquisition and evaluation of horizontal well dipole sonic logging data and geological applications in complex reservoirs such as fractured-vuggy carbonate rocks, fractured-pore clastic rocks, igneous rocks, and shale oil and gas reservoirs.

[0175] Example 9

[0176] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to perform the following steps:

[0177] Acquire acoustic logging signals and the corresponding spectral information of the acoustic logging signals;

[0178] Based on the spectral information, wave field separation is performed on the acoustic logging signal to obtain the separated formation wave signal;

[0179] Calculate the overall standard deviation of the waveform amplitude at each depth point of the formation wave signal relative to the average waveform amplitude;

[0180] For each depth point, determine whether the overall standard deviation of the waveform amplitude at that depth point relative to the average waveform amplitude is greater than a preset threshold.

[0181] When the overall standard deviation of the waveform amplitude at the depth point relative to the average waveform amplitude is greater than a preset threshold, the waveform at that depth point is determined to be a bad channel signal.

[0182] In one embodiment, the processor performs the following steps when executing the computer program:

[0183] The acoustic logging signal is subjected to discrete wavelet transform to obtain a multi-level decomposed signal in the wavelet domain;

[0184] Based on the spectrum information, the noise threshold of the interference signal in the acoustic logging signal is determined;

[0185] Based on the noise threshold, random interference signals in each level of the decomposed signal are removed to obtain the separated formation wave signals.

[0186] In one embodiment, the processor performs the following steps when executing the computer program:

[0187] Based on the spectral information, the intensity and frequency distribution characteristics of formation wave signals and interference signals in the acoustic logging signal are determined;

[0188] The noise threshold of the interference signal is determined based on the intensity and frequency distribution characteristics of the formation wave signal and the interference signal.

[0189] In one embodiment, the processor performs the following steps when executing the computer program:

[0190] The bad sector signal is corrected based on the non-bad sector signal adjacent to the bad sector signal.

[0191] In one embodiment, the processor performs the following steps when executing the computer program:

[0192] Determine the non-bad sector signal adjacent to the bad sector signal, and determine the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal;

[0193] Based on the matching position of the in-phase axis, the bad sector signal is corrected using bilinear interpolation.

[0194] In one embodiment, the processor performs the following steps when executing the computer program:

[0195] Calculate the cross-correlation coefficient of the non-bad sector signals adjacent to the bad sector signal;

[0196] The in-phase axis matching position of the non-bad sector signal and the bad sector signal is determined based on the largest cross-correlation coefficient among the cross-correlation coefficients.

[0197] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, performs the following steps:

[0198] Acquire acoustic logging signals and the corresponding spectral information of the acoustic logging signals;

[0199] Based on the spectral information, wave field separation is performed on the acoustic logging signal to obtain the separated formation wave signal;

[0200] Calculate the overall standard deviation of the waveform amplitude at each depth point of the formation wave signal relative to the average waveform amplitude;

[0201] For each depth point, determine whether the overall standard deviation of the waveform amplitude at that depth point relative to the average waveform amplitude is greater than a preset threshold.

[0202] When the overall standard deviation of the waveform amplitude at the depth point relative to the average waveform amplitude is greater than a preset threshold, the waveform at that depth point is determined to be a bad channel signal.

[0203] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0204] The acoustic logging signal is subjected to discrete wavelet transform to obtain a multi-level decomposed signal in the wavelet domain;

[0205] Based on the spectrum information, the noise threshold of the interference signal in the acoustic logging signal is determined;

[0206] Based on the noise threshold, random interference signals in each level of the decomposed signal are removed to obtain the separated formation wave signals.

[0207] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0208] Based on the spectral information, the intensity and frequency distribution characteristics of formation wave signals and interference signals in the acoustic logging signal are determined;

[0209] The noise threshold of the interference signal is determined based on the intensity and frequency distribution characteristics of the formation wave signal and the interference signal.

[0210] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0211] The bad sector signal is corrected based on the non-bad sector signal adjacent to the bad sector signal.

[0212] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0213] Determine the non-bad sector signal adjacent to the bad sector signal, and determine the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal;

[0214] Based on the matching position of the in-phase axis, the bad sector signal is corrected using bilinear interpolation.

[0215] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0216] Calculate the cross-correlation coefficient of the non-bad sector signals adjacent to the bad sector signal;

[0217] The in-phase axis matching position of the non-bad sector signal and the bad sector signal is determined based on the largest cross-correlation coefficient among the cross-correlation coefficients.

[0218] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, performs the following steps:

[0219] Acquire acoustic logging signals and the corresponding spectral information of the acoustic logging signals;

[0220] Based on the spectral information, wave field separation is performed on the acoustic logging signal to obtain the separated formation wave signal;

[0221] Calculate the overall standard deviation of the waveform amplitude at each depth point of the formation wave signal relative to the average waveform amplitude;

[0222] For each depth point, determine whether the overall standard deviation of the waveform amplitude at that depth point relative to the average waveform amplitude is greater than a preset threshold.

[0223] When the overall standard deviation of the waveform amplitude at the depth point relative to the average waveform amplitude is greater than a preset threshold, the waveform at that depth point is determined to be a bad channel signal.

[0224] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0225] The acoustic logging signal is subjected to discrete wavelet transform to obtain a multi-level decomposed signal in the wavelet domain;

[0226] Based on the spectrum information, the noise threshold of the interference signal in the acoustic logging signal is determined;

[0227] Based on the noise threshold, random interference signals in each level of the decomposed signal are removed to obtain the separated formation wave signals.

[0228] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0229] Based on the spectral information, the intensity and frequency distribution characteristics of formation wave signals and interference signals in the acoustic logging signal are determined;

[0230] The noise threshold of the interference signal is determined based on the intensity and frequency distribution characteristics of the formation wave signal and the interference signal.

[0231] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0232] The bad sector signal is corrected based on the non-bad sector signal adjacent to the bad sector signal.

[0233] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0234] Determine the non-bad sector signal adjacent to the bad sector signal, and determine the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal;

[0235] Based on the matching position of the in-phase axis, the bad sector signal is corrected using bilinear interpolation.

[0236] In one embodiment, when the computer program is executed by a processor, it performs the following steps:

[0237] Calculate the cross-correlation coefficient of the non-bad sector signals adjacent to the bad sector signal;

[0238] The in-phase axis matching position of the non-bad sector signal and the bad sector signal is determined based on the largest cross-correlation coefficient among the cross-correlation coefficients.

[0239] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods described in the above embodiments.

[0240] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0241] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0242] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0243] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0244] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0245] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0246] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0247] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method for recognizing acoustic signals, characterized in that, include: Acquire acoustic logging signals and the corresponding spectral information of the acoustic logging signals; Based on the spectral information, wave field separation is performed on the acoustic logging signal to obtain the separated formation wave signal; Calculate the overall standard deviation of the waveform amplitude at each depth point of the formation wave signal relative to the average waveform amplitude; For each depth point, determine whether the overall standard deviation of the waveform amplitude at that depth point relative to the average waveform amplitude is greater than a preset threshold. When the overall standard deviation of the waveform amplitude at the depth point relative to the average waveform amplitude is greater than a preset threshold, the waveform at that depth point is determined to be a bad channel signal.

2. The acoustic signal recognition method according to claim 1, characterized in that, The step of performing wavefield separation on the acoustic logging signal based on the spectral information to obtain the separated formation wave signal includes: The acoustic logging signal is subjected to discrete wavelet transform to obtain a multi-level decomposed signal in the wavelet domain; Based on the spectrum information, the noise threshold of the interference signal in the acoustic logging signal is determined; Based on the noise threshold, random interference signals in each level of the decomposed signal are removed to obtain the separated formation wave signals.

3. The acoustic signal recognition method according to claim 2, characterized in that, The step of determining the noise threshold of the interference signal in the acoustic logging signal based on the spectrum information includes: Based on the spectral information, the intensity and frequency distribution characteristics of formation wave signals and interference signals in the acoustic logging signal are determined; The noise threshold of the interference signal is determined based on the intensity and frequency distribution characteristics of the formation wave signal and the interference signal.

4. The acoustic signal recognition method according to claim 1, characterized in that, The formula for calculating the population standard deviation is: Among them, X i For a given moment in a wave train, n is the number of data points in the wave train, QC_Ave is the average amplitude, and QC_Std is the population standard deviation.

5. The acoustic signal recognition method according to claim 1, characterized in that, The method further includes: correcting the bad sector signal based on a non-bad sector signal adjacent to the bad sector signal.

6. The acoustic signal recognition method according to claim 5, characterized in that, The step of correcting the bad sector signal based on the non-bad sector signal adjacent to the bad sector signal includes: Determine the non-bad sector signal adjacent to the bad sector signal, and determine the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal; Based on the matching position of the in-phase axis, the bad sector signal is corrected using bilinear interpolation.

7. The acoustic signal recognition method according to claim 1, characterized in that, The steps of determining the non-bad sector signal adjacent to the bad sector signal and determining the in-phase axis matching position of the non-bad sector signal adjacent to the bad sector signal include: Calculate the cross-correlation coefficient of the non-bad sector signals adjacent to the bad sector signal; The in-phase axis matching position of the non-bad sector signal and the bad sector signal is determined based on the largest cross-correlation coefficient among the cross-correlation coefficients.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

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