Time difference determination method, device and equipment of array acoustic wave, and storage medium

Abstract

The application provides a time difference determination method, device and equipment of array acoustic wave and a storage medium. The method detects array acoustic wave through an array acoustic logging device located at a target depth, divides a full frequency band of the array acoustic wave into a frequency band of Stoneley wave and a frequency band of transverse-longitudinal wave according to a frequency demarcation point of the array acoustic wave, determines a Stoneley wave window length according to a first window cycle number of the Stoneley wave and the frequency band of the Stoneley wave, determines a transverse-longitudinal wave window length according to a second window cycle number of the transverse-longitudinal wave and the frequency band of the transverse-longitudinal wave, determines a time window length of a slowness time correlation algorithm based on the Stoneley wave window length and the transverse-longitudinal wave window length, and determines a time difference value of each component wave based on the algorithm and the time window length. The embodiments of the application can determine a corresponding time window length for the target depth, avoid the problems of complexity and inaccuracy of manual setting, and thus improve logging efficiency and logging accuracy.

Description

Technical Field

[0001] This application relates to the field of well logging technology, and more specifically, to a method, apparatus, device, and storage medium for determining the time difference of array acoustic waves. Background Technology

[0002] The time difference of sound waves is the reciprocal of their velocity, also known as slowness. Since sound waves propagate at different speeds in different media, changes in the time difference can reflect changes in lithology, porosity, and fluid properties within the well. For example, the time difference for cement is 64... Up to 111 The time difference for water is 189. The time difference for oil is 238. Based on the time difference curve, stratigraphic division, gas layer identification, differentiation between extensional and compressive strata, calculation of porosity, and estimation of formation pressure can be performed.

[0003] In related technologies, the Slowness Time Coherence (STC) method is commonly used to extract the time difference of array acoustic waves. The time length is one of the important parameters in the STC method. In engineering practice, the time window length is usually manually set based on prior experience. However, for well sections with thousands of depth units, the above-mentioned manual setting based on prior experience will affect logging efficiency and accuracy. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus, device and storage medium for determining the time difference of array acoustic waves, so as to at least solve the problems existing in the related art.

[0005] Specifically, this application is implemented through the following technical solution:

[0006] This application provides a method for determining the time difference of array acoustic waves, including:

[0007] Acquire array acoustic waves detected by an array acoustic logging device located at a target depth underground.

[0008] Based on the frequency boundary points of the array sound waves, the frequency bands of the Stoneley wave and the transverse and longitudinal waves in the array sound waves are determined from the frequency bands of the array sound waves; the transverse and longitudinal wave frequency bands include the frequency bands of the transverse wave and the frequency bands of the longitudinal wave.

[0009] The length of the Stoneley wave window is determined based on the number of periods of the first window of the Stoneley wave and the frequency band of the Stoneley wave; and the length of the transverse and longitudinal wave window is determined based on the number of periods of the second window of the transverse and longitudinal waves and the frequency band of the transverse and longitudinal waves.

[0010] Based on the Stoneley wave window length and the shear and longitudinal wave window lengths, the time window length of the slow time correlation algorithm corresponding to the target depth is determined, and the slow time correlation algorithm is used to determine the time difference values ​​corresponding to the shear wave, the longitudinal wave, and the Stoneley wave based on the time window length; the time difference values ​​corresponding to the shear wave, the longitudinal wave, and the Stoneley wave are used to determine the formation information at the target depth.

[0011] This application also provides a time difference determination device for array acoustic waves, comprising:

[0012] An array acoustic wave acquisition module is used to acquire array acoustic waves detected by an array acoustic wave logging device located at a target depth underground.

[0013] A frequency band determination module is used to determine the frequency band of the Stoneley wave and the frequency bands of the transverse and longitudinal waves in the array sound waves from the frequency bands of the array sound waves based on the frequency boundary points of the array sound waves; the transverse and longitudinal wave frequency bands include the frequency bands of the transverse waves and the frequency bands of the longitudinal waves.

[0014] The window length determination module is used to determine the Stoneley wave window length based on the first window period number of the Stoneley wave and the frequency band of the Stoneley wave, and to determine the transverse and longitudinal wave window length based on the second window period number of the transverse and longitudinal waves and the frequency band of the transverse and longitudinal waves.

[0015] The time difference determination module is used to determine the time window length of the slow time correlation algorithm corresponding to the target depth based on the Stoneley wave window length and the shear and longitudinal wave window lengths, and to use the slow time correlation algorithm to determine the time difference values ​​corresponding to the shear wave, the longitudinal wave and the Stoneley wave respectively based on the time window length; the time difference values ​​corresponding to the shear wave, the longitudinal wave and the Stoneley wave respectively are used to determine the formation information at the target depth.

[0016] This application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the array acoustic wave time difference determination method described in any of the foregoing embodiments.

[0017] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the array acoustic wave time difference determination method described in any of the foregoing embodiments.

[0018] This application also provides a computer program product, including a computer program that, when run by a processor, performs the steps of any of the possible array acoustic wave time difference determination methods described above.

[0019] The technical solutions provided by the embodiments of this application may include the following beneficial effects:

[0020] In this embodiment, based on a fixed number of window periods for different component waves (transverse and longitudinal waves, Stoneley waves) corresponding to the target depth, and combined with the frequency distribution of each component wave, the time window length is dynamically calculated. In this way, the corresponding time window length can be adaptively generated according to the target depth, avoiding the tediousness and inaccuracy of manual setting, thereby improving logging efficiency and logging accuracy.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of an array acoustic wave STC pattern shown in an exemplary embodiment of this application;

[0023] Figure 2 This is an exemplary embodiment of the present application illustrating the relationship between slowness and time when the time window length is 60 microseconds;

[0024] Figure 3 This is an exemplary embodiment of the present application illustrating the relationship between slowness and time with a time window length of 600 microseconds;

[0025] Figure 4 This is an exemplary embodiment of the present application illustrating the relationship between slowness and time, specifically a time window with a length of 1600 microseconds.

[0026] Figure 5 This is a spectrum diagram of a normal array sound wave shown in an exemplary embodiment of this application;

[0027] Figure 6 This is a spectrum diagram of an abnormal array acoustic wave shown in an exemplary embodiment of this application;

[0028] Figure 7 This is a diagram illustrating the relationship between the slowness and arrival time of an abnormal array sound wave, as shown in an exemplary embodiment of this application.

[0029] Figure 8 This is a flowchart illustrating an exemplary embodiment of the present application of a method for determining the time difference of array acoustic waves;

[0030] Figure 9 This is a flowchart illustrating a method for determining a frequency boundary point, as shown in an exemplary embodiment of this application.

[0031] Figure 10This is a time-domain comparison diagram of the STC spectrum of an array acoustic wave and the STC spectrum of a target array acoustic wave, as shown in an exemplary embodiment of this application.

[0032] Figure 11 This is a frequency domain comparison diagram of an array acoustic wave and a target array acoustic wave, as shown in an exemplary embodiment of this application.

[0033] Figure 12 This is a schematic diagram of the STC spectrum of a target array acoustic wave according to an exemplary embodiment of this application;

[0034] Figure 13 This is a graph illustrating the relationship between slowness and correlation coefficient, as shown in an exemplary embodiment of this application.

[0035] Figure 14 This is a flowchart illustrating a time difference determination process according to an exemplary embodiment of this application;

[0036] Figure 15 This is a schematic diagram of the structure of an array acoustic wave time difference determination device according to an exemplary embodiment of this application;

[0037] Figure 16 This is a hardware structure diagram of a computer device illustrated in an exemplary embodiment of this application. Detailed Implementation

[0038] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0039] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0040] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0041] The time difference of sound waves is the reciprocal of their velocity, also known as slowness. Since sound waves propagate at different speeds in different media, changes in the time difference can reflect changes in lithology, porosity, and fluid properties within the well. For example, the time difference for cement is 64... Up to 111 The time difference for water is 189. The time difference for oil is 238. Based on the time difference curve, stratigraphic division, gas layer identification, differentiation between extensional and compressive strata, calculation of porosity, and estimation of formation pressure can be performed.

[0042] In related technologies, the Slowness Time Coherence (STC) method is commonly used to extract the time difference of array acoustic waves.

[0043] As shown in formula (1), this is the expression for the slow time correlation method:

[0044] (1)

[0045] Where M represents the number of receiving probes in the array acoustic logging device, and there is a gap between every two adjacent receiving probes. △z The full-wavelength signal of the m-th receiving probe is r m , T W The time window length, in units of , s The slowness of each component wave is expressed in units of 1 / 2. , τ When the first wave arrives, ρ The correlation coefficient.

[0046] ρ The value of is between 0 and 1. ρ The larger the value, the more likely s is to be s. τ The greater the likelihood that it is the actual value, the more likely it is to be true. In practice, it is often given... s and τ Set a search range and retrieve all results within that range. s , τ ) combination ρ value.

[0047] The two-dimensional STC spectrum can be obtained based on the above formula, such as Figure 1 The image shown is a schematic diagram of the STC spectrum of an array acoustic wave provided in an exemplary embodiment of this application. Figure 1 As shown, the horizontal axis of the STC spectrum represents the slowness value, and the vertical axis represents the arrival time. The slowness value of the P-wave is between 50 and 60. Nearby, its arrival time is around 500. -1000 Nearby; the slowness value of the transverse wave is around 100. Nearby, its arrival time is around 1000 -2000 Nearby; the slowness value of the Stoneley wave is around 200. Nearby, its arrival time was around 2000 -3000 nearby.

[0048] Furthermore, based on the above formula, it can be seen that the time window length is one of the important parameters in the slow time correlation method. In engineering practice, the time window length is usually manually set based on prior experience. However, for well sections with thousands of depth units, the above-mentioned manual setting based on prior experience will affect logging efficiency and accuracy.

[0049] For example, please see Figures 2-4 , Figure 2 A graph showing the relationship between slowness and time with a time window length of 60 microseconds is provided as an exemplary embodiment of this application. Figure 3 A graph showing the relationship between slowness and time with a time window length of 600 microseconds is provided as an exemplary embodiment of this application. Figure 4 A graph showing the relationship between slowness and time with a time window length of 1600 microseconds is provided as an exemplary embodiment of this application.

[0050] Depend on Figures 2-4 It can be seen that when the time window length Tw is too short (60 microseconds), there is a lot of background noise; when the time window length Tw is too long (1600 microseconds), the transverse wave in the middle (i.e., the slowness value is 150) is more prominent. The blue area near the arrival time of 1200 microseconds almost disappeared; when the time window length Tw is 600 microseconds, all three component waves can be displayed normally.

[0051] In addition, the following problems exist in the time difference extraction process:

[0052] (1) Noise preprocessing of array acoustic waves: Noise is inevitable in array acoustic waves, so it is necessary to filter them.

[0053] For example, please see Figures 5-7 , Figure 5 A spectrum diagram of a normal array sound wave is provided as an exemplary embodiment of this application. Figure 6 A spectrum diagram of an abnormal array acoustic wave is provided as an exemplary embodiment of this application. Figure 7 A graph showing the relationship between the slowness and arrival time of an abnormal array sound wave, provided as an exemplary embodiment of this application.

[0054] like Figure 5 As shown, a normal array acoustic wave includes three effective wave components (longitudinal wave, transverse wave, and Stoneley wave). These three wave components occupy distinct frequency domains, with high-frequency noise primarily distributed beyond 15 kHz. Common filtering methods in related technologies include bandpass filtering, wavelet filtering, and tilt filters. However, the amplitude intensities of these three effective wave components can sometimes differ significantly, for example… Figure 6 In the above, the Stoneley wave intensity in the 0-5kHz frequency range is significantly excessive, masking the intensity of transverse and longitudinal waves with frequencies above 5kHz. Based on this, even for... Figure 5 The array acoustic waves shown are subjected to bandpass filtering to remove high-frequency noise beyond 15kHz, resulting in a two-dimensional STC spectrum ( Figure 7 It also cannot identify the three effective component waves.

[0055] Based on the above research, how to preprocess the noise of array acoustic waves is also an important topic.

[0056] (2) Slow extraction.

[0057] In obtaining such Figure 1 After obtaining the two-dimensional STC spectrum shown, extracting the slowness of the three red regions is also a problem. Related techniques typically use optimization algorithms such as genetic algorithms and simulated annealing to find the extreme values. However, these methods are computationally intensive and prone to getting trapped in local optima. While they may not pose a significant problem for extracting a single component, they are not very effective for full-wave extraction. Therefore, extracting multiple component waves is a particularly important aspect.

[0058] Based on the above research, this disclosure provides a method for determining the time difference of array acoustic waves. The method first acquires array acoustic waves detected by an array acoustic logging device located at a target depth underground. Second, based on the frequency boundary points of the array acoustic waves, the frequency bands of the Stoneley wave and the transverse and longitudinal waves are determined from the frequency bands of the array acoustic waves. The transverse and longitudinal wave frequency bands include the transverse wave frequency band and the longitudinal wave frequency band. Then, based on the first window period number of the Stoneley wave and the frequency band of the Stoneley wave, the Stoneley wave window is determined. The length of the transverse and longitudinal waves is determined based on the number of second window periods of the transverse and longitudinal waves and the frequency bands of the transverse and longitudinal waves. Finally, based on the Stoneley wave window length and the transverse and longitudinal wave window lengths, the time window length of the slow time correlation algorithm corresponding to the target depth is determined, and the slow time correlation algorithm is used to determine the time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave based on the time window length. The time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave are used to determine the formation information at the target depth.

[0059] In this embodiment, based on a fixed number of window periods for different component waves (transverse and longitudinal waves, Stoneley waves) corresponding to the target depth, and combined with the frequency distribution of each component wave, the time window length is dynamically calculated. In this way, the corresponding time window length can be adaptively determined for the target depth, avoiding the tediousness and inaccuracy of manual setting, thereby improving logging efficiency and logging accuracy.

[0060] To facilitate understanding of this embodiment, a detailed description of the time difference determination method for array acoustic waves disclosed in this disclosure is provided first. The execution entity of the time difference determination method for array acoustic waves provided in this disclosure is generally a computer device. This computer device can be a server, which can be an independent physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. In other embodiments, the computer device can also be a terminal device, which can be a mobile device, terminal, handheld device, computing device, vehicle-mounted device, etc.

[0061] In other embodiments, the method can also be applied to an implementation environment consisting of computer equipment and servers, or an implementation environment consisting of terminal equipment and servers. Furthermore, the method for determining the time difference of array acoustic waves can also be implemented by a processor calling computer-readable instructions stored in memory.

[0062] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0063] Please see the appendix Figure 8 The above is a flowchart illustrating a method for determining the time difference of array acoustic waves, as shown in an exemplary embodiment of this application. Figure 8 As shown, the method for determining the time difference of array acoustic waves in this embodiment may include the following steps S801~S805:

[0064] S801: Acquire array acoustic waves detected by an array acoustic logging device; the array acoustic logging device is located at the target depth underground.

[0065] As is well known, array acoustic logging is an acoustic logging method that uses a multi-probe acoustic system to measure multiple wave trains; it is also known as digital array acoustic logging. Its acoustic system consists of a transmitting transducer and an array of receiving probes. It achieves digital control acquisition of signals across the entire wave train through digital transmission technology and can record P-wave, S-wave, and Stoneley wave information.

[0066] An array acoustic logging device can refer to an array acoustic logging instrument or equipment. An array acoustic logging device can be placed at any target depth in a well section with thousands of depth units. It should be noted that, depending on the depth, the propagation characteristics of the acoustic waves will be changed by the formation conditions and other environmental factors. This will affect the main frequency distribution of the array acoustic waves and the frequency domain boundary points between the various component waves (for example, the frequency boundary points of array acoustic waves at different depths will be offset).

[0067] The array of acoustic waves includes transverse waves, longitudinal waves, and Stoneley waves.

[0068] S802: Based on the frequency boundary point of the array sound wave, determine the frequency band of the Stoneley wave and the frequency bands of the transverse and longitudinal waves in the array sound wave from the frequency band of the array sound wave; the transverse and longitudinal wave frequency bands include the frequency bands of the transverse waves and the frequency bands of the longitudinal waves.

[0069] The frequency boundary point is the boundary between the Stoneley wave and the transverse and longitudinal waves in the array sound waves.

[0070] It is understandable that after determining the frequency boundary point, the frequency bands of the Stoneley wave and the transverse and longitudinal waves can be determined from the frequency bands of the array sound waves, thereby obtaining the frequency bands of the Stoneley wave and the transverse and longitudinal waves of the array sound waves detected at the target depth.

[0071] In some implementations, the frequency band distribution for the Stoneley wave is as follows: The signal strength in this frequency domain is 90% of the total signal strength of the frequency band. Therefore, the frequency band... This constitutes the low-frequency noise band; for transverse and longitudinal waves, the frequency band distribution is as follows: This frequency domain The signal strength within this area is 90% of the total signal strength of the frequency band. Therefore, the frequency band... This constitutes a high-frequency noise band.

[0072] in, and It is usually a preset frequency.

[0073] That is, the full frequency band of the array sound waves can include the low-frequency noise band, the Stoneley wave band, the transverse and longitudinal wave bands, and the high-frequency noise band.

[0074] Optionally, the frequency boundary point of the array sound wave can be determined by the following steps: retrieving the number of valleys of the array sound wave, and determining the frequency boundary point of the array sound wave based on the retrieved number of valleys.

[0075] It should be noted that, as mentioned earlier, in the array acoustic logging method, the frequency domain distribution of different waveforms follows a clear pattern. For example, the energy of transverse and longitudinal waves is usually concentrated in the high-frequency band, while the energy of Stoneley waves is usually concentrated in the low-frequency band. Therefore, it should be understood that the energy at the intersection of transverse and longitudinal waves and Stoneley waves is relatively weak, which can form a single and obvious trough (that is, the trough corresponding to the frequency boundary point). The minimum frequency corresponding to this trough is the frequency boundary point, thus obtaining the frequency boundary point of the array acoustic waves corresponding to the target depth.

[0076] Specifically, when retrieving the number of troughs in the array sound waves and determining the frequency boundary points of the array sound waves based on the retrieved number of troughs, please refer to [link to relevant documentation]. Figure 9 The flowchart for determining a frequency boundary point, provided as an exemplary embodiment of this application, specifically includes the following steps S8021~S8031:

[0077] S8021: Get the window value of the preset sliding window.

[0078] Here, the preset sliding window is used for sliding filtering. The window value of the sliding window is usually an odd number. The window value size can be set according to actual needs, such as 3, 5 or 7, etc., without limitation here.

[0079] S8022: Determine the number of valleys in the preset transition frequency band of the array acoustic wave.

[0080] Here, the preset transition band can refer to the transition band between the Stoneley wave and the transverse and longitudinal waves. Therefore, it can be understood that the frequency boundary between the Stoneley wave and the transverse and longitudinal waves is located in the preset transition band.

[0081] For example, the preset transition frequency band can be 3KHz~6KHz. As mentioned above, the energy of transverse and longitudinal waves is usually concentrated in the high frequency band (usually greater than 6KHz), and the energy of Stoneley waves is usually concentrated in the low frequency band (usually less than 3KHz). Therefore, the preset transition frequency band can refer to 3KHz~6KHz.

[0082] It should be noted that in this implementation, the core objective is to determine a unique frequency boundary point, and the number of troughs in the transition frequency band reflects whether the array acoustic waves are affected by noise and whether the frequency boundary point can be accurately located.

[0083] S8023: Determine whether the number of valleys in the preset transition frequency band of the array acoustic wave is greater than the preset number. If yes, proceed to step S8024; otherwise, proceed to step S8030.

[0084] The preset quantity is 1.

[0085] Under normal circumstances, there is only one valley in the transition frequency band, which can directly locate the frequency boundary point. In abnormal circumstances, the number of valleys is greater than 1, indicating that it is a pseudo valley caused by noise. Therefore, in this application, it is first necessary to determine the number of valleys in the preset transition frequency band of the array acoustic wave.

[0086] S8024: Determine whether the window value of the sliding window is less than the preset threshold. If yes, proceed to step S8025; otherwise, proceed to step S8031.

[0087] The preset threshold refers to the maximum value of the sliding window, which can be adaptively set according to actual needs.

[0088] In other words, in this application, in order to avoid the loss of key frequency domain features (especially frequency boundary points) due to excessive smoothing caused by an excessively large sliding window, a boundary constraint is set to determine whether the window value (w) of the sliding window is less than a preset threshold (maximum time window).

[0089] S8025: Performs moving average filtering on the array acoustic waves within a sliding window to obtain the filtered signal.

[0090] As is well known, moving average is a basic linear smoothing filtering method. Its core logic is window sliding and local averaging. When the sliding window slides to a certain position, the arithmetic mean of all frequency domain signals within the window is taken, and this average value is used to replace the original signal amplitude value at the center position of the window.

[0091] S8026: Determine the number of troughs in the preset transition band of the filtered signal.

[0092] It is understandable that after obtaining the filtered signal, it is necessary to further determine the number of troughs in the preset transition frequency band of the filtered signal.

[0093] S8027: Determine whether the number of troughs in the preset transition frequency band of the filtered signal is the preset number. If yes, proceed to step S8028; otherwise, proceed to step S8029.

[0094] If the number of valleys in the preset transition frequency band of the filtered signal is the preset number, it means that a unique valley was obtained after the first filtering.

[0095] S8028: The frequency value corresponding to the minimum amplitude in the preset transition band of the filtered signal is used as the frequency boundary point.

[0096] Here, the minimum amplitude value in the preset transition frequency band of the filtered signal also refers to the amplitude value of the unique valley determined in step S8027, and the frequency value corresponding to the unique valley can be used as the frequency boundary point.

[0097] S8029: Increase the window value of the sliding window at preset intervals, and return to step S8024.

[0098] The preset interval can be set according to actual needs. In this embodiment, the preset interval is an even value, such as 2. Of course, in other embodiments, the preset interval can be other values, such as 4 or 6, etc., which are not limited here.

[0099] It is understandable that if the number of troughs in the preset transition frequency band of the filtered signal is not the preset number (not 1), it means that the noise has not been completely filtered out in this filtering. Therefore, the window value of the sliding window can be increased according to the preset interval, and the next filtering can be performed based on the increased window value until the number of troughs in the preset transition frequency band of the filtered signal is the preset number, and then step S8028 is executed.

[0100] S8030: The frequency value corresponding to the minimum amplitude in the preset transition band of the array acoustic wave is used as the frequency boundary point.

[0101] Here, it can be understood that if the number of troughs in the preset transition frequency band of the array sound wave is not greater than the preset number, that is, if the number of troughs in the preset transition frequency band of the array sound wave is 1, it means that there is no noise interference. In this case, the frequency value corresponding to the minimum amplitude in the preset transition frequency band of the array sound wave can be directly used as the frequency boundary point.

[0102] S8031: Adjust the window value of the sliding window to be less than a preset threshold, filter the array sound wave based on the adjusted window value, use the filtered array sound wave as the array sound wave, and return to step S8021.

[0103] In this embodiment, the window value of the sliding window is adjusted to be less than a preset threshold, which can be half of the maximum window value. In other embodiments, the preset threshold can be other values, such as one-third or two-thirds of the maximum window value, etc., which are not limited here.

[0104] Here, if increasing the window value of the sliding window to a preset threshold (i.e., the maximum window value) still fails to make the number of valleys 1, the window value of the sliding window can be adjusted to be less than the preset threshold (half of the maximum window value), the array sound wave is depth smoothed, and the filtered array sound wave is used as the array sound wave. That is, the initial array sound wave is replaced, and step S8021 is executed.

[0105] In this application, a single valley is used as the core judgment criterion. The boundary point is determined by searching the number of valleys in the preset transition frequency band, which effectively eliminates the problem of ambiguity in the positioning of the frequency boundary point caused by multiple pseudo valleys affected by noise. Furthermore, by dynamically adjusting the sliding average filtering method of the sliding window, a progression from light filtering to deep filtering is achieved, which can effectively suppress noise while preserving the detailed features of the sound wave.

[0106] Furthermore, in practical applications, array acoustic waves are easily affected by the environment. The above filtering method can improve the adaptability and stability of the subsequent time difference extraction method.

[0107] S803: Determine the length of the Stoneley wave window based on the number of periods of the first window of the Stoneley wave and the frequency band of the Stoneley wave; and determine the length of the transverse and longitudinal wave window based on the number of periods of the second window of the transverse and longitudinal waves and the frequency band of the transverse and longitudinal waves.

[0108] Here, for Stoneley waves and transverse and longitudinal waves, the number of periods contained in the window is fixed. Specifically, the first window of Stoneley waves has 1.75 periods, and the second window of transverse and longitudinal waves has 4 periods.

[0109] In this way, the number of the first window period of the Stoneley wave and the number of the second window period of the transverse and longitudinal waves can be obtained first. Then, the length of the Stoneley wave window can be determined based on the number of the first window period and the frequency band of the Stoneley wave, and the length of the transverse and longitudinal wave window can be determined based on the number of the second window period and the frequency band of the transverse and longitudinal waves.

[0110] Specifically, please refer to formula (3), which is the expression for the window length of any component wave (i.e., Stoneley wave and transverse and longitudinal waves):

[0111] (3)

[0112] Where P is the number of window periods for the component wave. This represents the lower limit of the frequency band of the component wave. This represents the upper limit of the frequency band of the component wave. is the window length of the component wave.

[0113] Thus, the lengths of the Stoneley wave window and the transverse and longitudinal wave windows can be determined according to the above formula (3).

[0114] S804: Based on the Stoneley wave window length and the shear and longitudinal wave window lengths, determine the time window length of the slow time correlation algorithm corresponding to the target depth, and use the slow time correlation algorithm to determine the time difference values ​​corresponding to the shear wave, the longitudinal wave, and the Stoneley wave based on the time window length; the time difference values ​​corresponding to the shear wave, the longitudinal wave, and the Stoneley wave are used to determine the formation information at the target depth.

[0115] It is understandable that after determining the lengths of the Stoneley wave window and the transverse and longitudinal wave windows, the time window length of the slow-motion time correlation algorithm can be determined, that is, the time window length corresponding to the target depth can be obtained.

[0116] Optionally, the average of the Stoneley wave window length and the transverse and longitudinal wave window lengths can be used to determine the time window length.

[0117] In this way, the slow time correlation algorithm (as shown in formula (1)) can be used to determine the time difference values ​​corresponding to the shear wave, longitudinal wave and Stoneley wave respectively based on the time window length, and the formation information at the target depth can be determined according to each time difference value.

[0118] The formation information may include formation type, lithology in the well, porosity, fluid properties, and formation pressure.

[0119] As can be seen from the foregoing, this application divides the entire frequency band of the array acoustic wave into a noise band, a Stoneley wave band, and transverse and longitudinal wave bands. Therefore, in order to reduce the impact of noise on the array acoustic wave, after determining the low-frequency noise band and the high-frequency noise band, the Stoneley wave and the transverse and longitudinal waves can be amplified to weaken the noise waves in the noise band (that is, the low-frequency noise signal in the low-frequency noise band and the high-frequency noise signal in the high-frequency noise band).

[0120] Based on this, in some implementations, when determining the time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave respectively using the slow time correlation algorithm based on the time window length, step S804 may include the following steps (1) to (2):

[0121] (1) The Stoneley wave, the transverse and longitudinal waves and the noise wave are respectively subjected to gain processing to obtain the target array sound wave.

[0122] Specifically, gain coefficients can be set for the Stoneley wave, transverse and longitudinal waves, and noise waves respectively, so as to perform gain processing on the Stoneley wave, transverse and longitudinal waves, noise waves and their corresponding gain coefficients to obtain the target array sound wave.

[0123] Optionally, gain processing (i) to (iii) can be implemented through the following steps:

[0124] (i) Determine the first average amplitude of the Stoneley wave and the second average amplitude of the transverse and longitudinal waves.

[0125] In this embodiment, the average amplitude of each amplitude of the Stoneley wave can be calculated to obtain the first average amplitude, and the average amplitude of each amplitude of the transverse and longitudinal waves can be calculated to obtain the second average amplitude.

[0126] (ii) Based on the first average amplitude and the second average amplitude, determine the first gain coefficient corresponding to the Stoneley wave and the second gain coefficient corresponding to the transverse and longitudinal waves, respectively.

[0127] Please refer to formula (4), which is the expression for the gain coefficient:

[0128] (4)

[0129] in, The first average amplitude, The second average amplitude, The first gain coefficient, 0.001 is the second gain coefficient, and 0.001 is the third gain coefficient.

[0130] (iii) Obtain the third gain coefficient of the noise wave, and perform gain processing on the Stoneley wave based on the first gain coefficient, on the transverse and longitudinal waves based on the second gain coefficient, and on the noise wave based on the third gain coefficient to obtain the target array acoustic wave.

[0131] Thus, based on the gain coefficient shown in formula (4), the corresponding component waves are multiplied to obtain the gain Stoneley wave, gain transverse and longitudinal waves and gain noise wave, respectively, and the target array sound wave is obtained.

[0132] For example, please see Figures 10-12 , Figure 10 A time-domain comparison diagram of the STC spectrum of an array acoustic wave and the STC spectrum of a target array acoustic wave, provided as an exemplary embodiment of this application. Figure 11 A frequency domain comparison diagram of array acoustic waves and target array acoustic waves is provided as an exemplary embodiment of this application. Figure 12 This is a schematic diagram of the STC spectrum of a target array acoustic wave provided for an exemplary embodiment of this application.

[0133] Figure 10 The waveforms shown are time-domain waveforms of the array acoustic waves collected by each probe of the array acoustic logging device. Figure 11 The waveforms shown are time-domain waveforms of the array acoustic waves acquired by each probe after gain processing. Figure 10 It can be seen that when the sound wave acquisition time of the array sound wave is 1000... Up to 3000 In this segment, the transverse and longitudinal waves are nearly straight lines, but after gain processing, the transverse and longitudinal wave signals (sound wave acquisition time is 1000) become more linear. Up to 3000 The waveform was enhanced, and the Stoneley wave signal (acoustic acquisition time was 3000) was amplified. -4000 The waveform is weakened, and similarly, Figure 11 In the array acoustic waves, the Stoneley wave has a large frequency domain waveform amplitude, while the frequency domain waveforms of the transverse and longitudinal waves are not obvious. In the target array acoustic waves, the frequency domain distribution of the three component waves can be clearly seen, and the STC spectrum calculated from the target array acoustic waves ( Figure 12 It also shows three component wave peak regions, compared to the STC spectrum of array acoustic waves ( Figure 7 There has been a significant improvement.

[0134] (2) Using the slow time correlation algorithm, based on the time window length, determine the time difference values ​​corresponding to the transverse wave, the longitudinal wave and the Stoneley wave in the target array acoustic wave.

[0135] Here, the target array acoustic wave and the time window length can be input into the slow time correlation algorithm to obtain the time difference values ​​corresponding to the transverse wave, longitudinal wave and Stoneley wave in the target array acoustic wave.

[0136] In this embodiment of the application, by designing gain coefficients to dynamically enhance or suppress sound waves in different frequency bands, the problem of Instone waves being too strong and masking transverse and longitudinal waves can be reduced.

[0137] Specifically, it may include the following steps (a) to (c):

[0138] (a) Using the slow time correlation algorithm, based on the time window length, a mapping curve of time difference and correlation coefficient for the target array acoustic wave is obtained.

[0139] It is understandable that by using a slowness time correlation algorithm, the correspondence between slowness and arrival time can be obtained based on the time window length. However, since the slowness value needs to be extracted in this application, the arrival time does not need to be considered. Therefore, in this embodiment, the dimension of arrival time is eliminated. Specifically, since each slowness value corresponds to multiple arrival times, it corresponds to multiple correlation coefficients. The slowness and correlation coefficient curve can be obtained by taking the maximum value among multiple correlation coefficients.

[0140] (b) Based on a preset initial correlation coefficient threshold, the mapping curve of the time difference and correlation coefficient is divided into multiple connected regions, and the number of connected regions greater than the initial correlation coefficient threshold is determined.

[0141] The initial correlation coefficient threshold can be set according to actual needs; for example, the initial correlation coefficient threshold can be 0.5.

[0142] like Figure 13 The image shown is a graph illustrating the relationship between slowness and correlation coefficient, provided as an exemplary embodiment of this application. Figure 13 As shown, a slowness value corresponds to a correlation coefficient, and the arrival time dimension is eliminated.

[0143] Furthermore, Figure 13 The curves shown have multiple distinct peaks, each corresponding to a different component wave. To find the peaks from the slowness-correlation coefficient curves and filter out unreasonable peaks (such as noise), this application constructs a threshold-based peak-finding algorithm to determine the peaks corresponding to the longitudinal wave, transverse wave, and Stoneley wave, respectively.

[0144] Please continue reading Figure 13 The red dashed line represents the initial correlation coefficient threshold (0.5). The red dashed line divides the curve of slowness and correlation coefficient into four connected regions A, B, C, and D.

[0145] (c) If the quantity is not less than the preset quantity, integrate each connected region to obtain the corresponding integral value, and determine the time difference value corresponding to the transverse wave, the longitudinal wave and the Stoneley wave based on each integral value.

[0146] The preset quantity is 3.

[0147] Following the above Figure 13 For example, different component waves are segmented into multiple connected regions under the action of an initial correlation coefficient threshold. The integral values ​​of these connected regions can be used to determine the credibility of the peak. Peaks with too small integral values ​​may be false peaks caused by glitch noise. Figure 13The integral values ​​of the four connected regions A, B, C and D are 33, 25, 61 and 21 respectively. The one with the smallest integral value (21) is the false peak.

[0148] Specifically, when determining the time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave based on each integral value, the target connected region with the largest integral value can be determined from each connected region. Then, according to the time difference value in ascending order, the time difference values ​​corresponding to the vertices of each target connected region are sequentially determined as the time difference values ​​corresponding to the longitudinal wave, the transverse wave, and the Stoneley wave, respectively.

[0149] The number of target connected regions is the preset number.

[0150] That is, if the number of connected regions is not less than the preset number, it means that the currently obtained connected regions include connected regions corresponding to longitudinal waves, transverse waves and Stoneley waves respectively. Since connected regions with small integral values ​​are usually caused by noise, in this embodiment, a preset number (i.e., 3) of target connected regions with the largest integral values ​​can be determined, and the time difference values ​​corresponding to the vertices of each target connected region are determined in order of increasing time difference values ​​as the time difference values ​​corresponding to longitudinal waves, transverse waves and Stoneley waves respectively.

[0151] In other implementations, if the number of connected regions is less than a preset number, the initial correlation coefficient threshold is reduced, and the process returns to the step of dividing the mapping curve of time difference and correlation coefficient into multiple connected regions based on the initial correlation coefficient threshold, until the number is not less than the preset number.

[0152] It is understandable that if the number of connected regions is less than the preset number, it means that the initial correlation coefficient threshold is set too high. Therefore, it is necessary to reduce the initial correlation coefficient threshold and, based on the reduced initial correlation coefficient threshold, divide the mapping curve of time difference and correlation coefficient into multiple connected regions until the number of connected regions is not less than the preset number.

[0153] like Figure 13 As shown, the connected regions A, B, and C are the target connected regions. As the slowness value increases from left to right, the time difference values ​​m1, m2, and m3 corresponding to the vertices a, b, and c of the target connected regions A, B, and C are respectively the time difference values ​​corresponding to the longitudinal wave, transverse wave, and Stoneley wave.

[0154] Please see Figure 14 This is a flowchart illustrating a time difference determination process provided in an exemplary embodiment of this application. Figure 14 As shown, the complete process for determining the time difference values ​​corresponding to the transverse wave, longitudinal wave, and Stoneley wave includes steps S1401 to S1406.

[0155] S1401: Using a slow time correlation algorithm, based on the time window length, a mapping curve of time difference and correlation coefficient for the target array acoustic wave is obtained.

[0156] S1402: Based on a preset initial correlation coefficient threshold, divide the mapping curve of time difference and correlation coefficient into multiple connected regions, and determine the number of connected regions that are greater than the initial correlation coefficient threshold.

[0157] S1403: Determine whether the number of connected regions with a value greater than the initial correlation coefficient threshold is not less than the preset number. If yes, proceed to step S1404; otherwise, proceed to step S1406.

[0158] S1404: Integrate each connected region separately to obtain the corresponding integral value.

[0159] S1405: From each connected region, determine the target connected region with the largest integral value in order, and determine the time difference value corresponding to the vertex of each target connected region as the time difference value corresponding to the longitudinal wave, transverse wave and Stoneley wave respectively, in order of the time difference value from smallest to largest.

[0160] S1406: Decrease the initial correlation coefficient threshold and return to step S1402.

[0161] The contents of S1401 to S1406 have been described sequentially in the foregoing embodiments, and will not be repeated here.

[0162] Through the aforementioned embodiments, the time difference values ​​corresponding to the longitudinal wave, transverse wave, and Stoneley wave can be extracted respectively. In order to further improve the accuracy of the time difference values, this application performs a second slowness search on this basis. Specifically, when determining the time difference values ​​corresponding to the vertices of each target connected region as the time difference values ​​corresponding to the longitudinal wave, transverse wave, and Stoneley wave respectively in ascending order of time difference values, the following steps (I) to (IV) can be included:

[0163] (I) In order of increasing time difference values, the time difference values ​​corresponding to the vertices of each target connected region are determined as the initial time difference value of the longitudinal wave, the initial time difference value of the transverse wave, and the initial time difference value of the Stoneley wave.

[0164] (II) Based on the initial time difference value of the longitudinal wave and the first preset time difference interval coefficient, determine the longitudinal wave time difference retrieval interval, and take the time difference value of the corresponding maximum correlation coefficient within the longitudinal wave time difference retrieval interval as the time difference value of the longitudinal wave.

[0165] The first preset time difference interval coefficient includes an upper limit coefficient and a lower limit coefficient. These upper and lower limit coefficients can be set according to actual needs; in this embodiment, the upper limit coefficient is 1.1 and the lower limit coefficient is 0.9.

[0166] Specifically, the lower limit of the interval is determined by multiplying the initial time difference of the P-wave with the lower limit coefficient, and the upper limit of the interval is determined by multiplying the initial time difference of the P-wave with the upper limit coefficient. Then, the P-wave time difference retrieval interval is determined based on the lower limit and the upper limit of the interval.

[0167] For example, the expression for the P-wave time difference retrieval interval can be expressed as: [0.9*SL P 1.1*SL P ], where SL P This represents the initial time difference of the longitudinal wave.

[0168] Furthermore, when using the time difference value of the maximum correlation coefficient within the longitudinal wave time difference retrieval interval as the time difference value of the longitudinal wave, formula (1) can be used first to determine each correlation coefficient within the longitudinal wave time difference retrieval interval, and the time difference value of the maximum correlation coefficient can be used as the time difference value of the longitudinal wave.

[0169] The expression for the time difference of the longitudinal wave is shown in formula (5):

[0170] (5)

[0171] in, This represents the time difference of the longitudinal wave. For the P-wave time difference retrieval interval, is the correlation coefficient expression in formula (1), and T is the arrival time retrieval interval.

[0172] (III) Based on the initial time difference value of the shear wave and the second preset time difference interval coefficient, determine the shear wave time difference retrieval interval, and take the time difference value of the corresponding maximum correlation coefficient within the shear wave time difference retrieval interval as the time difference value of the shear wave.

[0173] The second preset time difference interval coefficient can be the same as or different from the first preset time difference interval coefficient, and no limitation is made here.

[0174] Here, the process of determining the transverse wave time difference retrieval interval is similar to that of determining the longitudinal wave time difference retrieval interval, and will not be elaborated here.

[0175] Similarly, when using the time difference value of the maximum correlation coefficient within the transverse wave time difference retrieval interval as the time difference value of the transverse wave, formula (1) can be used to calculate each correlation coefficient corresponding to the transverse wave time difference retrieval interval, and the time difference value of the maximum correlation coefficient can be used as the time difference value of the transverse wave.

[0176] The expression for the time difference of the transverse wave is shown in formula (6):

[0177] (6)

[0178] in, This represents the time difference of the transverse wave. For the transverse wave time difference retrieval interval, is the correlation coefficient expression in formula (1), and T is the arrival time retrieval interval.

[0179] For example, the expression for the transverse wave time difference retrieval interval can be expressed as: [0.9*SLs, 1.1*SLs], where SLs is the initial time difference value of the transverse wave.

[0180] (IV) Based on the initial time difference value of the Stoneley wave and the third preset time difference interval coefficient, determine the Stoneley wave time difference retrieval interval, and take the time difference value of the corresponding maximum correlation coefficient within the Stoneley wave time difference retrieval interval as the time difference value of the Stoneley wave.

[0181] The third preset time difference interval coefficient can be the same as or different from the second preset time difference interval coefficient and the first preset time difference interval coefficient, and no limitation is made here.

[0182] Here, the process of determining the Stoneley wave time difference retrieval interval is similar to that of determining the longitudinal wave time difference retrieval interval, and will not be elaborated here.

[0183] Similarly, when using the time difference value of the maximum correlation coefficient within the Stoneley wave time difference retrieval interval as the time difference value of the Stoneley wave, formula (1) can be used to calculate each correlation coefficient corresponding to the Stoneley wave time difference retrieval interval, and the time difference value of the maximum correlation coefficient can be used as the time difference value of the Stoneley wave.

[0184] For example, the expression for the time difference of the Stoneley wave can be:

[0185] (7)

[0186] in, This represents the time difference of the Stoneley wave. For the Stoneley wave time difference retrieval interval, is the correlation coefficient expression in formula (1), and T is the arrival time retrieval interval.

[0187] Based on the above, it can be seen that the threshold-based connected component integral sorting algorithm of this application performs the first time difference extraction and the second time difference extraction is performed through the second depth interval search, which can improve the accuracy of the time difference.

[0188] In practical applications, after extracting the time differences corresponding to the P-wave, S-wave, and Stoneley wave, these time differences can be applied to the following scenarios:

[0189] 1. Oil and gas exploration and development:

[0190] It can be used for key well logging interpretation tasks such as formation segmentation, gas layer identification, porosity calculation, and formation pressure prediction, improving the accuracy and efficiency of reservoir evaluation. For scenarios with higher requirements for the accuracy and automation of sonic data processing, such as well logging interpretation of unconventional oil and gas resources (shale gas, tight oil, etc.), the above-mentioned high-precision slow detection method has advantages.

[0191] 2. Engineering logging and cementing quality evaluation:

[0192] It can be used in fields such as cementing quality inspection and cement bond evaluation, providing more reliable sonic transit time data support. It has real-time processing potential in logging while drilling (LWD), and its application in LWD can improve decision-making efficiency during the drilling process.

[0193] 3. Integration of intelligent logging system and software:

[0194] It can be integrated into well logging data processing software or connected to well logging systems to achieve a highly efficient automated processing tool. The above method is particularly suitable for full-wave extraction scenarios, significantly reducing labor costs and processing time.

[0195] Corresponding to the aforementioned embodiments of the time difference determination method for array acoustic waves, this application also provides embodiments of the time difference determination device for array acoustic waves.

[0196] Please refer to Figure 15 This is a schematic diagram illustrating the structure of a time difference determination device for arrayed acoustic waves, as shown in an exemplary embodiment of this application. Figure 15 As shown, the time difference determination device 1500 for the array acoustic waves includes:

[0197] The array acoustic wave acquisition module 1510 is used to acquire array acoustic waves detected by the array acoustic wave logging device; the array acoustic wave logging device is located at the target depth underground.

[0198] The frequency band determination module 1520 is used to determine the frequency band of the Stoneley wave and the frequency bands of the transverse and longitudinal waves in the array sound waves from the frequency band of the array sound waves based on the frequency boundary points of the array sound waves; the transverse and longitudinal wave frequency bands include the frequency bands of the transverse waves and the frequency bands of the longitudinal waves.

[0199] The window length determination module 1530 is used to determine the Stoneley wave window length based on the first window period number of the Stoneley wave and the frequency band of the Stoneley wave, and to determine the transverse and longitudinal wave window length based on the second window period number of the transverse and longitudinal waves and the frequency band of the transverse and longitudinal waves.

[0200] The time difference determination module 1540 is used to determine the time window length of the slow time correlation algorithm corresponding to the target depth based on the Stoneley wave window length and the shear and longitudinal wave window lengths, and to use the slow time correlation algorithm to determine the time difference values ​​corresponding to the shear wave, the longitudinal wave and the Stoneley wave respectively based on the time window length; the time difference values ​​corresponding to the shear wave, the longitudinal wave and the Stoneley wave respectively are used to determine the formation information at the target depth.

[0201] In some implementations, the frequency band determination module 1520 is specifically used to retrieve the number of valleys of the array sound wave and determine the frequency boundary point of the array sound wave based on the retrieved number of valleys.

[0202] In some embodiments, the frequency band determination module 1520 is specifically used for:

[0203] Get the window value of the preset sliding window;

[0204] The number of valleys in the transition frequency band of the array acoustic wave is determined. If the number of valleys is greater than a preset number, and the window value of the sliding window is less than a preset threshold, the array acoustic wave is subjected to moving average filtering within the sliding window to obtain a filtered signal.

[0205] The number of troughs in the transition band of the filtered signal is determined. If the number of troughs is greater than a preset number, the window value of the sliding window is increased at a preset interval and the process returns to the step of performing a moving average filtering process on the array sound wave within the sliding window if the window value of the sliding window is less than a preset threshold to obtain the filtered signal, until the number of troughs equals the preset number. The frequency value corresponding to the minimum amplitude in the transition band of the array sound wave is taken as the frequency boundary point.

[0206] In some embodiments, the frequency band determination module 1520 is further configured to:

[0207] If the number of troughs is greater than the preset number and the window value of the sliding window is not less than the preset threshold, the window value of the sliding window is adjusted to be less than the preset threshold. The array sound wave is filtered based on the adjusted window value, and the filtered array sound wave is used as the array sound wave. Then, the process returns to the step of obtaining the preset window value of the sliding window.

[0208] In some embodiments, the array acoustic wave further includes a noise wave; the time difference determination module 1550 is specifically used for:

[0209] Gain processing is performed on the Stoneley wave, the transverse and longitudinal waves, and the noise wave respectively to obtain the target array acoustic wave;

[0210] Using the slow time correlation algorithm, based on the time window length, the time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave in the target array acoustic wave are determined respectively.

[0211] In some implementations, the time difference determination module 1540 is specifically used for:

[0212] Determine the first average amplitude of the Stoneley wave and the second average amplitude of the transverse and longitudinal waves;

[0213] Based on the first average amplitude and the second average amplitude, a first gain coefficient corresponding to the Stoneley wave and a second gain coefficient corresponding to the transverse and longitudinal waves are determined respectively.

[0214] The third gain coefficient of the noise wave is obtained, and the Stoneley wave is gain processed based on the first gain coefficient, the transverse and longitudinal waves are gain processed based on the second gain coefficient, and the noise wave is gain processed based on the third gain coefficient to obtain the target array acoustic wave.

[0215] In some implementations, the time difference determination module 1540 is specifically used for:

[0216] Using the slow time correlation algorithm, based on the time window length, a mapping curve of time difference and correlation coefficient for the target array acoustic wave is obtained;

[0217] Based on a preset initial correlation coefficient threshold, the mapping curve between the time difference and the correlation coefficient is divided into multiple connected regions, and the number of connected regions greater than the initial correlation coefficient threshold is determined.

[0218] If the quantity is not less than the preset quantity, the integral value is obtained by integrating each connected region, and the time difference value corresponding to the transverse wave, the longitudinal wave and the Stoneley wave is determined based on each integral value.

[0219] In some implementations, the time difference determination module 1540 is specifically used for:

[0220] From each connected region, determine the target connected region with the largest integral value in that order; the number of the target connected regions is the preset number.

[0221] According to the time difference values ​​in ascending order, the time difference values ​​corresponding to the vertices of each target connected region are sequentially determined as the time difference values ​​corresponding to the longitudinal wave, the transverse wave, and the Stoneley wave, respectively.

[0222] In some implementations, the time difference determination module 1540 is specifically used for:

[0223] According to the time difference values ​​in ascending order, the time difference values ​​corresponding to the vertices of each target connected region are sequentially determined as the initial time difference values ​​of the longitudinal wave, the transverse wave, and the Stoneley wave.

[0224] Based on the initial time difference value of the P-wave and the first preset time difference interval coefficient, the P-wave time difference retrieval interval is determined, and the time difference value corresponding to the maximum correlation coefficient within the P-wave time difference retrieval interval is taken as the time difference value of the P-wave.

[0225] Based on the initial time difference value of the shear wave and the second preset time difference interval coefficient, the shear wave time difference retrieval interval is determined, and the time difference value corresponding to the maximum correlation coefficient within the shear wave time difference retrieval interval is taken as the time difference value of the shear wave.

[0226] Based on the initial time difference value of the Stoneley wave and the third preset time difference interval coefficient, the Stoneley wave time difference retrieval interval is determined, and the time difference value of the corresponding maximum correlation coefficient within the Stoneley wave time difference retrieval interval is taken as the time difference value of the Stoneley wave.

[0227] In some implementations, the time difference determination module 1540 is specifically used for:

[0228] If the number is less than the preset number, the initial correlation coefficient threshold is reduced, and the process returns to the step of dividing the mapping curve of time difference and correlation coefficient into multiple connected regions based on the initial correlation coefficient threshold, until the number is not less than the preset number.

[0229] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0230] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0231] Corresponding to the above-described method for determining the time difference of array acoustic waves, this disclosure also provides a computer device, such as... Figure 16 The diagram shown is a structural schematic of a computer device provided in an embodiment of this disclosure. Figure 16 As shown, the computer device 1600 includes a processor 1610, an internal bus 1620, memory 1630, a network interface 1640, and non-volatile memory 1650, and may also include other hardware required for its functions. One or more embodiments of this specification can be implemented in software, for example, the processor 1610 reads the corresponding computer program from the non-volatile memory 1650 into the memory 1630 and then runs it. Of course, besides software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0232] The memory 1630, also known as internal memory, is used to temporarily store the computational data in the processor 1610, as well as the data exchanged with non-volatile memory 1650 such as hard disk. The processor 1610 exchanges data with non-volatile memory 1650 through the memory 1630.

[0233] In this embodiment, memory 1630 is specifically used to store application code that executes the solution of this application, and its execution is controlled by processor 1610. That is, when the computer device is running, processor 1610 communicates with network interface 1640, memory 1630 and non-volatile memory 1650 through internal bus 1620, so that processor 1610 executes the application code stored in memory 1630 and non-volatile memory 1650, thereby executing the array acoustic wave time difference determination method described in the above method embodiment.

[0234] Processor 1610 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware microservices. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0235] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 1600. In other embodiments of this application, the computer device 1600 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0236] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the array acoustic wave time difference determination method described in the above-described method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0237] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the array acoustic wave time difference determination method in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0238] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0239] The embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0240] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by dedicated logic circuitry—such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as dedicated logic circuitry.

[0241] Computers suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. Basic computer microservices include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0242] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0243] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0244] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and microservices in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program microservices and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0245] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0246] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining the time difference of arrayed acoustic waves, characterized in that, include: Acquire array acoustic waves detected by an array acoustic logging device located at a target depth underground. The number of valleys in the array sound wave is retrieved, and based on the retrieved number of valleys, the frequency boundary point of the array sound wave is determined. Based on the frequency boundary point of the array sound wave, the frequency band of the Stoneley wave and the frequency bands of the transverse and longitudinal waves in the array sound wave are determined from the frequency band of the array sound wave. The transverse and longitudinal wave frequency bands include the frequency bands of the transverse waves and the frequency bands of the longitudinal waves. The length of the Stoneley wave window is determined based on the number of periods of the first window of the Stoneley wave and the frequency band of the Stoneley wave; and the length of the transverse and longitudinal wave window is determined based on the number of periods of the second window of the transverse and longitudinal waves and the frequency band of the transverse and longitudinal waves. Based on the Stoneley wave window length and the shear and p-wave window lengths, the time window length of the slow time correlation algorithm corresponding to the target depth is determined, and the slow time correlation algorithm is used to determine the time difference values ​​corresponding to the shear wave, the p-wave, and the Stoneley wave based on the time window length; the time difference values ​​corresponding to the shear wave, the p-wave, and the Stoneley wave are used to determine the formation information at the target depth; The step of retrieving the number of valleys in the array acoustic wave and determining the frequency boundary point of the array acoustic wave based on the retrieved number of valleys includes: Get the window value of the preset sliding window; The number of valleys in the transition frequency band of the array acoustic wave is determined. If the number of valleys is greater than a preset number, and the window value of the sliding window is less than a preset threshold, the array acoustic wave is subjected to moving average filtering within the sliding window to obtain a filtered signal. The number of troughs in the transition band of the filtered signal is determined. If the number of troughs is greater than a preset number, the window value of the sliding window is increased at a preset interval, and the process returns to the step of performing a moving average filtering process on the array sound wave within the sliding window if the window value of the sliding window is less than a preset threshold to obtain the filtered signal, until the number of troughs equals the preset number. The frequency value corresponding to the minimum amplitude in the transition band of the array sound wave is taken as the frequency boundary point.

2. The method according to claim 1, characterized in that, The method further includes: If the number of troughs is greater than the preset number and the window value of the sliding window is not less than the preset threshold, the window value of the sliding window is adjusted to be less than the preset threshold. The array sound wave is filtered based on the adjusted window value, and the filtered array sound wave is used as the array sound wave. Then, the process returns to the step of obtaining the preset window value of the sliding window.

3. The method according to claim 1, characterized in that, The array acoustic waves also include noise waves; the step of using the slow time correlation algorithm to determine the time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave based on the time window length includes: Gain processing is performed on the Stoneley wave, the transverse and longitudinal waves, and the noise wave respectively to obtain the target array acoustic wave; Using the slow time correlation algorithm, based on the time window length, the time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave in the target array acoustic wave are determined respectively.

4. The method according to claim 3, characterized in that, The process of gain processing the Stoneley wave, the transverse and longitudinal waves, and the noise wave to obtain the target array acoustic wave includes: Determine the first average amplitude of the Stoneley wave and the second average amplitude of the transverse and longitudinal waves; Based on the first average amplitude and the second average amplitude, a first gain coefficient corresponding to the Stoneley wave and a second gain coefficient corresponding to the transverse and longitudinal waves are determined respectively. The third gain coefficient of the noise wave is obtained, and the Stoneley wave is gain processed based on the first gain coefficient, the transverse and longitudinal waves are gain processed based on the second gain coefficient, and the noise wave is gain processed based on the third gain coefficient to obtain the target array acoustic wave.

5. The method according to claim 3, characterized in that, The step of using the slow time correlation algorithm to determine the time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave in the target array acoustic wave based on the time window length includes: Using the slow time correlation algorithm, based on the time window length, a mapping curve of time difference and correlation coefficient for the target array acoustic wave is obtained; Based on a preset initial correlation coefficient threshold, the mapping curve between the time difference and the correlation coefficient is divided into multiple connected regions, and the number of connected regions greater than the initial correlation coefficient threshold is determined. If the quantity is not less than the preset quantity, the integral value is obtained by integrating each connected region, and the time difference value corresponding to the transverse wave, the longitudinal wave and the Stoneley wave is determined based on each integral value.

6. The method according to claim 5, characterized in that, The step of determining the time difference values ​​corresponding to the transverse wave, the longitudinal wave, and the Stoneley wave based on each integral value includes: From each connected region, determine the target connected region with the largest integral value in that order; the number of the target connected regions is the preset number. According to the time difference values ​​in ascending order, the time difference values ​​corresponding to the vertices of each target connected region are sequentially determined as the time difference values ​​corresponding to the longitudinal wave, the transverse wave, and the Stoneley wave, respectively.

7. The method according to claim 6, characterized in that, The step of determining the time difference values ​​corresponding to the vertices of each target connected region in ascending order of time difference value as the time difference values ​​corresponding to the longitudinal wave, the transverse wave, and the Stoneley wave, respectively, includes: According to the time difference values ​​in ascending order, the time difference values ​​corresponding to the vertices of each target connected region are sequentially determined as the initial time difference values ​​of the longitudinal wave, the transverse wave, and the Stoneley wave. Based on the initial time difference value of the P-wave and the first preset time difference interval coefficient, the P-wave time difference retrieval interval is determined, and the time difference value corresponding to the maximum correlation coefficient within the P-wave time difference retrieval interval is taken as the time difference value of the P-wave. Based on the initial time difference value of the shear wave and the second preset time difference interval coefficient, the shear wave time difference retrieval interval is determined, and the time difference value corresponding to the maximum correlation coefficient within the shear wave time difference retrieval interval is taken as the time difference value of the shear wave. Based on the initial time difference value of the Stoneley wave and the third preset time difference interval coefficient, the Stoneley wave time difference retrieval interval is determined, and the time difference value of the corresponding maximum correlation coefficient within the Stoneley wave time difference retrieval interval is taken as the time difference value of the Stoneley wave.

8. The method according to claim 5, characterized in that, The method further includes: If the number is less than the preset number, the initial correlation coefficient threshold is reduced, and the process returns to the step of dividing the mapping curve of time difference and correlation coefficient into multiple connected regions based on the initial correlation coefficient threshold, until the number is not less than the preset number.

9. A time difference determination device for arrayed acoustic waves, characterized in that, include: An array acoustic wave acquisition module is used to acquire array acoustic waves detected by an array acoustic wave logging device located at a target depth underground. A frequency band determination module is used to retrieve the number of valleys in the array sound wave, and based on the retrieved number of valleys, determine the frequency boundary point of the array sound wave, and based on the frequency boundary point of the array sound wave, determine the frequency band of the Stoneley wave and the frequency bands of the transverse and longitudinal waves in the array sound wave from the frequency band of the array sound wave; the transverse and longitudinal wave frequency bands include the frequency bands of the transverse wave and the frequency bands of the longitudinal wave. The window length determination module is used to determine the Stoneley wave window length based on the first window period number of the Stoneley wave and the frequency band of the Stoneley wave, and to determine the transverse and longitudinal wave window length based on the second window period number of the transverse and longitudinal waves and the frequency band of the transverse and longitudinal waves. The time difference determination module is used to determine the time window length of the slow time correlation algorithm corresponding to the target depth based on the Stoneley wave window length and the shear and longitudinal wave window lengths, and to use the slow time correlation algorithm to determine the time difference values ​​corresponding to the shear wave, the longitudinal wave, and the Stoneley wave respectively based on the time window length; the time difference values ​​corresponding to the shear wave, the longitudinal wave, and the Stoneley wave respectively are used to determine the stratigraphic information at the target depth; The frequency band determination module is specifically used for: Get the window value of the preset sliding window; The number of valleys in the transition frequency band of the array acoustic wave is determined. If the number of valleys is greater than a preset number, and the window value of the sliding window is less than a preset threshold, the array acoustic wave is subjected to moving average filtering within the sliding window to obtain a filtered signal. The number of troughs in the transition band of the filtered signal is determined. If the number of troughs is greater than a preset number, the window value of the sliding window is increased at a preset interval, and the process returns to the step of performing a moving average filtering process on the array sound wave within the sliding window if the window value of the sliding window is less than a preset threshold to obtain the filtered signal, until the number of troughs equals the preset number. The frequency value corresponding to the minimum amplitude in the transition band of the array sound wave is taken as the frequency boundary point.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the time difference determination method for array acoustic waves according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the time difference determination method for array acoustic waves according to any one of claims 1-8.

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