A well logging acoustic wave automatic processing method, system and device for removing casing direct wave and a medium

Abstract

The application discloses a kind of well logging acoustic full-automatic processing method, system, device and medium of casing direct wave removal, comprising: collecting the array acoustic wave on logging at a certain depth as original waveform data;Original waveform data is standardized, and standardized waveform data is obtained;Then using the characteristics of constant casing wave velocity, automatically velocity filtering is carried out to standardized waveform data, to realize the automatic separation of formation wave and casing wave;STC analysis is carried out to the formation waveform obtained by separation, to obtain the slowness spectrum of formation wave;The slowness spectrum of formation wave is analyzed, to obtain the interval of formation longitudinal wave and transverse wave.The application uses the constant slowness characteristics of casing wave, to realize the automatic identification and removal of casing wave component using velocity characteristics from the original array acoustic waveform, to retain the true formation wave component, to realize the effective prediction and use of formation longitudinal wave and transverse wave interval.The application does not need to realize definition time window, and is widely applicable to various array acoustic logging data.

Description

Technical Field

[0001] This invention belongs to the field of geophysical technology and relates to a fully automated method, system, device and medium for removing direct-path waves from well logging acoustic waves. Background Technology

[0002] Casing wells constitute a large proportion of oil well logging applications. Estimating the acoustic transit time outside the casing, influenced by acoustic waves propagating directly along the casing (i.e., casing waves), has always been a technical challenge in the industry. During the oil and gas well development phase, changes in formation pore pressure and fluid composition significantly impact the formation transit time (i.e., slowness, the reciprocal of velocity) outside the casing, which is crucial for guiding precise production decisions and optimal well modification. The presence of casing waves, especially under conditions of poor cementation, severely affects formation transit time.

[0003] To achieve effective estimation of the casing wave time difference, technicians attempted to separate the casing wave by using the frequency difference between the casing wave and the formation wave and setting a bandpass filter in the frequency domain. However, this method is overly idealistic and requires manual interactive testing to design the filter. In reality, the frequency difference between the casing wave and the formation wave is very small in many cases, making it impossible to distinguish them by frequency alone. Hsu and Baggeroer proposed the maximum likelihood method to enhance the resolution of the formation signal, but the enhancement effect depends on the relative amplitude of the formation wave signal and the casing wave signal, making it unsuitable for signal processing in low signal-to-noise ratio formations. Valero et al. (2003) proposed a casing wave subtraction method to further extract the effective formation wave signal by subtracting the casing wave from the original waveform, but this method requires that the formation signal and the casing wave signal not overlap in the time domain. Bose et al. (2009) proposed the energy threshold method, introducing the influence of the energy threshold on the basis of the waveform correlation method, but this method cannot effectively extract the formation wave when the waveform energy of the formation wave is lower than that of the casing wave. Tang Xiaoming et al. proposed the waveform interferometry method (2015), which utilizes the modulation effect of the interference between casing waves and formation waves on waveform data to extract analytical signals related to formation information. This method can effectively extract formation wave velocity when the cementation quality is poor, but it requires knowledge of the propagation characteristics of the casing waves. Xu Song et al. (2022) effectively extracted the longitudinal wave information of the casing well by filtering the formation radiation curves of casing wells at different frequencies, selecting the optimal filtering frequency range, and performing time-slowness correlation processing on the filtered signal throughout the well section.

[0004] Methods for estimating time difference after casing typically utilize the differences between casing waves and formation waves in terms of arrival time, frequency, amplitude, power, and coherence to find solutions. These methods usually require long array acoustic acquisitions and manual design of frequency parameters and time windows, making truly automated processing impossible. Furthermore, the diverse types of casing wells and the varying quality of logging acoustic data in reality significantly limit the widespread application of these technologies. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies that utilize the differences between casing waves and formation waves in terms of arrival time, frequency, amplitude, power, and coherence. These technologies require long array acoustic acquisition times and manual design of frequency parameters and time windows, making truly automated processing impossible. Furthermore, the diverse types of casing wells and the varying quality of logging acoustic data in reality hinder the widespread application of such technologies. This invention provides a fully automated method, system, device, and medium for processing logging acoustic data to remove direct casing waves.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] A fully automated method for processing logging acoustic waves to remove direct casing waves includes:

[0008] Collect array acoustic waves from well logging at a certain depth as raw waveform data;

[0009] The original waveform data is standardized to obtain standardized waveform data;

[0010] Based on the constant velocity of the casing wave, automatic velocity filtering is performed on the standardized waveform data to achieve automatic separation of formation waves and casing waves.

[0011] STC analysis was performed on the separated formation waveforms to obtain the slowness spectrum of the formation waves;

[0012] The slowness spectrum of the formation waves was analyzed to obtain the time difference between the P-wave and S-wave.

[0013] A further improvement of the present invention is that:

[0014] Furthermore, the original waveform data is standardized to obtain standardized waveform data. Specifically, the waveform data is differentially processed along the time axis to suppress DC interference in the waveform data and improve the recognition of waveform information.

[0015] Furthermore, automatic velocity filtering is performed on the standardized waveform data to achieve automatic separation of formation waves and casing waves. Specifically, based on the characteristic that the slowness of casing waves in the standardized waveform data is relatively constant, velocity filtering is performed on the standardized waveform data to extract the casing wave waveform components, and then the formation wave components are separated.

[0016] Furthermore, velocity filtering employs Radon transform. Specifically, the standardized waveform data is processed based on Radon transform, the extraction window of the target waveform is automatically located in the Radon transform domain and extracted, and the extracted transform domain information is subjected to inverse Radon transform to obtain the target waveform in the time domain, thus completing the wave field separation and extraction process.

[0017] Furthermore, STC analysis is performed on the separated formation waveforms to obtain the slowness spectrum of the formation waves. Specifically, the STC method involves obtaining the inherent parameters of the array acoustic logging instrument used. On the entire array acoustic waveform, the length of the window is set, and two values ​​are selected for the arrival point and the corresponding component wave slowness range. The position of the arrival point determines the position of the window, while the magnitude of the slowness determines the time shift length of the waveforms of the receivers other than the first receiver. A correlation coefficient is calculated using this arrival point and slowness. After traversing the entire arrival point range and slowness range, the window is moved with a fixed step size, and the above calculation is repeated. After moving the window several times, the correlation coefficients are compared. The maximum value of the correlation coefficient corresponds to the most likely arrival point and slowness of the corresponding component wave. The correlation coefficient is calculated as follows:

[0018]

[0019] Where M is the number of receivers in the array acoustic logging instrument, typically 8; T is the wave arrival point; T w s is the window length, selected based on the actual waveform length; s is the slowness, r m (t) represents the waveform of the m-th receiver, z1 represents the distance from the 1st receiver to the transmitter, and z m Let m be the distance from the m-th receiver to the transmitter.

[0020] Furthermore, the instrument's inherent parameters include: the transmitter's position, the number of receivers, the spacing between adjacent receivers, and the sampling time interval.

[0021] A fully automated logging acoustic wave processing system for removing direct casing waves includes:

[0022] The acquisition module acquires array acoustic waves from well logging at a certain depth as raw waveform data;

[0023] A standardization processing module performs standardization processing on the original waveform data to obtain standardized waveform data.

[0024] A velocity filtering module, based on the characteristic of constant casing wave velocity, automatically performs velocity filtering on standardized waveform data to achieve automatic separation of formation waves and casing waves;

[0025] The STC analysis module performs STC analysis on the separated formation waveforms to obtain the slowness spectrum of the formation waves.

[0026] The acquisition module analyzes the slowness spectrum of the formation waves to obtain the time difference between the P-wave and S-wave.

[0027] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the steps of the method described above.

[0028] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] This invention standardizes the raw waveform data to obtain standardized waveform data. Then, based on the constant velocity of the casing wave, it automatically filters the standardized waveform data to achieve automatic separation of formation waves and casing waves. STC analysis is performed on the separated formation waveforms to obtain the slowness spectrum of the formation waves. Further analysis of the slowness spectrum yields the time differences between the P-waves and S-waves. This invention utilizes the constant slowness characteristic of casing waves to accurately extract and remove casing wave components from the original acquired array acoustic waveforms using velocity characteristics, thereby preserving the true formation wave components and achieving effective prediction and utilization of the time differences between formation P-waves and S-waves. This method is fully automated, requires no defined time windows, reduces human intervention, and is widely applicable to various array acoustic logging data. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating a fully automated logging acoustic wave processing method for removing direct casing waves according to the present invention.

[0033] Figure 2 This is a schematic diagram of the fully automated logging acoustic wave processing system for removing direct casing waves according to the present invention.

[0034] Figure 3 This is another flowchart illustrating the fully automated logging acoustic wave processing method for removing casing direct waves according to the present invention.

[0035] Figure 4 This is a schematic diagram illustrating the standardization process of the original array waveform data; where, Figure 4 (a) is a schematic diagram of the original array waveform data; Figure 4 (b) is a schematic diagram of the waveform data after standardization;

[0036] Figure 4 (c) is a schematic diagram of the two-dimensional STC spectrum of the original waveform data; Figure 4 (d) is a schematic diagram of the two-dimensional STC spectrum after standardization.

[0037] Figure 5 This is a schematic diagram illustrating the process of extracting the target waveform from a monopole array data: where, Figure 5 (a) is a schematic diagram of array unipolar waveform data; Figure 5 (b) is a schematic diagram of the extraction result of the known slowness in the Radon transform domain based on the target waveform; Figure 5 (c) is a schematic diagram of the target waveform after Radon inverse transform to the time domain;

[0038] Figure 6 This is a schematic diagram of the original well logging array acoustic waveform data and its STC analysis results; among which, Figure 6 (a) is a schematic diagram of the original array waveform data; Figure 6 (b) is a two-dimensional STC correlation diagram of the original array waveform data; Figure 6 (c) is a schematic diagram of the two-dimensional STC correlation contour lines of the original array waveform data; Figure 6 (d) is a schematic diagram of the one-dimensional slowness spectrum of the original array waveform data;

[0039] Figure 7 This is a schematic diagram of the processed formation wave data and its STC analysis results according to the present invention; wherein, Figure 7 (a) is a schematic diagram of the processed array waveform data; Figure 7 (b) is a schematic diagram of the two-dimensional STC correlation of the processed formation wave data; Figure 7 (c) is a schematic diagram of the two-dimensional STC correlation contour lines of the processed formation wave data; Figure 7 (d) is a schematic diagram of the one-dimensional slowness spectrum of the processed formation wave data;

[0040] Figure 8 This is a schematic diagram illustrating the formation wave processing analysis results for a specific well section; where, Figure 8 (a) is a schematic diagram of the original slowness spectrum; Figure 8 (b) is a schematic diagram of the slowness spectrum after processing according to the present invention; Figure 8 (c) is a schematic diagram of the real-time extraction results of the P-wave and S-wave time difference after processing according to the present invention;

[0041] Figure 9 This is a schematic diagram illustrating the effect of the present invention in suppressing drill collar direct waves in logging-while-drilling acoustic waves; wherein, Figure 9 (a) is a schematic diagram of the original slowness spectrum of logging-while-drilling acoustic waves; Figure 9 (b) is a schematic diagram of the slowness spectrum after processing according to the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0044] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0045] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0046] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0047] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0048] The present invention will now be described in further detail with reference to the accompanying drawings:

[0049] See Figure 1 This invention discloses a fully automated method for processing logging acoustic waves to remove direct casing waves, comprising:

[0050] S101 collects array acoustic waves from well logging at a certain depth as raw waveform data.

[0051] S102, standardize the original waveform data to obtain standardized waveform data.

[0052] Differential processing along the time axis of waveform data suppresses DC interference in the waveform data and improves the recognition of waveform information.

[0053] S103, based on the constant velocity of the casing wave, performs automatic velocity filtering on standardized waveform data to achieve automatic separation of formation waves and casing waves.

[0054] Based on the relatively constant slowness of casing waves in standardized waveform data, velocity filtering is performed on the standardized waveform data to extract the casing wave waveform components, thereby separating the formation wave components. The velocity filtering employs Radon transform. Specifically, the standardized waveform data is processed using Radon transform; the extraction window for the target waveform is automatically located in the Radon transform domain and extracted; then, the extracted transform domain information is subjected to an inverse Radon transform to obtain the target waveform in the time domain, completing the wavefield separation and extraction process.

[0055] S104. Perform STC analysis on the separated formation waveforms to obtain the slowness spectrum of the formation waves.

[0056] The STC method specifically involves: obtaining the inherent parameters of the array acoustic logging instrument; setting the window length across the entire array acoustic waveform; selecting two values ​​for the arrival point and the corresponding component wave slowness range; the location of the arrival point determines the window position, while the magnitude of the slowness determines the time shift length of the waveforms of all receivers except the first receiver; calculating the correlation coefficient using the arrival point and slowness; repeating the above calculation by moving the window with a fixed step size after traversing the entire arrival point and slowness range; comparing the correlation coefficients after moving the window several times, with the maximum correlation coefficient corresponding to the most likely arrival point and slowness of the corresponding wave component; the correlation coefficient is calculated as follows:

[0057]

[0058] In the formula: M is the number of receivers in the array acoustic logging instrument, typically 8; T is the wave arrival point; T w s is the window length, selected based on the actual waveform length; s is the slowness, r m (t) represents the waveform of the m-th receiver, z1 represents the distance from the 1st receiver to the transmitter, and z m Let be the distance from the m-th receiver to the transmitter. The instrument's inherent parameters include: the transmitter's location, the number of receivers, the spacing between adjacent receivers, and the sampling time interval.

[0059] S105 analyzes the slowness spectrum of the formation waves to obtain the time difference between the P-wave and S-wave.

[0060] See Figure 2 This invention discloses a fully automated logging acoustic wave processing system for removing direct casing waves, comprising:

[0061] The acquisition module acquires array acoustic waves from well logging at a certain depth as raw waveform data;

[0062] A standardization processing module performs standardization processing on the original waveform data to obtain standardized waveform data.

[0063] A velocity filtering module, based on the characteristic of constant casing wave velocity, automatically performs velocity filtering on standardized waveform data to achieve automatic separation of formation waves and casing waves;

[0064] The STC analysis module performs STC analysis on the separated formation waveforms to obtain the slowness spectrum of the formation waves.

[0065] The acquisition module analyzes the slowness spectrum of the formation waves to obtain the time difference between the P-wave and S-wave.

[0066] Example:

[0067] See Figure 3 This invention discloses a fully automated method for processing logging acoustic waves to remove direct casing waves, specifically as follows:

[0068] Step 1: Using the array acoustic waveform acquired at a certain depth as the processing unit, input the original waveform data.

[0069] Step 2: Considering the differences in response of each receiver in the acoustic logging instrument's receiving array and the potential interference, this invention employs a time-axis differential processing method for waveform data during standardization to suppress DC component interference and improve the recognition of waveform information. Figure 4 For example, the processed waveform features are more prominent, and interference on the STC spectrum is effectively suppressed. (Comparison) Figure 4 (a) Figure 4 (b) Figure 4 (c) and Figure 4 (d) It can be seen that the waveform characteristics after standardization are more significant, and irrelevant interference is effectively suppressed on the two-dimensional STC spectrum, laying the necessary groundwork for subsequent accurate time difference analysis and wave field separation.

[0070] Step 3: Utilizing the relatively constant slowness of the casing wave [~57 μs / ft], velocity filtering is performed on the original waveform data according to this velocity characteristic to extract the casing wave waveform components, thereby separating the formation wave components. This step is the core of the fully automated processing in this invention. There are many mathematical implementations of velocity filtering, such as Radon transform and FK transform; the method in this invention uses Radon transform. Figure 5 Taking the extraction of transverse wave information from a monopole waveform as an example, this process is illustrated. The monopole data is transformed to the Radon transform domain. Based on the known transverse wave velocity, the extraction window for the target waveform is automatically located in the Radon transform domain and extracted. Then, an inverse Radon transform is performed on the extracted transform domain information to obtain the target waveform in the time domain, thus completing the wavefield separation and extraction process. Figure 5 (a) Figure 5 (b) and Figure 5 (c) It can be seen that this step requires achieving the known slowness of the target waveform. For casing waves or drill collar waves, their fixed time difference characteristics make it easier to perform the separation process. It is worth noting that the previous separation step is also applicable to separating drill collar waves and formation waves from logging while drilling. Simply input the known slowness of the drill collar wave (e.g., 63 μs / ft) in this step and perform velocity filtering to achieve a similar effect.

[0071] Step 4: Perform STC analysis on the separated formation wave components to obtain the slowness spectrum containing formation waves.

[0072] Step 5: Perform post-processing analysis on the formation wave slowness spectrum to obtain the time differences between P-waves and S-waves. This step is a slowness analysis, which can be performed manually or automatically using a method to extract the P-wave and S-wave time differences.

[0073] In this case, the well logging acoustic acquisition was performed by five receivers (spaced 0.5 ft apart), with a source distance of 3 ft. For example... Figure 6 The original waveform shown in (a) is of poor quality, with a low signal-to-noise ratio. A significant portion of the waveform information is lost in the first and last receivers. STC analysis was performed on this array waveform data to obtain the similarity correlation plot and contour plot, as shown below. Figure 6 (b) and Figure 6 As shown in (c), further performing the projection towards the slowness axis yields... Figure 6 (d) Comprehensive comparison Figure 6 (b) Figure 6 (c) and Figure 6 (d) Casing wave, formation P-wave, and S-wave information coexist. The slowness value of the casing wave is 56 μs / ft (consistent with the actual casing velocity), and the slowness value of the formation P-wave is 78 μs / ft. However, due to the interference of the casing wave, the correlation between the formation P-wave and S-wave is not high, and the accuracy is limited to some extent. After processing using the method of this invention, the casing wave component can be accurately removed from the original waveform data, while the formation wave component is retained. Figure 7 As shown in (a), the processed first arrival of the formation wave is clearly displayed. Figure 7 (b) Figure 7 (c) and Figure 7 (d) The correlation energy of the original casing wave was effectively removed, and the waveform interference phenomenon caused by the casing wave was also suppressed, thus making the correlation between the P-wave and S-wave more prominent. The correlation of the formation P-wave increased from 0.74 to 0.81. Figure 8 (a) is the slowness spectrum obtained by performing STC analysis on the original data. Figure 8 (b) is the slowness spectrum obtained by performing STC analysis on the waveform data processed by the method of this invention. (Comparison) Figure 8 (a) and Figure 8 (b) In this example, casing waves are prevalent. Verification has shown that the technique of this invention can overcome the adverse effects of low signal-to-noise ratio, effectively removing casing wave characteristics from the processed slowness spectrum, thus providing an important data foundation for subsequent formation wave processing and analysis. In subsequent processing, manual interactive processing or automatic time difference analysis can be employed. Figure 8 (c) shows the automatic extraction of P-wave and S-wave time difference based on the data processed by the present invention. The results show that the P-wave and S-wave time difference of the formation can be accurately and reliably tracked and picked up in real time.

[0074] The method of this invention is also applicable to removing drill collar direct waves from drilling acoustic waves. Figure 9 (a) and Figure 9 (b) It is known that the slowness spectrum of the original waveform data exhibits visible drill collar wave influence at depths of 4200m to 4450m, with the slowness relatively fixed at around 63µs / ft. The resulting interference effect affects subsequent formation wave time difference analysis. After processing using the method of this invention, the influence of drill collars is effectively suppressed, providing key technical support for the field application of logging-while-drilling acoustic waves and the analysis of formation P-wave and S-wave time differences.

[0075] An embodiment of the present invention provides a terminal device. This terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0076] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0077] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0078] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0079] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0080] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0081] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fully automated method for processing logging acoustic waves to remove direct casing waves, characterized in that, include: Collect array acoustic waves from well logging at a certain depth as raw waveform data; The original waveform data is standardized to obtain standardized waveform data; Based on the constant velocity of the casing wave, automatic velocity filtering is performed on the standardized waveform data to achieve automatic separation of formation waves and casing waves. STC analysis was performed on the separated formation waveforms to obtain the slowness spectrum of the formation waves; The slowness spectrum of the formation waves is analyzed to obtain the time difference between the P-wave and S-wave of the formation. The automatic velocity filtering of standardized waveform data to achieve automatic separation of formation waves and casing waves is specifically as follows: based on the characteristic that the slowness of casing waves in standardized waveform data is relatively constant, velocity filtering is performed on the standardized waveform data to extract the casing wave waveform components, and then the formation wave components are separated. The velocity filtering employs Radon transform. Specifically, the Radon transform is used to process standardized waveform data, automatically locate the extraction window of the target waveform in the Radon transform domain and extract it, and then perform an inverse Radon transform on the extracted transform domain information to obtain the target waveform in the time domain, thus completing the wave field separation and extraction process.

2. The fully automated logging acoustic wave processing method for removing direct casing waves according to claim 1, characterized in that, The standardization process for the original waveform data to obtain standardized waveform data specifically involves: performing differential processing on the waveform data along the time axis to suppress DC component interference in the waveform data and improve the recognition of waveform information.

3. A fully automated logging acoustic wave processing system for removing direct-path waves from casing, characterized in that, The fully automated logging acoustic wave processing method for removing casing direct waves as described in any one of claims 1-2 includes: The acquisition module acquires array acoustic waves from well logging at a certain depth as raw waveform data; A standardization processing module performs standardization processing on the original waveform data to obtain standardized waveform data. A velocity filtering module, based on the characteristic of constant casing wave velocity, automatically performs velocity filtering on standardized waveform data to achieve automatic separation of formation waves and casing waves; The STC analysis module performs STC analysis on the separated formation waveforms to obtain the slowness spectrum of the formation waves. The acquisition module analyzes the slowness spectrum of the formation waves to obtain the time difference between the P-wave and S-wave.

4. A terminal 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 computer program, it implements the steps of the method as described in any one of claims 1-2.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-2.

Images