Logging sound wave full-automatic processing method, system and device for removing casing direct wave and medium

Abstract

The invention discloses a logging sound wave full-automatic processing method, system and device for removing casing direct waves and a medium. The logging sound wave full-automatic processing method comprises the steps that array sound waves on a logging well at a certain depth are collected to serve as original waveform data; performing standardization processing on the original waveform data to obtain standardized waveform data; performing automatic speed filtering on the standardized waveform data by utilizing the characteristic that the speed of the casing wave is constant, so as to realize automatic separation of the stratum wave and the casing wave; performing STC analysis on the separated formation waveform to obtain a slowness spectrum of the formation wave; and analyzing the slowness spectrum of the stratum wave to obtain the time difference between the stratum longitudinal wave and the stratum transverse wave. According to the method, the constant slowness characteristic of the casing wave is utilized, the casing wave component is automatically identified and removed from the originally collected array sound wave waveform by utilizing the speed characteristic, and the real stratum wave component is reserved, so that the effective prediction and utilization of the time difference of the stratum longitudinal and transverse waves are realized. A time window does not need to be defined, and the method is widely suitable for various array acoustic logging data.

Description

Technical Field

[0001] The invention belongs to the field of geophysical technology and relates to a fully automatic processing method, system, device and medium for well logging acoustic waves for removing casing direct waves. Background Art

[0002] Cased wells account for a large proportion in the field of oil logging. Affected by the sound waves directly propagating along the casing (i.e., casing waves), the estimation of the acoustic time difference outside the casing has always been a technical problem in the industry. During the development stage of oil and gas wells, due to the changes in formation pore pressure and fluid composition, the change in formation time difference (i.e., slowness, the inverse of the same speed) outside the casing is of great significance for guiding the fine production decision-making and optimization transformation of oil and gas wells. The existence of casing waves, especially under the condition of poor cementation quality, will seriously affect the formation time difference.

[0003] In order to achieve effective estimation of the post-casing time difference, technicians have tried to use the frequency difference between casing waves and formation waves to set up bandpass filters in the frequency domain to separate casing waves. This method is too idealistic and requires manual interactive testing to design the filter. In many practical examples, the frequency difference between casing waves and formation waves is very small and cannot be distinguished by frequency. Hsu and Baggeroer proposed the maximum likelihood method to enhance the resolution of formation signals, but the enhancement effect depends on the relative amplitude of formation wave signals and casing wave signals, which is not suitable for signal processing of low signal-to-noise ratio formations. The casing wave subtraction method proposed by Valero et al. (2003) further extracts effective formation wave signals by subtracting casing waves from the original waveform, but this method requires that the formation signal and the tube wave signal cannot overlap each other in the time domain. Bose et al. (2009) proposed the energy threshold method, which introduced the influence of energy threshold on the basis of the waveform correlation method, but when the waveform energy of the formation wave is lower than the tube wave energy, this method cannot effectively extract the formation wave. Tang Xiaoming et al. proposed the waveform interferometry method (2015), which uses the modulation effect of the interference between casing wave and formation wave on waveform data to extract analytical signals related to formation information. It can effectively extract formation wave velocity when the cementation quality is poor, but this method requires the propagation characteristics of casing wave to be known. Xu Song et al. (2022) filtered the formation radiation curve of cased well at different frequencies, selected the optimal filtering frequency interval, and processed the filtered signal by time-slowness correlation method in the whole well section, effectively extracting the longitudinal wave information of cased well.

[0004] Regarding the post-casing time difference estimation technology, all of them use the differences between casing waves and formation waves in terms of arrival time, frequency, amplitude, power and coherence to seek solutions. This type of technology usually requires a long array acoustic wave acquisition, and also requires manual interaction to design frequency parameters and time windows, and cannot achieve true automatic processing. In addition, the various types of casing wells and the widely varying quality of logging acoustic wave data in reality also largely restrict the promotion and application of this technology. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the existing technology uses the differences between casing waves and formation waves in terms of arrival time, frequency, amplitude, power and coherence, which requires a long array acoustic wave acquisition and requires manual interaction to design frequency parameters and time windows, and cannot achieve true automatic processing. In reality, the various casing well types and the vastly different logging acoustic wave data quality restrict the promotion and application of such technologies. A fully automatic logging acoustic wave processing method, system, device and medium for removing casing direct waves are provided.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A fully automatic processing method for well logging acoustic waves for removing casing direct waves, comprising:

[0008] Collecting array acoustic waves on the well logging at a certain depth as raw waveform data;

[0009] Performing standardization processing on the original waveform data to obtain standardized waveform data;

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

[0011] Perform STC analysis on the separated formation waveform to obtain the slowness spectrum of the formation wave;

[0012] The slowness spectrum of the formation wave is analyzed to obtain the time difference between the formation longitudinal wave and the transverse wave.

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

[0014] Furthermore, the original waveform data is standardized to obtain standardized waveform data, specifically: the waveform data is de-differentiated along the time axis to suppress the DC component interference in the waveform data and improve the recognition of the waveform information.

[0015] Furthermore, the standardized waveform data is automatically velocity filtered to achieve automatic separation of formation waves and casing waves. Specifically, based on the relatively constant slowness of the casing wave in the standardized waveform data, the standardized waveform data is velocity filtered to extract the casing wave waveform component, and then the formation wave component is separated.

[0016] Furthermore, velocity filtering adopts Radon transform, and Radon transform is used for velocity filtering, specifically: based on Radon transform, the standardized waveform data is processed, the extraction window of the target waveform is automatically located in the Radon transform domain and extraction is realized, and the extracted transform domain information is subjected to inverse Radon transform to obtain the target waveform in the time domain, thereby completing the wave field separation and extraction process.

[0017] Further, the separated formation waveform is subjected to STC analysis to obtain the slowness spectrum of the formation wave, specifically: The STC method is specifically as follows: according to the inherent parameters of the array acoustic logging instrument used, the length of the window is set on the entire array acoustic waveform and two values ​​are selected in the wave arrival point and the corresponding component wave slowness range respectively; the position of the wave arrival point determines the position of the window, and the size of the slowness determines the time shift length of the waveform of the remaining receivers except the first receiver; the correlation coefficient is calculated with the wave arrival point and the slowness; after traversing the entire wave arrival point range and the slowness range, the window is moved with a fixed step size to repeat the above calculation; after moving the window several times, the correlation coefficients are compared, and the maximum value of the correlation coefficient corresponds to the most likely wave arrival point and slowness of the component wave; the way to calculate the correlation coefficient is as follows:

[0018]

[0019] Where M is the number of receivers of the array acoustic logging instrument, which is generally 8; T is the wave arrival point; T w is the length of the window, which is selected according to the actual waveform length; s is the slowness, r m (t) is the waveform of the mth receiver, z 1 is the distance from the first receiver to the transmitter, z m is the distance from the mth receiver to the transmitter.

[0020] Furthermore, the intrinsic parameters of the instrument include: the location of the transmitter, the number of receivers, the spacing between adjacent receivers, and the sampling time interval.

[0021] A fully automatic logging acoustic wave processing system for removing casing direct waves, comprising:

[0022] An acquisition module, wherein the acquisition module acquires array acoustic waves on the well logging at a certain depth as raw waveform data;

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

[0024] A velocity filtering module, which automatically performs velocity filtering on the standardized waveform data based on the constant velocity of the casing wave, thereby realizing automatic separation of the formation wave and the casing wave;

[0025] An STC analysis module, wherein the STC analysis module performs STC analysis on the separated formation waveform to obtain a slowness spectrum of the formation wave;

[0026] An acquisition module analyzes the slowness spectrum of the formation wave to obtain the time difference between the formation longitudinal wave and the transverse wave.

[0027] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0028] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

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

[0030] The present invention obtains standardized waveform data by performing standardization processing on the original waveform data; then based on the constant velocity characteristic of the casing wave, the standardized waveform data is automatically velocity filtered to achieve automatic separation of the formation wave and the casing wave; the separated formation waveform is subjected to STC analysis to obtain the slowness spectrum of the formation wave; the slowness spectrum of the formation wave is analyzed to obtain the time difference between the longitudinal wave and the transverse wave of the formation. The present invention utilizes the constant slowness characteristic of the casing wave and the velocity characteristic from the original collected array acoustic wave waveform to achieve accurate extraction and removal of the casing wave component, thereby retaining the true formation wave component and achieving effective prediction and utilization of the time difference between the longitudinal and transverse waves of the formation. The method of the present invention is completely automatic, does not require the definition of the time window, reduces human intervention, and can be widely applied to various types of array acoustic logging data. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0032] Figure 1A schematic flow chart of a fully automatic logging acoustic wave processing method for removing casing direct waves of the present invention;

[0033] Figure 2 It is a structural schematic diagram of the fully automatic processing system for well logging acoustic waves removing casing direct waves of the present invention;

[0034] Figure 3 Another schematic flow chart of the fully automatic logging acoustic wave processing method for removing casing direct waves of the present invention;

[0035] Figure 4 Schematic diagram of the standardization process of raw array waveform data; Figure 4 (a) is a schematic diagram of the original array waveform data; Figure 4 (b) is a schematic diagram of 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 normalization;

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

[0038] Figure 6 It is a schematic diagram of the original logging array acoustic waveform data and its STC analysis results; Figure 6 (a) is a schematic diagram of the original array waveform data; Figure 6 (b) is a schematic diagram of the two-dimensional STC correlation of the original array waveform data; Figure 6 (c) is a schematic diagram of the two-dimensional STC correlation contours 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 It is a schematic diagram of the formation wave data processed by the present invention and its STC analysis results; 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 contours 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 The following is a schematic diagram of the formation wave processing and analysis results for a casing well section; 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 by the present invention; Figure 8 (c) is a schematic diagram of the real-time extraction result of longitudinal and transverse wave time differences after processing by the present invention;

[0041] Fig. 9 The figure is a schematic diagram of the processing effect of suppressing the drill collar direct wave in the acoustic wave of logging while drilling according to the present invention; wherein, Fig. 9 (a) is a schematic diagram of the original slowness spectrum of the LWD acoustic wave; Fig. 9 (b) is a schematic diagram of the slowness spectrum after processing by the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

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

[0046] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which 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 is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0049] See also Figure 1 The present invention discloses a fully automatic processing method for well logging acoustic waves for removing casing direct waves, comprising:

[0050] S101, collecting array acoustic waves on well logging at a certain depth as original waveform data.

[0051] S102, performing standardization processing on the original waveform data to obtain standardized waveform data.

[0052] The waveform data is de-differentiated along the time axis to suppress the DC component interference in the waveform data and improve the recognition of the waveform information.

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

[0054] Based on the relatively constant slowness of casing wave in the standardized waveform data, velocity filtering is performed on the standardized waveform data to extract the casing wave waveform component and then separate the formation wave component. Velocity filtering uses Radon transform, and the Radon transform is used for velocity filtering. 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 the extraction is realized, and the extracted transform domain information is subjected to inverse Radon transform to obtain the target waveform in the time domain, completing the wave field separation and extraction process.

[0055] S104, performing STC analysis on the separated formation waveform to obtain a slowness spectrum of the formation wave.

[0056] The STC method is specifically as follows: according to the inherent parameters of the array acoustic logging instrument used, the length of the window is set on the entire array acoustic waveform and two values ​​are selected in the wave arrival point and the corresponding component wave slowness range; the position of the wave arrival point determines the position of the window, and the size of the slowness determines the time shift length of the waveform of the remaining receivers except the first receiver; the correlation coefficient is calculated with the wave arrival point and the slowness; after traversing the entire wave arrival point range and the 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, and the maximum value of the correlation coefficient corresponds to the most likely wave arrival point and slowness of the component wave; the method of calculating the correlation coefficient is as follows:

[0057]

[0058] Where: M is the number of receivers of the array acoustic logging instrument, generally 8; T is the wave arrival point; T w is the length of the window, which is selected according to the actual waveform length; s is the slowness, r m (t) is the waveform of the mth receiver, z 1 is the distance from the first receiver to the transmitter, z m is the distance from the mth receiver to the transmitter. The intrinsic parameters of the instrument include: the location of the transmitter, the number of receivers, the spacing between adjacent receivers, and the sampling time interval.

[0059] S105, analyzing the slowness spectrum of the formation wave to obtain the time difference between the formation longitudinal wave and the transverse wave.

[0060] See also Figure 2 The present invention discloses a fully automatic processing system for well logging acoustic waves for removing casing direct waves, comprising:

[0061] An acquisition module, wherein the acquisition module acquires array acoustic waves on the well logging at a certain depth as raw waveform data;

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

[0063] A velocity filtering module, which automatically performs velocity filtering on the standardized waveform data based on the constant velocity of the casing wave, thereby realizing automatic separation of the formation wave and the casing wave;

[0064] An STC analysis module, wherein the STC analysis module performs STC analysis on the separated formation waveform to obtain a slowness spectrum of the formation wave;

[0065] An acquisition module analyzes the slowness spectrum of the formation wave to obtain the time difference between the formation longitudinal wave and the transverse wave.

[0066] Example:

[0067] See also Figure 3 The present invention discloses a fully automatic processing method for well logging acoustic waves for removing casing direct waves, which is specifically:

[0068] Step 1: Take the array acoustic wave waveform collected at a certain depth as the processing unit and input the original waveform data.

[0069] Step 2: Considering the response differences of each receiver in the acoustic logging instrument receiving array and the potential interference, the present invention adopts the process of de-differentiation of the waveform data along the time axis in the standardization process to suppress the DC component interference and improve the recognition of the waveform information. Figure 4 For example, the waveform features after processing are more prominent, and the interference on the STC spectrum is also effectively suppressed. 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 foundation for subsequent accurate time difference analysis and wave field separation.

[0070] Step 3: Taking advantage of the relatively constant slowness of casing wave [~57us / ft], the original waveform data is subjected to velocity filtering according to this velocity feature to extract the casing wave waveform component and then separate the formation wave component. This step is the core of the present invention to achieve full automatic processing. There are many mathematical methods to achieve velocity filtering, such as Radon transform, FK transform, etc. The method of the present invention adopts Radon transform. Figure 5 This process is explained by taking the extraction of shear wave information from a monopole waveform as an example. The monopole data is transformed into the Radon domain. According to the known shear wave velocity, the extraction window of the target waveform is automatically located in the Radon transform domain and the extraction is realized. The extracted transform domain information is then subjected to an inverse Radon transform to obtain the target waveform in the time domain, thereby completing the wave field separation and extraction process. Figure 5 (a) Figure 5 (b) and Figure 5 (c) It can be seen that this step needs to realize the slowness of the known target waveform. For casing wave or drill collar wave, its fixed time difference feature is more convenient for performing the separation process. It is worth noting that the separation link in the previous step is also applicable to separating the drill collar wave and formation wave of LWD. It only needs to input the known slowness of the drill collar wave (for example, 63us / 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 the formation wave.

[0072] Step 5: Perform subsequent processing and analysis on the formation wave slowness spectrum to obtain the time difference between the formation P-wave and S-wave. This step belongs to the slowness analysis, which can be done by manual interactive analysis or by automatic extraction of P-wave and S-wave time differences.

[0073] In this case, the logging acoustic wave acquisition is received by 5 receivers (with a spacing of 0.5 ft) and the source distance is 3 ft. Figure 6 As shown in the original waveform in (a), the waveform data quality is poor, the signal-to-noise ratio is low, and a considerable part of the waveform information is lost in the first and last receivers. The STC analysis of this array waveform data obtains similar correlation diagrams and contour diagrams as shown in Figure 1. Figure 6 (b) and Figure 6 As shown in (c), further projection to the slowness axis yields Figure 6 (d) Comprehensive comparison Figure 6 (b) Figure 6 (c) and Figure 6 (d), casing wave, formation longitudinal wave and shear wave information exist simultaneously, the casing wave slowness is 56us / ft (consistent with the actual casing velocity), and the formation longitudinal wave slowness is 78us / ft. However, due to the interference of casing wave, the correlation between formation longitudinal wave and shear wave is not high, and the accuracy is also limited to a certain extent. After processing by the method of the present invention, the casing wave component can be accurately removed from the original waveform data, and the formation wave component can be retained. Figure 7 As shown in (a), the first arrival of the processed formation wave is clearly displayed. Figure 7 (b) Figure 7 (c) and Figure 7 In (d), the original casing wave correlation energy is effectively removed, and the waveform interference phenomenon caused by the casing wave is also suppressed, making the correlation between the P-wave and S-wave more prominent. The correlation of the formation P-wave is 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 the present invention. Figure 8 (a) and Figure 8 (b) In this case, casing waves are ubiquitous. It has been verified that the technology of the present invention can overcome the adverse effects of low signal-to-noise ratio. The casing wave characteristics are well removed from the processed slowness spectrum, providing an important data basis for subsequent formation wave processing and analysis. In subsequent processing, manual interactive processing or automatic time difference analysis can be used. Figure 8 (c) shows the automatic extraction of P-wave and S-wave time differences based on the data processed by the present invention. The results show that the P-wave and S-wave time differences of the formation can be accurately and reliably tracked and picked up in real time.

[0074] The method of the invention is also suitable for removing the drill collar direct wave in the drilling acoustic wave. Fig. 9 (a) and Fig. 9 (b) It can be seen that the slowness spectrum of the original waveform data has visible influence of drill collar waves at a depth of 4200m to 4450m, and the slowness is relatively fixed at about 63us / ft. The interference effect caused by it affects the subsequent time difference analysis of formation waves. After being processed by the method of the present invention, the influence of drill collars is effectively suppressed, which can provide key technical support for the field application of logging while drilling acoustic waves and the analysis of the time difference between formation longitudinal and shear waves.

[0075] A terminal device is provided in one embodiment of the present invention. The terminal device of this embodiment 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, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

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

[0077] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

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

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

[0080] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased 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 electric carrier signals and telecommunication signals.

[0081] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A fully automatic processing method for well logging acoustic waves to remove casing direct waves, characterized in that: include: Collecting array acoustic waves on the well logging at a certain depth as raw waveform data; Performing standardization processing on the original waveform data to obtain standardized waveform data; Based on the constant velocity of casing wave, the standardized waveform data is automatically filtered to achieve automatic separation of formation wave and casing wave. Perform STC analysis on the separated formation waveform to obtain the slowness spectrum of the formation wave; The slowness spectrum of the formation wave is analyzed to obtain the time difference between the formation longitudinal wave and the transverse wave.

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

3. The fully automatic processing method for logging acoustic waves by removing casing direct waves according to claim 2 is characterized in that: The automatic velocity filtering of the standardized waveform data realizes the automatic separation of formation wave and casing wave, specifically: based on the relatively constant slowness of casing wave in the standardized waveform data, the standardized waveform data is velocity filtered to extract the casing wave waveform component, and then the formation wave component is separated.

4. The fully automatic processing method for logging acoustic waves by removing casing direct waves according to claim 3 is characterized in that: The velocity filtering adopts Radon transform, and Radon transform is used for velocity filtering, specifically: based on Radon transform, the standardized waveform data is processed, the extraction window of the target waveform is automatically located in the Radon transform domain and extraction is realized, and the extracted transform domain information is subjected to inverse Radon transform to obtain the target waveform in the time domain, thereby completing the wave field separation and extraction process.

5. The fully automatic processing method for logging acoustic waves for removing casing direct waves according to claim 4 is characterized in that: The STC analysis is performed on the separated formation waveform to obtain the slowness spectrum of the formation wave, specifically: The STC method is as follows: according to the inherent parameters of the array acoustic logging instrument, the length of the window is set on the entire array acoustic waveform and two values ​​are selected in the wave arrival point and the corresponding component wave slowness range; the position of the wave arrival point determines the position of the window, and the size of the slowness determines the time shift length of the waveform of the remaining receivers except the first receiver; the correlation coefficient is calculated based on the wave arrival point and the slowness; After traversing the entire wave arrival point range and slowness range, the above calculation is repeated by moving the window with a fixed step size; after moving the window several times, the correlation coefficients are compared, and the maximum value of the correlation coefficient corresponds to the most likely wave arrival point and slowness of the group of waves; the correlation coefficient is calculated as follows: Where M is the number of receivers of the array acoustic logging instrument, which is generally 8; T is the wave arrival point; T w is the length of the window, which is selected according to the actual waveform length; s is slowness, r m (t) is the waveform of the mth receiver, z1 is the distance from the first receiver to the transmitter, z m is the distance from the mth receiver to the transmitter.

6. The fully automatic processing method for logging acoustic waves for removing casing direct waves according to claim 5 is characterized in that: The intrinsic parameters of the instrument include: the position of the transmitter, the number of receivers, the spacing between adjacent receivers and the sampling time interval.

7. A fully automatic logging acoustic wave processing system for removing casing direct waves, characterized in that: include: An acquisition module, wherein the acquisition module acquires array acoustic waves on the well logging at a certain depth as raw waveform data; A standardization processing module, wherein the standardization processing module performs standardization processing on the original waveform data to obtain standardized waveform data; A velocity filtering module, which automatically performs velocity filtering on the standardized waveform data based on the constant velocity of the casing wave, thereby realizing automatic separation of the formation wave and the casing wave; An STC analysis module, wherein the STC analysis module performs STC analysis on the separated formation waveform to obtain a slowness spectrum of the formation wave; An acquisition module analyzes the slowness spectrum of the formation wave to obtain the time difference between the formation longitudinal wave and the transverse wave.

8. 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, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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