Denoising Method, Device, Electronic Device and Storage Medium for Slowness

Abstract

The present application provides a denoising method, apparatus, electronic device, and storage medium for slowness. The method includes: obtaining array acoustic logging data collected by an array acoustic logging device, obtaining a set of time slowness correlation coefficients based on the array acoustic logging data, determining a confidence coefficient for each slowness according to the set of time slowness correlation coefficients, and denoising each slowness according to the confidence coefficient of each slowness. By determining the confidence coefficient of each slowness from the set of time slowness correlation coefficients, the reliability of the extraction of each slowness can be clarified, that is, which slownesses are the slownesses corresponding to the effective signals and which slownesses are the slownesses corresponding to the noise. Furthermore, the slownesses corresponding to the effective signals are extracted, avoiding the extraction of the slownesses corresponding to the noise, making the extraction of the slowness more accurate.

Description

Technical Field

[0001] This application relates to the technical field of oil exploration, and particularly to a denoising method, device, electronic device, and storage medium for slowness. Background Art

[0002] Array acoustic logging is a commonly used logging technology in the field of oil exploration. By using array acoustic logging technology, mode waves at different depths downhole are measured to obtain array acoustic logging data. The slowness at different depths downhole is calculated from the array acoustic logging data, and effective reservoirs are found based on the slowness at different depths downhole, and data references are provided for geomechanics and seismic exploration.

[0003] In the prior art, the main methods for extracting slowness are the slowness time coherence method (Slowness Time Coherence, abbreviated as: STC) and the slowness distance coherence method (Slowness Distance Coherence, abbreviated as: SDC). However, the slowness extracted by STC and SDC is still interfered by noise, making the extracted slowness inaccurate. Summary of the Invention

[0004] This application provides a denoising method, device, electronic device, and storage medium for slowness to solve the problem that the slowness extracted in the prior art is interfered by noise.

[0005] In a first aspect, the present invention provides a denoising method for slowness, including:

[0006] Obtain array acoustic logging data collected by an array acoustic logging device;

[0007] Based on the array acoustic logging data, obtain a set of time slowness correlation coefficients, where the set of time slowness correlation coefficients includes multiple time slowness correlation coefficients, and each time slowness correlation coefficient is used to characterize the correlation between slowness and time;

[0008] Determine the confidence coefficient of each slowness according to the set of time slowness correlation coefficients;

[0009] Denoise each slowness according to the confidence coefficient of each slowness.

[0010] Optionally, determining the confidence coefficient of each slowness according to the set of time slowness correlation coefficients specifically includes:

[0011] Calculate the sum of the time slowness correlation coefficients corresponding to each slowness;

[0012] Obtain the confidence coefficient of each slowness according to the sum of the time slowness correlation coefficients corresponding to each slowness.

[0013] Optionally, denoising each slowness according to the confidence coefficient of each slowness includes:

[0014] Determining the maximum time-slowness correlation coefficient corresponding to each slowness from the set of time-slowness correlation coefficients, and using it as the initial value of each slowness;

[0015] Obtaining the denoising parameter of each slowness according to the confidence coefficient of each slowness and the initial value of each slowness;

[0016] Denoising each slowness according to the denoising parameter of each slowness.

[0017] Optionally, denoising each slowness according to the denoising parameter of each slowness specifically includes:

[0018] When the denoising parameter is less than the preset threshold, removing the slowness corresponding to the denoising parameter.

[0019] Optionally, obtaining the set of time-slowness correlation coefficients according to the array acoustic logging data specifically includes:

[0020] Obtaining the set of time-slowness correlation coefficients by using the time-slowness correlation method STC based on the array acoustic logging data.

[0021] Optionally, obtaining the set of time-slowness correlation coefficients by using the time-slowness correlation method STC based on the array acoustic logging data includes:

[0022] Performing gain recovery on the waveform amplitude in the array acoustic logging data so that the waveform amplitude after gain recovery is consistent with the actual waveform amplitude in the well;

[0023] Performing frequency-domain band-pass filtering on the array acoustic logging data after gain recovery to remove the noise above the first preset frequency and below the second preset frequency in the array acoustic logging data after gain recovery;

[0024] Obtaining the set of time-slowness correlation coefficients by using STC based on the array acoustic logging data after frequency-domain band-pass filtering.

[0025] In a second aspect, the present invention provides a slowness denoising device, including:

[0026] An acquisition module, configured to acquire the array acoustic logging data collected by the array acoustic logging device;

[0027] A processing module, configured to obtain a set of time-slowness correlation coefficients according to the array acoustic logging data, where the set of time-slowness correlation coefficients includes a plurality of time-slowness correlation coefficients, and each time-slowness correlation coefficient is used to characterize the correlation between slowness and time;

[0028] The processing module is further configured to determine the confidence coefficient of each slowness according to the set of time-slowness correlation coefficients;

[0029] The processing module is further configured to denoise each slowness according to the confidence coefficient of each slowness.

[0030] Optionally, the processing module is specifically configured to:

[0031] Calculate the sum of the time-slowness correlation coefficients corresponding to each slowness;

[0032] Obtain the confidence coefficient of each slowness according to the sum of the time-slowness correlation coefficients corresponding to each slowness.

[0033] In a third aspect, the present invention provides an electronic device, including: a memory and a processor;

[0034] The memory; a memory for storing processor-executable instructions;

[0035] Wherein, the processor is configured to execute the slowness denoising method involved in the first aspect and the optional solutions.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the slowness denoising method involved in the first aspect and the optional solutions.

[0037] The present application provides a slowness denoising method, device, electronic device and storage medium. The array acoustic logging data collected by the array acoustic logging device is obtained, a set of time-slowness correlation coefficients is obtained according to the array acoustic logging data, the confidence coefficient of each slowness is determined according to the set of time-slowness correlation coefficients, and each slowness is denoised according to the confidence coefficient of each slowness. By determining the confidence coefficient of each slowness from the set of time-slowness correlation coefficients, the reliability of the extraction of each slowness can be clarified, that is, which slownesses are the slownesses corresponding to the effective signals and which slownesses are the slownesses corresponding to the noise, and then the slownesses corresponding to the effective signals are extracted, avoiding the extraction of the slownesses corresponding to the noise, so that the extraction of the slownesses is more accurate. Description of the Drawings

[0038] Figure 1 It is a schematic diagram of the array acoustic logging device provided by the present application;

[0039] Figure 2 It is a schematic flow chart of the slowness denoising method shown according to an exemplary embodiment of the present invention;

[0040] Figure 3 It is a schematic diagram of obtaining a set of time-slowness correlation coefficients based on the array acoustic logging data by using STC according to the present invention;

[0041] Figure 4Schematic diagram for the present invention to determine the confidence coefficient of each slowness according to the set of time slowness correlation coefficients;

[0042] Figure 5 Schematic flowchart showing the denoising method of slowness according to another exemplary embodiment of the present invention;

[0043] FIG. 6(a) is a schematic diagram of the slowness at each depth in the well obtained without denoising according to an exemplary embodiment of the present invention;

[0044] FIG. 6(b) is a schematic diagram of the slowness at each depth in the well obtained after denoising according to an exemplary embodiment of the present invention;

[0045] Figure 7 Schematic structural diagram of the slowness denoising device according to an exemplary embodiment of the present invention;

[0046] Figure 8 Schematic hardware structure diagram of an electronic device according to an exemplary embodiment of the present invention. Detailed implementation manners

[0047] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0048] With the development of information technology and acoustic wave theory, the development process of acoustic logging has also accelerated. The acoustic logging signal contains rich rock physical information and plays an increasingly important role in geological and engineering applications. Array acoustic logging can not only determine rock mechanical parameters, fracture orientation and sand production analysis, but the most prominent advantage is that it can also determine microfractures and in-situ stress directions together with the well deviation azimuth instrument, providing azimuthal anisotropy analysis.

[0049] Figure 1 Schematic diagram of the array acoustic logging device provided by the present application. As Figure 1As shown in the figure, the array acoustic logging device includes an acoustic wave transmitting transducer T and an acoustic wave receiving probe R. In array acoustic logging, an acoustic wave transmitting transducer T emits acoustic waves underground, and mode waves are excited in the wellbore wall. Generally, there are three mode waves, namely shear waves, compressional waves, and Stoneley waves. The mode waves propagate in the wellbore wall, and the arrows in the figure indicate the propagation paths of the mode waves. Multiple linearly arrayed acoustic wave receiving probes R measure the mode waves at different depths underground to obtain array acoustic logging data. Furthermore, the underground conditions are determined based on the array acoustic logging data. Generally speaking, after obtaining the array acoustic logging data, the slowness at different depths underground can be calculated through the array acoustic logging data. Then, based on the slowness at different depths underground, effective reservoirs can be found, and data references can be provided for geomechanics and seismic exploration. It can be seen that the extraction of slowness is very important.

[0050] Currently, the main methods for extracting slowness are the time slowness correlation method STC and the slowness distance correlation method SDC. For STC, specific reference can be made to the real-time STC algorithm for array acoustic logging published by Tao Jun, Xiao Jiaqi, etc. For SDC, specific reference can be made to the slowness distance correlation - SDC method for picking up acoustic logging time differences published by Kang Xiaoquan, Su Yuanda, etc. However, the slowness extracted through STC and SDC is still interfered by noise, making the extracted slowness inaccurate. Therefore, this application provides a method, device, electronic device, and storage medium for denoising slowness. The array acoustic logging data collected by the array acoustic logging device is obtained. Based on the array acoustic logging data, a set of time slowness correlation coefficients is obtained. The confidence coefficient of each slowness is determined based on the set of time slowness correlation coefficients, and each slowness is denoised based on the confidence coefficient of each slowness. Through the above inventive concept, this method can suppress the noise interference of slowness and make the extracted slowness more accurate. The following is a detailed description of this method.

[0051] Figure 2 This is a schematic flowchart showing the slowness denoising method according to an exemplary embodiment of the present invention. As Figure 2 shown, the slowness denoising method includes the following steps:

[0052] S101: Obtain the array acoustic logging data collected by the array acoustic logging device.

[0053] More specifically, for the array acoustic logging device, refer to Figure 1 , and the array acoustic logging data is obtained by the array acoustic logging device. The acoustic wave transmitting transducer emits acoustic waves underground, and mode waves are excited in the wellbore wall. The mode waves propagate in the wellbore wall, and multiple linearly arrayed acoustic wave receiving probes measure the mode waves at different depths underground.

[0054] It should be noted that the array acoustic logging data obtained here is measured by the array acoustic logging device at different depths underground.

[0055] S102. Obtain a set of time slowness correlation coefficients according to the array acoustic logging data, where the set of time slowness correlation coefficients includes multiple time slowness correlation coefficients, and each time slowness correlation coefficient is used to characterize the correlation between slowness and time.

[0056] More specifically, the set of time slowness correlation coefficients is a set composed of multiple time slowness correlation coefficients, and each time slowness correlation coefficient is used to characterize the correlation between slowness and time. In the set of time slowness correlation coefficients, there are multiple groups of time slowness correlation coefficients. In each group of time slowness correlation coefficients, there are multiple time slowness correlation coefficients. Each time slowness correlation coefficient in each group of time slowness correlation coefficients corresponds to the same slowness, while each time slowness correlation coefficient in each group of time slowness correlation coefficients corresponds to a different time. That is to say, the time slowness correlation coefficient characterizes the correlation between the corresponding slowness and the corresponding time.

[0057] Optionally, based on the array acoustic logging data, the set of time slowness correlation coefficients can be obtained by using the time slowness correlation method STC. Of course, the time slowness correlation coefficients can also be obtained by other methods, and no specific limitation is made here.

[0058] Figure 3 This is a schematic diagram of the present invention for obtaining the set of time slowness correlation coefficients by using STC based on the array acoustic logging data. As Figure 3 shown, the abscissa is time, the ordinate is slowness, and each coordinate point represents a time slowness correlation coefficient. The larger the time slowness correlation coefficient at the coordinate point, the darker the color of the coordinate point, indicating that the correlation between the slowness corresponding to the coordinate point and the time corresponding to the coordinate point is greater, that is, the possibility that the time corresponding to the coordinate point has the slowness corresponding to the coordinate point is greater. Each slowness corresponds to multiple time slowness correlation coefficients with different magnitudes in time, and these multiple time slowness correlation coefficients with different magnitudes are a group of time slowness correlation coefficients. All groups of time slowness correlation coefficients are the set of time slowness correlation coefficients.

[0059] S103. Determine the confidence coefficient of each slowness according to the set of time slowness correlation coefficients.

[0060] Optionally, determining the confidence coefficient of each slowness according to the set of time slowness correlation coefficients specifically includes: calculating the sum of the time slowness correlation coefficients corresponding to each slowness. Obtain the confidence coefficient of each slowness according to the sum of the time slowness correlation coefficients corresponding to each slowness.

[0061] Among them, the confidence coefficient of each slowness is positively correlated with the sum of the time slowness correlation coefficients corresponding to each slowness in the set of time slowness correlation coefficients, and the confidence coefficient is used to characterize the reliability of slowness extraction.

[0062] More specifically, the time slowness correlation coefficients in each time slowness correlation coefficient group in the set of time slowness correlation coefficients can be summed within a given time range to obtain the confidence coefficients for each slowness. The number of confidence coefficients obtained is equal to the number of time slowness correlation coefficient groups in the set of time slowness correlation coefficients, and each confidence coefficient corresponds to a slowness, that is, the confidence coefficients for each slowness are obtained.

[0063] Of course, it is also possible to sum the time slowness correlation coefficients in each time slowness correlation coefficient group in the set of time slowness correlation coefficients within a given time range, and then multiply each sum by a preset value respectively, and further use the result after multiplication as the confidence coefficient for the corresponding slowness. For the specific values of the confidence coefficients for each slowness, as long as they are positively correlated with the sum of the time slowness correlation coefficients corresponding to each slowness, no specific limitation is made here.

[0064] Figure 4 This is a schematic diagram of the present invention for determining the confidence coefficients for each slowness according to the set of time slowness correlation coefficients. As Figure 4 shown, the set of time slowness correlation coefficients includes signal X, signal Y, and signal Z. Signal Y is the useful signal, and signals X and Z are noise. As can be seen from the left diagram in Figure 4 , at slowness A, signal X exists at time A, that is, the time slowness correlation coefficient here is α, and no signal exists at times B and C, that is, the time slowness correlation coefficient here is 0; at slowness B, signal Y exists at time B, that is, the time slowness correlation coefficient here is α, and no signal exists at times A and C, that is, the time slowness correlation coefficient here is 0; at slowness C, signal Z exists at time C, that is, the time slowness correlation coefficient here is α, and no signal exists at times B and C, that is, the time slowness correlation coefficient here is 0. At this time, the time slowness correlation coefficients on slowness A are summed from time A to time C, the time slowness correlation coefficients on slowness B are summed from time A to time C, and the time slowness correlation coefficients on slowness C are summed from time A to time C. In this way, the confidence coefficients corresponding to slowness A, slowness B, and slowness C are obtained, as shown in the right diagram in Figure 4 . Since the time span of time B is larger, the value obtained by summing in time is larger. The higher the peak, the higher the probability that the corresponding slowness is the true slowness. Therefore, signal Y is the useful signal, and signals X and Z are noise.

[0065] S104. Denoise each slowness according to the confidence coefficients of each slowness.

[0066] Since the magnitude of the confidence coefficient of a slowness can characterize the reliability of the slowness corresponding to the useful signal, that is, the larger the confidence coefficient of a slowness, the higher the probability that the signal corresponding to this slowness is the useful signal. Therefore, each slowness can be denoised according to the confidence coefficients of each slowness.

[0067] As shown Figure 4 in the figure, the normalized amplitude corresponding to the confidence coefficient of signal Y is relatively large, while the normalized amplitudes corresponding to the confidence coefficients of signal X and signal Z are relatively small. Therefore, the credibility that signal Y is useful noise is relatively high, while signal X and signal Z may be noise. In this way, the noise in the signal can be removed, the useful signal can be retained, and then the slowness of the useful signal can be extracted, making the extracted slowness more accurate.

[0068] It should be noted that regarding the specific value below which the confidence coefficient of the slowness is considered, the signal corresponding to the slowness is determined as noise and then removed as noise. This data can be set according to the actual situation and is not specifically limited here.

[0069] In the method provided in this embodiment, the array acoustic logging data collected by the array acoustic logging device is obtained. According to the array acoustic logging data, a set of time-slowness correlation coefficients is obtained. According to the set of time-slowness correlation coefficients, the confidence coefficient of each slowness is determined. Each slowness is denoised according to the confidence coefficient of each slowness. It can be seen that by determining the confidence coefficient of each slowness from the set of time-slowness correlation coefficients, the reliability of the extraction of each slowness can be clarified, that is, which slownesses are the slownesses corresponding to the effective signals and which slownesses are the slownesses corresponding to the noise. Then, the slownesses corresponding to the effective signals are extracted, avoiding the extraction of the slownesses corresponding to the noise, making the extraction of the slowness more accurate.

[0070] Figure 5 This is a schematic flowchart of the slowness denoising method according to another exemplary embodiment of the present invention. As Figure 5 shown, the slowness denoising method includes the following steps:

[0071] S201. Obtain the array acoustic logging data collected by the array acoustic logging device.

[0072] Among them, the acquisition method of the array acoustic logging data is the same as that of the array acoustic logging data in S101 of the embodiment Figure 2 shown, and will not be elaborated here.

[0073] S202. Perform gain recovery on the waveform amplitude in the array acoustic logging data so that the waveform amplitude after gain recovery is consistent with the actual waveform amplitude in the well.

[0074] Since the waveform amplitude in the measured array acoustic logging data may be reduced compared with the actual waveform amplitude in the well, in order to achieve consistency with the actual waveform amplitude in the well, therefore, it is necessary to perform gain recovery on the waveform amplitude in the array acoustic logging data so that the waveform amplitude after gain recovery is consistent with the actual waveform amplitude in the well.

[0075] More specifically, the gain recovery calculation formula can be used to perform gain recovery on the waveform amplitude in the array acoustic logging data:

[0076]

[0077] Among them, WF represents the waveform after gain recovery, WF Original represents the waveform of the original measured array acoustic logging data, and Gn represents the gain value, which can be read from the original measured array acoustic logging data.

[0078] S203. Perform frequency-domain band-pass filtering on the array acoustic logging data after gain recovery to remove the noise in the array acoustic logging data after gain recovery that is higher than the first preset frequency and lower than the second preset frequency.

[0079] In the specific implementation process, a band-pass filter can be used to perform frequency-domain band-pass filtering on the array acoustic logging data. The specific filtering range can be set according to actual needs and will not be specifically limited here. For example, in practical applications, the windowing range of the band-pass filter can be selected to be 6 kHz - 20 kHz, where 6 kHz is the first preset frequency and 20 kHz is the second preset frequency.

[0080] It should be noted here that steps S202 and S203 are preprocessing the array acoustic logging data. In the specific implementation process, the preprocessing of the array acoustic logging data can adopt but is not limited to the above two steps. Moreover, in addition to preprocessing the array acoustic logging data in the execution order of S202 first and then S203, it can also be preprocessed in the execution order of S203 first and then S202. The sequence of execution of the above two steps is not limited here.

[0081] S204. Obtain a set of time slowness correlation coefficients based on the array acoustic logging data after frequency-domain band-pass filtering using STC.

[0082] Since a set of time slowness correlation coefficients corresponds to the correlation between multiple slownesses and the measurement time measured at a certain depth in the well. And what actually needs to be extracted is not only the slowness at a certain depth in the well, but the slowness of a certain section of the well depth. Therefore, not only one set of time slowness correlation coefficients is obtained, but multiple sets of time slowness correlation coefficients, that is, sets of time slowness correlation coefficients corresponding to multiple depths. Multiple sets of time slowness correlation coefficients are to add a depth dimension to the set of time slowness correlation coefficients, that is, the time slowness correlation matrix.

[0083] In the specific implementation process, when calculating the time-slowness correlation matrix, at different depths, for the N-channel array acoustic logging data at that depth, within a given time and slowness range, through a scanning calculation method, a correlation matrix with different times and slownesses at different depth points can be obtained. The specific calculation is as follows:

[0084]

[0085] Among them, CR corr is the time-slowness correlation matrix, which is a function of time T and slowness Slow at different depth positions Dep. Wave is the array acoustic logging data, and the subscript m represents the channel number of the array acoustic logging data, successively representing the short source distance waveform - long source distance waveform from 1 - N. Wlength is the correlation coefficient calculation window length, and D is the receiver spacing of the logging tool.

[0086] In actual logging measurements, the measured waveform data is discrete in time. And when Wave m (t) does not fall on the discrete data value, interpolation can be performed using the data adjacent to t. The interpolation method can choose the linear interpolation method. The linear interpolation method is a method that uses a straight line connecting two known quantities to determine the value of an unknown quantity between these two known quantities.

[0087] S205. Determine the confidence coefficient of each slowness according to the set of time-slowness correlation coefficients.

[0088] Among them, the determination method of the confidence coefficient of each slowness is the same as that of the confidence coefficient of each slowness in S103 of the embodiment shown Figure 2 and will not be elaborated here.

[0089] When determining the confidence coefficient of each slowness at each depth downhole according to the time-slowness correlation matrix, the specific calculation is as follows:

[0090]

[0091] Among them, ZCR is the confidence coefficient of the slowness at different depths downhole, which is a function of depth Dep and slowness Slow. CR corr is the time-slowness correlation matrix.

[0092] S206. Determine the maximum time-slowness correlation coefficient corresponding to each slowness from the set of time-slowness correlation coefficients, and use it as the initial value of each slowness.

[0093] As Figure 4 shown, at a certain depth, the maximum time-slowness correlation coefficient of slowness A from time A to time C is the time-slowness correlation coefficient α at time A A, the maximum time slowness correlation coefficient of slowness B from time A to time C is the time slowness correlation coefficient α at time B B , the maximum time slowness correlation coefficient of slowness C from time A to time C is the time slowness correlation coefficient α at time C C . Therefore, the initial value of slowness A is α A , the initial value of slowness B is α B , the initial value of slowness C is α C . The specific calculation of the initial value of each slowness is as follows:

[0094]

[0095] Among them, SL is the initial value of the slowness of the mode wave at each depth point, which is a function of the depth position Dep and the slowness Slow. CR corr is the time slowness correlation matrix.

[0096] It should be noted that step S205 and step S206 can be executed simultaneously or one after the other. For the execution order of step S205 and step S206, no specific limitation is made here.

[0097] S207. Obtain the denoising parameters of each slowness according to the confidence coefficient of each slowness and the initial value of each slowness.

[0098] More specifically, multiply the confidence coefficient of each slowness at each depth underground by the corresponding initial value, and use the result as the denoising parameter of each slowness at each depth underground. It is also possible to multiply the confidence coefficient of each slowness at each depth underground by the corresponding initial value, and then multiply each of the multiplied results by a preset value. Furthermore, use the final multiplied result as the denoising parameter of each slowness at each depth underground. For the specific value of the denoising parameter of each slowness at each depth underground, as long as it is positively correlated with the product of the confidence coefficient of each slowness at each depth underground and the corresponding initial value, no specific limitation is made here. Among them, the larger the value of the denoising parameter, the higher the possibility that the slowness corresponding to the denoising parameter is the true slowness underground.

[0099] The specific calculation of the denoising parameter of each slowness at each depth underground is as follows:

[0100] SLF(Slow,Dep) = SL(Slow,Dep) × ZCR(Slow,Dep)

[0101] Among them, SLF is the denoising parameter of the slowness of the mode wave at each depth point, SL is the initial value of the slowness of the mode wave at each depth point, and ZCR is the confidence coefficient of the slowness of the mode wave at each depth point.

[0102] S208. Denoise each slowness according to the denoising parameter of each slowness.

[0103] Optionally, when the denoising parameter is less than a preset threshold, the slowness corresponding to the denoising parameter is removed.

[0104] More specifically, the preset threshold is set by a person skilled in the art according to the actual situation. If the denoising parameter is less than the preset threshold, it is determined that the signal corresponding to the slowness is noise, and the slowness corresponding to the denoising parameter is removed. If the denoising parameter is not less than the preset threshold, it is determined that the signal corresponding to the slowness is a useful signal, and the slowness corresponding to the denoising parameter is retained.

[0105] FIG. 6(a) is a schematic diagram of the slowness at each depth in the well obtained without denoising according to an exemplary embodiment of the present invention. FIG. 6(b) is a schematic diagram of the slowness at each depth in the well obtained after denoising according to an exemplary embodiment of the present invention. By comparing FIG. 6(a) and FIG. 6(b), it can be seen that in FIG. 6(b), the noise in FIG. 6(a) is significantly suppressed, and the continuity of FIG. 6(b) as a whole is better than that of FIG. 6(a). Thus, it can be seen that the denoising method for slowness in array acoustic logging in the embodiment of the present invention has a good denoising effect on the slowness at each depth in the well, and thus more accurate slowness at each depth in the well can be obtained.

[0106] In the method provided in this embodiment, by determining the confidence coefficient of each slowness at each depth in the well from the time-slowness correlation matrix, the reliability of the extraction of each slowness at each depth in the well can be clarified, that is, which slownesses are the slownesses corresponding to effective signals and which slownesses are the slownesses corresponding to noise. Furthermore, the slownesses corresponding to effective signals are extracted, and the extraction of slownesses corresponding to noise is avoided, so that the extraction of slownesses is more accurate, providing a reliable data reference for finding effective reservoirs, as well as for geomechanics and seismic exploration.

[0107] Figure 7 FIG. 15 is a schematic structural diagram of a slowness denoising device 70 according to an exemplary embodiment of the present invention. As Figure 7 shown, the present invention provides a slowness denoising device 70, and the device 70 includes:

[0108] An acquisition module 71, configured to acquire array acoustic logging data collected by an array acoustic logging device;

[0109] A processing module 72, configured to obtain a set of time-slowness correlation coefficients according to the array acoustic logging data, where the set of time-slowness correlation coefficients includes a plurality of time-slowness correlation coefficients, and each time-slowness correlation coefficient is used to characterize the correlation between slowness and time;

[0110] The processing module 72 is further configured to determine the confidence coefficient of each slowness according to the set of time-slowness correlation coefficients;

[0111] The processing module 72 is further configured to denoise each slowness according to the confidence coefficient of each slowness.

[0112] Optionally, the processing module 72 is specifically configured to:

[0113] Calculate the sum of the time-slowness correlation coefficients corresponding to each slowness;

[0114] Obtain the confidence coefficient of each slowness according to the sum of the time-slowness correlation coefficients corresponding to each slowness.

[0115] Specifically, this embodiment can refer to the above method embodiment, and its principle and technical effect are similar, so details are not described again.

[0116] Figure 8 This is a schematic diagram of the hardware structure of the electronic device 80 shown according to an exemplary embodiment of the present invention. As Figure 8 shown, the electronic device 80 in this embodiment includes: a processor 81 and a memory 82; wherein,

[0117] The memory 82 is used to store computer execution instructions;

[0118] The processor 81 is configured to execute the computer execution instructions stored in the memory to implement each step executed by the receiving device in the above embodiment. Specifically, reference can be made to the relevant descriptions in the foregoing method embodiment.

[0119] Optionally, the memory 82 can be either independent or integrated with the processor 81.

[0120] When the memory 82 is independently provided, the control device 80 further includes a bus 83 for connecting the memory 82 and the processor 81.

[0121] An embodiment of the present invention also provides a computer-readable storage medium, in which computer execution instructions are stored, and when the processor executes the computer execution instructions, the above-mentioned slowness denoising method is implemented.

[0122] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A denoising method for slowness, characterized in that, Including: Obtaining array acoustic logging data collected by an array acoustic logging device; Based on the array acoustic logging data, obtaining a set of time-slowness correlation coefficients, where there are multiple time-slowness correlation coefficient groups in the set of time-slowness correlation coefficients, each time-slowness correlation coefficient group includes multiple time-slowness correlation coefficients, each time-slowness correlation coefficient is used to characterize the correlation between slowness and time, and each time-slowness correlation coefficient in each time-slowness correlation coefficient group corresponds to the same slowness. The array acoustic logging data is characterized on a coordinate axis, with the abscissa being time and the ordinate being slowness, and each coordinate point represents a time-slowness correlation coefficient. Each slowness corresponds to multiple different time-slowness correlation coefficients in time, and the multiple different time-slowness correlation coefficients are a time-slowness correlation coefficient group; Determining the confidence coefficient of each slowness according to the set of time-slowness correlation coefficients; Denosing each slowness according to the confidence coefficient of each slowness; Determining the confidence coefficient of each slowness according to the set of time-slowness correlation coefficients, specifically including: Calculating the sum of the time-slowness correlation coefficients corresponding to each slowness; Obtaining the confidence coefficient of each slowness according to the sum of the time-slowness correlation coefficients corresponding to each slowness; The denoising of each slowness according to the confidence coefficient of each slowness includes: Determining the maximum time-slowness correlation coefficient corresponding to each slowness from the set of time-slowness correlation coefficients and using it as the initial value of each slowness; Obtaining the denoising parameter of each slowness according to the confidence coefficient of each slowness and the initial value of each slowness; When the denoising parameter is less than a preset threshold, removing the slowness corresponding to the denoising parameter.

2. The method according to claim 1, characterized in that, The obtaining of the set of time-slowness correlation coefficients based on the array acoustic logging data specifically includes: Obtaining the set of time-slowness correlation coefficients by using the time-slowness correlation method STC based on the array acoustic logging data.

3. The method according to claim 2, wherein The obtaining of the set of time-slowness correlation coefficients by using the time-slowness correlation method STC based on the array acoustic logging data includes: Performing gain recovery on the waveform amplitude in the array acoustic logging data so that the waveform amplitude after gain recovery is consistent with the actual waveform amplitude in the well; Performing frequency-domain band-pass filtering on the array acoustic logging data after gain recovery to remove the noise above the first preset frequency and below the second preset frequency in the array acoustic logging data after gain recovery; Obtaining the set of time-slowness correlation coefficients by using STC based on the array acoustic logging data after frequency-domain band-pass filtering.

4. A slowness denoising device, characterized in that, Including: An acquisition module for acquiring array acoustic logging data collected by an array acoustic logging device; A processing module, configured to obtain a set of time slowness correlation coefficients according to the array acoustic logging data, wherein there are multiple time slowness correlation coefficient groups in the set of time slowness correlation coefficients, each time slowness correlation coefficient group includes multiple time slowness correlation coefficients, and each time slowness correlation coefficient is used to characterize the correlation between slowness and time, and the time slowness correlation coefficients in each time slowness correlation coefficient group correspond to the same slowness; the array acoustic logging data is represented on a coordinate axis, with the abscissa being time and the ordinate being slowness, and each coordinate point represents a time slowness correlation coefficient, and each slowness corresponds to multiple different time slowness correlation coefficients in time, and the multiple different time slowness correlation coefficients are a time slowness correlation coefficient group; The processing module is further configured to determine a confidence coefficient of each slowness according to the set of time slowness correlation coefficients; Specifically, the processing module is configured to calculate the sum of the time slowness correlation coefficients corresponding to each slowness; and obtain the confidence coefficient of each slowness according to the sum of the time slowness correlation coefficients corresponding to each slowness; The processing module is further configured to denoise each slowness according to the confidence coefficients of each slowness; Specifically, the processing module is configured to determine the maximum time slowness correlation coefficient corresponding to each slowness from the set of time slowness correlation coefficients and use it as the initial value of each slowness; obtain the denoising parameter of each slowness according to the confidence coefficient of each slowness and the initial value of each slowness; when the denoising parameter is less than a preset threshold, remove the slowness corresponding to the denoising parameter.

5. An electronic device, characterized in that, Comprising: A memory and a processor; A memory; A memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the denoising method of slowness as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by the processor, they are used to implement the denoising method of slowness as described in any one of claims 1 to 3.

7. A computer program product comprising computer-executable instructions, characterized in that, When the computer-executable instructions are executed by the processor, the method as described in any one of claims 1-3 is implemented.

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