Method, system, device and storage medium for real-time extraction of compressional wave time difference in acoustic logging

Abstract

The disclosed embodiments relate to the field of geophysical well logging technology and disclose a method, system, device, and storage medium for real-time extraction of P-wave time difference in acoustic well logging. The method comprises: forming a time-slowness-correlation coefficient spectrum at the current depth point based on waveform data of the current depth point; forming an average time-slowness-correlation coefficient spectrum based on waveform data corresponding to multiple depth points before the current depth point; finding and determining the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point; determining a P-wave time difference slowness observation value based on the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point; processing and correcting the P-wave time difference slowness value, and outputting a P-wave time difference slowness prediction value. The disclosed exemplary embodiments greatly shorten the acoustic wave data processing cycle and no longer rely on human intervention and correction.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of geophysical well logging technology, and in particular to a method, system, device, and storage medium for real-time extraction of longitudinal wave time difference in acoustic well logging. Background Art

[0002] In the field of acoustic logging, array acoustic logging information contains a wide range of information, including rich formation physics information and corresponding P- and S-wave information. Rock mechanical parameters can be determined through the time difference between the two, and anisotropy of the formation can also be studied and analyzed, providing support for formation identification. With the rapid development of oil and gas field exploration technology, the application of this technology in petroleum engineering has continued to increase, making it increasingly necessary to analyze P- and S-wave time difference information.

[0003] Methods for extracting acoustic time-of-flight (OTF) are primarily categorized into the time domain and the frequency domain. Currently, the main time-domain methods include first-wave extraction and time-slowness correlation (STC). The first-wave extraction method uses waveform features to extract the initial arrival times of waves of different vibration modes at different source distances. The velocity of these waves is calculated based on the propagation time of the acoustic waves between specific known distances. The time-slowness correlation (STC) method uses the similarity of the waveforms received by an array of acoustic transducers to determine the propagation slowness of the waves. The energy integration method is an improvement on the first-wave extraction method. It integrates the energy of the waveform within a time window to reduce the influence of weak signals and noise, thereby improving the accuracy of first-wave extraction. The similarity correlation method (Slowness-Time Coherence, STC) is an alternative solution for automatic time-of-flight extraction. This method comprehensively considers the consistency of waveform phase and amplitude in the calculation of the correlation spectrum. It calculates the correlation coefficient by scanning a time window of a certain length in both the time and slowness dimensions. This method exhibits good noise immunity and has therefore gradually become a key technique for analyzing time-of-flight (i.e., slowness, or the inverse of velocity) in acoustic well logging. Many scholars have conducted research on STC (slowness-time correlation) algorithms. For example, when studying the STC algorithm for array acoustic logging data processing, it was found that the width of the time window significantly affects the processing results, leading to the idea of ​​variable window width. Existing technologies have also proposed array acoustic logging data processing methods that combine slowness-time correlation methods with genetic algorithms, and the processing results can meet the accuracy requirements of field interpretation. However, these methods do not address how to overcome the shortcomings of common waveform data in practice, nor do they propose new STC calculation formulas and analysis processes. Consequently, the processing results are less than ideal when encountering data with relatively noisy data, and they rely to a certain extent on manual post-processing.

[0004] In summary, the field of sonic logging still lacks a highly efficient and accurate automatic time difference picking method. The main problems are as follows:

[0005] 1. Due to the presence of signal noise, when the signal-to-noise ratio is low, using a fixed threshold value may not be able to find the true first wave position, thus causing the so-called "first wave jump" problem. In addition, due to the difference in signal amplitude of waveforms at different source distances, using the same threshold value for all waveforms may also cause problems.

[0006] 2. Most acoustic time difference extraction methods (frequency domain methods) have complex processing processes, low timeliness, high computing equipment requirements, and do not meet the conditions for real-time extraction;

[0007] 3. Most existing STC analysis methods focus on improving accuracy and efficiency, and there is no algorithm research specifically applied to waveform data defects. Summary of the Invention

[0008] The embodiments of the present disclosure provide a method, system, device, and storage medium for real-time extraction of compressional wave time difference in acoustic logging to solve or alleviate one or more of the above technical problems in the prior art.

[0009] According to one aspect of the present disclosure, a method for real-time extraction of compressional wave time difference in acoustic logging is provided, comprising:

[0010] Acquire and store waveform data of the current depth point in real time;

[0011] Based on the waveform data of the current depth point, a time-slowness-correlation coefficient spectrum of the current depth point is formed;

[0012] Based on the waveform data corresponding to multiple depth points before the current depth point, an average time-slowness-correlation coefficient spectrum is formed;

[0013] Find and determine the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point respectively;

[0014] Determine the P-wave time difference slowness observation value based on the first prominent peak of the time-slowness-correlation coefficient spectrum at the current depth point and the average time-slowness-correlation coefficient spectrum;

[0015] The P-wave time difference slowness value is processed and corrected, and a P-wave time difference slowness prediction value is output.

[0016] In a possible implementation, forming a time-slowness-correlation coefficient spectrum of the current depth point based on the waveform data of the current depth point includes:

[0017] Preprocessing the waveform data of the current depth point acquired in real time to form preprocessed waveform data;

[0018] The pre-processed waveform data is analyzed using a time-slowness-correlation method to obtain a time-slowness-correlation coefficient result of the current depth point, thereby forming a time-slowness-correlation coefficient spectrum of the current depth point.

[0019] In one possible implementation, analyzing the preprocessed waveform data using a time-slowness-correlation method to obtain a time-slowness-correlation coefficient result at the current depth point to form a time-slowness-correlation coefficient spectrum at the current depth point includes:

[0020]

[0021] Where ρ is the similarity coefficient of the similarity correlation method; s is the slowness, in μs / ft; T is the arrival time of the first wave of a certain component wave, in μs; T w is the time window length, in μs; X m (t) is the acoustic wave signal at the mth receiver in the N receiver array at time t, in mV; d is the distance between the receivers, in ft.

[0022] In a possible implementation, forming an average time-slowness-correlation coefficient spectrum based on waveform data corresponding to multiple depth points before the current depth point includes:

[0023] Get the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0024] Calculate the average time-slowness-correlation coefficient result based on the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0025] An average time-slowness-correlation coefficient spectrum is formed based on the average time-slowness-correlation coefficient results.

[0026] In a possible implementation, the steps of searching and determining the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point respectively include:

[0027] Find and determine the first prominent peak of the time-slowness-correlation coefficient spectrum at the current depth point in the direction from small to large slowness, and record it as the current spectrum peak;

[0028] The first prominent peak of the average time-slowness-correlation coefficient spectrum is found and determined in the direction from small to large slowness, and recorded as the average spectrum peak.

[0029] In a possible implementation, determining the P-wave time difference slowness observation value based on the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point includes:

[0030] Determine whether the current spectrum peak value is within the set range of the average spectrum peak value, or determine whether the current spectrum peak value exceeds the set physical significance limit;

[0031] When the current spectrum peak value is not within the set range of the average spectrum peak value, or the current spectrum peak value exceeds the set physical significance limit, the average spectrum peak value is determined as the longitudinal wave time difference slowness observation value;

[0032] When the current spectrum peak value is within the set range of the average spectrum peak value and the current spectrum peak value does not exceed the set physical significance limit, the current spectrum peak value is determined as the longitudinal wave time difference slowness observation value.

[0033] In one possible implementation, the following steps are included:

[0034] The target filter parameters are obtained through the adaptive Kalman filter parameter estimation mechanism.

[0035] In one possible implementation, the processing and correcting the P-wave move slowness value and outputting a P-wave move slowness prediction value includes:

[0036] The longitudinal wave time difference slowness value is processed and corrected by the Kalman filter algorithm. The Kalman filter algorithm formula is:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Where, represents the posterior state estimate at time k; represents the posterior state estimate at time k-1; u k-1 represents the control input at time k-1; p k represents the posterior estimated covariance at time k; p k-1 represents the posterior estimated covariance at time k-1; H is the transformation matrix from state variables to observations; Z k represents the observation value at time k; K k is the filter gain matrix; A is the state transfer matrix; Q is the process excitation noise covariance; R is the measurement noise covariance; B is the control matrix.

[0043] According to one aspect of the present disclosure, a system for real-time extraction of compressional wave time difference in acoustic logging is provided, comprising:

[0044] An acquisition unit, used for acquiring and storing waveform data of the current depth point in real time;

[0045] A first forming unit is configured to form a time-slowness-correlation coefficient spectrum of the current depth point based on the waveform data of the current depth point;

[0046] A second forming unit is configured to form an average time-slowness-correlation coefficient spectrum based on waveform data corresponding to a plurality of depth points before the current depth point;

[0047] a search unit, for respectively searching and determining the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point;

[0048] a determination unit for determining a P-wave time difference slowness observation value based on a time-slowness-correlation coefficient spectrum at a current depth point and a first prominent peak of an average time-slowness-correlation coefficient spectrum;

[0049] The processing unit is used to process and correct the P-wave time difference slowness value and output the P-wave time difference slowness prediction value.

[0050] In a possible implementation, the first forming unit includes:

[0051] A preprocessing module is used to preprocess the waveform data of the current depth point acquired in real time to form preprocessed waveform data;

[0052] The analysis module is used to analyze the pre-processed waveform data using a time-slowness-correlation method to obtain a time-slowness-correlation coefficient result of the current depth point and form a time-slowness-correlation coefficient spectrum of the current depth point.

[0053] In a possible implementation, the analysis module includes:

[0054]

[0055] Where ρ is the similarity coefficient of the similarity correlation method; s is the slowness, in μs / ft; T is the arrival time of the first wave of a certain component wave, in μs; T w is the time window length, in μs; X m (t) is the acoustic wave signal at the mth receiver in the N receiver array at time t, in mV; d is the distance between the receivers, in ft.

[0056] In a possible implementation, the second forming unit includes:

[0057] An acquisition module is used to obtain the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0058] A calculation module, configured to calculate an average time-slowness-correlation coefficient result based on the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0059] A forming module is used to form an average time-slowness-correlation coefficient spectrum according to the average time-slowness-correlation coefficient result.

[0060] In a possible implementation, the searching unit includes:

[0061] The first search module is used to search and determine the first prominent peak of the time-slowness-correlation coefficient spectrum at the current depth point in the direction of slowness from small to large, and record it as the current spectrum peak;

[0062] The second search module is used to search and determine the first prominent peak of the average time-slowness-correlation coefficient spectrum in the direction of slowness from small to large, and record it as the average spectrum peak.

[0063] In a possible implementation, the determining unit includes:

[0064] A judgment module is used to judge whether the current spectrum peak value is within the set range of the average spectrum peak value, or to judge whether the current spectrum peak value exceeds the set physical meaning limit;

[0065] The first determination module is configured to determine the average spectrum peak value as the longitudinal wave slowness observation value when the current spectrum peak value is not within a set range of the average spectrum peak value or the current spectrum peak value exceeds a set physical significance limit;

[0066] The second determination module is used to determine the current spectrum peak value as the longitudinal wave time difference slowness observation value when the current spectrum peak value is within the set range of the average spectrum peak value and the current spectrum peak value does not exceed the set physical significance limit.

[0067] In one possible implementation, the following steps are included:

[0068] The parameter acquisition unit is used to acquire target filter parameters through an adaptive Kalman filter parameter estimation mechanism.

[0069] In a possible implementation, the processing unit includes:

[0070] The processing and correction module is used to process and correct the longitudinal wave time difference slowness value through the Kalman filter algorithm. The Kalman filter algorithm formula is:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] Where, represents the posterior state estimate at time k; represents the posterior state estimate at time k-1; u k-1 represents the control input at time k-1; p k represents the posterior estimated covariance at time k; p k-1 represents the posterior estimated covariance at time k-1; H is the transformation matrix from state variables to observations; Z k represents the observation value at time k; K k is the filter gain matrix; A is the state transfer matrix; Q is the process excitation noise covariance; R is the measurement noise covariance; B is the control matrix.

[0077] According to one aspect of the present disclosure, a device for real-time extraction of compressional wave time difference in acoustic logging is provided, comprising:

[0078] processor and memory;

[0079] The memory is used to store computer programs, and the processor calls the computer programs stored in the memory to execute any one of the above-mentioned methods for extracting the time difference of compressional waves in acoustic logging.

[0080] According to one aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the processor is enabled to execute any one of the above-mentioned methods for extracting the time difference of compressional waves in acoustic logging.

[0081] The exemplary embodiments of the present disclosure have the following beneficial effects: the exemplary embodiments of the present disclosure effectively improve the data quality of the original data through a targeted preprocessing method, calculate the conventional STC spectrum and the average STC spectrum, rely on a dual peak-finding mechanism to ensure the reliability of the peak-finding, and finally use the Kalman filter algorithm to provide trend guidance for the extraction of longitudinal wave time difference to achieve final calibration. The exemplary embodiments of the present disclosure greatly shorten the cycle of acoustic wave data processing, no longer rely on human participation in correction, and also contribute to the development of remote well logging.

[0082] The details of one or more embodiments of the present application are set forth in the following drawings and description. Other features and advantages of the present application will become apparent from the accompanying drawings. It should be understood that the above general description and the detailed description that follows are merely exemplary and explanatory and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0084] Figure 1 This is one of the flow charts of a method for real-time extraction of compressional wave time difference in acoustic logging according to the exemplary embodiment;

[0085] Figure 2 This is the second flow chart of a method for real-time extraction of compressional wave time difference in acoustic logging according to this exemplary embodiment;

[0086] Figure 3 This is a schematic diagram of the results of longitudinal wave time difference picking in a standard well according to this exemplary embodiment. Figure 3 (a) is a schematic diagram of the STC spectrum, (b) is a schematic diagram of the initial picked longitudinal wave time difference result, and (c) is a schematic diagram of the final output longitudinal wave time difference extraction result;

[0087] Figure 4 This is a schematic diagram of the results of the longitudinal wave time difference picking during well logging in a southwest oil field according to this exemplary embodiment. Figure 4 (a) is a schematic diagram of the STC spectrum, (b) is a schematic diagram of the initial picked longitudinal wave time difference result, and (c) is a schematic diagram of the final output longitudinal wave time difference extraction result;

[0088] Figure 5 This is a schematic diagram of the longitudinal wave time difference picking results of this exemplary embodiment during well logging in an oil field in Northeast China. Figure 5 (a) is a schematic diagram of the STC spectrum, (b) is a schematic diagram of the initial picked longitudinal wave time difference result, and (c) is a schematic diagram of the final output longitudinal wave time difference extraction result;

[0089] Figure 6 This is a schematic diagram of the longitudinal wave time difference picking results of this exemplary embodiment during well logging in a cased well in North China. Figure 6 (a) is a schematic diagram of the STC spectrum, (b) is a schematic diagram of the initial picked longitudinal wave time difference result, and (c) is a schematic diagram of the final output longitudinal wave time difference extraction result;

[0090] Figure 7 is a block diagram of a real-time extraction system for acoustic logging compressional wave time difference according to the exemplary embodiment;

[0091] Figure 8 It is a structural schematic diagram of a real-time extraction device for acoustic logging longitudinal wave time difference according to this exemplary embodiment. DETAILED DESCRIPTION

[0092] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0093] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0094] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to actual circumstances.

[0095] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the application described herein can, for example, be implemented in an order other than that illustrated or described herein.

[0096] In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or submodules is not necessarily limited to those steps or submodules explicitly listed, but may include other steps or submodules not explicitly listed or inherent to such process, method, product or apparatus.

[0097] Figure 1 This is one of the flow charts of a method for real-time extraction of acoustic logging compressional wave time difference according to this exemplary embodiment. Figure 1As shown, an exemplary embodiment of the present disclosure provides a method for real-time extraction of acoustic logging compressional wave time difference, comprising:

[0098] S1 acquires and stores the waveform data of the current depth point in real time;

[0099] S2 forms a time-slowness-correlation coefficient spectrum of the current depth point based on the waveform data of the current depth point;

[0100] S3 forms an average time-slowness-correlation coefficient spectrum based on the waveform data corresponding to multiple depth points before the current depth point;

[0101] S4 respectively searches for and determines the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point;

[0102] S5 determines the P-wave time difference slowness observation value based on the first prominent peak of the time-slowness-correlation coefficient spectrum of the current depth point and the average time-slowness-correlation coefficient spectrum;

[0103] S6 processes and corrects the P-wave time difference slowness value and outputs the P-wave time difference slowness prediction value.

[0104] For example, the acoustic logging instrument starts running at a depth of 4000 meters downhole, acquires and stores waveform data at the current depth of 4000 meters downhole, and as the acoustic logging instrument moves upward, it continues to acquire and store waveform data at depths of 3999 meters, 3998 meters, and so on. For example, when the acoustic logging instrument is raised from a depth of 4000 meters downhole to a depth of 3000 meters downhole, waveform data corresponding to multiple depth points before the current depth of 3000 meters downhole (i.e., each depth point between 3000-4000 meters, each depth point between 3000-4000 meters is acquired and stored in real time as the acoustic logging instrument is raised) is acquired to form an average time-slowness-correlation coefficient spectrum. It is worth noting that the above-mentioned depth point setting is only one example, and the setting of other depth points should also fall within the scope of protection of this embodiment.

[0105] This exemplary embodiment effectively considers and overcomes the various unfavorable factors faced by actual acoustic logging, reliably extracts the longitudinal wave time difference from the array acoustic waveform data in real time, and proposes a method for enhancing the real-time extraction and stable tracking of the longitudinal wave time difference based on the Kalman filter algorithm. The method mainly includes targeted data preprocessing, conventional STC spectrum calculation, dual peak search strategy, STC calculation based on the Kalman filter algorithm, real-time automatic correction method and adaptive filter parameter estimation, which greatly shortens the acoustic data processing cycle, no longer relies on human participation in correction, and also contributes to the development of remote logging.

[0106] Specifically, S2 forms a time-slowness-correlation coefficient spectrum of the current depth point based on the waveform data of the current depth point, including:

[0107] S21 pre-processes the waveform data of the current depth point acquired in real time to form pre-processed waveform data;

[0108] S22 uses a time-slowness-correlation method to analyze the pre-processed waveform data, obtains a time-slowness-correlation coefficient result of the current depth point, and forms a time-slowness-correlation coefficient spectrum of the current depth point.

[0109] Specifically, S22 uses the time-slowness-correlation method to analyze the pre-processed waveform data to obtain the time-slowness-correlation coefficient result of the current depth point, and forms the time-slowness-correlation coefficient spectrum of the current depth point including:

[0110]

[0111] Where ρ is the similarity coefficient of the similarity correlation method; s is the slowness, in μs / ft; T is the arrival time of the first wave of a certain component wave, in μs; T w is the time window length, in μs; X m (t) is the acoustic wave signal at the mth receiver in the N receiver array at time t, in mV; d is the distance between the receivers, in ft.

[0112] Specifically, S3 forms an average time-slowness-correlation coefficient spectrum based on waveform data corresponding to multiple depth points before the current depth point, including:

[0113] S31 obtains the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0114] S32 calculates an average time-slowness-correlation coefficient result based on the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0115] S33 forms an average time-slowness-correlation coefficient spectrum according to the average time-slowness-correlation coefficient result.

[0116] Specifically, S4 searches for and determines the first prominent peaks of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point, including:

[0117] S41 searches for and determines the first prominent peak of the time-slowness-correlation coefficient spectrum at the current depth point in the direction of slowness from small to large, and records it as the current spectrum peak;

[0118] S42 searches for and determines the first prominent peak of the average time-slowness-correlation coefficient spectrum in the direction from small to large slowness, and records it as the average spectrum peak.

[0119] Specifically, S5 determines the P-wave time difference slowness observation value based on the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point, including:

[0120] S51 determines whether the current spectrum peak value is within the set range of the average spectrum peak value, or determines whether the current spectrum peak value exceeds the set physical significance limit;

[0121] S52: when the current spectrum peak value is not within the set range of the average spectrum peak value, or the current spectrum peak value exceeds the set physical significance limit, the average spectrum peak value is determined as the longitudinal wave time difference slowness observation value;

[0122] S53: When the current spectrum peak value is within the set range of the average spectrum peak value and the current spectrum peak value does not exceed the set physical significance limit, the current spectrum peak value is determined as the longitudinal wave time difference slowness observation value.

[0123] In this embodiment, first, the slowness value corresponding to the peak value found by the conventional spectrum is used as the preliminary picking result, and the average spectrum picking result is used as the reference value. If it deviates too much from the reference value, or exceeds the physical meaning limit (for example, 0-200, which can be set as needed), the reference value is used as the final picking result. Otherwise, the preliminary picking result is used as the final picking result. The final picking result is the Kalman filter observation value Z k .

[0124] Specifically, it includes:

[0125] S7 obtains the target filter parameters through the adaptive Kalman filter parameter estimation mechanism.

[0126] Specifically, S6 processes and corrects the P-wave slowness value, and outputs the P-wave slowness prediction value including:

[0127] The Kalman filter algorithm is used to process and correct the longitudinal wave time difference slowness value. The Kalman filter algorithm formula is:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] Where, represents the posterior state estimate at time k; represents the posterior state estimate at time k-1; u k-1 represents the control input at time k-1; p k represents the posterior estimated covariance at time k; p k-1 represents the posterior estimated covariance at time k-1; H is the transformation matrix from state variables to observations; Z k represents the observation value at time k; K k is the filter gain matrix; A is the state transfer matrix; Q is the process excitation noise covariance; R is the measurement noise covariance; B is the control matrix.

[0134] Figure 2 This is the second flow chart of a method for real-time extraction of acoustic logging compressional wave time difference of this exemplary embodiment; Figure 2 As shown, this exemplary embodiment proposes a method for realizing real-time stable extraction of longitudinal wave time difference based on Kalman filtering algorithm, which specifically comprises the following steps:

[0135] (1) Perform array acoustic logging to collect data, starting from a certain depth, and use the original array waveform data as data input to enter the array acoustic longitudinal wave real-time analysis process.

[0136] (2) Appropriate data preprocessing of the input waveform data can effectively improve the quality of the waveform data and make the subsequent processing process more accurate.

[0137] (3) Perform STC analysis on the waveform data after preprocessing.

[0138]

[0139] In formula (1), ρ is the similarity coefficient of the similarity correlation method; S is the slowness (μs / ft); T is the arrival time of the first wave of a certain component wave (μs); T w is the time window length (μs); X m (t) is the acoustic wave signal (mV) at the mth receiver in the N-receiver array at time t; d is the distance between the receivers (ft). The STC spectrum at the depth point can be calculated according to formula (1).

[0140] (4) The STC results calculated for each depth point are stored, and the mean of a certain number of data before the depth point is selected as the average STC result for real-time correction and to assist in the automatic picking of the longitudinal wave time difference.

[0141] (5) A dual peak-finding mechanism is used for the conventional STC spectrum at the current depth and the average STC spectrum. The first prominent peak is found and determined in the direction of slowness from small to large. The two sets of picking results are comprehensively compared to determine the reliable position of the P-wave time difference. Because the average STC spectrum carries the picking information of historical depth points to a certain extent, it can be used to solve the problem of information loss in the STC spectrum in low signal-to-noise ratio areas, providing a good reference for finding and distinguishing the STC peak position.

[0142] (6) Appropriate filter parameters, including A, Q, and R in formula (2), are obtained through the adaptive Kalman filter parameter estimation mechanism.

[0143] (7) The filter parameters obtained in step (6) and the longitudinal wave time difference slowness value generated in step (5) are used as the observation value z of the Kalman filter algorithm. k Input and cycle through formula (2) to get the predicted value x k , as the pickup value of the longitudinal wave time difference at this depth point. The Kalman filter algorithm formula is:

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] In formula (2), Represents the posterior state estimate at time k, which is one of the filtering results, that is, the updated result, also called the optimal estimate; p k represents the posterior estimated covariance at time k, indicating the instability of the state; H is the transformation matrix from state variables to measurements (observations), which represents the relationship connecting the state and observations. In the Kalman filter, it is a linear relationship. It is responsible for converting the m-dimensional measurement value to the n-dimensional one so that it conforms to the mathematical form of the state variable. It is one of the prerequisites for filtering; Z k Represents the measured value (observation value) at time k, which is the input of the filter; K kIt is the filter gain matrix, which is the intermediate calculation result of the filter, the Kalman gain, or the Kalman coefficient. A is the state transfer matrix, which is actually a conjecture model of the target state transition. For example, in maneuvering target tracking, the state transfer matrix is ​​often used to model the motion of the target, and its model may be uniform linear motion or uniform acceleration motion. When the state transfer matrix does not conform to the state transition model of the target, the filter will diverge quickly; Q is the process excitation noise covariance (covariance of the system process). This parameter is used to represent the error between the state transition matrix and the actual process. Because the process signal cannot be directly observed, the value of Q is difficult to determine. R is the measurement noise covariance. When the filter is actually implemented, the measurement noise covariance R can generally be observed and is a known condition of the filter; B is the control matrix (converting the input into the state), which can generally be taken as 0, u k-1 represents the control input at time k-1.

[0150] (8) Read the waveform data of the next depth point and execute the above analysis process.

[0151] In summary, to address the shortcomings of current real-time acoustic transit time extraction methods in the field of acoustic logging, this exemplary embodiment proposes a real-time compressional transit time extraction method based on a Kalman filter algorithm. This method effectively improves the quality of raw data through targeted preprocessing, calculates conventional and average STC spectra, and relies on a dual peak-finding mechanism to ensure peak-finding reliability. Finally, a Kalman filter algorithm is used to provide trend guidance for compressional transit time extraction, achieving final calibration. After extensive data testing, the algorithm of this exemplary embodiment has been shown to be able to effectively and rapidly extract compressional slowness values ​​between 40 and 200 μs / ft in a fully automated manner. It can accurately identify the transition states between various formations, enabling real-time tracking. Furthermore, the results obtained by this algorithm in cased-hole sections meet acceptance criteria. According to statistics, the algorithm processes data for a single depth point in approximately 20 milliseconds, meeting time requirements. This is of great significance for rapid reservoir quality assessment and real-time exploration decision-making. This innovative achievement significantly shortens the acoustic data processing cycle, eliminates the need for human intervention and correction, and contributes to the development of remote logging. In addition, this innovative achievement will also benefit the promotion of the company's independently produced acoustic logging instruments at home and abroad.

[0152] The following are the results of processing actual logging data from five representative wells using this exemplary embodiment. This exemplary embodiment does not require human intervention, can be fully automated, and is universally applicable. The results of the following five representative wells are all obtained using a consistent process. The technical process mainly includes: 1) Appropriate preprocessing of the input waveform data can effectively improve the quality of the waveform data and make the subsequent processing process more accurate; 2) STC analysis is performed on the waveform data after preprocessing; 3) The STC results calculated for each depth point are stored, and the average of a certain number of data before the depth point is selected to generate an average STC spectrum for real-time correction to assist in the correction of the P-wave time difference picking results; 4) A dual peak search mechanism is used for the average STC spectrum of the current depth and the conventional STC spectrum, and the first prominent peak is searched and determined in the direction of slowness from small to large. The two sets of picking results (referred to as P1 and P2, respectively) are comprehensively compared to determine the reliable position of the P-wave time difference; 5) Real-time adaptive Kalman filter parameter estimation; 6) Kalman filter processing is performed on the picking results to achieve final correction to ensure the accuracy of the P-wave time difference value.

[0153] Figure 3 This is a schematic diagram of the results of longitudinal wave time difference picking in a standard well according to this exemplary embodiment. Figure 3 (a) is a schematic diagram of the STC spectrum. Figure 3 In the figure, depth represents depth, slowness represents slowness, (b) is a schematic diagram of the initial picked-up longitudinal wave time difference result, and (c) is a schematic diagram of the final output longitudinal wave time difference extraction result; Figure 3 (b) and Figure 3 (c) It can be clearly seen that the acoustic transit time characteristics extracted by the traditional acoustic transit time extraction method (i.e., primary acoustic transit time extraction) are not obvious in low signal-to-noise ratio areas. The acoustic transit time extraction method based on Kalman filtering proposed in this exemplary embodiment, through a dual peak-finding mechanism, Kalman filtering processing, and secondary correction processes, can grasp the trend in low signal-to-noise ratio areas, effectively overcome data deficiencies, and provide a reliable reference for compressional transit time extraction in the entire well section.

[0154] Figure 4 This is a schematic diagram of the results of the longitudinal wave time difference picking during well logging in a southwest oil field according to this exemplary embodiment. Figure 4 In the , depth means depth, slowness means slowness, Figure 4 (a) is a schematic diagram of the STC spectrum, (b) is a schematic diagram of the initial picked-up longitudinal wave time difference result, and (c) is a schematic diagram of the final output longitudinal wave time difference extraction result; in this exemplary embodiment, it can be clearly seen from the conventional STC spectrum that there is coherent interference energy in the overall background. However, thanks to the dual peak-seeking strategy, the acoustic wave time difference extraction method of this exemplary embodiment can overcome the influence of this unfavorable factor and achieve accurate picking and tracking of the longitudinal wave time difference.

[0155] Figure 5 This is a schematic diagram of the longitudinal wave time difference picking results of this exemplary embodiment during well logging in an oil field in Northeast China. Figure 5 In the , depth means depth, slowness means slowness, Figure 5 (a) is a schematic diagram of the STC spectrum, (b) is a schematic diagram of the initial P-wave time difference result, and (c) is a schematic diagram of the final output P-wave time difference extraction result; it can be seen that the application effect of this exemplary embodiment in large time difference formations is good, and the slowness range of most formations in this well is around 150μs / ft, indicating that the present invention can effectively deal with formations with large time differences and sudden changes in P-wave time differences, and realize stable extraction of P-wave time differences for this type of formation.

[0156] Figure 6 This is a schematic diagram of the longitudinal wave time difference picking results of this exemplary embodiment during well logging in a cased well in North China. Figure 6 In the , depth means depth, slowness means slowness, Figure 6 (a) is a schematic diagram of the STC spectrum, (b) is a schematic diagram of the initial P-wave time difference result, and (c) is a schematic diagram of the final P-wave time difference extraction result. The entire logging process is the casing section, which can be seen from Figure 6 It can be seen that the longitudinal wave time difference extracted by the method of this exemplary embodiment in the casing section is about 56 μs / ft, which is close to the theoretical slowness value of 57 μs / ft of the casing and meets the actual acceptance standard.

[0157] Figure 7 FIG. 1 is a block diagram of a real-time extraction system for acoustic logging compressional wave time difference according to an exemplary embodiment of the present invention. Figure 7 As shown, an exemplary embodiment of the present disclosure provides a real-time extraction system for acoustic logging compressional wave time difference, comprising:

[0158] An acquisition unit 10 is used to acquire and store waveform data of the current depth point in real time;

[0159] A first forming unit 20 is configured to form a time-slowness-correlation coefficient spectrum of the current depth point based on the waveform data of the current depth point;

[0160] The second forming unit 30 is configured to form an average time-slowness-correlation coefficient spectrum based on waveform data corresponding to a plurality of depth points before the current depth point;

[0161] a searching unit 40 for searching and determining the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point;

[0162] a determination unit 50 for determining a P-wave time difference slowness observation value based on a first prominent peak of a time-slowness-correlation coefficient spectrum and an average time-slowness-correlation coefficient spectrum at a current depth point;

[0163] The processing unit 60 is used to process and correct the P-wave move-slowness value and output a P-wave move-slowness prediction value.

[0164] Specifically, the first forming unit 20 includes:

[0165] A preprocessing module is used to preprocess the waveform data of the current depth point acquired in real time to form preprocessed waveform data;

[0166] The analysis module is used to analyze the pre-processed waveform data using the time-slowness-correlation method to obtain the time-slowness-correlation coefficient result of the current depth point and form the time-slowness-correlation coefficient spectrum of the current depth point.

[0167] Specifically, the analysis module includes:

[0168]

[0169] Where ρ is the similarity coefficient of the similarity correlation method; s is the slowness, in μs / ft; T is the arrival time of the first wave of a certain component wave, in μs; T w is the time window length, in μs; X m (t) is the acoustic wave signal at the mth receiver in the N receiver array at time t, in mV; d is the distance between the receivers, in ft.

[0170] Specifically, the second forming unit 30 includes:

[0171] An acquisition module is used to obtain the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0172] A calculation module, configured to calculate an average time-slowness-correlation coefficient result based on the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0173] The forming module is used to form an average time-slowness-correlation coefficient spectrum according to the average time-slowness-correlation coefficient result.

[0174] Specifically, the searching unit 40 includes:

[0175] The first search module is used to search and determine the first prominent peak of the time-slowness-correlation coefficient spectrum at the current depth point in the direction of slowness from small to large, and record it as the current spectrum peak;

[0176] The second search module is used to search and determine the first prominent peak of the average time-slowness-correlation coefficient spectrum in the direction of slowness from small to large, and record it as the average spectrum peak.

[0177] Specifically, the determining unit 50 includes:

[0178] A judgment module is used to judge whether the current spectrum peak value is within the set range of the average spectrum peak value, or to judge whether the current spectrum peak value exceeds the set physical meaning limit;

[0179] The first determination module is configured to determine the average spectrum peak value as the longitudinal wave slowness observation value when the current spectrum peak value is not within a set range of the average spectrum peak value or the current spectrum peak value exceeds a set physical significance limit;

[0180] The second determination module is used to determine the current spectrum peak value as the longitudinal wave time difference slowness observation value when the current spectrum peak value is within the set range of the average spectrum peak value and the current spectrum peak value does not exceed the set physical significance limit.

[0181] Specifically, it includes:

[0182] The parameter acquisition unit is used to acquire target filter parameters through an adaptive Kalman filter parameter estimation mechanism.

[0183] Specifically, the processing unit includes:

[0184] The processing and correction module is used to process and correct the longitudinal wave time difference slowness value through the Kalman filter algorithm. The Kalman filter algorithm formula is:

[0185]

[0186]

[0187]

[0188]

[0189]

[0190] Where, represents the posterior state estimate at time k; represents the posterior state estimate at time k-1; u k-1 represents the control input at time k-1; p k represents the posterior estimated covariance at time k; p k-1 represents the posterior estimated covariance at time k-1; H is the transformation matrix from state variables to observations; Z k represents the observation value at time k; K kis the filter gain matrix; A is the state transfer matrix; Q is the process excitation noise covariance; R is the measurement noise covariance; B is the control matrix.

[0191] Figure 8 Schematic diagram of the structure of a real-time extraction device for acoustic logging longitudinal wave time difference according to this exemplary embodiment. Figure 8 As shown, corresponding to the above-mentioned method for extracting the time difference between longitudinal waves in acoustic logging, the present invention also provides a device for extracting the time difference between longitudinal waves in acoustic logging. Since the embodiment of this device is similar to the above-mentioned method embodiment, the description is relatively simple. For relevant details, please refer to the description of the above-mentioned method embodiment. The device described below is merely illustrative. The device may include: a processor 1, a memory 2, a communication bus (i.e., the above-mentioned device bus), and a search engine. The processor 1 and memory 2 communicate with each other via the communication bus and communicate with the outside world via a communication interface. The processor 1 can call logic instructions in the memory 2 to execute the method for extracting the time difference between longitudinal waves in acoustic logging.

[0192] In addition, the logic instructions in the above-mentioned memory 2 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: a memory chip, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0193] On the other hand, an embodiment of the present invention further provides a processor-readable storage medium, on which a computer program 3 is stored. When the computer program 3 is executed by the processor 1, the method for real-time extraction of acoustic logging compressional wave time difference provided in the above embodiments is implemented.

[0194] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor 1, including but not limited to magnetic storage (such as floppy disks, hard disks, tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSDs)), etc.

[0195] The above are merely preferred embodiments of the present disclosure. The scope of protection of the present disclosure is not limited to the above embodiments. All technical solutions based on the principles of the present disclosure are within the scope of protection of the present disclosure. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present disclosure should be considered within the scope of protection of the present disclosure.

Claims

1. A method for real-time extraction of longitudinal wave time difference in acoustic logging, characterized in that: include: Acquire and store waveform data of the current depth point in real time; Based on the waveform data of the current depth point, a time-slowness-correlation coefficient spectrum of the current depth point is formed; Based on the waveform data corresponding to multiple depth points before the current depth point, an average time-slowness-correlation coefficient spectrum is formed; Find and determine the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point respectively; Determine the P-wave time difference slowness observation value based on the first prominent peak of the time-slowness-correlation coefficient spectrum at the current depth point and the average time-slowness-correlation coefficient spectrum; The P-wave time difference slowness observation value is processed and corrected, and the P-wave time difference slowness prediction value is output.

2. The method for real-time extraction of compressional wave time difference in acoustic logging according to claim 1, characterized in that: The forming of the time-slowness-correlation coefficient spectrum of the current depth point according to the waveform data of the current depth point includes: Preprocessing the waveform data of the current depth point acquired in real time to form preprocessed waveform data; The pre-processed waveform data is analyzed using a time-slowness-correlation method to obtain a time-slowness-correlation coefficient result of the current depth point, thereby forming a time-slowness-correlation coefficient spectrum of the current depth point.

3. The method for real-time extraction of compressional wave time difference in acoustic logging according to claim 2, characterized in that: The method of analyzing the pre-processed waveform data using the time-slowness-correlation method to obtain the time-slowness-correlation coefficient result of the current depth point and forming the time-slowness-correlation coefficient spectrum of the current depth point includes: ； Where, is the similarity coefficient of the similarity correlation method; is the slowness, in μs / ft; is the arrival time of the first wave of a certain component wave, in μs; is the time window length, in μs; is the acoustic wave signal of the mth receiver in the N receiver array at time t, in mV; d is the distance between the receivers, in ft.

4. The method for real-time extraction of compressional wave time difference in acoustic logging according to claim 1, characterized in that: The forming of an average time-slowness-correlation coefficient spectrum based on waveform data corresponding to a plurality of depth points before the current depth point includes: Get the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point; Calculate the average time-slowness-correlation coefficient result based on the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point; An average time-slowness-correlation coefficient spectrum is formed based on the average time-slowness-correlation coefficient results.

5. The method for real-time extraction of compressional wave time difference in acoustic logging according to claim 1, characterized in that: The steps of respectively finding and determining the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point include: Find and determine the first prominent peak of the time-slowness-correlation coefficient spectrum at the current depth point in the direction from small to large slowness, and record it as the current spectrum peak; The first prominent peak of the average time-slowness-correlation coefficient spectrum is found and determined in the direction from small to large slowness, and recorded as the average spectrum peak.

6. The method for real-time extraction of compressional wave time difference in acoustic logging according to claim 5, characterized in that: Determining the P-wave time difference slowness observation value based on the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point includes: Determine whether the current spectrum peak value is within the set range of the average spectrum peak value, or determine whether the current spectrum peak value exceeds the set physical significance limit; When the current spectrum peak value is not within the set range of the average spectrum peak value, or the current spectrum peak value exceeds the set physical significance limit, the average spectrum peak value is determined as the longitudinal wave time difference slowness observation value; When the current spectrum peak value is within the set range of the average spectrum peak value and the current spectrum peak value does not exceed the set physical significance limit, the current spectrum peak value is determined as the longitudinal wave time difference slowness observation value.

7. The method for real-time extraction of compressional wave time difference in acoustic logging according to claim 1, characterized in that: include: The target filter parameters are obtained through the adaptive Kalman filter parameter estimation mechanism.

8. The method for real-time extraction of compressional wave time difference in acoustic logging according to claim 7, characterized in that: The processing and correction of the P-wave move slowness observation value and the output of the P-wave move slowness prediction value include: The longitudinal wave time difference slowness value is processed and corrected by the Kalman filter algorithm. The Kalman filter algorithm formula is: ； Where, represents the posterior state estimate at time k; represents the posterior state estimate at time k-1; represents the control input at time k-1; represents the posterior estimated covariance at time k; represents the posterior estimated covariance at time k-1; H is the transformation matrix from state variables to observations; represents the observation value at time k; is the filter gain matrix; A is the state transfer matrix; Q is the process excitation noise covariance; R is the measurement noise covariance; B is the control matrix.

9. A real-time extraction system for acoustic logging longitudinal wave time difference, characterized in that: include: An acquisition unit, used for acquiring and storing waveform data of the current depth point in real time; A first forming unit is configured to form a time-slowness-correlation coefficient spectrum of the current depth point based on the waveform data of the current depth point; A second forming unit is configured to form an average time-slowness-correlation coefficient spectrum based on waveform data corresponding to a plurality of depth points before the current depth point; a search unit, for respectively searching and determining the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point; a determination unit for determining a P-wave time difference slowness observation value based on a time-slowness-correlation coefficient spectrum at a current depth point and a first prominent peak of an average time-slowness-correlation coefficient spectrum; The processing unit is used to process and correct the P-wave time difference slowness observation value and output the P-wave time difference slowness prediction value.

10. The real-time extraction system for acoustic logging P-wave time difference according to claim 9, characterized in that: The first forming unit includes: A preprocessing module is used to preprocess the waveform data of the current depth point acquired in real time to form preprocessed waveform data; The analysis module is used to analyze the pre-processed waveform data using a time-slowness-correlation method to obtain a time-slowness-correlation coefficient result of the current depth point and form a time-slowness-correlation coefficient spectrum of the current depth point.

11. The real-time extraction system for acoustic logging P-wave time difference according to claim 10, characterized in that: The analysis module includes: ； Where, is the similarity coefficient of the similarity correlation method; is the slowness, in μs / ft; is the arrival time of the first wave of a certain component wave, in μs; is the time window length, in μs; is the acoustic wave signal of the mth receiver in the N receiver array at time t, in mV; d is the distance between the receivers, in ft.

12. The real-time extraction system for acoustic logging P-wave time difference according to claim 9, characterized in that: The second forming unit includes: An acquisition module is used to obtain the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point; A calculation module, configured to calculate an average time-slowness-correlation coefficient result based on the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point; A forming module is used to form an average time-slowness-correlation coefficient spectrum according to the average time-slowness-correlation coefficient result.

13. The real-time extraction system for acoustic logging P-wave time difference according to claim 9, characterized in that: The searching unit comprises: The first search module is used to search and determine the first prominent peak of the time-slowness-correlation coefficient spectrum at the current depth point in the direction of slowness from small to large, and record it as the current spectrum peak; The second search module is used to search and determine the first prominent peak of the average time-slowness-correlation coefficient spectrum in the direction of slowness from small to large, and record it as the average spectrum peak.

14. The real-time extraction system for acoustic logging P-wave time difference according to claim 13, characterized in that: The determining unit includes: A judgment module is used to judge whether the current spectrum peak value is within the set range of the average spectrum peak value, or to judge whether the current spectrum peak value exceeds the set physical meaning limit; The first determination module is configured to determine the average spectrum peak value as the longitudinal wave slowness observation value when the current spectrum peak value is not within a set range of the average spectrum peak value or the current spectrum peak value exceeds a set physical significance limit; The second determination module is used to determine the current spectrum peak value as the longitudinal wave time difference slowness observation value when the current spectrum peak value is within the set range of the average spectrum peak value and the current spectrum peak value does not exceed the set physical significance limit.

15. The real-time extraction system for acoustic logging P-wave time difference according to claim 9, characterized in that: include: The parameter acquisition unit is used to acquire target filter parameters through an adaptive Kalman filter parameter estimation mechanism.

16. The real-time extraction system for acoustic logging P-wave time difference according to claim 15, characterized in that: The processing unit includes: The processing and correction module is used to process and correct the longitudinal wave slowness observation value through the Kalman filter algorithm. The Kalman filter algorithm formula is: ； Where, represents the posterior state estimate at time k; represents the posterior state estimate at time k-1; represents the control input at time k-1; represents the posterior estimated covariance at time k; represents the posterior estimated covariance at time k-1; H is the transformation matrix from state variables to observations; represents the observation value at time k; is the filter gain matrix; A is the state transfer matrix; Q is the process excitation noise covariance; R is the measurement noise covariance; B is the control matrix.

17. A real-time extraction device for acoustic logging longitudinal wave time difference, characterized in that: include: processor and memory; The memory is used to store a computer program, and the processor calls the computer program stored in the memory to execute the method for real-time extraction of acoustic logging compressional wave time difference according to any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute the method for real-time extraction of acoustic logging compressional wave time difference according to any one of claims 1 to 8.

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