Acoustic logging longitudinal wave time difference real-time extraction method, system and equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of geophysical logging, and discloses an acoustic logging longitudinal wave time difference real-time extraction method, system and device and a storage medium, and the method comprises the steps: forming a time-slowness-correlation coefficient spectrum of a current depth point according to the waveform data of the current depth point; forming an average time-slowness-correlation coefficient spectrum according to waveform data corresponding to a plurality of depth points before the current depth point; respectively searching and determining the first prominent peak values of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum of the current depth point; determining a longitudinal wave time difference slowness observed value according to the first prominent peak values of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum of the current depth point; and processing and correcting the longitudinal wave time difference slowness value, and outputting a longitudinal wave time difference slowness predicted value. According to the embodiment of the invention, the period of sound wave data processing is greatly shortened, and the method does not depend on manual correction.

Description

Technical Field

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

[0002] In the field of acoustic logging, array acoustic logging information contains many aspects of information, such as rich formation physical information, corresponding longitudinal and transverse wave information, which can determine rock mechanical parameters through the time difference between the two, and can also study and analyze the anisotropy of the formation, providing certain support for formation identification. With the rapid development of oil and gas field exploration technology, the application ratio of this technology in the field of petroleum engineering is also increasing, so it is necessary to analyze the longitudinal and transverse wave time difference information.

[0003] The methods of acoustic time difference extraction are mainly divided into two categories: time domain and frequency domain. At present, the main methods in the time domain are: first wave extraction method and time-slowness correlation method (STC). The first wave extraction method uses waveform characteristics to extract the initial arrival time of different vibration mode waves at different source distances, and calculates the wave velocity of different vibration mode waves according to the propagation time of the sound wave between specific known spacings. The time-slowness correlation method (STC) uses the similarity of the waveforms received by the array acoustic wave receiving transducer to determine the propagation slowness of the wave. The energy integration method is an improvement on the first wave extraction method. It mainly calculates the energy integral of the waveform within the time window to reduce the influence of weak signals and noise and thus improve the accuracy of first wave extraction. The similarity correlation method (Slowness-Time Coherence, referred to as STC) is an alternative solution to achieve automatic time difference extraction. This method comprehensively considers the consistency of waveform phase and amplitude in the calculation process of correlation spectrum, and calculates the correlation coefficient according to a certain length of time window scanning in the two dimensions of time and slowness. It has good noise resistance, so it has gradually developed into the main technical means for acoustic logging time difference (i.e. slowness, or the inverse of speed) analysis. Many scholars have conducted research on the STC (slowness-time correlation) algorithm. For example, when studying the STC algorithm for array acoustic logging data processing, it was found that the width of the time window has a great influence on the processing results, so the idea of ​​variable window width was proposed. The prior art also proposed an array acoustic logging data processing method that combines the slowness-time correlation method with a genetic algorithm, and the processing results can meet the requirements of field interpretation accuracy. However, the above method does not involve how to overcome the defects of common waveform data in practice, nor does it propose a new STC calculation formula and analysis process. The processing results are not ideal when encountering data with relatively large noise, and to a certain extent rely on post-measurement processing with human participation.

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

[0005] 1. First wave extraction method Due to the presence of signal noise, when the signal-to-noise ratio is low, the fixed threshold value may not be used to find the true first wave position, resulting in the so-called "first wave jump" problem. In addition, due to the difference in the amplitude of waveform signals at different source distances, selecting the same threshold value for all waveforms may also cause problems;

[0006] 2. Most of the acoustic time difference extraction methods (frequency domain methods) have complex processing processes, low timeliness, high computing equipment thresholds, 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 acoustic logging compressional wave time difference, so as 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 acoustic logging compressional wave time difference is provided, comprising:

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

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

[0012] According to the waveform data corresponding to the 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 according to the first prominent peak of the time-slowness-correlation coefficient spectrum of the current depth point and the average time-slowness-correlation coefficient spectrum;

[0015] The longitudinal wave time difference slowness value is processed and corrected, and the longitudinal 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 according to 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 a possible implementation, the preprocessed waveform data is analyzed using a time-slowness-correlation method to obtain a time-slowness-correlation coefficient result of the current depth point, and the time-slowness-correlation coefficient spectrum of the current depth point is formed, including:

[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 head wave of a certain component, in μs; T w is the time window length, in μs; X m (t) 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.

[0022] In a possible implementation, forming an average time-slowness-correlation coefficient spectrum according to waveform data corresponding to a plurality of 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 according to the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point;

[0025] Based on the average time-slowness-correlation coefficient results, an average time-slowness-correlation coefficient spectrum is formed.

[0026] In a possible implementation, 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 of the current depth point 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 according to 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 meaning 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 meaning 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 a possible implementation, it includes:

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

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

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

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] In the formula, 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 observed 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 real-time extraction system for acoustic logging compressional wave time difference 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, used for forming a time-slowness-correlation coefficient spectrum of the current depth point according to the waveform data of the current depth point;

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

[0047] A search unit, used 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, used for determining the P-wave time difference slowness observation value according to the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point;

[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, used for preprocessing 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 a 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 head wave of a certain component, in μs; T w is the time window length, in μs; X m (t) 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.

[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, used to calculate an average time-slowness-correlation coefficient result according to 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 of the current depth point in the direction from small to large slowness, 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 from small to large slowness, and record it as the average spectrum peak.

[0063] In a possible implementation manner, 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 used to determine the average spectrum peak value as the longitudinal wave time difference slowness observation value 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 meaning 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 meaning limit.

[0067] In a possible implementation, it includes:

[0068] The parameter acquisition unit is used to acquire the 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] In the formula, 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 observed 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 real-time extraction device for acoustic logging longitudinal wave time difference 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 of the above-mentioned methods for extracting the time difference of longitudinal waves in acoustic logging.

[0080] According to one aspect of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the processor is enabled to execute any of the above-mentioned methods for extracting the time difference of longitudinal 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 presented in the following drawings and descriptions. 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 descriptions below are only exemplary and explanatory and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings herein are incorporated into the specification 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 accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

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

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

[0086] Figure 3 is a schematic diagram of the longitudinal wave time difference picking result in a standard well of 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 longitudinal wave time difference picking result of this exemplary embodiment during well logging in a certain oil field in the southwest. 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 is a schematic diagram of the longitudinal wave time difference picking result 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 of 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 being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. 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 other methods, components, devices, steps, etc. may be adopted. In other cases, 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 only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware units or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0094] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the 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 the actual situation.

[0095] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example.

[0096] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or sub-modules is not necessarily limited to those steps or sub-modules explicitly listed, but may include other steps or sub-modules not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0097] Figure 1 FIG. 1 is a flow chart of a method for real-time extraction of acoustic logging longitudinal wave time difference according to an exemplary embodiment of the present invention. Figure 1As shown, an exemplary embodiment of the present disclosure provides a method for real-time extraction of acoustic logging longitudinal 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 according to the waveform data of the current depth point;

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

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

[0102] S5 determines the P-wave time difference slowness observation value according to 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] Exemplarily, the acoustic logging instrument starts running from a position of 4000 meters underground, acquires and stores waveform data of the current depth point of 4000 meters underground, and as the acoustic logging instrument moves upward, continues to acquire and store waveform data of the current depth points of 3999 meters, 3998 meters, etc. underground; for example, when the acoustic logging instrument is lifted from a depth point of 4000 meters underground to a depth point of 3000 meters underground, the waveform data corresponding to multiple depth points before the current depth point of 3000 meters underground (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 lifted) are 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 protection scope of this embodiment.

[0105] This exemplary embodiment effectively considers and overcomes the various unfavorable factors faced by actual acoustic logging, extracts the longitudinal wave time difference from the array acoustic waveform data in real time and reliably, 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. It mainly includes targeted data preprocessing, conventional STC spectrum calculation, double peak search strategy, STC calculation based on the Kalman filter algorithm, real-time automatic correction method and adaptive filter parameter estimation, etc., 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 according to the waveform data of the current depth point, including:

[0107] S21 preprocesses the waveform data of the current depth point acquired in real time to form preprocessed waveform data;

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

[0109] Specifically, S22 uses the time-slowness-correlation method to analyze the preprocessed waveform data to obtain the time-slowness-correlation coefficient result of the current depth point, and the time-slowness-correlation coefficient spectrum of the current depth point is formed, 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 head wave of a certain component, in μs; T w is the time window length, in μs; X m (t) 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.

[0112] Specifically, S3 forms an average time-slowness-correlation coefficient spectrum according to the 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 according to 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 and determines the first prominent peaks of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum of the current depth point respectively, including:

[0117] S41 searches and determines 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 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 according to 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 meaning 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 meaning 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] In the formula, 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 observed 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 FIG. 2 is a flowchart of a method for real-time extraction of a time difference of a longitudinal wave in acoustic logging according to an exemplary embodiment of the present invention; 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 consists of 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 flow 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 head wave of a certain component (μs); T w is the time window length (μs); X m (t) is the acoustic wave signal (mV) of the mth receiver in the N receiver array at time t; d is the distance between the receivers (ft). The STC spectrum of the depth point can be calculated according to formula (1).

[0140] (4) 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 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 search mechanism is used for the conventional STC spectrum and the average STC spectrum at the current depth. The first prominent peak is searched 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 longitudinal 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, and provides 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 then loop 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), indicating the relationship connecting the state and observations. In Kalman filtering, it is a linear relationship. It is responsible for converting the m-dimensional measurement value to the n-dimensional one to conform to the mathematical form of the state variable, and 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 kis 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 uniformly accelerated 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 observed directly, 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 is generally 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, in view of the existing shortcomings of the real-time extraction method of acoustic time difference in the field of acoustic logging, this exemplary embodiment proposes a real-time extraction method of longitudinal wave time difference based on Kalman filter algorithm, which effectively improves the data quality of the original data through targeted preprocessing methods, calculates the conventional STC spectrum and the average STC spectrum, relies on the double peak search mechanism to ensure the reliability of peak search, and finally uses the Kalman filter algorithm to provide trend guidance for the extraction of longitudinal wave time difference to achieve final calibration. After a large amount of data testing, under the condition of full automation, the algorithm of this exemplary embodiment can effectively and quickly extract the longitudinal wave slowness value between 40-200μs / ft, can accurately identify the transformation state between a variety of different formations, and achieve the purpose of real-time tracking, and the results obtained by the algorithm in the casing section meet the acceptance criteria. According to statistics, the time for the algorithm of the present invention to process single depth point data is about 20 milliseconds, which meets the timeliness requirements and is of great significance for the rapid evaluation of reservoir quality and real-time decision-making in exploration. This innovative achievement greatly shortens the cycle of acoustic wave data processing, no longer relies on human participation in correction, and also 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 the actual logging data of five representative wells through this exemplary embodiment. This exemplary embodiment does not require human intervention, can be fully automated, and has universal applicability. The results of the following five representative wells are obtained by 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) Perform STC analysis on the waveform data after preprocessing; 3) Store the STC results calculated for each depth point, select the mean of a certain number of data before the depth point to generate an average STC spectrum, which is used for real-time correction and assists in the correction of the P-wave time difference picking results; 4) Use a dual peak search mechanism for the average STC spectrum of the current depth and the conventional STC spectrum, and search and determine the first prominent peak in the direction of slowness from small to large, and comprehensively compare the two sets of picking results (referred to as P1 and P2, respectively) to determine the credible position of the P-wave time difference; 5) Real-time adaptive Kalman filter parameter estimation; 6) Perform Kalman filter processing on the picking results to achieve final correction to ensure the accuracy of the P-wave time difference value.

[0153] Figure 3 is a schematic diagram of the longitudinal wave time difference picking result in a standard well of 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 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 time difference characteristics extracted by the traditional acoustic time difference extraction method (i.e., the initial acoustic time difference extraction) in the low signal-to-noise ratio area are not obvious. The acoustic time difference extraction method based on Kalman filtering proposed in this exemplary embodiment realizes the trend grasp in the low signal-to-noise ratio area through the double peak search mechanism, Kalman filtering processing and secondary correction processes, which can effectively overcome data defects and provide a reliable reference for the extraction of longitudinal wave time difference in the whole well section.

[0154] Figure 4 This is a schematic diagram of the longitudinal wave time difference picking result of this exemplary embodiment during well logging in a certain oil field in the southwest. Figure 4 In the above example, depth means depth and slowness means slowness. 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; 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 double peak search 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 above example, depth means depth and 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 P-wave time difference extraction result of the final output; 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, which means 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 is a schematic diagram of the longitudinal wave time difference picking result of this exemplary embodiment during well logging in a cased well in North China, Figure 6 In the above example, depth means depth and 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 was in 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 the casing 57 μs / ft and meets the actual acceptance standard.

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

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

[0159] A first forming unit 20, for forming a time-slowness-correlation coefficient spectrum of the current depth point according to the waveform data of the current depth point;

[0160] The second forming unit 30 is used to form an average time-slowness-correlation coefficient spectrum according to the waveform data corresponding to the multiple depth points before the current depth point;

[0161] A searching unit 40 is used to search and determine the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum of the current depth point respectively;

[0162] A determination unit 50, for determining a P-wave time difference slowness observation value according to 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 time difference slowness value and output the P-wave time difference slowness prediction value.

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

[0165] A preprocessing module, used for preprocessing 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 head wave of a certain component, in μs; T w is the time window length, in μs; X m (t) 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.

[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, used to calculate an average time-slowness-correlation coefficient result according to 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 of the current depth point in the direction from small to large slowness, 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 from small to large slowness, 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 used to determine the average spectrum peak value as the longitudinal wave time difference slowness observation value 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 meaning 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 meaning limit.

[0181] Specifically, it includes:

[0182] The parameter acquisition unit is used to acquire the 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] In the formula, 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 observed 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 the exemplary embodiment. Figure 8 As shown, corresponding to the above-mentioned acoustic logging longitudinal wave time difference real-time extraction method, the present invention also provides an acoustic logging longitudinal wave time difference real-time extraction device. Since the embodiment of the device is similar to the above-mentioned method embodiment, the description is relatively simple. For relevant parts, please refer to the description of the above-mentioned method embodiment part. The device described below is only schematic. The device may include: a processor (processor) 1, a memory (memory) 2 and a communication bus (i.e., the above-mentioned device bus) and a search engine, wherein the processor 1 and the memory 2 complete mutual communication through the communication bus and communicate with the outside through the communication interface. The processor 1 can call the logic instructions in the memory 2 to execute the acoustic logging longitudinal wave time difference real-time extraction method.

[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 it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions to enable 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 methods of each embodiment of the present invention. The aforementioned storage medium includes: a storage chip, a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a 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 longitudinal 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, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0195] The above are only preferred implementations of the present disclosure, and the protection scope of the present disclosure is not limited to the above embodiments. All technical solutions under the concept of the present disclosure belong to the protection scope of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present disclosure should be regarded as the protection scope 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 the waveform data of the current depth point in real time; According to the waveform data of the current depth point, a time-slowness-correlation coefficient spectrum of the current depth point is formed; According to the waveform data corresponding to the 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 according to the first prominent peak of the time-slowness-correlation coefficient spectrum of the current depth point and the average time-slowness-correlation coefficient spectrum; The longitudinal wave time difference slowness value is processed and corrected, and the longitudinal wave time difference slowness prediction value is output.

2. The method for real-time extraction of acoustic logging P-wave time difference 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 comprises: 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 longitudinal wave time difference of acoustic logging according to claim 2, characterized in that: The method of analyzing the preprocessed 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 ρ is the similarity coefficient of the similarity correlation method; s is the slowness, in μs / ft; T is the arrival time of the head wave of a certain component, in μs; T w is the time window length, in μs; X m (t) 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 longitudinal wave time difference of acoustic logging according to claim 1, characterized in that: The forming of the average time-slowness-correlation coefficient spectrum according to the waveform data corresponding to the plurality of depth points before the current depth point comprises: 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 according to the time-slowness-correlation coefficient results corresponding to multiple depth points before the current depth point; Based on the average time-slowness-correlation coefficient results, an average time-slowness-correlation coefficient spectrum is formed.

5. The method for real-time extraction of acoustic logging P-wave time difference 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 of 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 acoustic logging P-wave time difference according to claim 5, characterized in that: Determining the P-wave time difference slowness observation value according to the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point comprises: 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 meaning 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 meaning 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 acoustic logging P-wave time difference 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 acoustic logging P-wave time difference according to claim 7, characterized in that: The processing and correction of the longitudinal wave time difference slowness value and outputting the longitudinal wave time difference slowness prediction value comprises: The longitudinal wave time difference slowness value is processed and corrected by the Kalman filter algorithm, and the Kalman filter algorithm formula is: In the formula, 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 observed 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.

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, used for forming a time-slowness-correlation coefficient spectrum of the current depth point according to the waveform data of the current depth point; A second forming unit is used to form an average time-slowness-correlation coefficient spectrum according to waveform data corresponding to a plurality of depth points before the current depth point; A search unit, used 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, used for determining the P-wave time difference slowness observation value according to the first prominent peak of the time-slowness-correlation coefficient spectrum and the average time-slowness-correlation coefficient spectrum at the current depth point; 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.

10. The real-time extraction system of acoustic logging longitudinal wave time difference according to claim 9, characterized in that: The first forming unit comprises: A preprocessing module, used for preprocessing 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 a current depth point and form a time-slowness-correlation coefficient spectrum of the current depth point.

11. The real-time extraction system of acoustic logging longitudinal wave time difference according to claim 10, characterized in that: The analysis module comprises: 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 head wave of a certain component, in μs; T w is the time window length, in μs; X m (t) 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 of acoustic logging longitudinal wave time difference according to claim 9, characterized in that: The second forming unit comprises: 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, used to calculate an average time-slowness-correlation coefficient result according to 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 of acoustic logging longitudinal 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 of the current depth point in the direction from small to large slowness, 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 from small to large slowness, and record it as the average spectrum peak.

14. The real-time extraction system of acoustic logging longitudinal wave time difference according to claim 13, characterized in that: The determining unit comprises: 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 used to determine the average spectrum peak value as the longitudinal wave time difference slowness observation value 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 meaning 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 meaning limit.

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

16. The real-time extraction system of acoustic logging longitudinal wave time difference according to claim 15, characterized in that: The processing unit comprises: 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: In the formula, 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 observed 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.

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 computer programs, and the processor calls the computer programs 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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