A method and device for real-time and rapid extraction of downhole acoustic travel time

Abstract

The present invention relates to the field of logging technology, and particularly to a method for rapidly extracting downhole acoustic time difference in real time, comprising the following steps: S1. Collect acoustic waveform data at different formation depths through a downhole acoustic time difference real-time rapid extraction device; S2. Perform band-pass filtering on the acoustic waveform data; S3. Perform waveform discrete binary processing on the filtered waveform data; S4. Use cross-correlation calculation based on wave arrival time and time difference window for the waveform data after waveform discrete binary processing to obtain two-dimensional correlation coefficients under different wave arrival times and time difference windows; S5. Obtain one-dimensional correlation coefficients corresponding to different time differences according to the two-dimensional correlation coefficients; S6. Search for the maximum value of the one-dimensional correlation coefficient to obtain the time difference values corresponding to the longitudinal wave, transverse wave, and Stoneley wave. The present invention reduces the time complexity of calculation, improves the data processing speed, and can meet the requirement of real-time uploading of formation time difference measurement information by downhole instruments.

Description

Technical Field

[0001] The present invention relates to the technical field of well logging in oil exploration and development, and particularly relates to a method and device for real-time and rapid extraction of downhole acoustic wave slowness. Background Art

[0002] Acoustic wave slowness is one of the key physical property parameters for reservoir evaluation in oil well logging. Acoustic full waveform logging extracts slowness information of downhole reservoirs. The slowness is defined as the reciprocal of the acoustic wave velocity and can indirectly reflect the pore characteristics of the reservoir. The smaller the slowness, the faster the acoustic wave propagates in the reservoir, and the denser the reservoir. Combining relevant waveform processing techniques can measure the wave velocities of longitudinal waves, transverse waves, and Stoneley waves in the formation, and can also identify the properties of pore fluids, estimate the formation permeability, evaluate formation anisotropy, and identify fractures, etc.

[0003] The measurement principle of the acoustic full waveform logging tool is to adopt a single-shot - multi-receiver tool structure. The acoustic wave is emitted to the formation through a transmitting transducer, and the acoustic wave signals from the formation are received at different source distances. In this acoustic full waveform logging method based on a monopole sound source, the waveform signals received in sequence are three mode waves: longitudinal wave, transverse wave, and Stoneley wave.

[0004] Currently, the mainstream acoustic full waveform logging tools adopt a single-shot - eight-receiver structure, receive the acoustic waveforms from 8 different source distances in the formation, and calculate the formation slowness using the slowness - wave arrival time similarity principle of waveforms at different source distances. However, due to the huge amount of waveform data collected by downhole tools, it cannot meet the requirement of real-time upload. Therefore, it is necessary to implement the extraction of slowness based on waveform data in the downhole tool processing chip, so as to upload the calculated slowness data, which can greatly compress the upload data volume. However, the current conventional similarity algorithms have a high time complexity and require a large amount of chip resources, resulting in the inability to meet the requirement of real-time calculation and upload of acoustic wave slowness data during well logging operations. Therefore, there is a more urgent need for a method for real-time and rapid extraction of acoustic wave slowness. Summary of the Invention

[0005] The purpose of the present invention is to propose a method and device for real-time and rapid extraction of downhole acoustic wave slowness. Without changing the mechanical structure of the tool, it simplifies the design of the FPGA processing chip of the downhole tool hardware circuit. Based on this extraction method, a 2-bit FPGA adder design for downhole can be realized, using fewer FPGA processing chip resources, reducing the time complexity of the slowness extraction algorithm, meeting the requirement of real-time upload of formation slowness measurement information by downhole tools during well logging operations, and providing reliable data for real-time and rapid monitoring of downhole formation acoustic wave slowness data for deep oil and gas reservoir evaluation.

[0006] The technical solution adopted by the present invention to solve its technical problems is: to provide a method for real-time and rapid extraction of downhole acoustic wave slowness, including the following steps:

[0007] S1. Collect acoustic wave form data at different formation depths through the downhole acoustic wave time difference real-time rapid extraction device;

[0008] S2. Perform band-pass filtering on the acoustic wave form data;

[0009] S3. Perform waveform discrete binary processing on the filtered waveform data;

[0010] S4. Use cross-correlation calculation based on wave arrival time and time difference window for the waveform data after waveform discrete binary processing to obtain two-dimensional correlation coefficients under different wave arrival times and time difference windows;

[0011] S5. Obtain one-dimensional correlation coefficients corresponding to different time differences according to the two-dimensional correlation coefficients;

[0012] S6. Search for the maximum value of the one-dimensional correlation coefficient to obtain the time difference values corresponding to the longitudinal wave, transverse wave, and Stoneley wave.

[0013] Further, the S2 band-pass filtering preprocessing includes:

[0014] Set the working frequency range [f1, f2] of the transmitting transducer;

[0015] Transform the time-domain acoustic wave signal of the receiving transducer into a frequency-domain signal;

[0016] Perform band-pass filtering within the set frequency range [f1, f2], retain the signals within this frequency range and filter out the interference signals outside the range;

[0017] Perform inverse transformation on the frequency-domain signal after band-pass filtering to convert it back to a time-domain signal.

[0018] Further, the S3 waveform discrete binary preprocessing includes:

[0019] Set the positive threshold PosThrehold and the negative threshold NegThrehold;

[0020] ;

[0021] Where:

[0022] Wave represents the waveform after band-pass filtering;

[0023] SWave represents the waveform after discrete binary processing;

[0024] i represents the serial number of the receiving transducer;

[0025] j represents the number of sampling points of the waveform;

[0026] Compare the actual amplitude of the acoustic wave signal with the set positive and negative thresholds;

[0027] When the amplitude of the acoustic wave signal is greater than the positive threshold, set the signal amplitude to +1;

[0028] When the amplitude of the acoustic wave signal is less than the negative threshold, set the signal amplitude to -1;

[0029] When the amplitude of the acoustic wave signal does not meet the above two cases, set the signal amplitude to 0.

[0030] Furthermore, the setting of the positive threshold PosThrehold and the negative threshold NegThrehold considers the minimum signal amplitude of the background noise, and the steps include:

[0031] Analyze the amplitude distribution of the background noise to determine the minimum signal amplitude of the background noise;

[0032] Based on the minimum signal amplitude of the background noise, set the positive threshold PosThrehold and the negative threshold NegThrehold.

[0033] Furthermore, the S4 cross-correlation calculation includes the following steps:

[0034] At different depth points, given a time difference range [s1, s2] and an arrival time range [t1, t2];

[0035] In the outer loop, traverse the time difference variable s, and in the inner loop, traverse the arrival time variable t. Determine a time window under a certain set of (s, t) loop variables;

[0036] In each determined time window, perform a two-dimensional cross-correlation calculation on the received waveform data to obtain the two-dimensional correlation coefficient under this time window;

[0037] Repeat the above process until all possible combinations of time difference and arrival time variable values are traversed to complete the cross-correlation calculation process.

[0038] Furthermore, the cross-correlation calculation formula is:

[0039] ;

[0040] Where:

[0041] R2D represents the two-dimensional correlation coefficient;

[0042] S[k] is the time difference sequence;

[0043] ;

[0044] s1 represents the starting time difference;

[0045] s2 represents the ending time difference;

[0046] ;

[0047] t1 represents the starting time of the calculation area;

[0048] t2 represents the ending time of the calculation area;

[0049] Twin represents the length of the calculation window;

[0050] int represents the integer operation;

[0051] ts represents the sampling time interval of each waveform.

[0052] Furthermore, the calculation formula for the one-dimensional correlation coefficient R1D of S5 is:

[0053] ;

[0054] Among them, R1D(S[k]) represents the one-dimensional correlation coefficient corresponding to the time difference value. When calculating, fix each S[k] value and calculate the maximum value of the two-dimensional correlation coefficient corresponding to the range of m changes.

[0055] Furthermore, the search for the maximum value of the one-dimensional correlation coefficient of S6 includes:

[0056] Set the time difference search ranges for the P-wave, S-wave, and Stoneley wave respectively;

[0057] Search for the maximum value of the one-dimensional correlation coefficient within the time difference ranges corresponding to the P-wave, S-wave, and Stoneley wave, and select the time difference value corresponding to when the corresponding coefficient reaches a local maximum as the time difference values of the P-wave, S-wave, and Stoneley wave.

[0058] Furthermore, the setting of the time difference search ranges for the P-wave, S-wave, and Stoneley wave includes: generating the time difference search ranges for the P-wave, S-wave, and Stoneley wave based on the lithology parameters of the target formation.

[0059] This application also provides a device for real-time and rapid extraction of downhole acoustic wave time differences, including:

[0060] A full-waveform acoustic logging tool, which includes a transmitting transducer and several receiving transducers;

[0061] The transmitting transducer and several receiving transducers are installed on a metal mandrel and arranged coaxially with the metal mandrel;

[0062] Several receiving transducers are all on the same side of the transmitting transducer, and the distance between the first receiving transducer and the transmitting transducer is L;

[0063] The distance between adjacent two receiving transducers is S;

[0064] The sound insulation body is used to isolate the acoustic wave signal that directly propagates along the metal mandrel to the receiving transducer. The sound insulation body is installed on the metal rod between the first receiving transducer and the transmitting transducer.

[0065] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0066] (1) The present invention adopts steps such as band-pass filtering preprocessing and waveform discrete binarization to convert the original high-precision acoustic wave signal into simplified binarized waveform data, making the subsequent cross-correlation calculation more efficient. The present invention reduces the time complexity of the calculation, improves the data processing speed, and meets the requirement of the downhole instrument for real-time uploading of formation travel time measurement information.

[0067] (2) Through band-pass filtering preprocessing, the present invention effectively removes interference signals and retains the acoustic wave signals within the effective frequency band related to formation characteristics. This not only improves the data quality but also enhances the accuracy and reliability of the subsequent analysis results.

[0068] (3) As oil and gas exploration and development gradually move towards complex geological conditions such as deep and ultra-deep layers, the technical solution provided by the present invention can provide timely, rapid, and accurate formation acoustic wave travel time data, which helps to more precisely evaluate reservoir characteristics, identify pore fluid properties, estimate formation permeability, evaluate formation anisotropy, and identify fractures, etc.

[0069] (4) Since the data transmission volume is reduced and the processing speed is improved, the present invention enables the on-site logging operation to obtain the required data and make decisions in a shorter time, thus greatly improving the work efficiency and response speed. Description of the Drawings

[0070] Figure 1 Schematic diagram of the steps for real-time and rapid extraction of downhole acoustic wave travel time provided by the embodiment of the present invention;

[0071] Figure 2 Schematic diagram of the acoustic wave emission and reception measurement model provided by the embodiment of the present invention;

[0072] Figure 3 Full-wave train 8-channel original waveform diagram provided by the embodiment of the present invention;

[0073] Figure 4 Effect diagram of band-pass filtering and waveform discrete binarization processing of the full-wave train data provided by the embodiment of the present invention;

[0074] Figure 5 8-channel received waveform correlation coefficient two-dimensional contour map provided by the embodiment of the present invention;

[0075] Figure 6 8-channel received waveform correlation coefficient one-dimensional curve diagram provided by the embodiment of the present invention;

[0076] Figure 7 This is the full-wave train acoustic travel time curve diagram provided by the embodiments of the present invention.

[0077] Figure 8 This is a schematic structural diagram of a full-wave train acoustic logging device provided by the embodiments of the present invention; Specific embodiments

[0078] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope protected by the present invention. Embodiment

[0079] As Figure 1 shown, a method for real-time and rapid extraction of downhole acoustic travel time includes the following steps:

[0080] S1. Through a real-time and rapid extraction device for downhole acoustic travel time, record the acoustic waveform data of 8 channels of 8 receiving transducers at different formation depths;

[0081] S2. Perform band-pass filtering on the acoustic waveform data;

[0082] S3. Perform waveform discrete binary processing on the filtered waveform data, and retain the phase information of the waveform data;

[0083] S4. Perform cross-correlation calculation based on wave arrival time and time difference window on the 8-channel waveform data after waveform discrete binary processing to obtain two-dimensional correlation coefficients under different wave arrival times and time difference windows;

[0084] S5. According to the two-dimensional correlation coefficients, obtain one-dimensional correlation coefficients corresponding to different time differences;

[0085] S6. Search for the maximum value of the one-dimensional correlation coefficient to obtain the time difference values corresponding to the longitudinal wave, transverse wave, and Stoneley wave.

[0086] During the downhole movement of a single - shot and eight - receiver full - wave train acoustic logging tool, 8 - channel raw acoustic waveform data at different depth measurement points are collected. The sampling time interval for each waveform is \(t_s\), and the number of sampled data points is \(N_s\). First, given the band - pass frequency range \([f_1, f_2]\), the 8 - channel raw waveforms are subjected to band - pass filtering to remove the influence of interference signals. On this basis, the positive threshold \(PosThrehold\) and negative threshold \(NegThrehold\) of the acoustic signal are set, and the waveform amplitude is discretely binarized while preserving the phase information of the 8 - channel waveforms. Further, given the calculation range \([t_1, t_2]\) of the wave arrival time window and the calculation range \([s_1, s_2]\) of the time - difference window, cross - correlation calculations are performed on the 8 - channel discretely binarized waveforms to obtain the two - dimensional correlation coefficient regarding the wave arrival time and time - difference. The two - dimensional correlation coefficient is projected onto the time - difference axis to be converted into a one - dimensional correlation coefficient of the time - difference. Finally, the maximum value of the one - dimensional correlation coefficient is searched to obtain the time - difference values of different mode waves such as longitudinal waves, transverse waves, and Stoneley waves. Through the above data - processing steps, the acoustic time - difference information of the downhole formation can be quickly obtained.

[0087] As Figure 2 shown, it depicts the measurement model of the full - wave train acoustic logging device downhole. This measurement model consists of three parts: the borehole, the measurement device, and the formation. The transmitting transducer emits acoustic signals within a certain frequency range. The path it travels through is the borehole, the formation, and then back to the borehole. The acoustic signals are received by 8 receiving transducers. According to the difference in the propagation velocities of different mode waves, the acoustic signals received by the receiving transducers are the formation longitudinal wave \(V_p\), transverse wave \(V_s\), and Stoneley wave \(V_{st}\) in sequence. And as the transmit - receive distance increases, the arrival time of the received waveform increases. \(R_1\) receives the acoustic signal earliest, and \(R_8\) receives the acoustic signal latest.

[0088] Furthermore, the S2 band - pass filtering process includes the following steps:

[0089] Determine the operating frequency range \([f_1, f_2]\) of the transmitting transducer;

[0090] Perform Fourier transform on the time - domain acoustic signals of the receiving transducers to convert them into frequency - domain signals;

[0091] Perform band - pass filtering within the set frequency range \([f_1, f_2]\), retaining the signals within this frequency range and filtering out the interference signals outside the range;

[0092] Perform inverse Fourier transform on the frequency - domain signals after band - pass filtering to convert them back into time - domain signals.

[0093] Furthermore, S3 performs waveform discrete binarization on the filtered waveform data, including the following steps:

[0094] Set the positive threshold \(PosThrehold\) and negative threshold \(NegThrehold\);

[0095] Compare the actual amplitude of the acoustic wave signal with the set positive and negative thresholds;

[0096] When the amplitude of the acoustic wave signal is greater than the positive threshold, set the signal amplitude to +1;

[0097] When the amplitude of the acoustic wave signal is less than the negative threshold, set the signal amplitude to -1;

[0098] When the amplitude of the acoustic wave signal does not meet the above two cases, set the signal amplitude to 0.

[0099] Furthermore, the setting of the positive threshold PosThrehold and the negative threshold NegThrehold takes into account the minimum signal amplitude of the background noise, and its steps include:

[0100] Analyze the amplitude distribution of the background noise to determine the minimum signal amplitude of the background noise;

[0101] Based on the minimum signal amplitude of the background noise, set the positive threshold PosThrehold and the negative threshold NegThrehold.

[0102] In logging operations, the acoustic wave signal is usually interfered by background noise. To ensure the reliability of the signal, it is necessary to analyze the background noise of the acquired acoustic wave waveform data. The amplitude of the background noise is usually small, so the minimum signal amplitude can be determined by analyzing the amplitude distribution of the background noise. The setting of the positive and negative thresholds needs to consider the minimum signal amplitude to ensure that the signal and noise can be effectively distinguished and important signal features are not missed. The specific steps include: analyzing the waveform data after band-pass filtering, calculating the amplitude mean and standard deviation of the background noise. The amplitude of the background noise is usually small, so it can be used as a reference. Set the positive and negative thresholds according to the amplitude distribution of the background noise. The positive threshold is set to the background noise mean plus a certain multiple of the standard deviation, and the negative threshold is set to the background noise mean minus a certain multiple of the standard deviation. The formulas are: PosThreshold = μ + nσ and NegThreshold = μ - nσ; n is the multiple, usually 2 - 5. This can ensure that the threshold can effectively filter the noise and retain the key features of the signal.

[0103] Furthermore, the formula for the S3 waveform discrete binary processing is:

[0104] ; Formula 1;

[0105] Where:

[0106] Wave represents the waveform after band-pass filtering;

[0107] SWave represents the waveform after discrete binary processing;

[0108] i represents the serial number of the receiving transducer (i = 1, 2, …, 8);

[0109] j represents the number of sampling points of the waveform (j = 1, 2, …, Ns).

[0110] The waveform data SWave processed by Formula 1, whose signal amplitude will be represented by three values: 1, -1, and 0. This signal amplitude can be represented by 2-bit binary numbers, while the existing acoustic wave signal amplitude is represented by 16-bit binary numbers. Thus, the design of a 16-bit FPGA adder can be simplified to the design of a 2-bit FPGA adder for downhole real-time calculation. Compared with the design of a 16-bit FPGA adder for the real acoustic wave signal amplitude, the design of a 2-bit FPGA adder is more simplified, and at the same time, the calculation efficiency is significantly improved, and fewer chip resources are used.

[0111] Such as Figure 3 and 4 shown, respectively represent the original 8-channel waveform data collected by the full-wave train acoustic logging tool and the waveform curve after preprocessing such as band-pass filtering and waveform discrete binarization. As can be seen from Figure 4 it, the waveform amplitude has been limited, and only the phase information of the waveform is retained, converting the original floating-point waveform data into integer binarized data.

[0112] Further, the S4 cross-correlation calculation includes the following steps:

[0113] At different depth points, given a time difference range [s1, s2] and an arrival time range [t1, t2];

[0114] The outer loop traverses the time difference variable s, and the inner loop traverses the arrival time variable t. A time window is determined under a certain set of (s, t) loop variables;

[0115] In each determined time window, two-dimensional cross-correlation calculation is performed on the 8-channel received waveform data to obtain the two-dimensional correlation coefficient under this time window;

[0116] Repeat the above process until all possible combinations of time difference and arrival time variable values are traversed to complete the entire cross-correlation calculation process.

[0117] Further, the cross-correlation calculation formula is:

[0118] ; Formula 2;

[0119] Where:

[0120] R2D represents the two-dimensional correlation coefficient;

[0121] S[k] is the time difference sequence;

[0122] ;

[0123] s1 represents the starting time difference;

[0124] s2 represents the ending time difference;

[0125] ;

[0126] t1 represents the starting time of the calculation area;

[0127] t2 represents the ending time of the calculation area;

[0128] Twin represents the length of the calculation window;

[0129] int represents the integer operation;

[0130] ts represents the sampling time interval of each trace waveform.

[0131] Such as Figure 5 and 6 shown, respectively represent the two-dimensional correlation coefficient contour map and the one-dimensional correlation coefficient curve map obtained after the cross-correlation calculation of 8-channel discrete binary waves. The data of the 8-channel waveforms participating in the calculation are Figure 4 the waveform data in the parallelogram frame in . The solid frame and the dashed frame respectively represent the current calculation window and the next calculation window after moving one time step at a given time difference.

[0132] Furthermore, the calculation formula of the one-dimensional correlation coefficient R1D in S5 is:

[0133] ; Formula 3.

[0134] Among them, R1D(S[k]) represents the one-dimensional correlation coefficient corresponding to the time difference value. When calculating, each S[k] value is fixed, and the maximum value of the two-dimensional correlation coefficient corresponding to the range of m changes is calculated.

[0135] Furthermore, searching for the maximum value of the one-dimensional correlation coefficient in S6 includes: respectively setting the time difference search ranges for the longitudinal wave, transverse wave, and Stoneley wave. Among them, the longitudinal wave time difference range is [DTC1, DTC2], the transverse wave time difference range is [DTS1, DTS2], and the Stoneley wave time difference range is [DTST1, DTST2]; searching for the maximum value of the one-dimensional correlation coefficient within the time difference ranges corresponding to the longitudinal wave, transverse wave, and Stoneley wave, and selecting the time difference value corresponding to the local maximum value of the corresponding coefficient as the time difference values of the longitudinal wave, transverse wave, and Stoneley wave. The abscissa of the maximum value corresponds to the time difference values of the three mode waves propagating in the formation, that is, the longitudinal wave time difference, transverse wave time difference, and Stoneley wave time difference of the formation.

[0136] Furthermore, the setting of the time difference search ranges for the P-wave, S-wave, and Stoneley wave includes: generating the time difference search ranges for the P-wave, S-wave, and Stoneley wave based on the lithological parameters of the target formation.

[0137] Before logging operations, obtain the lithological parameters of the target formation through geological surveys, core analysis, or adjacent well data. These parameters include: rock type (such as sandstone, shale, carbonate rock, etc.), porosity range, rock density, mineral composition, formation pressure, and temperature. Based on the lithological parameters, combined with geophysical principles and experimental data, analyze the propagation characteristics of the P-wave, S-wave, and Stoneley wave in the target formation: Dense rocks: The acoustic wave propagation speed is relatively fast, and the time difference is relatively small. Rocks with high porosity: The acoustic wave propagation speed is relatively slow, and the time difference is relatively large. Rocks containing fluids: The time differences of the S-wave and Stoneley wave will vary due to the fluid properties (such as oil, gas, water).

[0138] According to the lithological parameters and acoustic wave propagation characteristics, set reasonable time difference search ranges for the P-wave, S-wave, and Stoneley wave respectively: P-wave time difference search range: Set the P-wave time difference range according to the density and porosity of the target formation. S-wave time difference search range: Set the S-wave time difference range according to the rigidity and pore structure of the rock. Stoneley wave time difference search range: Set the Stoneley wave time difference range according to the formation fluid properties and pore pressure. Combine historical data and adjacent well logging results to further optimize the time difference search range to ensure that the search range covers the actual time difference values of the target formation while reducing unnecessary computational effort. During the logging process, according to the set time difference search range, extract the time difference values of the P-wave, S-wave, and Stoneley wave in real time to provide reliable data for oil and gas reservoir evaluation.

[0139] As Figure 7 shown, given the measured waveform data, the downhole formation time difference curve is obtained by processing with the real-time rapid extraction method of downhole acoustic wave time difference. The first trace is the depth trace, and the second trace is the time difference curve. Based on Formula 3, a one-dimensional correlation coefficient curve regarding the time difference is obtained. By searching the one-dimensional curve, three maximum values can be obtained. The abscissas of the maximum values correspond to the time difference values of the three mode waves propagating in the formation, that is, the P-wave time difference, S-wave time difference, and Stoneley wave time difference of the formation.

[0140] Through the real-time rapid extraction method of downhole acoustic wave time difference, the requirement of real-time uploading of formation measurement information by downhole instruments during logging operations can be realized, providing reliable data for real-time rapid monitoring of downhole formation acoustic wave time difference data for deep oil and gas reservoir evaluation.

[0141] As Figure 8 shown, a real-time rapid extraction device for downhole acoustic wave time difference includes:

[0142] A single-shot - eight-receiver full-wave train acoustic logging tool, including a transmitting transducer T and eight receiving transducers R1 to R8;

[0143] The transmitting transducer T and the receiving transducers R1 to R8 are installed on a metal mandrel and arranged coaxially with the metal mandrel;

[0144] The receiving transducers R1 to R8 are all located on the same side of the transmitting transducer T. The distance between the first receiving transducer R1 and the transmitting transducer T is L, which serves as the source distance;

[0145] The distance between the receiving transducers R1 and R2 is S, and the distances between all eight receiving transducers are all S; that is, the distance between adjacent two receiving transducers is S;

[0146] The sound insulation body is used to isolate the acoustic signal directly propagating along the metal mandrel to the receiving transducer, ensuring that the received acoustic signal comes from the formation.

[0147] In this article, specific examples are used to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A real-time and rapid extraction method for downhole acoustic travel time, characterized in that, It includes the following steps: S1. Collect acoustic wave form data at different formation depths through an in - well acoustic wave slowness real - time rapid extraction device; S2. Perform band - pass filtering on the acoustic wave form data; S3. Perform waveform discrete binary processing on the filtered waveform data; S4. Perform cross - correlation calculation based on wave arrival time and slowness window on the waveform data after waveform discrete binary processing to obtain two - dimensional correlation coefficients under different wave arrival times and slowness windows; S5. Obtain one - dimensional correlation coefficients corresponding to different slownesses according to the two - dimensional correlation coefficients; S6. Search for the maximum value of the one - dimensional correlation coefficient to obtain the slowness values corresponding to the longitudinal wave, transverse wave, and Stoneley wave; Among them, the cross - correlation calculation in S4 includes the following steps: At different depth points, given a slowness range [s1, s2] and an arrival time range [t1, t2]; The outer loop traverses the slowness variable s, and the inner loop traverses the arrival time variable t. Determine a time window under a certain set of (s, t) loop variables; In each determined time window, perform two - dimensional cross - correlation calculation on the received waveform data to obtain the two - dimensional correlation coefficient under this time window; Repeat the above process until all possible combinations of slowness and arrival time variable values are traversed to complete the cross - correlation calculation process; The cross - correlation calculation formula is: Wherein: R2D represents the two - dimensional correlation coefficient; S[k] is the slowness sequence; ； s1 represents the starting slowness; s2 represents the ending slowness; ； τ[m] is the arrival time sequence; t1 represents the starting time of the calculation area; t2 represents the ending time of the calculation area; Twin represents the calculation window length; i represents the serial number of the receiving transducer; j represents the number of sampling points of the waveform; int represents the integer - taking operation; ts represents the sampling time interval of each channel waveform.

2. A real-time and rapid extraction method for downhole acoustic time difference according to claim 1, characterized in that The band - pass filtering pre - processing in S2 includes: Set the working frequency range [f1, f2] of the transmitting transducer; Transform the time - domain acoustic wave signal of the receiving transducer into a frequency - domain signal; Perform band - pass filtering processing within the set frequency range [f1, f2], retain the signals within this frequency range and filter out the interference signals outside the range; Perform inverse transformation on the frequency - domain signal after band - pass filtering processing to convert it back to the time - domain signal.

3. A real-time fast extraction method for downhole acoustic travel time according to claim 2, characterized in that The waveform discrete binary pre - processing in S3 includes: Set the positive threshold PosThrehold and the negative threshold NegThrehold; ； Among them: Wave represents the waveform after band - pass filtering; SWave represents the discrete binary waveform; i represents the serial number of the receiving transducer; j represents the number of sampling points of the waveform; Compare the actual amplitude of the acoustic wave signal with the set positive and negative thresholds; When the amplitude of the acoustic wave signal is greater than the positive threshold, set the signal amplitude to + 1; When the amplitude of the acoustic wave signal is less than the negative threshold, set the signal amplitude to - 1; When the amplitude of the acoustic wave signal does not meet the above two situations, set the signal amplitude to 0.

4. A real-time and fast extraction method for downhole acoustic time difference according to claim 3, characterized in that The setting of the positive threshold PosThrehold and the negative threshold NegThrehold considers the minimum signal amplitude of the background noise, and its steps include: Analyze the amplitude distribution of the background noise to determine the minimum signal amplitude of the background noise; Set the positive threshold PosThrehold and the negative threshold NegThrehold based on the minimum signal amplitude of the background noise.

5. A real-time and rapid extraction method for downhole acoustic travel time according to claim 1, characterized in that The calculation formula for the one-dimensional correlation coefficient R1D in S5 is: R1D(S[k]) = max(R2D(S[k], [m])); Among them, R1D(S[k]) represents the one-dimensional correlation coefficient corresponding to the time difference value. When calculating, fix each S[k] value and calculate the maximum value of the two-dimensional correlation coefficient within the range of m changes.

6. A real-time and fast extraction method for downhole acoustic travel time according to claim 1, characterized in that S6 Searching for the maximum value of the one-dimensional correlation coefficient includes: Set the time difference search ranges for the longitudinal wave, transverse wave, and Stoneley wave respectively; Search for the maximum value of the one-dimensional correlation coefficient within the time difference ranges corresponding to the longitudinal wave, transverse wave, and Stoneley wave, and select the time difference value corresponding to when the corresponding coefficient reaches the local maximum as the time difference values of the longitudinal wave, transverse wave, and Stoneley wave.

7. A real-time and rapid extraction method for downhole acoustic travel time according to claim 1, characterized in that The setting of the time difference search ranges for the longitudinal wave, transverse wave, and Stoneley wave includes: Generate the time difference search ranges for the longitudinal wave, transverse wave, and Stoneley wave based on the lithology parameters of the target formation.

8. A downhole acoustic time difference real-time and rapid extraction device for the downhole acoustic time difference real-time and rapid extraction method according to any one of claims 1-7, characterized in that, Include: A full-wave acoustic logging tool, which includes a transmitting transducer and several receiving transducers; The transmitting transducer and several receiving transducers are installed on the metal mandrel and arranged coaxially with the metal mandrel; Several receiving transducers are all on the same side of the transmitting transducer, and the distance between the first receiving transducer and the transmitting transducer is L; The spacing between adjacent two receiving transducers is all S; An acoustic isolation body, which is used to isolate the acoustic signal directly propagating along the metal mandrel to the receiving transducer. The acoustic isolation body is installed on the metal rod between the first receiving transducer and the transmitting transducer.

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