CN112558159A - Acoustic logging first arrival picking method - Google Patents
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Abstract
The invention discloses a sound wave logging first arrival picking method, which is characterized in that based on collected logging wave train data, on the basis of band-pass filtering and fuzzy nested multistage median filtering denoising, a dynamic self-adaptive first arrival picking range is obtained through time difference obtained by an STC method, and a waveform enveloping energy ratio method is adopted in the range to obtain a sound wave first arrival. The method can accurately pick up the sound wave first arrival of the stratum with low signal-to-noise ratio and serious attenuation and the stratum sections with thin interbed and interlayer development.
Description
Technical Field
The invention belongs to the field of geophysical logging, and relates to a sound wave logging first arrival picking method.
Background
In the process of processing acoustic data, accurate pickup of first arrival time is the basis of establishment of a rock physical model and is also an important parameter of longitudinal wave radial velocity imaging based on ray tracing. In recent years, many research results are obtained for predecessors of first-arrival picking methods, and georgel et al propose a mesh subdivision slowness model to perform first-arrival travel time calculation from four directions (CN 110568497A); the method comprises the following steps that a schargent slope and the like obtain a plurality of first arrivals through an energy ratio method, calculate first arrival confidence coefficients on the basis, and select the first arrival path with high confidence coefficient to perform fitting to obtain a first arrival predicted value (CN 102313901A); xukixiang provides a first-motion wave travel time picking method and a device, and the device extracts first-motion wave travel time (CN103616722A) in a processing time window of each seismic channel on a given initial reference line according to a calculated characteristic function value. Tomb et al have proposed a wave envelope-based anisotropic medium ultrasonic velocity automatic calculation method, obtain possible envelope first-break time according to solving its minimum value, set up the first-break screening condition of the wave packet, pick up the first-break time of the wave packet automatically (CN 106525979A); the method comprises the steps of providing a full-automatic seismic wave first arrival picking algorithm, calculating a reliability factor of a first arrival by analyzing the energy ratio difference between an accurate first arrival and an inaccurate first arrival, quantifying the change trend of the energy ratio, and setting reliability threshold value constraint to obtain the first arrival (CN 108072896A); li inspired to propose a long-short time mean ratio method for automatically picking up p-wave arrival times (CN201710842194.3), Heile proposed to adopt an improved energy ratio first arrival picking algorithm to calculate the first arrival wave position (CN110045417A) of each seismic channel in a template-based range band, Liuxin et al proposed a window fractional dimension method, Zhang et proposed a fractal box dimension method, Yuelong et al proposed a first arrival picking method based on time-frequency analysis; in recent years, with the popularity of artificial intelligence and intelligent image processing technology, artificial neural network methods and first-arrival wave picking methods based on image processing have become hot of research.
Comprehensively analyzing the existing sound wave first arrival picking algorithm, and finding that the current first arrival picking technology at least has the following problems: 1. the influence of signal-to-noise ratio is very obvious; 2. the accuracy of first arrival picking up is not high in stratum sections with serious attenuation of acoustic signals such as coal seams, diameter expansion and soft strata; 3. the difficulty in identifying stratum sections with low signal resolution such as thin interbed and interlayer is high.
Disclosure of Invention
The invention aims to provide a sound wave logging first arrival picking method which can more accurately pick sound waves of weak signal stratum sections with serious attenuation and stratum sections with low signal resolution such as thin interbed and interlayer on the basis of eliminating random noise and reducing the influence of signal to noise ratio.
The invention is realized by the following technical scheme:
a sonic logging first arrival pickup method, comprising:
s1, performing acoustic logging to obtain wave train data;
s2, comprehensively analyzing waveform distribution characteristics and frequency spectrum distribution characteristics of the wave train data, and determining the distribution range of the wave train effective signals in the wave train data in a time domain and a frequency domain;
s3, sequentially performing band-pass filtering and fuzzy nested multilevel median filtering on the frequency section of the wave train data in the frequency domain distribution range to eliminate noise signals;
s4, solving a sound wave time difference curve of the wave train data after the noise signal is eliminated by adopting an STC method; calculating a self-adaptive first arrival picking range based on the obtained sound wave time difference curve;
and S5, calculating a dynamic first arrival picking range on the basis of S4, and calculating a sound wave first arrival by adopting a waveform envelope energy ratio method in the dynamic first arrival picking range.
Preferably, in S1, the wave train logging is digital sonic logging, variable density sonic logging or array sonic logging.
Preferably, S2 is specifically: carrying out waveform distribution characteristic analysis on the wavetrain data, and determining the distribution range of the wavetrain effective signals in the wavetrain data in a time domain and the distribution range of longitudinal waves; and carrying out Fourier transform on the wavetrain data to obtain a spectrogram, analyzing the spectral distribution characteristics of the wavetrain data according to the spectrogram, and determining the distribution range of the wavetrain effective signals in the wavetrain data in a frequency domain.
Preferably, the fuzzy nested multistage median filtering in S3 is specifically: setting the waveform signal of the wave train data as x (t), Y (t) as a signal sequence of x (t) which is ordered from small to large, and alpha (t) as the middle value of Y (t);
the long filter length filtering results are:
the short filter length filtering results are:
the setting method of the threshold value comprises the following steps:
in the formula (3), m is
The total number of (2), C, represents the average of the amplitudes of the output of the wave train data after being filtered by the long filter.
Then fuzzy nested multistage median filtering outputs signals: when in use
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
preferably, S4 finds the adaptive first arrival picking range, and the specific method is as follows:
S＝Δt×(LR+n×LP)/ts-WF+LP×Δt×n/ts (4)
E＝Δt×(LR+n×LP)/ts+WR+LP×Δt×n/ts (5)
s is a first arrival picking starting position; e is a first arrival picking end position; delta t is the sound wave time difference obtained by STC; l isRIs the source distance; l isPIs the receiving track pitch; n is the track number (0, 1.. 7); t is tsIs the sampling rate; wFThe energy of the front and rear time windows of the waveform envelope is longer than that of the front time window; wRThe energy of the front and rear time windows of the waveform envelope is longer than that of the rear time window.
Preferably, S5 specifically includes:
s5.1, calculating a dynamic first arrival picking range on the basis of S4;
s5.2, solving a waveform envelope signal of the wave train data in the dynamic first arrival picking range;
s5.3, solving the energy ratio of the time windows before and after the waveform envelope based on the waveform envelope signal;
and S5.4, solving the first wave first arrival TT of the wave train data based on the energy ratio of the time windows before and after the waveform envelope.
Further, S5.1 specifically is: and repeating the step S4 to obtain the dynamic first arrival picking range on the basis of the sound wave time difference obtained by the STC.
Further, S5.2 specifically is: let the waveform signal of the wave train data be x (t),
hilbert transform for x (t);
the waveform envelope signal is:
further, S5.3 specifically is: the energy ratio of the time window before and after the waveform envelope is
Wherein the content of the first and second substances,
is a relative energy of the wave train, where N is the number of points of the wave train.
Further, S5.2 specifically is: the energy ratio R of the time window before and after the envelope of the waveform is maximummaxThe time point t corresponding to the time plus the energy of the time window before and after the waveform envelope is longer than the time window before, namely the first arrival point, and the first arrival point is multiplied by the sampling rate tsAnd obtaining the first wave first arrival TT.
Compared with the prior art, the invention has the following beneficial technical effects:
on the basis of band-pass filtering and fuzzy nested multistage median filtering denoising, the sound wave time difference is obtained by adopting an STC method, a dynamic self-adaptive first arrival picking range is obtained according to the obtained sound wave time difference, and a waveform envelope energy ratio is adopted in the obtained range to obtain a sound wave first arrival. The waveform envelope energy ratio is based on Hilbert transform, the waveform envelope energy ratio can play a role in amplifying sound waves, the effect of first arrival on weak signal stratums with serious attenuation is better, but the influence of random noise is large, and the stability is poor. The STC method has good stability but low identification precision in weak signal stratum sections with serious attenuation, thin interbed sections and interlayer sections. The invention fully combines the advantages of the two, obtains the dynamic self-adaptive first arrival picking range through the sound wave time difference obtained by the STC method, obtains the first arrival by adopting the waveform envelope energy ratio on the basis, and reduces the artificial influence while simplifying the operation; in addition, the invention also adopts band-pass filtering and fuzzy nested multistage median filtering to preprocess the original sound wave signal, and eliminates the noise signal under the condition of keeping the effective signal as much as possible. Compared with the traditional waveform envelope energy ratio method, the method has better stability and small influence of signal-to-noise ratio, and compared with the STC method widely applied to actual processing and interpretation software, the method can more accurately pick up the sound wave first arrivals of weak signal stratum sections with serious attenuation, thin interbed sections with low resolution and high identification difficulty and interlayer development sections, and ensure that the sound wave first arrivals capable of truly reflecting corresponding stratum information are obtained.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 illustrates a distribution characteristic and a spectrum diagram of an acoustic waveform according to an embodiment of the present invention;
FIG. 3 illustrates a first arrival acoustic log pick according to an embodiment of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to specific examples, which are intended to be illustrative, but not limiting, of the invention.
Based on the collected logging wave train data, on the basis of band-pass filtering and fuzzy nested multistage median filtering denoising, a dynamic self-adaptive first arrival picking range is obtained through a time difference obtained by an STC method, and a waveform enveloping energy ratio method is adopted in the range to obtain a sound wave first arrival. The method comprises the following steps:
performing wave train logging, such as digital acoustic logging, variable density acoustic logging, array acoustic logging and the like to obtain wave train data;
step two, carrying out comprehensive analysis on waveform distribution characteristics and frequency spectrum distribution characteristics of the wave train data, and determining the distribution range of the wave train effective signals in a time domain and the distribution range of a frequency domain;
thirdly, selecting a proper frequency section for band-pass filtering according to the distribution range of the effective signal of the wave train obtained in the second step in the frequency domain, and eliminating noise caused by frequency; carrying out fuzzy nested multistage median filtering on the wave train data obtained by the band-pass filtering, and eliminating noise signals under the condition of keeping effective signals as much as possible;
step four, solving a sound wave time difference curve of the denoised wave train data by adopting an STC (slowness-time correlation method); calculating a self-adaptive first arrival picking range based on the obtained sound wave time difference curve;
and step five, calculating a dynamic range of signal first arrival pickup on the basis of the step four, and calculating the sound wave first arrival by adopting a waveform envelope energy ratio method in the dynamic range.
Examples
As shown in fig. 1, the first arrival pickup method for acoustic logging of the present invention has the following specific implementation processes:
performing acoustic logging, such as digital acoustic logging, variable density acoustic logging, array acoustic logging and the like, to obtain wave train data;
secondly, performing waveform distribution characteristic analysis on the wave train data to determine the approximate distribution range of the wave train effective signals in a time domain and the approximate distribution range of longitudinal waves; and carrying out Fourier transform on the wavetrain data to obtain a spectrogram, and analyzing the spectral distribution characteristics of the wavetrain data to determine the distribution range of the wavetrain effective signals in the frequency domain. See figure 2.
Thirdly, performing band-pass filtering according to the distribution range of the effective wave train signals obtained in the second step in the frequency domain to eliminate noise signals caused by frequency; and carrying out fuzzy nested multistage median filtering on the obtained wave train data. The fuzzy nested multistage median filtering principle is that a threshold value is designed as a judgment parameter, so that a long filter is adopted for filtering when random noise is eliminated, and a short filter is adopted for filtering when effective information is reserved, so that the random noise can be eliminated well, and the effective information can be not damaged to the maximum extent. Let the waveform signal of the wave train data be x (t), Y (t) be the signal sequence of x (t) ordered from small to large, and alpha (t) be the middle value of Y (t).
The long filter length filtering results are:
the short filter length filtering results are:
the setting method of the threshold value comprises the following steps:
in the formula (3), m is
The total number of the cells. C represents the average of the amplitudes of the output of the wave train data after being filtered by the long filter.
Outputting a signal: when in use
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
and fourthly, solving the sound wave time difference delta t of the denoised wave train data through an STC (slowness-time correlation method), and solving the self-adaptive first arrival picking range on the basis of the sound wave time difference delta t obtained by the STC method. The specific method comprises the following steps:
S＝Δt×(LR+n×LP)/ts-WF+LP×Δt×n/ts (4)
E＝Δt×(LR+n×LP)/ts+WR+LP×Δt×n/ts (5)
s is a first arrival picking starting position; e is a first arrival picking end position; delta t is the sound wave time difference obtained by an STC method; l isRIs the source distance; l isPIs the receiving track pitch; n is the track number (0, 1.. 7); t is tsIs the sampling rate; wFThe energy of the front and rear time windows of the waveform envelope is longer than that of the front time window; wRThe energy of the front and rear time windows of the waveform envelope is longer than that of the rear time window;
and step five, changing the traditional static picking range into a dynamic picking range by taking the first arrival calculated by the STC as a constraint, and calculating the first arrival time of the first wave by adopting a waveform envelope energy ratio method on the basis. The specific method comprises the following steps:
1) finding dynamic first arrival picking range
And (3) repeating the step (4) on the basis of the sound wave time difference obtained by the STC to obtain a dynamic first arrival picking range, changing the traditional static picking range into the dynamic picking range to restrict the picking of the first arrival, and solving the advantage of good stability of the sound wave time difference based on the STC method, wherein the dynamic range can greatly improve the problem of poor stability of the energy ratio method.
2) Evaluating a waveform envelope signal
Let the waveform signal of the wave train data be x (t),
the hilbert transform is x (t), and an analysis signal formed by hilbert only contains positive frequency components and is 2 times of the positive frequency components of the original signal, so that the waveform amplitude envelope energy of the acoustic wave signal is stronger than the amplitude energy, the seismic wave signal actually plays a role in amplifying seismic waves, and the weak first arrival recognition effect is better.
The waveform envelope signal is:
3) energy ratio of front time window and rear time window of waveform envelope is calculated
The energy ratio of the time window before and after the waveform envelope is
Wherein
Is a relative energy of the wave train, where N is the number of points of the wave train.
4) Finding first arrival TT of array acoustic wave
The energy ratio R of the time window before and after the envelope of the waveform is maximummaxThe time point t corresponding to the time plus the energy of the time window before and after the waveform envelope is longer than the time window before, namely the first arrival point, and the first arrival point is multiplied by the sampling rate tsThe first arrival TT of the first wave can be obtained. Namely:
TT＝(t+WF)*ts (8)
the result is shown in fig. 3, compared with the characteristic that the conventional waveform envelope energy ratio method is greatly influenced by random noise and has poor first-arrival stability, the method has better stability in the first-arrival picking of the whole well section; compared with the first arrivals obtained by an STC method widely applied to actual processing and interpretation software, the method has the advantage that the sound wave first arrival pick-up is more accurate in low-resolution stratum sections such as severely attenuated weak signal stratum sections (3607m-3613m), thin interbedders (3591m-3599m), and interlayers (3573m-3579 m).
The present invention is not limited to the above-mentioned embodiments, and based on the technical solutions disclosed in the present invention, those skilled in the art can make some substitutions and modifications to some technical features without creative efforts according to the disclosed technical contents, and these substitutions and modifications are all within the protection scope of the present invention.
Claims (10)
1. A sonic logging first arrival pickup method, comprising:
s1, performing acoustic logging to obtain wave train data;
s2, comprehensively analyzing waveform distribution characteristics and frequency spectrum distribution characteristics of the wave train data, and determining the distribution range of the wave train effective signals in the wave train data in a time domain and a frequency domain;
s3, sequentially performing band-pass filtering and fuzzy nested multilevel median filtering on the frequency section of the wave train data in the frequency domain distribution range to eliminate noise signals;
s4, solving the acoustic wave time difference of the wavetrain data after the noise signal is eliminated by adopting an STC method; calculating a self-adaptive first arrival picking range based on the obtained sound wave time difference;
and S5, calculating a dynamic first arrival picking range on the basis of S4, and calculating a sound wave first arrival by adopting a waveform envelope energy ratio method in the dynamic first arrival picking range.
2. The sonic logging first arrival pickup method of claim 1 wherein at S1 the wavetrain logging is digital sonic logging, variable density sonic logging or array sonic logging.
3. The sonic logging first arrival pickup method of claim 1, wherein S2 is embodied as: carrying out waveform distribution characteristic analysis on the wavetrain data, and determining the distribution range of the wavetrain effective signals in the wavetrain data in a time domain and the distribution range of longitudinal waves; and carrying out Fourier transform on the wavetrain data to obtain a spectrogram, analyzing the spectral distribution characteristics of the wavetrain data according to the spectrogram, and determining the distribution range of the wavetrain effective signals in the wavetrain data in a frequency domain.
4. The sonic logging first arrival pickup method of claim 1 wherein the fuzzy nested multistage median filtering in S3 is embodied as: setting the waveform signal of the wave train data as x (t), Y (t) as a signal sequence of x (t) which is ordered from small to large, and alpha (t) as the middle value of Y (t);
the long filter length filtering results are:
the short filter length filtering results are:
the setting method of the threshold value comprises the following steps:
in the formula (3), m is
C represents the average value of the amplitude output after the wavetrain data is filtered by the long filter;
then fuzzy nested multistage median filtering outputs signals: when in use
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
5. the sonic logging first arrival pick-up method of claim 1 wherein S4 finds an adaptive first arrival pick-up range by:
S＝Δt×(LR+n×LP)/ts-WF+LP×Δt×n/ts (4)
E＝Δt×(LR+n×LP)/ts+WR+LP×Δt×n/ts (5)
s is a first arrival picking starting position; e is a first arrival picking end position; delta t is the sound wave time difference obtained by STC; l isRIs the source distance; l isPIs the receiving track pitch; n is the track number (0, 1.)..7)；tsIs the sampling rate; wFThe energy of the front and rear time windows of the waveform envelope is longer than that of the front time window; wRThe energy of the front and rear time windows of the waveform envelope is longer than that of the rear time window.
6. The sonic logging first arrival pickup method of claim 1 wherein S5 specifically comprises:
s5.1, calculating a dynamic first arrival picking range on the basis of S4;
s5.2, solving a waveform envelope signal of the wave train data in the dynamic first arrival picking range;
s5.3, solving the energy ratio of the time windows before and after the waveform envelope based on the waveform envelope signal;
and S5.4, solving the first wave first arrival TT of the wave train data based on the energy ratio of the time windows before and after the waveform envelope.
7. The acoustic logging first arrival pickup method according to claim 6, wherein S5.1 is specifically: and repeating the step S4 to obtain the dynamic first arrival picking range on the basis of the sound wave time difference obtained by the STC.
8. The acoustic logging first arrival pickup method according to claim 6, wherein S5.2 is specifically: let the waveform signal of the wave train data be x (t),
hilbert transform for x (t);
the waveform envelope signal is:
9. the acoustic logging first arrival pickup method according to claim 6, wherein S5.3 is specifically: the energy ratio of the time window before and after the waveform envelope is
Wherein the content of the first and second substances,
is a relative energy of the wave train, where N is the number of points of the wave train.
10. The acoustic logging first arrival pickup method according to claim 6, wherein S5.2 is specifically: the energy ratio R of the time window before and after the envelope of the waveform is maximummaxThe time point t corresponding to the time plus the energy of the time window before and after the waveform envelope is longer than the time window before, namely the first arrival point, and the first arrival point is multiplied by the sampling rate tsAnd obtaining the first wave first arrival TT.
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| WO2023184055A1 (en) * | 2022-03-28 | 2023-10-05 | Saudi Arabian Oil Company | A method and system for determining first breaks of sonic waveforms |
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