CN111624657B - Wavelet extraction method for full waveform inversion technology - Google Patents
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- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/364—Seismic filtering
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/20—Trace signal pre-filtering to select, remove or transform specific events or signal components, i.e. trace-in/trace-out
- G01V2210/21—Frequency-domain filtering, e.g. band pass
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Abstract
The invention discloses a method for extracting wavelets for a full waveform inversion technology, which comprises the following steps: extracting data; forward modeling; calculating an average digital filter; obtaining a primary extracted wavelet through convolution operation; and extracting the final wavelet. The wavelet extracting method for the full waveform inversion technology, disclosed by the invention, extracts wavelets by utilizing observation data and forward data, is matched with the full waveform inversion technology, meets the full waveform inversion requirement, has a simple wavelet extracting process and a stable algorithm, and can reduce the risk of waveform dislocation in the application process of the full waveform inversion.
Description
Technical Field
The invention relates to the field of oil-gas exploration, in particular to a method for extracting wavelets for a full-waveform inversion technology.
Background
Wave propagation in a subsurface fluid porous medium is one of core research contents in the field of oil and gas exploration, and the wave propagation speed in a high-precision medium is an important parameter for constructing a low-frequency model and seismic data reservoir inversion, and can be used for explaining lithology and physical properties of the fluid porous medium, so that guidance is provided for indicating an oil and gas sweet spot. In recent years, in the process of forward modeling data to approach real data, full waveform inversion technology is realizing that an initial model increasingly approaches a real model. The full-waveform inversion technology simultaneously utilizes amplitude information and phase information of seismic data, and improves the precision of the propagation velocity parameter of the underground medium through inversion.
The application process of the full waveform inversion technology is a process of data matching of forward data and observation data, and wavelet extraction is the core in the process. The matching accuracy of forward data and observation data in the full waveform inversion process is determined by the wavelet extraction effect, waveform dislocation can easily occur in full waveform inversion due to poor wavelets, if the phase difference between the forward data and the observation data is more than half wavelength, inversion is directly caused to be locally minimum, and inversion is finished in failure.
In recent years, many scholars make a lot of research on the aspect of the traditional wavelet extraction method, but the existing wavelet extraction methods are not developed for a full waveform inversion technology and cannot be used for the full waveform inversion technology, and even if the full waveform inversion is applied, the full waveform inversion is easy to have higher waveform dislocation risk in the application process, so that the actual application effect is poor.
Disclosure of Invention
The invention aims to provide a wavelet extraction method for a full waveform inversion technology, which is used for reducing the risk of waveform dislocation in the application process of full waveform inversion.
The invention provides a method for extracting wavelets for a full waveform inversion technology, which comprises the following steps:
extracting data: extracting a shot set from data to be extracted, and extracting data in a rectangular window from a small-to-medium offset distance part of the shot set to enable the rectangular window to contain direct waves so as to obtain an observation data subset d;
forward modeling: by means of Ricker wavelets with lower dominant frequencies, forward modeling is carried out by a forward modeling module of full waveform inversion software to obtain forward modeling data, the forward modeling data are consistent with observation data in size and have the same structure, data extraction is carried out from the forward modeling data strictly according to the data extraction mode of the observation data, and a forward modeling data subset p which is consistent with the observation data subset in size and has the same structure is obtained;
calculating an average digital filter;
obtaining preliminary extraction wavelets through convolution operation: performing convolution by using an average digital filter and the Ricker wavelet to obtain a primary extracted wavelet;
extracting final wavelets: and (3) carrying out low-pass filtering on the preliminary extracted wavelet by using a FILTER module in seismic processing software 'ParadigmTM-2011.3-EchosTM', so as to obtain a final extracted wavelet.
Further, the method for preprocessing the observation data before extracting the data comprises the following steps:
step S1011: firstly, suppressing random noise by using an AMPSCAL amplitude balance noise suppression module in seismic processing software 'ParadigmTM-2011.3-EchosTM'; then, a SUPPRES band-limited noise attenuation module in seismic processing software 'ParadigmTM-2011.3-EchosTM' is used for suppressing nonlinear noise;
step S1012: suppressing low-frequency linear noise by using a LEAF linear noise suppression module in seismic processing software 'ParadigmTM-2011.3-EchosTM';
step S1013: firstly, realizing the band-pass filtering of seismic data by using a FILTER module in seismic processing software 'ParadigmTM-2011.3-EchosTM'; and then, the MUTE module in the seismic processing software 'ParadigmTM-2011.3-EchosTM' is used for realizing the cutting operation of the seismic data, and the OFFSET and TIME parameters are used for cutting the residual noise to obtain the data to be extracted.
Further, the averaging digital filter comprises the following steps:
step S1041: for each pair of single tracks d in the observation data subset d and the forward data subset p i And p i Calculating a digital filter w i Such that the convolution of the forward single pass data and the single pass digital filter equals the single pass observed data, i.e.
i is a track number of the track,
is a convolution symbol;
step S1042: repeating the step S1041, and calculating each digital filter in sequence until the digital filters corresponding to all the channels of the data subset are calculated;
step S1043: and performing arithmetic mean calculation on all the digital filters to obtain a mean digital filter.
The invention has the following advantages:
the method for extracting the wavelets for the full waveform inversion technology, disclosed by the invention, extracts the wavelets by utilizing the observation data and the forward data, is in accordance with the full waveform inversion technology, meets the full waveform inversion requirement, has a simple wavelet extraction process and a stable algorithm, and can reduce the risk of waveform dislocation in the application process of the full waveform inversion.
Detailed Description
Example 1
Embodiment 1 provides a method for extracting wavelets for a full waveform inversion technique, the method for extracting wavelets including the steps of:
step S101: and (5) preprocessing observation data, and removing nonlinear noise, linear noise, low-frequency data and high-frequency data to obtain data to be extracted.
The following data preprocessing work is carried out on the observation data, and the data preprocessing work comprises the following steps:
step S1011: nonlinear noise suppression, removing nonlinear noise;
specifically, the method comprises the following steps: firstly, suppressing random noise by using an AMPSCAL amplitude balance noise suppression module in seismic processing software 'ParadigmTM-2011.3-EchosTM'; and then, a SUPPRES band-limited noise attenuation module in seismic processing software 'ParadigmTM-2011.3-EchosTM' is used for suppressing nonlinear noise.
Step S1012: linear noise suppression is carried out to remove linear noise;
specifically, the method comprises the following steps: low frequency linear noise is suppressed using a LEAF linear noise suppression module in the seismic processing software "ParadigmTM-2011.3-EchosTM".
Step S1013: filtering and cutting off the seismic data to remove low-frequency data and high-frequency data;
specifically, the method comprises the following steps: firstly, realizing the band-pass filtering of seismic data by using a FILTER module in seismic processing software 'ParadigmTM-2011.3-EchosTM'; and then, the MUTE module in the seismic processing software 'ParadigmTM-2011.3-echo TM' is used for realizing the cutting operation of the seismic data, and the OFFSET and TIME parameters are used for cutting the residual noise to obtain the data to be extracted.
Step S102: extracting data from the data to be extracted to obtain an observation data subset d
Specifically, a shot set is extracted from the data to be extracted, and then data in a rectangular window is extracted from the small-to-medium offset distance part of the shot set, so that the rectangular window comprises direct waves, and an observation data subset d is obtained.
Step S103: forward modeling data and extracting the data to obtain a forward modeling data subset p;
by means of Ricker wavelets with lower dominant frequencies, forward modeling is carried out by a forward modeling module of full waveform inversion software, forward modeling data are obtained, and the forward modeling data are consistent with observation data in size and identical in structure. And data extraction is carried out from the forward data strictly according to the data extraction mode of the observation data, and a forward data subset p which is consistent with the observation data subset in size and has the same structure is obtained.
Step S104: the method for solving the average digital filter specifically comprises the following steps:
step S1041: computing single-channel digital filter
The calculation method of the single-channel digital filter comprises the following steps: for each pair of single tracks d in the observation data subset d and the forward data subset p i And p i Calculating a digital filter w i Such that the convolution of the forward single pass data and the single pass digital filter equals the single pass observed data, i.e.
i is the track number of the track,
convolution symbols.
Step S1042: calculating a multi-channel digital filter
The method comprises the following specific steps: repeating the step S1041, and calculating each digital filter in sequence until the digital filters corresponding to all the channels of the data subset are calculated;
step S1043: calculating average digital filter
Performing arithmetic mean calculation on all the digital filters to obtain a mean digital filter w;
step S105: obtaining preliminary extraction wavelet through convolution operation
Convolution using an averaging digital filter and the Ricker wavelet, i.e.
And obtaining the primary extracted wavelet.
Step S106: extracting final wavelets
And (3) carrying out low-pass filtering on the preliminary extracted wavelet by using a FILTER module in seismic processing software 'ParadigmTM-2011.3-EchosTM', so as to obtain a final extracted wavelet.
Example 2
For a set of actual observations, embodiment 2 provides a method of extracting wavelets from such actual observations, the method comprising the steps of:
step S201: preprocessing data, removing nonlinear noise, linear noise, low frequency data and high frequency data to obtain data to be extracted
(1) Pressing nonlinear noise to remove the nonlinear noise;
setting a FACTOR parameter as 2, and pressing random noise by using an AMPSCAL amplitude balance noise pressing module in seismic processing software 'ParadigmTM-2011.3-EchosTM'.
The FEND parameter is set to 15, and the SUPPRES band-limited noise attenuation module in the seismic processing software 'ParadigmTM-2011.3-EchosTM' is used for suppressing nonlinear noise.
(2) Linear noise suppression is carried out to remove linear noise;
parameters F1, F2 and VEL are set to be 2,15 and 1250 respectively, and low-frequency linear noise is suppressed by using a LEAF linear noise suppression module in seismic processing software 'ParadigmTM-2011.3-EchosTM'.
(3) Filtering and cutting off the seismic data, and removing low-frequency data and high-frequency data;
setting parameters F1, F2, F3 and F4 as 2.5,5, 20 and 28 respectively, and realizing the band-pass filtering of the seismic data by using a FILTER module in seismic processing software 'ParadigmTM-2011.3-EchosTM';
the method comprises the steps of utilizing OFFSET and TIME parameters to cut residual noise, utilizing MUTE modules in seismic processing software 'ParadigmTM-2011.3-EchosTM' to achieve seismic data cutting operation, and utilizing OFFSET and TIME parameters to cut residual noise.
Repeating the steps S102 to S106 of the embodiment 1 to obtain the wavelet extracted from the actual data of a certain area by the full waveform inversion technique, wherein the wavelet has a good effect in the full waveform inversion.
Although the invention has been described in detail with respect to the general description and the specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (2)
1. A method for extracting wavelets for use in full waveform inversion techniques, said method comprising the steps of:
extracting data: extracting a shot set from data to be extracted, and extracting data in a rectangular window from a small-to-medium offset distance part of the shot set to enable the rectangular window to contain direct waves so as to obtain an observation data subset d;
forward modeling: by means of Ricker wavelets with lower dominant frequencies, forward modeling is carried out by a forward modeling module of full waveform inversion software to obtain forward modeling data, the forward modeling data are consistent with the observation data in size and have the same structure, data extraction is carried out from the forward modeling data strictly according to the data extraction mode of the observation data, and a forward modeling data subset p which is consistent with the observation data subset in size and has the same structure is obtained;
calculating an average digital filter;
obtaining a primary extraction wavelet through convolution operation: performing convolution by using an average digital filter and the Ricker wavelet to obtain a primary extracted wavelet;
extracting final wavelets: performing low-pass filtering on the preliminary extracted wavelet by using a FILTER module in seismic processing software 'ParadigmTM-2011.3-EchosTM', and obtaining a final extracted wavelet;
the step of evaluating the digital filter comprises the steps of:
step S1041: for each pair of single-track d in the observation data subset d and the forward data subset p i And p i Calculating a digital filter w i Such that the convolution of the forward single pass data and the single pass digital filter equals the single pass observed data, i.e.
i is the track number of the track,
is a convolution symbol;
step S1042: repeating the step S1041, and calculating the digital filters corresponding to each channel in sequence until the digital filters corresponding to all channels of the data subset are calculated;
step S1043: and performing arithmetic mean calculation on all the digital filters to obtain a mean digital filter.
2. The method of extracting wavelets usable with full waveform inversion techniques according to claim 1 wherein said extracting data is preceded by the steps of:
step S1011: firstly, suppressing random noise by using an AMPSCAL amplitude balance noise suppression module in seismic processing software 'ParadigmTM-2011.3-EchosTM'; then, a SUPPRES band-limited noise attenuation module in seismic processing software 'ParadigmTM-2011.3-echo' is utilized to SUPPRESs nonlinear noise;
step S1012: suppressing low-frequency linear noise by using a LEAF linear noise suppression module in seismic processing software 'ParadigmTM-2011.3-EchosTM';
step S1013: firstly, realizing the band-pass filtering of seismic data by using a FILTER module in seismic processing software 'ParadigmTM-2011.3-EchosTM'; and then, realizing the cutting operation of the seismic data by utilizing an MUTE module in seismic processing software 'ParadigmTM-2011.3-echo TM', and cutting residual noise by utilizing OFFSET and TIME parameters.
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| CN104597489A (en) * | 2015-01-21 | 2015-05-06 | 中国石油天然气集团公司 | Seismic source wavelet optimal setting method and device |
| CN109100789A (en) * | 2018-07-30 | 2018-12-28 | 中国石油化工股份有限公司 | The full waveform inversion wavelet extraction method propagated based on the wave field inverse time |
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