CN103217713B - Oil-gas exploration SEISMIC VELOCTTY ANALYSIS AND data optimization methods - Google Patents
Oil-gas exploration SEISMIC VELOCTTY ANALYSIS AND data optimization methods Download PDFInfo
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Abstract
The invention provides a kind of oil-gas exploration SEISMIC VELOCTTY ANALYSIS AND data optimization methods, this oil-gas exploration SEISMIC VELOCTTY ANALYSIS AND data optimization methods comprises, and carries out the collection sorting of speed road, sub-elect the channel set for velocity analysis to geological data; Carry out the decay of split-band anomalous amplitude; Carry out the three-dimensional prestack random noise decay in F-X-Y territory; Carry out frequency filtering, the dominant frequency band energy of reflection wave is strengthened; And carry out wavelet shape deconvolution process, improve resolution.This oil-gas exploration SEISMIC VELOCTTY ANALYSIS AND data optimization methods solves the low problem of prior art medium velocity analysing energy reunion burnt difference precision, there is the energy that can strengthen usable reflection, improve the quality of velocity analysis data, improve signal to noise ratio (S/N ratio) and the resolution of velocity analysis road collection, make the advantage that the focusing of velocity spectrum improves.
Description
Technical Field
The invention relates to the field of seismic data processing, in particular to a seismic velocity analysis data optimization method for oil and gas exploration.
Background
The requirement of seismic imaging on the analysis precision of the velocity is higher and higher, the conventional velocity analysis method mainly adopts a post-stack energy maximum discrimination method at present, and the velocity analysis process does not carry out targeted optimization processing on seismic data participating in velocity analysis, so that the velocity energy is not focused, the velocity precision of a middle-deep layer is not enough, and the imaging precision of a geological area is influenced. Although the velocity analysis method of leveling the trace gather as much as possible by four-term velocity analysis is developed in the early stage, the problem of energy focusing of the intermediate-depth velocity cannot be solved. Aiming at the problems that the energy mass of the intermediate and deep velocity spectrum is unfocused and the velocity analysis precision is low, no corresponding velocity analysis method is available. In general, when a velocity spectrum is generated, the entire data processing is directly used. When the whole data is processed, the longitudinal and transverse fidelity of factors such as amplitude, frequency and the like are considered, and when a noise suppression technology is applied, the denoising effect is usually poor, and the influence on a velocity spectrum is large. Therefore, a new optimization method for seismic velocity analysis data of oil-gas exploration is invented, aiming at the problem that the velocity analysis data is poor in focusing performance of velocity spectrum energy clusters due to low signal-to-noise ratio and weak effective reflection, the data participating in velocity analysis is mainly targeted to improve the signal-to-noise ratio and the velocity spectrum quality of a gather, and a better velocity spectrum can be obtained by adopting a processing method and parameters different from those of the whole data.
Disclosure of Invention
The invention aims to provide an oil and gas exploration seismic velocity analysis data optimization method capable of improving the signal-to-noise ratio and the resolution of a velocity analysis gather.
The object of the invention can be achieved by the following technical measures: the optimization method of the seismic velocity analysis data of oil and gas exploration comprises the following steps: step 1, sorting a velocity gather of seismic data, and sorting a super gather for velocity analysis; step 2, performing sub-band abnormal amplitude attenuation on the seismic data generated in the step 1; step 3, performing F-X-Y domain three-dimensional prestack random noise attenuation on the seismic data generated in the step 2; step 4, frequency filtering is carried out on the seismic data generated in the step 3, so that the energy of the dominant frequency band of the reflected wave is enhanced; and step 5, performing wavelet shaping deconvolution processing on the seismic data generated in the step 4, and improving the resolution.
The object of the invention can also be achieved by the following technical measures:
the method for optimizing seismic velocity analysis data for oil and gas exploration further comprises inputting the seismic data which is processed conventionally before the step 1.
In step 2, the seismic data generated in step 1 is firstly subjected to sub-band scanning analysis, and after the noise frequency band range is determined, sub-band abnormal amplitude attenuation is carried out.
In step 2, when the abnormal amplitude attenuation of the frequency division band is carried out, a plurality of hyperbolic curve time windows are established according to the stacking speed, so that the hyperbolic curve time windows are basically consistent with the effective reflection axis, the length of the hyperbolic curve time windows is 200-500 milliseconds, the upper time window and the lower time window are mutually overlapped, the overlapping range is 30% -50%, the seismic data generated in the step 1 are divided into a plurality of frequency division band data, and then the amplitude energy statistics is carried out on the data time division window of the noise-containing audio frequency band according to the following formula:
，
in the formulaEIn order to be able to do so,Ain order to be the amplitude of the vibration,iin order to be the number of the sampling point,kthe number of the track is the number of the track,fin order to be a frequency band,tin order to be a time window,Nsetting the median filtering channel number for the total number of sampling pointsnAnd a threshold value based on the statistics andnthe statistical means are compared and median filtering is used to suppress anomalous amplitude noise when the ratio of the statistical value to the mean exceeds a threshold value.
In step 3, when F-X-Y domain three-dimensional prestack random noise attenuation is carried out on the seismic data generated in step 2, taking the CMP axis of each line as a vertical axis, taking a single channel in a CMP channel set arranged in the size of offset distance as a horizontal axis, forming planes in two directions and forming a three-dimensional data body with time, carrying out Fourier transform on the three-dimensional data body in the time direction to obtain data in the F-X-Y domain, designing a rectangular predictor by utilizing the complex least squares principle for each frequency component, and outputting prediction error energy after the three-dimensional seismic data corresponding to the frequency component is predicted by the rectangular predictorQ(f) At a minimum, i.e. aboutQ(f) The rectangular predictor is subjected to partial derivation and is made to be zero, and a matrix equation with a Hermite matrix as a coefficient can be obtained
R'·P=R
Wherein,R'is a Hermite matrix of multi-channel autocorrelation of the seismic data in the F-X-Y domain,Ris an F-X-Y domain seismic data multi-channel autocorrelation array,Pis an array of components of the rectangular predictor, solving for the rectangular predictorAnd calculating each component of the operator, then obtaining the rectangular predictor corresponding to the frequency component, performing complex two-dimensional convolution on the data of the F-X-Y domain of the frequency component by using the rectangular predictor to obtain a result of the F-X-Y domain after three-dimensional denoising, and performing inverse Fourier transform of the frequency domain to obtain a three-dimensional random noise attenuation result.
In step 4, the seismic data generated in step 3 is subjected to dominant frequency spectrum analysis, and after the dominant frequency band of the reflected wave is determined, frequency filtering is performed to enhance the energy of the dominant frequency band of the reflected wave.
In step 4, during frequency filtering, the low cut-off frequency of the band-pass filter corresponds to the low frequency value of the dominant frequency band, the low frequency transition region adopts 9dB up-conversion, the high cut-off frequency of the band-pass filter corresponds to the high frequency value of the dominant frequency band, and the high frequency transition region adopts 36dB down-conversion.
The seismic data participating in velocity analysis are selected from conventionally processed seismic data, the signal-to-noise ratio of the velocity analysis data is improved by optimizing noise attenuation technical parameters, the resolution of a velocity analysis gather is improved by wavelet shaping deconvolution, the effective reflected energy is enhanced by processing means such as frequency constraint and dominant frequency band reinforcement, and the quality of the velocity analysis data is improved. The method has the advantages of improving the signal-to-noise ratio and the resolution of the velocity analysis gather and improving the focusing property of the velocity spectrum.
Drawings
FIG. 1 is a flow chart of a method of optimizing seismic velocity analysis data for oil and gas exploration according to the present invention;
FIG. 2 is a schematic diagram of gathers before and after attenuation of composite multi-domain noise in an embodiment of the present invention;
FIG. 3 is a diagram of gathers before and after optimization in an embodiment of the present invention;
FIG. 4 is a schematic of the velocity spectrum before and after the optimization process in one embodiment of the invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
As shown in FIG. 1, FIG. 1 is a flow chart of the method for optimizing seismic velocity analysis data for oil and gas exploration of the present invention. At step 101, conventionally processed seismic data is input. The flow proceeds to step 102.
In step 102, the seismic data input in step 101 is subjected to velocity gather sorting to sort out a gather of super-gathers for velocity analysis. The flow proceeds to step 103.
In step 103, the seismic data generated in step 102 is analyzed by sub-band scanning to determine the noise band range, and sub-band abnormal amplitude attenuation is performed. The sub-band abnormal amplitude attenuation method is as follows:
and establishing a plurality of hyperbolic curve time windows according to the superposition speed, so that the time windows are basically consistent with the effective reflection axis, the length of the time windows is 200 plus 500 milliseconds, the upper time window and the lower time window are mutually overlapped, and the overlapping range is 30-50 percent. Dividing the seismic data into a plurality of sub-band data, and then carrying out amplitude energy statistics on the data of the noise-containing frequency band in a time division window according to the following formula:
，
in the formulaEIn order to be able to do so,Ain order to be the amplitude of the vibration,iin order to be the number of the sampling point,kthe number of the track is the number of the track,fin order to be a frequency band,tin order to be a time window,Nis the total number of samples. Setting median filter channel numbernAnd a threshold value based on the statistics andncomparing the statistical mean values, and using the median value when the ratio of the statistical value to the mean value exceeds a threshold valueThe filtering suppresses the abnormal amplitude noise. The flow proceeds to step 104.
In step 104, F-X-Y domain three-dimensional prestack random noise attenuation is performed on the seismic data generated in step 103. The CMP axis of each line is taken as a vertical axis, and a single track in the CMP track set arranged in the offset size is taken as a horizontal axis, so that planes in two directions are formed and form a three-dimensional data body with time. And performing Fourier transform on the data volume in the time direction to obtain data in an F-X-Y domain. For each frequency component, a rectangular predictor can be designed by utilizing the complex least square principle, and the output prediction error energy of the three-dimensional seismic data corresponding to the frequency component is predicted by the rectangular predictorQ(f) At a minimum, i.e. aboutQ(f) The partial derivative of the rectangular predictor is solved and made to be zero, and a matrix equation with a Hermite matrix as a coefficient can be obtained
R'·P=R
Wherein:R'the method comprises the following steps of (1) obtaining an Hermite matrix of multi-channel autocorrelation of seismic data in an F-X-Y domain;Ris an F-X-Y domain seismic data multi-channel autocorrelation array;Pis an array of components of a rectangular predictor.
According to the characteristics of the Hermite matrix, each component of the rectangular predictor can be solved quickly, and then the rectangular predictor corresponding to the frequency component is solved. And carrying out complex two-dimensional convolution on the data of the F-X-Y domain of the frequency component by using the operator to obtain a result of the F-X-Y domain after three-dimensional denoising. Then, inverse Fourier transform of frequency domain is carried out to obtain the final three-dimensional random noise attenuation result. The flow proceeds to step 105.
In step 105, dominant spectrum analysis is performed on the seismic data generated in step 104, and the dominant band range of the reflected wave, i.e., the band range corresponding to an amplitude of-18 dB, is determined, and then band-pass filtering is performed. When the band-pass filtering is carried out, the low cut-off frequency of the band-pass filter corresponds to the low-frequency value of the dominant frequency band, the low-frequency transition region adopts 9dB frequency increasing, the high cut-off frequency of the band-pass filter corresponds to the high-frequency value of the dominant frequency band, and the high-frequency transition region adopts 36dB frequency reducing. The flow proceeds to step 106.
In step 106, wavelet shaping deconvolution processing is performed on the seismic data generated in step 105. The expected output of the deconvolution is a zero-phase wavelet, the delay time is selected through experiments, and the output result is the high resolution. The flow ends.
Fig. 2 to 4 show schematic diagrams of gathers before and after composite multi-domain noise attenuation according to an embodiment of the present invention, fig. 3 shows schematic diagrams of gathers before and after optimization processing according to an embodiment of the present invention, and fig. 4 shows a velocity spectrum before and after optimization processing according to an embodiment of the present invention. As can be seen from fig. 2 to 4, by analyzing the seismic data characteristic analysis, after the data participating in the velocity analysis is comprehensively processed by the technologies of composite multi-domain noise attenuation (as shown in fig. 2), dominant frequency band energy enhancement, resolution improvement and the like, the quality of the gather is obviously improved (as shown in fig. 3), the quality of the formed velocity spectrum is obviously improved, the energy cluster of the velocity spectrum is focused, the focusing region is extended downwards for more than 1 second, and the accuracy of the seismic velocity analysis is improved (as shown in fig. 4).
Claims (7)
1. The optimization method of the oil and gas exploration seismic velocity analysis data is characterized by comprising the following steps of:
step 1, sorting a velocity gather of seismic data, and sorting a super gather for velocity analysis;
step 2, performing sub-band abnormal amplitude attenuation on the seismic data generated in the step 1;
step 3, performing F-X-Y domain three-dimensional prestack random noise attenuation on the seismic data generated in the step 2;
step 4, frequency filtering is carried out on the seismic data generated in the step 3, so that the energy of the dominant frequency band of the reflected wave is enhanced; and
and 5, performing wavelet shaping deconvolution processing on the seismic data generated in the step 4, and improving the resolution.
2. The method of optimizing hydrocarbon exploration seismic velocity analysis data of claim 1, further comprising inputting conventionally processed seismic data prior to step 1.
3. The method of optimizing seismic velocity analysis data for oil and gas exploration according to claim 1, wherein in step 2, sub-band sweep analysis is performed on the seismic data generated in step 1, and sub-band abnormal amplitude attenuation is performed after the noise frequency band range is determined.
4. The method as claimed in claim 3, wherein in step 2, when the abnormal amplitude attenuation of the sub-band is performed, a plurality of hyperbolic time windows are established according to the stacking velocity, so that the hyperbolic time windows are substantially consistent with the effective reflection axis, the hyperbolic time windows have a length of 200-500 ms, the upper and lower time windows overlap with each other within a range of 30% -50%, the seismic data generated in step 1 are divided into a plurality of sub-band data, and then the amplitude energy statistics is performed on the data time windows of the noise-containing frequency band according to the following formula:
wherein E is energy, A is amplitude, i is sample number, k is track number, f is frequency band, t is time window, N is total number of sample points, set median filtering track number N and threshold value, compare the statistic value in the time window with the average value of N statistics, and use median filtering to suppress abnormal amplitude noise when the ratio of the statistic value to the average value exceeds the threshold value.
5. The method of optimizing seismic velocity analysis data for hydrocarbon exploration according to claim 1, wherein in step 3, when F-X-Y domain three-dimensional prestack random noise attenuation is performed on the seismic data generated in step 2, a CMP axis of each line is taken as a vertical axis, a single channel in a CMP gather arranged in offset size is taken as a horizontal axis, a plane in two directions is formed and a three-dimensional data volume is formed with time, and the three-dimensional data volume is subjected to
Performing Fourier transform in time direction to obtain F-X-Y domain data, designing a rectangular predictor for each frequency component by using complex least square principle, so that after the three-dimensional seismic data corresponding to the frequency component is predicted by the rectangular predictor, the output prediction error energy Q (F) is minimum, i.e. Q (F) calculates partial derivative of the rectangular predictor and makes the partial derivative zero, thereby obtaining a matrix equation with Hermite matrix as coefficient
R'·P＝R
R' is an Hermite matrix of multi-channel autocorrelation of the seismic data in the F-X-Y domain, R is an F-X-Y domain seismic data multi-channel autocorrelation array, P is an array of each component of the rectangular predictor, each component of the rectangular predictor is solved, the rectangular predictor corresponding to the frequency component is solved, the rectangular predictor is used for carrying out complex two-dimensional convolution on the data in the F-X-Y domain of the frequency component, a result of the F-X-Y domain after three-dimensional denoising is obtained, and an inverse Fourier transform of the frequency domain is carried out to obtain a three-dimensional random noise attenuation result.
6. The method of optimizing hydrocarbon exploration seismic velocity analysis data according to claim 1, wherein in step 4, dominant spectrum analysis is performed on the seismic data generated in step 3, and after determining the dominant frequency band of the reflected wave, frequency filtering is performed to enhance the energy of the dominant frequency band of the reflected wave.
7. The method of optimizing hydrocarbon exploration seismic velocity analysis data according to claim 6, wherein in step 4, during frequency filtering, the low cutoff frequency of the band pass filter corresponds to the low frequency value of the dominant frequency band, the low frequency transition region is frequency up by 9dB, the high cutoff frequency of the band pass filter corresponds to the high frequency value of the dominant frequency band, and the high frequency transition region is frequency down by 36 dB.
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| CN103954993A (en) * | 2014-04-17 | 2014-07-30 | 中国石油天然气集团公司 | Scale domain multichannel filtering method and device based on seismic signal continuous wavelet transformation |
| CN105319586B (en) * | 2014-07-04 | 2018-02-02 | 中国石油化工股份有限公司 | A kind of normal-moveout spectrum three-dimensional filtering optimal velocity analysis |
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| CN106125134A (en) * | 2016-07-04 | 2016-11-16 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Based on the geological data signal-noise ratio computation method of window during hyperbolic |
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