CN109884706A - Non-stationary differential weighted superposition Processing Seismic Data - Google Patents
Non-stationary differential weighted superposition Processing Seismic Data Download PDFInfo
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Abstract
The present invention provides a kind of non-stationary differential weighted superposition Processing Seismic Data, which includes: step 1, gaussian filtering process is carried out to initial data, to reduce influence of the noise to reservoir signal；Step 2,3 smoothing processings of sliding are carried out to seismic channel；Step 3, even number order derivative road is calculated to smoothed out data, to highlight the high-frequency information of thin layer and thin interbed；Step 4, higher derivative road is standardized and the relative amplitude preserved processing based on Gaussian window, so that treated, data, which longitudinally have, preferable protects width；Step 5, by initial data, smoothed data and higher derivative according to certain weighted superposition, processing result to the end is obtained.The non-stationary differential weighted superposition Processing Seismic Data can provide the data of high quality for subsequent attributes extraction, oil-gas reservoir identification and reservoir prediction, to promote the exploration and development ability of thin layer and thin interbed oil-gas reservoir.
Description
Technical field
It the present invention relates to target-oriented seismic data processing and explains field, it is folded to especially relate to a kind of non-stationary differential weighting
Add Processing Seismic Data.
Background technique
Thin layer and thin interbed are a kind of critically important oil-gas reservoir, with the promotion of horizontal well technology and development technology, originally
This kind of reservoir less paid close attention to, is just being increasingly subject to the attention of In Oil Field Exploration And Development circle.But such reservoir is less than since thickness is thin
A quarter earthquake wavelength, seismic facies should can due to tuning effect and mutually superimposed interference, so difficulties in exploration is larger.Research
The method for improving such oil-gas reservoir resolution ratio has important theory significance and productive value.We have invented a kind of new thus
Non-stationary differential weighted superposition Processing Seismic Data, solve the above technical problem.
Summary of the invention
The object of the present invention is to provide one kind to be mainly used for the processing of seismic data interpretation target, and final purpose is to improve ground
The resolution ratio of shake data.
The purpose of the present invention can be achieved by the following technical measures: non-stationary differential weighted superposition seism processing side
Method, which includes: step 1, carries out gaussian filtering process to initial data,
To reduce influence of the noise to reservoir signal；Step 2,3 smoothing processings of sliding are carried out to seismic channel；Step 3, to after smooth
Data calculate even number order derivative road, to highlight the high-frequency information of thin layer and thin interbed；Step 4, higher derivative road is marked
Standardization processing and the relative amplitude preserved processing based on Gaussian window, so that treated, data longitudinally have preferable guarantor's width；Step 5, will
Initial data, smoothed data and higher derivative obtain processing result to the end according to certain weighted superposition.
The purpose of the present invention can be also achieved by the following technical measures:
In step 1, to data both ends extrapolated value, the number of extrapolated value is half σ/2 of Gaussian window, then to data into
Row windowing process, the preprocessed data after obtaining adding window and the number for relative amplitude preserved processing in the standard on dataization processing of step 4
According to
In step 1, Gaussian window are as follows:Wherein μ is the coordinate in Gaussian window, and σ is the length of Gaussian window,
According to actual seismic data selection parameter.
In step 2, data after windowing process are filtered, using 3 filter methods, filter times are Is times,
Specific number will be determined according to the case where actual seismic data, when noise is smaller, reduction filter times appropriate, instead
The number that should then increase filtering, reduce influence of the noise for higher derivative to the greatest extent, the data of obtained filtering are su。
In step 3, smoothed data road s step 2 generateduThe higher derivative for calculating even-order, seeks second order respectively,
Quadravalence, six order derivatives.
Step 3 includes:
1. in data both ends complement value, the phenomenon that preventing few value；Then two subdifferentials obtain second dervative；
2. two subdifferentials obtain second-order differential d2；
Forward difference:
Backward difference:
3. repeating to obtain quadravalence differential d4With six rank differential d6。
Z represents difference value, and y represents sampled value, and i represents ith sample point, and Δ t represents the distance between sampled point.
In step 4, front and back interpolation is carried out to data, because only getting six order derivatives, each slotting 3 values are at data both ends
Can, to meet the needs that marginal point seeks norm:
suFor filter curve,For the data after standard,For the data after windowing process；duFor higher derivative,For
Higher derivative after standardization, the second dervative standardized after treatmentFourth-DerivativeSix order derivatives
In steps of 5, by initial data, smoothed data and higher derivative according to the formula of certain weighted superposition are as follows:
W represents different weights, in formulaFor retaining the information of real data,For retaining the low frequency of data
Information, second dervativeFourth-DerivativeSix order derivativesFor highlighting the information of waveform, adjusted not according to real data
Important information is obtained with the weight of component.
Non-stationary differential weighted superposition Processing Seismic Data in the present invention, is related to guarantor's width based on higher derivative
Method for weighted overlap-add is mainly used in using thin layer and thin interbed as the seismic data of main feature.Initial data is carried out first
Gaussian filtering process, to reduce influence of the noise to reservoir signal；Then 3 smoothing processings of sliding are carried out to seismic channel；To flat
Data after cunning calculate even number order derivative road (second order, quadravalence, six ranks etc.), for highlighting the high-frequency information of thin layer and thin interbed；
Then higher derivative road is standardized and the relative amplitude preserved processing based on Gaussian window, so that treated, data longitudinally have
It is preferable to protect width；Finally initial data, smoothed data and higher derivative are obtained to the end according to certain weighted superposition
Processing result.The present invention can preferably improve the resolution ratio of seismic data thin layer and thin interbed.Non-stationary differential weighting is folded
Add Processing Seismic Data under the premise of not changing the low-frequency information of reflection tectonic information, emphasis is by calculating higher derivative
Road reflects the high-frequency information of thin layer and thin interbed to extract, then by organically coupling superposition, to improve seismic data with protecting width
Resolution ratio.This achievement can provide the data of high quality for subsequent attributes extraction, oil-gas reservoir identification and reservoir prediction, to mention
Rise the exploration and development ability of thin layer and thin interbed oil-gas reservoir.
The present invention reaches the sensibility of layer position information enhancing thin layer and thin interbed reflection comparison for higher derivative
The effect of degree.3 smoothing processings of gaussian filtering and sliding are carried out to initial data, noise can be effectively reduced to reservoir
The influence of signal retains the low-frequency component of seismic data；Thin layer and thin interbed can be effectively highlighted by calculating higher derivative
High-frequency information；Higher derivative road is standardized and the relative amplitude preserved processing based on Gaussian window, so that treated, data are vertical
Width is preferably protected to having；Finally by initial data, smoothed data and higher derivative according to certain weighted superposition, by ground
The informix of shake data together, improves the resolution ratio of seismic data thin layer and thin interbed.
Detailed description of the invention
Fig. 1 is the process of a specific embodiment of non-stationary differential weighted superposition Processing Seismic Data of the invention
Figure；
Fig. 2 is the bar graph of theoretical model of the invention and the schematic diagram of synthetic seismogram；
Fig. 3 (a) is comparison of the theoretical model composite traces Jing Guo filtered result and unfiltered synthetic seismogram
Figure is (b) comparison diagram by data after Fourier transformation is transformed into frequency spectrum；
Fig. 4 is the synthetic seismogram comparison diagram of second dervative and theoretical model；
Fig. 5 is the synthetic seismogram comparison diagram of Fourth-Derivative and theoretical model；
Fig. 6 is the synthetic seismogram comparison diagram of six order derivatives and theoretical model；
Fig. 7 is that the result figure obtained after the processing of non-stationary differential method for weighted overlap-add synthesizes earthquake note with theoretical model
The comparison diagram of record；
Fig. 8 is three order derivatives and five order derivatives and theoretical model synthetic seismogram comparison diagram；
Fig. 9 is the comparative result figure of different noise amplitudes；
Figure 10 is the comparative result figure of different filter times；
Figure 11 is the comparative result figure of different higher derivative weights；
Figure 12 is the schematic diagram for testing the theoretical model that dominant frequency influences；
The schematic diagram of the waveform of Figure 13 different frequency wavelet, (a) dominant frequency are 15Hz, and (b) dominant frequency is 35Hz, and (c) dominant frequency is
55Hz；
Figure 14 is the schematic diagram of the synthetic seismogram of different dominant frequency；
Figure 15 is the schematic diagram of the processing result of the synthetic seismogram of different dominant frequency；
Figure 16 is the schematic diagram of the seismic profile in present example；
Figure 17 is example by non-stationary differential method for weighted overlap-add treated effect picture.
Specific embodiment
To enable above and other objects, features and advantages of the invention to be clearer and more comprehensible, preferably implementation is cited below particularly out
Example, and cooperate shown in attached drawing, it is described in detail below.
As shown in FIG. 1, FIG. 1 is the flow charts of non-stationary differential weighted superposition Processing Seismic Data of the invention.
S1, to guarantee that final data and primary data are in the same size, first to data both ends extrapolated value, the number of extrapolated value
For half σ/2 of Gaussian window, windowing process, the preprocessed data after obtaining adding window and the number for S4 then are carried out to data
It is worth the data of relative amplitude preserved processing in standardizationGaussian window are as follows:Wherein μ is the coordinate in Gaussian window, σ
For the length of Gaussian window (according to actual seismic data selection parameter).
S2 is filtered data after windowing process, uses 3 filter methods here, and filter times are Is times, tool
The number of body will be determined according to the case where actual seismic data, when noise is smaller, can reduction filter times appropriate,
Number that is on the contrary then should increasing filtering, reduces influence of the noise for higher derivative to the greatest extent, and the data of obtained filtering are su。
Shown in theoretical model such as Fig. 2 (a).Shown in the synthetic seismogram of theoretical model such as Fig. 2 (b).Fig. 2 (a) is the theoretical model time
Interval is respectively 0,2,4,6,8,10 milliseconds；Fig. 2 (b) adds the synthetic seismogram after noise.Fig. 3 (a) is theoretical model conjunction
It (b) is to turn data by Fourier transformation at the comparison diagram for recording filtered result and unfiltered synthetic seismogram
Comparison diagram after changing to frequency spectrum retains from figure 3, it can be seen that it is more smooth to be filtered rear curve to theoretical model
The low-frequency information of data, reduces the influence of noise bring high frequency in data.
S3, the smoothed data road s that step S2 is generateduThe higher derivative for calculating even-order, seeks second order respectively, quadravalence,
Six order derivatives.Reach the effect of enhancing thin layer and thin interbed reflectance contrast for the sensibility of layer position information by higher derivative
Fruit, the distortion that phase is generated due to asymmetry after not using odd number order derivative that can avoid processing.Even number order derivative can benefit
It is balanced out with differential symmetry.Three ranks, five order derivative shift phenomenon is shown in Fig. 8, therefore need to choose even number order derivative, this is also this method
One feature.
1. in data both ends complement value, the phenomenon that preventing few value；Then two subdifferentials obtain second dervative；
2. two subdifferentials obtain second-order differential d2。
Forward difference:
Backward difference:
3. repeating to obtain quadravalence differential d4With six rank differential d6。
Z represents difference value, and y represents sampled value, and i represents ith sample point, and Δ t represents the distance between sampled point.Ns generation
The number of table filtering, Nd represent the number of front and back difference.
As a result respectively such as Fig. 4, Fig. 5, shown in Fig. 6.It can be seen that higher derivative generally waveform and it is original synthetically
Shake record is consistent, while Fourth-Derivative and six order derivatives have better waveform at wave crest, can reflect more information.
S4, is then standardized higher derivative road and the relative amplitude preserved processing based on Gaussian window, so that treated
Data, which do not all have in people having a common goal and road, preferable protects width.It also needs to carry out data front and back interpolation herein, because only
Six order derivatives are got, so only needing in each slotting 3 values in data both ends, to meet the needs that marginal point seeks norm.
suFor filter curve,For the data after standard,For the data after windowing process.duFor higher derivative,For
Higher derivative after standardization, the second dervative standardized after treatmentFourth-DerivativeSix order derivatives
It is added by S5, the data obtained using above-mentioned steps according to certain weight.
W represents different weights, in formulaFor retaining the information of real data,For retaining the low frequency of data
Information, second dervativeFourth-DerivativeSix order derivativesFor highlighting the information of waveform, adjusted not according to real data
Important information is obtained with the weight of component.The processing result of theoretical model is shown in Fig. 7, as can be seen from Figure 7 initial data
In the otherness that is not exhibited by highlighted by higher differentiation.
Test results are shown in figure 7 for theoretical model of the present invention.By comparison initial data with treated as a result, can be with
It observes that the waveform of original aliasing together is separated, can preferably improve the resolution ratio of seismic data thin layer and thin interbed.
In the theoretical model of the invention, the amplitude size of the noise of addition, the number Is of filtering, the weight of higher derivative
Size, size of dominant frequency etc. can all have an impact last resolution ratio.
1. the comparative result figure of different noise amplitudes is as shown in Figure 9, it can be seen that, when noise amplitude is greater than signal amplitude
When 8%, last result can not effectively suppress the influence of noise, so when the noise of real data is stronger, it can be appropriate
Increase filter times；
2. the comparative result figure of different filter times is as shown in Figure 10, when filter times are fewer, noise does not have
There is method effectively to be suppressed, higher derivative is influenced by noise, can be generated jagged waveform and is reflected in last result
In；
3. the comparative result figure of different higher derivative weights is as shown in figure 11, the weight for increasing higher derivative can be appropriate
The information of increase thin layer, but the excessive waveform distortions that will lead to data of the weight of higher derivative, can reduce real data instead
Resolution ratio；
4. the model for testing the influence of dominant frequency is as shown in figure 12.Model has 251 sampled points, is divided into 2 milliseconds, two layers
Time interval be respectively 8 milliseconds and 10 milliseconds.Choosing dominant frequency respectively is 15hz, the Ricker wavelet (wavelet waveforms of 35hz, 55hz
Convolution carried out to model as shown in figure 13), each dominant frequency corresponding 30, from left to right respectively 15hz, 35hz, 55hz, form
Multiply 251 section for one 90, sectional view is as shown in figure 14.The result figure obtained after handling model is as shown in figure 15.One
90 are shared, per pass there are 251 sampled points, and the sampling interval is 2 milliseconds.The road 1-30 dominant frequency is 15hz, and the road 31-60 dominant frequency is
The road 35hz, 61-90 dominant frequency is 55hz.When time interval is 8 milliseconds, the effect of 55hz dominant frequency is best, 15hz and 35hz dominant frequency
Effect is bad；When time interval is 10 milliseconds, the effect of 55hz and 35hz dominant frequency is preferable, and the resolving effect of 15hz dominant frequency is worst.
As can be seen that dominant frequency is bigger from last result, the waveform of wavelet is narrower, and practical thin layer separating effect is better.
By the discussion above it is recognised that for theoretical model, the amplitude of noise is less than 10, filtering time
Number is 10 times, and second order, quadravalence, effect is best when six order derivative weights are 1,2,2.And in the processing of real data, it is practical to provide
The dominant frequency of material can also have an impact last result, and major frequency components are higher, and last effect is better.
Non-stationary differential weighted superposition Processing Seismic Data of the invention, core concept are as follows: pass through smothing filtering
It is constant to retain low-frequency information for processing；According to feature of data, even number order derivative is calculated, obtains in seismic data reflection thin layer and thin
The high-frequency information of alternating layers information；It is handled by moving window, realizes non-stationary relative amplitude preserved processing；It is folded by the coupling of low-and high-frequency signal
Add, final realize proposes high-resolution purpose.
Here is specific application example of the invention:
Apply the present invention to certain work area, work area sectional view is as shown in figure 16, road Shuo Wei 141, and per pass there are 191 samplings
Point, sampling interval are 2 milliseconds.It can be seen that noise is moderate from sectional view, filter times can be set to 10 times, by second order,
Quadravalence, the weight of six order derivatives are set to 1,2,2 respectively, using non-stationary differential method for weighted overlap-add, to real data per pass into
Row processing, the sectional view finally obtained after non-stationary differential weighted superposition are as shown in figure 17.It can from oval frame
It is separated to interface, the information of thin layer is highlighted, and the resolution ratio of section improves, and data longitudinally have preferable guarantor's width.
The related technology contents that do not address in aforesaid way are taken or are used for reference prior art and can be realized.
It should be noted that those skilled in the art can also make such or such appearance under the introduction of this specification
Easy variation pattern, such as equivalent way or obvious mode of texturing.Above-mentioned variation pattern should all protection scope of the present invention it
It is interior.
Claims (8)
1. non-stationary differential weighted superposition Processing Seismic Data, which is characterized in that the non-stationary differential weighted superposition earthquake
Data processing method includes:
Step 1, gaussian filtering process is carried out to initial data, to reduce influence of the noise to reservoir signal；
Step 2,3 smoothing processings of sliding are carried out to seismic channel；
Step 3, even number order derivative road is calculated to smoothed out data, to highlight the high-frequency information of thin layer and thin interbed；
Step 4, higher derivative road is standardized and the relative amplitude preserved processing based on Gaussian window, so that treated, data are vertical
Width is preferably protected to having；
Step 5, by initial data, smoothed data and higher derivative according to certain weighted superposition, processing knot to the end is obtained
Fruit.
2. non-stationary differential weighted superposition Processing Seismic Data according to claim 1, which is characterized in that in step
In 1, to data both ends extrapolated value, the number of extrapolated value is half σ/2 of Gaussian window, then carries out windowing process to data, obtains
Preprocessed data after to adding window and the data for relative amplitude preserved processing in the standard on dataization processing of step 4
3. non-stationary differential weighted superposition Processing Seismic Data according to claim 2, which is characterized in that in step
In 1, Gaussian window are as follows:Wherein μ is the coordinate in Gaussian window, and σ is the length of Gaussian window, is provided according to actual seismic
Expect selection parameter.
4. non-stationary differential weighted superposition Processing Seismic Data according to claim 1, which is characterized in that in step
In 2, data after windowing process are filtered, using 3 filter methods, filter times are Is times, and specific number wants root
Factually the case where border seismic data, determines, when noise is smaller, reduction filter times appropriate are on the contrary then should increase filter
The number of wave, reduces influence of the noise for higher derivative to the greatest extent, and the data of obtained filtering are su。
5. non-stationary differential weighted superposition Processing Seismic Data according to claim 1, which is characterized in that in step
In 3, to the smoothed data road s of step 2 generationuThe higher derivative for calculating even-order, seeks second order, quadravalence, six order derivatives respectively.
6. non-stationary differential weighted superposition Processing Seismic Data according to claim 5, which is characterized in that step 3
Include:
1. in data both ends complement value, the phenomenon that preventing few value；Then two subdifferentials obtain second dervative；
2. two subdifferentials obtain second-order differential d2；
Forward difference:
Backward difference:
3. repeating to obtain quadravalence differential d4With six rank differential d6。
Z represents difference value, and y represents sampled value, and i represents ith sample point, and Δ t represents the distance between sampled point.
7. non-stationary differential weighted superposition Processing Seismic Data according to claim 1, which is characterized in that in step
In 4, front and back interpolation is carried out to data, because only getting six order derivatives, in each slotting 3 values in data both ends, to meet edge
Point seeks the needs of norm:
suFor filter curve,For the data after standard,For the data after windowing process；duFor higher derivative,For standard
Higher derivative after change, the second dervative standardized after treatmentFourth-DerivativeSix order derivatives
8. non-stationary differential weighted superposition Processing Seismic Data according to claim 1, which is characterized in that in step
In 5, by initial data, smoothed data and higher derivative according to the formula of certain weighted superposition are as follows:
W represents different weights, in formulaFor retaining the information of real data,For retaining the low-frequency information of data,
Second dervativeFourth-DerivativeSix order derivativesFor highlighting the information of waveform, different groups are adjusted according to real data
Point weight obtain important information.
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