CN114076980B - Method and system for thin layer depiction - Google Patents
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Abstract
The invention provides a method and a system for thin layer depiction, wherein the method comprises the steps of obtaining a research distinguishing angle superposition data body, and obtaining a background longitudinal and transverse wave speed ratio of a research area through prestack inversion. And obtaining an objective function for representing the relation between zero offset data and actually measured seismic data, wherein the offset data is a longitudinal wave impedance reflectivity convolution wavelet result. And carrying out inversion processing on the superimposed data body of the split angle by using the obtained objective function to obtain the real zero offset seismic reflection data of the research area. And acquiring parity component weight coefficients of the zero offset reflection data on the basis of the real zero offset reflection data obtained based on inversion, and acquiring the zero offset reflection data with improved resolution on the basis of the parity component weight coefficients. The method comprises the steps of obtaining zero offset information by using a step-by-step method, then decomposing parity components on the basis of the zero offset information, improving the longitudinal resolution of the reflection data, and enabling the whole process to be more stable than direct inversion of multi-angle superposition data.
Description
Technical Field
The invention relates to the technical field of seismic exploration processing, in particular to a thin layer characterization method and system.
Background
In conventional processing, in order to improve the signal-to-noise ratio of the seismic data, the seismic data acquired by multiple coverage techniques are often superimposed, and the superimposed data is used as actual self-excitation self-reception data, i.e., zero offset data (Mou Yongguang, etc., seismic data processing method, 2006). In the face of actual seismic data, the resolution of the superimposed data is often reduced due to inaccurate velocity of the mobile calibration, i.e., the seismic data at different offsets differ significantly (Z.Sun, Y.Zhangand C.Fan, an iterative AVO inversion workflow for pure P-wave computation and S-wave motion improvement.the First Break,2014,10 (32): 47-50). The superimposed seismic data is not exactly equivalent to actual zero offset data. On one hand, the amplitude of the AVO effect affects the amplitude of the AVO effect, and the amplitude of the AVO effect are different; on the other hand, when the effective reflection angle is larger, the superimposed data can weaken and even completely cover some reservoirs with obvious AVO effect, namely tuning effect of AVO, so that resolution and accuracy of the seismic data are reduced to a certain extent.
The seismic data can be converted into impedance information reflecting the elastic property of the underground medium based on inversion of the superimposed data, but the method is based on the assumption that the superimposed data is actual zero offset data, and actually the superimposed data and the superimposed data have differences in relative amplitude and frequency, so that inversion accuracy is restricted to a certain extent, and the definition accuracy of thin layers and reservoir curtain details is limited; meanwhile, due to the influence of the AVO tuning effect, accurate stratum interpretation is difficult to perform by using the superposition data.
The vertical resolution of the seismic data directly determines its thin layer characterization ability, chopra et al (Chopra, s., j. Castagna, and y. Xu. Thin-bed reflectivity inversion andsome applications. First Break,2009, 27:17-24.) by using wedge model testing, it was found that the reflection coefficient consisted of an odd component and an even component, wherein the odd component inhibited the improvement of the longitudinal resolution, which was detrimental to thin layer characterization, and the even component could improve the longitudinal resolution of the reflection coefficient, thereby improving the ability of the seismic reflection information to characterize thin layers. The conventional method for improving inversion resolution is mainly divided into two types, wherein the first method is an inversion method through random simulation, but the method has extremely high requirements on the matching degree of well shocks and the quantity and quality of well data, and is time-consuming in calculation and not strong in applicability; the second method is based on a deterministic inversion method, and the longitudinal resolution is improved by utilizing parity decomposition of reflection coefficients, and the method is usually based on superposition reflection data (Yin Xingyao and the like) at present, a model constraint basis tracking inversion method is used for petroleum geophysical prospecting, 2019,55 (1): 115-122), or inversion calculation is directly carried out by utilizing multi-angle superposition data, wherein the inversion accuracy is limited because of inherent problems of the superposition data, and the discomfort in the inversion process is strong because of the condition number of a Jacobian matrix is overlarge.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a method and a system for thin layer depiction, which are characterized in that the method comprises the steps of acquiring real zero offset information of underground based on prestack inversion, enriching and truly plotting the real information of underground relative superimposed data, improving longitudinal resolution, and then decomposing parity components based on the zero offset information, further improving longitudinal resolution of reflected data, thereby improving precision of thin layer depiction, and ensuring that the whole process is more stable relative to direct inversion of multi-angle superimposed data.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a method for thin layer characterization, comprising the steps of: s100, acquiring a research distinction angle superposition data body, and acquiring a background longitudinal and transverse wave speed ratio of a research area through pre-stack inversion. S200, obtaining an objective function for representing the relation between zero offset data and actually measured seismic data, wherein the offset data is a longitudinal wave impedance reflectivity convolution wavelet result. The objective function is obtained by algebraic calculation according to the longitudinal wave impedance reflectivity and a coefficient matrix of an approximate formula, wherein the longitudinal wave impedance reflectivity is the ratio of the impedance difference between the upper layer and the lower layer of the interface to the impedance sum of the upper layer and the lower layer of the interface, and the coefficient matrix of the approximate formula is the ratio of the incident angle to the longitudinal wave speed. S300, inversion processing is carried out on the angle-divided superimposed data body by using the obtained objective function, and real zero offset seismic reflection data of the research area are obtained. S400, constructing an objective function of the parity component weight coefficient based on the true zero offset reflection data obtained by inversion in the step S300, obtaining the parity component weight coefficient of the zero offset reflection data, and obtaining the zero offset reflection data with improved resolution based on the parity component weight coefficient.
According to the method for thin layer depiction, the influence of superposition and odd components of reflection coefficients on thin layer depiction is considered, zero offset reflection data is obtained through pre-superposition inversion, the problem that longitudinal resolution of gather data is reduced due to superposition is overcome, longitudinal resolution of seismic data is further improved on the basis of the zero offset reflection data through odd-even component decomposition inversion, noise pressing processing is carried out on the basis of the obtained high-resolution reflection coefficients, the whole calculation process is carried out step by step, the problem of discomfort enhancement caused by directly utilizing angle superposition data calculation is reduced, the problem of detail blurring caused by total superposition seismic reflection signal superposition effect can be overcome, high-resolution zero offset reflection signals can be obtained, underground more real reflection information is recovered, and the capability of seismic data on thin layer depiction is effectively improved.
Further improvements to the above described solution are possible as follows.
According to the method for thin layer characterization of the present invention, in a preferred embodiment, further comprises step S500: noise suppression and filtering processing are performed on the basis of the zero offset reflection data with improved resolution obtained according to step S400 to obtain zero offset reflection data with improved longitudinal resolution.
And performing noise suppression by utilizing principal component analysis on the basis of the obtained zero offset reflection data with improved resolution, and suppressing abnormal frequency bands by utilizing band-pass filtering to obtain high-resolution zero offset reflection information, and providing constraint data for geological analysis and elastic parameter extraction of a thin target layer.
Further, in a preferred embodiment, the method for thin layer characterization according to the present invention further comprises step S600: and (3) carrying out elastic parameter inversion on the basis of the zero offset reflection data with improved longitudinal resolution obtained in the step S500, and carrying out geological evolution analysis on the research area.
Specifically, in a preferred embodiment, step S100 includes the following sub-steps: s101, on the basis of the offset-domain common reflection point gather, converting the angle domain prestack gather by using the layer speed body. S102, based on the pre-stack gather processed in the step S101, respectively obtaining L different incidence angles theta-division angle superposition data bodies D (theta 1 ),D(θ 2 )...D(θ L ). Further, in a preferred embodiment, in step S101, before performing the conversion of the angle domain prestack gather using the layer velocity body, an optimization process targeting prestack inversion is further performed, wherein the optimization process includes ablation, prestack gather denoising, and gather flattening.
Specifically, in a preferred embodiment, in step S200, zero offset data S is characterized p The objective function F (m) of the relation with the measured seismic data D is:
F(m)＝min||Gm-d||+β·m T W T Wm (1)
in the formula (1), G is the ratio gamma=V of the incident angle and the background longitudinal and transverse wave speed p /V s Coefficient matrix formed of V p For longitudinal wave velocity, V s For transverse wave velocity, the specific expression of G is:
g in formula (2) L1 ＝sec 2 (θ L )，G L2 ＝-8sin 2 (θ L )/γ 2 ，G L3 ＝2sin 2 (θ L )/γ 2 -tan 2 (θ L ) 2, gamma value is obtained through pre-stack inversion;
zero offset data S obtained by using m in formula (1) p Normal incidence transverse wave data S s Density change rate data S d The specific form of the composition is as follows:
m＝[S p ,S s ,S d ] T (3)
d in the formula (1) is a matrix formed by the superimposed data body of the sub-angle obtained by utilizing the observation seismic trace set, and is specifically expressed as follows:
d＝[D(θ 1 ),D(θ 2 )...D(θ L )] T (4)
in the formula (1), W is a flatness matrix, which is used for inhibiting the influence of noise on the extraction of zero offset data, and specifically expressed as:
beta in the formula (1) is a weight coefficient, and the weight of the flatness matrix constraint can be adjusted.
Specifically, in a preferred embodiment, step S400 includes the following sub-steps: s401, establishing an objective function for solving the parity component weight coefficient based on the zero offset data, and solving the parity component reflection weight coefficient based on the zero offset data obtained in the step S300; s402, acquiring zero offset data for improving resolution based on the zero offset parity component reflection coefficient and the parity component weight coefficient matrix obtained in the step S401. In step S401, zero offset seismic data S p Into odd component r o And even component r e The product of the weighting coefficients o, e respectively corresponding thereto, the correlation thereof can be expressed as:
S p ＝W sp [r o ,r e ][o,e] T (6)
in step S401, the inversion objective function ψ (m') based on the parity component weight coefficient of the zero offset data is:
ψ(m')＝min{||W sp Fm'-S p || 2 +μ||m'||+σ||PFm'-T|| 2 } (7)
in the formula (7), P is an integration matrix, W sp The wavelet matrix is zero offset seismic data, and T is low-frequency information of longitudinal wave impedance obtained by performing difference on logging data; μ, σ is a weight adjustment parameter; m' = [ o, e] T ，F＝[r o ,r e ]Wherein r is o ，r e The specific expression of the components which are the Dike function xi is as follows:
r o (t,m,n,Δt)＝ξ(t-mΔt)-ξ(t-mΔt+nΔt) (8)
r e (t,m,n,Δt)＝ξ(t-mΔt)+ξ(t-mΔt+nΔt) (9)
in the formulas (8) and (9), t is a preset time, delta t is a sampling interval, m is a sampling point position corresponding to a thin layer top interface, and n is a sampling point position corresponding to a bottom interface.
In step S402, zero offset reflection data r with improved resolution is obtained by using the parity component weight coefficient obtained in step S401 and the parity component combination p The specific solving formula is as follows: r is (r) p ＝Fm' (10)。
The system for thin layer characterization according to the second aspect of the present invention includes a first processing module for acquiring a research distinction angle superposition data volume and a background longitudinal and transverse wave velocity ratio. And the second processing module is used for acquiring an objective function representing the relation between the zero offset data and the actually measured seismic data. And the third processing module is used for carrying out inversion processing according to the obtained objective function to obtain the real zero offset seismic reflection data of the research area. And the fourth processing module is used for constructing an objective function of the parity decomposition weight coefficient according to the technology of the real zero offset seismic reflection data, acquiring the weight coefficient of the parity component of the zero offset seismic reflection data, and acquiring the zero offset seismic reflection data with improved resolution by combining the parity decomposition matrix.
Similarly, the system for thin layer depiction of the invention considers the influence of superposition and odd components of reflection coefficient on thin layer depiction, obtains zero offset reflection data through pre-superposition inversion, overcomes the problem of longitudinal resolution reduction caused by superposition of gather data, further improves the longitudinal resolution of seismic data on the basis of the zero offset reflection data through odd-even component decomposition inversion, performs noise pressing processing on the basis of the obtained high resolution reflection coefficient, calculates step by step in the whole calculation process, reduces the problem of discomfort enhancement caused by directly utilizing angle superposition data calculation, can not only overcome the problem of detail blurring caused by the superposition effect of total superposition seismic reflection signals, but also obtain high-resolution zero offset reflection signals, recover more real reflection information underground and effectively improve the capability of seismic data on thin layer depiction.
Further improvements to the above described solution are possible as follows.
Further, in a preferred embodiment, the system for thin layer characterization of the present invention further includes an optimization processing module, configured to perform noise suppression and filtering processing on the basis of the zero offset reflection data after resolution enhancement to obtain zero offset reflection data with enhanced longitudinal resolution.
Further, in a preferred embodiment, the thin layer characterization system of the present invention further comprises a post-processing module for performing elastic parametric inversion based on the zero offset reflection data for improving the longitudinal resolution, and performing a geological meaning analysis of the investigation region.
Compared with the prior art, the invention has the advantages that: by utilizing the step-by-step method, the real zero offset information of the underground is acquired based on pre-stack inversion, the description of the real information of the underground is richer and more real relative to the superimposed data, the longitudinal resolution is improved, then the odd-even component decomposition is carried out on the basis of the zero offset information, the longitudinal resolution of the reflected data is further improved, the precision of the description of the thin layer is improved, and the whole process is more stable relative to the direct inversion of the multi-angle superimposed data.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
FIG. 1 schematically shows a flow of a method for thin layer characterization according to an embodiment of the invention;
FIG. 2 schematically illustrates measured well data for an embodiment of the present invention;
FIG. 3 schematically shows an angle gather calculated using theoretical log data;
FIG. 4 schematically illustrates a comparison of a full stack of traces and a zero offset trace;
FIG. 5 schematically illustrates a comparison of zero offset gathers with parity decomposed high resolution reflection coefficient convolutions of a 25Hz wavelet;
FIG. 6 schematically illustrates a comparison of zero offset gathers with a parity decomposed high resolution reflection coefficient convolved 35Hz wavelet;
FIG. 7 schematically shows the result of comparing zero offset data obtained by inversion of actual observation data with data obtained by the thin layer characterization method according to the embodiment of the present invention; wherein, fig. 7 (a) is a longitudinal wave reflection seismic trace obtained by using the convolution of the measured well data longitudinal wave impedance and the rake wavelet with the dominant frequency of 40Hz, fig. 7 (b) is the conventional superposition data obtained by observation, fig. 7 (c) is a zero offset seismic trace set obtained by using the inversion of the angle-divided superposition data volume, fig. 7 (d) is a reflection seismic trace obtained by using the thin layer characterization method proposed by the present invention, and fig. 7 (e) is the measured well data longitudinal wave impedance;
FIG. 8 schematically shows conventional full stack data, zero offset data obtained using pre-stack inversion, and reflection data obtained by the method of an embodiment of the present invention; wherein, fig. 8 (a) is conventional superimposed data, fig. 8 (b) is zero offset data obtained by using pre-stack inversion, and fig. 8 (c) is zero offset high resolution reflection seismic data after noise suppression by parity component decomposition, principal component analysis and filtering processing on the basis of the zero offset data in fig. 8 (b);
FIG. 9 schematically shows the comparison of the full stack data with the high resolution data obtained using the method of the embodiments of the present invention and the inversion impedance results using both; wherein, fig. 9 (a) is conventional superimposed seismic reflection data, fig. 9 (b) is longitudinal wave impedance data obtained by inversion using the reflection data in fig. 9 (a), fig. 9 (c) is seismic reflection data obtained by the method of the embodiment of the invention, and fig. 9 (d) is longitudinal wave impedance data obtained by inversion using the reflection data in fig. 9 (c).
In the drawings, like parts are designated with like reference numerals. The figures are not drawn to scale.
Detailed Description
The invention will now be described in further detail with reference to the drawings and the specific examples, which are not intended to limit the scope of the invention.
Example 1
Fig. 1 schematically shows a flow of a method for thin layer characterization according to an embodiment of the invention.
As shown in fig. 1, the method for thin layer characterization according to the embodiment of the invention includes the following steps: s100, acquiring a research distinction angle superposition data body, and acquiring a background longitudinal and transverse wave speed ratio of a research area through pre-stack inversion. S200, obtaining an objective function for representing the relation between zero offset data and actually measured seismic data, wherein the offset data is a longitudinal wave impedance reflectivity convolution wavelet result. The objective function is obtained by algebraic calculation according to the longitudinal wave impedance reflectivity and a coefficient matrix of an approximate formula, wherein the longitudinal wave impedance reflectivity is the ratio of the impedance difference between the upper layer and the lower layer of the interface to the impedance sum of the upper layer and the lower layer of the interface, and the coefficient matrix of the approximate formula is the ratio of the incident angle to the longitudinal wave speed. S300, inversion processing is carried out on the angle-divided superimposed data body by using the obtained objective function, and real zero offset seismic reflection data of the research area are obtained. S400, constructing an objective function of the parity component weight coefficient based on the true zero offset reflection data obtained by inversion in the step S300, obtaining the parity component weight coefficient of the zero offset reflection data, and obtaining the zero offset reflection data with improved resolution based on the parity component weight coefficient.
According to the method for thin layer depiction, the influence of superposition and odd components of reflection coefficients on thin layer depiction is considered, zero offset reflection data is obtained through pre-superposition inversion, the problem that longitudinal resolution of gather data is reduced due to superposition is overcome, longitudinal resolution of seismic data is further improved on the basis of the zero offset reflection data through odd-even component decomposition inversion, noise pressing processing is carried out on the basis of the obtained high-resolution reflection coefficients, the whole calculation process is carried out step by step, the problem of discomfort enhancement caused by directly utilizing angle superposition data calculation is reduced, the problem of detail blurring caused by total superposition seismic reflection signal superposition effect can be overcome, high-resolution zero offset reflection signals can be obtained, underground more real reflection information is recovered, and the capability of seismic data on thin layer depiction is effectively improved.
Specifically, in the present embodiment, step S100 includes the following sub-steps: s101, on the basis of the offset-domain common reflection point gather, converting the angle domain prestack gather by using the layer speed body. S102, based on the pre-stack gather processed in the step 101, respectively obtaining L different incidence angles theta and angle superposition data bodies D (theta 1 ),D(θ 2 )...D(θ L ). Further, in this embodiment, in step S101, before performing the conversion of the angle domain prestack gather using the layer velocity body, optimization processing targeting prestack inversion is further performed, where the optimization processing includes ablation, prestack gather denoising, and gather leveling.
Specifically, in the present embodiment, in step S200, zero offset data S is characterized p The objective function F (m) of the relation with the measured seismic data D is:
F(m)＝min||Gm-d||+β·m T W T Wm (1)
in the formula (1), G is the ratio gamma=V of the incident angle and the background longitudinal and transverse wave speed p /V s Coefficient matrix formed of V p For longitudinal wave velocity, V s For transverse wave velocity, the specific expression of G is:
g in formula (2) L1 ＝sec 2 (θ L )，G L2 ＝-8sin 2 (θ L )/γ 2 ，G L3 ＝2sin 2 (θ L )/γ 2 -tan 2 (θ L ) 2, gamma value is obtained through pre-stack inversion;
zero offset data S obtained by using m in formula (1) p Normal incidence transverse wave data S s Density change rate data S d The specific form of the composition is as follows:
m＝[S p ,S s ,S d ] T (3)
d in the formula (1) is a matrix formed by the superimposed data body of the sub-angle obtained by utilizing the observation seismic trace set, and is specifically expressed as follows:
d＝[D(θ 1 ),D(θ 2 )...D(θ L )] T (4)
in the formula (1), W is a flatness matrix, which is used for inhibiting the influence of noise on the extraction of zero offset data, and specifically expressed as:
beta in the formula (1) is a weight coefficient, and the weight of the flatness matrix constraint can be adjusted.
Specifically, in the present embodiment, step S400 includes the following sub-steps: s401, establishing an objective function for solving the parity component weight coefficient based on the zero offset data, and solving the parity component reflection weight coefficient based on the zero offset data obtained in the step S300; s402, acquiring zero offset data for improving resolution based on the zero offset parity component reflection coefficient and the parity component weight coefficient matrix obtained in the step S401. In step S401, zero offset seismic data S p Into odd component r o And even component r e The product of the weighting coefficients o, e respectively corresponding thereto, the correlation thereof can be expressed as:
S p ＝W sp [r o ,r e ][o,e] T (6)
in step S401, the inversion objective function ψ (m') based on the parity component weight coefficient of the zero offset data is:
ψ(m')＝min{||W sp Fm'-S p || 2 +μ||m'||+σ||PFm'-T|| 2 } (7)
in the formula (7), P is an integration matrix, W sp The wavelet matrix is zero offset seismic data, and T is low-frequency information of longitudinal wave impedance obtained by performing difference on logging data; μ, σ is a weight adjustment parameter; m' = [ o, e] T ，F＝[r o ,r e ]Wherein r is o ，r e The specific expression of the components which are the Dike function xi is as follows:
r o (t,m,n,Δt)＝ξ(t-mΔt)-ξ(t-mΔt+nΔt) (8)
r e (t,m,n,Δt)＝ξ(t-mΔt)+ξ(t-mΔt+nΔt) (9)
in the formulas (8) and (9), t is a preset time, delta t is a sampling interval, m is a sampling point position corresponding to a thin layer top interface, and n is a sampling point position corresponding to a bottom interface.
In step S402, zero offset reflection data r with improved resolution is obtained by using the parity component weight coefficient obtained in step S401 and the parity component combination p The specific solving formula is as follows: r is (r) p ＝Fm' (10)。
Example 2
The method for thin layer characterization according to the embodiment of the present invention preferably further includes step S500 on the basis of embodiment 1: noise suppression and filtering processing are performed on the basis of the zero offset reflection data with improved resolution obtained according to step S400 to obtain zero offset reflection data with improved longitudinal resolution. And performing noise suppression by utilizing principal component analysis on the basis of the obtained zero offset reflection data with improved resolution, and suppressing abnormal frequency bands by utilizing band-pass filtering to obtain high-resolution zero offset reflection information, and providing constraint data for geological analysis and elastic parameter extraction of a thin target layer.
Example 3
Further, the method for thin layer characterization according to the embodiment of the present invention further includes step S600 on the basis of embodiment 2: and (3) carrying out elastic parameter inversion on the basis of the zero offset reflection data with improved longitudinal resolution obtained in the step S500, and carrying out geological evolution analysis on the research area.
Fig. 2 schematically shows measured well data including longitudinal wave velocity, transverse wave velocity and density data, and using these three measured well data to determine reflection coefficients for different angles of incidence. Fig. 3 schematically shows angle gathers calculated using theoretical log data, in this embodiment, the incidence angle is 0 ° to 36 °, each 4 ° interval, and then the reflection coefficient is convolved with a rake wavelet with a main frequency of 25Hz, so as to obtain gather data of incidence at different angles, which can be used as observation seismic data.
Fig. 4 schematically shows a comparison of a full stack of tracks and a zero offset track. The trace identified as 1 in fig. 4 is a true longitudinal wave reflection data trace set (obtained by using a rake wavelet convolution of a longitudinal wave reflection coefficient obtained by logging data and a main frequency of 25 Hz), the trace identified as 2 in fig. 4 is a superimposed data trace obtained by performing full superposition by using the seismic data observed in fig. 3, the trace identified as 3 is a difference between the trace identified as 1 and the trace identified as 2, that is, a difference between the true longitudinal wave reflection data and the superimposed data, the trace identified as 4 is zero offset data obtained by using the present invention, and the trace identified as 5 is a difference between the trace identified as 1 and the trace identified as 4, that is, a difference between the true longitudinal wave reflection data and the zero offset data obtained by using the method of the embodiment of the present invention. As can be seen by comparing the 3, 5 trace data in fig. 4, the data obtained by the conventional superposition method is error-prone to the true longitudinal wave reflection data, and the true longitudinal wave reflection is represented as two in-phase axes in the box (3176-3226 ms) in fig. 4, i.e. two reflection layers are represented, but the data in the box in the 2 nd trace superposition data becomes a thicker reflection axis due to the influence of AVO effect, thus resulting in a decrease in the resolution of the stratum. Thus, as can be seen from the comparison of fig. 4, the zero offset information of the embodiment of the present invention is more capable of recovering the true reflection information of the subsurface than the superimposed data, and highlights the small layer display.
FIG. 5 schematically shows a comparison of zero offset gathers with parity decomposed high resolution reflection coefficient convolutions of a 25Hz wavelet. In fig. 5, trace 1 is a true longitudinal wave reflection seismic trace obtained by convoluting a 25Hz wavelet with a true longitudinal wave reflection coefficient, trace 2 is a zero offset reflection seismic trace obtained by inversion of sub-angle superposition data with a main frequency of 25Hz, and trace 3 is a seismic trace obtained by convoluting a 25Hz wavelet with a reflection coefficient with a parity component obtained by the method after decomposition. By contrast, when the reflection coefficient of resolution is improved after the wavelet convolution parity decomposition with the main frequency of 25Hz is utilized, the obtained seismic trace is consistent with the true longitudinal wave reflection seismic trace and the zero offset seismic trace, namely the reflection coefficient obtained by the thin layer characterization method provided by the invention does not generate an unreasonable false axis, namely the result is credible.
FIG. 6 schematically shows a comparison of zero offset gathers with a parity decomposed high resolution reflection coefficient convolved 35Hz wavelet. The 1 st channel in fig. 6 is a longitudinal wave reflection seismic channel obtained by convoluting 35Hz wavelet with the well data actually measured in fig. 2, the 2 nd channel is a zero offset reflection seismic channel obtained by inversion of angle-division superimposed data with a main frequency of 25Hz, and the 3 rd channel is a seismic channel obtained by processing the 2 nd channel data in steps S400 and 500 of the invention, wherein the main component analysis in step S500 mainly plays a role of removing noise, and the high-frequency filtering mainly filters out reflection coefficients higher than 70Hz and also plays a role of suppressing noise. The method provided by the invention can be used for obtaining thin-layer reflection interfaces (in the frame of fig. 6) which cannot be marked by conventional superposition data. In order to verify the authenticity of the thin layer depicted by the invention, the 1 st and 3 rd channels of fig. 6 can be compared, and the reflection layer after resolution improvement can also be seen in the synthetic record of 35Hz (1 st channel of fig. 6), so that the reflection information of the thin layer can be highlighted more relative to the zero offset seismic reflection channel (2 nd channel of fig. 6) obtained by inversion, and the feasibility and the effectiveness of the invention are verified.
Fig. 7 schematically shows the result of comparing zero offset data obtained by inversion of actual observation data with data obtained by the thin layer characterization method according to the embodiment of the present invention. Wherein, fig. 7 (a) is a longitudinal wave reflection seismic trace obtained by using the convolution of the measured well data longitudinal wave impedance and the rake wavelet with the dominant frequency of 40Hz, fig. 7 (b) is the conventional superposition data obtained by observation, fig. 7 (c) is a zero offset seismic trace set obtained by using the inversion of the angle-divided superposition data volume, fig. 7 (d) is a reflection seismic trace obtained by using the thin layer characterization method proposed by the present invention, and fig. 7 (e) is the measured well data longitudinal wave impedance. Comparing fig. 7 (b) with fig. 7 (c), it can be seen that the inverted zero offset data is more consistent with the well synthetic seismic record and can reflect a depiction of a thin layer relative to the superimposed data, for example at 3025ms dashed line. Fig. 7 (d) shows the reflection seismic trace obtained by the thin layer characterization method according to the present invention, and by comparing the reflection seismic trace obtained by the device according to the present invention with the zero offset information, the thin layer information is further more enriched, such as the inside of the square frame in the figure.
Fig. 8 schematically shows conventional full stack data, zero offset data obtained using pre-stack inversion, and reflection data obtained by the method of an embodiment of the present invention. Fig. 8 (a) is conventional superimposed data, fig. 8 (b) is zero offset data obtained by pre-stack inversion, and fig. 8 (c) is zero offset high-resolution reflection seismic data obtained by suppressing noise through parity component decomposition, principal component analysis, and filtering processing on the basis of the zero offset data in fig. 8 (b). Compared with the zero offset data, the method for thin layer characterization has the advantages that the capability of thin layer characterization is further improved, and the longitudinal resolution of seismic reflection information is obviously improved.
Fig. 9 schematically shows the comparison of the full stack data with the high resolution data obtained using the method of the embodiments of the present invention and the inversion impedance results using both. Wherein, fig. 9 (a) is conventional superimposed seismic reflection data, fig. 9 (b) is longitudinal wave impedance data obtained by inversion using the reflection data in fig. 9 (a), fig. 9 (c) is seismic reflection data obtained by the method of the embodiment of the invention, and fig. 9 (d) is longitudinal wave impedance data obtained by inversion using the reflection data in fig. 9 (c). In the figure, the well passing line is actually measured longitudinal wave impedance data, and comparison of inversion results shows that the inversion result obtained by the method for thin layer characterization not only improves the longitudinal resolution of a reservoir layer, but also is more prominent in the thin layer and is more consistent with the well data, and the reliability of the method provided by the invention is verified.
Example 4
The system for thin layer characterization according to the embodiment of the second aspect of the invention comprises a first processing module for acquiring a research distinction angle superposition data volume and a background longitudinal and transverse wave speed ratio. And the second processing module is used for acquiring an objective function representing the relation between the zero offset data and the actually measured seismic data. And the third processing module is used for carrying out inversion processing according to the obtained objective function to obtain the real zero offset seismic reflection data of the research area. And the fourth processing module is used for constructing an objective function of the parity decomposition weight coefficient according to the technology of the real zero offset seismic reflection data, acquiring the weight coefficient of the parity component of the zero offset seismic reflection data, and acquiring the zero offset seismic reflection data with improved resolution by combining the parity decomposition matrix.
Further, in this embodiment, the system further includes an optimization processing module, configured to perform noise suppression and filtering processing on the basis of the zero offset reflection data after resolution enhancement, to obtain zero offset reflection data with enhanced longitudinal resolution. Furthermore, in this embodiment, the system further includes a post-processing module, configured to perform elastic parameter inversion based on the zero offset reflection data with improved longitudinal resolution, and perform geological meaning analysis on the investigation region.
Similarly, the system for thin layer depiction of the invention considers the influence of superposition and odd components of reflection coefficient on thin layer depiction, obtains zero offset reflection data through pre-superposition inversion, overcomes the problem of longitudinal resolution reduction caused by superposition of gather data, further improves the longitudinal resolution of seismic data on the basis of the zero offset reflection data through odd-even component decomposition inversion, performs noise pressing processing on the basis of the obtained high resolution reflection coefficient, calculates step by step in the whole calculation process, reduces the problem of discomfort enhancement caused by directly utilizing angle superposition data calculation, can not only overcome the problem of detail blurring caused by the superposition effect of total superposition seismic reflection signals, but also obtain high-resolution zero offset reflection signals, recover more real reflection information underground and effectively improve the capability of seismic data on thin layer depiction.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the respective embodiments may be combined in any manner as long as there is no structural conflict. The present invention is not limited to the specific embodiments disclosed herein, but encompasses all technical solutions falling within the scope of the claims.
Claims (10)
1. A method for thin layer characterization, comprising the steps of:
s100, acquiring a research distinction angle superposition data body, and acquiring a background longitudinal and transverse wave speed ratio of a research area through prestack inversion;
s200, acquiring an objective function for representing the relationship between the zero offset data and the measured seismic data, wherein,
the zero offset data is a longitudinal wave impedance reflectivity convolution wavelet result;
the objective function is obtained by algebraic calculation according to the longitudinal wave impedance reflectivity and a coefficient matrix of an approximate formula, wherein the longitudinal wave impedance reflectivity is the ratio of the impedance difference between the upper layer and the lower layer of the interface to the impedance sum of the upper layer and the lower layer of the interface, and the coefficient matrix of the approximate formula is the ratio of the incident angle to the longitudinal wave speed;
s300, inversion processing is carried out on the angle-divided superimposed data body by using the obtained objective function, so that real zero offset seismic reflection data of a research area are obtained;
s400, constructing an objective function of the parity component weight coefficient based on the true zero offset reflection data obtained by inversion in the step S300, obtaining the parity component weight coefficient of the zero offset reflection data, and obtaining the zero offset reflection data with improved resolution based on the parity component weight coefficient.
2. The method for thin layer characterization according to claim 1, further comprising step S500: noise suppression and filtering processing are performed on the basis of the zero offset reflection data with improved resolution obtained according to step S400 to obtain zero offset reflection data with improved longitudinal resolution.
3. The method for thin layer characterization according to claim 2, further comprising step S600: and (3) carrying out elastic parameter inversion on the basis of the zero offset reflection data with improved longitudinal resolution obtained in the step (S500), and carrying out geological meaning analysis on a research area.
4. A method for thin layer characterization according to any of claims 1 to 3, wherein step S100 comprises the sub-steps of:
s101, on the basis of the offset-domain common reflection point gather, converting an angle domain prestack gather by using a layer speed body;
s102, based on the pre-stack channel set processed in the step S101, respectively obtaining L different incidence angles theta and angle superposition data bodies D (theta 1 ),D(θ 2 )...D(θ L )。
5. The method for thin-layer characterization according to claim 4, characterized in that in step S101, before the conversion of the angle-domain prestack gather with the layer velocity body, an optimization process targeting prestack inversion is performed, wherein the optimization process comprises ablation, prestack gather denoising and gather flattening.
6. The method for thin layer characterization according to claim 4 wherein, in step S200,characterizing zero offset data S p The objective function F (m) of the relation with the measured seismic data D is:
F(m)＝min||Gm-d||+β·m T W T Wm (1)
in the formula (1), G is the ratio gamma=V of the incident angle and the background longitudinal and transverse wave speed p /V s Coefficient matrix formed of V p For longitudinal wave velocity, V s For transverse wave velocity, the specific expression of G is:
g in formula (2) L1 ＝sec 2 (θ L )，G L2 ＝-8sin 2 (θ L )/γ 2 ，G L3 ＝2sin 2 (θ L )/γ 2 -tan 2 (θ L ) 2, gamma value is obtained through pre-stack inversion;
zero offset data S obtained by using m in formula (1) p Normal incidence transverse wave data S s Density change rate data S d The specific form of the composition is as follows:
m＝[S p ,S s ,S d ] T (3)
d in the formula (1) is a matrix formed by the superimposed data body of the sub-angle obtained by utilizing the observation seismic trace set, and is specifically expressed as follows:
d＝[D(θ 1 ),D(θ 2 )...D(θ L )] T (4)
in the formula (1), W is a flatness matrix, which is used for inhibiting the influence of noise on the extraction of zero offset data, and specifically expressed as:
beta in the formula (1) is a weight coefficient, and the weight of the flatness matrix constraint can be adjusted.
7. The method for thin layer characterization according to claim 6, wherein the step S400 comprises the sub-steps of:
s401, establishing an objective function for solving the parity component weight coefficient based on the zero offset data, and solving the parity component reflection weight coefficient based on the zero offset data obtained in the step S300;
s402, acquiring zero offset data for improving resolution based on the zero offset odd-even component reflection coefficient and the odd-even component weight coefficient matrix acquired in the step S401;
in the step S401, zero offset seismic data S p Into odd component r o And even component r e The product of the weighting coefficients o, e respectively corresponding thereto, the correlation thereof can be expressed as:
S p ＝W sp [r o ,r e ][o,e] T (6)
in the step S401, the inversion objective function ψ (m') of the parity component weight coefficient based on the zero offset data is:
ψ(m')＝min{||W sp Fm'-S p || 2 +μ||m'||+σ||PFm'-T|| 2 } (7)
in the formula (7), P is an integration matrix, W sp The wavelet matrix is zero offset seismic data, and T is low-frequency information of longitudinal wave impedance obtained by performing difference on logging data; μ, σ is a weight adjustment parameter; m' = [ o, e] T ，F＝[r o ,r e ]Wherein r is o ，r e The specific expression of the components which are the Dike function xi is as follows:
r o (t,m,n,Δt)＝ξ(t-mΔt)-ξ(t-mΔt+nΔt) (8)
r e (t,m,n,Δt)＝ξ(t-mΔt)+ξ(t-mΔt+nΔt) (9)
in the formulas (8) and (9), t is a preset time, delta t is a sampling interval, m is a sampling point position corresponding to a thin layer top interface, and n is a sampling point position corresponding to a bottom interface;
in the step S402, the resolution is improved by using the parity component weight coefficient and the parity component combination obtained in the step S401Zero offset reflectance data r of rate p The specific solving formula is as follows:
r p ＝Fm' (10)。
8. a system for thin layer characterization, comprising
The first processing module is used for acquiring a research distinguishing angle superposition data body and a background longitudinal and transverse wave speed ratio;
the second processing module is used for obtaining an objective function representing the relation between the zero offset data and the actually measured seismic data;
the third processing module is used for carrying out inversion processing according to the obtained objective function to obtain real zero offset seismic reflection data of the research area;
and the fourth processing module is used for constructing an objective function of the parity decomposition weight coefficient according to the technology of the real zero offset seismic reflection data, acquiring the weight coefficient of the parity component of the zero offset seismic reflection data, and acquiring the zero offset seismic reflection data with improved resolution by combining the parity decomposition matrix.
9. The system for thin layer characterization according to claim 8, further comprising an optimization processing module configured to perform noise suppression and filtering processing on the zero offset reflection data after resolution enhancement to obtain the zero offset reflection data for longitudinal resolution enhancement.
10. The system for thin-layer characterization according to claim 9, further comprising a post-processing module for performing elastic parameter inversion based on zero offset reflection data that improves longitudinal resolution for performing a geologic meaning analysis of the region of interest.
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