CN112305584B - Phase shift wavelet-based strong reflection separation method for thin coal seam - Google Patents
Phase shift wavelet-based strong reflection separation method for thin coal seam Download PDFInfo
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Abstract
The invention provides a phase-shift wavelet-based thin coal seam strong reflection separation method, a computer storage medium and computer equipment. The method firstly carries out research aiming at the thin coal seam reflection mode, determines the thin coal seam earthquake reflection mode, then selects 90-degree phase shift wavelets as matching wavelets to carry out strong reflection separation, and determines a strong reflection separation coefficient by analyzing the relation between strong reflection energy and total energy, thereby being capable of more reasonably and effectively separating the strong reflection of the thin coal seam. In actual data application, the improved algorithm achieves better application effect.
Description
Technical Field
The invention belongs to the technical field of seismic explanatory processing, and particularly relates to a phase shift wavelet-based thin coal seam strong reflection separation method, a computer storage medium and computer equipment. The method utilizes 90-degree phase shift wavelets to carry out matching tracking thin coal seam strong reflection identification, better separates the thin coal seam strong reflection, and highlights the reservoir weak reflection signal so as to better carry out reservoir prediction work.
Background
Due to the fact that the large wave impedance difference exists between the stratum of the thin coal seam and the surrounding rock, an obvious strong reflection homophase axis appears on the seismic section, the strong reflection can shield weak signals of a reservoir stratum, and adverse effects are brought to prediction of a target reservoir stratum (non-coal seam). In this regard, the scholars have conducted relevant studies. Common methods include conventional high resolution processing, attribute analysis, pre-stack and post-stack inversion, and strong reflector separation methods based on matching pursuit. However, due to the lack of deep knowledge of the reflection mode of the thin coal seam, the methods still have certain limitations in the practical application process, the application effect is not ideal, and the subsequent reservoir research work is not facilitated.
For example, mallat et al propose a Matching Pursuit algorithm (Matching Pursuit) that is capable of decomposing the original signal into a linear combination of atoms determined by a set of control parameters: center delay time, frequency, phase, scale factor, and amplitude. Original signals are reconstructed by selecting combinations of atoms with different parameters, so that the purposes of denoising, frequency division and the like are achieved. Aiming at the problem that the strong reflection in-phase axis of the thin coal seam shields the weak signal of the reservoir, the matching tracking algorithm is an effective method. However, in the actual operation process, due to the lack of deep knowledge on the reflection mode of the thin coal seam, parameters are not properly selected, and the application effect of the method is influenced.
Disclosure of Invention
Aiming at the defects of the prior art, the invention improves the prior algorithm and provides a novel phase-shift wavelet-based thin coal seam strong reflection separation method. In essence, the method is a method for acquiring weak reflection of a reservoir on the basis of performing strong reflection matching tracking by using 90-degree phase shift wavelets and separating the strong reflection.
According to one aspect of the invention, the method for separating the strong reflection of the thin coal seam based on the phase-shifted wavelet mainly comprises the following steps:
s100, analyzing original seismic data containing the strong reflection of the thin coal seam to determine a seismic reflection mode of the thin coal seam;
s200, determining to adopt phase shift wavelets as matching wavelets to carry out strong reflection identification on the thin coal seam according to the seismic reflection mode of the thin coal seam;
s300, aiming at the original seismic data, carrying out strong reflection identification on the thin coal seam by using the matched wavelets to obtain strong reflection characteristic wavelets of the thin coal seam;
s400, analyzing the relationship between the strong reflection energy of the thin coal seam and the total energy of the original seismic data to determine a strong reflection separation coefficient;
and S500, removing the wavelet with the strong reflection characteristics of the thin coal seam from the original seismic data according to the strong reflection separation coefficient to obtain a reservoir weak reflection signal in the original seismic data so as to carry out reservoir attribute analysis.
According to the embodiment of the invention, in the step S200, the 90-degree phase shift wavelet is adopted as the matching wavelet according to the thin coal seam seismic reflection mode.
According to an embodiment of the present invention, in step S200, it is preferable to study the seismic reflection pattern of the thin coal seam by using a convolution forward method, so as to determine that a 90-degree phase shift wavelet is adopted as a matching wavelet.
According to an embodiment of the present invention, in the above steps S200 and S300, the thin coal seam strong reflection is identified as a thin coal seam strong reflection in-phase axis identification.
According to an embodiment of the present invention, in step S300, the wavelet wt with strong reflection characteristic of the thin coal seam is obtained strong Comprises the following steps:
wherein f is m Is the wavelet dominant frequency, t is the time, u is the central delay time, k is the wavelet scale factor, and phi is the phase.
According to an embodiment of the present invention, preferably, φ is 90 degrees.
According to the embodiment of the invention, in the step S400, the strong reflection separation coefficient is equal to the ratio of the strong reflection energy of the thin coal seam to the total energy of the original seismic data.
According to an embodiment of the present invention, in the step S500, according to the strong reflection separation coefficient, the strong reflection of the thin coal seam is removed from the original seismic data by the following formula to obtain a reservoir weak reflection signal in the original seismic data;
S new ＝S original -λ*wt strong
wherein S is original For raw seismic data, wt strong Is a strong reflection characteristic wavelet of a thin coal seam, lambda is a strong reflection separation coefficient, S new Is a reservoir weak reflection signal.
According to another aspect of the present invention, there is also provided a computer storage medium characterized by a computer program stored therein for implementing the above method.
According to yet another aspect of the present invention, the present invention also provides a computer device, characterized by comprising a memory and a processor for executing a computer program stored in the memory, wherein the computer program is for implementing the above method.
Compared with the prior art, the phase-shift wavelet-based strong reflection separation method for the thin coal seam has the following advantages or beneficial effects:
1) The 90-degree phase shift wavelet is more in line with a thin coal seam seismic reflection mode, and the separation effect is better;
2) The matching wavelet separation parameters are more reasonable, the separation precision is high, and the weak reflection characteristics of the reservoir can be better highlighted.
In the existing method, the strong reflection phase axis is separated in the strong reflection separation process, a necessary analysis process is lacked, and the application effect is poor. According to the improved method, firstly, a thin coal seam reflection mode is researched, a theoretical seismic response mode of the thin coal seam is further determined, then strong reflection homophase axis separation is carried out by adopting 90-degree phase shift wavelets, the separation precision is higher, and the weak reflection characteristic of a reservoir stratum is highlighted to a greater extent. Practical application shows that the seismic characteristics after the strong reflection separation of the thin coal seam have higher goodness of fit with actual well data and the reservoir characteristics are more obvious by utilizing the method, and the practicability and the superiority of the method are fully proved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
Further advantages and details of the invention will become apparent from the embodiments described below and with reference to the accompanying drawings. The following are schematic and show:
fig. 1 is a flowchart of a method for separating strong reflection of a thin coal seam based on phase-shifted wavelets according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of forward analysis of earthquake in a thin coal seam according to a second embodiment of the present invention;
FIG. 3 is a graph comparing seismic reflection waveforms and 90-degree phase shift wavelets of a thin coal seam according to a second embodiment of the present invention;
FIG. 4 is a graph comparing the strong reflection separation effect of the model data in the second embodiment of the present invention;
FIG. 5 is a synthetic histogram of actual well logs and synthetic seismic records of a second embodiment of the invention;
FIG. 6 is a graph of the analysis of the strong reflected energy and the superimposed energy according to the second embodiment of the present invention;
FIG. 7 is a graph of the separation effect of the thin coal layer of the actual well data according to the second embodiment of the present invention;
FIG. 8 is a plot of root mean square amplitude of the reservoir before and after strong reflection separation for example two of the present invention.
Detailed Description
Example one
Aiming at the problem of the separation of the strong reflection of the thin coal seam, the invention fully considers the earthquake reflection mode of the actual thin coal seam, and clearly proposes the identification and characterization of the strong reflection of the thin coal seam by adopting 90-degree phase shift wavelets for the first time. The method mainly comprises three parts of thin coal seam response mode analysis, matching, tracking and separating strong reflection and weak reflection reservoir prediction of the thin coal seam.
As shown in fig. 1, in this embodiment, the method includes the following specific steps:
1) Analyzing an actual earthquake response mode (also called a thin coal layer earthquake reflection mode) of the thin coal layer;
2) Determining to adopt 90-degree phase shift wavelets as matching wavelets according to the seismic reflection mode of the thin coal seam;
3) Aiming at original seismic data, performing strong reflection homophase axis identification on a thin coal seam by adopting 90-degree phase shift wavelets to obtain matching wavelets (also called as strong reflection characteristic wavelets) of the thin coal seam;
4) Determining strong reflection separation coefficient by analyzing the relationship between strong reflection energy and total energy of original seismic data
5) And subtracting the wavelet with the strong reflection characteristics of the thin coal seam from the original seismic data according to the strong reflection separation coefficient, thereby obtaining a reservoir weak reflection signal in the original seismic data and carrying out reservoir attribute analysis.
The specific implementation of each of the above steps is described in detail below.
Aiming at the problem of strong reflection of the thin coal seam to be researched, firstly, the seismic reflection mode of the thin coal seam needs to be determined, so that the strong reflection characteristic can be more accurately identified. In this embodiment, a simple thin coal seam model is preferably established, and the seismic reflection mode of the thin coal seam is studied by using a convolution forward method, so as to determine that the strong reflection of the coal seam is characterized by adopting 90-degree phase shift.
And assuming that the forward wavelet is w (t) and the reflection coefficient of the coal bed bottom is r, the reflection coefficient of the coal bed top is-r. The synthetic seismic record at the bottom of the coal seam is s b (t) = w (t) × r, synthetic seismic record of coal seam roof is s t (t-t 0 )＝w(t-t 0 )*(-r)＝-w(t-t 0 )*r＝-s b (t-t 0 ) Wherein, t 0 For top-bottom two-way travel time difference, for a thin coal seam (the thickness is 1-5m generally), the time difference is small (1-3 ms), and the synthesized waveform is expressed as syn (t) = s b (t)+s t (t-t 0 )＝s b (t)-s b (t-t 0 )。
Wherein, due to t 0 And the top and bottom synthetic waveforms of the wedge body in the tuning thickness are very close to the time derivative of the wavelet and accord with the Widess criterion.
Any seismic signal can be decomposed into stacks of sinusoidal signals of different frequencies according to a fourier transform. Meanwhile, the time derivative of the sinusoidal signal is consistent with its 90 degree phase shifted signal, as follows:
sin(x+90)＝cos[90-(x+90)]＝cos(x)＝(sinx)′
therefore, according to the above description, 90-degree phase shift wavelets can be used for effectively characterizing seismic response characteristics of the thin coal seam, particularly strong reflection of the thin coal seam.
Therefore, aiming at actual seismic data containing the thin coal seam strong reflection, matching tracking strong reflection separation is carried out by adopting 90-degree phase shift wavelets to obtain the thin coal seam strong reflection characteristic wavelets. Taking Morlet wavelets as an example, the expression of the wavelet of the strong reflection characteristic of the thin coal seam is as follows:
wherein, f m Is the wavelet dominant frequency, t is the time, u is the central delay time, k is the wavelet scale factor, and phi is the phase.
Wherein phi is 90 degrees aiming at strong reflection of the thin coal seam.
And finally, according to actual well forward modeling analysis, comparing and analyzing the strong reflection energy of the thin coal seam with the total energy of the original seismic data, determining a strong reflection separation coefficient, and then subtracting the strong reflection of the thin coal seam from the original seismic data through the following formula according to the strong reflection separation coefficient to obtain a reservoir weak reflection signal, so that the analysis and the research of the attribute of the subsequent weak reflection reservoir can be carried out.
S new ＝S original -λ*wt strong
Wherein S is original For raw seismic data, wt strong Is a strong reflection characteristic wavelet of a thin coal seam, lambda is a strong reflection separation coefficient, S new The weak reflection signal of the reservoir obtained after strong reflection separation.
The method mainly develops the study of the seismic reflection mode of the thin coal seam, and definitely proposes that the strong reflection identification of the thin coal seam is developed by utilizing 90-degree phase shift wavelets for the first time; meanwhile, a reasonable separation coefficient is determined by analyzing the relation between the strong reflection energy and the total energy, so that the separation effect is more reliable, and a data base is finally established for better reservoir weak reflection characteristic research.
Example two
The method provided by the invention can effectively separate the strong reflection of the thin coal seam, so that the weak reflection characteristic of the reservoir is more obvious, and a better application effect is obtained.
In a certain exploration area, severe strong reflection of a thin coal seam exists, and a weak reflection signal of a reservoir is shielded, so that strong reflection separation research needs to be carried out, so that reservoir prediction work can be carried out better.
For this reason, a thin coal seam seismic reflection mode study is firstly carried out. Compared with surrounding rocks, the thin coal seam has the characteristic of low impedance, so that the reflection coefficient of the top of the thin coal seam is negative, and the reflection coefficient of the bottom of the thin coal seam is positive. A simple model of the reflection coefficient of the thin coal seam is established. As shown in fig. 2 (a), the three reflection coefficients are respectively the weak reflection coefficient, the coal seam strong reflection coefficient and the superposition reflection coefficient of the two, and fig. 2 (b) is the corresponding forward seismic record. According to the Widess criterion and related research, the strong reflection of the thin coal seam is close to 90-degree phase shift wavelets. The thin coal seam strong reflection record is characterized by using a matching pursuit algorithm, as shown in fig. 3. The superimposed waveform shown in fig. 3 (a) is a thin coal seam strong reflection, and fig. 3 (b) is a 90-degree phase shift wavelet obtained by matching, and it can be seen that the two waveforms are very close, which indicates that the 90-degree phase shift wavelet can be used for characterizing the thin coal seam strong reflection.
Aiming at the model synthetic seismic record, a matching pursuit algorithm is utilized to carry out the test of the thin coal seam strong reflection separation method, as shown in figure 4. Fig. 4 (a) and fig. 4 (b) are the separation result obtained by the prior art and the separation result obtained by the method of the present invention, respectively, and it can be seen by comparison that the separation effect obtained by the prior art has a larger error from the ideal weak reflection, but by adopting the method of the present invention, the separation effect is significantly improved, and the effectiveness of the method is verified.
Further carrying out analysis and research aiming at actual data. FIG. 5 is a synthetic histogram of well logs from a well in the prospect and the synthetic seismic record calibration results. As can be seen from fig. 5 (a), the coal seam (black box) exhibits a distinct low velocity, low density characteristic with low impedance. As can be seen from fig. 5 (b), the reflectance mode of the thin coal seam is up-negative-down-positive (arrow), which is consistent with theory. It can be found by comparing the strong reflected energy with the original total energy (strong + weak superimposed energy), that the strong reflected energy accounts for about 50% of the superimposed energy, as shown in fig. 6. The strong reflection separation factor is set to 0.5 here. Then, using 90 degree phase shifted wavelets, matching pursuit strong reflection separation is performed, as shown in fig. 7. It can be seen that the original separation result is greatly different from the ideal weak reflection, the separation result cannot well represent the weak reflection characteristic of the reservoir, and the method is not suitable for developing subsequent reservoir research work. On the contrary, the method provided by the invention can better separate the strong reflection of the thin coal seam, the separation result is very close to the ideal weak reflection of the reservoir, the weak reflection characteristic of the reservoir is better represented, and the separation effect is obviously improved.
According to the well data analysis result, the matching pursuit thin coal layer strong reflection separation is carried out on the seismic data of the whole work area. The root mean square amplitude attribute before and after separation was extracted as shown in fig. 8. As can be seen from FIG. 8, the data well highlights the weak reflection characteristics of the reservoir, the characteristics of the fluvial facies reservoir (shown by white arrows) are clear, the geological understanding is matched, and the application effect is obvious.
Implementation III
The present embodiment also provides a computer storage medium characterized in that a computer program for implementing the above method is stored therein.
Practice four
The present embodiment also provides a computer device, which is characterized by comprising a memory and a processor, wherein the processor is configured to execute a computer program stored in the memory, and wherein the computer program is configured to implement the method described above.
In conclusion, the improved method provided by the invention firstly researches the reflection mode of the thin coal seam, further determines the theoretical seismic response mode of the thin coal seam, and adopts 90-degree phase shift wavelets to carry out strong reflection homophase axis separation, so that the separation precision is higher, and the weak reflection characteristic of the reservoir stratum is highlighted to a greater extent. The application of the actual data shows that the seismic characteristics after the strong reflection separation of the thin coal seam have higher goodness of fit with well data, the reservoir characteristics are more obvious, and the practicability of the invention is proved.
It is to be understood that the disclosed embodiments of this invention are not limited to the particular process steps or materials disclosed herein but are extended to equivalents thereof as would be understood by those ordinarily skilled in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "an embodiment" means that a particular feature, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "an embodiment" appearing in various places throughout the specification are not necessarily all referring to the same embodiment.
It will be appreciated by those of skill in the art that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A strong reflection separation method of a thin coal seam based on phase shift wavelets is characterized by comprising the following steps:
s100, analyzing original seismic data containing the strong reflection of the thin coal seam to determine a seismic reflection mode of the thin coal seam;
s200, determining to adopt phase shift wavelets as matching wavelets to carry out strong reflection identification on the thin coal seam according to the seismic reflection mode of the thin coal seam;
s300, aiming at the original seismic data, carrying out strong reflection identification on the thin coal seam by using the matched wavelets to obtain strong reflection characteristic wavelets of the thin coal seam;
s400, analyzing the relationship between the strong reflection energy of the thin coal seam and the total energy of the original seismic data to determine a strong reflection separation coefficient;
in the step S400, the strong reflection separation coefficient is equal to the ratio of the strong reflection energy of the thin coal seam to the total energy of the original seismic data;
and S500, removing the wavelet with the strong reflection characteristics of the thin coal seam from the original seismic data according to the strong reflection separation coefficient to obtain a reservoir weak reflection signal in the original seismic data so as to carry out reservoir attribute analysis.
2. The method for separating the strong reflection of the thin coal seam based on the phase shift wavelets as claimed in claim 1, wherein:
in the step S200, 90-degree phase shift wavelets are adopted as matching wavelets according to the seismic reflection mode of the thin coal seam.
3. The method for separating the strong reflection of the thin coal seam based on the phase-shifted wavelets according to claim 1, wherein:
in the step S200, a convolution forward method is used to study the seismic reflection mode of the thin coal seam, so as to determine that 90-degree phase shift wavelets are adopted as matching wavelets.
4. The method for separating the strong reflection of the thin coal seam based on the phase shift wavelets as claimed in claim 1, wherein:
in the steps S200 and S300, the thin coal seam strong reflection is identified as a thin coal seam strong reflection homophase axis identification.
5. The method for separating strong reflection of thin coal seam based on phase shift wavelet as claimed in claim 1, wherein in said step S300, said characteristic wavelet wt of strong reflection of thin coal seam is defined as strong Comprises the following steps:
wherein f is m Is the wavelet dominant frequency, t is the time, u is the central delay time, k is the wavelet scale factor, and phi is the phase.
6. The method for separating the strong reflection of the thin coal seam based on the phase-shifted wavelets according to claim 5, wherein:
phi is 90 degrees.
7. The method for separating the strong reflection of the thin coal seam based on the phase shift wavelets as claimed in claim 1, wherein:
in the step S500, according to the strong reflection separation coefficient, subtracting the low seam strong reflection characteristic wavelet from the original seismic data by the following formula to obtain a reservoir weak reflection signal in the original seismic data;
S new ＝S original -λ*wt strong
wherein S is original For raw seismic data, wt strong Is a strong reflection characteristic wavelet of a thin coal seam, lambda is a strong reflection separation coefficient, S new Is a reservoir weak reflection signal.
8. A computer storage medium, characterized in that a computer program for implementing the method of any one of the preceding claims 1 to 7 is stored therein.
9. A computer device comprising a memory and a processor for executing a computer program stored in the memory, the computer program being adapted to implement the method of any of claims 1 to 7.
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