CN109143331B - Seismic wavelet extraction method - Google Patents
Seismic wavelet extraction method Download PDFInfo
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- CN109143331B CN109143331B CN201710501413.1A CN201710501413A CN109143331B CN 109143331 B CN109143331 B CN 109143331B CN 201710501413 A CN201710501413 A CN 201710501413A CN 109143331 B CN109143331 B CN 109143331B
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
Abstract
A seismic wavelet extraction method is disclosed. The method can comprise the following steps: obtaining reflection coefficient sequences of a plurality of wells and corresponding well-side seismic traces based on well data and seismic data, wherein the reflection coefficient sequences of the plurality of wells are represented by a reflection coefficient matrix, and the corresponding well-side seismic traces are represented by a well-side seismic trace matrix; establishing a dictionary learning equation related to the seismic wavelet matrix based on the reflection coefficient matrix and the well-side seismic channel matrix; establishing a target function based on the reflection coefficient matrix, the well-side seismic channel matrix and the seismic wavelet matrix, and calculating the seismic wavelet matrix which enables the value of the target function to be minimum to serve as a final seismic wavelet matrix; and extracting and processing the final seismic wavelet matrix to obtain average wavelets. The invention adopts a dictionary learning method, and can accurately and efficiently extract the seismic wavelets of a plurality of wells at the same time.
Description
Technical Field
The invention relates to the field of seismic exploration and development of oil gas and coal bed gas, in particular to a seismic wavelet extraction method.
Background
With the need of oil and gas exploration and development, reservoir prediction and fine description are more and more emphasized, exploration and research carried out around the purpose are more and more, and seismic inversion is the most important point. When the target of the inversion is the wave impedance, we refer to it as wave impedance inversion. The current seismic wave impedance inversion is divided into two categories of post-stack inversion and pre-stack inversion according to used data, and the essence of any inversion is to remove the influence of wavelets, so that a seismic profile is converted into a form capable of being directly compared with data such as well drilling, geology and the like, therefore, the inversion improves the resolution ratio of a conventional earthquake under many conditions and improves the level of reservoir parameter research, which has very important significance for researching the spatial distribution of a complex oil and gas reservoir and developing the fine description of the complex oil and gas reservoir.
To remove the effect of the wavelet, the seismic wavelet is first extracted. At present, methods for extracting seismic wavelets are mainly divided into two types: deterministic wavelet extraction and statistical wavelet extraction. The two wavelet extraction methods are based on the same physical basis, namely a seismic convolution model, and the noiseless convolution model is expressed as follows:
S(t)＝W(t)*R(t) (1)
wherein s (t) represents seismic signals, w (t) represents wavelets, r (t) represents a sequence of reflection coefficients, and x represents convolution.
Statistical wavelet extraction estimates wavelets from the seismic data itself. Assuming that the seismic wavelet is invariant and the sequence of reflection coefficients is a random sequence with a white noise spectrum, the autocorrelation of the seismic traces is equivalent to the autocorrelation estimation of the seismic wavelet, thereby obtaining the amplitude spectrum (wavelet second order statistic) of the seismic wavelet, and for the phase spectrum, it is generally assumed to be the minimum phase or zero phase. Higher order statistical methods involving wavelet phase spectra arise because seismic wavelets are actually mixed-phase. The advantage of this method is that no well data is needed, but it is a statistical-based method and therefore requires a large number of statistics, and in addition, methods based on higher order statistics often require solving equations, use optimization techniques, both of which increase the amount of computation, and the solution process does not ensure stability.
The deterministic wavelet extraction method is to calculate a reflection coefficient sequence by using logging data, and then combine well-side seismic channels to calculate seismic wavelets by a convolution theory, wherein commonly used calculation methods comprise least square wiener filtering, spectrum division and the like. Its advantage is no need of making any hypothesis on the distribution of reflection coefficient sequence, and more accurate wavelets can be obtained. However, there is not much choice in the calculation method, and the existing methods obtain one wavelet by using one well each time and then average a plurality of wavelets, so that a plurality of wells cannot be simultaneously used at one time. Therefore, it is necessary to develop an accurate and simple seismic wavelet extraction method.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a seismic wavelet extraction method, which can adopt a dictionary learning method and can accurately and efficiently extract seismic wavelets from a plurality of wells at the same time.
The invention provides a seismic wavelet extraction method. The method may include: obtaining reflection coefficient sequences of a plurality of wells and corresponding well-side seismic traces based on well data and seismic data, wherein the reflection coefficient sequences of the plurality of wells are represented by a reflection coefficient matrix, and the corresponding well-side seismic traces are represented by a well-side seismic trace matrix; establishing a dictionary learning equation related to the seismic wavelet matrix based on the reflection coefficient matrix and the well-side seismic channel matrix; establishing a target function based on the reflection coefficient matrix, the well-side seismic channel matrix and the seismic wavelet matrix, and calculating the seismic wavelet matrix which enables the value of the target function to be minimum to serve as a final seismic wavelet matrix; and extracting and processing the final seismic wavelet matrix to obtain average wavelets.
Preferably, obtaining a plurality of well reflection coefficient sequences and corresponding well-side seismic traces based on the well data and the seismic data, wherein the well reflection coefficient sequences are represented by a reflection coefficient matrix, and the corresponding well-side seismic traces are represented by a well-side seismic trace matrix, comprises: calculating a reflection coefficient sequence of each well based on the well data and the seismic data, and converting the reflection coefficient sequence of each well into a time domain; carrying out well-to-seismic calibration on each well to obtain a well-side seismic channel corresponding to each well; and taking each reflection coefficient sequence of the time domain as a column vector to obtain the reflection coefficient matrix, and taking the well-side seismic channel corresponding to each well as the column vector to obtain the well-side seismic channel matrix.
Preferably, the dictionary learning equation is:
Y＝D·X (2)
wherein Y represents a well-side seismic channel matrix, X represents a reflection coefficient matrix, and D represents a seismic wavelet matrix.
Preferably, the objective function is:
wherein f (X) represents an objective function, X represents a reflection coefficient matrix, Y represents a well-side seismic channel matrix, D represents a seismic wavelet matrix, and gamma represents a weight factor, | · |. upRepresenting the p-norm.
Preferably, extracting and processing the final seismic wavelet matrix to obtain an average wavelet comprises: extracting each row of the final seismic wavelet matrix as a seismic wavelet sequence respectively, and obtaining the initial positions and the lengths corresponding to a plurality of seismic wavelet sequences; for each seismic wavelet sequence, carrying out Fourier transform on the seismic wavelet sequence based on the corresponding initial position and length of the seismic wavelet sequence to obtain an amplitude spectrum and a phase spectrum of the seismic wavelet sequence within the length; and carrying out average calculation on the amplitude spectrum and the phase spectrum of a plurality of seismic wavelet sequences to obtain an average amplitude spectrum and an average phase spectrum, and further obtaining the average wavelet.
Preferably, the average wavelet is:
wherein A (ω) represents the average amplitude spectrum,
denotes the average phase spectrum, i denotes the imaginary unit, and w (ω) denotes the average wavelet in the frequency domain.
Preferably, the method further comprises the following steps: and converting the average wavelet of the frequency domain into a time domain through inverse Fourier transform to obtain the average wavelet of the time domain.
The invention has the beneficial effects that: by adopting the dictionary learning method, a plurality of wells and corresponding well-side seismic channel data can be simultaneously utilized, seismic wavelets can be simultaneously extracted from the plurality of wells, and regularization terms are set, so that the seismic wavelets can be more accurately extracted.
The method of the present invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
FIG. 1 shows a flow chart of the steps of a seismic wavelet extraction method according to the present invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 shows a flow chart of the steps of a seismic wavelet extraction method according to the present invention.
The seismic wavelet extraction method according to the present invention may include:
102, establishing a dictionary learning equation related to the seismic wavelet matrix based on the reflection coefficient matrix and the well-side seismic channel matrix; the dictionary learning method is mainly applied to sparse signal representation, namely, the signal is assumed to be sparsely represented, namely, the sparsity is that the signal at many sampling points is zero. This assumption can be satisfied in seismic wavelet extraction, because in actual work, the depth domain wave impedance curve obtained from logging data is usually square-shaped, and then when the curve is converted into a time domain and a time domain reflection coefficient sequence is calculated, many sampling point values are zero, so that a dictionary learning method can be adopted to extract seismic wavelets;
103, establishing a target function based on the reflection coefficient matrix, the well-side seismic channel matrix and the seismic wavelet matrix, and calculating the seismic wavelet matrix which enables the value of the target function to be minimum to serve as a final seismic wavelet matrix;
and 104, extracting and processing the final seismic wavelet matrix to obtain average wavelets.
In one example, obtaining a sequence of reflection coefficients for a plurality of wells and corresponding parawell seismic traces based on the well data and the seismic data, wherein the sequence of reflection coefficients for the plurality of wells is represented in a reflection coefficient matrix and the corresponding parawell seismic traces are represented in a parawell seismic trace matrix comprises: calculating a reflection coefficient sequence of each well based on the well data and the seismic data, and converting the reflection coefficient sequence of each well into a time domain; carrying out well-to-seismic calibration on each well to obtain a well-side seismic channel corresponding to each well; and taking each reflection coefficient sequence of the time domain as a column vector to obtain a reflection coefficient matrix, and taking the well-side seismic channel corresponding to each well as the column vector to obtain a well-side seismic channel matrix.
In one example, the dictionary learning equation is:
Y＝D·X (2)
wherein Y represents a well-side seismic channel matrix, X represents a reflection coefficient matrix, and D represents a seismic wavelet matrix.
In one example, the objective function is:
wherein f (X) represents an objective function, X represents a reflection coefficient matrix, Y represents a well-side seismic channel matrix, D represents a seismic wavelet matrix, and gamma represents a weight factor, which is generally 5-10 | · | |pDenotes the p-norm, p typically being 1. The optimization method of the objective function can adopt an optimal direction method, a K-SVD method or a gradient descent method, and when a proper step length is selected, the gradient descent method is possibly superior to the optimal direction method and the K-SVD method.
In one example, extracting and processing the final seismic wavelet matrix to obtain an average wavelet comprises: extracting each row of the final seismic wavelet matrix as a seismic wavelet sequence respectively, and obtaining the initial positions and the lengths corresponding to a plurality of seismic wavelet sequences; for each seismic wavelet sequence, carrying out Fourier transform on the seismic wavelet sequence based on the corresponding initial position and length of the seismic wavelet sequence to obtain an amplitude spectrum and a phase spectrum of the seismic wavelet sequence within the length; and carrying out average calculation on the amplitude spectrum and the phase spectrum of a plurality of seismic wavelet sequences to obtain an average amplitude spectrum and an average phase spectrum, and further obtaining average wavelets.
In one example, the average wavelet is:
wherein A (ω) represents the average amplitude spectrum,
denotes the average phase spectrum, i denotes the imaginary unit, and w (ω) denotes the average wavelet in the frequency domain.
In one example, further comprising: and converting the average wavelet in the frequency domain into the time domain through inverse Fourier transform to obtain the average wavelet in the time domain.
Specifically, the final seismic wavelet matrix contains information of seismic wavelets and is composed of seismic wavelets. These seismic wavelets are all long in length, but vary in origin. These seismic wavelets then need to be fused or reconstructed to form a seismic wavelet. Extracting each column in the final seismic wavelet matrix to be used as a seismic wavelet sequence, displaying the seismic wavelet sequence in a graph form to determine the starting point and the length of each seismic wavelet sequence, then obtaining the amplitude spectrum and the phase spectrum of the seismic wavelet sequence within the length by utilizing Fourier transform, finally respectively carrying out average calculation on the amplitude spectrum and the phase spectrum corresponding to the seismic wavelet sequence of each column according to the frequency to obtain an average amplitude spectrum and an average phase spectrum, obtaining the average wavelet of a frequency domain by utilizing a formula (4), and obtaining the average wavelet of a time domain through inverse Fourier transform to the time domain.
The invention adopts a dictionary learning method, can simultaneously utilize a plurality of wells and corresponding well-side seismic channel data, simultaneously extract seismic wavelets of the plurality of wells, and set regularization terms, and can more accurately extract the seismic wavelets.
Application example
To facilitate understanding of the aspects of the embodiments of the present invention and their effects, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
Inputting all well data and seismic data in a work area, calculating a reflection coefficient sequence on each well, converting the reflection coefficient sequence on each well into a time domain, positioning at time sampling intervals of 1 millisecond, then carrying out well-seismic calibration to obtain a well-side seismic channel corresponding to each well, namely obtaining a starting point and a terminating point corresponding to the reflection coefficient on each well, taking each reflection coefficient sequence of the time domain as a column vector to obtain a reflection coefficient matrix, and taking the well-side seismic channel corresponding to each well as the column vector to obtain a well-side seismic channel matrix; and establishing a dictionary learning equation related to the seismic wavelet matrix as a formula (2) based on the reflection coefficient matrix and the well-side seismic channel matrix. And (3) establishing an objective function as a formula (3) based on the reflection coefficient matrix, the well-side seismic channel matrix and the seismic wavelet matrix, wherein gamma is 7, and p is 1, and calculating the seismic wavelet matrix which minimizes the value of the objective function by adopting an optimal direction method to serve as a final seismic wavelet matrix.
Extracting each column in the final seismic wavelet matrix to be used as a seismic wavelet sequence, displaying the seismic wavelet sequence in a graph form to determine the starting point and the length of each seismic wavelet sequence, then obtaining the amplitude spectrum and the phase spectrum of the seismic wavelet sequence within the length by utilizing Fourier transform, finally respectively carrying out average calculation on the amplitude spectrum and the phase spectrum corresponding to the seismic wavelet sequence of each column according to the frequency to obtain an average amplitude spectrum and an average phase spectrum, obtaining the average wavelet of a frequency domain by utilizing a formula (4), and converting the average wavelet into a time domain by utilizing inverse Fourier transform to obtain the average wavelet of the time domain.
In conclusion, the invention adopts the dictionary learning method, can simultaneously utilize a plurality of wells and corresponding well-side seismic channel data, simultaneously extract seismic wavelets from the plurality of wells, and set regularization terms, thereby more accurately extracting the seismic wavelets.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the beneficial effects of embodiments of the invention and is not intended to limit embodiments of the invention to any of the examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (6)
1. A seismic wavelet extraction method, comprising:
obtaining reflection coefficient sequences of a plurality of wells and corresponding well-side seismic traces based on well data and seismic data, wherein the reflection coefficient sequences of the plurality of wells are represented by a reflection coefficient matrix, and the corresponding well-side seismic traces are represented by a well-side seismic trace matrix;
establishing a dictionary learning equation related to the seismic wavelet matrix based on the reflection coefficient matrix and the well-side seismic channel matrix;
establishing a target function based on the reflection coefficient matrix, the well-side seismic channel matrix and the seismic wavelet matrix, and calculating the seismic wavelet matrix which enables the value of the target function to be minimum to serve as a final seismic wavelet matrix;
extracting and processing the final seismic wavelet matrix to obtain an average wavelet;
wherein the objective function is:
wherein f (X) represents an objective function, X represents a reflection coefficient matrix, Y represents a well-side seismic channel matrix, D represents a seismic wavelet matrix, gamma represents a weight factor, | | X | | YpRepresenting the p-norm of X.
2. The seismic wavelet extraction method of claim 1, wherein obtaining a plurality of well reflection coefficient sequences and corresponding well-side seismic traces based on well data and seismic data, wherein the plurality of well reflection coefficient sequences are represented in a reflection coefficient matrix, and the corresponding well-side seismic traces are represented in a well-side seismic trace matrix comprises:
calculating a reflection coefficient sequence of each well based on the well data and the seismic data, and converting the reflection coefficient sequence of each well into a time domain;
carrying out well-to-seismic calibration on each well to obtain a well-side seismic channel corresponding to each well;
and taking each reflection coefficient sequence of the time domain as a column vector to obtain the reflection coefficient matrix, and taking the well-side seismic channel corresponding to each well as the column vector to obtain the well-side seismic channel matrix.
3. The seismic wavelet extraction method of claim 1, wherein the dictionary learning equation is:
Y＝D·X (2)
wherein Y represents a well-side seismic channel matrix, X represents a reflection coefficient matrix, and D represents a seismic wavelet matrix.
4. The seismic wavelet extraction method of claim 1, wherein extracting and processing the final seismic wavelet matrix to obtain average wavelets comprises:
extracting each row of the final seismic wavelet matrix as a seismic wavelet sequence respectively, and obtaining the initial positions and the lengths corresponding to a plurality of seismic wavelet sequences;
for each seismic wavelet sequence, carrying out Fourier transform on the seismic wavelet sequence based on the corresponding initial position and length of the seismic wavelet sequence to obtain an amplitude spectrum and a phase spectrum of the seismic wavelet sequence within the length;
and carrying out average calculation on the amplitude spectrum and the phase spectrum of a plurality of seismic wavelet sequences to obtain an average amplitude spectrum and an average phase spectrum, and further obtaining the average wavelet.
5. The seismic wavelet extraction method of claim 4, wherein the average wavelet is:
wherein A (ω) represents the average amplitude spectrum,
denotes the average phase spectrum, i denotes the imaginary unit, and w (ω) denotes the average wavelet in the frequency domain.
6. The seismic wavelet extraction method of claim 5, further comprising: and converting the average wavelet of the frequency domain into a time domain through inverse Fourier transform to obtain the average wavelet of the time domain.
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