CN112149614A - Pre-stack well-seismic combined intelligent denoising method - Google Patents
Pre-stack well-seismic combined intelligent denoising method Download PDFInfo
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Abstract
The invention discloses a prestack well-seismic combined intelligent denoising method, which comprises the following steps: firstly, carrying out data segmentation on a forward modeling gather on a well and forming all segmented sample data into a sample training data set; randomly extracting partial data from the sample training data set, and performing a dictionary learning algorithm of overcomplete dictionary and sparse coefficient iterative learning on the partial data to obtain an overcomplete dictionary; thirdly, performing data segmentation on the seismic prestack gather to obtain a prestack gather prediction sample data set; randomly extracting part of sample data from the prediction sample data set, solving a sparse representation coefficient of each sample data by using an over-complete dictionary, establishing a quadratic term objective function, and solving to obtain a pre-stack denoising gather; and fifthly, traversing all the gathers according to the third step and the fourth step to finish denoising. The invention achieves the goal of denoising by carrying out amplitude-preserving reconstruction on the effective signal, and the denoising of the prestack gather operated by the algorithm not only improves the signal-to-noise ratio, but also considers the AVO trend, thereby being beneficial to the inversion of prestack elastic parameters.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a prestack well-seismic combined intelligent denoising method.
Background
The prestack inversion is an important mode for explaining oil gas detection and geological model parameters at present, the theoretical basis of the prestack inversion is a Zoeppritz equation and an approximate expression thereof, the basic form of the prestack inversion is to simultaneously invert longitudinal waves, transverse waves and density by utilizing a common reflection point prestack gather under the theoretical constraint of the Zeoppritz equation, and then, by utilizing the three basic rock physical parameters, other elastic parameter data bodies representing hydrocarbon detection and physical properties are obtained through calculation.
The quality of the data of the prestack gather determines the prestack inversion effect, the data quality of the prestack gather is reflected in the aspects of signal-to-noise ratio, wide angle and resolution, wherein the signal-to-noise ratio has the largest influence on the stability of an inversion algorithm, and the inversion result becomes unstable because the signal-to-noise ratio influences the condition number of an inversion coefficient matrix. The noise of the prestack gather can be divided into two categories of linear noise and random noise, at present, the removal of the random noise of the prestack gather can be filtered by using a filter of a time domain aiming at Gaussian distribution random hypothesis or a frequency domain filter, and the removal of the linear noise can be filtered by using wave field transformation separation or two-dimensional Fourier transformation denoising. The objective of prestack denoising is amplitude-preserving denoising of effective signals, but in actual data, the effective signals and noise signals of a prestack gather are mixed together, the conditions of approximation and aliasing appear in the amplitude range, the frequency range and even the statistical attribute range, and the objective of preserving the effective signals while filtering noise is difficult to achieve by using a conventional noise filter.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a prestack well-seismic combined intelligent denoising method.
The purpose of the invention is realized by the following technical scheme:
a prestack well-seismic combined intelligent denoising method specifically comprises the following steps:
the method comprises the following steps: performing data segmentation of a uniform size on the forward gather on the well, wherein one piece of segmented block data is used as sample data, and all the sample data form a sample training data set;
step two: randomly extracting partial data from the sample training data set, and performing a dictionary learning algorithm on the partial sample data about an over-complete dictionary and sparse coefficient iterative learning to obtain an over-complete dictionary capable of representing the two-dimensional structure characteristics of the pre-stack gather;
step three: carrying out data segmentation on the seismic prestack gather to obtain a prestack gather prediction sample data set;
step four: randomly extracting part of sample data from the prediction sample data set, solving a sparse representation coefficient of each sample data by using the over-complete dictionary obtained in the step two, then establishing a quadratic term objective function taking the denoising prestack gather as a target, and solving to obtain the prestack denoising gather;
step five: traversing all the gather according to the sequence of the third step and the fourth step to complete the denoising of all the pre-stack gathers.
Specifically, the data segmentation process in the first step includes segmenting data by using a segmentation window with a size of M × M, where data in one segmentation window is a sample, the size of the segmentation window M is one wavelength, and data at a non-integral multiple window length position in the segmentation process is discarded.
Specifically, the second step specifically includes the following substeps:
s201, randomly taking K samples from a pre-stack trace set training sample set to form a training sample setThe learning objective is a two-dimensional structure comprising a prestack gather, each sample having thereon a sparsely represented overcomplete dictionary D and sparsely represented coefficients aiEstablishing an objective function expression as shown in the following formula:
wherein λ is a sparse constraint factor;
s202, fixing the overcomplete dictionary D, and solving a sparse coefficient of each training sample under the condition that D is known, wherein the sparse coefficient is shown as the following formula:
solving to obtain the sparse coefficient a of each sample by using an optimization algorithm of linear programmingiThe sparse coefficients of all samples form a sparse matrix a ═ a (a)1,a2,…,aK);
S203, deducing and expanding an object function formula related to D according to the object function expression as shown in the following formula, and performing iterative solution by using a projection gradient method to obtain an over-complete dictionary D
Specifically, the third step specifically includes: performing data segmentation on the seismic prestack gather by adopting two-dimensional windows with the same size, unfolding two-dimensional segmentation data into one-dimensional data serving as a prediction sample, and forming a prediction sample set by all segmentation data; when the data of the seismic prestack gather is segmented, a non-integral multiple window can be filled in a symmetrical continuation mode, and the filling data is cut after denoising operation.
Specifically, the fourth step specifically includes the following substeps:
s401, randomly extracting K sample data from the prediction sample set, sparsely reconstructing K prediction samples by using an over-complete dictionary, and meanwhile, calculating to obtain a denoising prestack gather of the whole effective signal, and establishing a target function about K prediction sample sparse representation coefficients and the denoising prestack gather by using an expression as follows:
in the above formula, the first and second carbon atoms are,is after denoisingI is the original pre-stack seismic gather, I, j are the two-dimensional serial numbers of the samples, RijI is the ijth sample data, RijIs a sample decimation matrix, DaijIs a reconstructed sub-sample, uijIs sparsity constrained sparsity;
s402, solving a by using a solving expression for each sampleijSolving the expression is shown as follows:
s403, solving sparse coefficient a of K sample data by using solving expressionijSolving a denoising prestack gather matrix based on the obtained solution to obtain a denoising prestack gather matrixAnd calculating to obtain a denoising prestack gather matrix by utilizing a quadratic matrix equation solution algorithmWherein denoising the prestack gather matrixIs represented by the following formula:
the invention has the beneficial effects that: the method comprises the steps of establishing an overcomplete atom library through an on-well forward modeling gather, and denoising a seismic prestack gather according to the sparsity of effective signals in the overcomplete atom library. The traditional denoising method mainly removes noise according to the spectrum distribution rule of effective signals and noise signals, but in many cases, the spectrum regions of the effective signals and the noise signals are mixed together and are difficult to separate, the effective signals are damaged while the noise is removed, and the uncertainty of pre-stack inversion is increased due to the distortion of the effective signals. Because the forward gather does not contain random noise and coherent noise, and the forward gather can reflect the accurate AVO trend, the method adopts the forward gather to establish an over-complete dictionary which can represent the two-dimensional structure characteristics of the pre-stack gather, effective signals can be obtained by the sparse combination addition of the over-complete dictionary, and noise signals do not have the characteristics, so that the dictionary sparse reconstruction of the seismic pre-stack gather can be carried out, and the de-noised pre-stack gather data can be obtained; the denoising process is different from the traditional thought of signal-noise separation, the method achieves the denoising target by preserving the amplitude of the effective signal and reconstructing the target, and the denoising of the prestack gather operated by the algorithm not only improves the signal-to-noise ratio, but also considers the AVO trend and is beneficial to the inversion of prestack elastic parameters.
Drawings
Fig. 1 is a flow diagram of the method of the present invention.
Figure 2 is a diagram of a near-well Zeoppritz forward course set of the present invention.
FIG. 3 is an overcomplete dictionary diagram for forward gather batch dictionary learning of the present invention.
Fig. 4(a) to (c) are comparison of the denoising effect of the present invention, where (a) is an original angle trace set diagram, (b) is an angle trace set diagram after denoising of the present invention, and (c) is a residual of the present invention.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, embodiments of the present invention will now be described with reference to the accompanying drawings.
In this embodiment, as shown in fig. 1, a method for pre-stack well-seismic joint intelligent denoising specifically includes the following steps:
the method comprises the following steps: performing data segmentation of a uniform size on the forward gather on the well, wherein one segmented block data is used as sample data, and all the sample data form a sample training data set:
as shown in fig. 2, the prestack gather is subjected to data segmentation with a size of a black frame in the graph, the segmentation window is (M × M), two-dimensional data of one segmentation window is expanded into one-dimensional data as one sample data, and all the prestack gather is traversed to obtain a training sample data set.
The method takes two-dimensional segmentation data as sample data, and aims to learn and obtain the over-complete dictionary with both waveform and horizontal AVO characteristics. The size M of the division window is preferably one wavelength length, and data at non-integral multiple window length positions in the division process is discarded.
Step two: randomly extracting partial data from the sample training data set, and performing a dictionary learning algorithm on the partial sample data to obtain an overcomplete dictionary capable of representing the two-dimensional structure characteristics of the prestack gather, wherein the dictionary learning algorithm is related to the overcomplete dictionary and sparse coefficient iterative learning:
the invention adopts a machine learning method of dictionary learning to establish an over-complete dictionary model represented by the waveform characteristics of a prestack gather, and firstly, K samples are randomly taken out from a prestack gather training sample set to form a training sample setThe learning objective is a two-dimensional structure comprising a prestack gather, each sample having thereon a sparsely represented overcomplete dictionary D and sparsely represented coefficients aiAn objective function is established as shown in the following equation (1-1):
where λ is the sparsity constraint factor.
Since the dictionary D and the sparse coefficient a are unknown, an iterative approximation strategy of fixing one and then solving the other can be adopted for solving, and if the overcomplete dictionary D is fixed first, the initial D is set as the cosine transform dictionary base.
(1) On the basis of the over-complete dictionary D, solving a sparse coefficient a:
d is fixed, and the sparse coefficient of each training sample under the condition that D is known is solved, as represented by formula (1-2):
(1-2) optimization algorithm using linear programmingSolving to obtain the sparse coefficient a of each sampleiThe sparse coefficients of all samples form a sparse matrix a ═ a (a)1,a2,…,aK)。
(2) On the basis of the sparse matrix a, solving a dictionary D:
the formula (1-3) of the objective function related to D is derived from the formula (1-1)
The formula (1-3) is developed to obtain (1-4)
The formula (1-4) belongs to a quadratic programming problem, and can be solved by a projection gradient method generally, that is, the overcomplete dictionary D can be obtained by performing iterative solution through the formula (1-5).
Through the iterative operations of (1-2), (1-4) and (1-5), an overcomplete dictionary D of the prestack gather representation two-dimensional waveform structure can be obtained.
As shown in fig. 3, which represents an overcomplete dictionary learned using the forward gather of traces on the well.
Step three: carrying out data segmentation on the seismic prestack gather to obtain a prestack gather prediction sample data set:
and (3) performing data segmentation on the seismic prestack gather by adopting two-dimensional windows with the same size, unfolding the two-dimensional segmentation data into one-dimensional data serving as a prediction sample, and forming a prediction sample set by all the segmentation data.
When the data of the seismic prestack gather is segmented, a non-integral multiple window can be filled in a symmetrical continuation mode, and the filling data is cut after the denoising operation.
Step four: randomly extracting part of sample data from the prediction sample data set, solving the sparse representation coefficient of each sample data by using the over-complete dictionary obtained in the step two, then establishing a quadratic term objective function taking the denoising prestack gather as a target, and solving to obtain the prestack denoising gather:
randomly extracting K sample data from the prediction sample set, carrying out sparse reconstruction on the K prediction samples by utilizing an over-complete dictionary, simultaneously calculating to obtain a denoising prestack gather of the whole effective signal, and establishing a target function about K prediction sample sparse representation coefficients and the denoising prestack gather by utilizing (2-1):
in the above formulaIs a denoised prestack gather, I is an original prestack seismic gather, I and j refer to two-dimensional serial numbers of samples, RijI is the ijth sample data, RijIs a sample decimation matrix, DaijIs a reconstructed sub-sample, uijIs sparsity constrained sparsity.
(1) For each sample, solve for aijAs shown in formula (2-2):
(2) solving sparse coefficient a of K sample data by using (2-2)ijThen (2-1) is carried in to solve the denoising prestack gather matrixTo obtain the formula (2-3):
Fig. 4(a) to (c) show the comparison of the denoising effect of the present invention, where (a) is the original angle trace set map, (b) is the denoised angle trace set map of the present invention, and (c) is the residual error of the present invention.
Step five: traversing all the gather according to the sequence of the third step and the fourth step to complete the denoising of all the pre-stack gathers.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A prestack well-seismic combined intelligent denoising method is characterized by comprising the following steps:
s1: performing data segmentation of a uniform size on the forward gather on the well, wherein one piece of segmented block data is used as sample data, and all the sample data form a sample training data set;
s2: randomly extracting partial data from the sample training data set, and performing a dictionary learning algorithm on the partial sample data about an over-complete dictionary and sparse coefficient iterative learning to obtain an over-complete dictionary capable of representing the two-dimensional structure characteristics of the pre-stack gather;
s3: carrying out data segmentation on the seismic prestack gather to obtain a prestack gather prediction sample data set;
s4: randomly extracting part of sample data from the prediction sample data set, solving a sparse representation coefficient of each sample data by using the over-complete dictionary obtained in the step two, then establishing a quadratic term objective function taking the denoising prestack gather as a target, and solving to obtain the prestack denoising gather;
s5: and traversing all the gathers according to the sequence of the step S3 and the step S4 to complete the denoising of all the prestack gathers.
2. The method of claim 1, wherein the data segmentation in step S1 includes segmenting the data with a segmentation window of size M, the data in one segmentation window is a sample, the size M of the segmentation window is a wavelength, and the data at non-integer window length position in the segmentation process is discarded.
3. The method for pre-stack well-seismic joint intelligent denoising of claim 1, wherein the step S2 specifically comprises the following sub-steps:
s201, randomly taking K samples from a pre-stack trace set training sample set to form a training sample setThe learning objective is a two-dimensional structure comprising a prestack gather, each sample having thereon a sparsely represented overcomplete dictionary D and sparsely represented coefficients aiEstablishing an objective function expression as shown in the following formula:
wherein λ is a sparse constraint factor;
s202, fixing the overcomplete dictionary D, and solving a sparse coefficient of each training sample under the condition that D is known, wherein the sparse coefficient is shown as the following formula:
Solving to obtain the sparse coefficient a of each sample by using an optimization algorithm of linear programmingiThe sparse coefficients of all samples form a sparse matrix a ═ a (a)1,a2,…,aK);
And S203, deducing and expanding an object function formula related to D according to the object function expression as shown in the following formula, and performing iterative solution by using a projection gradient method to obtain the overcomplete dictionary D.
4. The method for pre-stack well-seismic joint intelligent denoising of claim 1, wherein the step S3 specifically comprises: performing data segmentation on the seismic prestack gather by adopting two-dimensional windows with the same size, unfolding two-dimensional segmentation data into one-dimensional data serving as a prediction sample, and forming a prediction sample set by all segmentation data; when the data of the seismic prestack gather is segmented, a non-integral multiple window can be filled in a symmetrical continuation mode, and the filling data is cut after denoising operation.
5. The method for pre-stack well-seismic joint intelligent denoising of claim 1, wherein the step S4 specifically comprises the following sub-steps:
s401, randomly extracting K sample data from the prediction sample set, sparsely reconstructing K prediction samples by using an over-complete dictionary, simultaneously calculating to obtain a denoising prestack gather of the whole effective signal, and establishing a target function about K prediction sample sparse representation coefficients and the denoising prestack gather by using an expression as follows:
in the above formula, the first and second carbon atoms are,is a denoised prestack gather, I is an original prestack seismic gather, lambda is a sparse constraint factor, I and j refer to two-dimensional serial numbers of samples, RijI is the firstij sample data, RijIs a sample decimation matrix, DaijIs a reconstructed sub-sample, uijIs sparsity constrained sparsity;
s402, solving a by using a solving expression for each sampleijSolving the expression is shown as follows:
s403, solving sparse coefficient a of K sample data by using solving expressionijSolving a denoising prestack gather matrix based on the obtained solution to obtain a denoising prestack gather matrixAnd calculating to obtain a denoising prestack gather matrix by utilizing a quadratic matrix equation solution algorithmWherein denoising the prestack gather matrixIs represented by the following formula:
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