CN111025389A - Multi-scale coherent dimensionality reduction fusion fracture prediction method and system - Google Patents
Multi-scale coherent dimensionality reduction fusion fracture prediction method and system Download PDFInfo
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Abstract
A multi-scale coherent dimensionality reduction fusion fracture prediction method and system are disclosed. The method can comprise the following steps: preprocessing seismic data, extracting macro-surface elements and forming a plurality of macro-surface element channel sets in different directions; selecting a plurality of azimuth angles and full offset distances for stacking aiming at the macro-surface element channel set to obtain a plurality of seismic azimuth sub-volumes; calculating the coherence attribute of the seismic orientation subvolume data volume to obtain seismic data coherence volumes of different orientations, and further calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence volumes respectively to obtain a plurality of frequency division orientation seismic data coherence volumes; acquiring fracture data volumes with different sizes according to the frequency division azimuth seismic data coherence volumes; and carrying out normalization processing on the crack data body so as to obtain a crack prediction result. The method predicts the crack through azimuth full offset superposition and coherent calculation, realizes the dimensionality reduction of multi-scale coherent attributes, and describes the details of the crack more finely.
Description
Technical Field
The invention relates to the field of geophysical exploration, in particular to a multi-scale coherent dimensionality reduction fusion fracture prediction method and system.
Background
Seismic attribute technology, which began in the early 70 s of the 20 th century, developed very rapidly and has now become an important tool for reservoir and reservoir description. However, in recent years, the progress of seismic attribute analysis is mostly concentrated on a calculation method for attribute extraction, and the research on multi-scale comprehensive analysis is less.
Since the appearance of the coherence as an attribute of seismic data for computing seismic coherence, research has begun with seismic coherence for detecting fractures. Existing methods for coherently predicting fractures have evolved from C1 coherence, C3 coherence based on post-stack data to coherence based on pre-stack limited azimuth, optimal azimuth coherence, and multi-scale coherence based on post-stack seismic data, among others.
The existing dimension reduction fusion method of seismic attributes is usually based on data mining or dimension reduction is carried out by using an RGB (red, green and blue) data fusion method; this often results in loss of original reservoir characteristics of the multidimensional data. Therefore, it is necessary to develop a multi-scale coherent dimensionality reduction fusion fracture prediction method and system.
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 multi-scale coherent dimensionality reduction fusion fracture prediction method and a multi-scale coherent dimensionality reduction fusion fracture prediction system, which can predict fractures through azimuth full offset superposition and coherent calculation, retain the response characteristics of a fractured reservoir, retain the original details of reservoir attributes obtained by each coherence, solve the problem that the original reservoir characteristics of multi-dimensional data are lost in the prior art, realize the dimensionality reduction of multi-scale coherent attributes and describe the details of the fractures more finely.
According to one aspect of the invention, a multi-scale coherent dimensionality reduction fusion fracture prediction method is provided. The method may include: preprocessing seismic data, extracting macro-surface elements and forming a plurality of macro-surface element channel sets in different directions; selecting a plurality of azimuth angles and full offset distances for stacking aiming at the macro surface element channel set to obtain a plurality of seismic azimuth subvolumes; calculating the coherence attribute of the seismic orientation subvolume data volume to obtain seismic data coherence volumes of different orientations, and further calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence volumes respectively to obtain a plurality of frequency division orientation seismic data coherence volumes; acquiring fracture data volumes with different sizes according to the frequency division azimuth seismic data coherence volumes; and carrying out normalization processing on the crack data volume so as to obtain a crack prediction result.
Preferably, the calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence body respectively, and the obtaining of the plurality of frequency division azimuth seismic data coherence bodies comprises: converting the seismic data coherent body from a time domain to a frequency domain, and setting three frequencies of low frequency and medium frequency to obtain a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body; and respectively calculating a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body to obtain the plurality of frequency division azimuth seismic data coherent bodies.
Preferably, the plurality of frequency-division azimuth seismic data coherence bodies comprises a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies and a plurality of low-frequency azimuth seismic data coherence bodies.
Preferably, obtaining fracture data volumes of different sizes according to the plurality of frequency division azimuth seismic data coherence bodies comprises: respectively aiming at a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies or a plurality of low-frequency azimuth seismic data coherence bodies, the following steps are carried out: and searching a maximum value among all azimuths for each point of each path of seismic data, and respectively obtaining a high-frequency maximum seismic data coherent body, a medium-frequency maximum seismic data coherent body and a low-frequency maximum seismic data coherent body so as to further respectively obtain a small-size fracture data body, a medium-size fracture data body and a large-size fracture data body.
Preferably, the method further comprises the following steps: and obtaining the small-size fracture data volume according to the high-frequency maximum seismic data coherence, obtaining the medium-size fracture data volume according to the medium-frequency maximum seismic data coherence, and obtaining the large-size fracture data volume according to the low-frequency maximum seismic data coherence.
According to another aspect of the present invention, a multi-scale coherent dimensionality reduction fused fracture prediction system is provided, which is characterized in that the system comprises: a memory storing computer-executable instructions; a processor executing computer executable instructions in the memory to perform the steps of: preprocessing seismic data, extracting macro-surface elements and forming a plurality of macro-surface element channel sets in different directions; selecting a plurality of azimuth angles and full offset distances for stacking aiming at the macro surface element channel set to obtain a plurality of seismic azimuth subvolumes; calculating the coherence attribute of the seismic orientation subvolume data volume to obtain seismic data coherence volumes of different orientations, and further calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence volumes respectively to obtain a plurality of frequency division orientation seismic data coherence volumes; acquiring fracture data volumes with different sizes according to the frequency division azimuth seismic data coherence volumes; and carrying out normalization processing on the crack data volume so as to obtain a crack prediction result.
Preferably, the calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence body respectively, and the obtaining of the plurality of frequency division azimuth seismic data coherence bodies comprises: converting the seismic data coherent body from a time domain to a frequency domain, and setting three frequencies of low frequency and medium frequency to obtain a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body; and respectively calculating a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body to obtain the plurality of frequency division azimuth seismic data coherent bodies.
Preferably, the plurality of frequency-division azimuth seismic data coherence bodies comprises a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies and a plurality of low-frequency azimuth seismic data coherence bodies.
Preferably, obtaining fracture data volumes of different sizes according to the plurality of frequency division azimuth seismic data coherence bodies comprises: respectively aiming at a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies or a plurality of low-frequency azimuth seismic data coherence bodies, the following steps are carried out: and searching a maximum value among all azimuths for each point of each path of seismic data, and respectively obtaining a high-frequency maximum seismic data coherent body, a medium-frequency maximum seismic data coherent body and a low-frequency maximum seismic data coherent body so as to further respectively obtain a small-size fracture data body, a medium-size fracture data body and a large-size fracture data body.
Preferably, the method further comprises the following steps: and obtaining the small-size fracture data volume according to the high-frequency maximum seismic data coherence, obtaining the medium-size fracture data volume according to the medium-frequency maximum seismic data coherence, and obtaining the large-size fracture data volume according to the low-frequency maximum seismic data coherence.
The beneficial effects are that:
(1) the response characteristics of the fractured reservoir are kept, original details of the reservoir attributes obtained by each coherence are kept, and the problem that the original reservoir characteristics of multi-dimensional data are lost in the prior art is solved;
(2) the calculation process can be manually monitored, and can also be automatically assigned by a computer statistical method, so that the method is more convenient and faster.
The method and apparatus of the present invention have 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.
FIG. 1 shows a flow chart of the steps of a multi-scale coherent dimension-reduction fused fracture prediction method according to the present invention.
FIG. 2 shows an offset-azimuth profile of seismic data according to one embodiment of the invention.
Fig. 3a, 3b, 3c, 3d, 3e, 3f show schematic diagrams of 6 seismic azimuth sub-volumes, respectively.
FIGS. 4a, 4b, 4c, 4d, 4e, 4f each show a schematic of 6 high frequency azimuth seismic data coherent volumes, according to one embodiment of the invention.
FIGS. 5a, 5b, 5c, 5d, 5e, and 5f each show a schematic of 6 mid-frequency azimuth seismic data coherence volumes, according to one embodiment of the invention.
FIGS. 6a, 6b, 6c, 6d, 6e, and 6f each show a schematic of 6 low frequency azimuth seismic data coherence volumes, according to an embodiment of the invention.
Fig. 7a, 7b and 7c are schematic diagrams respectively illustrating a small-size fracture data volume, a medium-size fracture data volume and a large-size fracture data volume according to an embodiment of the invention.
FIG. 8 shows a schematic of the fracture prediction results according to one embodiment of the 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 multi-scale coherent dimension-reduction fused fracture prediction method according to the present invention.
In this embodiment, the multi-scale coherent dimensionality reduction fused fracture prediction method according to the present invention may include: 101, preprocessing seismic data, extracting macro-surface elements and forming a plurality of macro-surface element channel sets in different directions; 102, selecting a plurality of azimuth angles and full offset distances for stacking aiming at a macro-surface element channel set to obtain a plurality of seismic azimuth subvolumes; 103, calculating the coherence attributes of the seismic orientation subvolumes to obtain seismic data coherence volumes of different orientations, and further calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence volumes respectively to obtain a plurality of frequency division orientation seismic data coherence volumes; 104, acquiring fracture data volumes with different sizes according to the seismic data coherence bodies of the frequency division azimuths; and 105, performing normalization processing on the crack data body to further obtain a crack prediction result.
In one example, calculating high, medium, and low frequencies of seismic data coherence, respectively, obtaining a plurality of frequency-divided azimuth seismic data coherence comprises: the seismic data coherence body is converted from a time domain to a frequency domain, and three frequencies of low frequency and medium frequency are set to obtain a high-frequency seismic data coherence body, a medium-frequency seismic data coherence body and a low-frequency seismic data coherence body; and respectively calculating a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body to obtain a plurality of frequency division azimuth seismic data coherent bodies.
In one example, the plurality of frequency-divided azimuth seismic data coherence bodies includes a plurality of high frequency azimuth seismic data coherence bodies, a plurality of intermediate frequency azimuth seismic data coherence bodies, and a plurality of low frequency azimuth seismic data coherence bodies.
In one example, obtaining fracture data volumes of different sizes from a plurality of frequency-divided azimuth seismic data coherence volumes comprises: respectively aiming at a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies or a plurality of low-frequency azimuth seismic data coherence bodies, the following steps are carried out: and searching a maximum value among all azimuths for each point of each path of seismic data, and respectively obtaining a high-frequency maximum seismic data coherent body, a medium-frequency maximum seismic data coherent body and a low-frequency maximum seismic data coherent body so as to further respectively obtain a small-size fracture data body, a medium-size fracture data body and a large-size fracture data body.
In one example, further comprising: and obtaining a small-size fracture data volume according to the high-frequency maximum seismic data coherence, obtaining a medium-size fracture data volume according to the medium-frequency maximum seismic data coherence, and obtaining a large-size fracture data volume according to the low-frequency maximum seismic data coherence.
Specifically, in a seismic three-dimensional offset volume (i.e., offset imaging), N sampling points of adjacent J traces are taken to form a seismic sub-volume composition matrix D, that is, the matrix D is used to represent the seismic sub-volume as:
wherein, each column in D represents a seismic channel (jth channel) with N sampling points, each row represents the same time sampling point (nth sampling point) in the jth channel, and DnjI.e. the nth sample point of the jth track.
Expanding the formula (1) to obtain a formula (2):
the orthogonal relationship of J-dimensional variables can be mathematically represented by a covariance matrix, whose rank is related to the degree of freedom. Therefore, the covariance matrix C of the matrix D can be expressed by the following method, which notes the nth behavior D of Dn T＝[dn1dn2...dnJ]The covariance matrix C of n samples under the condition that the mean is zero is formula (7).
If d isnIs a non-zero vector, then equation (2) is a semi-positive definite symmetric one-rank array, dndn TThere is only one non-zero eigenvalue. Covariance matrix D of all samplesTD can be seen as the sum of N linear arrays, with at most N (or Min (N, J)) ranks, and the covariance of equation (2) is calculated as equation (3):
the covariance matrix C is a symmetric matrix C whose rank is determined by the number of positive eigenvalues of equation (3):
the number and relative size of the eigenvalues of the covariance matrix C determine how many degrees of freedom are in the seismic data subvolume, and the relative position of each degree of freedom in the total energy, so that the maximum eigenvalue and the fraction of the maximum eigenvalue in the whole volume are quantitative descriptions of the variance (similarity) in the subvolume, from which the coherence factor can be defined as formula (5):
wherein, Tr(C) Is the trace of matrix C, λiThe eigenvalue of C is obtained by characteristic analysis of the matrix: t isr(C) Representing the total energy of the selected entire data subvolume, the number of eigenvalues representing the number of independent variables in the subvolume, the magnitude of the eigenvalues representing the fraction (status) occupied by the subvolume, and the maximum eigenvalue max (λ:)i) Representing the variables that the subvolume plays the dominant role. Since C is also a semi-positive definite symmetric matrix, all eigenvalues λi≥0，0≤λi≤ΣλjThus satisfying 0. ltoreq. Ec≦ 1, representing the percentage of the dominant variable to the total variable, i.e., the proportion of similar (or non-similar) portions to the entire subvolume or the relevant factor.
Assuming the same horizontal reflection for all traces, D can be represented by scaling any row sample D (other than 0) to represent other rows without loss of generality1 T＝[a a... a]A is not equal to 0, then dn T＝kn[aa...a]＝knd1N is 2, 3. At the same time each row dn TCovariance matrix d ofndn TIs formula (6):
the total-subunit covariance matrix C is formula (7):
due to the fact that
Is a rank matrix, so C is also a rank matrix with only one eigenvalue. I.e. when the waveforms of all tracks are identical, Ec＝λ1/λ1The similarity is best when the value is 1; with the change of each waveform, the free variable gradually increases, and the energy is dispersed to each eigenvalue, so EcWith a consequent decrease, reflecting a deterioration in the daughter similarity.
The multi-scale coherent dimensionality reduction fused fracture prediction method can comprise the following steps:
preprocessing is carried out on seismic data, a macro-surface element is extracted, a plurality of macro-surface element channel sets in different directions are formed, and preprocessing mainly comprises channel editing, band-pass filtering, true amplitude recovery, static correction, speed analysis, residual static correction, ground surface amplitude consistency compensation, prestack deconvolution, dynamic correction and the like.
And selecting a plurality of azimuth angles and full offset distances for stacking aiming at the macro surface element channel set to obtain a plurality of seismic azimuth sub-volume data volumes.
Due to the acquisition system and cost of the prestack gather of the actual seismic data, when the prestack time difference is extracted, the factors of insufficient azimuth and offset always exist, therefore, in order to improve the signal-to-noise ratio between adjacent traces, avoid the defect caused by uneven shot-geophone distribution, and ensure that different shot-geophone gather distributions with enough density and more consistent stacking times exist in different azimuths, a CMP macro-bin is established by means of expanding the original CMP (common center point) bin, generally 3 multiplied by 3 or 5 multiplied by 5, for one seismic trace, 1 multiplied by 1 is obtained, if two gathers around are added, 3 multiplied by 3 is obtained, four gathers around are added, 5 multiplied by 5 is obtained, and the like in sequence. By the method, partial superposition can be performed on adjacent tracks of the cannon and the cannon, azimuth or offset distance superposition can be selected, the specific azimuth size, the offset size and the interval between the azimuth and the offset are different according to actual seismic data, the signal-to-noise ratio of pre-stack data can be improved, effective signal energy is enhanced, and the superposition times of different azimuths are basically consistent.
If the seismic work area is uniformly collected, the channel number of the prestack seismic channel set of a CDP point (common reflection point) is N, the selected macro-bin scale is 3 x 3, the channel number of the macro-bin channel set of the CDP point is 9 x N, the 9 x N seismic data correspond to an offset distance and an azimuth angle, therefore, an offset-azimuth distribution diagram of the 9 x N seismic data can be obtained, in the distribution diagram, the offset distance range covered by the maximum full azimuth angle can be observed, therefore, a plurality of azimuth angles and full offset distances are selected for superposition, if the number of azimuth subvolumes is 6, 30-degree intervals are selected, 4 azimuth subvolumes are selected, 45-degree intervals are selected, the offset distance is generally selected to be 0 meter to the maximum value of the full offset distance, the full offset prestack channel sets in a certain azimuth range are all added, and then the number of unequal azimuth total offset stacks are obtained, the seismic orientation subvolumes of different azimuths can be obtained, the line numbers, the track numbers and the sampling numbers of the seismic channels of the seismic orientation subvolumes of different azimuths are the same, and the seismic data possibly in some azimuths are different only due to the superposition of different azimuths.
Calculating the coherence attribute of the seismic orientation subvolume data volume to obtain seismic data coherence volumes of different orientations, and calculating the frequency division coherence volumes of the seismic data coherence volumes of different orientations respectively, namely calculating the coherence values of low-frequency, medium-frequency and high-frequency seismic data respectively, wherein the principle of the frequency division part can adopt the traditional frequency division algorithm, such as traditional Fourier transform, wavelet transform and the like. The seismic data coherence body is converted from a time domain to a frequency domain, and three frequencies of low frequency and medium frequency are set to obtain a high-frequency seismic data coherence body, a medium-frequency seismic data coherence body and a low-frequency seismic data coherence body; and respectively calculating a high-frequency seismic data coherent body, an intermediate-frequency seismic data coherent body and a low-frequency seismic data coherent body to obtain a plurality of frequency division azimuth seismic data coherent bodies, wherein the plurality of frequency division azimuth seismic data coherent bodies comprise a plurality of high-frequency azimuth seismic data coherent bodies, a plurality of intermediate-frequency azimuth seismic data coherent bodies and a plurality of low-frequency azimuth seismic data coherent bodies.
In a seismic data coherent body with a plurality of frequency division directions, the crack development may be most obvious in some directions due to the development ductility of the crack, but the crack development may not be shown in other directions, so that the omnibearing seismic data coherent body needs to be optimized. Respectively aiming at a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies or a plurality of low-frequency azimuth seismic data coherence bodies, the following steps are carried out: and searching a maximum value among all azimuths for each point of each path of seismic data, and respectively obtaining a high-frequency maximum seismic data coherent body, a medium-frequency maximum seismic data coherent body and a low-frequency maximum seismic data coherent body so as to further respectively obtain a small-size fracture data body, a medium-size fracture data body and a large-size fracture data body. The high-frequency maximum seismic data coherence, the medium-frequency maximum seismic data coherence and the low-frequency maximum seismic data coherence are sorted according to different crack indication degrees, namely large-scale cracks, medium-scale cracks and small-scale cracks, in general, the low-frequency coherence corresponds to the large-scale cracks, the medium-frequency coherence corresponds to the medium-scale cracks, the high-frequency coherence corresponds to the small-scale cracks, and due to the fact that frequency ranges of the high-frequency maximum seismic data coherence, the medium-frequency maximum seismic data coherence and the low-frequency maximum seismic data coherence are different in frequency division. The crack data volume is normalized, partial maximum values and partial minimum values are removed, a specific range is selected according to the experience of geologists, the normalized result is subjected to secondary processing of data according to different sizes, the cracks of the large, medium and small sizes after the secondary processing are directly added according to the format of the seismic data, and due to the factor of frequency division, when the crack results of the large, medium and small sizes are different from each other, the directly added seismic data fuse the crack prediction results of different sizes. In the actual data processing, the large, medium and small-scale cracks are classified according to colors, the result after multi-scale coherent dimensionality reduction fusion is obtained, and the crack prediction result is obtained.
The method predicts the fractures through azimuth full offset superposition and coherent calculation, reserves the response characteristics of the fractured reservoir, reserves the original details of the reservoir attributes obtained by each coherence, solves the problem that the original reservoir characteristics of multi-dimensional data are lost in the prior art, realizes the dimensionality reduction of multi-scale coherent attributes, and describes the details of the fractures more finely.
Application example
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, 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.
The multi-scale coherent dimensionality reduction fused fracture prediction method can comprise the following steps:
preprocessing is carried out on seismic data, a macro-surface element is extracted, a plurality of macro-surface element channel sets in different directions are formed, and preprocessing mainly comprises channel editing, band-pass filtering, true amplitude recovery, static correction, speed analysis, residual static correction, ground surface amplitude consistency compensation, prestack deconvolution, dynamic correction and the like.
And selecting a plurality of azimuth angles and full offset distances for stacking aiming at the macro surface element channel set to obtain a plurality of seismic azimuth sub-volume data volumes.
FIG. 2 shows an offset-azimuth profile of seismic data according to one embodiment of the invention.
The channel number of the prestack seismic channel set of a CDP point is N, the selected macro-surface element scale is 3 multiplied by 3, the channel number of the macro-surface element channel set of the CDP point is 9 multiplied by N, the 9 xn traces of seismic data each correspond to an offset and an azimuth, and thus an offset-azimuth profile of the 9 xn traces of seismic data can be obtained, as shown in figure 2, in the figure, the offset range covered by the maximum full azimuth angle can be observed, so that seismic azimuth subvolumes of different azimuths are obtained by selecting a plurality of azimuths and full offsets for superposition, wherein the azimuth subvolumes are 6 and are 30-degree intervals, as shown in fig. 3 a-3 f, the line number, track number, and seismic trace number of the seismic orientation subvolume for different orientations are the same, and the number of samples per trace is the same, except that the seismic data may differ at some orientations due to stacking at different orientations.
Fig. 3a, 3b, 3c, 3d, 3e, 3f show schematic diagrams of 6 seismic azimuth sub-volumes, respectively.
4a, 4b, 4c, 4d, 4e, 4f respectively show schematic diagrams of high frequency azimuth seismic data coherent volumes for 6 different azimuths according to one embodiment of the present invention, with the dark portions of the circles in the diagrams representing the azimuths.
Fig. 5a, 5b, 5c, 5d, 5e, 5f each show a schematic representation of mid-frequency azimuth seismic data coherent volumes for 6 different azimuths according to one embodiment of the present invention, with the dark portions of the circles in the diagrams representing the azimuths.
6a, 6b, 6c, 6d, 6e, 6f respectively show schematic diagrams of low frequency azimuth seismic data coherent volumes for 6 different azimuths according to one embodiment of the present invention, with the dark portions of the circles in the diagrams representing the azimuths.
Calculating the coherence attributes of the seismic orientation subvolumes shown in figures 3 a-3 f to obtain seismic data coherence volumes of different orientations, and calculating the frequency division coherence volumes of the seismic data coherence volumes of different orientations respectively, namely calculating the coherence values of the low-frequency seismic data, the medium-frequency seismic data and the high-frequency seismic data respectively. The seismic data coherence body is converted from a time domain to a frequency domain, the low frequency and the high frequency are respectively set to be 5-20Hz, 21-35Hz and 36-55Hz, and a high-frequency seismic data coherence body, a medium-frequency seismic data coherence body and a low-frequency seismic data coherence body are obtained; respectively calculating a high-frequency seismic data coherent body, an intermediate-frequency seismic data coherent body and a low-frequency seismic data coherent body to obtain 18 frequency division azimuth seismic data coherent bodies, wherein the frequency division azimuth seismic data coherent bodies are respectively 6 high-frequency azimuth seismic data coherent bodies shown in figures 4 a-4 f, and the 6 intermediate-frequency azimuth seismic data coherent bodies shown in figures 5 a-5 f and 6 low-frequency azimuth seismic data coherent bodies shown in figures 6 a-6 f.
Fig. 7a, 7b and 7c are schematic diagrams respectively illustrating a small-size fracture data volume, a medium-size fracture data volume and a large-size fracture data volume according to an embodiment of the invention.
And optimizing 18 frequency division azimuth seismic data coherent bodies. Respectively aiming at 6 high-frequency azimuth seismic data coherence bodies, 6 intermediate-frequency azimuth seismic data coherence bodies or 6 low-frequency azimuth seismic data coherence bodies, the following steps are carried out: searching a maximum value among each azimuth for each point of each path of seismic data, respectively obtaining a high-frequency maximum seismic data coherent body, a medium-frequency maximum seismic data coherent body and a low-frequency maximum seismic data coherent body, reducing the dimensions of 18 frequency division azimuth seismic data coherent bodies to 3, and further respectively obtaining a small-size fracture data body, a medium-size fracture data body and a large-size fracture data body, as shown in fig. 7 a-7 c.
FIG. 8 shows a schematic of the fracture prediction results according to one embodiment of the invention.
The method is characterized in that high-frequency maximum seismic data coherence, medium-frequency maximum seismic data coherence and low-frequency maximum seismic data coherence are sorted according to different crack indication degrees of large, medium and small, low-frequency coherence corresponds to large-scale cracks, medium-frequency coherence corresponds to medium-scale cracks, high-frequency coherence corresponds to small-scale cracks, and corresponding coherence values are different due to different frequency ranges during frequency division. The large-scale cracks range from 0 to 0.3, the medium-scale cracks range from 0 to 0.4, and the small-scale cracks range from 0 to 0.6, the high-frequency maximum seismic data coherent body, the medium-frequency maximum seismic data coherent body, and the low-frequency maximum seismic data coherent body are normalized, part of the maximum values and the minimum values are removed, the specific range is selected according to the experience of geological personnel, the normalized result is subjected to secondary processing of data according to different scales, after the processing, the small-scale cracks range is from 0 to 1000, the medium-scale cracks range is from 1000-doped 2000, and the large-scale cracks range is from 2000-doped 3000.
The cracks with large, medium and small scales after secondary treatment are directly added according to the format of the seismic data, and due to the frequency division scale, when the crack results with large, medium and small scales are different from each other, the directly added seismic data fuse the crack prediction results with different scales. In the actual data processing, the large, medium and small-scale fractures are classified according to colors, a multi-scale coherent dimension-reduction fused result is obtained, and a fracture prediction result is obtained, as shown in fig. 8, when the large, medium and small-scale fractures are different, the seismic data can exceed the range of 3000, but because the large data correspond to the large-scale fracture characteristics, the medium and small-scale fractures are often associated or fused with the large-scale fracture development in the actual geological situation, and therefore the data result has little influence on the whole fracture prediction result.
In conclusion, the method predicts the fractures through azimuth full offset superposition and coherent calculation, retains the response characteristics of the fractured reservoir, retains the original details of the reservoir attributes obtained through each coherence, solves the problem that the original reservoir characteristics of multi-dimensional data are lost in the prior art, realizes the dimensionality reduction of the multi-scale coherent attributes, and describes the details of the fractures more finely.
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 benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
According to an embodiment of the invention, a multi-scale coherent dimensionality reduction fused fracture prediction system is provided, which is characterized by comprising: a memory storing computer-executable instructions; a processor executing computer executable instructions in the memory to perform the steps of: preprocessing seismic data, extracting macro-surface elements and forming a plurality of macro-surface element channel sets in different directions; selecting a plurality of azimuth angles and full offset distances for stacking aiming at the macro-surface element channel set to obtain a plurality of seismic azimuth sub-volumes; calculating the coherence attribute of the seismic orientation subvolume data volume to obtain seismic data coherence volumes of different orientations, and further calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence volumes respectively to obtain a plurality of frequency division orientation seismic data coherence volumes; acquiring fracture data volumes with different sizes according to the frequency division azimuth seismic data coherence volumes; and carrying out normalization processing on the crack data body so as to obtain a crack prediction result.
In one example, calculating high, medium, and low frequencies of seismic data coherence, respectively, obtaining a plurality of frequency-divided azimuth seismic data coherence comprises: the seismic data coherence body is converted from a time domain to a frequency domain, and three frequencies of low frequency and medium frequency are set to obtain a high-frequency seismic data coherence body, a medium-frequency seismic data coherence body and a low-frequency seismic data coherence body; and respectively calculating a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body to obtain a plurality of frequency division azimuth seismic data coherent bodies.
In one example, the plurality of frequency-divided azimuth seismic data coherence bodies includes a plurality of high frequency azimuth seismic data coherence bodies, a plurality of intermediate frequency azimuth seismic data coherence bodies, and a plurality of low frequency azimuth seismic data coherence bodies.
In one example, obtaining fracture data volumes of different sizes from a plurality of frequency-divided azimuth seismic data coherence volumes comprises: respectively aiming at a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies or a plurality of low-frequency azimuth seismic data coherence bodies, the following steps are carried out: and searching a maximum value among all azimuths for each point of each path of seismic data, and respectively obtaining a high-frequency maximum seismic data coherent body, a medium-frequency maximum seismic data coherent body and a low-frequency maximum seismic data coherent body so as to further respectively obtain a small-size fracture data body, a medium-size fracture data body and a large-size fracture data body.
In one example, further comprising: and obtaining a small-size fracture data volume according to the high-frequency maximum seismic data coherence, obtaining a medium-size fracture data volume according to the medium-frequency maximum seismic data coherence, and obtaining a large-size fracture data volume according to the low-frequency maximum seismic data coherence.
The system predicts the fractures through azimuth full offset superposition and coherent calculation, reserves the response characteristics of the fractured reservoir, reserves the original details of the reservoir attributes obtained by each coherence, solves the problem that the original reservoir characteristics of multi-dimensional data are lost in the prior art, realizes the dimensionality reduction of multi-scale coherent attributes, and describes the details of the fractures more finely.
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 benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any 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.
Claims (10)
1. A multi-scale coherent dimensionality reduction fusion fracture prediction method is characterized by comprising the following steps:
preprocessing seismic data, extracting macro-surface elements and forming a plurality of macro-surface element channel sets in different directions;
selecting a plurality of azimuth angles and full offset distances for stacking aiming at the macro surface element channel set to obtain a plurality of seismic azimuth subvolumes;
calculating the coherence attribute of the seismic orientation subvolume data volume to obtain seismic data coherence volumes of different orientations, and further calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence volumes respectively to obtain a plurality of frequency division orientation seismic data coherence volumes;
acquiring fracture data volumes with different sizes according to the frequency division azimuth seismic data coherence volumes;
and carrying out normalization processing on the crack data volume so as to obtain a crack prediction result.
2. The multi-scale coherent dimensionality reduction fused fracture prediction method of claim 1, wherein calculating high, medium, and low frequencies of the seismic data coherence respectively, obtaining a plurality of frequency-divided azimuth seismic data coherence comprises:
converting the seismic data coherent body from a time domain to a frequency domain, and setting three frequencies of low frequency and medium frequency to obtain a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body;
and respectively calculating a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body to obtain the plurality of frequency division azimuth seismic data coherent bodies.
3. The multi-scale coherent dimension-reduced fusion fracture prediction method of claim 1, wherein the plurality of frequency-division azimuth seismic data coherence bodies comprises a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of medium-frequency azimuth seismic data coherence bodies, and a plurality of low-frequency azimuth seismic data coherence bodies.
4. The multi-scale coherent dimensionality reduction fused fracture prediction method of claim 3, wherein obtaining fracture data volumes of different sizes from the plurality of cross-bearing seismic data coherence volumes comprises:
respectively aiming at a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies or a plurality of low-frequency azimuth seismic data coherence bodies, the following steps are carried out:
and searching a maximum value among all azimuths for each point of each path of seismic data, and respectively obtaining a high-frequency maximum seismic data coherent body, a medium-frequency maximum seismic data coherent body and a low-frequency maximum seismic data coherent body so as to further respectively obtain a small-size fracture data body, a medium-size fracture data body and a large-size fracture data body.
5. The multi-scale coherent dimensionality reduction fused fracture prediction method according to claim 4, further comprising:
and obtaining the small-size fracture data volume according to the high-frequency maximum seismic data coherence, obtaining the medium-size fracture data volume according to the medium-frequency maximum seismic data coherence, and obtaining the large-size fracture data volume according to the low-frequency maximum seismic data coherence.
6. A multi-scale coherent dimension-reduction fused fracture prediction system, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
preprocessing seismic data, extracting macro-surface elements and forming a plurality of macro-surface element channel sets in different directions;
selecting a plurality of azimuth angles and full offset distances for stacking aiming at the macro surface element channel set to obtain a plurality of seismic azimuth subvolumes;
calculating the coherence attribute of the seismic orientation subvolume data volume to obtain seismic data coherence volumes of different orientations, and further calculating the high frequency, the medium frequency and the low frequency of the seismic data coherence volumes respectively to obtain a plurality of frequency division orientation seismic data coherence volumes;
acquiring fracture data volumes with different sizes according to the frequency division azimuth seismic data coherence volumes;
and carrying out normalization processing on the crack data volume so as to obtain a crack prediction result.
7. The multi-scale coherent dimension-reduced fused fracture prediction system of claim 6, wherein calculating high, medium, and low frequencies of the seismic data coherence comprises obtaining a plurality of frequency-divided azimuth seismic data coherence:
converting the seismic data coherent body from a time domain to a frequency domain, and setting three frequencies of low frequency and medium frequency to obtain a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body;
and respectively calculating a high-frequency seismic data coherent body, a medium-frequency seismic data coherent body and a low-frequency seismic data coherent body to obtain the plurality of frequency division azimuth seismic data coherent bodies.
8. The multi-scale coherent dimension-reduced fusion fracture prediction system of claim 6, wherein the plurality of frequency-divided azimuth seismic data coherence bodies comprises a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of medium-frequency azimuth seismic data coherence bodies, and a plurality of low-frequency azimuth seismic data coherence bodies.
9. The multi-scale coherent dimension-reduced fusion fracture prediction system of claim 8, wherein obtaining fracture data volumes of different sizes from the plurality of split-azimuth seismic data coherence volumes comprises:
respectively aiming at a plurality of high-frequency azimuth seismic data coherence bodies, a plurality of intermediate-frequency azimuth seismic data coherence bodies or a plurality of low-frequency azimuth seismic data coherence bodies, the following steps are carried out:
and searching a maximum value among each azimuth for each point of each path of seismic data, and respectively obtaining a high-frequency maximum seismic data coherent body, a medium-frequency maximum seismic data coherent body and a low-frequency maximum seismic data coherent body.
10. The multi-scale coherent dimensionality-reduced fusion fracture prediction system of claim 6, further comprising:
and obtaining the small-size fracture data volume according to the high-frequency maximum seismic data coherence, obtaining the medium-size fracture data volume according to the medium-frequency maximum seismic data coherence, and obtaining the large-size fracture data volume according to the low-frequency maximum seismic data coherence.
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