CN117406272A - Deconvolution broadband processing method and device for fast multi-element information constraint - Google Patents
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Abstract
The invention relates to a deconvolution broadband processing method and device for fast multi-element information constraint, comprising the following steps: based on a single-channel reflection coefficient inversion basis, constructing an inversion mapping matrix G and a sparse vector q from a convolution model; determining a real-time geological inner screen and a real-time geological boundary based on the pre-drilling information, the while-drilling information and the post-drilling information; determining a sparse matrix Q on the basis of the corresponding relation between the real-time geological inner curtain and the real-time geological boundary and the sparse vector Q; and fusing the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, constructing a new inversion equation constrained by the multi-element information, and calculating to obtain a deconvolution broadband processing result. The method can realize the high-resolution processing of the earthquakes of the offshore complex stratum, improve the earthquake imaging precision of risk areas such as volcanic channels, hidden mountain inner curtains, fracture distribution, abnormal lithology boundaries and the like in the stratum before drilling, and provide important technical support for completing safe, efficient and accurate drilling engineering.
Description
Technical Field
The invention relates to a deconvolution broadband processing method and device for fast multi-element information constraint, and relates to the fields of geophysical exploration, drilling engineering, oil-gas field development and the like.
Background
In recent years, offshore oil and gas exploration and development of China are in an important attack period, the pre-drilling accurate imaging difficulty of exploration reservoir target depiction and drilling engineering is gradually increased, and geological risks of drilling in engineering operation are also greatly increased, so that a seismic data broadband processing technology with higher resolution, better accuracy and stronger fidelity is explored, and an effective data basis is provided for drilling safety planning. However, in the high resolution technology, the multiple links of acquisition, processing, interpretation, etc. all have an impact on the resolution of the seismic data. In the aspect of deconvolution, the resolution and frequency of the seismic data are generally improved in the present stage through two modes of single-channel deconvolution based on longitudinal sparse assumption and multi-channel deconvolution based on transverse continuous assumption, so as to obtain a clearer underground image.
In the single-channel high-resolution research, the interference of marine ringing and multiple waves is successfully restrained by establishing a prediction deconvolution theoretical basis, the assumption of the minimum phase of the seismic wavelet and the white noise of the reflection coefficient is avoided, and the application range of high-resolution processing is greatly expanded. Meanwhile, a minimum entropy deconvolution method, a maximum likelihood deconvolution method, a blind deconvolution technique, an unsteady deconvolution method, a colored deconvolution method, and the like are also applied to seismic data processing. The methods have poor transverse continuity, high requirements on the seismic wavelets and still blur the high-resolution processed image under the influence of noise. In multichannel high-resolution research, seismic data are subjected to integral inversion by a multichannel inversion method based on transverse continuous assumption, so that a seismic image with higher resolution is obtained. The method can effectively utilize the correlation information of the seismic waves in space, and improve the accuracy and stability of inversion results, for example: tikhonov regularization method, multi-channel Bayesian inversion method, multi-channel inversion method of reflection coefficient sparse hypothesis, multi-channel deconvolution method of Markov-Bayesian random field modeling, inversion method of high-order total variation, multi-channel blind deconvolution method based on sparse term constraint tracks and deconvolution method based on spatial regularization constraint can be used for improving stability and transverse continuity of inversion results and reducing multiple solutions of knowledge, but inversion calculation efficiency is low.
In summary, in the technology of performing broadband processing on seismic data to obtain higher resolution, the single-channel high-resolution processing method has high calculation efficiency, which is the preferred scheme adopted by current commercial software, but the single-channel high-resolution processing technology has low resolution and limited resolution in the face of current increasingly complex drilling conditions, and cannot effectively meet the seismic data requirements of the current drilling engineering safety. The resolution ratio of the multi-channel high-resolution inversion result is higher, and the multi-channel high-resolution inversion result accords with geological laws, but the multi-channel high-resolution processing technology has low calculation efficiency, and has higher requirements on hardware equipment of a computer. In addition, the existing multi-channel method does not consider the distribution rule of actual underground geology, the constraint algorithm is still multi-channel constraint of some mathematical assumptions, manual interpretation or high-accuracy multi-information is difficult to add, available effective information quantity is limited, and the resolution of the seismic data cannot be greatly improved. Therefore, in the face of the complexity of the deepwater geological environment and the strong heterogeneity of the stratum, the conventional multi-channel high-resolution technology still has difficulty in realizing effective practical popularization and application.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention aims to provide a deconvolution broadband processing method, device, equipment and medium for fast multi-element information constraint, which can solve the problems of low single-channel high-resolution inversion precision and low multi-channel high-resolution inversion calculation efficiency in the traditional method, and can rapidly provide seismic data with higher resolution and better fidelity as reliable basic data support in the marine complex geological engineering risk prediction process.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for fast multi-element information constrained deconvolution broadband processing, including:
based on a single-channel reflection coefficient inversion basis, constructing an inversion mapping matrix G and a sparse vector q from a convolution model;
determining a real-time geological inner screen and a real-time geological boundary based on the pre-drilling information, the while-drilling information and the post-drilling information;
determining a sparse matrix Q on the basis of the corresponding relation between the real-time geological inner curtain and the real-time geological boundary and the sparse vector Q;
and fusing the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, constructing a new inversion equation constrained by the multi-element information, and calculating to obtain a deconvolution broadband processing result.
Further, the inversion mapping matrix G is:
G＝(WW T +λQ) -1 W T
in the formula, W is a seismic wavelet convolution matrix, lambda is a regularization parameter, and lambda Q is an inversion matrix.
Further, the sparse vector q is:
q＝diag(λQ)。
further, determining the real-time geological inner curtain and the real-time geological boundary based on the pre-drilling information, the while-drilling information and the post-drilling information, comprising: and determining the boundary position information of the geologic body with obvious impedance difference in the stratum, and determining the inner curtain range of the geologic body with strong internal homogeneity and continuous stability. The geological boundary comprises a stratum unconformity surface, a top interface of a submarine mountain, a top-bottom interface of a river channel and the like, and the geological inner curtain comprises a volcanic channel, a coal seam, a river channel with stable deposition and the like.
Further, determining the sparse matrix Q based on the real-time geological inner curtain and the corresponding relation between the real-time geological boundary and the sparse vector Q includes:
based on geological boundary position information, if tau position has geological boundary information, introducing constraint, and then endowing a minimum value or zero value with a q vector:
q τ ＝0
based on geological body inner curtain position information, introducing constraint, and then giving a maximum value to a q vector at a position without reflection coefficient of the geological inner curtain:
q τ ＝max(q)
wherein, tau is the position of the geological inner curtain, q vector is only assigned at the initial stage of iteration, and the maximum value after assignment is kept at a certain high value;
the sparse matrix Q is composed of a sparse vector Q arrangement:
Q＝[q 1 ,q 2 ,q 3 ,…,q n ]
wherein n is the number of seismic traces.
Further, fusing the introduced real-time multi-element geological structure constraint matrix and the sparse matrix, constructing a new inversion equation of multi-element information constraint, and calculating to obtain a deconvolution broadband processing result, wherein the method comprises the following steps of:
1) The logging-geological structure constraint body Geo matrix is introduced as a real-time multi-element geological structure constraint matrix, and the structure constraint adds multi-element information, specifically as follows:
Geo＝f(S)
in the formula, geo is a structural constraint body, S is input seismic data, and f () is an algorithm for extracting structural constraint;
the geological boundary position information is added in the structural constraint to obtain:
Geo τ,i ＝0
Wherein i is a seismic trace, tau is the interface position of the geological horizon and the interface position in q are kept consistent;
the geological inner curtain range information is added in the structural constraint to obtain:
Geo τ,i ＝max(Geo i )
wherein i is the trace of an earthquake; τ is the position of the geological inner curtain range and is consistent with the geological inner curtain range in q;
2) Fusing the logging-geological structure constraint Geo matrix with the sparse matrix Q to form a new constraint body, namely a logging-geological multi-element information structure constraint matrix L:
wherein,is Cronecker product;
3) Obtaining a new inversion equation of the multi-element information structure constraint, and obtaining an inversion result through iterative optimization rapidly and efficiently:
R＝(WW T +λL) -1 W T S。
in a second aspect, the present invention further provides a deconvolution broadband processing apparatus for fast multivariate information constraint, including:
a first unit configured to construct an inversion mapping matrix G and a sparse vector q from the convolution model based on the single-pass reflection coefficient inversion basis;
a second unit configured to determine a real-time geological inner screen and a real-time geological boundary based on the pre-drilling information, the while-drilling information, and the post-drilling information;
a third unit configured to determine a sparse matrix Q based on the real-time geological inner curtain and the real-time geological boundary and the corresponding relationship of the sparse vector Q;
And the fourth unit is configured to fuse the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, construct a new inversion equation constrained by the multi-element information, and calculate to obtain a deconvolution broadband processing result.
Further, fusing the introduced real-time multi-element geological structure constraint matrix and the sparse matrix, constructing a new inversion equation of multi-element information constraint, and calculating to obtain a deconvolution broadband processing result, wherein the method comprises the following steps of:
1) Introducing a logging-geological structure constraint body Geo matrix as a real-time multi-element geological structure constraint matrix, and adding multi-element information to the structure constraint as follows:
Geo＝f(S)
in the formula, geo is a structural constraint body, S is input seismic data, and f () is an algorithm for extracting structural constraint;
the geological boundary position information is added in the structural constraint to obtain:
Geo τ,i ＝0
wherein i is the channel of the earthquake, and tau is the geological horizon interface position;
the geological inner curtain range information is added in the structural constraint to obtain:
Geo τ,i ＝max(Geo i )
wherein i is the trace of an earthquake; τ is the position of the inner curtain range of the geologic body;
2) Fusing the logging-geological structure constraint Geo matrix with the sparse matrix Q to form a new constraint body, namely a logging-geological multi-element information structure constraint matrix L:
wherein,is Cronecker product;
3) Obtaining a new inversion equation of the multi-element information structure constraint, and obtaining an inversion result through iterative optimization rapidly and efficiently:
R＝(WW T +λL) -1 W T S
in the formula, λ is a regularization parameter.
In a third aspect, the present invention also provides an electronic device, including: one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
In a fourth aspect, the invention also provides a computer readable storage medium storing one or more programs, characterized in that the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
The invention adopts the technical proposal and has the following characteristics:
1. according to the invention, a logging-geological structure constraint Geo matrix is introduced from the mechanism of single-channel sparse inversion and on the basis of a classical sparse Q matrix, a multi-element information sparse structure constraint L matrix is provided, a structure constraint Geo extraction method is provided and organically fused with the classical sparse matrix, logging-geological structure information is taken as priori knowledge, and the requirements on seismic wavelet, mathematical sparse assumption and regular parameter precision are reduced; and the noise is perceived in a high dimension, so that the noise amplification is controlled while the compression wavelet is realized, and the signal-to-noise ratio of the image is effectively improved.
2. According to the method, the distribution of local solutions in the known space is changed, the convergence direction and step length of the algorithm are optimized, and the calculation efficiency of high-resolution stratum inversion is greatly improved.
3. The invention has higher operation efficiency in two-dimensional, three-dimensional and pre-stack data, the inversion speed is faster than that of the classical sparse reflection coefficient inversion method, and the convergence data is even faster than that of the traditional single-channel high-resolution processing method; the practicality and reliability of the new method and the new device are verified in the numerical model and the actual data.
4. According to the deconvolution broadband processing method for the rapid multi-element information constraint, the structural constraint body Geo is obtained through extraction, the sparse constraint matrix is unconstrained, the resolution is obviously improved while noise suppression is realized, and the spectrum expansion is obvious; in addition, the structural constraint provided by the invention is convenient for the introduction of the multi-element information, after the priori knowledge of logging-geological multi-element information is introduced, the resolution of the deconvolution result is improved, and the reliability of spectrum expansion is stronger.
In summary, the rapid multi-information constraint deconvolution broadband processing method and device provided by the invention combine multi-scale information such as logging, well drilling and logging to realize high-resolution processing of earthquakes of offshore complex stratum, improve the earthquake imaging precision of risk areas such as volcanic channels, hidden mountain inner curtains, fracture distribution and abnormal lithology boundaries in stratum before drilling, and provide important technical support for completing safe, efficient and accurate drilling engineering.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like parts are designated with like reference numerals throughout the drawings. In the drawings:
FIG. 1 is a schematic diagram of a fast multi-element information constrained deconvolution broadband processing method according to an embodiment of the present invention.
FIG. 2 shows a real wedge reflectance model and composite data, wherein (a) is a real wedge reflectance model with an inclination angle of 45 degrees, 60 sampling points in the longitudinal direction, 30 CDPs in total, and the model has a relatively simple structure, and is beneficial to the test of high-resolution formation imaging; (b) The synthetic data of 10% of noise is added simultaneously for the convolution of the true reflection coefficient and the 30Hz zero-phase Ricker seismic wavelet.
FIG. 3 shows the results of a sparse Q matrix and reflection coefficients for classical sparse deconvolution high resolution formation imaging in accordance with an embodiment of the present invention, where (a) is the sparse Q matrix for classical sparse deconvolution high resolution formation imaging, where low values occur at reflection coefficient positions and high values occur at non-reflection coefficient positions, but are subject to noise effects and errors; (b) The reflection coefficient result obtained by classical sparse deconvolution high-resolution stratum imaging is that the resolution limit is near one quarter wavelength, a great amount of noise exists in the inversion result, and the signal to noise ratio is low.
Fig. 4 shows a structural sparse constraint matrix L and a deconvolution result of a new device for deconvolution of multiple information constraint according to an embodiment of the present invention, where (a) the structural sparse constraint matrix L of the new device for deconvolution of multiple information constraint is added with constraints, and then more high values appear at the non-reflection coefficient positions, and (b) the deconvolution result of multiple information constraint is obtained, so that resolution is obviously improved, noise is weaker, and signal to noise ratio is high.
FIG. 5 shows a structural sparse constraint matrix L with added reflection coefficient position information constraint deconvolution and deconvolution results, wherein (a) the structural sparse constraint matrix L with added reflection coefficient position information constraint deconvolution is obtained by taking the wedge-shaped top reflection coefficient position as a priori knowledge, and the reflection coefficient position in the L matrix is a horizontal low-value line; (b) In order to add the deconvolution result of the reflection coefficient position information constraint, the longitudinal position of the reflection coefficient top inversion result is accurate, and the resolution is further improved.
FIG. 6 shows a structural sparse constraint matrix L added with geological inner curtain information constraint deconvolution and a constraint deconvolution result, wherein (a) the structural sparse constraint matrix L added with geological inner curtain information constraint deconvolution is provided with a maximum value above a wedge-shaped model (sampling points range is 1-19); (b) In order to add the constrained deconvolution result of the geological inner curtain information, no noise appears above the wedge model, and the resolution is better than that of the classical high-resolution deconvolution method.
Fig. 7 is a statistical comparison of the convergence speed of the method of the present invention, in which the ordinate is the error of the logarithmic domain and the abscissa is the iteration number, and the conventional deconvolution method has statistically significant rapid convergence characteristics.
FIG. 8 is a high resolution profile obtained from local post-stack seismic data and inversion for a region of a China offshore drilling rig according to an embodiment of the present invention, wherein FIG. (a) is the local post-stack seismic data for a region of the China offshore drilling rig, wherein log data is present at the CDP 44, shown as wave impedance, gamma log curves; (b) The method is used for inverting the obtained high-resolution profile. The inversion results in high matching degree between the reflection axis and the logging, which all appear near the abrupt change of wave impedance; (c) The spectrum is compared before and after the treatment by the method, and the treated spectrum is expanded by about 20 Hz.
FIG. 9 is a partial cross-section of a structure extracted from raw seismic data in accordance with an embodiment of the invention.
Fig. 10 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "includes," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless an order of performance is explicitly stated. It should also be appreciated that additional or alternative steps may be used.
Although the terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer or section from another region, layer or section. Terms such as "first," "second," and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.
For ease of description, spatially relative terms, such as "inner," "outer," "lower," "upper," and the like, may be used herein to describe one element or feature's relationship to another element or feature as illustrated in the figures. Such spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.
In the field of oil and gas exploration, development, and drilling, high resolution seismic data processing is extremely critical. Compression of the seismic wavelet and widening of the effective signal band range is the primary means of achieving resolution enhancement of the seismic data. In addition, to obtain high quality seismic data, it is desirable to process large amounts of disturbance information and to improve the signal-to-noise ratio in order to better identify and analyze the characteristics of the seismic wavefield. In order to more accurately simulate the underground characteristics, more data information is needed to be utilized by combining various information such as surface conditions, geological structures and the like so as to realize the maximum utilization of oil and gas resources. In order to solve the problems of the current deconvolution, the method, the device, the equipment and the medium for deconvolution broadband processing of the fast multi-element information constraint provided by the invention comprise the following steps: based on a single-channel reflection coefficient inversion basis, constructing an inversion mapping matrix G and a sparse vector q from a convolution model; determining a real-time geological inner screen and a real-time geological boundary based on the pre-drilling information, the while-drilling information and the post-drilling information; determining a sparse matrix Q on the basis of the corresponding relation between the real-time geological inner curtain and the real-time geological boundary and the sparse vector Q; and fusing the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, constructing a new inversion equation constrained by the multi-element information, and calculating to obtain a deconvolution broadband processing result. Therefore, the structural constraint body Geo is obtained through extraction, the sparse constraint matrix is unconstrained, the resolution is obviously improved while noise suppression is realized, and the spectrum expansion is obvious. In addition, the structural constraint provided by the invention is convenient for the introduction of the multi-element information, after the priori knowledge of logging-geological multi-element information is introduced, the resolution of the deconvolution result is improved, and the reliability of spectrum expansion is stronger.
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary 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.
Embodiment one: as shown in fig. 1, the fast multi-element information constraint deconvolution broadband processing method provided in this embodiment includes:
s1, based on a single-channel reflection coefficient inversion basis, constructing an inversion mapping matrix G and a sparse vector q from a convolution model, and taking the sparse matrix as a data basis for subsequent construction.
In this embodiment, the reflection coefficient inversion method needs to calculate the reflection coefficient from the seismic data S, where the inversion mapping matrix G is a key of method calculation, and the inversion expression is as follows:
R＝GS (1)
wherein G is an inversion mapping matrix, R is a reflection coefficient, and S is seismic data.
Assuming that the seismic wavelet is stationary over a range of time and space, the seismic wavelet is represented by a convolution model. For the inverse of the ideal convolution model, there is the objective function as follows:
Wherein W represents a wavelet matrix formed by an array of seismic wavelets representing a inversion, time-shift process in a convolution pattern.
In the ideal case of data, without noise, the reflection coefficient has an analytical solution by least squares solution:
R＝(WW T ) -1 W T S (3)
for the least squares smooth solution (3), its inversion mapping matrix G is related only to the seismic wavelet W, and its expression is as follows:
G＝(WW T ) -1 W T (4)
however, for an ideal convolution model the inverse process is difficult to process noisy, non-smooth seismic data, and for a noisy convolution model the inverse process has a general objective function:
in single-pass deconvolution, the noisy seismic data least squares iterative inversion mapping matrix G can be expressed as:
G＝(WW T +λQ) -1 W T (6)
in the formula, W is a seismic wavelet convolution matrix, and lambda is a regularization parameter.
The invention defines the diagonal element of the inversion matrix lambda Q as a sparse vector Q by analyzing the inversion mapping matrix G, as follows:
q＝diag(λQ) (7)
obviously, the q vector is affected by various sparsity assumptions, iteration numbers, and other factors.
S2, determining a real-time geological inner curtain and a real-time geological boundary based on the pre-drilling information, the while-drilling information and the post-drilling information.
In this embodiment, the effective and accurate geologic structure information provided by the logging curve, the drilling information, the geologic interpretation and the like is utilized to determine the boundary position information of the geologic body with obvious impedance difference in the stratum and the inner curtain range of the geologic body with strong internal homogeneity and continuous stability. The geological boundary comprises a stratum unconformity surface, a top interface of a submarine mountain, a top-bottom interface of a river channel and the like, the geological inner curtain comprises a volcanic channel, a coal seam, a river channel with stable deposition and the like, and the process is the prior art and is not repeated here.
S3, determining a sparse matrix Q on the basis of the geological boundary and geological inner curtain information and sparse vector Q corresponding relation.
Specifically, an inherent mathematical relationship between the sparse vector q and the reflection coefficient is determined, and geological boundary and geological internal curtain structure information are added at the same time, so that the sparse vector q and the reflection coefficient are integrated into a seismic data high-resolution imaging process, and a novel reflection coefficient inversion technology with multi-channel structure constraint is realized.
The wave impedance of the seismic data at the geological boundary has obvious change, and according to the geological boundary position information of the stratum unconformity surface, the coal seam top and bottom and the like obtained in the steps, the change or reflection sparse position is realized, if tau position has geological boundary information, the q vector is endowed with a minimum value or a zero value after constraint is introduced:
q τ ＝0 (8)
according to the geological body inner curtain position information such as the river distribution range, the pure coal layer distribution range and the like in the stratum obtained in the steps, introducing constraint and then giving a maximum value to the q vector at the position without reflection coefficient in the geological body:
q τ ＝max(q) (9)
where τ is the position of the geological inner curtain, q vector is assigned only at the initial iteration stage, and the maximum value after assignment will continue to keep a certain high value.
The sparse matrix Q consists of an array of the formula (7), and is a longitudinal sparse representation of the seismic data:
Q＝[q 1 ,q 2 ,q 3 ,…,q n ] (10)
Wherein n is the number of seismic traces, and the sparse matrix Q is formed by the joint constraint of seismic data and mathematical sparse assumptions.
S4, fusing the real-time multi-element geological structure constraint matrix and the sparse matrix to construct a multi-element information constraint inversion new equation, and calculating a high-resolution result.
In the embodiment, the structural constraint matrix does not have sparse requirements in the longitudinal direction and has good continuity in the transverse direction, so that the structural constraint matrix can reflect the approximate spatial distribution information of the underground high-resolution stratum image.
S41, introducing a logging-geological structure constraint body Geo matrix as a real-time multi-element geological structure constraint matrix, and adding multi-element information into the structure constraint, wherein the method specifically comprises the following steps:
Geo＝f(S) (11)
in the formula, geo is a structural restraint body which can reflect the basic structural form of underground geology; s is input seismic data, f () is an algorithm for extracting structural constraints, the algorithm can input the seismic data and output the approximate range of the reflection coefficient, the output range is much larger than the position of the real reflection coefficient, the lower the value is, the more the reflection coefficient is likely to appear, and the relationship between the q vector and the inversion reflection coefficient is consistent.
The geological boundary position information is added in the structural constraint to obtain:
Geo τ,i ＝0 (12)
Where i is the trace of the earthquake and τ is the boundary position of the geological horizon and the boundary position in q are kept consistent.
The geological inner curtain range information is added in the structural constraint to obtain:
Geo τ,i ＝max(Geo i ) (13)
wherein i is the trace of an earthquake; τ is the position of the geological inner curtain range and is consistent with the geological inner curtain range in q.
S42, providing a multi-element information sparse structure constraint matrix L
The logging-geological structure constraint Geo matrix is fused with Q obtained by sparse assumption to form a new constraint body, namely a logging-geological multi-element information structure constraint matrix L, which is expressed as follows:
wherein,for the kronecker product, the logging-geological structure constraint Geo matrix is used for realizing constraint of the multi-element information geological structure on the sparse matrix by adjusting the column vector extremum distribution mode in the sparse matrix Q.
S43, finally obtaining a new inversion equation constrained by the multi-element information structure, and obtaining an inversion result through iterative optimization rapidly and efficiently, wherein the high-resolution result has the characteristics of high noise resistance and wide frequency band.
R＝(WW T +λL) -1 W T S (15)
In summary, the invention improves the dimension of the one-dimensional sparse vector Q into the sparse matrix Q, invents a structure constraint extraction method and constructs a multi-element structure constraint matrix, utilizes the structure constraint matrix to constrain the sparse matrix, and provides a new single-channel reflection coefficient inversion method of the structure constraint, which has the calculation efficiency of single-channel high-resolution inversion and the calculation precision of multi-channel inversion. In addition, the invention has higher wavelet fault tolerance, noise immunity and higher convergence rate. Through multiple sets of seismic data tests, compared with a classical single-channel sparse high-resolution inversion method, the method has the advantages of higher calculation efficiency, better transverse continuity and higher image definition.
The invention integrates through the L matrix in an iterative regularization mode, namely, logging and geological information are integrated into the processing process of seismic data. The complementarity of the multiple information at different angles is utilized, so that the definition of the seismic data processing image can be improved. The actual data processing result shows that the high-resolution inversion result obtained by the method has relatively accurate frequency components, and the coincidence degree of the high-resolution inversion result and the wave impedance curve in the actual logging data is improved; the inversion speed is high, and the inversion result accords with the geological development rule better.
FIG. 2 shows a real wedge reflectance model and composite data, wherein (a) is a real wedge reflectance model with an inclination angle of 45 degrees, 60 sampling points in the longitudinal direction, 30 CDPs in total, and the model has a relatively simple structure, and is beneficial to the test of high-resolution formation imaging; (b) The synthetic data of 10% of noise is added simultaneously for the convolution of the true reflection coefficient and the 30Hz zero-phase Ricker seismic wavelet. FIG. 3 shows the results of sparse Q moment and reflection coefficient during classical sparse deconvolution high resolution formation imaging in accordance with an embodiment of the present invention, wherein (a) is a sparse Q matrix during classical sparse deconvolution high resolution formation imaging, where low values occur at reflection coefficient positions and high values occur at non-reflection coefficient positions, but are subject to noise effects and errors; (b) The reflection coefficient result obtained by classical sparse deconvolution high-resolution stratum imaging is that the resolution limit is near one quarter wavelength, a great amount of noise exists in the inversion result, and the signal to noise ratio is low. Fig. 4 shows a structural sparse constraint matrix L and a deconvolution result of a new device for deconvolution of multiple information constraint according to an embodiment of the present invention, where (a) the structural sparse constraint matrix L of the new device for deconvolution of multiple information constraint is added with constraints, and then more high values appear at the non-reflection coefficient positions, and (b) the deconvolution result of multiple information constraint is obtained, so that resolution is obviously improved, noise is weaker, and signal to noise ratio is high. FIG. 5 shows a structural sparse constraint matrix L with added reflection coefficient position information constraint deconvolution and deconvolution results, wherein (a) the structural sparse constraint matrix L with added reflection coefficient position information constraint deconvolution is obtained by taking the wedge-shaped top reflection coefficient position as a priori knowledge, and the reflection coefficient position in the L matrix is a horizontal low-value line; (b) In order to add the deconvolution result of the reflection coefficient position information constraint, the longitudinal position of the reflection coefficient top inversion result is accurate, and the resolution is further improved. FIG. 6 shows a structural sparse constraint matrix L added with geological inner curtain information constraint deconvolution and a constraint deconvolution result, wherein (a) the structural sparse constraint matrix L added with geological inner curtain information constraint deconvolution is provided with a maximum value above a wedge-shaped model (sampling points range is 1-19); (b) In order to add the constrained deconvolution result of the geological inner curtain information, no noise appears above the wedge model, and the resolution is better than that of the classical high-resolution deconvolution method. Fig. 7 is a statistical comparison of the convergence speed of the method of the present invention, in which the ordinate is the error of the logarithmic domain and the abscissa is the iteration number, and the conventional deconvolution method has statistically significant rapid convergence characteristics. FIG. 8 is a high resolution profile obtained from local post-stack seismic data and inversion for a region of a China offshore drilling rig according to an embodiment of the present invention, wherein FIG. (a) is the local post-stack seismic data for a region of the China offshore drilling rig, wherein log data is present at the CDP 44, shown as wave impedance, gamma log curves; (b) The method is used for inverting the obtained high-resolution profile. The inversion results in high matching degree between the reflection axis and the logging, which all appear near the abrupt change of wave impedance; (c) The spectrum is compared before and after the treatment by the method, and the treated spectrum is expanded by about 20 Hz. FIG. 9 is a partial cross-section of a structure extracted from raw seismic data in accordance with an embodiment of the invention.
Embodiment two: in contrast, the present embodiment provides a fast multi-element information constrained deconvolution broadband processing device. The device provided in this embodiment may implement the fast multi-element information constraint deconvolution broadband processing method of the first embodiment, and the device may be implemented by software, hardware or a combination of software and hardware. For convenience of description, the present embodiment is described while being functionally divided into various units. Of course, the functions of the units may be implemented in the same piece or pieces of software and/or hardware. For example, the apparatus may comprise integrated or separate functional modules or functional units to perform the corresponding steps in the methods of the first embodiment. Since the apparatus of this embodiment is substantially similar to the method embodiment, the description of this embodiment is relatively simple, and the relevant points may be referred to in the description of the first embodiment, and the embodiment of the fast multi-information constraint deconvolution broadband processing apparatus provided by the present invention is merely illustrative.
Specifically, this embodiment also provides a deconvolution broadband processing device of fast multivariate information constraint, including:
A first unit configured to construct an inversion mapping matrix G and a sparse vector q from the convolution model based on the single-pass reflection coefficient inversion basis;
a second unit configured to determine a real-time geological inner screen and a real-time geological boundary based on the pre-drilling information, the while-drilling information, and the post-drilling information;
a third unit configured to determine a sparse matrix Q based on the real-time geological inner curtain and the real-time geological boundary and the corresponding relationship of the sparse vector Q;
and the fourth unit is configured to fuse the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, construct a new inversion equation constrained by the multi-element information, and calculate to obtain a deconvolution broadband processing result.
Embodiment III: the present embodiment provides an electronic device corresponding to the fast multi-element information constraint deconvolution broadband processing method provided in the first embodiment, where the electronic device may be an electronic device for a client, for example, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., so as to execute the method of the first embodiment.
As shown in fig. 10, the electronic device includes a processor, a memory, a communication interface, and a bus, where the processor, the memory, and the communication interface are connected by the bus to complete communication with each other. The bus may be an industry standard architecture (ISA, industry Standard Architecture) bus, a peripheral component interconnect (PCI, peripheral Component) bus, or an extended industry standard architecture (EISA, extended Industry Standard Component) bus, among others. The memory stores a computer program that can be executed on the processor, and when the processor executes the computer program, the processor executes the method of the first embodiment, so that the principle and technical effects are similar to those of the first embodiment, and are not described herein again. Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the architecture relevant to the present application and is not limiting of the computing devices to which the present application may be applied, and that a particular computing device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In a preferred embodiment, the logic instructions in the memory described above may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an optical disk, or other various media capable of storing program codes.
In a preferred embodiment, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general purpose processor, which is not limited herein.
Embodiment four: the present embodiment provides a computer program product, which may be a computer program stored on a computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is capable of executing the method provided in the above embodiment, and its implementation principles and technical effects are similar to those of the embodiment and are not repeated herein.
In a preferred embodiment, the computer-readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device, such as, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing. The computer-readable storage medium stores computer program instructions that cause a computer to perform the method provided by the first embodiment described above.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In the description of the present specification, reference to the terms "one preferred embodiment," "further," "specifically," "in the present embodiment," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A deconvolution broadband processing method of fast multi-element information constraint is characterized by comprising the following steps:
based on a single-channel reflection coefficient inversion basis, constructing an inversion mapping matrix G and a sparse vector q from a convolution model;
Determining a real-time geological inner screen and a real-time geological boundary based on the pre-drilling information, the while-drilling information and the post-drilling information; determining a sparse matrix Q on the basis of the corresponding relation between the real-time geological inner curtain and the real-time geological boundary and the sparse vector Q;
and fusing the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, constructing a new inversion equation constrained by the multi-element information, and calculating to obtain a deconvolution broadband processing result.
2. The method for fast multivariate information constrained deconvolution wideband processing of claim 1, wherein the inversion mapping matrix G is:
G＝(WW T +λQ) -1 W T
in the formula, W is a seismic wavelet convolution matrix, lambda is a regularization parameter, and lambda Q is an inversion matrix.
3. The method for fast multivariate information constrained deconvolution wideband processing of claim 2, wherein the sparse vector q is:
q＝diag(λQ)。
4. the method of claim 1, wherein determining the real-time geological inner curtain and the real-time geological boundary based on pre-drilling information, while-drilling information and post-drilling information comprises: and determining geologic body boundary position information with good relative continuity and obvious longitudinal difference in the stratum and geologic body inner curtain range with continuous and stable interior and no obvious lithology difference, wherein the geologic boundary comprises a stratum non-integration surface, a top-bottom interface of a river channel, a top-bottom interface of a coal bed and the like, and the geologic inner curtain comprises a large set of coal bed and mudstone and a river channel with stable sediment.
5. The method for deconvolution broadband processing of fast multivariate information constraint according to claim 1, wherein determining the sparse matrix Q based on the real-time geological inner curtain and the correspondence between the real-time geological boundary and the sparse vector Q comprises:
based on geological boundary position information, if tau position has geological boundary information, introducing constraint, and then endowing a minimum value or zero value with a q vector:
q τ ＝0
based on geological body inner curtain position information, introducing constraint, and then giving a maximum value to a q vector at a position without reflection coefficient of the geological inner curtain:
q τ ＝max(q)
wherein, tau is the position of the geological inner curtain, q vector is only assigned at the initial stage of iteration, and the maximum value after assignment is kept at a certain high value;
the sparse matrix Q is composed of a sparse vector Q arrangement:
Q＝[q 1 ,q 2 ,q 3 ,…,q n ]
wherein n is the number of seismic traces.
6. The method for deconvolution broadband processing of fast multi-element information constraint according to claim 1, wherein the method for deconvolution broadband processing results by fusing the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, constructing a new inversion equation of multi-element information constraint and calculating comprises the following steps:
1) Introducing a logging-geological structure constraint Geo matrix as a real-time multi-element geological structure constraint matrix:
Geo＝f(S)
In the formula, geo is a structural constraint body, S is input seismic data, and f () is an algorithm for extracting structural constraint;
the geological boundary position information is added in the structural constraint to obtain:
Geo τ,i ＝0
wherein i is a seismic trace, tau is the interface position of the geological horizon and the interface position in q are kept consistent;
the geological inner curtain range information is added in the structural constraint to obtain:
Geo τ,i ＝max(Geo i )
wherein i is the trace of an earthquake; τ is the position of the geological inner curtain range and is consistent with the geological inner curtain range in q;
2) Fusing the logging-geological structure constraint Geo matrix with the sparse matrix Q to form a new constraint body, namely a logging-geological multi-element information structure constraint matrix L:
wherein,is Cronecker product;
3) Obtaining a new inversion equation of the multi-element information structure constraint, and obtaining an inversion result through iterative optimization rapidly and efficiently:
R＝(WW T +λL) -1 W T S。
7. a deconvolution broadband processing device of fast multivariate information constraint is characterized by comprising:
a first unit configured to construct an inversion mapping matrix G and a sparse vector q from the convolution model based on the single-pass reflection coefficient inversion basis;
a second unit configured to determine a real-time geological inner screen and a real-time geological boundary based on the pre-drilling information, the while-drilling information, and the post-drilling information;
A third unit configured to determine a sparse matrix Q based on the real-time geological inner curtain and the real-time geological boundary and the corresponding relationship of the sparse vector Q;
and the fourth unit is configured to fuse the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, construct a new inversion equation constrained by the multi-element information, and calculate to obtain a deconvolution broadband processing result.
8. The apparatus of claim 7, wherein the method for performing deconvolution broadband processing by fusing the introduced real-time multi-element geological structure constraint matrix with the sparse matrix, constructing a new inversion equation of multi-element information constraint, and performing computation comprises:
1) Introducing a logging-geological structure constraint Geo matrix as a real-time multi-element geological structure constraint matrix:
Geo＝f(S)
in the formula, geo is a structural constraint body, S is input seismic data, and f () is an algorithm for extracting structural constraint;
the geological boundary position information is added in the structural constraint to obtain:
Geo τ,i ＝0
wherein i is the channel of the earthquake, and tau is the geological horizon interface position;
the geological inner curtain range information is added in the structural constraint to obtain:
Geo τ,i ＝max(Geo i )
wherein i is the trace of an earthquake; τ is the position of the inner curtain range of the geologic body;
2) Fusing the logging-geological structure constraint Geo matrix with the sparse matrix Q to form a new constraint body, namely a logging-geological multi-element information structure constraint matrix L:
wherein,is Cronecker product;
3) Obtaining a new inversion equation of the multi-element information structure constraint, and obtaining an inversion result through iterative optimization rapidly and efficiently:
R＝(WW T +λL) -1 W T S
in the formula, λ is a regularization parameter.
9. An electronic device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-6.
10. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-6.
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