CN115963542A - Method, device, equipment and medium for determining anisotropic parameter sensitivity matrix - Google Patents
Method, device, equipment and medium for determining anisotropic parameter sensitivity matrix Download PDFInfo
- Publication number
- CN115963542A CN115963542A CN202111193074.8A CN202111193074A CN115963542A CN 115963542 A CN115963542 A CN 115963542A CN 202111193074 A CN202111193074 A CN 202111193074A CN 115963542 A CN115963542 A CN 115963542A
- Authority
- CN
- China
- Prior art keywords
- simulated
- azimuth angle
- actual
- anisotropic
- prestack
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Geophysics And Detection Of Objects (AREA)
Abstract
The application provides a method, a device, equipment and a storage medium for determining an anisotropic parameter sensitivity matrix, which comprise the following steps: acquiring an actual prestack azimuth angle gather, longitudinal wave impedance, transverse wave impedance, density and an anisotropic parameter initial model; constructing a simulated prestack azimuth angle gather based on the initial models of the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters; obtaining error functions of a plurality of actual seismic records and simulated seismic records with preset azimuth angles and preset incidence angles according to the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather; constructing a constraint term error function of an actual pre-stack azimuth angle gather and a simulated pre-stack azimuth angle gather based on error functions of each actual seismic record and each simulated seismic record; and determining an anisotropic parameter sensitivity matrix according to the partial derivative of the constraint term error function to the anisotropic parameter.
Description
Technical Field
The application relates to the technical field of petroleum geophysical exploration, in particular to a method, a device, equipment and a storage medium for determining an anisotropic parameter sensitivity matrix.
Background
Anisotropy refers to the property of a medium that is not constant in value, but varies with direction. Anisotropy in seismic exploration mainly refers to the characteristic that the propagation velocity of seismic waves in a subsurface medium changes along with the change of the propagation direction. At present, along with the gradual deepening of exploration and development degree, the geophysical exploration type of oil and gas has been developed from the fields of conventional energy coal, petroleum, natural gas to unconventional energy coal bed gas, oil shale, shale gas and the like, the exploration area is from inland to offshore or even deep sea, and the exploration depth is gradually developed from the middle layer to the middle-deep layer. The development of the oil and gas geophysical exploration inevitably meets the problem of seismic anisotropy, the offset distance of the acquired seismic data is gradually increased along with the increase of the exploration depth, and the anisotropy phenomenon is particularly prominent. In addition, offshore and deep-sea sedimentary formations are mostly anisotropic, and in the case of oil shale as an example of unconventional energy sources, the oil shale mostly develops into a continuous or discontinuous horizontal lamellar structure and is characterized in that fine layers are distributed in a lamellar manner, and the shale with high content of lamellar minerals and the oil shale generally have obvious anisotropic characteristics.
The anisotropy plays an extremely important role in oil and gas exploration, however, in the related technology, the anisotropy parameters capable of representing the anisotropy degree of a medium are difficult to obtain, so that the inversion work efficiency of the prestack anisotropy parameters is low and the accuracy is poor.
Disclosure of Invention
In view of the foregoing problems, the present application provides a method, an apparatus, a device, and a storage medium for determining an anisotropic parameter sensitivity matrix.
The application provides a method for determining an anisotropic parameter sensitivity matrix, which comprises the following steps:
acquiring an actual prestack azimuth angle gather, longitudinal wave impedance, transverse wave impedance, density and an anisotropic parameter initial model;
constructing a simulated prestack azimuth angle gather based on the initial models of the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters;
obtaining error functions of a plurality of actual seismic records and simulated seismic records with preset azimuth angles and preset incidence angles according to the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather;
constructing a constraint term error function of an actual prestack azimuth angle gather and a simulated prestack azimuth angle gather based on error functions of each actual seismic record and each simulated seismic record;
and determining an anisotropic parameter sensitivity matrix according to the partial derivative of the constraint term error function to the anisotropic parameter.
In some embodiments, the constructing a simulated prestack azimuth angle gather based on the longitudinal wave impedance, the transverse wave impedance, the density, and the initial model of anisotropy parameters includes:
establishing functional relations between longitudinal wave reflection coefficients and the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters;
and generating the simulated prestack azimuth angle gather by utilizing the functional relation and the seismic wavelets based on a convolution principle.
In some embodiments, constructing the constraint term error functions for the actual pre-stack azimuth angle gather and the simulated pre-stack azimuth angle gather based on the error functions for each of the actual seismic record and the simulated seismic record comprises:
accumulating error functions of a plurality of actual seismic records and simulated seismic records corresponding to different azimuth angles and different incidence angles to obtain matching items of simulated data and actual data;
calculating a constraint term of the anisotropic parameter initial model;
and obtaining a constraint term error function of the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather based on the matching term of the simulated data and the actual data and the constraint term of the initial model of the anisotropic parameters.
In some embodiments, the constraint terms of the initial model of anisotropic parameters comprise constraint terms of the initial model of anisotropic parameters in three different directions.
In some embodiments, said determining an anisotropy parameter sensitivity matrix from partial derivatives of said constraint term error function on anisotropy parameters comprises:
calculating partial derivatives of error functions of actual seismic records and simulated seismic records under a preset azimuth angle and a preset incidence angle;
stacking partial derivatives of error functions of the actual seismic records and the simulated seismic records to obtain partial derivatives of the constraint term error functions to anisotropic parameters;
and obtaining an anisotropic parameter sensitivity matrix based on the partial derivative of the constraint term error function to the anisotropic parameter.
In some embodiments, said stacking the partial derivatives of each of said actual seismic records with the error function of the simulated seismic records comprises:
and stacking the partial derivatives of the error functions of the actual seismic records and the simulated seismic records according to the sequence of the incidence angle and the azimuth angle.
In some embodiments, the predetermined azimuth angle has a first angle range and the difference between adjacent azimuth angles is 1 degree, the predetermined incident angle has a second angle range and the difference between adjacent incident angles is 1 degree.
The embodiment of the present application provides a device for determining an anisotropic parameter sensitivity matrix, including:
the acquisition module is used for acquiring an initial model of an actual prestack azimuth angle gather, longitudinal wave impedance, transverse wave impedance, density and anisotropic parameters;
the first construction module is used for constructing a simulated prestack azimuth angle gather based on the initial models of the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters;
the second construction module is used for obtaining error functions of a plurality of actual seismic records and simulated seismic records with preset azimuth angles and preset incidence angles according to the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather;
the third construction module is used for constructing a constraint term error function of the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather based on the error functions of the actual seismic records and the simulated seismic records;
and the determining module is used for determining the sensitivity matrix of the anisotropic parameters according to the partial derivatives of the constraint term error function to the anisotropic parameters.
An embodiment of the present application provides an apparatus for determining an anisotropic parameter sensitivity matrix, which includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the apparatus executes any one of the above methods for determining an anisotropic parameter sensitivity matrix.
The present application provides a storage medium storing a computer program, which can be executed by one or more processors, and can be used to implement any one of the above methods for determining an anisotropic parameter sensitivity matrix.
According to the determination method, the device, the equipment and the storage medium for the sensitivity matrix of the anisotropic parameters, a simulation prestack azimuth angle gather is constructed through an initial model of longitudinal wave impedance, transverse wave impedance, density and the anisotropic parameters, then a plurality of error functions of actual seismic records and simulation seismic records with preset azimuth angles and preset incidence angles are obtained through the simulation prestack azimuth angle gather and the actual prestack azimuth angle gather, a constraint term error function of the actual prestack azimuth angle gather and the simulation prestack azimuth angle gather with prior geological constraints is constructed based on the error functions of the actual seismic records and the simulation seismic records, finally the sensitivity matrix of the anisotropic parameters is determined according to partial derivatives of the constraint term error function on the anisotropic parameters, the obtained sensitivity matrix information can be used for prestack anisotropic parameter inversion work of extracting the anisotropic parameters from prestack seismic data, and the convergence speed of a prestack anisotropic parameter inversion work algorithm and the precision of inversion results are improved.
Drawings
The present application will be described in more detail below on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart illustrating an implementation of a method for determining an anisotropic parameter sensitivity matrix according to an embodiment of the present application;
fig. 2 is a schematic implementation flow diagram of a method for constructing a simulated prestack azimuth angle gather based on the longitudinal wave impedance, the transverse wave impedance, the density, and the initial anisotropic parameter model according to the embodiment of the present application;
FIG. 3 is a schematic diagram of an implementation flow of a method for constructing a constraint term error function of an actual prestack azimuth angle gather and a simulated prestack azimuth angle gather according to an embodiment of the present application;
fig. 4 is a schematic diagram of an implementation flow of a method for determining a sensitivity matrix of anisotropic parameters according to partial derivatives of the constraint term error function to the anisotropic parameters, provided in the embodiment of the present application;
FIG. 5 is a schematic diagram of actual pre-stack azimuth angle gathers at different angles provided by an embodiment of the present application;
FIG. 6 is a schematic diagram of actual well log data of a certain area provided by an embodiment of the present application;
FIG. 7 is a schematic diagram of a partial derivative curve of an actual pre-stack angle gather, a simulated pre-stack angle gather, and an error function for an azimuth angle of 180 degrees according to an embodiment of the present application;
FIG. 8 is a schematic diagram of sensitivity matrices of error functions for longitudinal wave impedance, transverse wave impedance, density and anisotropic parameters by calculating constraint terms according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an apparatus for determining an anisotropic parameter sensitivity matrix according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of a device for determining an anisotropic parameter sensitivity matrix according to an embodiment of the present application.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
The following description will be added if a similar description of "first \ second \ third" appears in the application file, and in the following description, the terms "first \ second \ third" merely distinguish similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under certain circumstances in a specific order or sequence, so that the embodiments of the application described herein can be implemented in an order other than that shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
Before describing a method for determining an anisotropic parameter sensitivity matrix provided by an embodiment of the present application, a brief description is given of problems existing in the related art:
anisotropy refers to the property of a medium that is not constant in value, but varies with direction. Anisotropy in seismic exploration mainly refers to the characteristic that the propagation speed of seismic waves in a subsurface medium changes along with the change of the propagation direction. At present, along with the gradual deepening of the exploration and development degree, the geophysical exploration types of oil and gas have been developed from the fields of conventional energy coal, petroleum, natural gas, unconventional energy coal bed gas, oil shale, shale gas and the like, the exploration area of the geophysical exploration type is from inland to offshore or even deep sea, and the exploration depth of the geophysical exploration type is gradually developed from the middle layer to the middle deep layer. The development of the oil and gas geophysical exploration inevitably meets the problem of seismic anisotropy, the offset distance of the acquired seismic data is gradually increased along with the increase of the exploration depth, and the anisotropy phenomenon is particularly prominent. In addition, offshore and deep-sea sedimentary formations are mostly anisotropic, and in the case of oil shale as an example of unconventional energy sources, the oil shale mostly develops into a continuous or discontinuous horizontal lamellar structure and is characterized in that fine layers are distributed in a lamellar manner, and the shale with high content of lamellar minerals and the oil shale generally have obvious anisotropic characteristics.
Therefore, the anisotropy plays an extremely important role in oil and gas exploration, and in order to research the influence of the anisotropy on the seismic wave velocity, a student Thomsen provides parameters epsilon, delta and gamma for representing the anisotropy of a medium, wherein epsilon is approximately equal to the relative difference between the horizontal velocity and the vertical velocity of a P wave, the magnitude of epsilon reflects the anisotropy degree of a longitudinal wave, delta represents the anisotropic change speed of the longitudinal wave between the transverse direction and the vertical direction, delta is the most important anisotropy parameter in the anisotropic seismic data processing, gamma represents the difference degree of the fast transverse wave velocity and the slow transverse wave velocity, reflects the fracture development strength, is a reference parameter for determining the well position of a fracture-type oil reservoir, and the research shows that the slight change of the anisotropy of the medium has great influence on the seismic wave reflection amplitude. In the aspect of anisotropic parameter inversion, alkhalifah et al originally proposed the inversion of anisotropic parameters in tilted TI media by using P-wave NMO velocity (AlkhalifahT, tsvann I. Velocity analysis in t ransverses visco anisotropy. Geophysics,1995,60 (5): 1550-1566), and no mature technology is available at present for obtaining anisotropic parameters representing the degree of anisotropy of media from seismic data.
In addition, the sensitivity matrix plays a role in searching direction in the numerical optimization algorithm, and the accuracy of the sensitivity matrix directly influences the convergence and the calculation efficiency of the inversion algorithm. The accuracy of the sensitivity matrix plays a crucial role in the inversion of the pre-stack anisotropic parameters, so the efficiency and the accuracy of the pre-stack anisotropic parameter inversion work are directly influenced by the quality of the determination method of the sensitivity matrix of the anisotropic parameters.
Based on the problems in the related art, embodiments of the present application provide a method for determining an anisotropic parameter sensitivity matrix, where the method is applied to a device for determining an anisotropic parameter sensitivity matrix, and the device for determining an anisotropic parameter sensitivity matrix may be an electronic device, such as a computer, a mobile terminal, and the like. The function implemented by the method for determining the anisotropic parameter sensitivity matrix provided in the embodiment of the present application may be implemented by calling a program code by a processor of an electronic device, where the program code may be stored in a computer storage medium.
Example one
An embodiment of the present application provides a method for determining an anisotropic parameter sensitivity matrix, and fig. 1 is a schematic implementation flow diagram of the method for determining an anisotropic parameter sensitivity matrix provided in the embodiment of the present application, and as shown in fig. 1, the method includes:
step S101, obtaining an actual prestack azimuth angle gather, longitudinal wave impedance, transverse wave impedance, density and an anisotropic parameter initial model.
In this embodiment of the application, the data information of the actual prestack azimuth angle gather, the longitudinal wave impedance, the transverse wave impedance, the density, and the anisotropic parameter initial model may be stored in a server in advance, and the anisotropic parameter sensitivity matrix determining device obtains corresponding data through communication connection with the server. In some embodiments, the anisotropic parameter sensitivity matrix determining device may also be connected to the above-mentioned respective data acquiring devices, and directly acquire the corresponding data from the respective data acquiring devices. Of course, in another embodiment, the data information may be directly input into the anisotropic parameter sensitivity matrix determination device by the user through a reverse input method.
And S102, constructing a simulated prestack azimuth angle gather based on the initial models of the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters.
In the embodiment of the present application, for the convenience of description, Z may be used p Representing the longitudinal wave impedance, Z s Representing transverse wave impedance, ρ representing density, δ, ε, and γ representing anisotropy parameters, respectively, M δ Initial model, M, representing the anisotropy parameter delta ε Initial model, M, representing the anisotropy parameter ε γ Representing an initial model of the anisotropy parameter γ, the simulated prestack azimuth angle gathers can be represented as
In a specific embodiment, Z pj Representing the longitudinal wave impedance, Z, of the j-th interface sj Represents the transverse wave impedance, p, of the jth interface j Denotes the density of the j-th interface, δ j 、ε j And gamma j Respectively represents the anisotropy parameter of the jth interface, wherein j represents the number of interfaces and takes the value of j =0.. M, then->
Is azimuthal angle theta, angle of incidence->
Time analog seismic records. />
Is to simulate a seismic recording->
I.e., is greater than>
And S103, obtaining error functions of a plurality of actual seismic records and simulated seismic records with preset azimuth angles and preset incidence angles according to the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather.
In the embodiment of the application, the actual prestack azimuth angle gather consists of actual seismic records with different azimuth angles and different incidence angles, and the simulated prestack azimuth angle gather consists of simulated seismic records with different azimuth angles and different incidence angles. In a particular embodiment, with
Represents the actual prestack azimuth angle gather, by +>
Denotes an azimuth angle theta and an angle of incidence->
Actual seismic recording of time, based on the time of day>
Is the actual seismic recording->
The ith sample value of (1). Then, it is>
Representing an azimuth angle theta, an angle of incidence->
The ith sample value of the error function of the actual seismic record and the simulated seismic record in time, i.e., ->
Can be formulated as:
then, in one particular embodiment, the azimuth angle is represented as θ and the angle of incidence is represented as
Error function of actual seismic record versus simulated seismic record of time->
Can be expressed as:
and step S104, constructing a constraint term error function of the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather based on the error functions of the actual seismic records and the simulated seismic records.
In the embodiment of the application, the error function of the actual seismic record and the simulated seismic record
The magnitude of the value indicates how well the simulated seismic record matches (fits) the actual seismic record. Specifically, F is used for representing the constraint term error function of the actual prestack azimuth angle trace set and the simulated prestack azimuth angle trace set, and the error function of the actual seismic record and the simulated seismic record corresponding to different azimuth angles and different incidence angles is based on the ^ 4>
And accumulating and adding the prior constraint term to obtain a constraint term error function F.
And step S105, determining an anisotropic parameter sensitivity matrix according to the partial derivative of the constraint term error function to the anisotropic parameter.
In the embodiment of the application, the error function F of the constraint term is an error function obtained by actual seismic recording and simulated seismic recording
Is constructed to come out and is->
And then collects the information through the actual prestack azimuth angle>
And simulating a pre-stack azimuth angle gather>
Is constructed. Thus for each direction in FPartial derivatives of heterosexual parameters, i.e. for Z separately p ，Z s The partial derivatives are calculated for p, δ, e, γ, and finally the anisotropy parameter sensitivity matrix G is determined from the partial derivatives.
According to the determination method of the anisotropic parameter sensitivity matrix, a simulated prestack azimuth angle gather is constructed through an initial model of longitudinal wave impedance, transverse wave impedance, density and anisotropic parameters, error functions of a plurality of actual seismic records with preset azimuth angles and preset incidence angles and the simulated seismic records are obtained through the simulated prestack azimuth angle gather and the actual prestack azimuth angle gather, a constraint term error function with prior geological constraint of the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather is constructed based on the error functions of the actual seismic records and the simulated seismic records, and the anisotropic parameter sensitivity matrix is determined according to partial derivatives of the constraint term error function on the anisotropic parameters, so that the obtained sensitivity matrix information can be used for prestack anisotropic parameter inversion work of extracting the anisotropic parameters from prestack seismic data, and the convergence speed of a prestack anisotropic parameter inversion work algorithm and the precision of an inversion result are improved.
Example two
Based on the foregoing embodiments, the present application further provides a method for constructing a simulated prestack azimuth angle gather based on the longitudinal wave impedance, the transverse wave impedance, the density, and the initial anisotropic parameter model, as shown in fig. 2, where the method includes:
step S201, functional relations between the longitudinal wave reflection coefficient and the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters are established.
In the examples of this application, Z is used p Representing the longitudinal wave impedance, Z s Representing transverse wave impedance, ρ representing density, δ, ε, and γ representing anisotropy parameters, respectively, M δ Initial model, M, representing the anisotropy parameter delta ε Initial model, M, representing the anisotropy parameter ε γ An initial model of the anisotropy parameter γ is represented. Z is a linear or branched member pj Longitudinal wave representing j-th interfaceImpedance, Z sj Represents the transverse wave impedance, p, of the jth interface j Denotes the density of the j-th interface, δ j 、ε j And gamma j And respectively representing the anisotropy parameters of the jth interface, wherein j represents the number of interfaces and takes the value of j =0. By using
Representing actual prestack azimuth angle gathers by &>
Denotes an azimuth angle theta and an angle of incidence->
Actual seismic recording of time, based on the time of day>
Is the actual seismic recording->
The ith sample value of (1).
By using
Expressing the longitudinal wave reflection coefficient, the functional relationship between the longitudinal wave reflection coefficient and the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropy parameter can be expressed as:
wherein the content of the first and second substances,
and j represents the longitudinal wave reflection coefficient of the jth interface, represents the number of interfaces and takes the value of j =0. Ln in the expression represents the coefficient c taken from the natural logarithm 1 、c 2 、c 3 、c 4 、c 5 And c 6 The entirety is shown in expression (4):
in expression (4), k is a ratio of shear wave velocity to longitudinal wave velocity, and in particular k = vs/vp, k is usually taken to be a constant value of 0.5.
In one embodiment, expression (3) may be simplified, specifically, as shown in the following expression (5), let:
then, expression (3) can be simplified to expression (6):
r pj ＝c 1 ΔL p +c 2 ΔL s +c 3 ΔL d +c 4 Δδ+c 5 Δε+c 6 Δγ (6)。
and S202, generating the simulated prestack azimuth angle gather by utilizing the functional relation and the seismic wavelets based on a convolution principle.
In the embodiment of the application, the seismic wavelet is represented by w
Represents a simulated prestack azimuth angle gather, then->
Is azimuthal angle theta, angle of incidence->
Time analog seismic record->
Is to simulate a seismic recording->
Is/is greater than or equal to>
Can be expressed as:
wherein m is the number of interfaces, and n is the number of sample points. It should be noted that, the expression (7) applies a convolution formula in functional analysis, and the specific calculation process is as follows: seismic wavelet time series w j Get the inverse so that w j Is turned 180 degrees by taking the longitudinal axis as the center and then has the reflection coefficient r of the longitudinal wave pj And summing after multiplication. Where j is the longitudinal wave reflection coefficient r p Subscript of (1), longitudinal wave reflection coefficient r pj A total of m samples, seismic wavelets w j There are a total of p spots. Longitudinal wave reflection coefficient r of m sampling points pj Seismic wavelets w with p samples j Signal obtained after convolution
N = m + p-1.
Furthermore, based on expression (6), expression (7) can be expressed again as:
in one embodiment, based on expression (8), expression (2) may in turn be expressed as:
according to the determination method of the anisotropic parameter sensitivity matrix, the obtained sensitivity matrix information can be used for prestack anisotropic parameter inversion work for extracting anisotropic parameters from prestack seismic data, and the convergence speed of a prestack anisotropic parameter inversion work algorithm and the accuracy of an inversion result are improved.
EXAMPLE III
Based on the foregoing embodiments, the embodiments of the present application further provide a method for constructing an error function of a constraint term of an actual prestack azimuth angle gather and a simulated prestack azimuth angle gather based on error functions of actual seismic records and simulated seismic records, as shown in fig. 3, where the method includes:
step S301, accumulating error functions of a plurality of actual seismic records and simulated seismic records corresponding to different azimuth angles and different incidence angles to obtain a matching item of simulated data and actual data.
In the embodiment of the present application, the azimuth angle is set
Having a first angular range, e.g. the range is [ P1, P2 ]]I.e., the minimum azimuth is P1, the maximum azimuth is P2, and the interval ^ is greater than>
1 degree was taken. The set azimuth angle θ has a second angular range, e.g., [ Q1, Q2 ]]That is, the minimum incident angle is Q1, the maximum incident angle is Q2, and the interval delt θ takes 1 degree.
The obtained matching term of the simulation data and the actual data is
Step S302, calculating a constraint term of the anisotropic parameter initial model.
In the embodiment of the present application, the constraint terms of the initial model of anisotropic parameters include constraint terms of the initial model of anisotropic parameters in three different directions, specifically, δ, ∈, and γ can be used to represent anisotropic parameters in three different directions, and M is used to represent anisotropic parameters in three different directions δ Initial model, M, representing the anisotropy parameter delta ε Initial model, M, representing the anisotropy parameter ε γ An initial model of the anisotropy parameter γ is shown.
The constraint term of the initial model of the anisotropy parameters is then α (δ -M) δ )+β(ε-M ε )+λ(γ-M γ )。
Wherein η, α, β, λ are weighting coefficients of each term in the error function, and are all greater than 0, and η + α + β + λ =1.
Step S303, based on the matching item of the simulation data and the actual data and the constraint item of the initial model of the anisotropic parameter, obtaining a constraint item error function of the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather.
In the embodiment of the application, the error functions of the actual seismic records with different azimuth angles and different incidence angles and the simulated seismic records are accumulated, the constraint terms of the anisotropic parameter initial model are added, and the final result is the constraint term error function of the actual prestack azimuth angle trace set and the simulated prestack azimuth angle trace set, which can be expressed as:
according to the determination method of the anisotropic parameter sensitivity matrix, the obtained sensitivity matrix information can be used for prestack anisotropic parameter inversion work for extracting anisotropic parameters from prestack seismic data, and the convergence speed of a prestack anisotropic parameter inversion work algorithm and the accuracy of an inversion result are improved.
Example four
Based on the foregoing embodiments, the present application further provides a method for determining a sensitivity matrix of an anisotropic parameter according to a partial derivative of the constraint term error function on the anisotropic parameter, as shown in fig. 4, where the method includes:
step S401, calculating partial derivatives of error functions of the actual seismic record and the simulated seismic record under the preset azimuth angle and the preset incidence angle.
In the embodiment of the application, the constraint term error function F is an error function obtained by actual seismic recording and simulated seismic recording
Is constructed to come out and is->
And then collects the information through the actual prestack azimuth angle>
And simulating a pre-stack azimuth angle gather>
Is constructed. Therefore, based on expression (10), the partial derivative of the error function of the actual seismic record and the simulated seismic record at the preset azimuth angle and the preset incidence angle can be calculated first.
Specifically, a given azimuth angle θ and an incident angle are first calculated
When is greater or less>
Partial derivatives of (a).
To L is paired with pj Calculating a partial derivative:
to L sj Calculating a partial derivative:
to L is paired with dj Calculating a partial derivative:
for delta j Calculating a partial derivative:
for epsilon j Calculating a partial derivative:
for gamma ray j Calculating a partial derivative:
and S402, stacking the partial derivatives of the error functions of the actual seismic records and the simulated seismic records to obtain the partial derivatives of the constraint term error functions to the anisotropic parameters.
In the embodiment of the application, the error function corresponding to each azimuth angle and incidence angle is used
The partial derivatives of (a) are accumulated one by one in the order of incidence angle first and azimuth angle second. Since the seismic data of the prestack azimuth gathers are stored according to the sequence of the incidence angles before and after, the accumulation sequence in the embodiment is performed according to the storage sequence of the actual seismic data, so that the prestack azimuth gathers are more intuitive and convenient to understand. Of course, in other embodiments, the superposition may be performed in the order of azimuth angle first and incidence angle second.
Based on the above expressions (11) - (16), the resulting partial derivatives of the constraint term error function with respect to the anisotropy parameter are as follows:
and S403, obtaining an anisotropic parameter sensitivity matrix based on the partial derivative of the constraint term error function to the anisotropic parameter.
In the embodiment of the present application, based on the above expressions (17) to (22), the following expression of the anisotropic parameter sensitivity matrix G is obtained:
the sensitivity matrix G of the error function obtained by the method can be directly used for inverting the anisotropic parameters of the actual three-dimensional prestack seismic data, and the convergence speed of the prestack anisotropic parameter inversion working algorithm and the accuracy of the inversion result are improved.
The application and effect of the sensitivity matrix will be described below by taking the actual well-side seismic data of a certain area as an example.
FIGS. 5 (a) - (f) are actual prestack azimuth angle gathers, obtained from the well-side seismic data. Fig. 5 (a) - (f) respectively show the situations when the azimuth angle is 0 degree, 90 degrees, 180 degrees, 210 degrees, 300 degrees and 360 degrees, the azimuth angle can be uniformly taken between 0 and 360 degrees, and the more the azimuth angle is taken, the larger the calculation amount is. The values of the incidence angles are all 1-45 degrees, and the value range of the incidence angles is determined by the quality of seismic data.
Fig. 6 is a graph of actual well log data of a certain area, and curves shown in the graph are longitudinal wave impedance, transverse wave impedance, density, and anisotropy parameters δ, ∈, and γ in the order from left to right.
The two graphs on the left side of fig. 7 are the actual pre-stack angle gather and the simulated pre-stack angle gather at an azimuth angle of 180 degrees, the right side curve, and the partial derivative curves of the error function for the longitudinal wave impedance, the transverse wave impedance, the density, and the anisotropic parameters.
In fig. 8, sensitivity matrices of the error function to longitudinal wave impedance, transverse wave impedance, density and anisotropy parameters by calculating the constraint terms are shown from left to right. The curve is marked obviously for the destination layer between 600 ms and 700ms through the value size or the amplitude value of the curve. The anisotropic parameter sensitivity matrix is determined by utilizing the partial derivative of the constraint term error function to the anisotropic parameters, so that the obtained sensitivity matrix information can be used for pre-stack anisotropic parameter inversion work for extracting the anisotropic parameters from the pre-stack seismic data, and the convergence speed of the pre-stack anisotropic parameter inversion work algorithm and the accuracy of the inversion result are improved.
EXAMPLE five
Based on the foregoing embodiments, the present application provides an apparatus for determining an anisotropic parameter sensitivity matrix, where the apparatus includes modules and units included in the modules, and the modules and the units may be implemented by a processor in a computer device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the processor may be a Central Processing Unit (CPU), a Microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
An embodiment of the present application provides a device for determining an anisotropic parameter sensitivity matrix, and fig. 9 is a schematic structural diagram of the device for determining an anisotropic parameter sensitivity matrix provided in the embodiment of the present application, as shown in fig. 9, the device 1000 for determining an anisotropic parameter sensitivity matrix includes:
an obtaining module 1001, configured to obtain an initial model of an actual prestack azimuth angle gather, longitudinal wave impedance, transverse wave impedance, density, and an anisotropic parameter;
a first constructing module 1002, configured to construct a simulated prestack azimuth angle gather based on the initial models of the longitudinal wave impedance, the transverse wave impedance, the density, and the anisotropy parameters;
a second constructing module 1003, configured to obtain an error function of the multiple actual seismic records and the simulated seismic records with a preset azimuth angle and a preset incident angle according to the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather;
a third constructing module 1004, configured to construct a constraint term error function of the actual pre-stack azimuth angle gather and the simulated pre-stack azimuth angle gather based on the error function of each actual seismic record and each simulated seismic record;
a determining module 1005, configured to determine an anisotropic parameter sensitivity matrix according to the partial derivatives of the constraint term error function on the anisotropic parameters.
In some embodiments, first building block 1002, comprises:
the first construction unit is used for establishing functional relations among the longitudinal wave reflection coefficient, the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters;
and the second construction unit is used for generating the simulated prestack azimuth angle gather by utilizing the functional relation and the seismic wavelets based on the convolution principle.
In one embodiment, the second building block 1003 includes:
the third construction unit is used for accumulating error functions of the actual seismic records and the simulated seismic records corresponding to different azimuth angles and different incidence angles to obtain a matching item of the simulated data and the actual data;
the fourth construction unit is used for calculating a constraint term of the anisotropic parameter initial model;
and the fifth construction unit is used for obtaining a constraint term error function of the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather based on the matching term of the simulation data and the actual data and the constraint term of the initial model of the anisotropic parameters.
In some embodiments, determining module 1005 includes:
the first determining unit is used for calculating the partial derivative of an error function of the actual seismic record and the simulated seismic record under the preset azimuth angle and the preset incidence angle;
the second determining unit is used for superposing the partial derivative of the error function of each actual seismic record and each simulated seismic record to obtain the partial derivative of the constraint term error function to the anisotropic parameter;
and the third determining unit is used for obtaining an anisotropic parameter sensitivity matrix based on the partial derivative of the constraint term error function to the anisotropic parameter.
In some embodiments, the second determining unit further includes:
and the second determining subunit is used for stacking the partial derivatives of the error functions of the actual seismic records and the simulated seismic records according to the sequence of the incidence angle and the azimuth angle.
It should be noted that, in the embodiment of the present application, if the determination method of the equivalent matrix modulus is implemented in the form of a software functional module and is sold or used as a separate product, it may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, the present application provides a storage medium, on which a computer program is stored, wherein the computer program is executed by a processor to implement the steps of the method for determining an equivalent matrix modulus provided in the above embodiments.
EXAMPLE six
The embodiment of the application provides equipment for determining an anisotropic parameter sensitivity matrix; fig. 10 is a schematic structural diagram of a determining apparatus for an anisotropic parameter sensitivity matrix according to an embodiment of the present application, and as shown in fig. 10, the determining apparatus 1100 for an anisotropic parameter sensitivity matrix includes: a processor 1101, at least one communication bus 1102, a user interface 1103, at least one external communication interface 1104, and a memory 1105. Wherein the communication bus 1102 is configured to enable connected communication between these components. Where user interface 1103 may include a display screen, external communication interface 1104 may include standard wired and wireless interfaces. The processor 1101 is configured to execute a program of the determination method of the anisotropic parameter sensitivity matrix stored in the memory to implement the steps in the determination method of the anisotropic parameter sensitivity matrix provided in the above-mentioned embodiments.
The above description of the embodiments of the determination apparatus and the storage medium of the anisotropic parameter sensitivity matrix is similar to the description of the above method embodiments, and has similar advantageous effects to the method embodiments. For technical details not disclosed in the embodiments of the computer device and the storage medium of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
Here, it should be noted that: the above description of the storage medium and device embodiments, similar to the description of the method embodiments above, has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and the apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit may be implemented in the form of hardware, or in the form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and shall cover the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for determining an anisotropic parametric sensitivity matrix, comprising:
acquiring an actual prestack azimuth angle gather, longitudinal wave impedance, transverse wave impedance, density and an anisotropic parameter initial model;
constructing a simulated prestack azimuth angle gather based on the initial models of the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters;
obtaining error functions of a plurality of actual seismic records and simulated seismic records with preset azimuth angles and preset incidence angles according to the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather;
constructing a constraint term error function of an actual prestack azimuth angle gather and a simulated prestack azimuth angle gather based on error functions of each actual seismic record and each simulated seismic record;
and determining an anisotropic parameter sensitivity matrix according to the partial derivative of the constraint term error function to the anisotropic parameter.
2. The method of claim 1, wherein the constructing a simulated prestack azimuth angle gather based on the compressional wave impedance, shear wave impedance, density, and the initial model of anisotropy parameters comprises:
establishing functional relations among longitudinal wave reflection coefficients, longitudinal wave impedance, transverse wave impedance, density and anisotropic parameters;
and generating the simulated prestack azimuth angle gather by utilizing the functional relation and the seismic wavelets based on a convolution principle.
3. The method of claim 1, wherein constructing the constraint term error functions for the actual pre-stack azimuth angle gather and the simulated pre-stack azimuth angle gather based on the error functions for each of the actual seismic record and the simulated seismic record comprises:
accumulating error functions of a plurality of actual seismic records and simulated seismic records corresponding to different azimuth angles and different incidence angles to obtain matching items of simulated data and actual data;
calculating a constraint term of the anisotropic parameter initial model;
and obtaining a constraint term error function of the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather based on the matching term of the simulated data and the actual data and the constraint term of the initial model of the anisotropic parameters.
4. The method according to claim 3, characterized in that the constraint terms of the initial model of anisotropic parameters comprise constraint terms of initial models of anisotropic parameters of three different directions.
5. The method of claim 1, wherein determining an anisotropy parameter sensitivity matrix based on partial derivatives of the constraint term error function on anisotropy parameters comprises:
calculating partial derivatives of error functions of actual seismic records and simulated seismic records under a preset azimuth angle and a preset incidence angle;
stacking the partial derivatives of the error functions of the actual seismic records and the simulated seismic records to obtain the partial derivatives of the constraint term error functions to the anisotropic parameters;
and obtaining an anisotropic parameter sensitivity matrix based on the partial derivative of the constraint term error function to the anisotropic parameter.
6. The method of claim 5, wherein stacking the partial derivatives of the error function for each of the actual seismic records and the simulated seismic records comprises:
and stacking the partial derivatives of the error functions of the actual seismic records and the simulated seismic records according to the sequence of the incidence angle and the azimuth angle.
7. The method of claim 1, wherein the predetermined azimuth angle has a first angle range and a difference between adjacent azimuth angles is 1 degree, the predetermined incident angle has a second angle range and a difference between adjacent incident angles is 1 degree.
8. An apparatus for determining an anisotropic parametric sensitivity matrix, comprising:
the acquisition module is used for acquiring an initial model of an actual prestack azimuth angle gather, longitudinal wave impedance, transverse wave impedance, density and anisotropic parameters;
the first construction module is used for constructing a simulated prestack azimuth angle gather based on the initial models of the longitudinal wave impedance, the transverse wave impedance, the density and the anisotropic parameters;
the second building module is used for obtaining error functions of a plurality of actual seismic records and simulated seismic records with preset azimuth angles and preset incidence angles according to the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather;
the third construction module is used for constructing a constraint term error function of the actual prestack azimuth angle gather and the simulated prestack azimuth angle gather based on the error functions of the actual seismic records and the simulated seismic records;
and the determining module is used for determining the sensitivity matrix of the anisotropic parameters according to the partial derivatives of the constraint term error function to the anisotropic parameters.
9. An apparatus for determining an anisotropic parametric sensitivity matrix, comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the method of determining an anisotropic parametric sensitivity matrix according to any of claims 1 to 7.
10. A storage medium storing a computer program executable by one or more processors and operable to implement the method of determining an anisotropic parametric sensitivity matrix as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111193074.8A CN115963542A (en) | 2021-10-13 | 2021-10-13 | Method, device, equipment and medium for determining anisotropic parameter sensitivity matrix |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111193074.8A CN115963542A (en) | 2021-10-13 | 2021-10-13 | Method, device, equipment and medium for determining anisotropic parameter sensitivity matrix |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115963542A true CN115963542A (en) | 2023-04-14 |
Family
ID=87358453
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111193074.8A Pending CN115963542A (en) | 2021-10-13 | 2021-10-13 | Method, device, equipment and medium for determining anisotropic parameter sensitivity matrix |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115963542A (en) |
-
2021
- 2021-10-13 CN CN202111193074.8A patent/CN115963542A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lee et al. | Full‐3‐D tomography for crustal structure in southern California based on the scattering‐integral and the adjoint‐wavefield methods | |
| US8830788B2 (en) | Sensitivity kernal-based migration velocity analysis in 3D anisotropic media | |
| Prieux et al. | Multiparameter full waveform inversion of multicomponent ocean-bottom-cable data from the Valhall field. Part 1: Imaging compressional wave speed, density and attenuation | |
| Chaillat et al. | A new fast multi-domain BEM to model seismic wave propagation and amplification in 3-D geological structures | |
| EP1047957B1 (en) | Method of prestack 3-d migration | |
| Bottero et al. | An axisymmetric time-domain spectral-element method for full-wave simulations: Application to ocean acoustics | |
| He et al. | Fast and slow flexural waves in a deviated borehole in homogeneous and layered anisotropic formations | |
| Wilson et al. | Seismic imaging of the crust and upper mantle using regularized joint receiver functions, frequency–wave number filtering, and multimode Kirchhoff migration | |
| Fang et al. | Reservoir fracture characterization from seismic scattered waves | |
| CN104316966A (en) | Fluid identification method and system | |
| Qu et al. | Multi-scale full waveform inversion for areas with irregular surface topography in an auxiliary coordinate system | |
| Bissig et al. | Multifrequency inversion of Ps and Sp receiver functions: methodology and application to USArray data | |
| CN102866426A (en) | Method for analyzing oil gas information of rock body by applying amplitude versus offset (AVO) large-angle trace gathers | |
| Rodgers et al. | WUS256: An adjoint waveform tomography model of the crust and upper mantle of the western United States for improved waveform simulations | |
| Eisen et al. | Age estimates of isochronous reflection horizons by combining ice core, survey, and synthetic radar data | |
| Peter et al. | Surface wave tomography: global membrane waves and adjoint methods | |
| Ajala et al. | Effect of merging multiscale models on seismic wavefield predictions near the southern San Andreas fault | |
| Wang et al. | Full‐waveform inversion of high‐frequency teleseismic body waves based on multiple plane‐wave incidence: Methods and practical applications | |
| Bruno | Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review | |
| Graves | The seismic response of the San Bernardino basin region during the 2001 Big Bear Lake earthquake | |
| Zhuang et al. | The rotated Cartesian coordinate method to remove the axial singularity of cylindrical coordinates in finite‐difference schemes for elastic and viscoelastic waves | |
| US20180299573A9 (en) | Method and system for efficient extrapolation of a combined source-and-receiver wavefield | |
| Buursink et al. | Crosshole radar velocity tomography with finite-frequency Fresnel volume sensitivities | |
| Wang et al. | Reciprocity relations for the elastodynamic fields generated by multipole sources in a fluid–solid configuration | |
| US20130173169A1 (en) | Method and system for efficient wavelength extrapolation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |