CN113985480B - AVO inversion method and device based on angle correction - Google Patents
AVO inversion method and device based on angle correction Download PDFInfo
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Abstract
The invention discloses an AVO inversion method and device based on angle correction, wherein the method comprises the following steps: performing edge detection on the two-dimensional seismic record or attribute data after post-stack migration based on a gradient structure tensor algorithm, and solving a local dip angle; according to the local inclination angle, angle correction is carried out on a Aki-Richards approximate formula, and an improved AVO expression is obtained; and acquiring an AVO three-parameter inversion result by adopting an AVO inversion method based on Bayes according to the improved AVO expression. According to the invention, the uneven mass scattering of the carbonate medium is considered, the deviation between the ray incidence angle and the incidence angle of the traditional assumed horizontal lamellar uniform medium is considered, the two-dimensional seismic record or attribute data are regarded as two-dimensional images, the local dip angle is calculated by adopting a gradient structure tensor algorithm, the traditional AVO approximate formula is further subjected to angle correction according to the calculated local dip angle, the improved AVO expression is more in accordance with the uneven characteristics of the carbonate, and the AVO inversion precision is improved.
Description
Technical Field
The invention relates to the field of oil and gas geophysical exploration, in particular to an AVO inversion method and device based on angle correction.
Background
The reservoir prediction technology of the carbonate rock is similar to that of the clastic rock, and the lithofacies, lithology physical properties and fluidities of the reservoir are mainly described through information such as earthquake, geology and logging, but the reservoir description of the carbonate rock has the difficulty that the carbonate rock is heterogeneous, the rock longitudinal and transverse properties are large in difference, and the carbonate rock reservoir is difficult to be described through summarizing space rules, so that great challenges are provided for the oil and gas exploration of the carbonate rock. AVO (Amplitude Versus Offset) technology is one of the current conventional and mainstream reservoir prediction methods. The AVO back-modeling technique has good application effect in clastic rock reservoirs such as sandstone, but the method has unsatisfactory effect in inverting carbonate rock reservoirs. On one hand, due to complex carbonate reservoir formation, the diversity of seismic reflection characteristics and seismic angle gather variation characteristics exists (application of AVO technology in carbonate fracture-cavity reservoir prediction, chen Jun, etc. 2014), and on the other hand, because AVO inversion is based on a convolution model, the medium is generally assumed to be layered and uniform in isotropy, and the method is not suitable for complex carbonate reservoir inversion.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides an AVO inversion method and device based on angle correction, wherein the deviation of the ray incidence angle and the incidence angle of a traditional assumed horizontal lamellar uniform medium is considered by the non-uniform mass scattering, and the angle is corrected.
In order to solve the problems in the prior art, the invention is realized by the following technical scheme:
an AVO inversion method based on angle correction, comprising:
s1, performing edge detection on two-dimensional seismic records or attribute data after post-stack migration based on a gradient structure tensor algorithm, and solving a local dip angle;
s2, performing angle correction on the Aki-Richards approximate formula according to the local inclination angle to obtain an improved AVO expression;
and S3, acquiring an AVO three-parameter inversion result by adopting an AVO inversion method based on Bayes according to the improved AVO expression.
Further, the step S1 specifically includes:
s11, regarding post-stack offset two-dimensional seismic records or instantaneous phases with different signal to noise ratios as two-dimensional images, and calculating horizontal gradient g for any point on the images x And vertical gradient g y The gradient squared tensor matrix is:
s12, performing feature decomposition on the gradient square tensor matrix to obtain:
wherein lambda is 1 ，λ 2 As a characteristic value, v 1 ，v 2 Is a feature vector;
s13, defining that the ascending angle is negative, and the descending angle is positive, the local inclination angle of the two-dimensional structure is:
further, in step S2: the correction angle is:the improved AVO expression is:
wherein,
in addition, in the case of the optical fiber,and->Respectively average values of longitudinal wave speed, transverse wave speed and density of an upper layer and a lower layer; deltaV P ,ΔV S And Deltaρ is the difference between the longitudinal wave speed, the transverse wave speed and the density of the upper and lower medium layers; θ is the average incident angle of longitudinal waves of the upper and lower media, +.>
Further, in step S3, the prior probability distribution function in the Bayesian-based AVO inversion method is:
wherein M is the seismic inversion parameter, I is the priori geological information, const is a constant, for MMultidimensional parametric model of variables, C m For a parameter covariance matrix of M x M, diagonal elements represent variances of model parameters, and non-diagonal elements represent cross-correlations between parameters; mu (mu) m Representing the most probable model in the prior information as the prior model parameter; obtaining parameter C from velocity and density log data m Sum mu m 。
Further, assume data d obs Is subject to gaussian distribution, the likelihood function is as follows:
wherein d obs For observation data, G is a known mapping of model space to data space, i.e., the forward operator of the improved AVO expression.
Further, the final posterior probability density function is:
further, the AVO three-parameter inversion result is:
m＝(G T C n -1 G+C m -1 ) -1 (G T C n -1 d obs +C m -1 u m )，
the covariance expression of the posterior distribution is:
C＝(G T C n -1 G+C m -1 ) -1 。
in another aspect, the present invention provides an apparatus for an AVO inversion method based on angle correction, including:
the local dip angle calculation module is used for carrying out edge detection on the two-dimensional seismic record or attribute data after post-stack migration based on a gradient structure tensor algorithm, and solving a local dip angle;
the AVO angle correction module is used for carrying out angle correction on the Aki-Richards approximate formula according to the local inclination angle to obtain an improved AVO expression;
and the three-parameter inversion module is used for acquiring an AVO three-parameter inversion result by adopting a Bayesian-based AVO inversion method according to the improved AVO expression.
The specific functions of each module realize the steps of the AVO inversion method based on angle correction.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the steps of the AVO inversion method based on angle correction when executing the computer program.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the aforementioned angle correction-based AVO inversion method.
Compared with the prior art, the beneficial technical effects brought by the invention are as follows:
according to the invention, the non-uniform mass scattering of the carbonate medium is fully considered, the deviation between the ray incidence angle and the incidence angle of the traditional assumed horizontal lamellar uniform medium is fully considered, the two-dimensional seismic record or attribute data are regarded as two-dimensional images, the local dip angle is calculated by adopting a gradient structure tensor algorithm, the angle correction is carried out on the traditional AVO approximate formula according to the calculated local dip angle, the improved AVO expression is more in accordance with the non-uniform characteristic of the carbonate, and the AVO inversion precision is improved.
According to the improved AVO expression, the three-parameter quick inversion is realized by adopting an AVO inversion method based on a Bayesian framework.
Drawings
FIG. 1 is a flow chart of an AVO inversion method based on angle correction in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram showing the definition of the tilt angle symbol of the two-dimensional reflection profile according to embodiment 1 of the present invention.
Detailed Description
The technical scheme of the invention is further elaborated below in conjunction with the description and drawings.
The embodiment provides an AVO inversion method based on angle correction, as shown in fig. 1, including:
s1, performing edge detection on two-dimensional seismic records or attribute data after post-stack migration based on a gradient structure tensor algorithm, and solving a local dip angle;
s2, performing angle correction on the Aki-Richards approximate formula according to the local inclination angle to obtain an improved AVO expression;
and S3, acquiring an AVO three-parameter inversion result by adopting an AVO inversion method based on Bayes according to the improved AVO expression.
The step S1 specifically comprises the following steps:
s11, regarding post-stack offset two-dimensional seismic records or instantaneous phases with different signal to noise ratios as two-dimensional images, and calculating horizontal gradient g for any point on the images x And vertical gradient g y The gradient squared tensor matrix is:
s12, performing feature decomposition on the gradient square tensor matrix to obtain:
wherein lambda is 1 ，λ 2 As a characteristic value, v 1 ，v 2 Is a feature vector;
s13, defining that the tilt-up angle is negative and the tilt-down angle is positive, as shown in fig. 2, the local tilt angle of the two-dimensional structure is:
in step S2: the correction angle is:
the improved AVO expression is:
wherein,
in addition, in the case of the optical fiber,and->Respectively average values of longitudinal wave speed, transverse wave speed and density of an upper layer and a lower layer; deltaV P ,ΔV S And Deltaρ is the difference between the longitudinal wave speed, the transverse wave speed and the density of the upper and lower medium layers; θ is the average incident angle of the longitudinal waves of the upper and lower media,
the step S3 adopts an inversion method based on a Bayesian framework.
The Bayesian theory represents posterior probability density distribution of model parameters through prior distribution and likelihood function, combines known prior information and observation data to obtain the best fit solution of the model parameters, and meanwhile describes the inversion result of the credibility through probability distribution so as to better know inversion multi-solution.
Let the seismic model parameters be m, d be the observed data, G be the known mapping of model space to data space. The modified AVO expression (5) is written in a matrix form as follows:
order the Can be abbreviated as: d=gm.
As stated above, the statistical inversion based on bayesian theory is to consider the inversion parameter m as a random variable subject to a certain probability distribution p (m) to obtain a corresponding solution by maximizing the posterior probability density function p (m|d). The p (m) is called prior probability, and the prior probability of model parameters is usually obtained through logging, geostatistics and other methods, which is equivalent to introducing additional constraint information into inversion, plays a role of regularization term, and can reduce the multi-solution property of inversion. Meanwhile, p (d|m), which is called a likelihood function, describes a conditional probability density of d for observed data given a model parameter m, which represents the degree of approximation between model values and true values calculated by forward computation given the model parameter m. From the bayesian formula (formula 8) in the probability statistics, the posterior probability p (m|d) and its distribution parameters can be presumed given the observation data and the prior distribution:
where p (d) is a constant, which can be used as a normalization constant, and is generally negligible, so the above formula can be written as:
p(m|d)＝p(m)p(d|m) (9)
the posterior probability distribution of the parameter is estimated by a bayesian method, and can be considered as a process of obtaining the posterior probability distribution by the prior probability distribution function through the action of the prior probability distribution function and the likelihood function. In geophysical inversion, given the measurement data d, the posterior probability of the model parameter m is typically:
p(m|d,I)∝p(m|I)p(d|m,I) (10)
where I is the geological information, we can obtain the final posterior probability density function given the prior distribution P (m|i) and the conditional probability distribution P (d|m, I) of the model observations. The prior distribution of the model can effectively reduce the discomfort of inversion. In geophysical inversion, the prior distribution of model parameters may obey different statistical distributions depending on different inversion requirements and actual geological conditions: such as gaussian distribution, cauchy distribution, long tail distribution, etc. Based on the assumption that parameters obey long tail distribution, a sparse pulse inversion result with higher resolution can be obtained; based on the assumption that the parameters obey the Cauchy distribution, an inverted thin fluffy can be obtained by introducing a correlation matrix. We take gaussian distribution as an example to solve for the solution under the bayesian framework.
By assuming that each variable in the prior information is subjected to Gaussian distribution, the model parameters are known to be subjected to M-dimensional Gaussian distribution, and the prior probability distribution function is as follows:
wherein, for a multidimensional parameter model with M variables, C m For a parameter covariance matrix of M x M, diagonal elements represent the variances of the model parameters, and non-diagonal elements represent the cross-correlations between the parameters. Mu (mu) m The prior model parameters represent the most probable model in the prior information. In AVO inversion, parameter C can be obtained from velocity and density log data m Sum mu m 。
Suppose data d obs Is subject to gaussian distribution, the likelihood function is as follows:
the posterior probability density function can be expressed as:
wherein C is n Is the covariance matrix of the data. The subsurface elasticity parameters are solved based on a linear approximation equation, whose forward operator G has the form:
F forward ＝Gm (14)
the final posterior probability density function can be obtained as:
when the posterior probability of the maximization of the model parameters is solved, the solution corresponds to the minimum value of the solution F (m). Order theThe value of parameter m can be solved. Due to the linear relation between the observed data and the model parameters, the AVO three-parameter inversion result can be finally displayed and expressed:
m＝(G T C n -1 G+C m -1 ) -1 (G T C n -1 d obs +C m -1 u m ) (17)
the covariance expression of the posterior distribution is also given:
C＝(G T C n -1 G+C m -1 ) -1 (18)
the embodiment also provides a device of the AVO inversion method based on angle correction, which comprises:
the local dip angle calculation module is used for carrying out edge detection on the two-dimensional seismic record or attribute data after post-stack migration based on a gradient structure tensor algorithm, and solving a local dip angle;
the AVO angle correction module is used for carrying out angle correction on the Aki-Richards approximate formula according to the local inclination angle to obtain an improved AVO expression;
and the three-parameter inversion module is used for acquiring an AVO three-parameter inversion result by adopting a Bayesian-based AVO inversion method according to the improved AVO expression.
The embodiment also provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the steps of the AVO inversion method based on angle correction when executing the computer program.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the aforementioned angle correction-based AVO inversion method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments disclosed.
Claims (9)
1. An AVO inversion method based on angle correction, comprising:
s1, performing edge detection on two-dimensional seismic records or attribute data after post-stack migration based on a gradient structure tensor algorithm, and solving a local dip angle;
s2, performing angle correction on the Aki-Richards approximate formula according to the local inclination angle to obtain an improved AVO expression;
s3, acquiring an AVO three-parameter inversion result by adopting an AVO inversion method based on Bayes according to the improved AVO expression;
in step S2: the correction angle is:gamma is the local tilt angle; the improved AVO expression is:
wherein,
in addition, in the case of the optical fiber,and->Respectively average values of longitudinal wave speed, transverse wave speed and density of an upper layer and a lower layer; deltaV P ,ΔV S And Deltaρ is the difference between the longitudinal wave speed, the transverse wave speed and the density of the upper and lower medium layers; θ is the average incident angle of longitudinal waves of the upper and lower media, +.>
2. The AVO inversion method according to claim 1, wherein step S1 specifically comprises:
s11, regarding post-stack offset two-dimensional seismic records or instantaneous phases with different signal to noise ratios as two-dimensional images, and calculating horizontal gradient g for any point on the images x And vertical gradient g y The gradient squared tensor matrix is:
s12, performing feature decomposition on the gradient square tensor matrix to obtain:
wherein lambda is 1 ，λ 2 As a characteristic value, v 1 ，v 2 Is a feature vector;
s13, defining that the ascending angle is negative, and the descending angle is positive, the local inclination angle of the two-dimensional structure is:
3. the AVO inversion method according to claim 1 or 2, wherein in step S3, the prior probability distribution function in the bayesian-based AVO inversion method is:
wherein m is the seismic inversion parameter, I is the priori geological information, const is a constant forMultidimensional parametric model with M variables, C m For a parameter covariance matrix of M x M, diagonal elements represent variances of model parameters, and non-diagonal elements represent cross-correlations between parameters; mu (mu) m Representing the most probable model in the prior information as the prior model parameter; obtaining parameter C from velocity and density log data m Sum mu m 。
4. The AVO inversion method of claim 3, wherein: suppose data d obs Is subject to gaussian distribution, the likelihood function is as follows:
wherein d=d obs For observation data, G is a known mapping of model space to data space, i.e., the forward operator of the improved AVO expression.
5. The AVO inversion method of claim 4, wherein: the final posterior probability density function is:
wherein C is n Is the covariance matrix of the data.
6. The AVO inversion method of claim 5, wherein: the AVO three-parameter inversion result is:
m＝(G T C n -1 G+C m -1 ) -1 (G T C n -1 d obs +C m -1 u m )，
the covariance expression of the posterior distribution is:
C＝(G T C n -1 G+C m -1 ) -1 。
7. an AVO inversion device based on angle correction, comprising:
the local dip angle calculation module is used for carrying out edge detection on the two-dimensional seismic record or attribute data after post-stack migration based on a gradient structure tensor algorithm, and solving a local dip angle;
the AVO angle correction module is used for carrying out angle correction on the Aki-Richards approximate formula according to the local inclination angle to obtain an improved AVO expression, and the specific mode is as follows:
the correction angle is:gamma is the local tilt angle; the improved AVO expression is:
wherein,
in addition, in the case of the optical fiber,and->Respectively the longitudinal wave speed and the transverse wave speed of the upper layer and the lower layerAverage value of speed and density; deltaV P ,ΔV S And Deltaρ is the difference between the longitudinal wave speed, the transverse wave speed and the density of the upper and lower medium layers; θ is the average incident angle of longitudinal waves of the upper and lower media, +.>
And the three-parameter inversion module is used for acquiring an AVO three-parameter inversion result by adopting a Bayesian-based AVO inversion method according to the improved AVO expression.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor performing the steps of the method of any of the preceding claims 1-6 when the computer program is executed.
9. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the preceding claims 1-6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0621928D0 (en) * | 2005-11-04 | 2006-12-13 | Westerngeco Seismic Holdings | 3D pre-stack full waveform inversion |
| CN106547020A (en) * | 2015-09-17 | 2017-03-29 | 中国石油化工股份有限公司 | A kind of relative amplitude preserved processing method of geological data |
| CN109490964A (en) * | 2018-11-12 | 2019-03-19 | 同济大学 | A kind of improved high-precision A VO elastic parameter fast inversion method |
| CN109521470A (en) * | 2018-12-29 | 2019-03-26 | 西南石油大学 | The method that analysis geological structure influences seismic inversion fracture spacing |
| CN112147683A (en) * | 2019-06-28 | 2020-12-29 | 中国石油化工股份有限公司 | Pre-stack sparse layer inversion method and system based on rock physical relationship constraint |
| CN112147682A (en) * | 2019-06-28 | 2020-12-29 | 中国石油化工股份有限公司 | AVO inversion method and system based on Bayes and series inversion theory |
| CN113341465A (en) * | 2021-06-11 | 2021-09-03 | 中国石油大学(北京) | Method, device, medium and equipment for predicting ground stress of orientation anisotropic medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6920084B2 (en) * | 2003-01-15 | 2005-07-19 | Western Geco, L.L.C. | Offset transformation to zero dip that preserves angle of incidence |
-
2021
- 2021-11-08 CN CN202111311337.0A patent/CN113985480B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0621928D0 (en) * | 2005-11-04 | 2006-12-13 | Westerngeco Seismic Holdings | 3D pre-stack full waveform inversion |
| CN106547020A (en) * | 2015-09-17 | 2017-03-29 | 中国石油化工股份有限公司 | A kind of relative amplitude preserved processing method of geological data |
| CN109490964A (en) * | 2018-11-12 | 2019-03-19 | 同济大学 | A kind of improved high-precision A VO elastic parameter fast inversion method |
| CN109521470A (en) * | 2018-12-29 | 2019-03-26 | 西南石油大学 | The method that analysis geological structure influences seismic inversion fracture spacing |
| CN112147683A (en) * | 2019-06-28 | 2020-12-29 | 中国石油化工股份有限公司 | Pre-stack sparse layer inversion method and system based on rock physical relationship constraint |
| CN112147682A (en) * | 2019-06-28 | 2020-12-29 | 中国石油化工股份有限公司 | AVO inversion method and system based on Bayes and series inversion theory |
| CN113341465A (en) * | 2021-06-11 | 2021-09-03 | 中国石油大学(北京) | Method, device, medium and equipment for predicting ground stress of orientation anisotropic medium |
Non-Patent Citations (8)
| Title |
|---|
| Amplitude-Versus-Angle Analysis of Local Angle-Domain Common Image Gathers With Prestack Gaussian-Beam Migration of Seismic Data;Hao Hu , Keyao Xia, Fred Hilterman, and Ying Zhang;IEEE Transactions on Geoscience and Remote Sensing;第58卷(第8期);全文 * |
| 叠前同时AVO反演在TX1工区的应用;许鸿;王力;陈骏;;内蒙古石油化工(第02期);全文 * |
| 基于三变量柯西分布先验约束的叠前三参数反演方法;张世鑫;印兴耀;张繁昌;;石油地球物理勘探(第05期);全文 * |
| 基于梯度平方结构张量算法的高密度二维立体层析反演;王宇翔;杨锴;杨小椿;薛冬;陈宝书;;地球物理学报(第01期);全文 * |
| 基于粒子群优化最小二乘支持向量机的非线性AVO反演;谢玮;王彦春;刘建军;苏建龙;毛庆辉;何润;;石油地球物理勘探(第06期);全文 * |
| 带粒子滤波约束的PP-PS联合反演的稀疏解算法;王彦飞;唐静;耿伟峰;王成祥;;地球物理学报(第03期);全文 * |
| 许鸿 ; 王力 ; 陈骏.叠前同时AVO反演在TX1工区的应用.内蒙古石油化工.2020,(第002期),全文. * |
| 适于Russell流体因子提取的孔隙弹性介质反射系数近似方程;张世鑫;杜向东;韩文明;孙林洁;;石油物探(第02期);全文 * |
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