CN-121980830-A - VTI medium fluid parameter Bayesian inversion method based on independent priori information

CN121980830ACN 121980830 ACN121980830 ACN 121980830ACN-121980830-A

Abstract

The invention discloses a VTI medium fluid parameter Bayesian inversion method based on independent priori information. The method extracts the fluid parameters (fluid factor, shear modulus, density) and anisotropic parameters to be inverted 、 ) And finally, obtaining a final inversion result through the transformation of the independent priori information. The technology solves the problem of coupling of conventional fluid parameter inversion results, and can obtain the fluid parameter inversion result which is more fit with the true value.

Inventors

WANG WEIHONG
CHI LIN
ZHAO HAIBO
XIAO YUFENG
XIAO GAOJIE
YU BO

Assignees

东北石油大学三亚海洋油气研究院

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (6)

1. A VTI medium fluid parameter Bayesian inversion method based on independent prior information comprises the following steps: The method comprises the steps of (1) forward modeling of pre-stack seismic data and superposition of sub-incidence angles, namely, superposition of sub-incidence angles of the pre-stack seismic data is carried out according to actual requirements, and superimposed profile data bodies with different incidence angles are output, wherein the number of the sub-incidence angles is at least 3; the initial model is obtained, namely an initial model of fluid parameters to be inverted and anisotropic parameters is output by combining logging information and horizon information and is used as low-frequency constraint applicable to Bayesian inversion of VTI media; The independent priori information operators are obtained, namely based on logging information, the independent priori information operators are calculated and used for updating the seismic data forward operators; Step (4) performing Bayesian inversion of the fluid parameters and the anisotropic parameters of the pre-stack VTI medium based on the independent priori information, wherein the output result is an inversion result containing the independent priori information; And (5) restoring the true fluid parameters and the anisotropic parameter inversion result by applying an anti-independent priori information operator.
2. The method according to claim 1, wherein in the step (1), the specific process is as follows: since different fluids have different fluid factors, the fluid type in saturated rock is predicted from the inverted fluid factors, and the fluid factor expression is: (1); Wherein, the Is a term of the fluid factor which is a factor of the fluid, Is the density of the saturated rock and, And The longitudinal wave velocity and the transverse wave velocity under the condition of saturated rock, Is the square of the longitudinal-transverse wave speed ratio under the condition of dry rock; An approximation formula in the form of Aki-Richards equations is proposed using the fluid factor, shear modulus and density terms for constructing the seismic reflection coefficients, expressed as: (2); Wherein, the Representing the reflection coefficient of the isotropic medium, 、 And Respectively representing the reflection coefficient variables corresponding to the fluid factor item, the shearing modulus item and the density item, And Respectively represent a shear modulus term and a density term, and Together referred to as the three parameters of the fluid, 、 And Respectively the average value of the fluid factor, the shear modulus and the density of the medium at two sides of the reflection interface; 、 And The difference value of the fluid factor, the shear modulus and the density of the media at the two sides of the reflecting interface; is the square of the longitudinal-transverse wave speed ratio under the condition of saturated rock; combining the formula (2) with the VTI anisotropic parameter term to obtain a fluid parameter reflection coefficient equation suitable for the VTI medium, which is expressed as follows: (3); Wherein, the Representing the reflection coefficient at the VTI medium conditions, Represents the anisotropic reflection coefficient, and is written as: (4); Wherein, the And Respectively the anisotropic parameters of the media at two sides of the reflecting interface Anisotropic parameters Is a difference in (2); positive algorithm for seismic recording Authoring seismic wavelet matrix Matrix of seismic reflection coefficients And a first order differential matrix In the form of convolutions of (a): (5); wherein the seismic reflection coefficient matrix The element in (2) is obtained by using the formula (3), and a first order differential matrix is obtained Can be written as: (6); in the framework of convolution forward, a seismic record derived from forward of fluid factor, shear modulus and density for a VTI medium can be expressed as: (7); Wherein, the The seismic records obtained for forward modeling; Is a seismic forward operator; to include fluid factor, shear modulus, density, anisotropy parameters Anisotropic parameters Models of 5 target parameters; is a noise term; In the simulation example, pre-stack seismic data with different incidence angles are obtained based on a formula, and in the actual seismic data, the required pre-stack seismic data are obtained through incidence angle sorting.
3. The method of claim 1, wherein in the step (2), the initial model is obtained by Gaussian smoothing of the existing model in the simulation data, interpolation calculation and Gaussian smoothing are performed on the unknown information between wells and between layers based on the existing logging information and horizon information for the actual seismic data to build the initial model, and the fluid factors to be inverted, the shear modulus, the density and the anisotropic parameters are output Anisotropic parameters For bayesian inversion as a low frequency constraint.
4. The method of claim 2, wherein in the step (3), the model parameters are based on the above formula (7) Gao Jiexie variance matrices of (a) are written as: (8); Wherein, the For the sampling point number of the seismic data, the submatrices on the main diagonal respectively represent the high-order variance matrixes of five parameters to be inverted, the submatrices on the auxiliary diagonal respectively represent the high-order covariance matrixes between every two of the five parameters to be inverted, and the size of each submatrix is ; Firstly, parameter independent information at a certain point of a parameter model is obtained, and model parameters are extracted Three parameters at a certain point in (3) constitute a target parameter vector Its corresponding fifth order variance matrix is expressed as: (9); Wherein, the element on the main diagonal is the variance of each parameter about the element, and the element on the auxiliary diagonal is the covariance between the three parameters by pairs And (3) carrying out principal component analysis to obtain: (10); Wherein, the For the target parameter vector Is used for the principal component analysis operator of (a), Is that Is represented by a matrix transpose, expressed as: (11); (12); Wherein the five parameters of the principal diagonal in formula (12) represent the covariance of the five parameters to be inverted with respect to themselves after principal component analysis, respectively; If it is to Seen as a new variance matrix, the corresponding new parameter model is recorded as At this time, the variance matrix of the new parameter model only has the variance of the parameters, and the covariance among the parameters becomes zero, which means that the new parameters are independent from each other; next, the third-order principal component analysis operator is extended to meet the number of sampling points The form of (2): (13); Wherein, the In the same form as each sub-matrix of (a) For example, write as: (14); Then the matrix Is suitable for sampling point number Is a parametric model of (2) Is provided.
5. The method according to claim 1, wherein in the step (4), based on that the result of the linear Bayesian inversion is the maximum probability distribution characteristic of the parameter to be inverted, uncertainty analysis is performed on the inversion result by using a posterior mean value, so that the target parameter and the noise item are subjected to a multi-element Gaussian distribution: (15); (16); Wherein N represents Gaussian distribution; And Respectively represent models Mean and covariance of (a); Representing noise items Mean and covariance of (a); the new independent model parameters after principal component analysis are defined as: (17); Wherein, the Representing a parameter model after principal component analysis; The independent model parameters still satisfy the gaussian distribution: (18); Wherein, the And Respectively represent models Mean and covariance of (a); In addition, the corresponding positive algorithm The updating is as follows: (19); Wherein, the Representation model A corresponding forward operator; Then, the relationship between the seismic data and the updated parametric model is written as: (20); According to the linear gaussian principle, the distribution function of seismic data is expressed as: (21); Wherein, the And Representing the mean and covariance of the seismic records respectively, Because the target parameter and the noise item are assumed to be Gaussian distributions, the posterior distribution corresponding to the target parameter is also multi-element Gaussian according to the statistical correlation characteristics, and finally the posterior Gaussian distribution of the target parameter under the constraint of the seismic information is obtained, and the posterior mean value is obtained Sum posterior covariance Can be expressed as: (22)。
6. The method according to claim 1, wherein in the step (5), the initial model parameters are subjected to the anti-independent information restoration by the independent information operator: (23)。

Description

VTI medium fluid parameter Bayesian inversion method based on independent priori information Technical Field The invention belongs to the technical field of shale reservoir exploration and development, and particularly relates to a VTI medium fluid parameter Bayesian inversion method based on independent priori information. Background Fluid identification and lithology prediction are of great significance in shale reservoir exploration and development, wherein the accuracy of fluid identification directly affects reservoir hydrocarbon distribution prediction. The shale gas reservoir layer has relatively developed horizontal layer structure, obvious VTI medium characteristics and obvious change on seismic reflection characteristics. In terms of fluid identification, russell et al propose Russell fluid factors for describing corresponding solid and fluid components in elastic reservoirs based on pore elasticity theory, and as the fluid properties have a clear physical meaning, uncertainty in fluid identification is reduced. In the theory based on deterministic inversion, the prestack seismic inversion of the frequency-dependent viscoelastic fluid factor is realized by establishing the relation between the broadband impedance and the frequency-dependent viscoelastic fluid factor under the condition of the viscoelastic medium. The quality of fluid detection can be effectively improved by applying the joint inversion method of the PP wave and the PS wave. In addition, the square of the longitudinal-transverse wave speed ratio under the condition of dry rock is an important parameter in the process of fluid factor inversion, and the square is regarded as a dynamic variable which varies with depth, so that the problem that the accuracy of fluid factor prediction is limited when DVRS is regarded as a constant can be solved. In bayesian theory based inversion, the prior information is represented by a prior probability distribution function of model parameters, with gaussian distributions being most common among many types of distribution functions. Hansen et al have proposed a linear geostatistical inversion method by combining Gaussian linear inversion with geostatistics under model parameters that satisfy Gaussian distribution. Because the AVO prestack gather contains more accurate fluid information, the prestack Bayesian inversion based on AVO data is widely applied to estimation of fluid factors, and a better effect is obtained in practical application. For the VTI medium, the reflection, transmission exact propagation equation of plane waves in the VTI medium was derived Garebner on the basis of the Zoeppritz equation based on the isotropic assumption. Hou Dong A and the like realize the VTI medium multi-wave prestack joint inversion based on the Bayesian theory. Yin Xingyao and the like derive a new elastic wave impedance formula expressed by a fluid factor, a Ramey constant and density based on an approximate formula of Russell, and parameters such as the fluid factor are obtained through direct inversion of the elastic impedance. Most of the existing inversion methods are based on deterministic inversion ideas, however, for shale oil and gas exploration and development, since anisotropy in a VTI medium is not negligible, seismic data is noisy, and meanwhile, a matrix inversion problem exists in an inversion process, a fluid factor prediction result based on deterministic inversion is low in precision and high in calculation cost. However, the existing inversion method based on the Bayesian theory is difficult to overcome the correlation between the parameters to be inverted, so that the predictive trend coupling phenomenon inevitably occurs in the inversion result. Meanwhile, the uncertainty analysis on the anisotropic parameters of the VTI medium is also absent in the prior art. Disclosure of Invention Based on the problems existing in the background technology, the application provides a VTI medium fluid parameter Bayesian inversion method based on independent prior information. The application extracts the fluid parameters (fluid factor, shear modulus, density) and anisotropic parameters to be inverted、) And finally, obtaining a final inversion result through the transformation of the independent priori information. The application solves the problem of coupling of the conventional fluid parameter inversion result, and can obtain the fluid parameter inversion result which is more fit with the true value. The technical scheme provided by the invention is that the VTI medium fluid parameter Bayesian inversion method based on independent prior information comprises the following steps: The method comprises the steps of (1) forward modeling of pre-stack seismic data and superposition of sub-incidence angles, namely, superposition of sub-incidence angles of the pre-stack seismic data is carried out according to actual requirements, and superimposed profile data bodies with different incidence angles are outpu