CN115327625A - Reservoir lithology identification method - Google Patents
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Abstract
The invention discloses a reservoir lithology identification method, which comprises the steps of constructing an initial model; constructing a generalized prior probability model; constructing a posterior probability model and solving a maximum posterior probability solution; constructing a sensitive brittleness indicator factor, and identifying lithology based on the sensitive brittleness indicator factor; the method has the following advantages: 1. the method is based on p * Norm constraint is carried out, a generalized prior distribution model is given, and model parameters are corrected through logging data, so that prediction deviation caused by assumption that prior obeys a certain specific distribution in probabilistic prediction is avoided, and the probabilistic prediction precision is improved; 2. according to the method, the Young modulus and the Poisson ratio are utilized to construct a sensitive brittleness indicator of weighted representation after normalization of the Young modulus and the Poisson ratio, well logging information is utilized to correct the sensitive brittleness indicator, the parameter is more sensitive to lithology, and the influence of reservoir prior information is corrected and considered by utilizing the well logging information, so that the lithology can be better distinguished.
Description
Technical Field
The invention relates to the field of lithology identification, in particular to a reservoir lithology identification method.
Background
Seismic inversion is an important means for obtaining underground reservoir parameters, elastic parameters such as longitudinal and transverse wave speeds and density can be effectively obtained, a rock physical model is combined to obtain a fluid factor sensitive to pore fluid, a brittleness indicator factor capable of identifying reservoir lithology and the like, and therefore the oil-gas reservoir is precisely described.
With continuous deepening of oil and gas exploration and development, post-stack inversion presents limitation, a single elastic parameter hardly meets the requirement of reservoir fine description, and pre-stack inversion research of multi-parameter prediction needs to be developed, wherein AVA (Amplitude Versusang, amplitude changes along with the change of an incidence angle) inversion is widely applied to the field of oil and gas exploration.
Bayes theory is widely applied to probabilistic reservoir prediction, prediction accuracy of Bayes theory is influenced by prior information, a reasonable prior model can effectively reduce multiple solutions of inversion, but the prior probabilistic prediction method usually assumes that model parameters obey a certain specific distribution, which limits the prediction accuracy of reservoir parameters, so that in probabilistic reservoir lithology, a prior distribution model needs to be improved to improve reliability of Young modulus and Poisson ratio prediction results.
In summary, the problems of the existing reservoir lithology identification method based on seismic data are as follows: 1. the deterministic inversion cannot carry out uncertainty evaluation on an inversion result, and the prior information of model parameters is limited in utilization, so that the prediction precision of reservoir parameters is restricted; 2. the existing probability prediction method based on Bayesian theory usually assumes that the prior obeys a certain specific distribution, which limits the prediction precision of reservoir parameters; 3. the existing brittleness indicator factor is generally expressed as the mean value of normalized Young modulus and Poisson ratio, the physical property difference of different reservoirs is ignored, and the lithological distinguishing capability of the existing brittleness indicator factor is limited; 4. a brittleness analysis method based on combination of a plurality of elastic parameters has certain complexity in distinguishing lithology, and a single brittleness indication factor sensitive to the lithology needs to be researched to directly identify the lithology of a reservoir.
In view of the above, a method for identifying reservoir lithology is urgently needed to solve the problems in the prior art.
Disclosure of Invention
The invention aims to provide a reservoir lithology identification method to solve the problem of low rock stratum identification precision in the prior art, and the specific technical scheme is as follows:
a reservoir lithology identification method comprises the following steps:
step S1: constructing an initial model of the Young modulus, the Poisson ratio and the density;
step S2: constructing a generalized prior probability model;
and step S3: constructing a posterior probability model by using the initial model in the step S1 and the generalized prior probability model in the step S2, and solving a maximum posterior probability solution of the Young modulus, the Poisson ratio and the density based on the posterior probability model;
and step S4: and constructing a sensitive brittleness indicator factor by utilizing a maximum posterior probability solution of the Young modulus, the Poisson ratio and the density, and identifying lithology based on the sensitive brittleness indicator factor.
Preferably, in the step S1, an initial model is constructed according to the logging information and the seismic horizon information.
Preferably, in the step S2, the generalized prior probability model P (m) 1 ) As shown in formula 1);
wherein m is 1 Is young's modulus, poisson's ratio or density; μ is the parameter m 1 The mean value of (a); delta * Is a parameter m 1 The equivalent variance of (c); Γ (·) is a gamma function; p is a radical of * Is an adjustable parameter of the generalized prior probability model; exp (·) represents an exponential function with a natural constant e as the base.
Preferably, in the above technical solution, in the step S3, the posterior probability model P (m | d) is as shown in formula 2):
P(m|d)∝P(d|m)P(m) 2)；
wherein P (d | m) represents a conditional probability; p (m) represents a joint prior probability model of Young's modulus, poisson's ratio and density; m represents Young's modulus, poisson's ratio and density; d is the seismic AVA angle gather.
Preferably, in the technical scheme, the seismic AVA angle gather is shown as a formula 2.1);
d＝w*r pp (E,σ,ρ) 2.1)；
wherein w is a statistical wavelet obtained according to seismic data; r is pp (E, σ, ρ) is a linear AVA approximation relating Young's modulus E, poisson's ratio σ, and density ρ.
Preferred of the above technical solutions, r pp (E, σ, ρ) is represented by the formula 2.11);
wherein the content of the first and second substances,
and
reflectance coefficients for young's modulus, poisson's ratio, and density, respectively; a (θ), b (θ), and c (θ) are weighting coefficients related to the incident angle θ.
Preferably, in the step S4, the sensitive brittleness indicator EBI is represented by formula 3);
wherein λ is a weighting coefficient; e ave And σ ave Normalized young's modulus and poisson ratio.
Preferably, in the above technical scheme, the value of λ is [0 ].
Preferably, said E ave And σ ave As shown in formula 3.1);
where E denotes the young's modulus, σ denotes the poisson ratio, and subscripts max and min denote the maximum and minimum values, respectively.
Preferably, the lithology recognition is performed based on the sensitive brittleness indicator factor EBI in the above technical scheme, which specifically includes:
the first step is as follows: based on the logging data and the formula 3), obtaining the lithologic boundary value EBI of the sensitive brittleness indicator factor * ；
The second step is that: based on formula 3), calculating a sensitive brittleness indicator factor based on the seismic data by using the maximum posterior probability solution of the Young modulus, the Poisson ratio and the density obtained in the step S3, and then obtaining the sensitive brittleness indicator factor and the EBI according to the sensitive brittleness indicator factor and the EBI of the seismic data * The size relationship of (a) distinguishes lithology.
The technical scheme of the invention has the following beneficial effects:
(1) Storage of the inventionThe lithology identification method has the following advantages: 1. the method is based on p * Norm constraint, namely, a prior model in Bayesian probabilistic prediction is expanded, a generalized prior distribution model (namely, a generalized prior probability model) is given, and model parameters are corrected through logging data, so that prediction deviation caused by assumption prior obeying a certain specific distribution in probabilistic prediction is avoided, and the probabilistic prediction precision is improved; 2. according to the method, the Young modulus and the Poisson ratio are utilized to construct the sensitive brittleness indicator factor of the weighted representation after normalization of the Young modulus and the Poisson ratio, the sensitive brittleness indicator factor is corrected by utilizing logging information, the parameter is more sensitive to lithology, the influence of reservoir prior information is corrected and considered by utilizing the logging information, the lithology can be better distinguished, and the lithology recognition precision is improved.
(2) In step S1, statistical wavelets are extracted based on actual seismic angle gather data, initial models of Young modulus, poisson ratio and density are established by utilizing logging information and known horizon information, the initial models can provide low-frequency information for seismic inversion, and the statistical wavelets are helpful for obtaining more accurate reflection coefficient information.
(3) Step S2 of the invention, on the basis of Gaussian distribution and Laplace distribution analysis, more generalized p is constructed * The norm-constrained prior distribution avoids the prediction deviation caused by prior obeying a certain specific distribution in probabilistic prediction, the equivalent variance and the mean value can be obtained through logging data statistics, and the adjustable parameters in the generalized prior distribution model can be obtained through logging data adjustment, so that the prior probability distribution form which is more in line with the Young modulus, the Poisson ratio and the density of a target reservoir stratum is obtained.
(4) In step S3, firstly, an earthquake forward model is established, namely, an AVA approximate expression related to Young modulus, poisson ratio and density is convoluted with the statistical wavelet obtained in step S1, on the basis, a Gaussian condition probability meeting the noise mean value of 0 is established by using the difference between the earthquake forward result and the actually measured earthquake data, then a posterior probability (namely a posterior probability model) is established by combining the generalized prior distribution provided in step S2 and based on a Bayes theory, a three-parameter inversion posterior probability (namely a posterior probability model) of Young modulus, poisson ratio and density is established, finally, a three-parameter result of the maximum posterior probability is iteratively obtained by using a McMC (Markov chain Monte Carlo ) MH (Metropolis-Hastings, mei Teluo Polies-Musteins) algorithm, and reliability evaluation is performed by using the posterior probability of the prediction result, and the iteration initial value is the initial model obtained in step S1.
(5) In step S4, firstly, the existing brittleness indication factors are expanded by utilizing the logging Young modulus and the Poisson ratio, a weighting form of the normalized Young modulus and the Poisson ratio is given, the weighting coefficient is determined by utilizing logging information, sensitive brittleness indication factors are obtained, then the Young modulus and the Poisson ratio obtained in step S3 are utilized, and sensitive brittleness indication factor sections based on seismic inversion results are determined.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification.
In the drawings:
FIG. 1 is a flow chart of a reservoir lithology identification method of the present embodiment;
FIG. 2 is an input seismic AVA Angle gather of the present embodiment, where Angle is the incident Angle and Time is the depth Time;
FIG. 3 shows the Young's modulus (E), poisson's ratio (σ) and density (ρ) Prediction results obtained by the present embodiment using McMC MH algorithm based on a Bayesian probabilistic Prediction framework, where the prior model is an extended generalized prior probability model, wellData is logging data, prediction is a Prediction Result, initial model is an initial model, and Posterior Result is a Posterior probability solution;
FIG. 4 is a plot of Young's modulus (E) and Poisson's ratio (σ) for the well log of this example, where Sand is sandstone and Shale is mudstone;
FIG. 5 is a normalized Young's modulus (E) of the well log data of this example ave ) And a sensitive brittleness indicator factor (EBI) cross-plot, wherein Sand is sandstone, shale is mudstone, and the dotted line is a lithologic boundary characterized by EBI, namely the lithologic boundary value EBI of the sensitive brittleness indicator factor * ；
FIG. 6 is a normalized Young's modulus (E) of the prediction result of the present example ave ) And a sensitive brittleness indicator factor (EBI) cross-plot, wherein Sand is sandstone, shale is mudstone, and the dotted line is a lithologic boundary characterized by EBI, namely the lithologic boundary value EBI of the sensitive brittleness indicator factor * ；
FIG. 7 shows Predicted lithology according to an embodiment of the present invention, where Time is depth Time, real Life is well logging lithology, predicted Life is Predicted lithology, sand is sandstone, and Shale is mudstone.
Detailed Description
Embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways, which are defined and covered by the claims.
Example (b):
the embodiment discloses a reservoir lithology identification method, and preferably, the reservoir lithology identification method in the embodiment is provided on the basis of researching the following problems:
(1) Compared with deterministic inversion, reservoir parameter probabilistic prediction based on Bayesian theory can not only provide prediction results but also give posterior probability, but the prior model of the reservoir parameter probabilistic prediction usually assumes to obey a certain specific distribution, which restricts the accuracy of the probabilistic prediction;
(2) Rock differentiation based on elastic parameters is realized by adopting a plurality of attributes to realize combined identification, so that the complexity of lithology differentiation is increased, and lithology is identified by adopting a normalized mean value of Young modulus and Poisson ratio as a brittleness indicator, but the lithology identification capability is limited because the physical property difference of reservoirs is not considered.
The main principle of the reservoir lithology identification method in the embodiment for solving the problems is as follows:
firstly, acquiring an initial model of Young modulus, poisson's ratio and density and covariance of the Young modulus, the Poisson's ratio and the density by using logging data; then, a generalized prior probability model is utilized, a posterior probability model of three-parameter inversion is constructed based on a Bayesian theory, and a McMC MH algorithm is adopted for solving; and then, performing parameter correction on the sensitive brittleness indicating factor by using logging information, and giving out a lithology boundary value of the sensitive brittleness indicating factor capable of effectively distinguishing the lithology of the target reservoir, thereby realizing reservoir lithology identification based on the seismic data.
The reservoir lithology identification method of the present embodiment is explained as follows:
the reservoir lithology identification method of the embodiment comprises the following steps of S1 to S4:
step S1: constructing an initial model of Young modulus, poisson ratio and density, which comprises the following steps:
before carrying out probabilistic prediction, determining statistical wavelets and an initial model, wherein the statistical wavelets are obtained according to seismic data; the initial models of the Young modulus, the Poisson ratio and the density are obtained according to well logging information and seismic horizon information; the method for establishing the partial statistical wavelets and the method for establishing the initial model are widely applied to geophysical inversion and belong to common knowledge in the field;
step S2: after obtaining statistical wavelets and an initial model, developing earthquake probabilistic prediction based on Bayesian theory, firstly improving a Gaussian prior model, and providing a more generalized prior probability model, namely a generalized prior probability model, wherein the prior probability model P (m) is 1 ) As shown in formula 1):
wherein m is 1 Is a reservoir parameter, referred to herein as young's modulus, poisson's ratio, or density; μ is the parameter m 1 Mean value of (d), d * Is a parameter m 1 Is to be exemplified here if m 1 Is Young's modulusE, then μ and δ * Respectively, the mean value and equivalent variance corresponding thereto (i.e., young's modulus E), and similarly, when m is 1 When the Poisson ratio is sigma or the density rho, the mean value and the equivalent variance are also corresponding according to the sigma or the rho; in this example, μ and δ * The method can be obtained through logging data statistics; Γ (·) is a gamma function; p is a radical of formula * Tunable parameters for generalized prior probabilistic models, Γ (·) and p * May be determined by the log probability distribution of the reservoir parameters; exp (·) is expressed as an exponential function with a natural constant e as the base;
since the above formula 1) gives a generalized prior probability model of young's modulus, poisson's ratio or density, the above formula 1) can be written as formula 1.a);
to be noted in step S2 are: formula 1) has a more generalized form than deterministic prior distributions such as gaussian and laplacian, which can be represented by the parameter p * The real probability distribution of the reservoir parameters which are more consistent with the actual work area is obtained through adjustment, and the expression form is more flexible, so that the prior probability distribution form which is more consistent with the Young modulus, poisson ratio and density of the target reservoir is obtained, and the probabilistic prediction precision is improved;
and step S3: establishing a posterior probability model of the Young modulus, the Poisson ratio and the density by using the statistical wavelets and the initial model obtained in the step S1 and the generalized prior probability model constructed in the step S2 based on a Bayesian theory, and solving a maximum posterior probability solution of the three parameters (namely the Young modulus, the Poisson ratio and the density), wherein the method specifically comprises the following steps:
step S3.1: according to Bayesian theory, a posterior probabilistic model P (m | d) of reservoir parameters can be formed from the prior probabilistic model P (m |) 1 ) And conditional probability P (d | m), as shown in equation 2:
P(m|d)∝P(d|m)P(m) 2)；
where m represents a reservoir parameter, here young's modulus E, poisson's ratio σ, and density ρ; d represents the seismic AVA angle gather; p (d | m) represents a conditional probability; p (m) represents a joint prior probability model of young's modulus E, poisson ratio σ, and density ρ, and since m represents young's modulus E, poisson ratio σ, and density ρ, equation 2) can be further written as equation 2.a);
P(E,σ,ρ|d)∝P(d|E,σ,ρ)P(E,σ,ρ) 2.a)；
assuming that the statistical regularity of the reservoir parameters m satisfies the independent homodistributive assumption, the joint prior probability model P (E, σ, ρ) can be expressed as a respective prior probability model P (m) of young's modulus E, poisson's ratio σ, and density ρ 1 ＝E)、P(m 1 = σ) and P (m) 1 Product of = ρ), the formula 2.a) can be further rewritten as formula 2.a 1);
P(E,σ,ρ|d)∝P(d|E,σ,ρ)P(m 1 ＝E)P(m 1 ＝σ)P(m 1 ＝ρ) 2.a1)；
wherein, P (m) 1 = E) represents a prior probability model of young's modulus E; p (m) 1 = σ) a prior probability model representing poisson's ratio σ; p (m) 1 = ρ) a prior probability model of density ρ, i.e. P (m) 1 ＝E)、P(m 1 = σ) and P (m) 1 = ρ) are given by a generalized prior probability model;
to be noted for step S3.1 are: in the formula 2), the seismic AVA angle gather is shown as a formula 2.1);
d＝w*r pp (E,σ,ρ) 2.1)；
wherein w is the statistical wavelet obtained according to the step S1; r is a radical of hydrogen pp (E, σ, ρ) is a linear AVA approximation relating Young's modulus E, poisson's ratio σ, and density ρ;
r pp (E, σ, ρ) is as shown in equation 2.11):
wherein, the first and the second end of the pipe are connected with each other,
and
reflectance coefficients for young's modulus, poisson's ratio, and density, respectively; in inversion
And
may be given according to the initial model obtained in step S1; a (theta) is a weighting coefficient of the Young modulus reflection coefficient in relation to the incident angle theta; b (theta) is a weighting coefficient of the poisson's ratio reflection coefficient relative to the incident angle theta; c (theta) is a weighting coefficient of the density reflection coefficient related to the incident angle theta; a (theta), b (theta) and c (theta) are determined by the initial model obtained in step S1 and the incident angle theta, and in the embodiment, the determination of a (theta), b (theta) and c (theta) refers to the prior art;
thus, the posterior probability model P (m | d) is obtained in step S3.1;
step S3.2, solving the maximum posterior probability solution of the three parameters (the three parameters are Young modulus, poisson ratio and density) by using a McMCMH algorithm, specifically: referring to fig. 2 and fig. 3, fig. 2 is an input seismic AVA angle gather in the present embodiment, and fig. 3 is a young modulus, poisson ratio, and density prediction result obtained by using McMCMH algorithm according to formula 2) in the present embodiment, which shows that the prediction result is well matched with logging data; the mcmmh algorithm references the prior art;
and step S4: constructing a sensitive brittleness indicator factor EBI by utilizing the Young modulus, the Poisson ratio and the density prediction result obtained in the step S3.2, and identifying lithology based on the sensitive brittleness indicator factor;
step S4.1, constructing a sensitive brittleness indicator factor EBI, wherein the EBI is shown as a formula 3),
wherein, λ is a weighting coefficient, and the value range of λ is [0 1]]The lithology of a target reservoir can be effectively identified by introducing the lambda, and sensitive brittleness indication factors suitable for different reservoirs can be obtained by adjusting the lambda;E ave and σ ave To normalize Young's modulus and Poisson's ratio, E ave And σ ave As shown in the formula 3.1),
wherein the indices max and min refer to the maximum and minimum values, respectively, e.g. E max Represents the maximum value of Young's modulus; wherein E is max 、E min 、σ max And σ min The data are obtained according to the statistics of logging data;
s4.2, determining lithology identification step based on a formula 3), wherein the identification strategy firstly determines sensitive brittleness indicator factors capable of identifying lithology based on logging data, namely lithology boundary values EBI of the sensitive brittleness indicator factors * This value is then applied to the seismic inversion results to identify lithology, as follows:
the first step is as follows: by utilizing the Young modulus E, the Poisson ratio sigma and the density rho of the well logging, using the formula 3), calculating a sensitive brittleness indicating factor based on the well logging data, and determining the lithological boundary value EBI of the sensitive brittleness indicating factor for distinguishing different lithological characters by adjusting the parameter lambda * ；
The second step is that: according to the Young modulus E, the Poisson ratio sigma and the density rho prediction results obtained by adopting the seismic data in the step S3.2, a sensitive brittleness indicating factor based on the seismic data is obtained by using the formula 3), and then the lithological boundary value EBI of the sensitive brittleness indicating factor is determined according to the sensitive brittleness indicating factor * To distinguish lithology from EBI based on sensitive brittleness indicator based on seismic data * For example, if the lithology of the study area consists of sandstone and mudstone, the lithology boundary value EBI is generally smaller than the sensitive brittleness indicator factor * Sandstone, and mudstone which is larger than the value, and based on the mudstone, a lithology prediction section based on the seismic inversion result can be drawn, namely, the lithology identification is completed.
To be noted in step S4 are:
1. referring to fig. 4, fig. 4 is a cross plot of young's modulus and poisson's ratio of the logging data of the present embodiment, and it can be seen that the ability to distinguish lithology is limited by using only a single young's modulus or poisson's ratio; referring to fig. 5, fig. 5 is an intersection graph of normalized young's modulus and sensitive brittleness indicator factor of the logging data of the present embodiment, and it can be seen that, compared with fig. 4, the sensitive brittleness indicator factor constructed in the present embodiment can effectively distinguish lithology, as shown by a dotted line in fig. 5, the single property can separate sandstone and mudstone, which illustrates the feasibility of the sensitive brittleness indicator factor provided in the present embodiment.
2. In step S4, first, a boundary value EBI capable of distinguishing lithology is determined according to the well log data * As can be seen from fig. 5, in this embodiment, the sensitive brittleness indicator obtained from the well logging data can effectively distinguish lithology at the position of the dashed line, and thus the value at the dashed line is the boundary value EBI * (ii) a Then, a sensitive brittleness indicator factor based on seismic data is obtained, see fig. 6, and the position of the broken line in fig. 6 is the lithologic boundary value EBI of the sensitive brittleness indicator factor determined according to the logging data in fig. 5 * It can be seen that the sensitive brittleness indicator factor can effectively distinguish sandstone reservoirs from mudstone reservoirs, and finally, the boundary value EBI determined according to fig. 5 is used in the embodiment * And drawing a comparison graph of the predicted lithology and the well logging lithology, wherein the comparison graph is shown in FIG. 7, so that the predicted lithology and the well logging lithology are well matched, and the lithology identification method provided by the embodiment can effectively identify the lithology.
3. In the embodiment, lithology is identified by solving the sensitive brittleness indicator factor, and the method has the following advantages: 1. compared with the conventional Gauss prior and Laplace prior, the generalized prior probability model provided by the embodiment has more flexible expression form, and the parameter p is adjusted * Can obtain a more generalized probability distribution form and a parameter p * The method can be determined through logging data, can better describe the parameter distribution characteristics of the reservoir in the actual work area, and is beneficial to improving the seismic probability prediction precision; 2. the sensitive brittleness indicator factor constructed in the embodiment comprehensively considers the influence of the Young modulus and the Poisson ratio, and the weighting coefficient lambda can be adjusted according to logging information, so that the obtained brittleness indicator factor is more sensitive to reservoir lithology and has stronger distinguishing capability; 3. book (I)According to the embodiment, firstly, the probability prediction precision of the earthquake is improved by improving the prior distribution, then the lithology identification reliability of the reservoir is improved by establishing the sensitive brittleness indicator, a high-precision reservoir lithology prediction process based on the earthquake data is provided, and the prediction process is beneficial to improving the lithology distinguishing capability of the reservoir.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1.A reservoir lithology identification method is characterized by comprising the following steps:
step S1: constructing an initial model of the Young modulus, the Poisson ratio and the density;
step S2: constructing a generalized prior probability model;
and step S3: constructing a posterior probability model by using the initial model in the step S1 and the generalized prior probability model in the step S2, and solving a maximum posterior probability solution of the Young modulus, the Poisson ratio and the density based on the posterior probability model;
and step S4: and constructing a sensitive brittleness indicator factor by utilizing a maximum posterior probability solution of the Young modulus, the Poisson ratio and the density, and identifying lithology based on the sensitive brittleness indicator factor.
2.A reservoir lithology identification method as claimed in claim 1, wherein in step S1, an initial model is constructed from well log data and seismic horizon information.
3. Reservoir lithology identification method according to claim 1, wherein in step S2, the generalized prior probability model P (m) is 1 ) As shown in formula 1);
wherein m is 1 Is young's modulus, poisson's ratio or density; μ is the parameter m 1 The mean value of (a); delta. For the preparation of a coating * Is a parameter m 1 The equivalent variance of (c); Γ (·) is a gamma function; p is a radical of formula * Is an adjustable parameter of the generalized prior probability model; exp (-) denotes an exponential function with a natural constant e as the base.
4. A reservoir lithology identification method according to claim 3, wherein in the step S3, the posterior probability model P (m | d) is represented by formula 2):
P(m|d)∝P(d|m)P(m) 2)；
wherein P (d | m) represents a conditional probability; p (m) represents a joint prior probability model of Young's modulus, poisson's ratio and density; m represents Young's modulus, poisson's ratio and density; d is the seismic AVA angle gather.
5. The reservoir lithology identification method of claim 4, wherein the seismic AVA angle gather is as shown in equation 2.1);
d＝w*r pp (E,σ,ρ) 2.1)；
wherein, w is a statistical wavelet obtained according to seismic data; r is pp (E, σ, ρ) is a linear AVA approximation relating Young's modulus E, poisson's ratio σ, and density ρ.
6. Reservoir lithology identification method of claim 5, wherein r is pp (E, sigma, rho) is shown as the formula 2.11);
wherein the content of the first and second substances,
and
reflectance coefficients for young's modulus, poisson's ratio, and density, respectively; a (θ), b (θ), and c (θ) are weighting coefficients related to the incident angle θ.
7. The reservoir lithology identification method of any one of claims 1 to 6, wherein in the step S4, the sensitive brittleness indicator EBI is represented by the formula 3);
wherein λ is a weighting coefficient; e ave And σ ave Normalized young's modulus and poisson ratio.
8. A reservoir lithology identification method as claimed in claim 7, wherein λ is at a value of [0 ].
9. Reservoir lithology identification method of claim 7, wherein E is ave And σ ave As shown in formula 3.1);
where E denotes the young's modulus, σ denotes the poisson ratio, and subscripts max and min denote the maximum and minimum values, respectively.
10. The reservoir lithology identification method of claim 7, characterized in that lithology identification is performed based on a sensitive brittleness indicator EBI, as follows:
the first step is as follows: based on the logging data and the formula 3), obtaining the lithologic boundary value EBI of the sensitive brittleness indicator factor * ；
The second step: based on equation 3), the sensitivity based on the seismic data is obtained by using the maximum posterior probability solution of the Young 'S modulus, poisson' S ratio and density obtained in step S3Brittleness indicator, sensitive brittleness indicator and EBI based on seismic data * The size relationship of (a) distinguishes lithology.
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