Shale reservoir fracture and brittleness prediction method and system based on Bayesian inversion

Abstract

The present disclosure provides a shale reservoir fracture and brittleness prediction method based on Bayesian inversion, including: obtaining azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model; obtaining prediction results based on the obtained azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model, and a preset shale reservoir fracture and brittleness prediction model; wherein, the shale reservoir fracture and brittleness prediction model is established in the following process: deriving an approximate equation of the HTI medium longitudinal wave azimuth reflection coefficient containing a new brittleness indicator factor and fracture density; and establishing a shale reservoir fracture and brittleness prediction model based on the above relationship and the Bayesian AVAZ inversion method. The present disclosure effectively avoids the cumulative error in the indirect parameter conversion process and improves the accuracy of shale reservoir fracture and brittleness prediction.

Description

Technical Field

[0001] The present invention belongs to the technical field of shale reservoir inversion prediction, and in particular to a shale reservoir fracture and brittleness prediction method and system based on Bayesian inversion. Background Art

[0002] Shale reservoirs are characterized by low porosity, ultra-low permeability and density. Commercial production capacity can only be achieved by fracturing to form a complex fracture network. Fracturability is defined as the property of shale reservoirs that can be effectively fractured to obtain increased production capacity. The main factors affecting fracturability include shale brittleness and natural fractures. Generally, the more developed the natural fractures in the reservoir, the better the brittleness, and the better the fracturability. The brittleness of shale is generally expressed by a brittleness index. Brittleness evaluation methods mainly include the mineral composition method and the rock mechanics parameter method. The mineral composition method mainly calculates the brittleness index based on the content of brittle minerals in the shale, while the rock mechanics parameter method mainly uses Young's modulus (E) and Poisson's ratio (σ) to calculate the brittleness index. The seismic brittleness prediction of shale reservoirs generally adopts the rock mechanics parameter method. Generally, the higher the Young's modulus of shale and the lower the Poisson's ratio, the better the brittleness.

[0003] The inventors of the present disclosure have found that the existing methods for inversion prediction of shale reservoirs have the following problems:

[0004] 1. Due to the influence of factors such as organic carbon content, porosity and fluid in shale reservoirs, Young's modulus is not sufficient to characterize shale brittleness in some cases. In addition, the elastic parameters (P and S wave velocities and density) obtained by inversion in traditional methods are indirectly converted into brittleness indicator factors, which inevitably leads to cumulative errors;

[0005] 2. The traditional method indicates the development of fractures by inverting anisotropic parameters. Anisotropic parameters are easily affected by fluids and cannot indicate the development of reservoir fractures well. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a shale reservoir fracture and brittleness prediction method and system based on Bayesian inversion. The present invention first derives an approximate equation for the longitudinal wave azimuthal reflection coefficient of the HTI (Horizontal Transversely Isotropy) medium containing a new brittleness indicator factor and fracture density; secondly, an AVAZ (Amplitude Variation with Azimuth) inversion method with Cauchy constraint and smoothing model constraint regularization is developed under the Bayesian framework to achieve synchronous inversion prediction of shale gas reservoir fracture density and new brittleness indicator factor; finally, the effectiveness of the present invention in shale gas reservoir fracture and brittle seismic prediction is verified by model testing and practical application; the cumulative error in the indirect parameter conversion process is effectively avoided, and the accuracy of shale reservoir fracture and brittleness prediction is improved.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0008] In a first aspect, the present disclosure provides a shale reservoir fracture and brittleness prediction method based on Bayesian inversion, comprising:

[0009] Obtain azimuth partial angle stacked seismic data, azimuth seismic wavelet and model parameter low-frequency model;

[0010] The prediction results are obtained based on the acquired azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model, and the preset shale reservoir fracture and brittleness prediction model;

[0011] The process of establishing the shale reservoir fracture and brittleness prediction model is as follows: deriving the approximate equation of the HTI medium longitudinal wave azimuthal reflection coefficient containing the new brittleness indicator factor and fracture density; and establishing the shale reservoir fracture and brittleness prediction model based on the above relationship and the Bayesian AVAZ inversion method.

[0012] Furthermore, the new brittleness indicator factor is set to be the ratio of Young's modulus to the first Lame constant.

[0013] Furthermore, the derivation process of the approximate equation of the HTI medium longitudinal wave azimuthal reflection coefficient containing the new brittleness indicator factor and crack density includes:

[0014] Substituting the approximate equation of the longitudinal wave reflection coefficient characterized by Young's modulus, Poisson's ratio and density into the approximate equation of the longitudinal wave reflection coefficient of the pore fluid term in the saturated HTI medium and the dry fracture weakness decoupling, the intermediate equation is obtained;

[0015] According to the relationship between crack density and dry crack weakness, the relationship between the new brittleness indicator factor and Poisson's ratio and the intermediate equation, an approximate equation for the longitudinal wave azimuthal reflection coefficient of the HTI medium characterized by Young's modulus, new brittleness indicator factor, density and crack density is obtained.

[0016] Furthermore, when performing inversion, the influence of seismic wavelets is taken into consideration and the approximate equation of the HTI medium longitudinal wave azimuthal reflection coefficient is rewritten into a matrix form.

[0017] Furthermore, the Bayesian AVAZ inversion method is adopted.

[0018] Furthermore, the prior distribution of model parameters adopts Cauchy distribution.

[0019] Furthermore, during inversion, the initial inversion objective function is derived by maximizing the posterior distribution, and the missing low-frequency information in the inversion results is supplemented by logging data; the low-frequency constraints of the initial model are added to obtain the final inversion objective function as a shale reservoir fracture and brittleness prediction model.

[0020] In a second aspect, the present disclosure also provides a shale reservoir fracture and brittleness prediction system based on Bayesian inversion, including a data acquisition module and a prediction module;

[0021] The data acquisition module is configured to: obtain azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model;

[0022] The prediction module is configured to obtain prediction results based on the acquired azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model, and a preset shale reservoir fracture and brittleness prediction model;

[0023] The process of establishing the shale reservoir fracture and brittleness prediction model is as follows: deriving the approximate equation of the HTI medium longitudinal wave azimuthal reflection coefficient containing the new brittleness indicator factor and fracture density; and establishing the shale reservoir fracture and brittleness prediction model based on the above relationship and the Bayesian AVAZ inversion method.

[0024] In a third aspect, the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the shale reservoir fracture and brittleness prediction method based on Bayesian inversion described in the first aspect.

[0025] In a fourth aspect, the present disclosure further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the shale reservoir fracture and brittleness prediction method based on Bayesian inversion described in the first aspect are implemented.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The present disclosure effectively avoids the cumulative error in the process of indirect parameter conversion and improves the prediction accuracy of shale reservoir fractures and brittleness;

[0028] 2. In the present disclosure, firstly, an approximate equation for the longitudinal wave azimuthal reflection coefficient of the HTI medium containing the new brittleness indicator factor and the fracture density is derived; secondly, an AVAZ inversion method with Cauchy constraint and smoothing model constraint regularization is developed under the Bayesian framework to realize the simultaneous inversion prediction of the fracture density and the new brittleness indicator factor of the shale gas reservoir; finally, the effectiveness of the present invention in the prediction of fractures and brittle earthquakes in shale gas reservoirs is verified by model testing and practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0030] Figure 1 This is a flow chart for predicting shale reservoir fracture density and new brittleness indicator factors in Example 1 of the present disclosure;

[0031] Figure 2 The data of Well A in Example 1 of the present disclosure;

[0032] Figure 3 The synthesized noise-free azimuth gather of Example 1 of the present disclosure;

[0033] Figure 4 The synthesized azimuth gather with a signal-to-noise ratio of 2 in Example 1 of the present disclosure;

[0034] Figure 5 are the model parameters inverted in the noise-free case of Example 1 of the present disclosure (the solid line represents the true value, and the dotted line represents the inversion result);

[0035] Figure 6 : are the model parameters inverted when the signal-to-noise ratio is 2 in Example 1 of the present disclosure (the solid line represents the true value, and the dotted line represents the inversion result);

[0036] Figure 7 It is the longitudinal wave impedance of the azimuth partial angle stacking seismic data at a small angle of Example 2 of the present disclosure (the solid line represents the longitudinal wave impedance);

[0037] Figure 8 It is the longitudinal wave impedance of the azimuth partial angle stacking seismic data at a medium angle of Example 2 of the present disclosure (the solid line represents the longitudinal wave impedance);

[0038] Fig. 9It is the longitudinal wave impedance of the azimuth partial angle stacking seismic data at a large angle of Example 2 of the present disclosure (the solid line represents the longitudinal wave impedance);

[0039] Fig.10 This is the predicted model parameter profile of Example 2 of the present disclosure (the solid line represents the longitudinal wave impedance). DETAILED DESCRIPTION

[0040] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0042] Embodiment 1:

[0043] This embodiment provides a shale reservoir fracture and brittleness prediction method based on Bayesian inversion, including:

[0044] Obtain azimuth partial angle stacked seismic data, azimuth seismic wavelet and model parameter low-frequency model;

[0045] The prediction results are obtained based on the acquired azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model, and the preset shale reservoir fracture and brittleness prediction model;

[0046] The process of establishing the shale reservoir brittleness prediction model is as follows: deriving the approximate equation of the HTI medium longitudinal wave azimuthal reflection coefficient containing the new brittleness indicator factor and fracture density; and establishing the shale reservoir fracture and brittleness prediction model based on the above relationship and the Bayesian AVAZ inversion method.

[0047] Specifically, Figure 1 As shown in the figure, the prediction process of shale reservoir fracture density and new brittleness indicator factor based on Bayesian inversion is as follows:

[0048] The azimuth pre-stack seismic gathers are processed in azimuth and incident angle stacked to obtain azimuth partial angle stacked seismic data volumes, and the azimuth partial angle stacked seismic data and well logging data are combined to extract azimuth seismic angle wavelets;

[0049] Combining well logging data and seismic horizon data, the well curve is interpolated and extrapolated along the seismic horizon to establish a low-frequency model of Young's modulus, new brittleness indicator factor, density and fracture density;

[0050] Combining the azimuthal partial angle stacking seismic data, azimuthal seismic angle wavelet, Young's modulus, new brittleness indicator factor, low-frequency models of density and fracture density, and prior information on fracture azimuth, Bayesian AVAZ inversion was carried out to obtain the data bodies of Young's modulus, new brittleness indicator factor, density and fracture density. The fracture density and new brittleness indicator factor were used to predict reservoir fractures and brittleness respectively.

[0051] The specific implementation content is:

[0052] A set of rotationally invariant vertical fractures embedded in an isotropic background can cause HTI anisotropy. In this embodiment, the approximate equation for the P-wave azimuthal reflection coefficient of the pore fluid term and dry fracture weakness decoupled in the saturated HTI medium is as follows:

[0053]

[0054] in,

[0055]

[0056] Where f, μ and ρ represent the pore fluid term, shear modulus and density respectively, δ N and δ T denote the normal and tangential weaknesses caused by vertical cracks in dry rock, respectively, g s It represents the square of the ratio of the shear wave velocity to the longitudinal wave velocity of the saturated rock, g d It represents the square of the ratio of the shear wave velocity to the longitudinal wave velocity of dry rock. The symbol Δ represents the difference in rock properties on both sides of the interface. The symbol - represents the average value of the rock properties on both sides of the interface. θ represents the incident angle. φ represents the azimuth, i.e. the observation azimuth of the seismic line. Azimuth of the crack The angle between.

[0057] In this embodiment, the approximate equation of the longitudinal wave reflection coefficient characterized by Young's modulus E, Poisson's ratio σ and density in an isotropic medium is as follows:

[0058]

[0059] Substituting equation (3) into equation (1), we get the intermediate equation as follows:

[0060]

[0061] Generally speaking, the higher the Young's modulus of shale, the lower the Poisson's ratio and the better the brittleness; however, due to the influence of factors such as organic carbon content, porosity and fluid in shale reservoirs, in some cases Young's modulus is not sufficient to characterize the brittleness of shale.

[0062] In this embodiment, the ratio of Young's modulus to the first Lame constant (BI = E / λ) is used as a new brittleness indicator factor. Rock physical analysis shows that the new brittleness indicator factor is more sensitive to shale brittleness. The higher the new brittleness indicator factor, the better the brittleness. According to the conversion relationship between elastic parameters, the relationship between the new brittleness indicator factor and Poisson's ratio can be obtained:

[0063]

[0064] make

[0065]

[0066] ψ=1-2σ (7)

[0067] but

[0068]

[0069] According to equations (6) and (7), we can deduce:

[0070]

[0071]

[0072] Substituting equations (9) and (10) into equation (8) yields:

[0073]

[0074] because

[0075]

[0076] Substituting equation (12) into equation (11), we can deduce:

[0077]

[0078] The relationship between crack density and dry crack weakness can be expressed as:

[0079]

[0080]

[0081] Substituting equations (13)-(15) into equation (4), we can obtain the approximate equation for the azimuthal longitudinal wave reflection coefficient of the HTI medium characterized by Young's modulus, new brittleness indicator factor, density and crack density:

[0082] R PP (θ,φ)=a E (θ)R E +a BI (θ)RBI +a ρ (θ)R ρ +a e (θ,φ)Δe (16)

[0083] in,

[0084]

[0085] In the formula, and represent Young's modulus, new brittleness indicator factor and reflection coefficient of density respectively.

[0086] Bayesian AVAZ inversion:

[0087] In the case of M azimuths, N incident angles, and K time samples, considering the influence of seismic wavelets, equation (16) can be rewritten as the following matrix form:

[0088] d=Gm (18)

[0089] in,

[0090]

[0091] In the formula, W(θ i ,φ j ) represents the azimuth angle wavelet matrix, and the superscript T represents the transpose of the matrix.

[0092] In this embodiment, the inversion objective function is constructed using Bayesian theory. Based on Bayesian theory, the posterior probability distribution P(m|d) can be expressed by the likelihood function P(d|m) of the observed data and the prior distribution P(m) of the model parameters as follows:

[0093] P(m|d)∝P(d|m)P(m) (20)

[0094] It is generally assumed that the noise in the observed data follows a Gaussian distribution, so the likelihood function is expressed as:

[0095]

[0096] In the formula, represents the variance of the noise.

[0097] Using different prior distributions of model parameters will produce different solutions. Since the Cauchy distribution can produce sparse solutions, the obtained inversion results have higher resolution. Therefore, it is assumed that the prior distribution obeys the Cauchy distribution, which is specifically expressed as:

[0098]

[0099] Substituting equations (21) and (22) into equation (20), by maximizing the posterior distribution, the initial inversion objective function is derived as follows:

[0100]

[0101] Due to the band-limited nature of seismic data, logging data is usually used to supplement the missing low-frequency information in the inversion results. Adding the low-frequency constraints of the initial model, the final inversion objective function is obtained as the shale reservoir fracture and brittleness prediction model:

[0102]

[0103] Among them, α E , α BI , α ρ and α e They represent Young's modulus, new brittleness indicator factor, density and the constraint coefficient of crack density, respectively. P represents the integral matrix, and D represents the difference matrix. Since equation (24) has weak nonlinearity, an iterative reweighted least squares algorithm is used to solve it.

[0104] In this embodiment, the above model is tested as follows:

[0105] The effectiveness of the present invention is verified by using the data of Well A in a certain work area to produce synthetic seismic records; Figure 2 The data of Well A are displayed, including Young's modulus, new brittleness indicator factor, density and fracture density curve. According to the logging and core interpretation results, the reservoir is roughly located at 1910-1940 ms, the reservoir is relatively brittle, and the fractures are relatively developed; Figure 2 It can be seen that the Young's modulus at the reservoir location is low and cannot effectively indicate the brittle area of ​​the reservoir, while the new brittle factor is high and can better indicate the brittle area of ​​the reservoir. Using equation (16), the synthetic seismic record is produced by combining the A well data and the 35Hz Ricker wavelet. Figure 3 A noise-free synthetic azimuth gather is shown. Figure 5 The model parameters inverted without noise are shown. It can be seen that the new brittleness indicator factor and crack density inverted without noise are basically consistent with the true value. In order to verify the noise resistance of the method, Gaussian noise with a signal-to-noise ratio of 2 is added to the synthetic azimuth gather, and the synthetic azimuth gather with a signal-to-noise ratio of 2 is obtained as shown in Figure 4 shown. Figure 6 The inversion results of model parameters are shown when the signal-to-noise ratio is 2. It can be seen that the new brittleness indicator factor and fracture density inverted when the signal-to-noise ratio is 2 are still basically consistent with the true value, which verifies the effectiveness of this method in predicting fractures and brittleness in shale reservoirs.

[0106] Embodiment 2:

[0107] This embodiment puts the method proposed in Embodiment 1 into practical application, as follows:

[0108] The effectiveness of this method was verified using two-dimensional seismic data from a well A in a certain work area in the Sichuan Basin. Imaging logging data and core data show that gas-bearing shale reservoirs mainly develop nearly vertical high-angle fractures, so they can be equivalent to HTI media. The pre-stack azimuth gathers have been processed with amplitude preservation and denoising in advance. 12 partial-angle stacking seismic data are obtained through azimuth and partial angle stacking processing. The azimuths are 20°, 65°, 110° and 155°, and the incident angles are 10° (small angle), 20° (medium angle) and 30° (large angle). Figure 7 , Figure 8 and Fig. 9 Using the partial azimuth angle stacking seismic data, Bayesian AVAZ inversion is carried out, and the model parameter profile obtained by inversion is shown as follows Fig.10 As shown. It can be seen that the inverted new brittleness indicator factor and fracture density profile have good lateral continuity. At the reservoir location (about 1910-1940 milliseconds), the longitudinal wave impedance shows a relatively low value, and the inverted fracture density shows a relatively high value, which is consistent with the characteristics of reservoir fracture development. The inverted Young's modulus shows a relatively low value and cannot indicate the brittleness of the reservoir, while the inverted new brittleness indicator factor shows a relatively high value, indicating that the reservoir brittleness is good, which is basically consistent with the logging interpretation results. The comprehensive use of the inverted new brittleness indicator factor and fracture density is helpful for the lateral identification of favorable fracturing areas.

[0109] Embodiment 3:

[0110] This embodiment provides a shale reservoir fracture and brittleness prediction system based on Bayesian inversion, including a data acquisition module and a prediction module;

[0111] The data acquisition module is configured to: obtain azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model;

[0112] The prediction module is configured to obtain prediction results based on the acquired azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model, and a preset shale reservoir fracture and brittleness prediction model;

[0113] The process of establishing the shale reservoir brittleness prediction model is as follows: deriving the approximate equation of the HTI medium longitudinal wave azimuthal reflection coefficient containing the new brittleness indicator factor and fracture density; and establishing the shale reservoir fracture and brittleness prediction model based on the above relationship and the Bayesian AVAZ inversion method.

[0114] Embodiment 4:

[0115] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the shale reservoir fracture and brittleness prediction method based on Bayesian inversion described in Example 1 are implemented.

[0116] Embodiment 5:

[0117] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the shale reservoir fracture and brittleness prediction method based on Bayesian inversion described in Example 1 are implemented.

[0118] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A method for predicting fractures and brittleness of shale reservoirs based on Bayesian inversion. It is characterized in that include: Obtain azimuth partial angle stacked seismic data, azimuth seismic wavelet and model parameter low-frequency model; The prediction results are obtained based on the acquired azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model, and the preset shale reservoir fracture and brittleness prediction model; Specifically, the azimuth pre-stack seismic gathers are processed in azimuth and incident angle stacked to obtain azimuth partial angle stacked seismic data volumes, and the azimuth partial angle stacked seismic data and well logging data are combined to extract azimuth seismic angle wavelets; Combining well logging data and seismic horizon data, the well curve is interpolated and extrapolated along the seismic horizon to establish a low-frequency model of Young's modulus, new brittleness indicator factor, density and fracture density; The process of establishing the shale reservoir fracture and brittleness prediction model is as follows: deriving an approximate equation for the HTI medium longitudinal wave azimuthal reflection coefficient containing a new brittleness indicator factor and fracture density; and establishing a shale reservoir fracture and brittleness prediction model based on the approximate equation and the Bayesian AVAZ inversion method.

2. The shale reservoir fracture and brittleness prediction method based on Bayesian inversion according to claim 1, It is characterized in that The new brittleness indicator factor is set as the ratio of Young's modulus to the first Lame constant.

3. The shale reservoir fracture and brittleness prediction method based on Bayesian inversion according to claim 1, It is characterized in that The derivation process of the approximate equation of the HTI medium longitudinal wave azimuthal reflection coefficient including the new brittleness indicator factor and crack density includes: Substitute the approximate equation of the longitudinal wave reflection coefficient characterized by Young's modulus, Poisson's ratio and density into the pore fluid term in the saturated HTI medium and the approximate equation of the longitudinal wave azimuthal reflection coefficient decoupled by dry fracture weakness to obtain the intermediate equation; According to the relationship between crack density and dry crack weakness, the relationship between the new brittleness indicator factor and Poisson's ratio and the intermediate equation, an approximate equation for the longitudinal wave azimuthal reflection coefficient of the HTI medium characterized by Young's modulus, new brittleness indicator factor, density and crack density is obtained.

4. The shale reservoir fracture and brittleness prediction method based on Bayesian inversion according to claim 3, It is characterized in that When performing inversion, the influence of the seismic wavelet is taken into consideration and the approximate equation of the longitudinal wave azimuth reflection coefficient is rewritten into a matrix form.

5. The shale reservoir fracture and brittleness prediction method based on Bayesian inversion according to claim 1, It is characterized in that The Bayesian AVAZ inversion method was adopted.

6. The shale reservoir fracture and brittleness prediction method based on Bayesian inversion according to claim 1, It is characterized in that The prior distribution of model parameters adopts Cauchy distribution.

7. The shale reservoir fracture and brittleness prediction method based on Bayesian inversion according to claim 1, It is characterized in that During inversion, the initial inversion objective function is derived by maximizing the posterior distribution, and the missing low-frequency information in the inversion results is supplemented by logging data. The low-frequency constraints of the initial model are added to obtain the final inversion objective function as a prediction model for shale reservoir fractures and brittleness.

8. Shale reservoir fracture and brittleness prediction system based on Bayesian inversion, It is characterized in that Including data collection module and prediction module; The data acquisition module is configured to: obtain azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model; The prediction module is configured to obtain prediction results based on the acquired azimuth partial angle stacking seismic data, azimuth seismic wavelet and model parameter low-frequency model, and a preset shale reservoir fracture and brittleness prediction model; Specifically, the azimuth pre-stack seismic gathers are processed in azimuth and incident angle stacked to obtain azimuth partial angle stacked seismic data volumes, and the azimuth partial angle stacked seismic data and well logging data are combined to extract azimuth seismic angle wavelets; Combining well logging data and seismic horizon data, the well curve is interpolated and extrapolated along the seismic horizon to establish a low-frequency model of Young's modulus, new brittleness indicator factor, density and fracture density; The process of establishing the shale reservoir brittleness prediction model is as follows: deriving an approximate equation for the HTI medium longitudinal wave azimuthal reflection coefficient containing a new brittleness indicator factor and fracture density; and establishing a shale reservoir fracture and brittleness prediction model based on the approximate equation and the Bayesian AVAZ inversion method.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the program is executed by a processor, the steps of the shale reservoir fracture and brittleness prediction method based on Bayesian inversion as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, the steps of the shale reservoir fracture and brittleness prediction method based on Bayesian inversion as described in any one of claims 1 to 7 are implemented.

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