Transverse wave velocity estimation method and device based on equivalent self-compatibility improved model

Abstract

The invention relates to a shear wave velocity estimation method and device based on an equivalent self-compatibility improved model, and the method comprises the following steps: S1, changing an equivalent self-compatibility model through the longitudinal wave velocity and density data in logging information, and the mineral content, porosity and water saturation data in a logging interpretation result; s2, solving a coupling equation about the bulk modulus and the shear modulus into solving a nonlinear function about the transverse wave velocity through the assumed transverse wave velocity, the actually measured longitudinal wave velocity and the density data; s3, calculating by using an empirical formula to obtain a shear wave velocity and taking the shear wave velocity as an initial inversion value; and S4, according to the inversion initial value of the shear wave velocity, using a simulated annealing algorithm to obtain an estimated shear wave velocity. On one hand, the method simplifies the modeling process and can quickly and effectively estimate the shear wave velocity, and on the other hand, the method reduces the solving parameters through formula deformation and can obtain the high-precision shear wave velocity under the constraints of the actual measurement density and the longitudinal wave velocity.

Description

Technical Field

[0001] The present invention relates to the technical field of well logging rock physics, and in particular to a shear wave velocity estimation method and device based on an equivalent self-consistent improved model. Background Art

[0002] In oil and gas geophysical exploration, rock physics theory, as a bridge between elastic parameters and reservoir physical parameters, is an important tool for analyzing reservoir characteristics and plays a crucial role in guiding the prediction of favorable reservoirs and oil and gas accumulations. Shear wave logging data is essential for seismic rock physics analysis, AVO analysis, prestack inversion, and reservoir prediction. In practical applications, acquiring shear wave logging data is expensive, or due to problems such as poor wellbore conditions in the target logging interval, shear wave data is missing or of poor quality, hindering further reservoir prediction. To address this, various methods for shear wave velocity estimation and correction have been proposed, including: 1. empirical formula methods; 2. rock physics modeling methods; and 3. machine learning methods.

[0003] For example, Chinese patent CN201610776581.7 discloses a method for estimating shear wave velocity in shale gas formations. This method uses the equivalent modulus self-consistent model (SCA) for rock physics modeling, fully considers pore morphology and pore flattening, and performs fluid substitution based on the Gassmann equation. This improves the accuracy of prediction results, and the algorithm has high computational efficiency, making it applicable to the calculation of shear wave data in shale gas wells in actual production practice, reducing the ambiguity of the calculation results.

[0004] Another example is the academic literature: "Application of Reservoir Prediction Method Based on Rock Physics in the Guansan Reservoir in the DLJ Area" in China Science and Technology Expo 2015, Issue 36, which discloses a method of using rock physics modeling to invert shear wave velocity, estimate and analyze elastic parameters that are sensitive to reservoir characteristics, and accurately extract formation elastic parameters through pre-stack high-precision inversion methods, and use pre-stack inversion sensitive parameters to characterize reservoir and oil field characteristics.

[0005] It is worth mentioning that the applicability of the shear wave estimation method varies greatly under different types of target layers and different data abundance conditions. When establishing the shear wave estimation process, verification wells are required to ensure the accuracy of the estimation results.

[0006] Rocks are composed of a rock matrix, pores, and fluids in the pores. The diversity of mineral types in the rock matrix, the diversity of pore types and pore fluids, as well as factors such as porosity and saturation, make it difficult to accurately predict the elastic properties of rocks. Therefore, researchers have considered using the concept of equivalent media to describe and analyze rock characteristics. The earliest proposed models were the Voigt and Reuss limit models and the VRH average model for roughly estimating the elastic modulus of rocks. Subsequently, adaptive and scattering theories that consider the influence of rock pore fillings and pore structure on rock elastic properties were proposed, such as the Kuster- Various classic equivalent medium theories have been proposed, including the KT model, the Mori-Tanaka theory (MT model), the self-consistent equivalent model (SCA), and the differential equivalent medium (DEM) model. Xu et al., after comprehensively considering applicable conditions and combining equivalent medium theoretical models with the Gassmann fluid substitution formula, established rock physics modeling frameworks such as the Xu-White model and the Xu-Payne model. These frameworks and their improved versions are currently the most widely used rock physics modeling processes and the most commonly used methods for shear wave estimation.

[0007] In the field of well logging petrophysics, the selection of equivalent medium theory is crucial when estimating shear waves through modeling. While the KT and MT models are explicit analytical formulas, their applicability is limited, typically applied to low-porosity, low-permeability rocks with a matrix fraction greater than approximately 80%. Models such as SCA and DEM are widely applicable, but because their formulas are implicit, coupled, they require a series of simultaneous iterations or differential operations during the solution process. For complex lithologic reservoirs, the increased number of mineral species complicates the calculation process, making it difficult for the coupled formulas to converge quickly to the optimal solution.

[0008] In summary, the existing well logging shear wave velocity estimation method has technical problems such as complex calculation and low accuracy of estimated shear wave velocity. Summary of the Invention

[0009] The purpose of the present invention is to propose a shear wave velocity estimation method and device based on an equivalent self-consistent improved model, aiming to solve the technical problems of the existing well logging shear wave velocity estimation method having complex calculations and low estimated shear wave velocity accuracy.

[0010] To achieve the above object, the present invention proposes a shear wave velocity estimation method based on an equivalent self-consistent improved model, comprising the following steps:

[0011] S1: Using the P-wave velocity and density data from the well logging data and the mineral content, porosity, and water saturation data from the well logging interpretation results, the equivalent self-consistent model is changed;

[0012] S2: By using the assumed shear wave velocity and the measured longitudinal wave velocity and density data, the coupled equations for bulk modulus and shear modulus are transformed into nonlinear functions for shear wave velocity.

[0013] S3: Calculate the shear wave velocity using the empirical formula and use it as the initial value for inversion;

[0014] S4: Based on the initial inversion value of the shear wave velocity, the estimated shear wave velocity is obtained using the simulated annealing algorithm.

[0015] Furthermore, the method further comprises the following steps:

[0016] S21: Assuming the shear wave velocity, calculate the elastic modulus of saturated rock based on the longitudinal wave velocity and density;

[0017] S22: Calculate the equivalent bulk modulus and shear modulus of the mixed fluid based on the porosity and water saturation data;

[0018] S23: Calculating the elastic modulus of dry rock using the inverse Gassmann equation according to the elastic modulus of the saturated rock;

[0019] S24: Calculate the equivalent bulk modulus and shear modulus of the rock matrix using the matrix mineral curve;

[0020] S25: constructing a nonlinear function related to shear wave velocity based on an equivalent self-consistent model formula according to the elastic modulus of the dry rock, the equivalent bulk modulus and shear modulus of the mixed fluid, and the equivalent bulk modulus and shear modulus of the rock matrix;

[0021] The step S3 comprises:

[0022] S31: Calculate the initial inversion value of shear wave velocity based on the Greenberg-Castagna sandstone empirical formula.

[0023] Furthermore, the step S21 includes:

[0024] Assuming the shear wave velocity V S , combined with the measured longitudinal wave velocity and density, the saturated rock elastic modulus is calculated using the formula:

[0025]

[0026] Where ρ,V P are the measured density and longitudinal wave velocity, K sat (V S ),μ sat (V S ) are the shear wave velocities V S Bulk modulus and shear modulus of time-saturated rock.

[0027] Furthermore, the step S22 includes:

[0028] The Wood formula is used to calculate the equivalent bulk modulus and shear modulus of the mixed fluid, combined with the porosity and water saturation data, where the shear modulus of the mixed fluid is 0:

[0029]

[0030] Where K f is the equivalent bulk modulus of the mixed fluid, f i and K i are the volume fraction and bulk modulus of each fluid component.

[0031] Furthermore, step S23 includes: in the low-frequency case, when the SCA model is applied to the pores, there is enough time for the seismic wave to pass through to increase the pressure inside the pores, causing the pore fluid to flow. The usual practice is to first calculate the equivalent modulus of dry rock, then use the low-frequency Gassmann formula to add the fluid to the rock, and calculate the elastic modulus of the dry rock through step S21. According to the inverse Gassmann equation:

[0032]

[0033] Where K dry (V S ),μ dry (V S ) are the shear wave velocities V S The bulk modulus and shear modulus of dry rock, φ is the effective porosity of rock, K ma is the bedrock bulk modulus.

[0034] Furthermore, the step S24 includes:

[0035] The equivalent bulk modulus and shear modulus of the rock matrix are calculated using the matrix mineral curve and the Voigt-Reuss-Hill average model:

[0036]

[0037] Among them, M v The equivalent bulk modulus calculated for the Voigt upper bound model of rock, M R is the equivalent bulk modulus calculated by the Reuss lower limit model of rock;

[0038] or, M v The shear modulus calculated by the Voigt upper limit model for rock, M R is the shear modulus calculated by the Reuss lower limit model of rock.

[0039] Furthermore, the step S25 includes:

[0040] The SCA model based on the equivalent medium self-compatibility approximation of N-phase mineral mixtures:

[0041]

[0042] Where K i 、μ i Represent the volume and shear modulus of the i-th type inclusion respectively; x i represents the volume fraction of the i-th type inclusion; is the equivalent volume and shear modulus calculated by the SCA model; P *i and Q *i is called the geometric factor,

[0043]

[0044] Where:

[0045] T iijj =3F1 / F2

[0046]

[0047] in:

[0048]

[0049]

[0050]

[0051] F4=1+(A / 4)[f+3θ-R(f-θ)]

[0052]

[0053] F6=1+A[1+fR(f+θ)]+B(1-θ)(3-4R)

[0054] F7=2+(A / 4)[3f+9θ-R(3f+5θ)]+Bθ(3-4R)

[0055] F8=A[1-2R+(f / 2)(R-1)+(θ2)(5R-3)]+B(1-θ)(3-4R)

[0056] F9=A[(R-1)f-Rθ]+Bθ(3-4R)

[0057] In the above formula, A, B, R and θ are

[0058]

[0059]

[0060]

[0061]

[0062] K i 、μ i are the bulk modulus and shear modulus of each inclusion, α is the pore aspect ratio of each inclusion, are the bulk modulus and shear modulus of rock, is the Poisson's ratio of rock, which can be obtained by the formula to obtain;

[0063] In formula (5) and (6), Replace K in formula (3) dry (V S ), μ dry (V S ), construct a nonlinear function of the desired shear wave velocity:

[0064]

[0065] Furthermore, in step S31, the sandstone empirical formula is:

[0066] V S0 =V P / ab / a (16)

[0067] V P is the measured shear wave velocity, a and b are the empirical parameters for estimating the shear wave velocity from the longitudinal wave velocity for different lithologies.

[0068] Furthermore, in step S4, the simulated annealing algorithm is a probability-based algorithm derived from the solid annealing principle, which mainly includes two parts: the annealing process and the Metropolis algorithm. The annealing process is the process of decreasing the initial temperature T0 to the termination temperature T according to a certain decrease coefficient. The Metropolis algorithm is the basis of simulated annealing. Its advantage is that it ensures that in the process of iterative optimization, it can jump out of the local optimal solution and finally reach the global optimal solution. The specific process is shown in the attached Figure 2 ; Apply the V calculated in step S31 S0 As the initial value of the simulated annealing inversion, it speeds up the algorithm optimization and obtains the final estimated value of the shear wave velocity.

[0069] The present invention also proposes a device for estimating shear wave velocity based on an equivalent self-consistent improved model, comprising:

[0070] A memory, a processor, and a shear wave velocity estimation program stored in the memory and operable on the processor. When the shear wave velocity estimation program is executed by the processor, the steps of the shear wave velocity estimation method are performed.

[0071] The present invention also provides a computer-readable storage medium, on which a shear wave velocity estimation program is stored. When the shear wave velocity estimation program is executed by a processor, the steps of the above-mentioned shear wave velocity estimation method are implemented.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] This technical solution, based on equivalent medium theory and the Gassmann formula, employs an improved equivalent self-consistent model by assuming shear wave velocity and integrating measured well data. This model constructs an objective function for shear wave velocity and applies a simulated annealing algorithm to estimate shear wave velocity. This method simplifies the modeling process, enabling rapid and efficient shear wave velocity estimation. Furthermore, through formula modification, it reduces the number of solution parameters and, within the constraints of measured density and compressional wave velocity, achieves highly accurate shear wave velocity, providing fundamental data for precise reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0075] Figure 1 This is a flow chart of the rock physics modeling technology based on the equivalent self-consistent improved model of the present invention;

[0076] Figure 2 It is a flow chart of the principle of the simulated annealing algorithm of the present invention;

[0077] Figure 3 This is an analysis diagram of the shear wave velocity estimation effect of an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0079] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0080] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features limited to "first" and "second" may explicitly or implicitly include at least one of such features. In addition, if "and / or" or "and / or" appears in the full text, its meaning includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or solutions that satisfy both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0081] In one embodiment of the present invention, a method for estimating shear wave velocity based on an equivalent self-consistent improved model is provided. Figure 1 , the shear wave velocity estimation method includes the following steps:

[0082] S1: Using the P-wave velocity V in the logging data P , density data ρ and mineral content, porosity, and water saturation data in the logging interpretation results, and change the equivalent self-compatible model;

[0083] S2: By assuming the shear wave velocity V S and the measured longitudinal wave velocity V P , density data ρ, the coupled equations for bulk modulus and shear modulus are changed to the nonlinear function for shear wave velocity;

[0084] S3: Calculate the shear wave velocity using the empirical formula and use it as the initial value for inversion;

[0085] S4: Based on the initial inversion value of the shear wave velocity, the estimated shear wave velocity is obtained using the simulated annealing algorithm.

[0086] Furthermore, the shear wave velocity estimation method further includes the following steps:

[0087] S21: Assuming the shear wave velocity V S , according to the longitudinal wave velocity V P Calculate the elastic modulus of saturated rock with density ρ;

[0088] S22: Calculate the equivalent bulk modulus and shear modulus of the mixed fluid based on the porosity and water saturation data;

[0089] S23: Calculating the elastic modulus of dry rock using the inverse Gassmann equation according to the elastic modulus of the saturated rock;

[0090] S24: Calculate the equivalent bulk modulus and shear modulus of the rock matrix using the matrix mineral curve;

[0091] S25: constructing a nonlinear function related to shear wave velocity based on an equivalent self-consistent model formula according to the elastic modulus of the dry rock, the equivalent bulk modulus and shear modulus of the mixed fluid, and the equivalent bulk modulus and shear modulus of the rock matrix;

[0092] The step S3 comprises:

[0093] S31: Calculate the initial inversion value of shear wave velocity based on the Greenberg-Castagna sandstone empirical formula.

[0094] Furthermore, the step S21 includes:

[0095] Assuming the shear wave velocity V S , combined with the measured longitudinal wave velocity and density, the saturated rock elastic modulus is calculated using the formula:

[0096]

[0097] Where ρ,V P are the measured density and longitudinal wave velocity, K sat (V S ),μ sat (V S ) are the shear wave velocities V S Bulk modulus and shear modulus of time-saturated rock.

[0098] Furthermore, the step S22 includes:

[0099] The Wood formula is used to calculate the equivalent bulk modulus and shear modulus of the mixed fluid, combined with the porosity and water saturation data, where the shear modulus of the mixed fluid is 0:

[0100]

[0101] Where K f is the equivalent bulk modulus of the mixed fluid, f i and K i are the volume fraction and bulk modulus of each fluid component.

[0102] Furthermore, step S23 includes: in the low-frequency case, when the SCA model is applied to the pores, there is enough time for the seismic wave to pass through to increase the pressure inside the pores, causing the pore fluid to flow. The usual practice is to first calculate the equivalent modulus of dry rock, then use the low-frequency Gassmann formula to add the fluid to the rock, and calculate the elastic modulus of the dry rock through step S21. According to the inverse Gassmann equation:

[0103]

[0104] μ dry (V S )=μ sat (V S )

[0105] Where K dry (V S ),μ dry (V S ) are the shear wave velocities V S The bulk modulus and shear modulus of dry rock, φ is the effective porosity of rock, K ma is the bedrock bulk modulus.

[0106] Furthermore, the step S24 includes:

[0107] The equivalent bulk modulus and shear modulus of the rock matrix are calculated using the matrix mineral curve and the Voigt-Reuss-Hill average model:

[0108]

[0109] Among them, M v The bulk or shear modulus calculated for the Voigt upper bound model of rock, M R is the bulk or shear modulus of rock calculated using the Reuss lower bound model.

[0110] Furthermore, the step S25 includes:

[0111] The SCA model based on the equivalent medium self-compatibility approximation of N-phase mineral mixtures:

[0112]

[0113] Where K i 、μ i Represent the volume and shear modulus of the i-th type inclusion respectively; x i represents the volume fraction of the i-th type inclusion; is the equivalent volume and shear modulus calculated by the SCA model; P *i and Q *i is called the geometric factor,

[0114]

[0115] Where:

[0116] T iijj =3F1 / F2

[0117]

[0118] in:

[0119]

[0120]

[0121]

[0122] F4=1+(A / 4)[f+3θ-R(f-θ)]

[0123]

[0124] F6=1+A[1+fR(f+θ)]+B(1-θ)(3-4R)

[0125] F7=2+(A / 4)[3f+9θ-R(3f+5θ)]+Bθ(3-4R)

[0126] F8=A[1-2R+(f / 2)(R-1)+(θ2)(5R-3)]+B(1-θ)(3-4R)

[0127] F9=A[(R-1)f-Rθ]+Bθ(3-4R)

[0128] In the above formula, A, B, R and θ are

[0129]

[0130]

[0131]

[0132]

[0133] K i 、μ i are the bulk modulus and shear modulus of each inclusion, α is the pore aspect ratio of each inclusion, are the bulk modulus and shear modulus of rock, is the Poisson's ratio of rock, which can be obtained by the formula to obtain;

[0134] In formula (5) and (6), Replace K in formula (3) dry (V S ), μ dry (V S ), construct a nonlinear function of the desired shear wave velocity:

[0135]

[0136] Furthermore, in step S31, the sandstone empirical formula is:

[0137] V S0 =V P / ab / a (24)

[0138] V P is the measured shear wave velocity, a and b are the empirical parameters for estimating the shear wave velocity from the longitudinal wave velocity for different lithologies.

[0139] Furthermore, in step S4, the simulated annealing algorithm is a probability-based algorithm derived from the solid annealing principle, which mainly includes two parts: the annealing process and the Metropolis algorithm. The annealing process is the process of decreasing the initial temperature T0 to the termination temperature T according to a certain decrease coefficient. The Metropolis algorithm is the basis of simulated annealing. Its advantage is that it ensures that in the process of iterative optimization, it can jump out of the local optimal solution and finally reach the global optimal solution. The specific process is shown in the attached Figure 2 ; Apply the V calculated in step S31 S0 As the initial value of the simulated annealing inversion, it speeds up the algorithm optimization and obtains the final estimated value of the shear wave velocity.

[0140] On the one hand, the present invention simplifies the modeling process and can quickly and effectively estimate the shear wave velocity. On the other hand, the present invention reduces the solution parameters through formula deformation and can obtain high-precision shear wave velocity under the constraints of measured density and longitudinal wave velocity, providing basic data for precise reservoir prediction.

[0141] The present invention provides a device for estimating shear wave velocity based on an equivalent self-consistent improved model, comprising:

[0142] A memory, a processor, and a shear wave velocity estimation program stored in the memory and operable on the processor. When the shear wave velocity estimation program is executed by the processor, the steps of the shear wave velocity estimation method are performed.

[0143] The present invention also provides a computer-readable storage medium, on which a shear wave velocity estimation program is stored. When the shear wave velocity estimation program is executed by a processor, the steps of the above-mentioned shear wave velocity estimation method are implemented.

[0144] This method was tested and analyzed in an exploration area in Xinjiang. The region has complex geological conditions and strong reservoir heterogeneity, making reservoir prediction challenging. Core analysis revealed a complex reservoir mineral composition, including 6-7 mineral species. Conventional rock physics modeling methods are complex and have limited estimation accuracy. However, the abundance of well logging data, including actual drilling P-wave velocity, density, and logging interpretation data, enabled a reasonable estimation of S-wave velocity using the proposed method.

[0145] The above descriptions are merely optional embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present description and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A method for estimating shear wave velocity based on an equivalent self-consistent improved model, characterized in that: The steps include: S1: Using the P-wave velocity and density data in the logging data and the mineral content, porosity and water saturation data in the logging interpretation results, the equivalent self-compatible model is changed; S2: By using the assumed shear wave velocity and the measured longitudinal wave velocity and density data, the coupled equations for bulk modulus and shear modulus are transformed into nonlinear functions for shear wave velocity. S3: Calculate the shear wave velocity using the empirical formula and use it as the initial value for inversion; S4: Based on the inversion initial value of the shear wave velocity, the estimated shear wave velocity is obtained using the simulated annealing algorithm.

2. The method for estimating shear wave velocity based on an equivalent self-consistent improved model according to claim 1, characterized in that: The step S2 comprises: S21: Assuming the shear wave velocity, calculate the elastic modulus of the saturated rock based on the longitudinal wave velocity and density; S22: Combine porosity and water saturation data to calculate the equivalent bulk modulus and shear modulus of the mixed fluid; S23: calculating the elastic modulus of dry rock using the inverse Gassmann equation according to the elastic modulus of the saturated rock; S24: Calculate the equivalent bulk modulus and shear modulus of the rock matrix using the matrix mineral curve; S25: constructing a nonlinear function about shear wave velocity based on an equivalent self-consistent model formula according to the elastic modulus of the dry rock, the equivalent bulk modulus and shear modulus of the mixed fluid, and the equivalent bulk modulus and shear modulus of the rock matrix; The step S3 comprises: S31: Calculate the initial inversion value of shear wave velocity according to the Greenberg-Castagna sandstone empirical formula.

3. The method for estimating shear wave velocity based on an equivalent self-consistent improved model according to claim 2, characterized in that: The step S21 comprises: Assuming the shear wave velocity V S , combined with the measured longitudinal wave velocity and density, the saturated rock elastic modulus is calculated by the formula: Where ρ,V P are the measured density and longitudinal wave velocity, K sat (V S ),μ sat (V S ) are the shear wave velocities V S Bulk modulus and shear modulus of time-saturated rock.

4. The method for estimating shear wave velocity based on an equivalent self-consistent improved model according to claim 3 is characterized in that: The step S22 comprises: Wood's formula is used to calculate the equivalent bulk modulus and shear modulus of the mixed fluid in combination with the porosity and water saturation data, where the shear modulus of the mixed fluid is 0: In the formula, K f is the equivalent bulk modulus of the mixed fluid, f i and K i are the volume components and bulk modulus of each fluid component.

5. The method for estimating shear wave velocity based on an equivalent self-consistent improved model according to claim 4, characterized in that: In step S23, the inverse Gassmann equation is: m dry (V S )=μ sat (V S ) Where K dry (V S ),μ dry (V S ) are the shear wave velocities V S The bulk modulus and shear modulus of dry rock, φ is the effective porosity of rock, K ma is the bedrock bulk modulus.

6. The method for estimating shear wave velocity based on an equivalent self-consistent improved model according to claim 5, characterized in that: The step S24 comprises: Using the matrix mineral curve, the Voigt-Reuss-Hill average model is used to calculate the equivalent bulk modulus and shear modulus of the rock matrix: Among them, M v The equivalent bulk modulus calculated by the Voigt upper limit model for rock, M R is the equivalent bulk modulus calculated by the Reuss lower limit model of rock; or, M v The shear modulus calculated by the Voigt upper limit model for rock, M R is the shear modulus calculated by the Reuss lower limit model of rock.

7. The method for estimating shear wave velocity based on an equivalent self-consistent improved model according to claim 6, characterized in that: The step S25 comprises: The SCA model based on the equivalent medium self-compatibility approximation of N-phase mineral mixtures: Where K i , μ i Respectively represent the volume and shear modulus of the i-th type inclusion; x i represents the volume fraction of the i-th type inclusion; is the equivalent volume and shear modulus calculated by the SCA model; P *i and Q *i is called the geometric factor, Where: T iijj =3F1 / F2 in: F4=1+(A / 4)[f+3θ-R(f-θ)] F6=1+A[1+fR(f+θ)]+B(1-θ)(3-4R) F7=2+(A / 4)[3f+9θ-R(3f+5θ)]+Bθ(3-4R) F8=A[1-2R+(f / 2)(R-1)+(θ / 2)(5R-3)]+B(1-θ)(3-4R) F9=A[(R-1)f-Rθ]+Bθ(3-4R) In the above formula, A, B, R and θ are K i , μ i are the bulk modulus and shear modulus of each inclusion, α is the pore aspect ratio of each inclusion, μ S * C are the bulk modulus and shear modulus of rock, is the Poisson's ratio of rock, which can be obtained by the formula to seek; The formulas (5) and (6) are Substitute K in formula (3) dry (V S ), μ dry (V S ), construct a nonlinear function of the desired shear wave velocity:

8. The method for estimating shear wave velocity based on an equivalent self-consistent improved model according to claim 7 is characterized in that: In step S31, the sandstone empirical formula is: In S0 =V P / ab / a (8) V P is the measured shear wave velocity, a and b are the empirical parameters for estimating the shear wave velocity from the longitudinal wave velocity for different lithologies.

9. A shear wave velocity estimation device based on an equivalent self-consistent improved model, characterized in that: The shear wave velocity estimation device includes: a memory, a processor, and a shear wave velocity estimation program stored in the memory and executable on the processor. When the shear wave velocity estimation program is executed by the processor, the steps of the shear wave velocity estimation method based on the equivalent self-consistent improved model as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a shear wave velocity estimation program, which, when executed by a processor, implements the steps of the shear wave velocity estimation method based on the equivalent self-consistent improved model as claimed in any one of claims 1 to 8.

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