Shear flexibility factor solving method based on constraint solution

Abstract

The invention belongs to the technical field of permeability prediction, and particularly relates to a shear flexibility factor solving method based on constraint solution. According to the method, on the basis of the rock physics theory, shear flexibility factor modeling is carried out, a theoretical formula of the shear flexibility factor is obtained through derivation, and on the basis of logging curve splicing, completion and singular value elimination processing, a target function for solving the shear flexibility factor in a constraint mode is established by deriving the theoretical formula of the shear flexibility factor; and solving the established objective function by adopting an L2 norm regularization least square method, and finally obtaining a shear flexibility factor of constraint solution. According to the method, constraint solving of the shear flexibility factor is achieved, compared with direct solving of the shear flexibility factor, the correlation of constraint solving of the shear flexibility factor and the permeability is improved by about 30%, and basic data are provided for accurate prediction of the permeability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of permeability prediction, and particularly relates to a shear compliance factor calculation method based on constraint solving. BACKGROUND

[0002] Reservoir permeability prediction using seismic data has always been a frontier and difficult problem of reservoir prediction. Since permeability and porosity are not simply one-to-one corresponding relationship, but also affected by pore structure, the corresponding relationship between porosity and permeability of different pore structure rocks is obviously different. Only by combining reservoir pore structure for permeability prediction can the multi-solution be reduced and the prediction accuracy be improved.

[0003] In recent years, a new idea of combining porosity and skeleton compliance factor for permeability prediction has been successfully applied in carbonate reservoirs such as Texas, Sichuan Basin Puguang Gas Field, etc. The method first calculates the skeleton compliance factor in the logging data, combines the skeleton compliance factor and rock thin section to classify the rock, and then calculates the regression of porosity and permeability in each type of rock.

[0004] Therefore, how to accurately calculate the skeleton compliance factor is one of the keys to improve the permeability prediction. The traditional method is to directly solve the skeleton compliance factor by formula. However, in actual application, it is found that due to various noises in logging data, the direct solution of skeleton compliance factor is very unstable, which further leads to inaccurate prediction of permeability. Therefore, an accurate method for calculating shear compliance factor is needed. SUMMARY

[0005] To achieve the above purpose, the shear compliance factor calculation method based on constraint solving improves the stability and accuracy of shear compliance factor calculation, and adopts the following technical solutions: Step 1, based on rock physics theory, shear compliance factor modeling is carried out, and the theoretical formula of shear compliance factor is derived, (1) wherein, V p represents the longitudinal wave velocity, V s represents the transverse wave velocity, K s represents the bulk modulus, µ s represents the shear modulus, p and represents the density. (2) wherein, K d represents the dry rock bulk modulus, Km denotes the bulk modulus of the rock matrix, µ d denotes the dry rock shear modulus, µ m denotes the shear modulus of the rock matrix, denotes the porosity, g denotes the bulk skeleton compliance factor, g µ denotes the shear compliance factor; Taking the logarithm of both sides of the shear skeleton compliance factor equation (2) gives: (3) where, µ d denotes the dry rock shear modulus, µ m denotes the shear modulus of the rock matrix, denotes the porosity, g µ denotes the shear compliance factor; Rearranging equation (3) gives: (4) where, µ d denotes the dry rock shear modulus, µ m denotes the shear modulus of the rock matrix, denotes the porosity, g µ denotes the shear compliance factor; Since the fluid has no effect on the shear modulus of the rock, we have: (5) µ d denotes the dry rock shear modulus, µ s denotes the shear modulus; Finally, we obtain the theoretical equation for directly solving the shear compliance factor g µ (6) where, g µ denotes the shear compliance factor; V s denotes the S-wave velocity; p denotes the density; m m ​The solution of the equation adopts Voigt-Reuss-Hill average method, Voigt model assumes that various minerals of the rock are arranged in parallel along the stress direction, and the equivalent modulus is solved by space volume average method; denotes porosity; Step 2, splicing, completing and singular value removing treatment are performed on the well logging curve; Step 3, the target function for solving the shear flexibility factor is established by deducing the theoretical formula of the shear flexibility factor, Further arrangement of formula (3) obtains: (10) wherein: V s denotes shear modulus of rock matrix, p denotes density; µ m denotes shear modulus of rock matrix, denotes porosity, g µ denotes shear flexibility factor; The above formula is simplified as: (11) wherein, D =log( V s 2 p ) - log µ m ; A =log( 1- ); X = g µ ; µ m denotes shear modulus of rock matrix; V s denotes shear modulus of rock matrix, p denotes density; denotes porosity, g µ denotes shear flexibility factor; n denotes noise; The target function is established as follows by solving X , and the target function is established as follows: (12) wherein, D =log( V s 2 p ) - logµ m ; A = log( 1- ); X = log( g µ ; µ m denotes the shear modulus of rock matrix; V s denotes the S-wave velocity; p denotes the density; denotes the porosity, g µ denotes the shear compliance factor; By minimizing the objective function, i.e., the constrained shear compliance factor ; Step 4, the established objective function is solved by L2 norm regularization least square method, and the final constrained solution of the shear compliance factor is obtained; the objective function formula (12) is extended as follows: (13) wherein: is the L2 norm; D = log( V s 2 p ) - log µ m ; A = log( 1- ); X = log( g µ ; V s denotes the S-wave velocity; p denotes the density; µ m denotes the shear modulus of rock matrix; denotes the porosity, g µ denotes the shear compliance factor; l is a constant; x is the scalar form of the vector X ; Derivation of formula (13) is zero, and the final form of the constrained solution of the shear compliance factor is: (14) wherein, D = log( V s 2 p )- log µ m ; A =log( 1- ); X = g µ ; A T denotes the transpose of the matrix A ; µ m denotes the shear modulus of the rock matrix; V s denotes the S-wave velocity; p denotes the density; denotes the porosity, g µ denotes the shear compliance factor; I is a diagonal matrix; l is a constant.

[0006] The present application has the following beneficial effects: In view of the problems of low accuracy and poor stability in directly solving the shear compliance factor in the current complex reservoir permeability prediction process, a shear compliance factor calculation method based on constraint solving is invented. The constraint solving of the shear compliance factor is realized for the first time, and the correlation between the constraint solving of the shear compliance factor and the permeability is improved by about 30% compared with the direct solving of the shear compliance factor, which provides basic data for accurately predicting the permeability. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 A flowchart of a shear compliance factor calculation method based on constraint solving; Figure 2 Definition diagram of the pore aspect ratio; Figure 3 Diagram showing the relationship between the pore aspect ratio, porosity and velocity; Figure 4 Diagram showing the relationship between the shear compliance factor, permeability and pore aspect ratio; Figure 5 Diagram showing the pre-processing of the well logging curve in the specific embodiment research area; Figure 6 Logging curve required for solving the shear compliance factor; Figure 7 Comparison of the shear compliance factor solving effects of different methods; Figure 8 Comparison of the correlation between the shear compliance factor solving results and the permeability; Figure 9 Intersection plot of the permeability fitted by the constraint solving of the shear compliance factor and the original permeability. DETAILED DESCRIPTION

[0008] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions will be described clearly and completely below with reference to the embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0009] The skeleton flexibility factor, also known as the pore structure factor, is used to describe the influence of pores of different shapes and sizes on the elastic properties of a formation. Pore structure is one of the important factors affecting reservoir permeability. The influence of pore structure on the characteristics of seismic response is mainly reflected in the rock skeleton model, and the pore structure has a certain sensitivity to permeability. Fluid has a greater influence on bulk modulus, while shear modulus is not affected by fluid. Therefore, fluid has an influence on the skeleton flexibility factor, but has no influence on the bulk skeleton flexibility factor.

[0010] There are many parameters representing pore structure in geology, and the pore aspect ratio (α) is usually used to represent pore structure in seismic. The pore aspect ratio has obvious indicative effect on pore type and pore shape. In theory, the pore aspect ratio of circular pores is the largest, the pore aspect ratio of pore+fracture is the second, and the pore aspect ratio of fracture is the smallest. For the same porosity, the larger the α, the larger the velocity; for the same velocity, the larger the α, the larger the porosity, as shown in Figure 2 .

[0011] The main influencing factors of the shear skeleton flexibility factor include pore size, pore shape, permeability, and rock cementation degree. In theory, the shear flexibility factor has a good relationship with the pore type. Generally, the larger the pore aspect ratio, the smaller the shear flexibility factor; the smaller the pore aspect ratio, the larger the shear flexibility factor. The shear flexibility factor has a negative correlation with the pore aspect ratio, and has a positive correlation with the permeability, as shown in Figure 3 . The relationship among the shear flexibility factor, the permeability, and the pore aspect ratio is shown in Figure 4 .

[0012] Taking XH recessed west sub-sag as an example, through physical property analysis, the reservoir porosity of the target layer is between 1.9~12%, mainly distributed in 5~10%, and the average is 7.4%; the permeability is between 0.029~44.1mD, mainly distributed in 0.05~1mD, and the average is 0.82, belonging to low porosity and low permeability~low permeability reservoir, and the reservoir physical property difference is large, the argillaceous content is low, and the physical property sweet spot is locally developed, which is one of the target areas of the next new area production, the sweet spot distribution prediction is the main restricting factor of the new area scheme compilation and the implementation scheme optimization, and the permeability is an important sweet spot characteristic parameter, and accurately obtaining the shear flexibility factor is one of the key factors to obtain accurate permeability.

[0013] Step 1, shear flexibility factor modeling The seismic parameters representing the petrophysical characteristics mainly include the elastic modulus of rock, P-wave velocity, S-wave velocity, density and attenuation, etc., which is an important parameter for identifying lithology and oil and gas, and is also a parameter for connecting reservoir characteristics and a bridge for quantitative seismic reservoir description, for example, porosity, water saturation, permeability and formation pressure.

[0014] One of the purposes of establishing a petrophysical model is to establish a quantitative relationship between reservoir physical properties, fluid parameters and elastic parameters. The basic research of petrophysics and related analysis method research are the basis of seismic inversion and attribute analysis, which lay the foundation for further identification of lithology and hydrocarbon, and are also the basis for obtaining the skeleton flexibility factor.

[0015] Based on the theory of petrophysics, the shear flexibility factor modeling is carried out, and the theoretical formula of the shear flexibility factor is derived. The skeleton flexibility factor is a parameter introduced in the new petrophysical model derived by Sun, mainly including the bulk skeleton flexibility factor and the shear skeleton flexibility factor. These skeleton flexibility factor parameters can be used to describe the influence of different shapes and sizes of pores on the elastic properties of the formation, among which the bulk skeleton flexibility factor explains the bulk strain under stress conditions, and the shear skeleton flexibility factor explains the shear deformation.

[0016] (1) Wherein: V p Vp represents the P-wave velocity, V s Vs represents the S-wave velocity, K s K represents the bulk modulus, µ s μ represents the shear modulus, p ρ represents the density.

[0017] (2) Wherein: K ddenotes the bulk modulus of dry rock, K m denotes the bulk modulus of rock matrix, µ d denotes the shear modulus of dry rock, µ m denotes the shear modulus of rock matrix, denotes the porosity, g denotes the bulk skeleton compliance factor, g µ denotes the shear compliance factor.

[0018] Taking the logarithm of both sides of the shear skeleton compliance factor equation (2) gives: (3) where: µ d denotes the shear modulus of dry rock, µ m denotes the shear modulus of rock matrix, denotes the porosity, g µ denotes the shear compliance factor.

[0019] Rearranging equation (3) gives: (4) where: µ d denotes the shear modulus of dry rock, µ m denotes the shear modulus of rock matrix, denotes the porosity, g µ denotes the shear compliance factor.

[0020] Since the fluid has no effect on the shear modulus of the rock, we have: (5) µ d denotes the shear modulus of dry rock, µ s denotes the shear modulus.

[0021] Finally, we can obtain the theoretical equation for directly solving the shear compliance factor g µ : (6) where, g µ denotes the shear compliance factor; m mThe solution of Voigt-Reuss-Hill average method, Voigt model assumes that various minerals of the rock are arranged in parallel along the stress direction, and the equivalent modulus is obtained by spatial volume average method. V s represents the shear wave velocity; p represents the density; represents the porosity.

[0022] (7) wherein: represents the Voigt model, f i represents the percentage of different components, M i represents the modulus of different components (quartz, clay, etc.).

[0023] Ruess model assumes that various minerals of the rock are arranged in parallel perpendicular to the stress direction, and the equivalent modulus is obtained by spatial volume average method.

[0024] (8) wherein: represents the Ruess model; f i represents the percentage of different components, M i represents the modulus of different components Voigt-Reuss-Hill average is the arithmetic average of Voigt upper limit and Reuss lower limit: (9) wherein , m m The solution of Voigt-Reuss-Hill average method, Voigt model assumes that various minerals of the rock are arranged in parallel along the stress direction, and the equivalent modulus is obtained by spatial volume average method represents the Voigt model; represents the Ruess model.

[0025] Step 2, well logging curve processing In the field of oil and gas exploration and development, well logging curve is an important means to obtain underground geological information. However, in actual operation, affected by factors such as instrument and environment, well logging curve often appears segmented measurement, data missing and uneven data distribution. Therefore, well logging curve splicing, missing curve completion and singular value elimination processing become key technical links.

[0026] Logging curve splicing: In logging operation, due to instrument tripping or measurement time limit, the same type of logging curve may be segmented and distributed in different depth sections. For example, in complex formations, multiple trips are required for measurement, which leads to segmented curves. To meet the needs of subsequent data synthesis research, the same type of curves in different depth sections need to be spliced into a continuous curve. Traditional manual splicing is not only low in efficiency, but also easy to introduce human error, affecting data accuracy. With the growth of logging data, automatic splicing technology has emerged. The automatic splicing algorithm first determines the overlapping area between curves, finds the overlapping part by comparing depth information and curve characteristics. In the overlapping section, the correlation coefficient is calculated by using curve correlation analysis to determine the part with the highest matching degree. Then, to unify the curve amplitude in the overlapping area, methods such as mean correction and trend correction are used. For example, if the mean values of two curves in the overlapping section are biased, the bias value is calculated, and one of the curves is translated and corrected to make the overlapping section data smooth transition.

[0027] Missing curve completion: During logging, due to instrument failure, signal interference, etc., the curve may have missing data. Missing data will destroy the integrity of the curve and affect the accuracy of subsequent geological analysis. For missing curve completion, interpolation method can be used in simple scenarios. If the missing section is short, linear interpolation estimates the missing value by connecting the known data points at both ends of the missing section according to linear relationship; cubic spline interpolation can construct a smoother curve, which uses known data points to generate a curve with continuous second derivative to estimate the missing value. When the missing section is long or there are multiple curve correlations, machine learning methods are more advantageous. Taking the random forest algorithm as an example, first construct a feature matrix, select other logging curve data related to the target curve as features, and the non-missing part of the target curve itself as training labels. Divide the data into valid data and missing data index, train the random forest model using valid data, and predict the missing data after the model learns the relationship between features and target values, thereby completing the curve.

[0028] Outlier removal: During the acquisition process, due to instrument failure, severe changes in downhole environment or signal transmission interference, etc., abnormal values often appear in the logging curve, which are out of the normal fluctuation range. These outliers are represented as data jump points, extreme outliers or sudden changes that do not conform to geological rules. If not removed, they will seriously interfere with the accuracy of reservoir parameter calculation, and even cause deviations in lithology identification, fluid property judgment and other geological interpretation. Outlier removal technology needs to combine statistical characteristics and geological logic. The statistical threshold method marks values outside a certain range as abnormal by calculating the mean and standard deviation of the curve, which is suitable for normal distribution of conventional logging curves. The sliding window method removes outliers based on the median and mean of the data in the window, which can better adapt to the trend of the curve. For multi-curve joint processing scenarios, the isolated forest algorithm captures isolated points in the data distribution by building a binary tree model, which has strong robustness for high-dimensional logging data. The wavelet transform method can separate the high-frequency noise corresponding to the outliers from the effective signal and remove them.

[0029] Before calculating the shear compliance factor, the logging curve splicing ensures the continuity of the curve, the missing curve completion repairs the integrity of the data, and the outlier removal. The three work together to lay a solid foundation for accurately calculating the shear compliance factor, which is an indispensable key step in the logging data processing flow.

[0030] As can be seen from formula (6), the logging curves needed to solve the shear compliance factor include the shear wave curve, the density curve, the porosity curve, and the shale content curve. Before calculating the shear compliance factor, the logging curve splicing ensures the continuity of the curve, the missing curve completion repairs the integrity of the data, and the outlier removal. The three work together to lay a solid foundation for accurately calculating the shear compliance factor. In the study area, there are two problems with logging curves: outlier removal and missing curve completion. For outlier removal, a method combining median filtering and mean filtering is used to remove outliers. For missing curve completion, a random forest curve fitting method is used to complete the missing curve, as shown in FIG. 6. After outlier removal, the outliers of the original curve are well removed, and the signal-to-noise ratio of the curve is significantly improved. After missing curve completion, the missing part of the original curve is well completed, providing a good data basis for subsequent shear compliance factor calculation. Finally, all the logging curves (shale content, porosity, shear wave velocity, and density) needed to solve the shear compliance factor are shown in FIG. 7. Figure 5 Figure 6

[0031] Step 3, target function establishment By deriving the theoretical formula of the shear compliance factor, the target function for constrained solution of the shear compliance factor is established.

[0032] ​​The traditional method is to directly calculate the shear compliance factor by formula (6) g µ However, due to various noises in the logging data, such as the measured S-wave velocity and the calculated density m d , the Voigt-Reuss-Hill average method m m , etc. directly solving the parameters g µ is not suitable, and directly solving by formula (6) g µ will be very unstable, so the method of minimizing the objective function is used to constrain the solution g µ .

[0033] The objective function is the difference between the model prediction result and the actual result, which is usually expressed by a mathematical formula. The form of the objective function varies depending on the problem, but it is usually a convex function, which means that there is a global minimum in the entire parameter space.

[0034] Further rearranging formula (3) gives: (10) Where: V s represents the S-wave velocity; p represents the density; µ m represents the shear modulus of the rock matrix, represents the porosity, g µ represents the shear compliance factor.

[0035] Simplify the above formula to: (11) Where, D =log( V s 2 p ) - log µ m ; A =log( 1- ); X = g µ ; µ m represents the shear modulus of the rock matrix; V s represents the S-wave velocity; p represents the density; denotes porosity, g µ denotes shear compliance factor; n denotes noise.

[0036] solved by objective function optimization X , the following objective function is established: (12) wherein, D = log( V s 2 p ) - log µ m ; A = log( 1- ); X = g µ ; µ m denotes shear modulus of rock matrix; V s denotes S-wave velocity; p denotes density; denotes porosity, g µ denotes shear compliance factor.

[0037] By minimizing the objective function, i.e. the constrained shear compliance factor g µ .

[0038] Step 4: Shear compliance factor solution The shear compliance factor solution is actually the objective function optimization solution. Generally speaking, the optimization methods of the objective function mainly include gradient descent method, Newton method, genetic algorithm, simulated annealing algorithm, least square method, etc. The gradient descent method is an iterative algorithm, which calculates the gradient of the objective function according to the current parameters each time, and then updates the parameters according to the gradient; the Newton method is an iterative algorithm, which calculates the second-order derivative of the objective function according to the current parameters each time, and then updates the parameters according to the second-order derivative; the genetic algorithm is a random search algorithm, which finds the optimal solution by constantly selecting, crossing and mutating the population; the simulated annealing algorithm is a random search algorithm, which gradually reduces the temperature by constantly accepting small improvements, and finally reaches the global minimum value.

[0039] The least square method is widely used in seismic exploration. In order to further improve the stability and accuracy of the shear compliance factor solution, the least square method is used to solve the established objective function (12). However, there are often a lot of noise in the logging data. Therefore, in order to improve the stability and uniqueness of the solution, the solution process needs to be regularized and constrained.

[0040] Regularization refers to that in linear algebra theory, an ill-posed problem is usually defined by a set of linear algebra equations, and the set of equations is usually derived from an ill-posed inverse problem with a large condition number. When the linear equation solution does not exist or is not unique, it is called an ill-posed problem. Many times, the ill-posed problem needs to be solved. For the case where the solution does not exist, the solution is to add some conditions to find an approximate solution. For the case where the solution is not unique, the solution is to add some restrictions to narrow the solution range. This method of solving the ill-posed problem by adding conditions or restrictions is called the regularization method.

[0041] The L2 norm regularization least square method is used to solve the established objective function, that is, the constraint solution of the shear compliance factor can be realized. The L2 norm refers to the square sum of each element in the vector and then taking the square root. For L2 norm regularization, in terms of statistics, it can be understood as prior knowledge, that is, X should not be too large; in terms of prediction, when the model is applied to a new instance, a larger X value will lead to AX a larger change, which may not be able to achieve high prediction performance, and vice versa. A smaller X value can achieve higher prediction performance; in terms of optimization, L2 norm regularization gives a compromise between solving the system and having a small X . Through L2 norm regularization, the objective function formula (12) is expanded as follows: (13) , wherein: is the L2 norm; D = log( V s 2 p ) - log µ m ; A = log( 1- ); X = g µ ; V s denotes the shear wave velocity; p denotes the density; µm represents the rock matrix shear modulus; represents the porosity, g µ represents the shear flexibility factor; l is a constant; x is a vector X scalar form of .

[0042] Taking the derivative of formula (13) and setting it to zero, the final constraint solution of the shear flexibility factor is in the form of: (14) in, D =log( V s 2 p ) - log µ m ; A =log( 1- ); X = g µ ; A T Representation matrix A The transpose of µ m represents the rock matrix shear modulus; V s represents the shear wave velocity; p Indicates density; represents the porosity, g µ represents the shear flexibility factor; I is a diagonal matrix; l is a constant.

[0043] Comparison of shear flexibility factor solution results using different methods Figure 7 As shown in the figure, it can be seen that compared with directly solving the shear flexibility factor, the local changes of the shear flexibility factor results solved by the constraint solution are more contrasting, the overall changes are more stable, and the resolution is also improved. Figure 8 As shown, it can be seen that the correlation coefficient between the directly calculated shear compliance factor and the permeability is 0.23, while the correlation coefficient between the shear compliance factor and the permeability by constraint solution is 0.52. The correlation is significantly improved, which shows the accuracy of the shear compliance factor by constraint solution. Figure 9 The cross-plot of the permeability obtained by linearly fitting the constrained shear compliance factor and the original permeability shows a correlation of 0.86. It can be seen that the constrained shear compliance factor improves the accuracy of permeability fitting and prediction.

[0044] It should be understood by those of ordinary skill in the art that the above discussion of the embodiments is merely exemplary in nature and not intended to suggest the scope of the application, which is disclosed herein to include all such embodiments and equivalents thereof. Under the teachings of the application, the technical features of the above embodiments or technical features among different embodiments can be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the application as described above, which are not provided in detail for the sake of brevity. Any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for obtaining shear compliance factor based on constraint solving, characterized in that: The following steps are involved: Step 1: Based on rock physics theory, the shear flexibility factor is modeled and the theoretical formula of the shear flexibility factor is derived: (1) in, V p represents the longitudinal wave velocity, V s represents the shear wave velocity, K s represents the bulk modulus, µ s represents the shear modulus, ρ Indicates density; (2) in, K d represents the dry rock bulk modulus, K m represents the rock matrix bulk modulus, µ d represents the dry rock shear modulus, µ m represents the rock matrix shear modulus, represents the porosity, γ represents the body skeleton flexibility factor, γ µ represents the shear flexibility factor; right Take the logarithm of both sides: (3) in, µ d represents the dry rock shear modulus, µ m represents the rock matrix shear modulus, represents the porosity, γ µ represents the shear flexibility factor; Formula (3) can be sorted out to obtain: (4) in, µ d represents the dry rock shear modulus, µ m represents the rock matrix shear modulus, represents the porosity, γ µ represents the shear flexibility factor; Since the fluid has no effect on the shear modulus of the rock, therefore: (5) µ d represents the dry rock shear modulus, µ s represents the shear modulus; Finally, the shear flexibility factor is solved γ µ The theoretical formula: (6) in, γ µ represents the shear flexibility factor; V s represents the shear wave velocity; ρ Indicates density; μ m represents the rock matrix shear modulus; represents porosity; Step 2: splicing, completing and singular value elimination of the logging curves; Step 3: By deriving the theoretical formula of the shear flexibility factor, establish the objective function of constrained solution of the shear flexibility factor; Formula (3) can be further sorted out to obtain: (10) in: V s represents the shear wave velocity; ρ Indicates density; µ m represents the rock matrix shear modulus, represents the porosity, γ µ represents the shear flexibility factor; Simplify the above formula to: (11) in, D =log( V s 2 ρ ) - log µ m ; A =log( 1- ); X = γ µ ; V s represents the shear wave velocity; ρ Indicates density; µ m represents the rock matrix shear modulus; represents the porosity, γ µ represents the shear flexibility factor; n represents the noise; Solving by optimizing the objective function X , establish the following objective function: (12) in, D =log( V s 2 ρ ) - log µ m ; A =log( 1- ); X = γ µ ; V s represents the shear wave velocity; ρ Indicates density; µ m represents the rock matrix shear modulus; represents the porosity, γ µ represents the shear flexibility factor; Step 4: Use the L2 norm regularized least squares method to solve the established objective function and obtain the shear flexibility factor of the constraint solution. The objective function formula (12) is expanded as follows: (13) in: That is the L2 norm; D =log( V s 2 ρ ) - log µ m ; A =log( 1- ); X = γ µ ; V s represents the shear wave velocity; ρ Indicates density; µ m represents the rock matrix shear modulus; represents the porosity, γ µ represents the shear flexibility factor; λ is a constant; x is a vector X The scalar form of Taking the derivative of formula (13) and setting it to zero, the constraint solution of the shear flexibility factor is in the form of: (14) in, D =log( V s 2 ρ ) - log µ m ; A =log( 1- ); X = γ µ ; A T Representation matrix A The transpose of µ m represents the rock matrix shear modulus; V s represents the shear wave velocity; ρ Indicates density; represents the porosity, γ µ represents the shear flexibility factor; I is a diagonal matrix; λ is a constant.

2. The method for obtaining the shear compliance factor based on constraint solving according to claim 1, characterized in that: Rock matrix shear modulus in step 1 μ m The solution is obtained using the Voigt-Reuss-Hill averaging method.

3. The method for obtaining the shear compliance factor based on constraint solving according to claim 1, characterized in that: In step 2, a method combining median filtering and mean filtering is used to remove singular values ​​from the logging curve, and a random forest curve fitting method is used to complete the missing curves.

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