Shear wave prediction method for organic-rich shales based on Xu-white model with variable pore aspect ratio

Abstract

The present invention provides a shear wave prediction method for organic-rich shale based on the Xu-white model with a variable pore aspect ratio, the method comprising the following steps: according to the characteristics of kerogen, it is simultaneously equivalent to matrix minerals and pore fluids; based on the Xu-white model, the Voigt-Reuss-Hill average theory is introduced to calculate the equivalent elastic modulus of the organic-rich mineral matrix, the Wood formula is used to calculate the equivalent bulk modulus of the mixed fluid, the Gassmann fluid substitution equation is used to calculate the bulk modulus and shear modulus of the fluid-saturated organic-rich shale, and the density, porosity, mud content and longitudinal wave velocity of the fluid-saturated shale rock are used as the variable pore aspect ratio to construct the Xu-White model with a variable pore aspect ratio, and then predict the shear wave velocity. The Xu-White model constructed by the present invention can be effectively applied to the prediction of shear wave velocity of organic-rich shale reservoirs, takes into account the special elastic properties of kerogen, enhances the applicability of the model, avoids the error caused by the rock physical parameters being constants, and improves the accuracy of the prediction results.

Description

Technical Field

[0001] The invention relates to the technical field of well logging of shale gas reservoir rocks, and in particular to a shear wave prediction method for organic-rich shale based on a Xu-white model with a variable pore aspect ratio. Background Art

[0002] Shear wave velocity is essential in prestack seismic AVO analysis and prestack seismic inversion, and is also an important parameter for fracture fluid identification, reservoir physical property analysis, and rock physical property inversion. Due to the limitations of well location conditions, technical means, and logging costs, shear wave velocity information is relatively scarce in actual logging data, so accurate prediction of shear wave velocity information is of great significance. In order to obtain accurate shear wave velocity information, many scholars have used conventional logging data and related theoretical methods to predict shear wave velocity, which are mainly divided into empirical formula method, rock physics model method, and machine learning intelligent prediction method.

[0003] Methods based on empirical formulas establish a simple and direct relationship between shear wave velocity and logging data. Castagna et al. established a linear relationship between shear wave velocity and compressional wave velocity in water-bearing clastic silicate rocks; Han et al. improved the relationship between velocity, porosity and clay content and gave new equations at different pressures. Castagna and Backus established the relationship between shear wave velocity and compressional wave velocity by fitting the experimental data with the least squares polynomial; Yan et al. combined the Han equation with the Xu-White model to obtain a new fitting equation and considered the effect of aspect ratio on velocity; Vernik et al. studied a hybrid technique based on the Greenberg-Castagna method, taking kerogen into account and improving the shear wave velocity prediction method. In recent years, intelligent prediction methods based on machine learning and artificial neural networks have emerged. Intelligent prediction methods can obtain accurate shear wave velocity information, but these methods require real data of the target parameters to train the system, so there is a lack of extrapolation beyond the range of training data. Rock physics models describe the influence of microstructure on the overall properties of rocks, so the relationship between velocity or modulus and microstructural properties can be established. Berryman proposed the self-compatible equivalent medium model (SCM) assuming that rocks are mixtures of ellipsoidal components with different aspect ratios; Xu and White established the relationship between the velocity of mudstone and the porosity and clay content and proposed the Xu-White model; Keys and Xu introduced the approximate equation of dry rock modulus into the Xu-White model; Bai Junyu modified the Xu-White model by replacing the fixed aspect ratio with a variable aspect ratio; Guo and Li estimated the shear wave velocity of Barnett shale using the SCM model that combines given rock properties, aspect ratio and volume fraction of each component; Sohail and Hawkes evaluated the empirical formula method and rock physics model method for predicting shear wave velocity, and believed that the modified Xu-White model is the best option currently available.

[0004] Kerogen is an important indicator of the oil and gas content of shale reservoirs. Its bulk modulus and shear modulus are small. It is neither a solid mineral nor a fluid. The bulk modulus is similar to that of water, which is quite different from that of solid minerals. However, it has a shear modulus. In conventional rock physics modeling, kerogen is equivalent to a fluid. The elastic properties of the rock matrix and the properties of the pore fluid will change with the pressure and temperature of the actual surrounding rock. In the conventional Xu-White model, a fixed pore aspect ratio is used, ignoring the changes in rock physical parameters with depth, resulting in inaccurate prediction results. Summary of the invention

[0005] To address the above-mentioned defects, the present invention provides a shear wave prediction method for organic-rich shale based on the Xu-white model with variable pore aspect ratio. The method can be effectively applied to the prediction of shear wave velocity in organic-rich shale reservoirs, takes into account the special elastic properties of kerogen, avoids the errors caused by the constants of rock physical parameters, and improves the accuracy of the prediction results.

[0006] A shear wave prediction method for organic-rich shale based on the Xu-white model with a variable pore aspect ratio, the method comprising the following steps: S1: according to the characteristics of kerogen, it is simultaneously equivalent to matrix minerals and pore fluids; the mineral matrix is ​​equivalent to a mixture of inorganic minerals and kerogen, and the pore fluid is equivalent to a mixture of water, gas and kerogen, wherein the kerogen is evenly distributed in the mineral matrix and the pore fluid according to the volume fraction; S2: based on the Xu-white model, the Voigt-Reuss-Hill average theory is introduced to calculate the equivalent elastic modulus of the organic-rich mineral matrix, the equivalent bulk modulus of the mixed fluid is calculated using the Wood formula, the bulk modulus and shear modulus of the fluid-saturated organic-rich shale are calculated using the Gassmann fluid substitution equation, and the variable pore aspect ratio is constructed with the density, porosity, mud content and P-wave velocity of the fluid-saturated shale rock as constraints, so as to predict the shear wave velocity.

[0007] In one embodiment of the present invention, in step S2, the equivalent elastic modulus of the organic-rich mineral matrix obtained by introducing the Voigt-Reuss-Hill average theory is as follows:

[0008]

[0009] In the formula, i is an inorganic mineral in the matrix, M i is the modulus corresponding to inorganic minerals, f i is the volume fraction of inorganic minerals, M k is the modulus of kerogen, f k is the volume fraction of kerogen, M H is the equivalent elastic modulus.

[0010] In one embodiment of the present invention, in step S2, the equivalent bulk modulus of the mixed fluid calculated using the Wood formula is as follows:

[0011]

[0012] In the formula, f w 、f g 、f k are the volume fractions of water, gas and kerogen, respectively, K f , K w , Kg and K k are the equivalent bulk modulus of the mixed fluid, the bulk modulus of water, the bulk modulus of gas, and the bulk modulus of kerogen, respectively, where f w =S w is the water saturation,

[0013] In one embodiment of the present invention, in step S2, the density of the fluid-saturated shale rock is given by the following formula:

[0014] ρ=(1-φ)ρ0+φρ f

[0015]

[0016]

[0017] In the formula, ρ0 and ρ f are the density of the mineral matrix and the density of the saturated fluid, respectively; φ is the total porosity; ρ s , c , gas , water and ρ k are the densities of sand, clay, air, water and kerogen respectively; f s 、f c and f k are the volume fraction of sand, clay and kerogen.

[0018] In one embodiment of the present invention, the calculation formula of the sand-related pore aspect ratio is as follows:

[0019] α s =0.17114-0.24477φ+0.004314f c ;

[0020] In the formula, α s is the aspect ratio of sand-related pores; φ is the total porosity; f c is the volume fraction of clay.

[0021] In one embodiment of the present invention, the objective function is constructed as follows, with the density, porosity, shale content and P-wave velocity of the fluid-saturated shale rock as the constraint conditions for the variable pore aspect ratio:

[0022] F(f c ,φ,α s , α c ,ρ)=|V p -V pc |

[0023] In the formula, f cis the volume fraction of clay; φ is the total porosity; α s is the sand-related pore aspect ratio; α c is the aspect ratio of clay-related pores; ρ is the density of fluid-saturated shale rock; V p is the P-wave velocity in conventional logging curve, V pc is the longitudinal wave velocity calculated using the organic-rich Xu-White model.

[0024] In one embodiment of the present invention, the simulated annealing particle swarm algorithm is used to solve the objective function, and the search parameter |V p -V pc | is the optimal solution to make the posterior distribution F(f c ,φ,α s , α c ,ρ) is maximum.

[0025] In summary, the present invention provides a method for predicting shear waves in organic-rich shale based on the Xu-white model with variable pore aspect ratio. The beneficial effects of the present invention are:

[0026] According to the characteristics of kerogen, the present invention simultaneously equates it to matrix minerals and pore fluids, introduces the Gassmann fluid substitution equation, the Voigt-Reuss-Hill average theory, and the Wood formula based on the Xu-white model, and uses the simulated annealing particle swarm algorithm (SA-PSO) to solve the variable pore aspect ratio constrained by density, porosity, shale content, and longitudinal wave velocity, and constructs the Xu-White model of the variable pore aspect ratio, thereby predicting the shear wave velocity. The effectiveness of the present invention is verified by testing the actual shale conventional logging data.

[0027] Furthermore, the organic-rich Xu-White rock physics model constructed by the present invention has good applicability, and the prediction result is significantly better than that of the Xu-White model method using a fixed pore aspect ratio. Under constraints, the pore aspect ratio inverted by the simulated annealing particle swarm algorithm can truly reflect the changes in the rock pore structure. The Xu-White model constructed under the condition of changing pore aspect ratio can be effectively applied to the prediction of shear wave velocity of organic-rich shale reservoirs, avoiding the errors caused by the rock physical parameters being constants and improving the accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is composed of organic-rich shale material.

[0029] Figure 2 Modeling process of Xu-white model for organic-rich shale.

[0030] Figure 3This is a flow chart for predicting shear wave velocity of organic-rich shale based on the Xu-White model with variable porosity ratio.

[0031] Figure 4 Conventional logging curve of target well location.

[0032] Figure 5 Information on mineral composition of formations at target well locations.

[0033] Figure 6 These are the results of the model calculation; among them, 6-1 is the clay-related pore aspect ratio curve; 6-2 is the model-calculated P-wave velocity curve, 6-3 is the actual P-wave velocity curve; 6-4 is the error analysis between the model-calculated P-wave velocity curve and the actual P-wave velocity curve.

[0034] Figure 7 Histogram of clay-related pore aspect ratio distribution.

[0035] Figure 8 Shear wave velocity prediction results based on the Xu-White model of organic-rich fixed pore aspect ratio; among them, 8-1 is the model predicted shear wave velocity curve, 8-2 is the actual shear wave velocity curve; 8-3 is the error analysis curve between the model predicted shear wave velocity curve and the actual shear wave velocity curve of well logging.

[0036] Fig. 9 Shear wave velocity prediction results of the Xu-White model based on the variable pore aspect ratio of kerogen fluid equivalent; among them, 9-1 is the model predicted shear wave velocity curve, 9-2 is the actual shear wave velocity curve; 9-3 is the error analysis curve between the model predicted shear wave velocity curve and the actual shear wave velocity curve of well logging.

[0037] Fig.10 Shear wave velocity prediction results based on the Xu-White model of organic-rich pore aspect ratio variation; 10-1 is the predicted shear wave velocity curve, 10-2 is the actual shear wave velocity curve; 10-3 is the error analysis curve between the model predicted shear wave velocity curve and the actual shear wave velocity curve of well logging.

[0038] In the figure, 1. pyrite; 2. pores; 3. kerogen; 4. calcite; 5. quartz; 6. clay. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0040] 1. Basic principles

[0041] 1.1 Xu-White rock physics model

[0042] Xu and White considered factors such as porosity, matrix properties, fluid and pore aspect ratio, and proposed the Xu-White model of sand-related pores with larger pore aspect ratios and clay-related pores with smaller pore aspect ratios based on the Gassmann fluid substitution equation, KT equation and differential equivalent medium theory (DEM). The formula is expressed as follows:

[0043] φ=φ s +φ c (1)

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] In order to avoid the problem of excessive iterative calculation in DEM differential calculation, Keys and Xu improved the Xu-White model and determined the rock skeleton elastic modulus by solving a first-order linear ordinary differential equation group. The expression is as follows:

[0050] K d =K0(1-φ) p (7)

[0051] μ d =μ0(1-φ) q (8)

[0052] In formula (1-8), φ, φ s ,φ c are total porosity, sand-related porosity, clay-related porosity, and f s and f c is the volume fraction of sand and clay, K d and K0 are the bulk moduli of the rock skeleton and rock mineral matrix, respectively, μ d and μ0 are the shear moduli of the rock skeleton and rock mineral matrix, respectively. ijij (α), T iijj (α) is a function of the pore aspect ratio α, and p and q are geometric factors.

[0053] The relationship between p and q is as follows:

[0054]

[0055]

[0056] In the formula, f l is the volume fraction of the relevant mineral components.

[0057] Berryman (1980) proposed the N-phase mixture self-consistent approximation model (SCA) to estimate the equivalent elastic modulus of multiphase media, and also gave the scalars required to calculate p and q.

[0058]

[0059]

[0060] in:

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] F6=1+A[1+fR(f+θ)]+B(1-θ)(3-4R) (18)

[0067]

[0068]

[0069] F9=A[(R-1)f-Rθ]+Bθ(3-4R) (21)

[0070] Some parameters in the above formula are obtained by the following formula:

[0071]

[0072]

[0073] In the formula, K j and μ j are the bulk modulus and shear modulus of each mineral component, K m and μ m are the bulk modulus and shear modulus of the mineral matrix, respectively.

[0074] 1.2 Shear wave velocity prediction

[0075] The Gassmann fluid substitution equation can predict the velocity of rock saturated by one fluid through the velocity of rock saturated by another fluid. It can also predict the velocity of rock saturated by fluid through the velocity of rock skeleton. The relationship is as follows:

[0076]

[0077] μ sat =μ d (twenty three)

[0078] Where: K sat and K1 are the bulk moduli of fluid-saturated rock and pore fluid, respectively, μ sat and μ d are the shear moduli of fluid-saturated rock and pore fluid, respectively.

[0079] According to the relationship between rock elastic parameters and rock seismic wave propagation velocity, the wave velocity can be expressed by the bulk modulus, shear modulus, and density of fluid-saturated rock:

[0080]

[0081]

[0082] Where ρ is the density of fluid-saturated rock, V p 、V s are the longitudinal and shear wave velocities of fluid-saturated rock.

[0083] 1.3 Prediction of shale shear wave velocity based on the improved Xu-White model

[0084] Kerogen is related to the oil and gas content of shale reservoirs, and maturity characterizes its occurrence form. The higher the maturity, the higher the degree of conversion into oil. When modeling, it is necessary to fully consider the occurrence form, porosity and aspect ratio of kerogen on shale. The pore structure of kerogen is extremely fine, so its pore structure problem is not considered in modeling. Figure 1 As shown, the present invention equates kerogen with lower maturity to matrix minerals, and kerogen with higher maturity to pore fluids. The matrix minerals are equated to a mixture of inorganic rock minerals such as quartz, feldspar and clay and kerogen, and the pore fluid is equated to a mixture of water, gas and kerogen, wherein the kerogen is evenly distributed to the matrix minerals and the pore fluid according to the volume fraction.

[0085] The Voigt-Reuss-Hill average theory can be used to obtain the equivalent elastic modulus of a mixture of N-phase minerals without considering the mineral geometry. The equivalent elastic modulus of the model matrix is ​​as follows:

[0086]

[0087] In the formula, i is an inorganic mineral in the matrix, M i is the modulus corresponding to inorganic minerals, f i is the volume fraction of inorganic minerals, M k is the modulus of kerogen, f k is the volume fraction of kerogen, M H is the Hill effective modulus.

[0088] The equivalent modulus of the shale pore fluid mixture can be calculated using the Wood formula as follows:

[0089]

[0090] In the formula, f w 、f g 、f k are the volume fractions of water, gas and kerogen, respectively, K f , K w , K g and K k are the equivalent bulk modulus of the mixed fluid, the bulk modulus of water, the bulk modulus of gas, and the bulk modulus of kerogen, respectively, where f w =S w is the water saturation,

[0091] The density of fluid-saturated shale rock is given by:

[0092] ρ=(1-φ)ρ0+φρ f (28)

[0093]

[0094]

[0095] In the formula, ρ0 and ρ f are the density of the mineral matrix and the density of the saturated fluid, respectively; ρ s , c , gas , water and ρ k are the densities of sand, clay, air, water, and kerogen, respectively.

[0096] The conventional Xu-White model uses a fixed pore aspect ratio, which cannot accurately describe the reservoir pore structure, especially when predicting rock physical parameters, there will be a large deviation. The clay-related pore aspect ratio has a great influence on the model prediction results. Using this feature, such as Figure 2 As shown, the present invention adopts the Xu-White model based on the condition of changing pore aspect ratio. The model uses density, porosity, mud content and longitudinal wave velocity as constraints, fits the changing clay-related pore aspect ratio, and initializes the Xu-White model with the changing pore aspect ratio.

[0097] Sams and Andrea gave a formula that takes into account the pore aspect ratio of sand relative to the shale content as follows:

[0098] α s =0.17114-0.24477φ+0.004314f c (31)

[0099] Taking density, porosity, shale content and P-wave velocity as constraints, the objective function is as follows:

[0100] F(f c ,φ,α s , α c ,ρ)=|V p -V pc | (32)

[0101] Among them, α c is the clay-related pore aspect ratio, V p is the velocity of the P- and S-waves in the conventional logging curve, V pc are the P- and S-wave velocities calculated using the organic-rich Xu-White model.

[0102] The simulated annealing particle swarm algorithm (SA-PSO) is a cluster optimization algorithm that can solve the problem of solving nonlinear objective functions in inversion. It is based on the idea of ​​simulated annealing and improves the local optimization ability of the particle swarm algorithm. It has been applied to the nonlinear inversion problem of reservoir description. It is applied to solve the objective function and searches for the parameter |V p -V pc | is the optimal solution to make the posterior distribution F(f c ,φ,α s , α c ,ρ) is maximum. Figure 3 This is a flow chart for predicting shear wave velocity of organic-rich shale based on the Xu-White model with variable porosity ratio.

[0103] A Xu-White model with changing pore aspect ratio is constructed in organic-rich shale. When the volume fraction, density and intrinsic modulus of sand, clay, kerogen and pores, and the aspect ratio of sand-related pores and clay-related pores are given, the elastic modulus of the mineral matrix, saturated fluid and skeleton of the rock are calculated through the model. The elastic modulus of fluid-saturated rock is calculated using the Gassmann fluid substitution equation, and finally the shear wave velocity is calculated using the velocity equation.

[0104] Example

[0105] The embodiment selects actual logging data from a well in the Jiaoshiba area in the southeast of the Sichuan Basin in southwestern China for processing and research. The study area is in a tectonic transition area, the main structure is weakly deformed, the structural layer morphology is basically the same, and the structural unit as a whole presents an anticline shape, with a wide and gentle top and steep wings. The target layer is located at the bottom of the Longmaxi Formation of the Silurian System, with a high organic matter content. The upper and lower strata are shales with poor porosity, which plays a good sealing role. The drilling results show that the thickness of the organic-rich shale is about 80-120 meters, the kerogen content at the bottom is high, and there is a stable distribution of about 20-40 meters of high-quality shale. This well section is a reservoir well section. The well logging data of the target well location is complete and has relevant geological information and core data of the well location. The measured shear wave velocity information can be used to compare the prediction results and verify the effectiveness of the method.

[0106] The target well location logging data provides conventional logging curves: gamma ray (GR), longitudinal wave velocity (V p ), shear wave velocity (V s ), density (ρ), porosity (φ), and water saturation (S w ),like Figure 4As shown in the figure. The gamma-ray line in the figure indicates the intensity of rock radioactivity, which indirectly reflects the radioactivity of the mineral components, and is generally negatively correlated with the velocity curve. The longitudinal and transverse wave velocity curves show a strong correlation, and the obvious velocity change can identify the 2354 and 2380 meter formation boundaries. The density curve shows a decreasing trend with increasing depth. The information of each mineral component within the well depth is as follows Figure 5 As shown, the formation minerals in the target area are mainly clay, quartz, calcite, kerogen and pyrite, and the pore fluids are water, gas and part of kerogen, among which the kerogen content gradually increases with depth.

[0107] According to the logging data of the target well location and the elastic parameters of each component, as shown in Table 1, the kerogen is divided equally by volume fraction, half of which is equivalent to the matrix mineral component and the other half is equivalent to the pore fluid. The elastic parameters of the organic-rich mineral matrix are calculated by the VRH average theory, and the Xu-White model of the variable pore aspect ratio of organic-rich shale is constructed. The equivalent modulus of the mixed fluid is calculated using the Wood formula, and the elastic modulus of the fluid-saturated organic-rich shale is calculated using the Gassmann fluid substitution equation. The longitudinal wave velocity V of the model is calculated using the velocity equation. pc , in the framework of simulated annealing particle swarm algorithm, density, porosity, shale content and P-wave velocity are used as constraints to search for parameter |V p -V pc |The optimal solution of the variable pore aspect ratio is obtained and the Xu-White model is initialized. The Xu-White model with a variable pore aspect ratio of organic-rich shale is constructed, and then the shear wave velocity of the model is predicted. The prediction results are then compared with those of the Xu-White model with a fixed pore aspect ratio to analyze the superiority of the method of the present invention.

[0108] Table 1 Mineral components and pore fluid elastic parameters

[0109]

[0110] The sand-related pore aspect ratio curve can be directly calculated by formula (31), and the clay-related pore aspect ratio curve can be fitted by formula (32). The results are shown in Figure 6 As shown, |V p -V pc |The curve fit is good, the error is stable and very small, Figure 7The distribution histogram of clay-related pore aspect ratio at the target well position is approximately normal distribution, which proves the validity of the obtained clay-related pore aspect ratio curve. It can be seen from the figure that the pore aspect ratio of the overburden rock (2330-2380 meters) varies widely, while the pore aspect ratio of the reservoir well section (2380-2415 meters) is relatively stable.

[0111] In order to verify the accuracy of the method of the present invention, the fixed pore aspect ratio Xu-White model of organic-rich shale, the variable pore aspect ratio Xu-White model and the variable pore aspect ratio Xu-White model equivalent to kerogen fluid are used to predict the shear wave velocity of the target well position, and the results are quantitatively evaluated by the root mean square error (RMSE) and the correlation coefficient (r). The evaluation formula is as follows:

[0112]

[0113]

[0114] Among them, i is the depth point, and V i is the predicted speed and actual speed, N is the total number of depth points, cov(·) is the covariance, and σ is the standard deviation of the speed.

[0115] The prediction results of the shear wave velocity at the target well position by the three methods are as follows: Figure 8-10 The error evaluation is shown in Table 2.

[0116] Table 2 Root mean square error (RMSE) and correlation coefficient (r) of Vs predicted by different models

[0117]

[0118] Comparing the above prediction results, the effectiveness of the method of the present invention in predicting shear wave velocity in the well logging area of ​​organic-rich shale reservoirs is verified. The prediction results based on the Xu-White model with fixed pore aspect ratio in organic-rich shale reservoirs are shown in Figure 2. Figure 8 As shown in the figure, the predicted shear wave velocity curve and the logging shear wave velocity curve have roughly the same change trend and high correlation. Because the model uses a fixed pore aspect ratio and does not consider the influence of rock pore aspect ratio and formation depth, surrounding rock pressure and rock properties, the root mean square error of the prediction result is large and there is a global error. The error of the reservoir section result gradually increases with the depth. The prediction results of the Xu-White model based on the pore aspect ratio of the kerogen equivalent fluid are shown in Figure 1. Fig. 9As shown in the figure, the predicted shear wave velocity curve is similar to the well logging shear wave velocity curve. Due to the neglect of the kerogen shear modulus, the root mean square error of the result is large and there is a global error, which is overall worse than the prediction result based on the Xu-White model with a fixed pore aspect ratio of organic-rich matter. The prediction results based on the Xu-White model with a variable pore aspect ratio of organic-rich matter are shown in the figure. Fig.10 Therefore, the predicted shear wave velocity curve in the figure has a high degree of fit with the logging shear wave velocity curve, and the correlation is greatly improved compared with the prediction results of the other two methods, the root mean square error is greatly reduced, and the global error is corrected. Compared with the Xu-White model with a changing pore aspect ratio based on kerogen equivalent fluid, it is shown that according to the particularity of kerogen, the applicability of constructing an organic-rich Xu-White rock physics model by simultaneously equating it to matrix minerals and pore fluids is good. The prediction result is significantly better than the Xu-White model method with a fixed pore aspect ratio, indicating that under constraints, the pore aspect ratio inverted by the simulated annealing particle swarm algorithm can truly reflect the changes in the rock pore structure. The Xu-White model constructed under the condition of changing pore aspect ratio can be effectively applied to the prediction of shear wave velocity of organic-rich shale reservoirs, avoiding the errors caused by the constant rock physical parameters, and verifying the accuracy of the method of the present invention.

[0119] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A shear wave prediction method for organic-rich shale based on the Xu-white model with variable pore aspect ratio, characterized in that: The method comprises the following steps: S1: Based on the characteristics of kerogen, it is equivalent to both mineral matrix and pore fluid; The mineral matrix is ​​equivalent to a mixture of inorganic minerals and kerogen, and the pore fluid is equivalent to a mixture of water, gas and kerogen, wherein the kerogen is evenly distributed in the mineral matrix and the pore fluid according to the volume fraction; S2: Based on the Xu-white model, the Voigt-Reuss-Hill average theory is introduced to calculate the equivalent elastic modulus of the organic-rich mineral matrix, the Wood formula is used to calculate the equivalent bulk modulus of the mixed fluid, and the Gassmann fluid substitution equation is used to calculate the bulk modulus and shear modulus of fluid-saturated organic-rich shale. The Xu-White model of variable pore aspect ratio is constructed with the density, porosity, shale content and P-wave velocity of fluid-saturated shale rock as constraints to predict the S-wave velocity. In step S2, the equivalent elastic modulus of the organic-rich mineral matrix obtained by introducing the Voigt-Reuss-Hill average theory is as follows: In the formula, i is an inorganic mineral in the matrix, M i is the modulus corresponding to inorganic minerals, f i is the volume fraction of inorganic minerals, M k is the modulus of kerogen, f k is the volume fraction of kerogen, M H is the equivalent elastic modulus; In step S2, the equivalent bulk modulus of the mixed fluid calculated using the Wood formula is as follows: In the formula, f w 、f g 、f k are the volume fractions of water, gas and kerogen, respectively, K f , K w , K g and K k are the equivalent bulk modulus of the mixed fluid, the bulk modulus of water, the bulk modulus of gas, and the bulk modulus of kerogen, respectively, where f w =S w is the water saturation, Taking the density, porosity, shale content and P-wave velocity of fluid-saturated shale rock as the constraints for varying pore aspect ratio, the objective function is constructed as follows: F(f c ,f,a s ,a c ,ρ)=|V p -V pc | In the formula, f c is the volume fraction of clay; φ is the total porosity; α s is the sand-related pore aspect ratio; α c is the aspect ratio of clay-related pores; ρ is the density of fluid-saturated shale rock; V p is the P-wave velocity in conventional logging curve, V pc is the longitudinal wave velocity calculated using the organic-rich Xu-White model.

2. The method for predicting shear waves of organic-rich shale based on the Xu-white model with variable pore aspect ratio according to claim 1, characterized in that: In step S2, the density of the fluid-saturated shale rock is given by the following formula: ρ=(1-φ)ρ0+φρ f In the formula, ρ0 and ρ f are the density of the mineral matrix and the density of the saturated fluid, respectively; φ is the total porosity; ρ s , c , gas , water and ρ k are the densities of sand, clay, air, water and kerogen respectively; f s 、f c and f k are the volume fractions of sand, clay and kerogen respectively.

3. The method for predicting shear waves of organic-rich shale based on the Xu-white model with variable pore aspect ratio according to claim 2, characterized in that: The calculation formula of sand-related pore aspect ratio is as follows: a s =0.17114-0.24477φ+0.004314f c In the formula, α s is the aspect ratio of sand-related pores; φ is the total porosity; f c is the volume fraction of clay.

4. The method for predicting shear waves of organic-rich shale based on the Xu-white model with variable pore aspect ratio according to claim 3 is characterized in that: The simulated annealing particle swarm algorithm is used to solve the objective function. p -V pc | is the optimal solution to make the posterior distribution F(f c ,φ,α s , α c ,ρ) is maximum.

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