Shale anisotropy parameter inversion method based on logging data and electronic device

Abstract

The embodiment of the present application discloses a shale anisotropy parameter inversion method based on logging data and electronic equipment. Wherein, the shale anisotropy parameter inversion method based on logging data comprises: based on the obtained logging mineral component information, calculating the upper and lower boundaries of the mixture of multiple minerals to obtain the modulus variation range of the background medium; based on the obtained P-wave and S-wave velocities and density of the actual logging data, obtaining the stiffness coefficient of the corresponding actual logging data; obtaining the stiffness coefficient corresponding to the rock physical model; constructing a target function based on the error of the stiffness coefficient corresponding to the rock physical model and the stiffness coefficient of the actual logging data; obtaining the inversion parameter by using the grid search method based on the target function and the modulus variation range of the background medium; obtaining the elastic parameter based on the inversion parameter and the rock physical model, and obtaining the anisotropy parameter by combining the anisotropy parameter definition. The purpose of considering the anisotropy caused by the directional clay mineral and obtaining the actual theoretical support of the rock physics is achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of rock physics numerical simulation, and more particularly relates to a shale anisotropy parameter inversion method based on logging data and an electronic device. BACKGROUND

[0002] Shale contains various clay minerals, organic matter, clay particles arranged in a certain direction, and microcracks, which make the influence on the elastic parameters of shale complex. Compared with conventional sandstone and shale, shale has more complex mineral composition and pore structure, and stronger anisotropy. The anisotropy of shale mainly reflects the diversity of mineral composition, microcracks and the directional arrangement of clay mineral particles. The anisotropic elastic parameters of shale are widely used in seismic imaging, seismic data processing and reservoir characterization. However, the anisotropic parameters of shale cannot be directly measured by logging data. At present, Thomsen parameters are mainly used to characterize anisotropy. The inversion of shale anisotropy parameters is mainly based on the theory of shale rock physics model, and the actual core or logging data is used for constraint to obtain the corresponding anisotropy parameters.

[0003] In the process of implementing the present application, the inventors have found that at least the following problems exist in the prior art:

[0004] Due to the complexity of the rock physics model of shale, various factors need to be considered, and the existing rock physics model mostly only considers the influence of cracks and does not consider the anisotropy caused by directional clay minerals. Moreover, the existing inversion algorithm is mostly a statistical or probabilistic method, which lacks the support of rock physics theory. SUMMARY

[0005] Therefore, the embodiments of the present application provide a shale anisotropy parameter inversion method based on logging data and an electronic device, which at least solve the problems that the anisotropy caused by directional clay minerals is not considered and the rock physics theory is not supported in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a shale anisotropy parameter inversion method based on logging data, comprising:

[0007] Based on the obtained logging mineral composition information, the upper and lower boundaries of the mixture of multiple minerals are calculated to obtain the range of the modulus variation of the background medium;

[0008] Based on the actual logging data obtained by the P-wave and S-wave velocities and the density, the stiffness coefficient of the corresponding actual logging data is obtained;

[0009] Based on the stiffness coefficient of the actual logging data, the stiffness coefficient of the corresponding rock physics model is obtained;

[0010] constructing a target function based on the error between the stiffness coefficient of the corresponding rock physical model and the stiffness coefficient of the actual logging data;

[0011] obtaining an inversion parameter by using a grid search method based on the target function and a background medium modulus variation range;

[0012] obtaining an elastic parameter based on the inversion parameter and the rock physical model, and obtaining an anisotropy parameter by combining the anisotropy parameter definition.

[0013] Optionally, the inversion parameter comprises:

[0014] a bulk modulus K0 of a background mineral skeleton, a Lame constant μ0, a crack density d, and a pore aspect ratio α of an ellipsoidal crack. c

[0015] Optionally, the stiffness coefficient of the corresponding rock physical model is obtained based on the P-wave velocity and the S-wave velocity and the density obtained from the actual logging data, and the stiffness coefficient of the corresponding rock physical model comprises:

[0016] obtaining the stiffness coefficient based on the P-wave velocity and the S-wave velocity and the density curve obtained from the actual logging data and the stiffness coefficient

[0017]

[0018] wherein ρ is the density curve, V p is the P-wave velocity, and V s is the S-wave velocity.

[0019] Optionally, the stiffness coefficient of the corresponding rock physical model is obtained based on the stiffness coefficient of the actual logging data, and the stiffness coefficient of the corresponding rock physical model comprises:

[0020] calculating the stiffness coefficient of the corresponding rock physical model by using the following formula,

[0021]

[0022]

[0023] wherein C 33Model and C 44Model are the stiffness coefficients of the rock physical model, λ and μ are the Lame constants, and U3 and U1 are the fluid-filled weak inclusions.

[0024] Optionally, in the step of constructing the target function based on the error between the stiffness coefficient of the corresponding rock physical model and the stiffness coefficient of the actual logging data, the target function is:

[0025]

[0026] ​wherein K0 is the bulk modulus of the background mineral matrix, μ0 is the Lame's constant, d c is the fracture density and is the pore aspect ratio of the alpha ellipsoidal fracture, and J is the objective function.

[0027] Optionally, the elastic parameter is obtained based on the inversion parameter and the rock physics model, and the anisotropy parameter is defined by combining the anisotropy parameter, comprising:

[0028] The inversion parameter is substituted into the rock physics model to obtain the elastic parameter.

[0029] The anisotropy parameter is obtained based on the definition of the anisotropy parameter and the elastic parameter.

[0030] Optionally, the rock physics model is a Hudson-Cheng model.

[0031] The rock physics model is:

[0032] The first-order approximation of the Hudson-Cheng model is used by ignoring the second-order correction of the Hudson-Cheng model.

[0033] Optionally, the inversion parameter is obtained by using a grid search method based on the objective function and the range of the modulus of the background medium, comprising: setting a search interval.

[0034] The search interval is set, comprising:

[0035] The range of the volume modulus and the shear modulus of the mixed background medium and the pore aspect ratio is set as the search interval.

[0036] In a second aspect, the embodiments of the present application further provide an electronic device, comprising:

[0037] A memory storing executable instructions.

[0038] A processor running the executable instructions in the memory to implement the method of any one of the first aspect.

[0039] The present application obtains the range of the modulus of the background medium based on the obtained logging mineral composition information, then obtains the corresponding stiffness coefficient based on the compressional and shear wave velocities in the logging data, and obtains the stiffness coefficient of the rock physics model, and constructs an objective function to obtain the anisotropy parameter. The logging mineral composition information is considered, so that the anisotropy caused by the directional clay mineral is considered, and the purpose of obtaining the actual theoretical support of the rock physics is achieved.

[0040] Other features and advantages of the present application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0041] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which like reference characters refer to like parts throughout the figures, and wherein:

[0042] Figure 1 A flow chart of a shale anisotropy parameter inversion method based on logging data according to an embodiment of the present application is shown;

[0043] Figures 2a to 2f A schematic diagram of logging curve data in the prior art is shown;

[0044] Figures 3a to 3i A schematic diagram of the effect of the prior inversion method is shown;

[0045] Figures 4a to 4i A schematic diagram of the search effect of the inversion method according to an embodiment of the present application fixing the pore aspect ratio to 0.01 is shown;

[0046] Figures 5a to 5i A schematic diagram of the search effect of the inversion method according to an embodiment of the present application fixing the pore aspect ratio to 0.025 is shown;

[0047] Figures 6a to 6i A schematic diagram of the effect of the inversion method according to an embodiment of the present application optimizing and adjusting the pore aspect ratio is shown. DETAILED DESCRIPTION

[0048] Preferred embodiments of the present application will be described in more detail below. Although the following describes preferred embodiments of the present application, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0049] Embodiment One:

[0050] As shown in the drawings, a shale anisotropy parameter inversion method based on logging data includes: Figure 1

[0051] Step S101: Based on the obtained logging mineral composition information, the upper and lower boundaries of a plurality of mineral mixtures are calculated to obtain the range of background medium modulus variation;

[0052] Step S102: Based on the obtained P-wave and S-wave velocities and density of the actual logging data, the stiffness coefficient of the corresponding actual logging data is obtained;

[0053] Optionally, based on the obtained P-wave and S-wave velocities and density of the actual logging data, the stiffness coefficient of the corresponding actual logging data is obtained, including:

[0054] Based on the obtained P-wave and S-wave velocities and density curves of the actual logging data, the stiffness coefficient is obtained​ and stiffness coefficient

[0055]

[0056] Where ρ is the density curve, V p is the longitudinal wave velocity, V s is the shear wave velocity.

[0057] The density curve is the density logging curve of the logging data in the study area, and is an attribute curve that can be calculated from the original logging data.

[0058] The density curve and the P- and S-wave velocities can be calculated together to obtain and In the anisotropic parameter inversion framework, they can match the appropriate background medium modulus within the range of background medium modulus variation.

[0059] Generally speaking, the range of variation of the background medium modulus is a range, and the density curve is a parameter in this overall inversion framework. These parameters are used together to actually determine the size of the background medium modulus.

[0060] Step S103: obtaining a stiffness coefficient of a rock physics model based on the stiffness coefficient of the actual well logging data;

[0061] Optionally, obtaining the stiffness coefficient of the rock physics model based on the stiffness coefficient of the actual well logging data includes:

[0062] The elastic modulus of the rock physics model is calculated using the following formula:

[0063]

[0064]

[0065] Among them, C 33Model and C 44Model is the elastic modulus of the rock physics model, λ and μ are both Lame' constants, and U3 and U1 are both fluid-filled weak inclusions.

[0066] Step S104: constructing an objective function based on the error between the stiffness coefficient of the corresponding rock physics model and the stiffness coefficient of the actual logging data;

[0067] Optionally, in constructing an objective function based on the error between the stiffness coefficient of the corresponding rock physics model and the stiffness coefficient of the actual logging data, the objective function is:

[0068]

[0069] where K0 is the bulk modulus of the background mineral matrix, μ0 is the Lame's constant, d c is the fracture density and α is the aspect ratio of the α-elliptical fractures, and J is the objective function.

[0070] Step S105: obtaining the inversion parameter by using the grid search method based on the objective function and the range of the background medium modulus;

[0071] Optionally, the inversion parameter comprises:

[0072] the bulk modulus K0 of the background mineral matrix, the Lame's constant μ0, the fracture density d c and the aspect ratio α of the elliptical fracture.

[0073] Step S106: obtaining the elastic parameter based on the inversion parameter and the rock physics model, and obtaining the anisotropic parameter by combining the definition of the anisotropic parameter.

[0074] Optionally, obtaining the elastic parameter based on the inversion parameter and the rock physics model, and obtaining the anisotropic parameter by combining the definition of the anisotropic parameter, comprises:

[0075] substituting the inversion parameter into the rock physics model to obtain the elastic parameter;

[0076] obtaining the anisotropic parameter based on the definition of the anisotropic parameter and the elastic parameter.

[0077] Optionally, the rock physics model is the Hudson-Cheng model, which comprises: ignoring the second-order correction of the Hudson-Cheng model, and using the first-order approximation of the Hudson-Cheng model.

[0078] obtaining the inversion parameter by using the grid search method based on the objective function and the range of the background medium modulus, comprises: setting a search interval;

[0079] Optionally, the setting of the search interval comprises:

[0080] setting the range of the mixed background medium bulk modulus, the shear modulus upper and lower boundaries and the aspect ratio as the search interval.

[0081] Embodiment two:

[0082] The Hudson-Cheng model used in the rock physics model is a second-order approximation model of the equivalent modulus of fractures based on elliptical inclusions constructed by Hudson (1981) and Cheng (1993). The model assumes that the fracture density in the complete shale formation is very small, and thus ignores the second-order correction of the background medium in the original formula. The first-order approximation is used in this embodiment. The corresponding stiffness coefficient is given in the following format:

[0083]

[0084] where the equivalent modulus can be obtained from well logs. is the isotropic background medium modulus, which is assumed to contain spherical inclusions of the rock matrix and the filling water or brine. is the first order correction term due to the presence of penny-shaped cracks. The background medium modulus is given by

[0085]

[0086]

[0087]

[0088] where λ and μ are the Lame's constants in the isotropic medium. The first order correction term is given by

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] where the crack density is determined by the crack porosity φ s and the pore aspect ratio α of the elliptical cracks. For fluid-filled weak inclusions:

[0095]

[0096]

[0097] where

[0098]

[0099] where K f is the bulk modulus of the fluid, and K0is the bulk modulus of the background mineral matrix. In a vertical well, the compressional and shear wave velocities and the density curve provide the and equivalent moduli, where

[0100]

[0101]

[0102] The idea of this embodiment is to improve the existing anisotropy parameter inversion framework according to the mineral composition information of the logging curve, so that the anisotropy parameters of shale data can be calculated in the case of logging curve with only mineral composition and P-S wave velocity information. The main method adopted is the optimal matching of model data and actual data.

[0103] The specific implementation steps are as follows:

[0104] 1. For multi-mineral calculation, the upper and lower boundaries of the mixed minerals need to be calculated in combination with the logging mineral composition information to obtain the medium modulus variation range of the background, wherein the mineral composition of clay needs to be reasonably estimated. The upper and lower boundaries calculated can contain multiple possible cases.

[0105] 2. In combination with the P-S wave velocity and density of the logging data, the corresponding stiffness coefficients can be calculated. and

[0106] 3. The stiffness coefficients C 33Model and C 44Model of the rock physics model are calculated by using formula 13 and formula 14.

[0107]

[0108]

[0109] 4. The mixed background medium volume modulus and shear modulus upper and lower boundaries and the pore aspect ratio variation range are set as the search interval by using the grid search method, then the fluid is replaced, and the optimal values of the volume modulus, shear modulus and pore aspect ratio within the 5% range are calculated by the constraint of formula 15 target function. The volume modulus K0, Lame constant μ0 and crack density d c of the background mineral skeleton and the pore aspect ratio α of the ellipsoidal crack can be calculated.

[0110]

[0111] Formula 15 minimizes the difference between the actual observation and the model calculation of the vertical P-S wave velocity. For this inversion, the only adjustable parameter is the boundary and the step size of the search space for each model parameter. The boundary set by the algorithm is as wide as possible to meet the model parameters under various conditions. The search step size is small enough to have the highest accuracy for the final solution.

[0112] 5. The parameters obtained by inversion are substituted into the Hudson-Cheng model to calculate the elastic parameters of the model. The anisotropy parameters are calculated by using the definition of Thomsen anisotropy parameters.​

[0113] In a specific application scenario, the invention was used to simulate the anisotropic parameter inversion of logging data on a shale logging curve in Sichuan, achieving good calculation results.

[0114] Figures 2a to 2f The well data is from the Sichuan Basin in China, primarily focusing on the Longmaxi Formation. The well log curves are distributed from a depth of 4050m to 4250m. For ease of analysis, the well logs are divided into 13 sections from top to bottom. Each section contains information on P-wave and S-wave velocities, density, porosity, gamma-ray curves, and mineral composition. This low-porosity log curve is primarily composed of clay, calcite, and quartz, with organic matter and pyrite intercalation, representing a relatively complex multi-mineral model. The density curve is relatively flat, and only minerals below 4180m can be distinguished.

[0115] Figures 3a to 3i From left to right are shear wave velocity, compressional wave velocity, fracture density, matrix shear modulus, matrix Lame modulus, anisotropy parameter δ, relative error in compressional and shear wave velocities, anisotropy parameter ε, and lithologic composition. The matrix elastic modulus is calculated using the average of the Hashin-Shtrikman bounds, and the anisotropy parameters are derived using conventional methods. The pore aspect ratio coefficient was empirically selected as 0.01. Information such as fracture density is miscalculated, and the errors in compressional and shear wave velocities are too large to be reliable, indicating that the model is no longer suitable for prediction.

[0116] Figures 4a to 4i To use the Hashin-Shtrikman mixture of solid as the upper and lower bounds to search for K0, μ0, and fracture density, the pore aspect ratio α is fixed to 0.01. Then the well logging data is inverted to obtain Figures 5a to 5i .from Figures 4a to 4i It can be seen that the error in the shear wave velocity is large, while the error in the longitudinal wave velocity is acceptable. The bulk modulus and shear modulus of the matrix tend to be minimum.

[0117] Figures 5a to 5i The pore aspect ratio α was adjusted to 0.025. The pore aspect ratio has a significant impact on the search results. The bulk modulus and shear modulus of the matrix show that changing the pore aspect ratio causes these moduli to approach the middle of the elastic limit, which is more consistent with rock physics. Figures 5a to 5i The error of the P-wave velocity is significantly reduced, and the error of the S-wave velocity is also smaller. A reasonable pore aspect ratio is very important for the inversion result.

[0118] Figures 6a to 6iThe results of the optimization adjustment calculation of the pore aspect ratio are shown in the figure. The errors of the P and S wave velocities calculated in this way are relatively small, and the well logging curves can be seen to be nearly completely overlapped. The information of each well logging data point can obtain the corresponding value and the corresponding fracture density belongs to the reasonable interval. By using the Hashin-Shtrikman upper and lower bounds as the search interval, K0 and μ0 and α are inverted to obtain the anisotropic parameters. Here, two methods are used to control the P and S wave velocities, and the error rate of the calculation results is very low. Overall, the dependence on the interval is relatively large, and the calculation amount is relatively large compared with the previous method.

[0119] Example three:

[0120] The electronic device provided in the embodiments of the present application includes a memory and a processor,

[0121] The memory stores executable instructions.

[0122] The processor runs the executable instructions in the memory to implement the shale anisotropic parameter inversion method based on well logging data.

[0123] The memory is used to store non-transitory computer readable instructions. Specifically, the memory can include one or more computer program products, which can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a flash memory, etc.

[0124] The processor can be a central processing unit (CPU) or other forms of processing units with data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. In an embodiment of the present application, the processor is used to run the computer readable instructions stored in the memory.

[0125] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience effect, the embodiments can also include well-known structures such as communication buses, interfaces, etc., which should also be included in the protection scope of the present application.

[0126] The detailed description of the embodiments can refer to the corresponding description in the foregoing embodiments, which will not be repeated here.

[0127] The embodiments of the present application provide a computer readable storage medium storing a computer program, which is executed by a processor to implement a shale anisotropic parameter inversion method based on well logging data.

[0128] The computer readable storage medium according to an embodiment of the present application has non-transitory computer readable instructions stored thereon. When the non-transitory computer readable instructions are run by a processor, all or part of the steps of the method according to the embodiments of the present application described above are performed.

[0129] The computer readable storage medium described above includes, but is not limited to, optical storage media (for example, CD-ROM and DVD), magneto-optical storage media (for example, MO), magnetic storage media (for example, magnetic tape or a removable hard disk), media with a built-in rewritable nonvolatile memory (for example, a memory card), and media with a built-in ROM (for example, a ROM cartridge).

[0130] The embodiments of the present application have been described above with the foregoing description, which is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A shale anisotropy parameter inversion method based on well logging data, characterized in that: include: Based on the mineral composition information obtained from well logging, the upper and lower boundaries of multiple mineral mixtures are calculated to obtain the range of background medium modulus variation; Based on the P-wave and S-wave velocities and densities obtained from actual well logging data, the stiffness coefficients of the corresponding actual well logging data are obtained; Obtaining a stiffness coefficient of a corresponding rock physics model based on the stiffness coefficient of the actual well logging data; constructing an objective function based on an error between the stiffness coefficient of the corresponding rock physics model and the stiffness coefficient of actual logging data; Based on the objective function and the range of variation of the background medium modulus, a grid search method is used to obtain the inversion parameters; Obtaining elastic parameters based on the inversion parameters and the rock physics model, and obtaining anisotropic parameters in combination with anisotropic parameter definitions; The stiffness coefficient of the corresponding actual logging data is obtained based on the P-wave and S-wave velocities and densities obtained from the actual logging data, including: Based on the P-wave and S-wave velocity and density curves obtained from actual logging data, the stiffness coefficient is obtained. and stiffness coefficient Where ρ is the density curve, V p is the longitudinal wave velocity, V s is the shear wave velocity; The obtaining of the stiffness coefficient of the corresponding rock physics model based on the stiffness coefficient of the actual well logging data includes: The stiffness coefficient corresponding to the rock physics model is calculated using the following formula: Among them, C 33Model and C 44Model is the stiffness coefficient of the rock physics model, λ and μ are both Lame' constants, U3 and U1 are both fluid-filled weak inclusions; The error between the stiffness coefficient of the corresponding rock physics model and the stiffness coefficient of the actual logging data is used to construct the objective function, and the objective function is: Where K0 is the bulk modulus of the background mineral skeleton, μ0 is the Lame' constant, and d c is the crack density and is the pore aspect ratio of the ellipsoidal crack, and J is the objective function.

2. The shale anisotropy parameter inversion method based on well logging data according to claim 1, characterized in that: The inversion parameters include: The bulk modulus K0, Lame' constant μ0, and crack density d of the background mineral skeleton c and the pore aspect ratio α of the ellipsoidal fracture.

3. The shale anisotropy parameter inversion method based on well logging data according to claim 1, characterized in that: The elastic parameters are obtained based on the inversion parameters and the rock physics model, and the anisotropic parameters are obtained in combination with the anisotropic parameter definition, including: Substituting the inversion parameters into a rock physics model to obtain elastic parameters; Based on the definition of anisotropic parameters and the elastic parameters, anisotropic parameters are obtained.

4. The shale anisotropy parameter inversion method based on well logging data according to claim 3 is characterized in that: The rock physics model is the Hudson-Cheng model; The rock physics model is: Ignore the second-order correction of the Hudson-Cheng model and use the first-order approximation of the Hudson-Cheng model.

5. The shale anisotropy parameter inversion method based on well logging data according to claim 1, characterized in that: The method of obtaining inversion parameters by using a grid search method based on the objective function and the range of change of the background medium modulus includes: setting a search interval; The setting of the search interval includes: The upper and lower boundaries of the bulk modulus and shear modulus of the mixed background medium and the range of pore aspect ratio are set as the search interval.

6. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the method according to any one of claims 1 to 5.

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