Shale reservoir anisotropy parameter inversion method and device, electronic equipment and medium

Abstract

The invention discloses a shale reservoir anisotropy parameter inversion method and device, electronic equipment and a medium. The method comprises the following steps: establishing a mathematical statistical model according to shale core experimental test data, a logging elastic parameter curve and seismic data of a target reservoir; seismic elastic parameter inversion is carried out according to seismic, logging and horizon data, and sensitive parameters P epsilon and P gamma are obtained; constraining through a plurality of elastic parameters, and carrying out rock core, logging and earthquake multi-scale multi-parameter combined constraining; and substituting the sensitive parameters P epsilon and P gamma into the mathematical statistical model to obtain anisotropic parameters epsilon and gamma. Through multi-scale and multi-parameter combined constraint of core, logging and seismic data, mathematical statistical analysis is performed according to core elastic parameter data, a mathematical statistical model with anisotropic parameters is established, anisotropic parameter inversion is completed, operation is simple and easy to implement, and prediction precision is high.

Description

Technical Field

[0001] The present invention relates to the field of geophysical technologies, and more particularly, to a method, device, electronic device and medium for inverting anisotropic parameters of a shale reservoir. Background Art

[0002] The great success of unconventional shale gas resources exploitation in North America has led to a new revolution in the energy industry, and a boom in shale oil and gas exploration and development has also emerged in China. Compared with conventional reservoirs, due to the directional arrangement of clay minerals, organic matter components and microfractures, the shale reservoir has obvious anisotropy. Therefore, when evaluating the energy storage and production capacity of the shale reservoir, anisotropic parameters of the reservoir are usually used to improve the accuracy of the evaluation results.

[0003] However, in most work areas, the anisotropic parameters of shale are missing because the anisotropic parameters of shale cannot be directly obtained from well logging data. At present, the inversion of most shale anisotropic parameters is mainly completed by establishing a shale rock physics model based on core test data and / or well logging data.

[0004] Establishing a reliable shale rock physics model requires not only rock and mineral data, microstructural characteristic data, and elastic parameter data, but also a suitable calculation model to be selected according to the physical contact mode between each part. Due to the complex mineral composition, pore structure and organic matter distribution of the shale reservoir, there are problems such as large modeling difficulty, low prediction accuracy and high requirements for personnel experience in using the shale rock physics model to predict anisotropic parameters.

[0005] Therefore, it is necessary to develop a method, device, electronic device and medium for inverting anisotropic parameters of a shale reservoir.

[0006] The information disclosed in the background art part of the present invention is only intended to deepen the understanding of the general background art of the present invention, and should not be regarded as an admission or any form of implication that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0007] The present invention provides a method, device, electronic device and medium for inverting anisotropic parameters of a shale reservoir, which can perform multi-scale and multi-parameter joint constraints through core, well logging and seismic data, conduct mathematical statistics analysis based on core elastic parameter data, establish a mathematical statistics model with anisotropic parameters, and complete the inversion of anisotropic parameters, with simple and easy operation and high prediction accuracy.

[0008] In a first aspect, an embodiment of the present disclosure provides a method for inverting anisotropic parameters of a shale reservoir, including:

[0009] Establish a mathematical statistical model based on the experimental test data of shale cores, logging elastic parameter curves, and seismic data of the target reservoir;

[0010] Conduct seismic elastic parameter inversion based on seismic, logging, and horizon data to obtain the sensitive parameter P ε 、P γ ;

[0011] Perform joint constraints of core, logging, and seismic multi-scale and multi-parameter through the constraints of multiple elastic parameters;

[0012] Substitute the sensitive parameters P ε 、P γ into the above mathematical statistical model to obtain the anisotropic parameters ε and γ.

[0013] As a specific implementation manner of the embodiment of the present disclosure, establishing a mathematical statistical model based on the experimental test data of shale cores, logging elastic parameter curves, and seismic data of the target reservoir includes:

[0014] Calculate the elastic parameters and Thomsen anisotropic parameters in the direction perpendicular to the bedding according to the experimental test data of the core;

[0015] Evaluate the correlation between different elastic parameters and the two anisotropic parameters respectively with the Spearman rank correlation coefficient as the constraint, and select the two elastic parameters P ε 、P γ with the highest correlation as the sensitive parameters, and establish mathematical statistical models for predicting ε and γ respectively.

[0016] As a specific implementation manner of the embodiment of the present disclosure, the elastic modulus in the direction perpendicular to the bedding includes: longitudinal wave impedance, shear wave impedance, Lame constant, Young's modulus, shear modulus, Poisson's ratio, bulk modulus, longitudinal wave modulus.

[0017] As a specific implementation manner of the embodiment of the present disclosure, the calculation formula of the Thomsen anisotropic parameter ε is:

[0018]

[0019] where V p represents the longitudinal wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0020] As a specific implementation manner of the embodiment of the present disclosure, the calculation formula of the Thomsen anisotropic parameter γ is:

[0021]

[0022] where V s represents the shear wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0023] As a specific implementation manner of the embodiments of the present disclosure, assume that the number of variables X and Y is N, and the data x i and y i are sorted in ascending order, and the sorted positions are denoted as x i ' and y i ', and denote d i = x i '- y i ', then the Spearman rank correlation coefficient is:

[0024]

[0025] As a specific implementation manner of the embodiments of the present disclosure, through multiple elastic parameters for constraint, the multi-scale and multi-parameter joint constraint of core, logging, and seismic includes:

[0026] Combined with the actual situation of the research area, according to the reservoir structure characteristics, core distribution, and core experiment conditions, determine the relative error threshold between the core test data and the logging data, establish multi-parameter constraints between the core test data and the logging curves, and realize the coarsening of the core data to the logging scale.

[0027] In a second aspect, the embodiments of the present disclosure further provide a shale reservoir anisotropic parameter inversion device, including:

[0028] A modeling module that establishes a mathematical statistical model according to the shale core experiment test data, logging elastic parameter curves, and seismic data of the target reservoir;

[0029] An inversion module that performs seismic elastic parameter inversion according to seismic, logging, and horizon data to obtain sensitive parameters P ε and P γ ;

[0030] A constraint module that performs multi-scale and multi-parameter joint constraint of core, logging, and seismic through multiple elastic parameters for constraint;

[0031] A calculation module that substitutes the sensitive parameters P ε and P γ into the mathematical statistical model to obtain anisotropic parameters ε and γ.

[0032] As a specific implementation manner of the embodiments of the present disclosure, establishing a mathematical statistical model according to the core experiment test data, logging elastic parameter curves, and seismic data of the target reservoir includes:

[0033] According to the core experiment test data, calculate the elastic parameters in the direction perpendicular to the bedding plane and the Thomsen anisotropic parameters respectively;

[0034] Taking the Spearman rank correlation coefficient as a constraint, the correlations between different elastic parameters and two anisotropic parameters are evaluated respectively, and the two elastic parameters P ε and P γ with the highest correlations are selected as sensitive parameters, and mathematical statistical models for predicting ε and γ are established respectively.

[0035] As a specific implementation manner of the embodiment of the present disclosure, the elastic moduli in the direction perpendicular to the bedding include: longitudinal wave impedance, shear wave impedance, Lame constant, Young's modulus, shear modulus, Poisson's ratio, bulk modulus, longitudinal wave modulus.

[0036] As a specific implementation manner of the embodiment of the present disclosure, the calculation formula for the Thomsen anisotropic parameter ε is:

[0037]

[0038] where V p represents the longitudinal wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0039] As a specific implementation manner of the embodiment of the present disclosure, the calculation formula for the Thomsen anisotropic parameter γ is:

[0040]

[0041] where V s represents the shear wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0042] As a specific implementation manner of the embodiment of the present disclosure, assuming that the number of variables X and Y is N, the data x i and y i are re-sorted in ascending order, and the sorted positions are denoted as x i ' and y i ', and denoting d i =x i '-y i ', then the Spearman rank correlation coefficient is:

[0043]

[0044] As a specific implementation manner of the embodiment of the present disclosure, through multiple elastic parameters for constraint, the multi-scale and multi-parameter joint constraint of core, logging, and seismic includes:

[0045] Combined with the actual situation of the study area, according to the reservoir structure characteristics, core distribution, and core experiment conditions, determine the relative error threshold between the core test data and the logging data, establish a multi-parameter constraint between the core test data and the logging curves, and realize the coarsening of the core data to the logging scale.

[0046] In a third aspect, embodiments of the present disclosure further provide an electronic device, which includes:

[0047] a memory storing executable instructions;

[0048] a processor that runs the executable instructions in the memory to implement the anisotropic parameter inversion method for shale reservoirs described above.

[0049] In a fourth aspect, embodiments of the present disclosure further provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the anisotropic parameter inversion method for shale reservoirs.

[0050] The beneficial effects are as follows:

[0051] (1) The anisotropic parameter prediction method provided by the present invention only considers the mathematical statistical relationship between elastic parameters and anisotropic parameters and has nothing to do with the process mechanism, and has the advantages of simple operation and high prediction accuracy.

[0052] (2) The present invention directly establishes a prediction model for anisotropic parameters from core elastic experimental data in two directions of parallel bedding and perpendicular bedding, does not rely on complex rock physics models, has clear mathematical meaning, and high credibility.

[0053] (3) The present invention uses multi-scale and multi-parameter joint constraints of core, logging, and seismic data to ensure that the established anisotropic parameter prediction model can be reasonably extrapolated.

[0054] The methods and apparatuses of the present invention have other characteristics and advantages, which will be apparent from or will be described in detail in the accompanying drawings and subsequent specific embodiments incorporated herein. These accompanying drawings and specific embodiments together are used to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more obvious. Among them, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0056] Figure 1 A flowchart showing the steps of an anisotropic parameter inversion method for shale reservoirs according to an embodiment of the present invention is shown.

[0057] Figure 2 A schematic diagram showing the direction of a shale core according to an embodiment of the present invention is shown.

[0058] Figure 3a and Figure 3b respectively show schematic diagrams of the results of inverting the anisotropic parameters ε and γ according to an embodiment of the present invention.

[0059] Figure 4 shows a block diagram of an anisotropic parameter inversion device for a shale reservoir according to an embodiment of the present invention.

[0060] Description of reference numerals:

[0061] 201, modeling module; 202, inversion module; 203, constraint module; 204, calculation module. Detailed implementation manners

[0062] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0063] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that this example is only for facilitating the understanding of the present invention, and any specific details are not intended to limit the present invention in any way.

[0064] Example 1

[0065] Figure 1 shows a flowchart of the steps of an anisotropic parameter inversion method for a shale reservoir according to an embodiment of the present invention.

[0066] As Figure 1 shown, the anisotropic parameter inversion method for the shale reservoir includes: Step 101, establishing a mathematical statistical model based on the experimental test data of the shale core of the target reservoir, the logging elastic parameter curve, and the seismic data; Step 102, performing seismic elastic parameter inversion based on the seismic, logging, and horizon data to obtain the sensitive parameters P ε 、P γ ; Step 103, performing joint constraints of multi-scale and multi-parameters of the core, logging, and seismic through the constraints of multiple elastic parameters; Step 104, substituting the sensitive parameters P ε 、P γ into the mathematical statistical model to obtain the anisotropic parameters ε and γ.

[0067] In one example, establishing a mathematical statistical model based on the experimental test data of the core of the target reservoir, the logging elastic parameter curve, and the seismic data includes:

[0068] Calculating the elastic parameters in the direction perpendicular to the bedding and the Thomsen anisotropic parameters respectively according to the experimental test data of the core;

[0069] With the Spearman rank correlation coefficient as a constraint, the correlations between different elastic parameters and two anisotropic parameters are evaluated respectively, and the two elastic parameters P with the highest correlation are selected. ε and P γ are used as sensitive parameters, and mathematical statistical models for predicting ε and γ are established respectively.

[0070] In one example, the elastic moduli in the direction perpendicular to the bedding include: longitudinal wave impedance, shear wave impedance, Lame constant, Young's modulus, shear modulus, Poisson's ratio, bulk modulus, and longitudinal wave modulus.

[0071] In one example, the calculation formula for the Thomsen anisotropic parameter ε is:

[0072]

[0073] where V p represents the longitudinal wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0074] In one example, the calculation formula for the Thomsen anisotropic parameter γ is:

[0075]

[0076] where V s represents the shear wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0077] In one example, assuming the number of variables X and Y is N, the data x i and y i are sorted in ascending order, and the sorted positions are denoted as x i ' and y i '. Denote d i =x i '-y i ', then the Spearman rank correlation coefficient is:

[0078]

[0079] In one example, through the constraint of multiple elastic parameters, the multi-scale and multi-parameter joint constraints of core, logging, and seismic include:

[0080] Combined with the actual situation of the study area, according to the reservoir structure characteristics, core distribution, and core experiment conditions, determine the relative error threshold between the core test data and the logging data, establish multi-parameter constraints between the core test data and the logging curves, and realize the coarsening of the core data to the logging scale.

[0081] Specifically, data preparation includes experimental test data of the target reservoir shale core, logging elastic parameter curves of the target reservoir, and seismic data of the target reservoir. Among them, the core experimental test data should at least include density ρ, longitudinal and transverse wave velocities (V p(0°) , V s(0°) ) in the direction perpendicular to the bedding plane, and longitudinal and transverse wave velocities (V p(90°) , V s(90°) ) in the direction parallel to the bedding plane. Among them, the logging curves should at least include density ρ, longitudinal wave velocity V p(0°) and transverse wave velocity curve V s(0°) .

[0082] Establish a mathematical statistical model: According to the core experimental test data, calculate the commonly used elastic parameters and Thomsen anisotropy parameters ε, γ in the direction perpendicular to the bedding plane respectively. With the Spearman rank correlation coefficient as the constraint, evaluate the correlation between different elastic parameters and the two anisotropy parameters respectively, and select the elastic parameters P ε , P γ with the best correlation, and establish mathematical statistical models for predicting ε and γ respectively. Among them, the commonly used elastic moduli in the direction perpendicular to the bedding plane include but are not limited to: longitudinal wave impedance Ip(0°), transverse wave impedance Is(0°), Lame constant λ(0°), Young's modulus E(0°), shear modulus μ(0°), Poisson's ratio ν(0°), bulk modulus K(0°), longitudinal wave modulus M(0°).

[0083] The calculation formula for Thomsen anisotropy parameter ε is:

[0084]

[0085] The calculation formula for Thomsen anisotropy parameter γ is:

[0086]

[0087] The rank correlation coefficient between input parameters is calculated by the formula proposed by Spearman(1904). Specifically, let the number of variables X and Y be N, and reorder the data x i and y i in ascending order, and record the sorted positions as x i ' and y i '. Denote d i = x i ' - y i ', then the Spearman rank correlation coefficient is:

[0088]

[0089] Seismic elastic parameter inversion: Integrate seismic, logging, and horizon data to obtain pre-stack inversion results, and further transform to obtain ρ, V p(0°) 、V s(0°) and the elastic parameter P with the best selected correlation ε 、P γ 。

[0090] Multi-scale and multi-parameter joint constraint of core, logging, and seismic data: Use multiple elastic parameters for constraint to complete the coarsening from core data to logging scale and from logging data to seismic scale.

[0091] Among them, the coarsening of core data to logging scale includes that the user combines the actual situation of the study area, and according to the reservoir structure characteristics, core distribution, core experiment situation, etc., comprehensively evaluates and stipulates the relative error threshold between core test data and logging data, and establishes multi-parameter (ρ, V p(0°) 、V s(0°) ) constraints to ensure that the relative error of each parameter is within the threshold range, so as to realize the coarsening of core data to logging scale and ensure the prediction accuracy. The coarsening of logging data to seismic scale includes that the user confirms the accuracy of the seismic inversion results of multi-parameters (ρ, V p(0°) 、V s(0°) ) through the well-crossing profile.

[0092] Anisotropic parameter inversion: Further obtain anisotropic parameters ε, γ according to the mathematical statistical model and elastic parameter P ε 、P γ .

[0093] Example 2

[0094] The present invention also provides a device for inverting anisotropic parameters of shale reservoirs, including:

[0095] A modeling module that establishes a mathematical statistical model based on the shale core experiment test data, logging elastic parameter curves, and seismic data of the target reservoir;

[0096] An inversion module that performs seismic elastic parameter inversion based on seismic, logging, and horizon data to obtain sensitive parameters P ε 、P γ ;

[0097] A constraint module that performs multi-scale and multi-parameter joint constraint of core, logging, and seismic data through multiple elastic parameters;

[0098] A calculation module that substitutes the sensitive parameters P ε 、P γ into the mathematical statistical model to obtain anisotropic parameters ε, γ.

[0099] In one example, based on the core experimental test data, well logging elastic parameter curves, and seismic data of the target reservoir, a mathematical statistical model is established, including:

[0100] According to the core experimental test data, calculate the elastic parameters in the direction perpendicular to the bedding plane and the Thomsen anisotropy parameters respectively;

[0101] With the Spearman rank correlation coefficient as a constraint, evaluate the correlations between different elastic parameters and the two anisotropy parameters respectively, and select the two elastic parameters P ε 、P γ as sensitive parameters, and establish mathematical statistical models for predicting ε and γ respectively.

[0102] In one example, the elastic moduli in the direction perpendicular to the bedding plane include: longitudinal wave impedance, shear wave impedance, Lame constant, Young's modulus, shear modulus, Poisson's ratio, bulk modulus, and longitudinal wave modulus.

[0103] In one example, the calculation formula for the Thomsen anisotropy parameter ε is:

[0104]

[0105] where V p represents the longitudinal wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0106] In one example, the calculation formula for the Thomsen anisotropy parameter γ is:

[0107]

[0108] where V s represents the shear wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0109] In one example, assume the number of variables X and Y is N, and re - sort the data x i and y i in ascending order, and record the sorted positions as x i ' and y i ', and record d i =x i '-y i ', then the Spearman rank correlation coefficient is:

[0110]

[0111] In one example, through the constraint of multiple elastic parameters, the multi - scale and multi - parameter joint constraint of core, well logging, and seismic includes:

[0112] Combined with the actual situation of the study area, according to the reservoir structure characteristics, core distribution, and core experiment conditions, determine the relative error threshold between core test data and logging data, establish multi-parameter constraints between core test data and logging curves, and realize the coarsening of core data to the logging scale.

[0113] Specifically, data preparation: including shale core experiment test data of the target reservoir, logging elastic parameter curves of the target reservoir, and seismic data of the target reservoir. Among them, the core experiment test data should at least include density ρ, longitudinal and transverse wave velocities (V p(0°) , V s(0°) ) in the direction perpendicular to the bedding, and longitudinal and transverse wave velocities (V p(90°) , V s(90°) ) in the direction parallel to the bedding. Among them, the logging curves should at least include density ρ, longitudinal wave velocity V p(0°) and transverse wave velocity curve V s(0°) .

[0114] Establish a mathematical statistical model: According to the core experiment test data, calculate the commonly used elastic parameters and Thomsen anisotropic parameters ε, γ in the direction perpendicular to the bedding respectively. Using the Spearman rank correlation coefficient as a constraint, evaluate the correlation between different elastic parameters and the two anisotropic parameters respectively, and select the elastic parameters P ε , P γ with the best correlation, and establish mathematical statistical models for predicting ε and γ respectively. Among them, the commonly used elastic moduli in the direction perpendicular to the bedding include but are not limited to: longitudinal wave impedance Ip(0°), transverse wave impedance Is(0°), Lame constant λ(0°), Young's modulus E(0°), shear modulus μ(0°), Poisson's ratio ν(0°), bulk modulus K(0°), longitudinal wave modulus M(0°).

[0115] The calculation formula for Thomsen anisotropic parameter ε is:

[0116]

[0117] The calculation formula for Thomsen anisotropic parameter γ is:

[0118]

[0119] The rank correlation coefficient between input parameters is calculated by the formula proposed by Spearman (1904). Specifically: Let the number of variables X and Y be N, and reorder the data x i and y i in ascending order, and record the sorted positions as x i ' and y i ', and record d i = x i ' - yi ', the Spearman rank correlation coefficient is as follows:

[0120]

[0121] Seismic elastic parameter inversion: Integrating seismic, logging, and horizon data to obtain pre-stack inversion results, and further transforming to obtain ρ, V p(0°) , V s(0°) and the elastic parameter P with the best selected correlation ε , P γ .

[0122] Core, logging, and seismic multi-scale and multi-parameter joint constraint: Using multiple elastic parameters for constraint to complete the coarsening from core data to logging scale and from logging data to seismic scale.

[0123] Among them, the coarsening from core data to logging scale includes that the user combines the actual situation of the study area, and according to reservoir structure characteristics, core distribution, core experiment conditions, etc., after comprehensive evaluation, stipulates the relative error threshold between core test data and logging data, and establishes multi-parameter (ρ, V p(0°) , V s(0°) ) constraints between core test data and logging curves to ensure that the relative errors of each parameter are within the threshold range, so as to realize the coarsening from core data to logging scale and ensure the prediction accuracy. The coarsening from logging data to seismic scale includes that the user confirms the accuracy of the seismic inversion results of multi-parameters (ρ, V p(0°) , V s(0°) ) through the well-crossing section.

[0124] Anisotropic parameter inversion: Further obtaining anisotropic parameters ε, γ according to the mathematical statistical model and elastic parameters P ε , P γ .

[0125] Example 3

[0126] Figure 2 FIG. shows a schematic diagram of the shale core direction according to an embodiment of the present invention.

[0127] Data preparation: Including shale core experiment test data of the target reservoir, logging elastic parameter curves of the target reservoir, and seismic data of the target reservoir. The prepared core experiment test data includes density ρ, longitudinal and transverse wave velocities (V p(0°) , V s(0°) ) in the direction perpendicular to the bedding plane and longitudinal and transverse wave velocities (V p(90°) , V s(90°) ) in the direction parallel to the bedding plane. The schematic diagram of the core direction is as shown in Figure 2 . The logging curves include density ρ, longitudinal wave velocity V p(0°)and the shear wave velocity curve V s(0°) 。

[0128] Establish a mathematical statistical model: According to the core experimental test data, calculate the commonly used elastic parameters and Thomsen anisotropic parameters ε and γ in the direction perpendicular to the bedding respectively. With the Spearman rank correlation coefficient as the constraint, evaluate the correlation between different elastic parameters and the two anisotropic parameters respectively, and select the elastic parameter P with the best correlation ε 、P γ , and establish mathematical statistical models for predicting ε and γ respectively.

[0129] According to the magnitude of the rank correlation coefficient, screen out the elastic parameter P with the best correlation with ε ε as V p(0°) , and the established mathematical statistical model is: ε = 239.5 * V p (0°) -4.76 。 The elastic parameter P with the best correlation with γ γ is V s(0°) , and the established mathematical statistical model is: γ = 19.79 * V s (0°) -4.749 。

[0130] The commonly used elastic moduli calculated in the direction perpendicular to the bedding include: longitudinal wave impedance Ip(0°), shear wave impedance Is(0°), Lame constant λ(0°), Young's modulus E(0°), shear modulus μ(0°), Poisson's ratio ν(0°), bulk modulus K(0°), longitudinal wave modulus M(0°).

[0131] The calculation formula for Thomsen anisotropic parameter ε is:

[0132]

[0133] The calculation formula for Thomsen anisotropic parameter γ is:

[0134]

[0135] The rank correlation coefficients between different elastic parameters and the two anisotropic parameters are:

[0136] Let the number of variables X and Y be N, and reorder the data x i and y i in ascending order, and record the sorted positions as x i ' and y i ', and record d i = x i '- y i ', then the Spearman rank correlation coefficient is:

[0137]

[0138] Seismic elastic parameter inversion: Integrating seismic, logging, and horizon data to obtain pre-stack inversion results, and further transforming to obtain ρ, V p(0°) , V s(0°) and the elastic parameter V with the best correlation selected in Step 2 p(0°) , V s(0°) .

[0139] Multi-scale and multi-parameter joint constraint of core, logging, and seismic data: Using multiple elastic parameters for constraint to complete the coarsening from core data to logging scale and from logging data to seismic scale.

[0140] The coarsening of core data to logging scale includes that the user combines the actual situation of the study area, and according to the reservoir structure characteristics, core distribution, core experiment situation, etc., after comprehensive evaluation, stipulates that the relative error threshold between core test data and logging data is 10%, and establishes ρ, V p(0°) , V s(0°) constraints to ensure that the relative error of each parameter is within the threshold range, thereby realizing the coarsening of core data to logging scale.

[0141] The coarsening of logging data to seismic scale includes that the user confirms the accuracy of the seismic inversion results of multiple parameters (ρ, V p(0°) , V s(0°) ) through the well-crossing profile.

[0142] Figure 3a and Figure 3b respectively show schematic diagrams of the inversion results of anisotropic parameters ε, γ according to an embodiment of the present invention.

[0143] Anisotropic parameter inversion: According to the mathematical statistical model and elastic parameter V p(0°) , V s(0°) further obtain anisotropic parameters ε, γ, as Figure 3a , Figure 3b shown.

[0144] Example 4

[0145] Figure 4 shows a block diagram of an anisotropic parameter inversion device for a shale reservoir according to an embodiment of the present invention.

[0146] As Figure 4 shown, the anisotropic parameter inversion device for a shale reservoir includes:

[0147] Modeling module 201, which establishes a mathematical statistical model based on the shale core experiment test data, logging elastic parameter curves, and seismic data of the target reservoir;

[0148] The inversion module 202 performs seismic elastic parameter inversion based on seismic, logging, and horizon data to obtain the sensitive parameters P ε 、P γ ;

[0149] The constraint module 203 performs joint constraints on cores, logs, and seismic data at multiple scales and with multiple parameters by constraining through multiple elastic parameters;

[0150] The calculation module 204 substitutes the sensitive parameters P ε 、P γ into a mathematical statistical model to obtain the anisotropic parameters ε and γ.

[0151] As an alternative, based on the core experimental test data, logging elastic parameter curves, and seismic data of the target reservoir, a mathematical statistical model is established, including:

[0152] According to the core experimental test data, calculate the elastic parameters and Thomsen anisotropic parameters in the direction perpendicular to the bedding respectively;

[0153] With the Spearman rank correlation coefficient as the constraint, evaluate the correlations between different elastic parameters and the two anisotropic parameters respectively, and select the two elastic parameters P ε 、P γ with the highest correlations as sensitive parameters, and establish mathematical statistical models for predicting ε and γ respectively.

[0154] As an alternative, the elastic modulus in the direction perpendicular to the bedding includes: longitudinal wave impedance, shear wave impedance, Lame constant, Young's modulus, shear modulus, Poisson's ratio, bulk modulus, longitudinal wave modulus.

[0155] As an alternative, the calculation formula for the Thomsen anisotropic parameter ε is:

[0156]

[0157] where V p represents the longitudinal wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0158] As an alternative, the calculation formula for the Thomsen anisotropic parameter γ is:

[0159]

[0160] where V s represents the shear wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

[0161] As an alternative, assuming the number of variables X and Y is N, the data x i and yi Re - sort in ascending order, and denote the sorted position as x i ' and y i ', denote d i = x i ' - y i ', then the Spearman rank correlation coefficient is:

[0162]

[0163] As an alternative, it is constrained by multiple elastic parameters. The multi - scale and multi - parameter joint constraint of core, logging, and seismic includes:

[0164] Combined with the actual situation of the study area, according to the reservoir structure characteristics, core distribution, and core experiment conditions, determine the relative error threshold between core test data and logging data, establish multi - parameter constraints between core test data and logging curves, and realize the coarsening of core data to the logging scale.

[0165] Example 5

[0166] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the above - mentioned anisotropic parameter inversion method for shale reservoirs.

[0167] The electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0168] The memory is used to store non - transient computer - readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer - readable storage media, such as volatile memory and / or non - volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non - volatile memory may include, for example, read - only memory (ROM), hard disk, flash memory, etc.

[0169] The processor may be a central processing unit (CPU) or other forms of processing units with data - processing capabilities and / or instruction - execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer - readable instructions stored in the memory.

[0170] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well - known structures such as communication buses and interfaces, and these well - known structures should also be included in the protection scope of the present disclosure.

[0171] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0172] Example 6

[0173] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the anisotropic parameter inversion method for shale reservoirs described above.

[0174] The computer-readable storage medium according to the embodiment of the present disclosure stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the methods of the foregoing embodiments of the present disclosure are executed.

[0175] The above computer-readable storage medium includes but is not limited to: optical storage media (such as CD-ROMs and DVDs), magneto-optical storage media (such as MOs), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROMs (such as ROM cartridges).

[0176] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any example given.

[0177] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. An anisotropic parameter inversion method for shale reservoirs, characterized in that, it includes: establishing a mathematical statistical model based on the experimental test data of shale cores, well logging elastic parameter curves, and seismic data of the target reservoir; Based on seismic, logging, and horizon data, perform seismic elastic parameter inversion to obtain sensitive parameters P ε and P γ ; performing joint constraints of multi-scale and multi-parameters of cores, well logging, and seismic by constraining multiple elastic parameters; Bring the sensitive parameter P ε and P γ into the mathematical statistical model to obtain the anisotropic parameters ε and γ.

2. The anisotropic parameter inversion method for shale reservoirs according to claim 1, wherein, establishing a mathematical statistical model based on the experimental test data of cores, well logging elastic parameter curves, and seismic data of the target reservoir includes: calculating the elastic parameters and Thomsen anisotropic parameters in the direction perpendicular to the bedding respectively according to the experimental test data of cores; With the Spearman rank correlation coefficient as the constraint, the correlations between different elastic parameters and two anisotropic parameters are evaluated respectively, and the two elastic parameters P with the highest correlation are selected. ε , P γ As sensitive parameters, mathematical statistical models for predicting ε and γ are established respectively.

3. The anisotropic parameter inversion method for shale reservoirs according to claim 2, wherein, the elastic moduli in the direction perpendicular to the bedding include: longitudinal wave impedance, shear wave impedance, Lame constant, Young's modulus, shear modulus, Poisson's ratio, bulk modulus, longitudinal wave modulus.

4. The anisotropic parameter inversion method for shale reservoirs according to claim 2, wherein, the calculation formula of Thomsen anisotropic parameter ε is: Among them, V p represents the longitudinal wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

5. The anisotropic parameter inversion method for shale reservoirs according to claim 2, wherein, the calculation formula of Thomsen anisotropic parameter γ is: Among them, V s represents the shear wave velocity, and the angle represents the angle between the wave vector and the symmetry axis.

6. The anisotropic parameter inversion method for shale reservoirs according to claim 2, wherein, Let the number of variables X and Y be N, and the data x i and y i are re-sorted in ascending order, and the sorted positions are denoted as x i ' and y i ', and let d i = x i ' - y i ', then the Spearman rank correlation coefficient is:

7. The anisotropic parameter inversion method for shale reservoirs according to claim 1, wherein, performing joint constraints of multi-scale and multi-parameters of cores, well logging, and seismic by constraining multiple elastic parameters includes: combining the actual situation of the study area, determining the relative error threshold between the core test data and well logging data according to the reservoir structure characteristics, core distribution, and core experiment situation, and establishing multi-parameter constraints between the core test data and well logging curves to realize the coarsening of core data to the well logging scale.

8. An anisotropic parameter inversion device for shale reservoirs, characterized in that, it includes: a modeling module, which establishes a mathematical statistical model based on the experimental test data of shale cores, well logging elastic parameter curves, and seismic data of the target reservoir; Inversion module, which performs seismic elastic parameter inversion based on seismic, logging, and horizon data to obtain sensitive parameters P ε and P γ ; a constraint module, which performs joint constraints of multi-scale and multi-parameters of cores, well logging, and seismic by constraining multiple elastic parameters; Computing module, which substitutes the sensitive parameters P ε and P γ into the mathematical statistical model to obtain the anisotropic parameters ε and γ.

9. An electronic device, characterized in that, the electronic device includes: a memory storing executable instructions; a processor, and the processor runs the executable instructions in the memory to implement the anisotropic parameter inversion method for shale reservoirs according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the anisotropic parameter inversion method for shale reservoirs according to any one of claims 1-8.

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