Method and device for determining anisotropy value of shaft rock and processor

Abstract

The invention discloses a method and device for determining anisotropy values of shaft rocks and a processor. The method comprises the steps that a digital rock image of a shaft is obtained, and the digital rock image of the shaft is a digital image used for representing the rock of the shaft; establishing a digital rock physical model of the shaft rock based on the shaft digital rock image; based on the digital rock physical model and a constraint model, a first simulation value of the shaft rock is obtained, the first simulation value comprises a longitudinal wave simulation speed and a transverse wave simulation speed, and the constraint model is used for constraining the process of obtaining the first simulation value; correcting the first simulation value by using the first actual measurement value to obtain a first target simulation value; based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, a second simulation value of the shaft rock is determined, and the second simulation value is an anisotropic simulation value. The technical problem that the accuracy of determining the anisotropy value of the shaft rock is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wellbore rock parameter determination, and in particular to a wellbore rock anisotropy value determination method, device and processor. Background Art

[0002] With the development of microscopic imaging technology and advanced numerical simulation methods, digital rock physics method research uses three-dimensional digital modeling of rock structure and numerical methods to represent the velocity of wellbore rock, but does not pay attention to the anisotropy value of wellbore rock. In addition, in the laboratory, it is impossible to achieve the simulation conditions for obtaining the anisotropy value, which leads to the technical problem of low accuracy in determining the anisotropy value of wellbore rock.

[0003] With respect to the above-mentioned technical problem of low accuracy in determining the anisotropy value of wellbore rock, no effective solution has been proposed so far. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and processor for determining the anisotropy value of wellbore rock, so as to at least solve the technical problem of low accuracy in determining the anisotropy value of wellbore rock.

[0005] According to one aspect of the embodiment of the invention, a method for determining the anisotropy value of a wellbore rock is provided. The method may include: obtaining a digital rock image of the wellbore, wherein the digital rock image of the wellbore is a digital image used to represent the wellbore rock; based on the digital rock image of the wellbore, establishing a digital rock physics model of the wellbore rock; based on the digital rock physics model and the constraint model, obtaining a first simulation value of the wellbore rock, wherein the first simulation value includes: a longitudinal wave simulation velocity and a shear wave simulation velocity, and the constraint model is used to constrain the process of obtaining the first simulation value; correcting the first simulation value using the first actual measurement value to obtain a first target simulation value; determining a second simulation value of the wellbore rock based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, wherein the second simulation value is an anisotropy simulation value.

[0006] Optionally, based on the digital rock physics model and the constraint model, a first simulation value of the wellbore rock is obtained, including: generating a first calculation model based on the stress tensor of the wellbore rock and the strain tensor of the wellbore rock, wherein the first calculation model is used to characterize the mapping relationship between the stress tensor and the strain tensor of the wellbore rock on a two-dimensional plane; generating a second calculation model based on the stress tensor of the pore fluid in the wellbore rock and the strain rate of the pore fluid, wherein the second calculation model is used to characterize the mapping relationship between the stress tensor of the pore fluid and the strain rate of the pore fluid on a two-dimensional plane; generating a third calculation model based on the bulk density of the wellbore rock and the first-order spatial derivative of the stress tensor of the wellbore rock, wherein the third calculation model is used to characterize the mapping relationship between the bulk density of the wellbore rock and the first-order spatial derivative of the stress tensor; combining the first calculation model, the second calculation model and the third calculation model into a constraint model, and obtaining the first simulation value based on the constraint model.

[0007] Optionally, the first calculation model, the second calculation model and the third calculation model are combined into a constraint model, and based on the constraint model, a first simulation value is obtained, including: generating a stress-strain curve according to the constraint model, wherein the stress-strain curve includes: a stress-strain curve simulated by longitudinal waves and a stress-strain curve simulated by transverse waves, the stress-strain curve simulated by longitudinal waves is used to characterize the mapping relationship between the normal stress of the wellbore rock and the strain force of the wellbore rock, and the stress-strain curve simulated by transverse waves is used to characterize the mapping relationship between the shear stress of the wellbore rock and the strain force of the wellbore rock; based on the stress-strain curve, the first simulation value is obtained.

[0008] Optionally, based on the stress-strain curve, obtaining a first simulation value includes: converting the stress-strain curve to obtain the stress rate and the strain rate of the wellbore rock, wherein the stress rate includes: normal stress rate and shear stress rate, and the strain rate includes: normal strain rate and shear strain rate; based on the stress rate and the strain rate, obtaining the complex modulus of the first simulation value, wherein the complex modulus includes: complex shear wave modulus and complex longitudinal wave modulus, the complex shear wave modulus is obtained through the shear stress rate and the shear strain rate, and the complex longitudinal wave modulus is obtained through the normal stress rate and the normal strain rate; based on the complex modulus, obtaining the first simulation value.

[0009] Optionally, based on the first target simulation value in the direction parallel to the bedding direction and the first target simulation value in the direction perpendicular to the bedding direction, the second simulation value of the wellbore rock is determined, including: subtracting the first target simulation value in the direction parallel to the bedding direction from the first target simulation value in the direction perpendicular to the bedding direction to obtain a first calculation result; and determining the ratio of the first calculation result to the first target simulation value in the direction perpendicular to the bedding direction as the second simulation value.

[0010] Optionally, the method further includes: comparing the second simulation value with the second actual measurement value to determine a second target simulation value, wherein the second actual measurement value is used to verify the second simulation value.

[0011] According to one aspect of an embodiment of the present invention, a device for determining anisotropy values ​​of wellbore rocks is provided, and the device may include: a first acquisition unit, used to acquire a digital rock image of the wellbore, wherein the digital rock image of the wellbore is a digital image used to represent the wellbore rock; an establishment unit, used to establish a digital rock physics model of the wellbore rock based on the digital rock image of the wellbore; a second acquisition unit, used to acquire a first simulation value of the wellbore rock based on the digital rock physics model and a constraint model, wherein the first simulation value includes: a longitudinal wave simulation velocity and a shear wave simulation velocity, and the constraint model is used to constrain the process of acquiring the first simulation value; a third acquisition unit, used to correct the first simulation value using a first actual measurement value to obtain a first target simulation value; a determination unit, used to determine a second simulation value of the wellbore rock based on a first target simulation value parallel to the bedding direction and a first target simulation value perpendicular to the bedding direction, wherein the second simulation value is an anisotropic simulation value.

[0012] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored program, wherein when the program is executed by a processor, the device where the storage medium is located is controlled to execute the method for determining the anisotropy value of the wellbore rock of an embodiment of the present invention.

[0013] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, which includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the method for determining the anisotropy value of the wellbore rock according to an embodiment of the present invention.

[0014] According to another aspect of an embodiment of the present invention, a processor is provided, which is used to run a program, wherein when the program is run, the method for determining the anisotropy value of the wellbore rock according to an embodiment of the present invention is executed.

[0015] In an embodiment of the present invention, a digital rock image of a wellbore is obtained, wherein the digital rock image of the wellbore is a digital image used to represent the wellbore rock; based on the digital rock image of the wellbore, a digital rock physics model of the wellbore rock is established; based on the digital rock physics model and the constraint model, a first simulation value of the wellbore rock is obtained, wherein the first simulation value includes: a longitudinal wave simulation velocity and a shear wave simulation velocity, and the constraint model is used to constrain the process of obtaining the first simulation value; the first simulation value is corrected using the first actual measurement value to obtain a first target simulation value; based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, a second simulation value of the wellbore rock is determined, wherein the second simulation value is an anisotropic simulation value. That is to say, the embodiment of the present invention can establish a digital rock physics model based on the acquired digital rock image of the wellbore, and then based on the digital rock physics model and the constraint model, a first simulation value of the wellbore rock can be obtained, and the first simulation value is corrected through the first actual measurement value to obtain a first target simulation value, and finally based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, the second simulation value of the wellbore rock is determined to achieve the purpose of obtaining the anisotropic simulation value of the wellbore rock, thereby solving the technical problem of low accuracy in determining the anisotropic value of the wellbore rock and achieving the technical effect of improving the accuracy of determining the anisotropic value of the wellbore rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a method for determining anisotropy value of wellbore rock according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a wave propagating along a vertical bedding direction on a two-dimensional model according to an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of a wave propagating along a bedding direction parallel to a two-dimensional model according to an embodiment of the present invention;

[0020] Figure 4 is a flow chart of another method for determining anisotropy value of wellbore rock according to an embodiment of the present invention;

[0021] Figure 5 is a flow chart of anisotropy values ​​of longitudinal and transverse wave velocities at logging scale based on wellbore rock characterization according to an embodiment of the present invention;

[0022] Figure 6is a schematic diagram of a grayscale image analysis result of a wellbore rock according to an embodiment of the present invention;

[0023] Figure 7 is a schematic diagram of comparing simulation results of a core-resolution digital rock physics model with ultrasonic experimental measurement results according to an embodiment of the present invention;

[0024] Figure 8 is a schematic diagram comparing a simulation result of a digital rock physics model with a logging resolution according to an embodiment of the present invention with a measurement result of an acoustic logging experiment;

[0025] Fig. 9 Schematic diagram of a device for determining anisotropy values ​​of wellbore rocks according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Example 1

[0029] According to an embodiment of the present invention, a method for determining the anisotropy value of wellbore rock is provided. It should be noted that in the flowchart of the accompanying drawings, the steps shown therein can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0030] Figure 1is a flow chart of a method for determining anisotropy value of wellbore rock according to an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0031] Step S101, obtaining a wellbore digital rock image, wherein the wellbore digital rock image is a digital image used to represent wellbore rocks.

[0032] In the technical solution provided in the above step S101 of the present invention, a computed tomography (CT) scan can be performed on the wellbore rock image to obtain a digital rock image of the wellbore, so as to achieve the purpose of representing the wellbore rock. The digital rock image of the wellbore can include shale heterogeneous lithology information and structural information. For example, a digital rock image of a wellbore with a length of 100 feet (ft) can be obtained. It should be noted that this is only a preferred implementation method for obtaining a digital rock image of a wellbore, and the method and process for obtaining a digital rock image of a wellbore are not specifically limited.

[0033] Optionally, continuous core CT images with a target wellbore length of up to 100ft are collected, and the lithofacies category of the image can be determined based on the grayscale histogram of the continuous CT images. The lithofacies category can be a pre-set value. For example, there can be five lithofacies categories, organic matter as the main phase, clay as the main phase, felsic as the main phase, calcite and quartz mixed phase, and calcite as the main phase, wherein each phase is a mixture of multiple minerals, and the above-mentioned lithofacies category names (components) can be determined based on element logging results, X-ray Diffraction (XRD) analysis results, corresponding depth core scanning electron microscope photos, thin section images, etc. This is only an example, and the method and content of determining the lithofacies category are not specifically limited.

[0034] Step S102: establishing a digital rock physics model of the wellbore rock based on the wellbore digital rock image.

[0035] In the technical solution provided in the above step S102 of the present invention, after obtaining the digital rock image of the wellbore, a digital rock physics model of the wellbore rock is established based on the digital rock image of the wellbore, so as to achieve the purpose of obtaining the simulation value of the wellbore rock by using the digital rock physics model.

[0036] Optionally, the maximum inter-class variance method (OTSU) is used to perform threshold segmentation on the wellbore digital rock image to determine the lithofacies with low to high density, so as to achieve the purpose of establishing a digital rock physics model of the wellbore rock. Among them, the maximum inter-class variance method is an adaptive threshold segmentation method, also known as the Otsu method, which can determine a suitable threshold by maximizing the inter-class variance, thereby realizing automatic image segmentation.

[0037] Alternatively, in most cases, the length of the continuous core column of the wellbore is several meters to more than ten meters, which is much longer than the logging acoustic wave measurement resolution. In addition, due to the scale dependence of the elastic properties of highly heterogeneous shales, it is necessary to segment the large-scale two-dimensional digital rock physics model according to different resolutions and construct multiple small-scale digital rock physics models of the wellbore rock, such as a digital rock physics model with core resolution or a digital rock physics model with logging resolution.

[0038] For example, a 13.3ft*0.1ft continuous core column image and the corresponding ultrasonic experimental measurement results and field acoustic logging results are intercepted from a 100ft continuous core CT image. The 13.3ft*0.1ft continuous core column image is segmented into 16 images according to the size of 25 centimeters (cm)*3.048cm, and is determined as a subset of the digital rock physics model for establishing the logging resolution. The 13.3ft*0.1ft continuous core column image is segmented into 51 images according to the size of 5cm*3.048cm (some invalid images are discarded), and is determined as a subset of the digital rock physics model with core resolution. Among them, the sizes of the two sets of digital rock physics model subsets are consistent with the sizes of the ultrasonic experimental measurement and field acoustic wave measurement resolution. The components of the digital rock physics model subset are consistent with the components of the 100ft continuous core column digital rock physics model.

[0039] Step S103, based on the digital rock physics model and the constraint model, obtain the first simulation value of the wellbore rock, wherein the first simulation value includes: longitudinal wave simulation velocity and shear wave simulation velocity, and the constraint model is used to constrain the process of obtaining the first simulation value.

[0040] In the technical solution provided in the above step S103 of the present invention, after the digital rock physics model of the wellbore rock is established, the longitudinal wave simulation velocity and the shear wave simulation velocity of the wellbore rock can be obtained according to the digital rock physics model and the constraint model. Among them, the constraint model can constrain the process of obtaining the longitudinal wave simulation velocity and the shear wave simulation velocity. The longitudinal wave simulation velocity can be referred to as the longitudinal wave velocity, the shear wave simulation velocity can be referred to as the shear wave velocity, and the longitudinal wave velocity and the shear wave velocity can be collectively referred to as the longitudinal and shear wave velocities. The constraint conditions of the constraint model can be expressed by a wave equation, and the wave equation can be composed of multiple equations.

[0041] Optionally, based on the digital rock physics model, a first simulation value of the wellbore rock is obtained using a dynamic stress-strain method, which is an experimental method for studying the dynamic response of a material, and can measure the stress and strain of the material by applying a dynamic load (such as impact, vibration, etc.), thereby studying the dynamic mechanical properties of the material.

[0042] For example, based on a subset of the digital rock physics model with core resolution and a subset of the digital rock physics model with logging resolution, a digital rock physics model with core resolution and a digital rock physics model with logging resolution are established, and the two models are processed by the dynamic stress-strain method to obtain the longitudinal wave simulation velocity and the shear wave simulation velocity of the vertical bedding. This is only an example, and the simulation method and process for obtaining the first simulation value are not specifically limited.

[0043] Optionally, the boundary conditions of the digital rock image can determine the simulation direction of the digital rock physics model. When simulating the longitudinal wave velocity and the shear wave velocity, different boundary conditions can be set for the digital rock physics model. For example, when simulating the longitudinal wave, the right edge of the model is compressed or pulled along the y direction, its opposite side is fixed, and the other two sides are applied with periodic boundary conditions. When simulating the shear wave, the right edge of the model is dragged along the x direction, while its opposite side is fixed, and the other two sides are applied with periodic boundary conditions.

[0044] Step S104: Correct the first simulation value using the first actual measurement value to obtain a first target simulation value.

[0045] In the technical solution provided in the above step S104 of the present invention, after obtaining the first simulation value, the first simulation value can be corrected using the first actual measurement value obtained by the experiment, so that the error between the first actual measurement value and the first simulation value is minimized, so as to achieve the purpose of obtaining the first target simulation value. The first actual measurement value may be the actually measured shear wave velocity and longitudinal wave velocity. This is only for example description, and the representation method of the first actual measurement value is not specifically limited.

[0046] Optionally, a pixel in the digital rock image of the wellbore no longer corresponds to a single mineral, but to a mixed phase composed of multiple mineral components. Therefore, it is difficult to completely determine the lithofacies category of the elastic input through the geological background and image information. Therefore, the lithofacies information can be combined to preliminarily determine the mineral components of each phase, and the first simulation value of the wellbore rock can be obtained through the digital rock physics model. The first simulation value includes the shear wave simulation velocity and the longitudinal wave simulation velocity, and the first simulation value is corrected using the first actual measurement value to minimize the error between the first actual measurement value and the first simulation value, thereby obtaining the first target simulation value.

[0047] It should be noted that this is only a preferred implementation method for obtaining the first target simulation value, and no further examples are given of the method and process for obtaining the first target simulation value. As long as the first simulation value is corrected by using the first actual measurement value, the method and process for obtaining the first target simulation value are within the protection scope of the present invention and are not listed one by one here.

[0048] Step S105, determining a second simulation value of the wellbore rock based on the first target simulation value in the direction parallel to the bedding direction and the first target simulation value in the direction perpendicular to the bedding direction, wherein the second simulation value is an anisotropic simulation value.

[0049] In the technical solution provided in the above step S105 of the present invention, the second simulation value of the wellbore rock can be determined according to the first target simulation value in the direction parallel to the bedding direction and the first target simulation value in the direction perpendicular to the bedding direction, wherein the second simulation value can be an anisotropic simulation value, which can be referred to as an anisotropic value for short. The anisotropic value refers to the different values ​​of a physical property (such as resistivity, magnetic permeability, dielectric constant, thermal conductivity, etc.) in different directions in a material or medium.

[0050] Optionally, the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction are calculated to determine the anisotropy simulation value of the wellbore rock, and the anisotropy simulation value is compared with the anisotropy value actually measured to verify the accuracy of the anisotropy simulation value, so as to achieve the purpose of determining the anisotropy value of the wellbore rock. It should be noted that this is only a preferred implementation for determining the first simulation value of the wellbore rock, and the method and process for determining the first simulation value of the wellbore rock are not specifically limited.

[0051] In the above steps S101 to S105 of the present invention, a digital rock image of the wellbore is obtained, wherein the digital rock image of the wellbore is a digital image used to represent the wellbore rock; based on the digital rock image of the wellbore, a digital rock physics model of the wellbore rock is established; based on the digital rock physics model and the constraint model, a first simulation value of the wellbore rock is obtained, wherein the first simulation value includes: a longitudinal wave simulation velocity and a shear wave simulation velocity, and the constraint model is used to constrain the process of obtaining the first simulation value; the first simulation value is corrected using the first actual measurement value to obtain a first target simulation value; based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, a second simulation value of the wellbore rock is determined, wherein the second simulation value is an anisotropic simulation value. That is to say, the embodiment of the present invention can establish a digital rock physics model based on the acquired digital rock image of the wellbore, and then based on the digital rock physics model and the constraint model, a first simulation value of the wellbore rock can be obtained, and the first simulation value is corrected through the first actual measurement value to obtain a first target simulation value, and finally based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, the second simulation value of the wellbore rock is determined to achieve the purpose of obtaining the anisotropic simulation value of the wellbore rock, thereby solving the technical problem of low accuracy in determining the anisotropic value of the wellbore rock and achieving the technical effect of improving the accuracy of determining the anisotropic value of the wellbore rock.

[0052] The above method of this embodiment is further introduced below.

[0053] As an optional implementation method, in step S103, based on the digital rock physics model and the constraint model, a first simulation value of the wellbore rock is obtained, including: based on the stress tensor of the wellbore rock and the strain tensor of the wellbore rock, a first calculation model is generated, wherein the first calculation model is used to characterize the mapping relationship between the stress tensor and the strain tensor of the wellbore rock on a two-dimensional plane; based on the stress tensor of the pore fluid in the wellbore rock and the strain rate of the pore fluid, a second calculation model is generated, wherein the second calculation model is used to characterize the mapping relationship between the stress tensor of the pore fluid and the strain rate of the pore fluid on a two-dimensional plane; based on the bulk density of the wellbore rock and the first-order spatial derivative of the stress tensor of the wellbore rock, a third calculation model is generated, wherein the third calculation model is used to characterize the mapping relationship between the bulk density of the wellbore rock and the first-order spatial derivative of the stress tensor; the first calculation model, the second calculation model and the third calculation model are combined into a constraint model, and based on the constraint model, the first simulation value is obtained.

[0054] In this embodiment, a first calculation model may be generated according to the stress tensor and the strain tensor of the wellbore rock, wherein the first calculation model may be represented by the following formula:

[0055] σij =λe kk δ ij +2μe ij ,i,j,k=1,2.

[0056] The above formula can also be used to express the two-dimensional constitutive relationship of wellbore rock, and the matrix of wellbore rock can be considered to be homogeneous, isotropic and linear elastic. In the above formula, λ and μ can be used to represent the Lame constant, σ ij Can be used for the stress tensor of the wellbore rock, e kk and e ij It can be used to represent the strain tensor of the wellbore rock, δ ij Can be used to represent the Kronnick function.

[0057] Optionally, a second calculation model is generated according to the stress tensor of the pore fluid in the wellbore rock and the strain rate of the pore fluid, wherein the second calculation model can be expressed by the following formula:

[0058]

[0059] Assuming that the pore fluid is a uniform Newtonian fluid, the above formula can also be used for the constitutive relation of two-dimensional Newtonian fluid. λ and η μ is the viscosity, and can be used to express the strain rate of the pore fluid, σ ij can be used to represent the stress tensor of the pore fluid, λ can be used to represent the Lame constant, δ ij Can be used to represent the Kronnick function.

[0060] Optionally, a third calculation model may be generated according to the volume density of the wellbore rock and the first-order spatial derivative of the stress tensor of the wellbore rock, wherein the third calculation model may be expressed by the following formula:

[0061]

[0062] The above formula can also be used to express the two-dimensional motion equation of a particle in a digital rock image of a wellbore. In the above formula, is the second-order time derivative of displacement, ρ is the volume density, σ ij,j is the first-order spatial derivative of stress, g i Indicates the physical force, where g i =0.

[0063] Optionally, the first calculation model, the second calculation model and the third calculation model may be combined to form a constraint model, and based on the constraint model, the first simulation value may be obtained. In other words, the constraint model may be combined to form a constraint model by the expression formula of the first calculation model, the expression formula of the second calculation model and the expression formula of the third calculation model, and based on the constraint model, the first simulation value may be obtained.

[0064] As an optional implementation method, the first calculation model, the second calculation model and the third calculation model are combined into a constraint model, and based on the constraint model, a first simulation value is obtained, including: generating a stress-strain curve according to the constraint model, wherein the stress-strain curve includes: a stress-strain curve simulated by longitudinal waves and a stress-strain curve simulated by transverse waves, the stress-strain curve simulated by longitudinal waves is used to characterize the mapping relationship between the normal stress of the wellbore rock and the strain force of the wellbore rock, and the stress-strain curve simulated by transverse waves is used to characterize the mapping relationship between the shear stress of the wellbore rock and the strain force of the wellbore rock; based on the stress-strain curve, the first simulation value is obtained.

[0065] In this embodiment, a stress-strain curve of longitudinal wave simulation and a stress-strain curve of transverse wave simulation can be generated according to the obtained constraint model, and a first simulation value can be obtained according to the stress-strain curve of longitudinal wave simulation and the stress-strain curve of transverse wave simulation. The stress-strain curve of longitudinal wave simulation can be an average stress-strain curve of longitudinal wave simulation, and the stress-strain curve of transverse wave simulation can be an average stress-strain curve of transverse wave simulation.

[0066] Optionally, after obtaining the constraint model, the boundary of the digital rock physics model can be fixed, and a wave perpendicular to the bedding direction can be applied to force the model to vibrate, and then the finite difference method of the rotating staggered grid can be used to solve the wave equation on the digital rock image. After completing the simulation of the wave field on the digital rock using the finite difference method, the average stress-strain curve of the longitudinal wave simulation and the average stress-strain curve of the transverse wave simulation can be obtained through formula calculation, so as to achieve the purpose of obtaining the strain rate and stress rate of the wellbore rock.

[0067] Alternatively, the average stress-strain curve of the longitudinal wave simulation can be expressed by the following formula:

[0068]

[0069] Among them, σ xx (t) and ε xx (t) are used to represent the average normal stress and strain of the entire digital rock, and They are used to represent the normal stress and strain of each grid, respectively. Each grid refers to a two-dimensional rectangular grid. Each grid can contain a pixel or other elements, etc. This is only an example and is not limited to a specific example. i and j are used to represent the index of the grid center in the x and y directions, respectively. <·> is the volume average operator.

[0070] Alternatively, the average stress-strain curve of the shear wave simulation can be expressed by the following formula:

[0071]

[0072] Among them, σ xy (t) and ε xx (t) are used to represent the average shear stress and strain of the entire digital rock, and are used to represent the shear stress and strain for each mesh, respectively.

[0073] As an optional implementation method, based on the stress-strain curve, a first simulation value is obtained, including: converting the stress-strain curve to obtain the stress rate and the strain rate of the wellbore rock, wherein the stress rate includes: normal stress rate and shear stress rate, and the strain rate includes: normal strain rate and shear strain rate; based on the stress rate and the strain rate, the complex modulus of the first simulation value is obtained, wherein the complex modulus includes: complex shear wave modulus and complex longitudinal wave modulus, the complex shear wave modulus is obtained through the shear stress rate and the shear strain rate, and the complex longitudinal wave modulus is obtained through the normal stress rate and the normal strain rate; based on the complex modulus, the first simulation value is obtained.

[0074] In this embodiment, after obtaining the average stress-strain curve, the average stress-strain curve can be converted to achieve the purpose of obtaining the normal stress rate, shear stress rate, normal strain rate and shear strain rate of the wellbore rock, and the stress rate and strain rate obtained above are calculated to obtain the complex modulus of the first simulation value, and the shear wave simulation velocity and the longitudinal wave simulation velocity can be obtained according to the obtained complex modulus. Among them, the complex modulus includes: complex longitudinal wave modulus and complex transverse wave modulus. The complex longitudinal wave modulus and the complex transverse wave modulus are the ratios of the Fourier transforms of the stress rate and the strain rate, and the longitudinal wave simulation velocity and the transverse wave simulation velocity are calculated from the complex longitudinal wave modulus, the complex transverse wave modulus and the density of the wellbore rock.

[0075] It should be noted that this is only a preferred implementation method for obtaining the first simulation value, and the acquisition method and processing process of the first simulation value are not specifically limited. As long as the strain rate and stress rate are obtained according to the stress-strain curve to obtain the complex modulus of the first simulation value, and the complex modulus is calculated, the method and process for obtaining the first simulation value are within the protection scope of the present invention and are not listed one by one here.

[0076] Optionally, the stress-strain rate curve is converted to the frequency domain by Fourier transform to obtain the stress rate and strain rate of the wellbore rock. The above process can be expressed by the following formula:

[0077]

[0078]

[0079] Among them, σ xx and ε xx They are used to represent the average normal stress and average normal strain of the wellbore rock in the x direction, σ xy and ε xy They are used to represent the average shear stress and average strain of the wellbore rock, and are used to represent normal stress rate and normal strain rate, respectively. and are used to represent shear stress rate and shear strain rate, respectively, and FT[·] can be used to represent Fourier transform.

[0080] Optionally, after the stress rate and the strain rate are obtained, the complex modulus of the first simulation value can be obtained by calculation, and the above process can be expressed by the following formula:

[0081]

[0082] Among them, M(ω) can be used to represent the complex longitudinal wave modulus, and G(ω) can be used to represent the complex shear wave modulus.

[0083] Optionally, according to the obtained complex longitudinal wave modulus and complex shear wave modulus, the shear wave simulation velocity and the longitudinal wave simulation velocity of the wellbore rock can be obtained, and the calculation method of the above process can be implemented by the following formula:

[0084]

[0085]

[0086] Among them, V Pc (ω) can be used to represent the intermediate value of the longitudinal wave simulation velocity, V P Can be used to represent the longitudinal wave simulation velocity, V Sc (ω) can be used to represent the intermediate value of the shear wave simulation velocity, V s It can be used to represent the shear wave simulation velocity, and the Re() function can be used to return the real part of the complex number.

[0087] As an optional implementation method, the second simulation value of the wellbore rock is determined based on the first target simulation value in the direction parallel to the bedding direction and the first target simulation value in the direction perpendicular to the bedding direction, including: subtracting the first target simulation value in the direction parallel to the bedding direction from the first target simulation value in the direction perpendicular to the bedding direction to obtain a first calculation result; and determining the ratio of the first calculation result to the first target simulation value in the direction perpendicular to the bedding direction as the second simulation value.

[0088] In this embodiment, after the first simulation value is corrected by the first actual measurement to obtain the first target simulation value, the first target simulation value in the direction parallel to the bedding direction and the first target simulation value in the direction perpendicular to the bedding direction are subtracted to obtain the first calculation result. The second simulation value is the ratio of the first calculation result to the first target simulation value in the direction perpendicular to the bedding direction.

[0089] For example, the ratio of the velocity difference between the bedding direction parallel to the bedding direction and the bedding direction perpendicular to the bedding direction velocity can be determined as the anisotropy value of the wellbore rock. The above process can be expressed by the following formula:

[0090]

[0091] Optionally, according to the image of the wave along the direction perpendicular to the bedding, the boundary conditions are set, and after calculations are performed through steps S101, S102, S103 and S104, the corrected P-wave simulation velocity and the corrected S-wave simulation velocity in the direction perpendicular to the bedding can be obtained. Figure 2 is a schematic diagram of a wave propagating along a vertical bedding direction on a two-dimensional model according to an embodiment of the present invention, Figure 2 It can be seen that the propagation of waves changes along the direction perpendicular to the bedding.

[0092] Optionally, the image of the wave along the direction perpendicular to the bedding direction is rotated 90 degrees (°) to obtain an image of the wave along the direction parallel to the bedding direction. The same boundary conditions are set, and after calculations through steps S101, S102, S103 and S104, the corrected longitudinal wave simulation velocity and the corrected shear wave simulation velocity in the direction parallel to the bedding direction can be obtained. Figure 3 is a schematic diagram of a wave propagating along a bedding direction parallel to a two-dimensional model according to an embodiment of the present invention, Figure 3 It can be seen that the propagation of waves changes along the direction parallel to the bedding.

[0093] As an optional embodiment, the method further includes: comparing the second simulation value with the second actual measurement value to determine a second target simulation value, wherein the second actual measurement value is used to verify the second simulation value.

[0094] In this embodiment, after obtaining the second simulation value, the second simulation value can be compared with the second actual measurement value to determine the second target simulation value, so as to achieve the purpose of verifying the second simulation value by using the second actual measurement value, wherein the second actual measurement value is the real data of the actual measurement.

[0095] Optionally, after obtaining the anisotropy simulation value of the wellbore rock, the value is compared with the actually measured anisotropy value, so as to verify the accuracy and feasibility of the obtained anisotropy simulation value, thereby achieving the purpose of accurately representing the shear wave simulation velocity, longitudinal wave simulation velocity, and anisotropy simulation value of the wellbore rock. Therefore, under the limited environment in the laboratory, digital rock physics models of different resolutions can be constructed based on image subsets of digital rocks of different resolutions, so as to expand the method of simulating the anisotropy value of the wellbore rock to rock images of other scales, and achieve the technical effect of accurately characterizing the anisotropy value.

[0096] In this embodiment, a digital rock physics model can be established according to the acquired digital rock image of the wellbore, and then a first simulation value of the wellbore rock can be obtained based on the digital rock physics model and the constraint model. The first simulation value is corrected by the first actual measurement value to obtain a first target simulation value, and finally the second simulation value of the wellbore rock is determined based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, so as to achieve the purpose of obtaining the anisotropic simulation value of the wellbore rock, thereby solving the technical problem of low accuracy in determining the anisotropic value of the wellbore rock and achieving the technical effect of improving the accuracy of determining the anisotropic value of the wellbore rock.

[0097] Example 2

[0098] The technical solution of the embodiment of the present invention is illustrated below in conjunction with preferred implementation modes.

[0099] The elastic and anisotropic properties of rocks are the key to understanding seismic field measurements in sedimentary basins. At present, traditional rock physics experiments and theories are widely used to explain the macroscopic elastic anisotropy characteristics of reservoirs. Ultrasonic experimental measurements, traveling vertical seismic profiles, cross-well seismic and cross-dipole acoustic wave data are all common methods for directly measuring elastic anisotropy. However, due to the complex underground structure, these methods are difficult to implement and costly.

[0100] With the development of microscopic imaging technology and advanced numerical simulation methods, digital rock physics research uses three-dimensional digital modeling of rock structure and numerical methods to represent the velocity of wellbore rocks. Methods based on digital rock physics have been applied to characterize the elastic properties and related parameters of sandstone, shale and carbonate rocks, and have been verified by experimental data to prove their feasibility. However, no attention has been paid to the anisotropy value of wellbore rocks, and in the laboratory, it is impossible to achieve the simulation conditions for obtaining the anisotropy value, which leads to the technical problem of low accuracy in determining the anisotropy value of wellbore rocks.

[0101] Therefore, in order to solve the above problems, an embodiment of the present invention provides a method, device and processor for determining the anisotropy value of wellbore rock. The method is to establish a digital rock physics model based on the acquired digital rock image of the wellbore, and then based on the digital rock physics model and the constraint model, a first simulation value of the wellbore rock can be obtained. The first simulation value is corrected through the first actual measurement value to obtain a first target simulation value. Finally, based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, the second simulation value of the wellbore rock is determined to achieve the purpose of obtaining the anisotropy simulation value of the wellbore rock, thereby achieving the technical effect of improving the accuracy of determining the anisotropy value of the wellbore rock.

[0102] Figure 4 FIG. 4 is a flow chart of another method for determining anisotropy value of wellbore rock according to an embodiment of the present invention. Figure 4 As shown, the method may include the following steps:

[0103] Step S401, obtaining a digital rock image of the wellbore and establishing a digital rock physics model of the wellbore rock.

[0104] In this embodiment, a digital rock image of the wellbore may be acquired first, and a digital rock physics model of the wellbore rock may be established based on the digital rock image of the wellbore. For example, a digital rock physics model of core resolution and a digital rock physics model of logging resolution may be established based on a subset of the digital rock physics model of core resolution and a subset of the digital rock physics model of logging resolution.

[0105] Optionally, the Otsu method is used for threshold segmentation to determine the low to high density lithofacies and reconstruct the digital rock physics model of the wellbore rock. Different from the conventional digital core model, each phase in the lithofacies category of the image of the constructed digital rock physics model is a mixture of multiple minerals, and the composition can be determined based on element logging results, XRD analysis results, corresponding depth core scanning electron microscope photos, thin section images, etc.

[0106] Optionally, the length of the continuous core column of the wellbore is several meters to more than ten meters, which is much longer than the resolution of the well logging acoustic wave measurement. Due to the scale dependence of the elastic properties of the highly heterogeneous shale, it is necessary to segment the large-scale two-dimensional digital rock physics model according to different resolutions and construct multiple small-scale digital rock physics models of the wellbore rock. For example, a digital rock physics model with core resolution or a digital rock physics model with logging resolution.

[0107] Step S402, based on the digital rock physics model of the wellbore rock, a dynamic stress-strain method is used to obtain the longitudinal wave simulation velocity and the shear wave simulation velocity of the wellbore rock.

[0108] In this embodiment, the dynamic stress-strain method can be used to obtain the P-wave simulation velocity and S-wave simulation velocity of the wellbore rock according to the established digital rock physics model with core resolution. The dynamic stress-strain method can also be used to obtain the P-wave simulation velocity and S-wave simulation velocity of the wellbore rock according to the established digital rock physics model with logging resolution.

[0109] Optionally, considering the strong heterogeneity of shale, the dynamic stress-strain method is selected based on the digital rock physics model to obtain the P-wave simulation velocity and S-wave simulation velocity. The strong heterogeneity of shale refers to the obvious differences in physical and chemical properties inside the rock, that is, the spatial inhomogeneity of the rock composition, structure, porosity, permeability, etc. This heterogeneity leads to the complexity of shale reservoirs.

[0110] Optionally, the boundary conditions of the digital rock image can determine the simulation direction of the digital rock physics model. When simulating the longitudinal wave velocity and the shear wave velocity, different boundary conditions can be set for the digital rock physics model. For example, when simulating the longitudinal wave, the right edge of the model is compressed or pulled along the y direction, its opposite side is fixed, and the other two sides are applied with periodic boundary conditions. When simulating the shear wave, the right edge of the model is dragged along the x direction, while its opposite side is fixed, and the other two sides are applied with periodic boundary conditions.

[0111] Optionally, based on the digital rock physics model, the dynamic stress-strain method is selected to simulate the longitudinal wave velocity and the transverse wave velocity, which can be achieved through the two-dimensional constitutive relationship of the wellbore rock, the constitutive relationship of the two-dimensional Newtonian fluid and the two-dimensional motion equation of the particles in the digital rock image of the wellbore, and the corresponding calculation process.

[0112] Optionally, the two-dimensional constitutive relation of the wellbore rock: σ ij =λe kk δ ij +2μe ij ,i,j,k=1,2, where λ and μ in the above formula can be used to represent the Lame constant, σ ij It can be used to represent the stress tensor of the wellbore rock, ekk and e ij It can be used to represent the strain tensor of the wellbore rock, δ ij Can be used to represent the Kronnick function.

[0113] Alternatively, the constitutive relation of a two-dimensional Newtonian fluid can be expressed by the following formula:

[0114]

[0115] Among them, η in the above formula λ and η μ Can be used to express viscosity, and can be expressed as the strain rate of the pore fluid, σ ij can be used to express the stress tensor of the pore fluid, λ can be used to express the Lame constant, δ ij can be used to represent the Kronnick function.

[0116] Optionally, the two-dimensional motion equation of a particle in the digital rock image of the wellbore is: Among them, in the above formula can be expressed as the second-order time derivative of displacement, ρ can be expressed as volume density, σ ij,j can be used to express the first-order spatial derivative of stress, g i Can be used to express the physical force component, where g i =0.

[0117] Optionally, the two-dimensional constitutive relationship of the wellbore rock, the constitutive relationship of the two-dimensional Newtonian fluid and the two-dimensional motion equation of particles in the digital rock image of the wellbore written above together constitute a constraint model. After obtaining the constraint model, the boundary of the digital rock physics model can be fixed, and a wave perpendicular to the bedding direction can be applied to force the model to vibrate, and then the finite difference method of the rotating staggered grid is used to solve the wave equation on the digital rock image. After completing the simulation of the wave field on the digital rock using the finite difference method, the average stress-strain curve of the longitudinal wave simulation and the average stress-strain curve of the transverse wave simulation can be obtained by formula calculation.

[0118] Alternatively, the average stress-strain curve of the longitudinal wave simulation can be expressed by the following formula:

[0119]

[0120] Among them, σ xx (t) and ε xx (t) are used to represent the average normal stress and strain of the entire digital rock, and They are used to represent the normal stress and strain of each grid, respectively. Each grid refers to a two-dimensional rectangular grid. Each grid can contain a pixel or other elements, etc. This is only an example and is not limited to a specific example. i, j represent the index of the grid center in the x and y directions, respectively. <·> is the volume average operator.

[0121] Alternatively, the average stress-strain curve of the shear wave simulation can be expressed by the following formula:

[0122]

[0123] Among them, σ xy (t) and ε xx (t) are used to represent the average shear stress and strain of the entire digital rock, and are used to represent the shear stress and strain for each mesh, respectively.

[0124] Optionally, after obtaining the average stress-strain curve, the average stress-strain curve can be converted to obtain the stress rate and strain rate of the wellbore rock, and then the stress rate and strain rate obtained above are calculated to obtain the complex longitudinal wave modulus and the complex transverse wave modulus, and finally the complex longitudinal wave modulus and the complex transverse wave modulus are calculated accordingly to achieve the purpose of obtaining the transverse wave simulation velocity and the longitudinal wave simulation velocity. Among them, the complex longitudinal wave modulus and the complex transverse wave modulus are the ratios of the Fourier transform of the stress rate and the strain rate, and the longitudinal wave simulation velocity and the transverse wave simulation velocity are calculated from the complex longitudinal wave modulus, the complex transverse wave modulus and the density of the wellbore rock.

[0125] Optionally, the stress-strain rate curve is converted to the frequency domain by Fourier transform to obtain the stress rate and strain rate of the wellbore rock. The above process can be expressed by the following formula:

[0126]

[0127]

[0128] Among them, σ xx and ε xx They are used to represent the average normal stress and average normal strain of the wellbore rock in the x direction, σ xy and ε xy They are used to represent the average shear stress and average strain of the wellbore rock, and are used to represent normal stress rate and normal strain rate, respectively. and are used to represent shear stress rate and shear strain rate, respectively, and FT[·] can be used to represent Fourier transform.

[0129] Optionally, after the stress rate and the strain rate are obtained, the complex modulus of the first simulation value can be obtained by calculation, and the above process can be expressed by the following formula:

[0130]

[0131] Among them, M(ω) can be used to represent the complex longitudinal wave modulus, and G(ω) can be used to represent the complex shear wave modulus.

[0132] Optionally, according to the obtained complex longitudinal wave modulus and complex shear wave modulus, the shear wave simulation velocity and the longitudinal wave simulation velocity of the wellbore rock can be obtained, and the calculation method of the above process can be implemented by the following formula:

[0133]

[0134]

[0135] Among them, V Pc (ω) can be used to represent the intermediate value of the longitudinal wave simulation velocity, V P Can be used to represent the longitudinal wave simulation velocity, V Sc (ω) can be used to represent the intermediate value of the shear wave simulation velocity, V s It can be used to represent the shear wave simulation velocity, and the Re() function can be used to return the real part of the complex number.

[0136] Step S403, using the actually measured longitudinal wave velocity and shear wave velocity, the obtained longitudinal wave simulation velocity and shear wave simulation velocity of the wellbore rock are corrected.

[0137] In this embodiment, a pixel in the digital rock image of the wellbore no longer corresponds to a single mineral, but corresponds to a mixed phase composed of multiple mineral components. Therefore, it is difficult to completely determine the lithofacies category of the elastic input through the geological background and image information. Therefore, the lithofacies information can be combined to preliminarily determine the mineral components of each phase, and the shear wave simulation velocity and the longitudinal wave simulation velocity of the wellbore rock can be obtained through the digital rock physics model. The longitudinal wave velocity and the transverse wave velocity of the wellbore rock obtained by simulation are corrected using the actually measured longitudinal wave velocity and the transverse wave velocity, so as to minimize the error between the actually measured longitudinal wave velocity and the transverse wave velocity and the longitudinal wave velocity and the transverse wave velocity of the wellbore rock obtained by simulation, thereby obtaining the corrected shear wave simulation velocity and the longitudinal wave simulation velocity, and using the corrected values ​​to obtain the anisotropic simulation value of the wellbore rock.

[0138] Step S404, obtaining anisotropic simulation values ​​of the wellbore rock based on the corrected shear wave simulation velocity and longitudinal wave simulation velocity.

[0139] In this embodiment, according to the image of the wave along the direction perpendicular to the bedding, the boundary conditions are set, and the corrected P-wave simulation velocity and the corrected S-wave simulation velocity in the direction perpendicular to the bedding can be obtained through steps S401, S402 and S403. The image of the wave along the direction perpendicular to the bedding is rotated 90° to obtain the image of the wave along the direction parallel to the bedding, and the same boundary conditions are set, and the corrected P-wave simulation velocity and the corrected S-wave simulation velocity in the direction parallel to the bedding can be obtained through steps S401, S402 and S403.

[0140] Optionally, the simulated velocity of the corrected wave in the direction parallel to the bedding direction and the simulated velocity of the corrected wave in the direction perpendicular to the bedding direction are subtracted, and the ratio of the result of the subtraction operation to the simulated velocity of the corrected wave in the direction perpendicular to the bedding direction is determined as the anisotropy simulation value of the wellbore rock. For example, the ratio of the velocity difference between the direction parallel to the bedding direction and the direction perpendicular to the bedding direction and the velocity in the direction perpendicular to the bedding direction can be determined as the anisotropy value of the wellbore rock, and the above process can be expressed by the following formula:

[0141]

[0142] Optionally, after obtaining the anisotropy simulation value of the wellbore rock, the value is compared with the actually measured anisotropy value, so as to verify the accuracy and feasibility of the obtained anisotropy simulation value, thereby achieving the purpose of accurately representing the shear wave simulation velocity, longitudinal wave simulation velocity, and anisotropy simulation value of the wellbore rock. Therefore, under the limited environment in the laboratory, digital rock physics models of different resolutions can be constructed based on image subsets of digital rocks of different resolutions, so as to expand the method of simulating the anisotropy value of the wellbore rock to rock images of other scales, and achieve the technical effect of accurately characterizing the anisotropy value.

[0143] In step S401 to step S404, a digital rock image of the wellbore is obtained, a digital rock physics model of the wellbore rock is established, and based on the digital rock physics model of the wellbore rock, the P-wave velocity and S-wave velocity of the wellbore rock are simulated by using a dynamic stress-strain method. The P-wave velocity and S-wave velocity of the wellbore rock obtained by simulation are corrected using the actually measured P-wave velocity and S-wave velocity. Based on the corrected S-wave simulation velocity and P-wave simulation velocity, an anisotropy simulation value of the wellbore rock is obtained, thereby solving the technical problem of low accuracy in determining the anisotropy value of the wellbore rock and achieving the technical effect of improving the accuracy in determining the anisotropy value of the wellbore rock.

[0144] Figure 5 1 is a flow chart of anisotropy values ​​of longitudinal and transverse wave velocities at logging scale based on wellbore rock characterization according to an embodiment of the present invention. Figure 5As shown, the method may include the following steps:

[0145] Step S501, establishing a digital rock physics model based on the wellbore rock image.

[0146] In this embodiment, continuous core CT images of the target wellbore with a length of 100 ft and the corresponding high-resolution element logging results can be collected first. According to the grayscale histogram of the continuous CT image, it is determined that it can be divided into five phases, which can be used as the elastic input of the digital rock physics model. The digital rock physics model is constructed using the OSTU threshold segmentation method. According to the element logging results, the five phases are determined to be organic matter as the main phase, clay as the main phase, felsic as the main phase, calcite and quartz mixed phase, and calcite as the main phase.

[0147] Optionally, Figure 6 is a schematic diagram of the grayscale image analysis results of wellbore rocks. Figure 6 It can be seen that Figure 6 a) is an unconventional continuous core grayscale image. Figure 6 b) is the logging analysis result of high-resolution elements in the corresponding interval. Figure 6 c) is the segmentation result of Otsu threshold. Since there is no more information to further confirm the specific mineral components of each phase, organic matter, clay, quartz, 50% quartz and 50% calcite, and calcite are determined as the five phases.

[0148] Step S502: Acquire a subset of the digital rock physics model at core resolution and a subset of the digital rock physics model at logging resolution.

[0149] In this embodiment, a 13.3ft*0.1ft continuous core column image and the corresponding ultrasonic experimental measurement results and field acoustic logging results are intercepted from a 100ft continuous core CT image. The 13.3ft*0.1ft continuous core column image is divided into 16 images according to the size of 25cm*3.048cm, and is determined as a subset of a digital rock physics model for establishing a logging resolution. The 13.3ft*0.1ft continuous core column image is divided into 51 images according to the size of 5cm*3.048cm (some invalid images are discarded), and is determined as a subset of a digital rock physics model with core resolution. Among them, the sizes of the two sets of digital rock physics model subsets are consistent with the sizes of the ultrasonic experimental measurement and field acoustic wave measurement resolution. The components of the digital rock physics model subset are consistent with the components of the 100ft continuous core column digital rock physics model.

[0150] Step S503, for two sets of digital rock physics model subsets with different resolutions, respectively use the dynamic stress-strain method to simulate the vertical bedding longitudinal and shear wave velocities, and compare and calibrate the simulation results with the ultrasonic experimental measurement results and the sonic logging results.

[0151] In this embodiment, two sets of digital rock physics model subsets with different resolutions are respectively simulated using the dynamic stress-strain method for vertical bedding P- and S-wave velocity simulations, and the simulation results are compared and calibrated with ultrasonic experimental measurements and sonic logging results.

[0152] Optionally, Figure 7 This is a schematic diagram of the comparison between the simulation results of a core-resolution digital rock physics model and the ultrasonic experimental measurement results. Figure 7 It can be seen that the first two columns are experimental comparison diagrams of the P- and S-wave velocity simulation results of the digital rock physics model with core resolution and the actually measured P- and S-wave velocities. It can be seen from the figures that the P- and S-wave velocity simulation results are in good agreement with the actual measurement results, that is, the elastic simulation based on the digital rock of the wellbore can characterize the velocity change characteristics caused by mineral distribution, and the five-phase elastic input determined in step S501 is also proven to be reasonable.

[0153] Optionally, Figure 8 This is a schematic diagram comparing the simulation results of a digital rock physics model with the experimental measurement results of acoustic logging. Figure 8 It can be seen that the first two columns are experimental comparison diagrams of the P- and S-wave velocity simulation results of the digital rock physics model with logging resolution and the actually measured P- and S-wave velocities. It can be seen from the figure that the P- and S-wave velocity simulation results are in good agreement with the actual measured results.

[0154] Step S504: Compare and calibrate the anisotropy simulation value of the digital rock physics model based on the core resolution with the ultrasonic experimental measurement result.

[0155] In this embodiment, a subset of the digital rock physics model with core resolution is used to further calculate the bedding parallel P-wave and S-wave velocities, and the corresponding anisotropy value of the wellbore rock is calculated, and the obtained anisotropy value is compared and calibrated. The anisotropy value may also be referred to as anisotropy. This is only for illustration and is not specifically limited.

[0156] Optionally, the calculated anisotropy value of the wellbore rock is compared with the ultrasonic test measurement result, and the comparison result is as follows: Figure 7 As shown in the third and fourth columns, it can be seen from the figure that the simulation results are consistent with the change trend of the measured data, and the values ​​are also within a reasonable range. Therefore, the scheme proposed by the present invention can better describe the anisotropic characteristics caused by the heterogeneous layered structure of the lithology.

[0157] Step S505, obtaining anisotropy simulation values ​​of the digital rock physics model based on the logging resolution.

[0158] In this embodiment, the five phases in step S501 are used as elastic inputs, and the digital rock physics model subset with logging resolution is used to calculate the P-wave and S-wave velocities parallel to and perpendicular to the bedding. Based on the obtained P-wave and S-wave velocities, the anisotropy values ​​are calculated, that is, Figure 8 As shown in the third and fourth columns, the anisotropy values ​​of the logging resolution are slightly lower than the simulation results of the digital rock physics model of the core resolution, but this is consistent with the actual observed results and further proves the superiority of the proposed scheme in the characterization of the anisotropy values ​​of multi-scale rock images.

[0159] In this embodiment, a digital rock physics model is established based on the wellbore rock image, and a subset of the digital rock physics model with core resolution and a subset of the digital rock physics model with logging resolution are obtained. The dynamic stress-strain method is used to simulate the vertical bedding longitudinal and transverse wave velocities and the anisotropy value for the two sets of digital rock physics model subsets with different resolutions, and the simulation results are compared and calibrated with the actual measured values, thereby solving the technical problem of low accuracy in determining the anisotropy value of the wellbore rock and achieving the technical effect of improving the accuracy of determining the anisotropy value of the wellbore rock.

[0160] Example 3

[0161] According to an embodiment of the present invention, a device for determining anisotropy values ​​of wellbore rocks is provided. It should be noted that the device for determining anisotropy values ​​of wellbore rocks can be used to execute a method for determining anisotropy values ​​of wellbore rocks in Example 1.

[0162] Fig. 9 Schematic diagram of a device for determining anisotropy value of wellbore rock according to an embodiment of the present invention. Fig. 9 As shown, a device 900 for determining anisotropy values ​​of wellbore rocks may include: a first acquisition unit 901 , an establishment unit 902 , a second acquisition unit 903 , a third acquisition unit 904 and a determination unit 905 .

[0163] The first acquisition unit 901 is used to acquire a wellbore digital rock image, wherein the wellbore digital rock image is a digital image used to represent wellbore rocks.

[0164] The establishing unit 902 is used to establish a digital rock physics model of the wellbore rock based on the wellbore digital rock image.

[0165] The second acquisition unit 903 is used to acquire a first simulation value of the wellbore rock based on the digital rock physics model and the constraint model, wherein the first simulation value includes: a longitudinal wave simulation velocity and a shear wave simulation velocity, and the constraint model is used to constrain the process of acquiring the first simulation value.

[0166] The third acquisition unit 904 is used to correct the first simulation value by using the first actual measurement value to obtain a first target simulation value.

[0167] The determination unit 905 is used to determine a second simulation value of the wellbore rock based on the first target simulation value in the direction parallel to the bedding direction and the first target simulation value in the direction perpendicular to the bedding direction, wherein the second simulation value is an anisotropic simulation value.

[0168] Optionally, the second acquisition unit 903 includes: a first generation module, which is used to generate a first calculation model based on the stress tensor of the wellbore rock and the strain tensor of the wellbore rock, wherein the first calculation model is used to characterize the mapping relationship between the stress tensor and the strain tensor of the wellbore rock on a two-dimensional plane; a second generation module, which is used to generate a second calculation model based on the stress tensor of the pore fluid in the wellbore rock and the strain rate of the pore fluid, wherein the second calculation model is used to characterize the mapping relationship between the stress tensor of the pore fluid and the strain rate of the pore fluid on a two-dimensional plane; a third generation module, which is used to generate a third calculation model based on the bulk density of the wellbore rock and the first-order spatial derivative of the stress tensor of the wellbore rock, wherein the third calculation model is used to characterize the mapping relationship between the bulk density of the wellbore rock and the first-order spatial derivative of the stress tensor; a first acquisition module, which is used to combine the first calculation model, the second calculation model and the third calculation model into a constraint model, and obtain a first simulation value based on the constraint model.

[0169] Optionally, the first acquisition module may include: a generation submodule, used to generate a stress-strain curve according to a constraint model, wherein the stress-strain curve includes: a stress-strain curve simulated by longitudinal waves and a stress-strain curve simulated by transverse waves, the stress-strain curve simulated by longitudinal waves is used to characterize the mapping relationship between the normal stress of the wellbore rock and the strain force of the wellbore rock, and the stress-strain curve simulated by transverse waves is used to characterize the mapping relationship between the shear stress of the wellbore rock and the strain force of the wellbore rock; an acquisition submodule, used to acquire a first simulation value based on the stress-strain curve.

[0170] Optionally, the acquisition submodule may include: converting the stress-strain curve to obtain the stress rate and strain rate of the wellbore rock, wherein the stress rate includes: normal stress rate and shear stress rate, and the strain rate includes: normal strain rate and shear strain rate; based on the stress rate and strain rate, obtaining the complex modulus of the first simulation value, wherein the complex modulus includes: complex shear wave modulus and complex longitudinal wave modulus, the complex shear wave modulus is obtained through the shear stress rate and the shear strain rate, and the complex longitudinal wave modulus is obtained through the normal stress rate and the normal strain rate; based on the complex modulus, obtaining the first simulation value.

[0171] Optionally, the determination unit 905 may include: a second acquisition module, used to subtract the first target simulation value in the direction parallel to the bedding direction from the first target simulation value in the direction perpendicular to the bedding direction to obtain a first calculation result; and a first determination module, used to determine the ratio of the first calculation result to the first target simulation value in the direction perpendicular to the bedding direction as the second simulation value.

[0172] Optionally, the determination unit 905 may further include: a second determination module, configured to compare the second simulation value with the second actual measurement value to determine a second target simulation value, wherein the second actual measurement value is used to verify the second simulation value.

[0173] In this embodiment, a digital rock image of the wellbore is acquired by a first acquisition unit, wherein the digital rock image of the wellbore is a digital image used to represent the wellbore rock; an establishment unit is used to establish a digital rock physics model of the wellbore rock based on the digital rock image of the wellbore; a second acquisition unit is used to acquire a first simulation value of the wellbore rock based on the digital rock physics model and the constraint model, wherein the first simulation value includes: a longitudinal wave simulation velocity and a shear wave simulation velocity, and the constraint model is used to constrain the process of acquiring the first simulation value; a third acquisition unit is used to correct the first simulation value using the first actual measurement value to obtain a first target simulation value; a determination unit is used to determine a second simulation value of the wellbore rock based on the first target simulation value parallel to the bedding direction and the first target simulation value perpendicular to the bedding direction, wherein the second simulation value is an anisotropic simulation value, thereby solving the technical problem of low accuracy in determining the anisotropic value of the wellbore rock and achieving the technical effect of improving the accuracy in determining the anisotropic value of the wellbore rock.

[0174] Example 4

[0175] According to an embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored program, wherein when the program is executed by a processor, the device where the storage medium is located is controlled to execute the method for determining the anisotropy value of the wellbore rock in Example 1.

[0176] Example 5

[0177] According to an embodiment of the present invention, there is also provided an electronic device, which includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the method for determining the anisotropy value of the wellbore rock in Example 1.

[0178] Example 6

[0179] According to an embodiment of the present invention, a processor is also provided, which is used to run a program, wherein the method for determining the anisotropy value of the wellbore rock in Example 1 is executed when the program is run.

[0180] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0181] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0182] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic, for example, the division of units can be a logical function division, and there can be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0183] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0184] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0185] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0186] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining anisotropy value of wellbore rock, characterized in that: include: Acquire a wellbore digital rock image, wherein the wellbore digital rock image is a digital image used to represent wellbore rocks; Based on the wellbore digital rock image, establishing a digital rock physics model of the wellbore rock; Based on the digital rock physics model and the constraint model, a first simulation value of the wellbore rock is obtained, wherein the first simulation value includes: a longitudinal wave simulation velocity and a shear wave simulation velocity, and the constraint model is used to constrain the process of obtaining the first simulation value; Correcting the first simulation value using the first actual measurement value to obtain a first target simulation value; Based on the first target simulation value in a direction parallel to the bedding direction and the first target simulation value in a direction perpendicular to the bedding direction, a second simulation value of the wellbore rock is determined, wherein the second simulation value is an anisotropic simulation value.

2. The method according to claim 1, characterized in that Based on the digital rock physics model and the constraint model, obtaining a first simulation value of the wellbore rock includes: Based on the stress tensor of the wellbore rock and the strain tensor of the wellbore rock, a first calculation model is generated, wherein the first calculation model is used to characterize the mapping relationship between the stress tensor and the strain tensor of the wellbore rock on a two-dimensional plane; Based on the stress tensor of the pore fluid in the wellbore rock and the strain rate of the pore fluid, a second calculation model is generated, wherein the second calculation model is used to characterize the mapping relationship between the stress tensor of the pore fluid and the strain rate of the pore fluid on the two-dimensional plane; Based on the volume density of the wellbore rock and the first-order spatial derivative of the stress tensor of the wellbore rock, a third calculation model is generated, wherein the third calculation model is used to characterize the mapping relationship between the volume density of the wellbore rock and the first-order spatial derivative of the stress tensor; The first calculation model, the second calculation model and the third calculation model are combined into the constraint model, and the first simulation value is obtained based on the constraint model.

3. The method according to claim 2, characterized in that Combining the first calculation model, the second calculation model, and the third calculation model into the constraint model, and obtaining the first simulation value based on the constraint model, comprises: Generate a stress-strain curve according to the constraint model, wherein the stress-strain curve includes: a stress-strain curve simulated by longitudinal waves and a stress-strain curve simulated by transverse waves, wherein the stress-strain curve simulated by longitudinal waves is used to characterize the mapping relationship between the normal stress of the wellbore rock and the strain force of the wellbore rock, and the stress-strain curve simulated by transverse waves is used to characterize the mapping relationship between the shear stress of the wellbore rock and the strain force of the wellbore rock; Based on the stress-strain curve, the first simulation value is obtained.

4. The method according to claim 3, characterized in that Based on the stress-strain curve, obtaining the first simulation value includes: Converting the stress-strain curve to obtain the stress rate and strain rate of the wellbore rock, wherein the stress rate includes: normal stress rate and shear stress rate, and the strain rate includes: normal strain rate and shear strain rate; Based on the stress rate and the strain rate, obtaining a complex modulus of the first simulation value, wherein the complex modulus includes: a complex shear wave modulus and a complex longitudinal wave modulus, the complex shear wave modulus is obtained through the shear stress rate and the shear strain rate, and the complex longitudinal wave modulus is obtained through the normal stress rate and the normal strain rate; Based on the complex modulus, the first simulation value is obtained.

5. The method according to claim 1, characterized in that: Determining a second simulation value of the wellbore rock based on the first target simulation value in a direction parallel to the bedding direction and the first target simulation value in a direction perpendicular to the bedding direction includes: Subtracting the first target simulation value in the direction parallel to the bedding from the first target simulation value in the direction perpendicular to the bedding to obtain a first calculation result; The ratio of the first calculation result to the first target simulation value in the vertical bedding direction is determined as the second simulation value.

6. The method according to claim 5, characterized in that The method further comprises: The second simulation value is compared with a second actual measurement value to determine a second target simulation value, wherein the second actual measurement value is used to verify the second simulation value.

7. A device for determining anisotropy value of wellbore rock, characterized in that: include: A first acquisition unit is used to acquire a wellbore digital rock image, wherein the wellbore digital rock image is a digital image used to represent wellbore rocks; An establishing unit, used for establishing a digital rock physics model of the wellbore rock based on the wellbore digital rock image; A second acquisition unit is used to acquire a first simulation value of the wellbore rock based on the digital rock physics model and the constraint model, wherein the first simulation value includes: a longitudinal wave simulation velocity and a shear wave simulation velocity, and the constraint model is used to constrain the process of acquiring the first simulation value; a third acquisition unit, configured to correct the first simulation value by using the first actual measurement value to obtain a first target simulation value; A determination unit is used to determine a second simulation value of the wellbore rock based on the first target simulation value in a direction parallel to the bedding direction and the first target simulation value in a direction perpendicular to the bedding direction, wherein the second simulation value is an anisotropic simulation value.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: The method comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any one of claims 1 to 6.

10. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 6 when running.

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