Digital rock core modeling method and device, electronic equipment and medium

Abstract

The invention discloses a digital rock core modeling method and device, electronic equipment and a medium. The method comprises the following steps: acquiring a core image of a to-be-processed core, and performing threshold segmentation to obtain a binary image; according to the binarized image, obtaining structure parameters of filler in the rock core sample, and further determining filling structure parameters; constructing a three-dimensional digital core model according to the filling structure parameters; and performing numerical simulation on the three-dimensional digital core model to realize digital core modeling. On the basis of the image morphological algorithm, the filler type and the pore filling proportion can be controlled, and an effective method is provided for exploring the influence of the filling type and the filling proportion on the reservoir.

Description

Technical Field

[0001] This invention relates to the field of digital rock physics, and more specifically, to a digital core modeling method, apparatus, electronic device, and medium. Background Technology

[0002] Rock infilling is a common geological phenomenon, occurring during the filling of fractures and pores in strata by various materials. In oil and gas exploration, rock infilling can bring a series of problems, affecting the effectiveness and efficiency of oil and gas resource exploration and development. Traditional rock physics experimental methods cannot control the regular changes in core properties caused by a single variable, making it difficult to study specific infill materials and types. Digital core technology is an effective method for studying the characteristics of rock infilling. Under certain geological constraints, digital core technology uses mathematical modeling, computer image processing, and high-resolution CT imaging to digitally represent rock samples. It then uses numerical simulation and other methods to equivalently measure the physical parameters of the constructed digital core model, further obtaining core properties.

[0003] However, density measurements of the fillers revealed that the density differences between different fillers and the matrix were relatively small. High-precision CT scanning is one of the most commonly used methods for digital core modeling, capable of accurate imaging of actual cores and showing good imaging results for core samples with well-developed pores and favorable physical properties. However, CT scanning methods struggle to distinguish media with small density differences through grayscale segmentation, making it difficult to construct digital core models with different fillers and structures using CT methods.

[0004] Therefore, it is necessary to develop a digital core modeling method, device, electronic equipment, and medium.

[0005] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention proposes a digital core modeling method, device, electronic equipment, and medium. Based on image morphology algorithms, it can control the type of filling material and the pore filling ratio, providing an effective method for exploring the influence of filling type and filling ratio on reservoirs.

[0007] In a first aspect, embodiments of this disclosure provide a digital core modeling method, including:

[0008] Obtain the core image of the core to be processed, and then perform threshold segmentation to obtain a binarized image;

[0009] Based on the binarized image, the structural parameters of the filling material in the core sample are obtained, and then the filling structure parameters are determined.

[0010] A three-dimensional digital core model is constructed based on the filling structure parameters.

[0011] Numerical simulation was performed on the three-dimensional digital core model to achieve digital core modeling.

[0012] As a specific implementation of this disclosure, multiple initial three-dimensional digital core models of clayey infill and calcite infill under various aspect ratios are constructed according to the filling structure parameters. The multiple initial three-dimensional digital core models are superimposed to obtain the three-dimensional digital core model.

[0013] As a specific implementation of this disclosure, the three-dimensional digital core model is numerically simulated using the static finite element method.

[0014] As a specific implementation of this disclosure, numerical simulation of the three-dimensional digital core model using the static finite element method includes:

[0015] Establish the relationship between the flexibility tensor and the elastic stiffness tensor to obtain the simplified elastic stiffness tensor;

[0016] Based on bulk modulus and shear modulus, calculate the Poisson's ratio and Young's modulus of isotropic materials;

[0017] Calculate the longitudinal and transverse wave velocities of the rock.

[0018] As a specific implementation of this disclosure, the simplified elastic stiffness tensor is:

[0019]

[0020] Where σ and ε represent stress and strain, respectively, λ is Lamé constant, and μ is shear modulus.

[0021] As a specific implementation of this disclosure, the Poisson ratio is:

[0022]

[0023] Young's modulus is:

[0024]

[0025] As one specific implementation of this disclosure, the longitudinal wave velocity is:

[0026]

[0027] The transverse wave velocity is:

[0028]

[0029] Among them, V p V is the longitudinal wave velocity. s This represents the transverse wave velocity.

[0030] Secondly, this disclosure also provides a digital core modeling device, comprising:

[0031] The binarization module acquires the core image of the core to be processed, and then performs threshold segmentation to obtain a binarized image;

[0032] The parameter acquisition module obtains the structural parameters of the filling material in the core sample based on the binarized image, and then determines the filling structure parameters.

[0033] The modeling module constructs a three-dimensional digital core model based on the filling structure parameters;

[0034] The calculation module performs numerical simulation on the three-dimensional digital core model to realize digital core modeling.

[0035] As a specific implementation of this disclosure, multiple initial three-dimensional digital core models of clayey infill and calcite infill under various aspect ratios are constructed according to the filling structure parameters. The multiple initial three-dimensional digital core models are superimposed to obtain the three-dimensional digital core model.

[0036] As a specific implementation of this disclosure, the three-dimensional digital core model is numerically simulated using the static finite element method.

[0037] As a specific implementation of this disclosure, numerical simulation of the three-dimensional digital core model using the static finite element method includes:

[0038] Establish the relationship between the flexibility tensor and the elastic stiffness tensor to obtain the simplified elastic stiffness tensor;

[0039] Based on bulk modulus and shear modulus, calculate the Poisson's ratio and Young's modulus of isotropic materials;

[0040] Calculate the longitudinal and transverse wave velocities of the rock.

[0041] As a specific implementation of this disclosure, the simplified elastic stiffness tensor is:

[0042]

[0043] Where σ and ε represent stress and strain, respectively, λ is Lamé constant, and μ is shear modulus.

[0044] As a specific implementation of this disclosure, the Poisson ratio is:

[0045]

[0046] Young's modulus is:

[0047]

[0048] As one specific implementation of this disclosure, the longitudinal wave velocity is:

[0049]

[0050] The transverse wave velocity is:

[0051]

[0052] Among them, V p V is the longitudinal wave velocity. s This represents the transverse wave velocity.

[0053] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0054] Memory, which stores executable instructions;

[0055] A processor that executes the executable instructions in the memory to implement the digital core modeling method.

[0056] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the digital core modeling method described above.

[0057] Its beneficial effects are as follows:

[0058] This invention applies image processing and analysis technology to process and statistically analyze actual reservoir cores. By using the statistically obtained filling structure information, an equivalent model is constructed. This invention integrates commonly used digital core functions such as image processing technology, image information statistical methods, and 3D image construction and combination. It can construct an equivalent digital core model with variable filling types and ratios that conforms to the characteristics of actual cores, laying the foundation for subsequent research on the impact of karst filling characteristics on reservoirs.

[0059] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0060] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0061] Figure 1 A flowchart illustrating the steps of a digital core modeling method according to an embodiment of the present invention is shown.

[0062] Figure 2 A schematic diagram showing the CT scan and grayscale analysis results for a typical sample is presented.

[0063] Figure 3 A flowchart of image processing and fill parameter statistics according to an embodiment of the present invention is shown.

[0064] Figure 4 A schematic diagram illustrating the construction and combination of different filling materials, filling structures, and according to an embodiment of the present invention is shown.

[0065] Figure 5 A schematic diagram showing a comparison between numerical simulation results and experimental results of longitudinal wave velocity according to an embodiment of the present invention is presented.

[0066] Figure 6 A schematic diagram showing the comparison between numerical simulation results and experimental results of shear wave velocity according to an embodiment of the present invention is presented.

[0067] Figure 7 A block diagram of a digital core modeling apparatus according to an embodiment of the present invention is shown.

[0068] Explanation of reference numerals in the attached figures:

[0069] 201. Binarization module; 202. Parameter acquisition module; 203. Modeling module; 204. Calculation module. Detailed Implementation

[0070] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0071] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0072] Example 1

[0073] Figure 1A flowchart illustrating the steps of a digital core modeling method according to an embodiment of the present invention is shown.

[0074] like Figure 1 As shown, this digital core modeling method includes:

[0075] Step 101: Obtain the core image of the core to be processed, and then perform threshold segmentation to obtain a binarized image;

[0076] Step 102: Based on the binarized image, obtain the structural parameters of the filling material in the core sample, and then determine the filling structure parameters;

[0077] Step 103: Construct a three-dimensional digital core model based on the filling structure parameters;

[0078] Step 104: Perform numerical simulation on the three-dimensional digital core model to realize digital core modeling.

[0079] In one example, multiple initial three-dimensional digital core models of clayey infill and calcite infill under various aspect ratios are constructed based on the infill structure parameters. The multiple initial three-dimensional digital core models are then superimposed to obtain a three-dimensional digital core model.

[0080] In one example, a three-dimensional digital core model is numerically simulated using the static finite element method.

[0081] In one example, numerical simulation of a three-dimensional digital core model using the static finite element method includes:

[0082] Establish the relationship between the flexibility tensor and the elastic stiffness tensor to obtain the simplified elastic stiffness tensor;

[0083] Based on bulk modulus and shear modulus, calculate the Poisson's ratio and Young's modulus of isotropic materials;

[0084] Calculate the longitudinal and transverse wave velocities of the rock.

[0085] In one example, the simplified elastic stiffness tensor is:

[0086]

[0087] Where σ and ε represent stress and strain, respectively, λ is Lamé constant, and μ is shear modulus.

[0088] In one example, Poisson's ratio is:

[0089]

[0090] Young's modulus is:

[0091]

[0092] In one example, the longitudinal wave velocity is:

[0093]

[0094] The transverse wave velocity is:

[0095]

[0096] Among them, V p V is the longitudinal wave velocity. s This represents the transverse wave velocity.

[0097] Specifically, a high-resolution macro camera is used to photograph the surface of the core sample to be processed. One image of the core surface is taken every 90° from the center, resulting in four images for each core sample. These four images cover all the surface information of the core. The captured images are then cropped to a fixed sample side length.

[0098] After a series of processing steps including denoising, smoothing, and filtering, the core images were thresholded for segmentation. Binary images of the matrix-calcite infill and the matrix-clay infill were obtained. Based on these binarized images, relevant structural parameters of the infill in the core samples can be statistically analyzed. The infill is divided into independent regions, and the structural parameters of each region are statistically analyzed. The interpretations of the different structural information are as follows:

[0099] Area: Calculates the area of ​​the selected region in pixels;

[0100] Perimeter: The perimeter of the selected area is calculated in pixels.

[0101] Aspect Ratio (AR): Minor axis length / Major axis length;

[0102] Feret's Diameter: The longest distance between any two points in the selected area, also known as the maximum caliper.

[0103] Minimum Feret diameter: The shortest distance boundary between any two points in the selected area, also known as the minimum caliper.

[0104] The structural parameter information was summarized, and the information of clay and calcite infill materials with different aspect ratios and filling areas were equivalent to four infill structures with aspect ratios of 0.875, 0.625, 0.375, and 0.125. The corresponding infill proportion was calculated by area, and the major and minor axis lengths of the particles in the digital core model were determined by Fretter diameter and minimum Fretter diameter.

[0105] Based on the infill structure parameters, three-dimensional digital core models of clayey infill and calcite infill were constructed under four aspect ratios. These multiple models were then superimposed to form a complete three-dimensional digital core model.

[0106] Based on the three-dimensional digital core model, the static finite element method is applied to numerically simulate the model.

[0107] For a given microstructure, subject to the applied field or other boundary conditions, the voltage or elastic displacement distribution causes the total energy stored in the elastic shell to be dissipated to extremes; for example, the energy gradient with respect to voltage or elastic displacement is zero. The variable En (hereinafter referred to as energy), even in the case of conductivity, is actually the energy dissipation per unit time or power. For En to be valid, all partial derivatives must be equal and zero. Elasticity theory describes the propagation characteristics of sound waves in rocks. A representative law is Hooke's law, which is formulated as follows:

[0108]

[0109] Where, σ ik Represents the given stress tensor, ε ik Represents the strain tensor, C iklm Representing the elastic stiffness tensor, we have:

[0110]

[0111] It can also be abbreviated as

[0112] ε ik =D iklm σ lm

[0113] Among them, D iklm For the flexibility tensor.

[0114] The flexibility tensor and the elastic stiffness tensor are inverse tensors, satisfying the following relationship:

[0115]

[0116] Where, δ ij This is the Kronecker function. The parameters σ and ε represent stress and strain, respectively, and are expressed as a matrix result based on nine components. Therefore, the above system of equations consists of nine equations, each composed of nine strain components. The elastic stiffness tensor C will then be represented as a 9th-order matrix, with 81 elastic constants. These constants have the following intrinsic relationships:

[0117] C ijkl =C jikl =C ijlk =C jilk (4)

[0118] Because of the symmetry between the stress and strain tensors, the number of independent variables in C can be reduced to 36. Considering the symmetry of C, the following results can be obtained:

[0119] C ijkl =C klij (5)

[0120] The above changes reduce the number of independent variables within the elastic constants to 21, representing the maximum limit of independent constants that can be accommodated in the elastic medium. After analyzing the elastic medium, if the results show a clear isotropic characteristic, it indicates that there are two independent variables in the elastic stiffness matrix, which can be represented by the Lamé constant λ and the shear modulus μ. Therefore, C can be expressed using the following matrix:

[0121]

[0122] Furthermore, if we use parameter K to represent its bulk modulus and the ratio of ring stress to volumetric deformation, and use shear modulus to represent the ratio of shear stress to shear deformation, then we can obtain the following formula:

[0123]

[0124] σ ij =2με ij (8)

[0125] Based on the bulk modulus and shear modulus of a material, the Poisson's ratio and Young's modulus of an isotropic material can be calculated and determined using the following formulas:

[0126]

[0127] If the density of the rock sample is known, then the P-wave and S-wave velocities of the rock are (V p V is the longitudinal wave velocity. s (For transverse wave velocity):

[0128]

[0129] After calculating the stress and strain values ​​of the rock using the finite element method, the P-wave and S-wave velocities or other elastic moduli can be calculated according to the generalized Hooke's law for isotropic media. This is the basic principle of the static finite element method. Comparing the simulation results with experimental test results can verify the effectiveness of the modeling method of this invention. Further research can be conducted on the simulation of variable calcite infill to explore the physical properties of rocks under different mineral infill ratios.

[0130] Based on rock physics experiments, the elastic moduli of the corresponding infill material and matrix were obtained as parameters for numerical simulation. The elastic moduli of the matrix, calcite, and clay were calculated using velocity analysis. The bulk modulus of the matrix was 53.4 GPa, and the shear modulus was 28.7 GPa; the bulk modulus of calcite was 39.4 GPa, and the shear modulus was 19 GPa; and the bulk modulus of clay was 48 GPa, and the shear modulus was 22.5 GPa. These elastic moduli were used as parameters for digital core simulation.

[0131] Example 2

[0132] The present invention also provides a digital core modeling device, comprising:

[0133] The binarization module acquires the core image of the core to be processed, and then performs threshold segmentation to obtain a binarized image;

[0134] The parameter acquisition module obtains the structural parameters of the filling material in the core sample based on the binarized image, and then determines the filling structure parameters.

[0135] The modeling module constructs a three-dimensional digital core model based on the filling structure parameters.

[0136] The calculation module performs numerical simulations on the three-dimensional digital core model to achieve digital core modeling.

[0137] In one example, multiple initial three-dimensional digital core models of clayey infill and calcite infill under various aspect ratios are constructed based on the infill structure parameters. The multiple initial three-dimensional digital core models are then superimposed to obtain a three-dimensional digital core model.

[0138] In one example, a three-dimensional digital core model is numerically simulated using the static finite element method.

[0139] In one example, numerical simulation of a three-dimensional digital core model using the static finite element method includes:

[0140] Establish the relationship between the flexibility tensor and the elastic stiffness tensor to obtain the simplified elastic stiffness tensor;

[0141] Based on bulk modulus and shear modulus, calculate the Poisson's ratio and Young's modulus of isotropic materials;

[0142] Calculate the longitudinal and transverse wave velocities of the rock.

[0143] In one example, the simplified elastic stiffness tensor is:

[0144]

[0145] Where σ and ε represent stress and strain, respectively, λ is Lamé constant, and μ is shear modulus.

[0146] In one example, Poisson's ratio is:

[0147]

[0148] Young's modulus is:

[0149]

[0150] In one example, the longitudinal wave velocity is:

[0151]

[0152] The transverse wave velocity is:

[0153]

[0154] Among them, V p V is the longitudinal wave velocity. s This represents the transverse wave velocity.

[0155] Specifically, a high-resolution macro camera is used to photograph the surface of the core sample to be processed. One image of the core surface is taken every 90° from the center, resulting in four images for each core sample. These four images cover all the surface information of the core. The captured images are then cropped to a fixed sample side length.

[0156] After a series of processing steps including denoising, smoothing, and filtering, the core images were thresholded for segmentation. Binary images of the matrix-calcite infill and the matrix-clay infill were obtained. Based on these binarized images, relevant structural parameters of the infill in the core samples can be statistically analyzed. The infill is divided into independent regions, and the structural parameters of each region are statistically analyzed. The interpretations of the different structural information are as follows:

[0157] Area: Calculates the area of ​​the selected region in pixels;

[0158] Perimeter: The perimeter of the selected area is calculated in pixels.

[0159] Aspect Ratio (AR): Minor axis length / Major axis length;

[0160] Feret's Diameter: The longest distance between any two points in the selected area, also known as the maximum caliper.

[0161] Minimum Feret diameter: The shortest distance boundary between any two points in the selected area, also known as the minimum caliper.

[0162] The structural parameter information was summarized, and the information of clay and calcite infill materials with different aspect ratios and filling areas were equivalent to four infill structures with aspect ratios of 0.875, 0.625, 0.375, and 0.125. The corresponding infill proportion was calculated by area, and the major and minor axis lengths of the particles in the digital core model were determined by Fretter diameter and minimum Fretter diameter.

[0163] Based on the infill structure parameters, three-dimensional digital core models of clayey infill and calcite infill were constructed under four aspect ratios. These multiple models were then superimposed to form a complete three-dimensional digital core model.

[0164] Based on the three-dimensional digital core model, the static finite element method is applied to numerically simulate the model.

[0165] For a given microstructure, subject to the applied field or other boundary conditions, the voltage or elastic displacement distribution causes the total energy stored in the elastic shell to be dissipated to extremes; for example, the energy gradient with respect to voltage or elastic displacement is zero. The variable En (hereinafter referred to as energy), even in the case of conductivity, is actually the energy dissipation per unit time or power. For En to be valid, all partial derivatives must be equal and zero. Elasticity theory describes the propagation characteristics of sound waves in rocks. A representative law is Hooke's law, which is formulated as follows:

[0166] σ ik =C iklm ε lm (1)

[0167] Where, σ ik Represents the given stress tensor, ε ik Represents the strain tensor, C iklm Representing the elastic stiffness tensor, we have:

[0168]

[0169] It can also be abbreviated as

[0170] ε ik =D iklm σ lm

[0171] Among them, D iklm For the flexibility tensor.

[0172] The flexibility tensor and the elastic stiffness tensor are inverse tensors, satisfying the following relationship:

[0173]

[0174] Where, δ ijThis is the Kronecker function. The parameters σ and ε represent stress and strain, respectively, and are expressed as a matrix result based on nine components. Therefore, the above system of equations consists of nine equations, each composed of nine strain components. The elastic stiffness tensor C will then be represented as a 9th-order matrix, with 81 elastic constants. These constants have the following intrinsic relationships:

[0175] C ijkl =C jikl =C ijlk =C jilk (4)

[0176] Because of the symmetry between the stress and strain tensors, the number of independent variables in C can be reduced to 36. Considering the symmetry of C, the following results can be obtained:

[0177] C ijkl =C klij (5)

[0178] The above changes reduce the number of independent variables within the elastic constants to 21, representing the maximum limit of independent constants that can be accommodated in the elastic medium. After analyzing the elastic medium, if the results show a clear isotropic characteristic, it indicates that there are two independent variables in the elastic stiffness matrix, which can be represented by the Lamé constant λ and the shear modulus μ. Therefore, C can be expressed using the following matrix:

[0179]

[0180] Furthermore, if we use parameter K to represent its bulk modulus and the ratio of ring stress to volumetric deformation, and use shear modulus to represent the ratio of shear stress to shear deformation, then we can obtain the following formula:

[0181]

[0182] σ ij =2με ij (8)

[0183] Based on the bulk modulus and shear modulus of a material, the Poisson's ratio and Young's modulus of an isotropic material can be calculated and determined using the following formulas:

[0184]

[0185] If the density of the rock sample is known, then the P-wave and S-wave velocities of the rock are (V p V is the longitudinal wave velocity. s (For transverse wave velocity):

[0186]

[0187]

[0188] After calculating the stress and strain values ​​of the rock using the finite element method, the P-wave and S-wave velocities or other elastic moduli can be calculated according to the generalized Hooke's law for isotropic media. This is the basic principle of the static finite element method. Comparing the simulation results with experimental test results can verify the effectiveness of the modeling method of this invention. Further research can be conducted on the simulation of variable calcite infill to explore the physical properties of rocks under different mineral infill ratios.

[0189] Based on rock physics experiments, the elastic moduli of the corresponding infill material and matrix were obtained as parameters for numerical simulation. The elastic moduli of the matrix, calcite, and clay were calculated using velocity analysis. The bulk modulus of the matrix was 53.4 GPa, and the shear modulus was 28.7 GPa; the bulk modulus of calcite was 39.4 GPa, and the shear modulus was 19 GPa; and the bulk modulus of clay was 48 GPa, and the shear modulus was 22.5 GPa. These elastic moduli were used as parameters for digital core simulation.

[0190] Example 3

[0191] Figure 2 A schematic diagram showing the CT scan and grayscale analysis results for a typical sample is presented.

[0192] like Figure 2 As shown, CT scans and grayscale analysis results were performed on typical samples. Matrices, calcite infill, and clay infill were drilled and fixed together for CT scans. The grayscale analysis results showed a single main peak pattern, making it difficult to distinguish different types of infill using threshold segmentation methods.

[0193] A formation in a basin is divided into three sections from bottom to top, with a stratigraphic thickness of 188-240m. Controlled by tectonic movements, the first and second sections are stable carbonate platform deposits; the third section exhibits sedimentary differentiation, developing sedimentary facies zones from the platform margin to the slope. The shallow shoal reservoirs of the third section are mainly composed of calcareous dolomite and sparry bioclastic limestone, with measured core porosity ranging from 1.26% to 8.37%. Due to early selective dissolution alteration, the reservoir space types are mainly biocavitary pores, intercrystalline pores, and intercrystalline pores, with well-developed dissolution pores and fractures, but severe calcite mineral infilling. Discrepancies exist between actual drilling and the analysis, revealing strong reservoir heterogeneity, severe infilling, and low predictive accuracy. Applying this invention to construct a digital core model and conduct numerical simulations can effectively study different infill materials and types, further exploring the influence of single infill factors on the petrophysical characteristics of the reservoir.

[0194] This invention selects carbonate rock cores from a basin, obtains the corresponding infill material type and infill structure information parameters through this invention, and verifies the effectiveness of the method by modeling and numerical simulation and comparing the simulation results with experiments.

[0195] Figure 3 A flowchart of image processing and fill parameter statistics according to an embodiment of the present invention is shown.

[0196] Figure 4 A schematic diagram illustrating the construction and combination of different filling materials, filling structures, and according to an embodiment of the present invention is shown.

[0197] like Figure 3 , Figure 4 As shown, multi-angle photographs of carbonate rock cores were taken, and image processing and analysis were performed on the core surface images to obtain filling parameters. A three-dimensional digital core equivalent model was constructed based on the filling parameters, and static finite element numerical simulation was performed on the three-dimensional digital core equivalent model to obtain the P-wave and S-wave velocities of the digital core. Taking a typical plunger sample as an example, the main filling materials (the top fifteen structural components) and their structural information are shown in Table 1. Based on the proportions of different aspect ratios and parameters such as Fretter diameter, an equivalent three-dimensional digital core model can be constructed.

[0198] Table 1. Main filling materials and structural information of typical samples

[0199]

[0200]

[0201] Figure 5 A schematic diagram showing a comparison between numerical simulation results and experimental results of longitudinal wave velocity according to an embodiment of the present invention is presented.

[0202] Figure 6 A schematic diagram showing the comparison between numerical simulation results and experimental results of shear wave velocity according to an embodiment of the present invention is presented.

[0203] The reliability of the modeling method of this invention was verified by comparing the simulated data with the experimental data. The numerical simulation results correspond to the P-wave and S-wave velocities of the core under normal pressure, which should be slightly lower than the experimental velocities under 5 MPa pressure. Figure 5 , Figure 6 The results show that the velocity obtained by simulation using the core model constructed using this invention is reasonable and within the expected error range compared with the experimental results. Therefore, the reliability of this method can be determined.

[0204] Example 4

[0205] Figure 7A block diagram of a digital core modeling apparatus according to an embodiment of the present invention is shown.

[0206] like Figure 7 As shown, the digital core modeling device includes:

[0207] The binarization module 201 acquires the core image of the core to be processed, and then performs threshold segmentation to obtain a binarized image;

[0208] The parameter acquisition module 202 obtains the structural parameters of the filling material in the core sample based on the binarized image, and then determines the filling structure parameters.

[0209] Modeling module 203 constructs a three-dimensional digital core model based on the filling structure parameters;

[0210] The calculation module 204 performs numerical simulations on the three-dimensional digital core model to realize digital core modeling.

[0211] In one example, multiple initial three-dimensional digital core models of clayey infill and calcite infill under various aspect ratios are constructed based on the infill structure parameters. The multiple initial three-dimensional digital core models are then superimposed to obtain a three-dimensional digital core model.

[0212] In one example, a three-dimensional digital core model is numerically simulated using the static finite element method.

[0213] In one example, numerical simulation of a three-dimensional digital core model using the static finite element method includes:

[0214] Establish the relationship between the flexibility tensor and the elastic stiffness tensor to obtain the simplified elastic stiffness tensor;

[0215] Based on bulk modulus and shear modulus, calculate the Poisson's ratio and Young's modulus of isotropic materials;

[0216] Calculate the longitudinal and transverse wave velocities of the rock.

[0217] In one example, the simplified elastic stiffness tensor is:

[0218]

[0219] Where σ and ε represent stress and strain, respectively, λ is Lamé constant, and μ is shear modulus.

[0220] In one example, Poisson's ratio is:

[0221]

[0222] Young's modulus is:

[0223]

[0224] In one example, the longitudinal wave velocity is:

[0225]

[0226] The transverse wave velocity is:

[0227]

[0228] Among them, V p V is the longitudinal wave velocity. s This represents the transverse wave velocity.

[0229] Example 5

[0230] This disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the aforementioned digital core modeling method.

[0231] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0232] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0233] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0234] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0235] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0236] Example 6

[0237] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the digital core modeling method.

[0238] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0239] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0240] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0241] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A digital core modeling method, characterized in that, include: Obtain the core image of the core to be processed, and then perform threshold segmentation to obtain a binarized image; Based on the binarized image, the structural parameters of the filling material in the core sample are obtained, and then the filling structure parameters are determined. A three-dimensional digital core model is constructed based on the filling structure parameters. Numerical simulation was performed on the three-dimensional digital core model to achieve digital core modeling.

2. The digital core modeling method according to claim 1, wherein, Based on the filling structure parameters, multiple initial three-dimensional digital core models of clayey filling material and calcite filling material under various aspect ratios are constructed. The multiple initial three-dimensional digital core models are superimposed to obtain the three-dimensional digital core model.

3. The digital core modeling method according to claim 1, wherein, The three-dimensional digital core model was numerically simulated using the static finite element method.

4. The digital core modeling method according to claim 3, wherein, Numerical simulation of the three-dimensional digital core model using the static finite element method includes: Establish the relationship between the flexibility tensor and the elastic stiffness tensor to obtain the simplified elastic stiffness tensor; Based on bulk modulus and shear modulus, calculate the Poisson's ratio and Young's modulus of isotropic materials; Calculate the longitudinal and transverse wave velocities of the rock.

5. The digital core modeling method according to claim 4, wherein, The simplified elastic stiffness tensor is: Where σ and ε represent stress and strain, respectively, λ is Lamé constant, and μ is shear modulus.

6. The digital core modeling method according to claim 4, wherein, Poisson's ratio is: Young's modulus is:

7. The digital core modeling method according to claim 4, wherein, The longitudinal wave velocity is: The transverse wave velocity is: Among them, V p V is the longitudinal wave velocity. s This represents the transverse wave velocity.

8. A digital core modeling device, characterized in that, include: The binarization module acquires the core image of the core to be processed, and then performs threshold segmentation to obtain a binarized image; The parameter acquisition module obtains the structural parameters of the filling material in the core sample based on the binarized image, and then determines the filling structure parameters. The modeling module constructs a three-dimensional digital core model based on the filling structure parameters; The calculation module performs numerical simulation on the three-dimensional digital core model to realize digital core modeling.

9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the digital core modeling method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the digital core modeling method according to any one of claims 1-7.

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