Digital modeling method, medium and equipment for enhancing connectivity of low-porosity rock core

Abstract

The invention provides a digital modeling method for enhancing connectivity of a low-porosity rock core, a medium and equipment, and relates to the field of digital modeling of rock cores, and the method comprises the following steps: reconstructing a Gaussian random field based on SAXS data to obtain an initial model of a three-dimensional digital rock core pore structure; extracting pore center points of the initial model, and constructing an initial network connecting all the pore center points; based on a minimum spanning tree algorithm, identifying mutually isolated pore clusters in the initial network, and selecting a most efficient seepage path skeleton connected with the isolated clusters; based on the seepage path skeleton, constructing a throat with fractal characteristics; and according to the total volume of the throat, performing morphological corrosion operation on an original pore area in the initial model, embedding the constructed throat into the corroded model, and then performing controllable morphological expansion operation until the volume variation of the final model is smaller than a preset error threshold. According to the method, the reconstruction precision of the digital rock core in microstructure and macroscopic connectivity is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of core digital modeling, in particular to a low porosity core connectivity enhanced digital modeling method, medium and equipment. BACKGROUND

[0002] The accurate study of the macroscopic physical properties of rock and other porous medium materials, including fluid transport behavior, thermal response characteristics and mechanical properties, is of great significance in the fields of carbon dioxide injection and storage and enhanced oil and gas recovery. Due to the complexity of the pore structure of the rock, the dynamic of the multiphase fluid and the mutual coupling between the external physical field, it is difficult to fully capture and accurately characterize the internal mechanism by relying solely on traditional physical experiments. For the evolution behavior of the core under the action of heat-flow-solidification multi-field coupling, digital core modeling technology has become an indispensable tool for simulation research. This technology realizes the accurate restoration of the micro-nano scale topological structure of the sample through advanced imaging methods such as high-resolution CT scanning, and then obtains a digital model with analytical value. However, the pore size distribution of shale covers several orders of magnitude, resulting in a need to balance the imaging resolution and the observation field of view during the reconstruction of the digital core. At the same time, the high-precision imaging method of the digital core based on physical experiments generally has the challenges of scale limitation, high cost and time consumption, which restricts the wide application of large-scale and high-precision digital core construction.

[0003] Computational reconstruction methods have emerged, which can reconstruct the target porous structure with corresponding statistical characteristics, physical mechanisms or properties with the help of two-dimensional images or limited three-dimensional structure samples. The random field method and the technology based on statistical correlation function have been widely used in the digital reconstruction of porous media. However, most of the current reconstruction strategies simplify the porous medium as an ideal two-phase system, which can better capture the statistical characteristics of the micro-pores, but the generated model is essentially a discrete set of pore clusters, ignoring the existence and role of isolated solid clusters between the pore domains. This simplification has significant limitations in describing long-range connectivity systems, as the pore structure separated by the solid phase does not match the micro-distribution of the actual complex core, leading to significant deviations in transport property prediction, thereby limiting the universality of its practical application. Especially in dense shale cores with significant multi-scale pore structure and low porosity, traditional methods are difficult to fully and accurately characterize the microstructure, and the resulting digital core model performs poorly in percolation simulation. SUMMARY

[0004] The purpose of the present application is to improve the accuracy of the characterization of the pore connectivity of low porosity core digital modeling. A low porosity core connectivity enhanced digital modeling method is proposed, comprising the following steps: S1, reconstructing a three-dimensional digital core pore structure initial model based on the Gaussian random field of SAXS data; S2, extracting the pore center points of the initial model, and constructing an initial network connecting all the pore center points; S3, based on the minimum spanning tree algorithm, identifying mutually isolated pore clusters in the initial network, and selecting the most efficient percolation path skeleton connecting the isolated clusters; S4, based on the percolation path skeleton, constructing a throat with fractal characteristics; S5, according to the total volume of the constructed throat, performing a morphological erosion operation on the original pore region in the initial model, embedding the constructed throat into the model after erosion, and then performing one or more controllable morphological dilation operations until the volume change of the final model is less than a preset error threshold.

[0005] Further, S1 is specifically: S11, scanning the sheet shale sample by using a synchrotron radiation SAXS line station, and collecting an original two-dimensional scattering image; S12, converting the original two-dimensional scattering image into a one-dimensional scattering intensity and scattering vector function curve; S13, based on the one-dimensional scattering intensity and scattering vector function curve, obtaining the volume fraction of the pore and skeleton two phases by calculating the scattering invariant; S14, based on the volume fraction of the two phases, calculating the probability that two points with a distance of in space belong to the same phase, obtaining the Debye correlation function, and determining the threshold of two-phase separation; S15, Fourier transforming the Debye correlation function to obtain the spectral function; S16, based on the spectral function, constructing a continuous three-dimensional Gaussian random field; S17, comparing the voxel value of the three-dimensional Gaussian random field with the threshold of two-phase separation, separating the pore phase and the solid phase, and reconstructing to obtain the initial model of the three-dimensional digital core pore structure of the pore-skeleton binary structure.

[0006] Further, the calculation formula for obtaining the volume fraction of the porosity and the skeleton two phases by calculating the scattering invariant is:

[0007] wherein, represents the scattering invariant, q represents the scattering vector, represents the one-dimensional scattering intensity and scattering vector function curve, represents the electron density difference between the two phases, represents the porosity volume fraction, represents the skeleton volume fraction, .

[0008] Further, the Debye correlation function is calculated as follows:

[0009] wherein, denotes the threshold value related to the Debye correlation function, and r denotes the distance between two points in space. the threshold value of two-phase separation is calculated as follows:

[0010]

[0011] wherein, denotes the inverse error function, and x denotes the integral variable.

[0012] Further, the Debye correlation function is Fourier transformed to obtain the spectral function, which is calculated as follows:

[0013] wherein, denotes the spectral function, denotes the pair distribution function, r denotes the distance between two points in space, and k denotes the variable in reciprocal space describing the structural characteristics.

[0014] Further, a continuous three-dimensional Gaussian random field is constructed based on the spectral function, which is calculated as follows:

[0015] wherein, denotes the three-dimensional Gaussian random field, x denotes the spatial position vector, , , denote the number of discrete wave vector components selected in three orthogonal directions in the three-dimensional reciprocal space, denotes the amplitude of each spatial frequency component weighted according to the spectral function , , , denote the wave vector components selected in three orthogonal directions, , denotes the initial phase corresponding to each wave vector component.

[0016] Further, an initial network connecting all the pore center points is constructed by Delaunay triangulation.

[0017] Further, S4 is specifically: S41. Based on the seepage path framework, identify a set of pore pairs to be connected, and first calculate the geometric midpoint of the pore pairs. :

[0018] in, and Represents any two adjacent points; S42 is the midpoint. Apply a controlled random displacement to generate a new path node. , represented as:

[0019] in, Indicates the magnitude of the random displacement. Represents a three-dimensional random vector; The magnitude d of the random displacement follows the following normal distribution:

[0020] in, The degree of throat tortuosity is represented by n, and the iteration level is represented by n; Direction vector of random displacement By analyzing a three-dimensional random vector that follows a standard normal distribution Normalization is performed to determine this, where:

[0021] S43. Iterate S41-S42 repeatedly until the preset iteration depth or geometric accuracy requirement is reached, generating a throat path with fractal characteristics that connects the two pores.

[0022] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned digital modeling method for enhancing connectivity in low-porosity cores.

[0023] The present invention also proposes an electronic device, including a processor and a memory, wherein the processor is interconnected with the memory, the memory is used to store a computer program, the computer program includes computer-readable instructions, and the processor is configured to invoke the computer-readable instructions to execute the above-described digital modeling method for enhancing the connectivity of low-porosity cores.

[0024] The beneficial effects of the technical solution provided by this invention are: The application firstly reconstructs an initial model based on SAXS, ensures the structural authenticity of the model on the microscale, uses Delaunay triangulation to establish a neighboring relationship network for the pore clusters in the initial model, and then uses a minimum spanning tree algorithm to screen out the shortest channel skeleton connecting all the pore clusters, so as to realize the improvement of topological connectivity. On the basis of the skeleton screening, a three-dimensional fractal algorithm based on the midpoint displacement method is introduced, and the morphological real and complex connected throat is generated between the potential pore and throat, so that the geometric characteristics are more in line with the actual reservoir. In the method, the macroscopic physical parameters (such as tortuosity) are fused to constrain the geometric morphology, and the microstructure and macroscopic seepage capacity of the dense reservoir are accurately represented. At the same time, in order to ensure the physical consistency of the macroscopic porosity, a morphological erosion-inflation compensation mechanism is designed, part of the large pores is subjected to volume distribution regulation before the throat is embedded, and then the smoothness of the pore surface is restored through the inflation process, so as to realize the volume invariance. The application effectively improves the reconstruction accuracy of the digital core in the microstructure statistical authenticity and macroscopic network connectivity. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is a flow chart of a low porosity core connectivity enhanced digital modeling method according to an embodiment of the application; Figure 2 is a 2D SAXS pattern collected according to an embodiment of the application; Figure 3 is a spectrum function obtained by Fourier transform on the Debye correlation function according to an embodiment of the application; Figure 4 is a continuous three-dimensional Gaussian random field constructed by the amplitude superposition method according to an embodiment of the application; Figure 5 is a result image obtained by separating the pore phase and the solid phase according to an embodiment of the application; Figure 6 is an initial model of the three-dimensional digital core pore structure of the pore-skeleton binary structure reconstructed according to an embodiment of the application, Figure 6 in (a) is a three-dimensional digital core pore structure reconstructed based on the Gaussian random field of the SAXS data, Figure 6 in (b) is a three-dimensional meshing effect image of the same model; Figure 7 is a key node schematic diagram of a method for constructing a connected throat with fractal characteristics in combination with high-pressure mercury injection (MICP) throat information according to an embodiment of the application, Figure 7 in a, an initial network connecting all the pore center points, Figure 7 in b, the most possible and most efficient percolation path skeleton connecting the isolated clusters is identified, Figure 7 in c, a throat is generated, Figure 7 in d, a three-dimensional throat structure with fractal characteristics is finally formed. Figure 8 This is a digital core pore distribution and meshing effect diagram based on SAXS-MICP hybrid modeling according to an embodiment of the present invention. Figure 8 In the image, 'a' represents a visualized 3D reconstruction of the digital rock core. Figure 8 In the middle b, the meshing effect of the three-dimensional digital core structure is shown. Figure 9 These are simulation images of the fluid intrusion process in 3D digital cores under different reconstruction methods. Figure 9 In the image, 'a' represents a traditional CT reconstruction model. Figure 9 In the model b, a single SAXS reconstruction model is used. Figure 9 In this invention, c represents the reconstruction model proposed by the present invention; Figure 10 This is a comparison chart of three-dimensional digital core fluid intrusion curves under different reconstruction methods; Figure 11 This is a block diagram of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0027] The flowchart of the digital modeling method for enhancing the connectivity of low-porosity cores according to an embodiment of the present invention is as follows: Figure 1 Specifically, it includes the following steps: S1. Based on Gaussian random field reconstruction of SAXS (Small Angle X-ray Scattering) data, an initial model of the three-dimensional digital core pore structure is obtained. The specific steps are as follows: S11. To ensure data accuracy, the flaky shale samples were first dried at 80°C for 72 hours to completely remove residual moisture and volatile components from the pores. Subsequently, the processed samples were scanned with high precision using a synchrotron radiation SAXS beamline. The X-ray wavelength was set to... The distance between the sample and the detector is maintained at This ensures the scattering vector The collection range is within Between these points, this range precisely corresponds to the area within shale. The key pore size distribution range was determined. Background correction was performed on the test data from the empty sample cell and standard quartz particles, and an SRM 3600 glass carbon sheet was used as the absolute intensity calibration standard. Original two-dimensional scattering images were acquired, and referenced... Figure 2 2DSAXS pattern.

[0028] S12, using professional software such as ScatterX10 to process the collected original two-dimensional scattering image, and converting it into one-dimensional scattering intensity and the function curve of scattering vector , , so as to obtain the two-dimensional small-angle X-ray scattering pattern representing the average structure information of the sample.

[0029] S13, based on the function curve of one-dimensional scattering intensity and scattering vector, the volume fractions of the pore phase and the skeleton phase are obtained by calculating the scattering invariant, and the microstructure information of the material is analyzed from the function curve of one-dimensional scattering intensity and scattering vector.

[0030] The calculation formula is:

[0031]

[0032] Among them, represents the scattering invariant, q represents the scattering vector, represents the function curve of one-dimensional scattering intensity and scattering vector, represents the electron density difference between the two phases, represents the volume fraction of the porosity, represents the volume fraction of the skeleton, , represents the scattering angle, that is, the angle between the incident light and the scattered light in the scattering process.

[0033] S14, based on the volume fractions of the two phases, the probability that two points in space with a distance of belong to the same phase is calculated, the Debye correlation function is obtained, and the threshold value of two-phase separation is determined.

[0034] The calculation formula of Debye correlation function is as follows:

[0035] Among them, represents the Debye correlation function related to the threshold value , The calculation formula of the threshold value of two-phase separation is as follows:

[0036]

[0037] Among them, represents the inverse error function, that is, the volume fraction of the porosity , and x represents the integral variable.

[0038] S15, Fourier transform is carried out on the Debye correlation function, and a spectral function is obtained, and the spectral function is referenced Figure 3 , and the calculation formula is as follows:

[0039] Among them, The spectral function is represented by The pair probability distribution function is represented by , r represents the distance between two points in space, and k represents a variable describing the structural characteristics in reciprocal space.

[0040] S16, a continuous three-dimensional Gaussian random field is constructed based on the spectral function. First, an initial phase information group consistent with the statistical characteristics of the spectral function is generated by a random algorithm . Then, the amplitude information of the spectral function and the random phase are used to construct a continuous three-dimensional Gaussian random field by amplitude superposition method, and the continuous three-dimensional Gaussian random field is referenced Figure 4 , Figure 4 The continuous three-dimensional Gaussian random field constructed by the amplitude superposition method in the embodiment of the application.

[0041] The calculation formula is as follows:

[0042] Among them, The three-dimensional Gaussian random field is represented by , , The number of discrete wave vector components selected in three orthogonal directions in three-dimensional reciprocal space is represented by The amplitude of each spatial frequency component weighted according to the spectral function , , , The wave vector components selected in three orthogonal directions are represented by The direction of the wave vector is uniformly distributed on the unit sphere, and the modulus is determined by the acceptance-rejection method according to the distribution of the spectral function , The initial phase corresponding to each wave vector component is represented by , which is usually randomly distributed in the interval. By adjusting the amplitude and phase of each frequency component, the structure field with specific spatial statistical characteristics is reconstructed.

[0043] S17, compare each voxel value of the three-dimensional Gaussian random field with a two-phase separation threshold value, separate the pore phase and the solid phase, and the reference Figure 5 ,Figure 5 is the result image obtained by separating the pore phase and the solid phase in an embodiment of the present application, and a three-dimensional random field is generated. The voxel value of each voxel of the three-dimensional random field is compared with the threshold value calculated previously. If the voxel value is less than the threshold value, the voxel is determined to be a "pore" phase; otherwise, if the voxel value is greater than the threshold value, the voxel is determined to be a "solid" phase. An initial model of the three-dimensional digital core pore structure of the pore-skeleton binary structure is reconstructed.

[0044] As shown in Figure 6 , Fig. (a) in the figure is a three-dimensional digital core pore structure reconstructed based on a Gaussian random field of SAXS data, and different colors in the figure represent various independent pore clusters distributed in space. It can be seen that the pore clusters are uniformly distributed in three-dimensional space and have different morphologies, fully embodying the good restoration capability of the model on the microscopic statistical characteristics such as pore size distribution, volume fraction and specific surface area, and so on, on the real rock. Figure 6 Fig. (b) in the figure shows a three-dimensional meshing effect image of the same model. The pores and the matrix are finely divided into a large number of mesh elements. Although the preliminary digital core model can highly restore the microscopic structure statistical characteristics of the real rock, from the overall connectivity point of view, most of the pore clusters present an isolated state from each other, lack effective interconnection channels, and cannot form a complete percolation network. Therefore, the structure still has certain limitations in reflecting the actual permeability and fluid transport capacity of the reservoir. It is necessary to further introduce a connectivity enhancement strategy for optimization and improvement. Figure 6 S2, first, the key parameters (such as throat tortuosity and pore throat ratio) reflecting the percolation characteristics of the reservoir are obtained by using the MICP experiment as the physical constraint condition to guide the structure optimization. The pore center points of the initial three-dimensional digital core model are extracted, the topological analysis of the pore network in the model is performed, and an initial network connecting all the pore center points is constructed by establishing a preliminary spatial connection network through Delaunay triangulation. The network mathematically contains all potential connection paths based on the proximity principle (as shown in Fig. (a) in

[0045] ). Figure 7

[0046] S3, based on the minimum spanning tree algorithm, the mutually isolated pore clusters in the initial network are identified, and the most efficient percolation path skeleton connecting the isolated clusters is selected.

[0047] ​The minimum spanning tree is extracted from the Delaunay network to distinguish connected pore regions from isolated pore clusters in the model. By applying the minimum spanning tree algorithm, the initial spatial connectivity network is systematically pruned to eliminate redundant connections, thereby accurately identifying isolated pore clusters at the topological level and identifying the most probable and efficient seepage path framework connecting these isolated clusters (e.g., ...). Figure 7 (As shown in b).

[0048] S4. Based on the seepage path framework, construct a throat with fractal characteristics. The specific steps are as follows: S41. Based on the identified seepage path framework, throats with fractal characteristics are programmatically constructed to achieve physical connectivity between isolated pore clusters. After identifying a set of pore pairs to be connected, this invention employs a recursive iterative algorithm based on the midpoint displacement method to generate geometrically realistic throats (such as...). Figure 7 (As shown in c). In each iteration, for any two adjacent points... and First, calculate its geometric midpoint. :

[0049] in, and It represents any two adjacent points.

[0050] S42. Next, apply a controlled random displacement to the midpoint to generate a new path node. The magnitude of the displacement It is not arbitrary; the variance of its sampling distribution is determined by the current line segment length and the iteration level. Furthermore, the key physical parameter extracted from the high-pressure mercury intrusion (MICP) experiment—tortuosity—is strictly constrained, specifically following the normal distribution:

[0051] To ensure the unbiasedness of the displacement direction in three-dimensional space, the direction vector of the random displacement... By analyzing a three-dimensional random vector of a standard normal distribution Normalization is performed to determine:

[0052]

[0053] in, Indicates the magnitude of the random displacement. Represents a three-dimensional random vector. denoted by , where n represents the degree of throat tortuosity, and represents the iteration level.

[0054] S43, iteratively iterating S41-S42 until a preset iteration depth or geometric accuracy requirement is reached, to generate a throat path connecting the two pores and having fractal characteristics.

[0055] S5, according to the total volume of the constructed throat, performing a morphological erosion operation on the original pore region in the initial model, embedding the constructed throat into the eroded model, and then performing one or more controllable morphological dilation operations until the volume change of the final model is less than a preset error threshold.

[0056] To ensure that the total porosity of the model can be strictly conserved after introducing these new throats, the morphological erosion-dilation algorithm is used for volume compensation. First, according to the total volume of all new throats , a corresponding morphological erosion operation is performed on the original pore region in the model to create an equivalent volume space. Subsequently, the generated fractal throat network is embedded into the eroded model. Finally, one or more controllable morphological dilation operations are performed on the overall model, and the dilation radius is iteratively optimized until the volume change of the final model is less than a preset minimum error threshold , so as to realize the "volume neutral" structure optimization.

[0057] The complete three-dimensional pore-throat network is generated and visualized, and the final digital core structure is output for subsequent physical property analysis. Through this recursive construction method, a three-dimensional throat structure with fractal characteristics is finally formed (as shown in d of Figure 7 ), and the geometric characteristics are controlled by the input tortuosity parameter and the iteration level. The throat structure generated by the method described in the present application not only has good self-similarity, but also can effectively simulate the complex and multi-scale pore connectivity characteristics in real shale reservoirs.

[0058] The hybrid reconstruction model finally obtained by the above method is shown in Figure 8 , which not only retains the structural statistical authenticity of the initial SAXS model at the microscale, but also enhances the macroscopic topological connectivity. Figure 8 In a, the visualized three-dimensional reconstruction of the digital core clearly shows the complex distribution and connectivity characteristics of the pore and throat network in space. Different colors in the figure can be used to represent multiple independent and connected pore clusters and their topological structures, with blue showing, for example, the dominant flow path. The throat network generated by the fractal algorithm recursively has rich scales and complex morphology. Figure 8Figure b illustrates the meshing effect of the three-dimensional digital core structure. It is evident that the pore-throat system is precisely and conformally embedded into a high-quality three-dimensional mesh. This provides a solid structural foundation for subsequent multiphysics simulations and analyses of seepage, diffusion, and mechanics based on numerical methods such as the finite element method or grid method, balancing geometric details with global topological connectivity. This effectively ensures the accuracy and stability of numerical calculations. In summary, the method described in this invention has significant technical advantages in achieving realistic microstructure reproduction and ensuring the friendliness of numerical simulations.

[0059] To further verify the effectiveness of the proposed hybrid reconstruction method and its superiority in characterizing the seepage characteristics of tight reservoirs, comparative numerical simulations of fluid intrusion were conducted. For example... Figure 9 As shown, by numerically simulating the fluid (mercury) intrusion process using different reconstruction models, their ability to reproduce the real seepage physics can be intuitively evaluated. Traditional CT reconstruction models ( Figure 9 (a) Due to its fundamental limitation in imaging resolution, it cannot capture nanoscale pore structures, resulting in fluid intrusion exhibiting a non-physical, large-scale, discontinuous distribution that severely deviates from the complex seepage path dominated by capillary forces in dense rock cores. A single SAXS reconstruction model ( Figure 9 (b) Although it has statistical fidelity in porosity, the pore clusters it generates are isolated from each other and lack an effective macroscopic connectivity network, which prevents the fluid in the simulation from entering the pores to form a penetrating seepage front.

[0060] In stark contrast, the hybrid reconstruction model proposed in this invention ( Figure 9 (c) By creatively introducing macroscopic connectivity characteristics with real physical significance constrained by MICP experimental data, a topologically complex and geometrically realistic throat network was successfully constructed. Simulation results show that fluid can penetrate extensively and deeply along these physically plausible paths, exhibiting a diffuse and pervasive saturation distribution consistent with real physical processes. Therefore, the hybrid reconstruction model of this invention significantly outperforms both the traditional CT reconstruction model and the single SAXS model in terms of both the plausibility of the fluid (mercury) spatial distribution and the topological connectivity of the seepage path.

[0061] This advantage is not only reflected in the macroscopic visual effect, but is also strongly confirmed in the quantitative comparison of mercury intrusion curves. Figure 10The comparison between the mercury intrusion simulation results of different models and the real experimental data is shown. The real experimental data (pressure pump test results) show that the throat diameters of the real rock sample are mainly distributed in the peak area of about 10 nanometers, which is the key to determine its permeability. The CT reconstruction model (blue curve) completely fails to capture the pore throat structure smaller than 1000 nanometers due to the limitation of its resolution, and its simulation results have nothing in common with the experimental data. The simple SAXS reconstruction model (SAXS-RM) can reflect the existence of small-size pores, but its simulated mercury intrusion pressure and pore throat distribution have obvious deviation from the experimental values.

[0062] In contrast, the simulated mercury intrusion curve of the SAXS-MICP hybrid reconstruction model (SAXS-MICP-HRM) proposed in the present application has a high consistency with the experimental data (green curve) in the entire throat diameter range, especially in the key small-size throat area that determines the reservoir permeability, accurately reproducing the peak position, peak shape and cumulative mercury intrusion of the experimental curve. This highly consistent result fundamentally verifies the correctness and effectiveness of the present method in constraining the geometry by fusing macroscopic physical parameters (such as tortuosity) when constructing the throat network. Therefore, whether it is qualitative permeability simulation or quantitative physical property prediction, it has proved that the present application has unique technical advantages and great practical value in accurately characterizing the microstructure and macroscopic permeability of the dense reservoir.

[0063] In an exemplary embodiment, a computer readable storage medium is included, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the above-mentioned digital modeling method for connectivity enhancement of low porosity cores.

[0064] Referring to Figure 11 In an exemplary embodiment, an electronic device is also included, which includes at least one processor, at least one memory, and at least one communication bus.

[0065] The memory stores a computer program, and the computer program includes computer readable instructions. The processor invokes the computer readable instructions stored in the memory through the communication bus to execute the above-mentioned digital modeling method for connectivity enhancement of low porosity cores.

[0066] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A digital modeling method for enhancing connectivity in low-porosity rock cores, characterized in that, Includes the following steps: S1. An initial model of the three-dimensional digital core pore structure is obtained by reconstructing the Gaussian random field based on SAXS data. S2. Extract the pore center points of the initial model and construct an initial network connecting all pore center points; S3. Based on the minimum spanning tree algorithm, identify mutually isolated pore clusters in the initial network and filter the seepage path skeleton connecting these mutually isolated pore clusters. S4. Based on the seepage path framework, construct a throat with fractal characteristics; S5. Based on the total volume of the constructed throat, perform morphological erosion on the original pore region in the initial model to embed the constructed throat into the eroded model. Then, perform a controlled morphological expansion operation until the volume change of the final model is less than a preset error threshold.

2. The digital modeling method for enhancing the connectivity of low-porosity cores according to claim 1, characterized in that, S1 specifically refers to: S11. Scan the scaly shale sample using the SAXS synchrotron radiation beamline to acquire raw two-dimensional scattering images; S12. Transform the original two-dimensional scattering image into a one-dimensional function curve of scattering intensity versus scattering vector; S13. Based on the function curve of one-dimensional scattering intensity and scattering vector, the volume fraction of porosity and framework phases is obtained by calculating the scattering invariant. S14. Based on the volume fraction of the two phases, calculate the spatial distance between the phases. The probability that two points belong to the same phase is used to obtain the Debye correlation function and determine the threshold for phase separation. S15. Perform a Fourier transform on the Debye correlation function to obtain the spectral function; S16. Construct a continuous three-dimensional Gaussian random field based on the spectral function; S17. Compare each voxel value of the three-dimensional Gaussian random field with the threshold for two-phase separation to separate the pore phase and the solid phase, and reconstruct the initial model of the three-dimensional digital core pore structure of the pore-skeleton binary structure.

3. The digital modeling method for enhancing the connectivity of low-porosity cores according to claim 2, characterized in that, The formulas for calculating the volume fraction of porosity and framework phases by calculating the scattering invariant are as follows: in, Let q represent the scattering invariant and q represent the scattering vector. The curve representing the function curve of one-dimensional scattering intensity versus scattering vector. This represents the electron density difference between the two phases. Indicates the porosity volume fraction. Indicates the volume fraction of the skeleton. .

4. The digital modeling method for enhancing the connectivity of low-porosity cores according to claim 2, characterized in that, The formulas for calculating Debye-related functions are as follows: in, Representation and threshold The related Debye functions, where r represents the distance between two points in space; The threshold for phase separation The calculation formula is as follows: in, Let x represent the inverse error function, and let x represent the integration variable.

5. The digital modeling method for enhancing the connectivity of low-porosity cores according to claim 2, characterized in that, Performing a Fourier transform on the Debye correlation function yields the spectral function, calculated using the following formula: in, Represents the spectral function. Let represent the probability distribution function of the two bodies, r represent the distance between two points in space, and k represent the variable describing the structural characteristics in the reciprocal space.

6. The digital modeling method for enhancing connectivity of low-porosity cores according to claim 2, characterized in that, A continuous three-dimensional Gaussian random field is constructed based on the spectral function, and the calculation formula is as follows: in, This represents a three-dimensional Gaussian random field, where x represents the spatial position vector. , , These represent the number of discrete wave vector components selected along the three orthogonal directions in the three-dimensional reciprocal space, respectively. Indicated based on spectral function The amplitudes of each weighted spatial frequency component, , , These represent the wave vector components selected in the three orthogonal directions. For three-dimensional wave vectors, This represents the initial phase corresponding to each wave vector component.

7. The digital modeling method for enhancing the connectivity of low-porosity cores according to claim 1, characterized in that, An initial network connecting all pore center points is constructed using Delaunay triangulation.

8. The digital modeling method for enhancing connectivity of low-porosity cores according to claim 1, characterized in that, S4 specifically refers to: S41. Based on the seepage path framework, identify a set of pore pairs to be connected, and first calculate the geometric midpoint of the pore pairs. : in, and Represents any two adjacent points; S42 is the midpoint. Apply a controlled random displacement to generate a new path node. , is represented as: in, Indicates the magnitude of the random displacement. Represents a three-dimensional random vector; The magnitude d of the random displacement follows the following normal distribution: in, The degree of throat tortuosity is represented by n, and the iteration level is represented by n; Direction vector of random displacement By analyzing a three-dimensional random vector of a standard normal distribution Normalization is performed to determine this, where: S43. Iterate S41-S42 repeatedly until the preset iteration depth or geometric accuracy requirement is reached, generating a throat path with fractal characteristics that connects the two pores.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

10. An electronic device, characterized in that, The device includes a processor and a memory interconnected thereto, wherein the memory is used to store a computer program, the computer program including computer-readable instructions, and the processor is configured to invoke the computer-readable instructions to perform the method as described in any one of claims 1-8.

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