Method and system for digitally calculating core permeability based on X-ray CT

Abstract

The invention provides a method and a system for digitally calculating rock core permeability based on X-ray CT. The method comprises the following steps: establishing a calculation model of rock core permeability K according to a pore throat radius median value, a pore throat radius and porosity; x-ray CT is adopted to scan the rock core to obtain CT images of different positions of the rock core, binarization processing is carried out on the CT images, and a binary image only with pores and a skeleton is obtained; performing in-situ stacking construction on the binary image to obtain a three-dimensional digital core model, and extracting a pore axis network from the three-dimensional digital core model; and based on the pore axis network, extracting throat data by using a Dijkstra algorithm to obtain a pore throat radius and a porosity phi, and substituting the pore throat radius and the porosity phi into the calculation model of the rock core permeability K to obtain permeability data of the rock core. According to the invention, rapid determination of the permeability parameter based on the digital core is realized, and the seepage characteristics of the complex reservoir rock can be accurately calculated.

Description

Technical Field

[0001] The invention belongs to the technical field of reservoir physical property evaluation in oil and gas exploration and development, and specifically belongs to a method and system for calculating core permeability based on X-ray CT digital technology. Background Art

[0002] Underground reservoir rocks are porous media with very complex pore structures. In particular, the existence of low-permeability reservoirs such as tight reservoirs has caused many problems in the evaluation and interpretation of underground reservoirs using existing conventional theories. It is difficult to directly study the seepage mechanism of reservoirs and calculate reservoir parameters. Some macroscopic manifestations of reservoir properties are usually determined by microscopic mechanisms. The connectivity of the pore structure, the size and distribution of pore throats, etc. will seriously affect the migration speed of fluids in the pore space. Therefore, to study the seepage characteristics of macroscopic reservoirs, theoretical and technical research cannot be limited to the macroscopic level, but can be started from the microscopic pore structure characteristics. At present, the methods for macroscopic experiments on reservoirs are mainly based on Darcy's law equations. With the gradual deepening of exploration and development, more tight oil reservoirs have come into view. The main characteristics of these reservoirs are complex microstructures and poor connectivity, which have a certain impact on the accuracy of macroscopic experiments. Darcy's law equations cannot be applied to the microscopic scale. Therefore, it is necessary to study a technical method system suitable for the microscopic scale, and conduct in-depth research on the seepage characteristics of reservoirs at two levels to make the permeability measurement results more accurate and reliable. In addition, in traditional rock physics research, the most commonly used and basic method is experiment. However, as the research deepens, for some complex special reservoirs, the problems of coring and displacement difficulties have led to the common experimental methods being unable to meet the accuracy requirements of in-depth research. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the present invention provides a method and system for calculating core permeability based on X-ray CT digital, mainly for formations with complex pore structures and difficult fluid flow, to establish a new relationship between the median pore throat radius Rm and the permeability K and porosity The relationship between them is analyzed, and Rm=1 is taken as the critical value. The permeability parameter calculation model is established in sections. By performing CT scanning on the core, the CT image is binarized using the iterative threshold method to establish a three-dimensional digital core. Then the pore mid-circumference network is extracted, and the porosity and pore throat parameters are statistically analyzed to improve the speed and accuracy of obtaining the core permeability parameters. The core permeability parameters can be determined quickly and accurately, solving the problem of rock physical displacement experiments.

[0004] To achieve the above object, the present invention provides the following technical solution: a method and system for digitally calculating core permeability based on X-ray CT, comprising the following steps:

[0005] S1 establishes a calculation model for core permeability K based on the median pore throat radius, pore throat radius, and porosity;

[0006] lgR m =M1lg(K / φ)-M0 (1)

[0007] Where: R m is the median value of the pore throat radius, M0 and M1 are empirical coefficients related to geological characteristics;

[0008] S2 uses X-ray CT to scan the core to obtain CT images at different positions of the core, and performs binarization on the CT images to obtain a binary image of only pores and skeletons;

[0009] S3 constructs a three-dimensional digital core model by stacking the binary images in situ, and extracts the pore medial axis network from the three-dimensional digital core model;

[0010] Based on the pore medial axis network, S4 uses the Dijkstra algorithm to extract throat data to obtain the pore throat radius and porosity φ, and substitutes the pore throat radius and porosity φ into the calculation model of the core permeability K to obtain the permeability data of the core.

[0011] Furthermore, the critical pore throat radius median R m临 The data is 1, according to the median value of the critical pore throat radius R m临 The calculation model of core permeability K is divided into:

[0012] R m临 >1,lgR m =M1lg(K / φ)-M0 (2)

[0013] R m临 <1,lgR m =-M1'lg(K / φ)-M0' (3)

[0014] Where: R m is the median value of the pore throat radius, M0 and M1 are empirical coefficients related to geological characteristics, which are determined by least squares fitting.

[0015] Furthermore, in S2, the core is cleaned, sampled and cemented before X-ray CT scanning, the diameter of the core sample is not less than 2 mm, and the step angle of the X-ray CT scanning is not greater than 5 degrees.

[0016] Furthermore, in S3, an iterative threshold segmentation algorithm is used to perform binarization processing on the CT image.

[0017] Furthermore, in S3, the specific steps of binarization processing are as follows:

[0018] (1) All skeleton voxels in the CT image are marked as 0, and the pore voxels are marked as 1, and n = 2;

[0019] (2) A pore voxel is randomly selected and labeled as n. With this pore voxel as the center, all the pore voxels around this pore voxel that have twenty-six connectivity with this pore voxel are labeled as n;

[0020] (3) Taking the pore voxel marked as n as the center, the pore voxels around the pore voxel with twenty-six connectivity are marked as n, and the above operation is repeated until all pore voxels that can be marked are marked as n, and the number of n is f(n);

[0021] (4) Let n = n + 1, randomly select a pore voxel labeled n + 1 from the pore voxels that are still labeled 1, and repeat steps (2) and (3) until the labels of all pore voxels are not 1. Record the number f(n + 1) and obtain a binary image containing only pores and skeletons.

[0022] Furthermore, in S3, a distance calibration algorithm is used to extract the pore medial axis network in the three-dimensional digital core model.

[0023] Furthermore, in S3, the specific steps of extracting the pore medial axis network are as follows:

[0024] (1) All skeleton voxels in the 3D digital core model are marked as 0, and pore voxels are not marked;

[0025] (2) Let k be the distance radius, mark the pore voxel adjacent to the skeleton voxel marked as 0 as k, and record the direction from 0 to k;

[0026] (3) Mark the unlabeled pore voxel adjacent to the pore voxel labeled k as k+1, and record the direction in which k points to k+1;

[0027] (4) Repeat step (3), increasing k by 1 each time, until all pore voxels are calibrated with distance radii;

[0028] (5) Delete branch paths and pinhole paths, merge redundant pore nodes, correct boundary paths, and obtain the pore medial axis network.

[0029] The present invention also provides a system for calculating core permeability based on X-ray CT digital data, comprising:

[0030] A model building module is used to establish a calculation model for core permeability K based on the median pore throat radius, pore throat radius, and porosity;

[0031] lgR m =M1lg(K / φ)-M0 (1)

[0032] Where: R m is the median value of the pore throat radius, M0 and M1 are empirical coefficients related to geological characteristics;

[0033] An image acquisition module is used to scan the core using X-ray CT to obtain CT images of different positions of the core, and to perform binarization processing on the CT images to obtain a binary image of only pores and skeletons;

[0034] A three-dimensional model building module is used to stack the binary images in situ to obtain a three-dimensional digital core model, and extract the pore medial axis network in the three-dimensional digital core model;

[0035] The permeability calculation module is used to extract throat data based on the pore medial axis network using the Dijkstra algorithm to obtain the pore throat radius and porosity φ, and substitute the pore throat radius and porosity φ into the calculation model of the core permeability K to obtain the permeability data of the core.

[0036] The present invention provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above method when executing the computer program, or implements the functions of each module in the above system when executing the computer program.

[0037] The present invention provides a computer-readable medium, in which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are implemented.

[0038] Compared with the prior art, the present invention has at least the following beneficial effects:

[0039] The invention provides a method for calculating the core permeability based on X-ray CT digital data, establishes a new relationship between the median value Rm of the pore throat radius and the permeability K and the porosity φ, establishes a permeability parameter calculation model, and based on the core CT scanning imaging, uses an iterative threshold method to binarize the CT image, establishes a three-dimensional digital core, and then extracts the pore mid-circle network, and statistically calculates the porosity and pore throat parameters to achieve rapid acquisition of the core permeability parameters. The invention can achieve rapid and effective acquisition of the permeability parameters for the core with complex pore structure and low permeability, which makes it difficult to conduct a permeability physical experiment, and can more accurately calculate the seepage characteristics of complex reservoir rocks.

[0040] Furthermore, the present invention takes Rm=1 as the critical value and establishes a permeability parameter calculation model in sections, which provides a reliable basis for the further development of current complex reservoir evaluation and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of the method of the present invention;

[0042] Figure 2 This is a cross-sectional view of the core CT scan;

[0043] Figure 3 It is a three-dimensional binary digital core model;

[0044] Figure 4 is the extracted porosity median axis map;

[0045] Figure 5 Taking Rm = 1 as the critical value, the relationship between the median pore throat radius and permeability is statistically analyzed;

[0046] Figure 6 Comparison of calculated permeability with measured permeability. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0048] See also Figure 1 The present invention provides a method for calculating core permeability based on X-ray CT digital data, comprising the following steps:

[0049] Step 101, scanning the core sample using X-ray CT to obtain a CT image;

[0050] Step 102, segmenting and binarizing the image to obtain a binary image with only two components, pores and skeletons;

[0051] Step 103, constructing a three-dimensional digital core and extracting the pore medial axis network;

[0052] Step 104, based on the pore medial axis network, the Dijkstra algorithm is used to extract throat data, calculate the pore throat radius, length, cross-sectional area, porosity, and pore volume, and establish a calculation formula for the core permeability K based on the calculated pore throat radius and porosity φ:

[0053] lgR m =M1lg(K / φ)-M0 (1)

[0054] Where: R m is the median value of the pore throat radius, and M0 and M1 are empirical coefficients related to geological characteristics.

[0055] Preferably, based on the digital core, the present invention can obtain the pore throat radius at different spatial positions inside the core, which is not a unique value, but a series of distributions that vary with space. Based on these pore throat radius distributions, the unique median pore throat radius of the core can be calculated.

[0056] Preferably, M0 and M1 are mainly determined based on data such as pore throat radius, porosity, permeability, etc., using formula (1) as a mathematical model and determined by least squares fitting.

[0057] Step 105: construct a critical pore throat radius median R m临 , calculate the above M0 and M1 parameters respectively, and obtain the permeability calculation model;

[0058] Step 106, using the CT image of the core to be tested to extract the central axis network, statistically analyze the pore throat parameters, and bring them into the model to calculate the permeability.

[0059] Specifically, before performing step 101, conventional core cleaning, sample preparation and cementation are required, wherein the cleaning includes oil washing, salt washing and cementation solidification.

[0060] Specifically, in step 101, the core sample is a low porosity and permeability core sample of an oil field, the diameter of the core sample is not less than 2 mm, and the CT scanning step angle is not greater than 5 degrees.

[0061] Furthermore, in step 102, an optimized iterative threshold segmentation algorithm is used for the binarization algorithm of image segmentation to distinguish only the spatial distribution of the skeleton and pores. The calculation steps are as follows:

[0062] (1) All skeleton voxels in the space are marked as 0, and pore voxels are marked as 1, and n = 2;

[0063] (2) Randomly select a voxel labeled n from all the voxels labeled 1. With this voxel as the center, all the voxel points around it that have 26 connections are also labeled n;

[0064] (3) Taking all these points marked as n as the center, mark the voxels with twenty-six connectivity with them as n again, and repeat the operation until all voxels that can be marked are marked. At this time, the number of n is f(n);

[0065] (4) Let n = n + 1, and then randomly select a voxel labeled n + 1 from the voxels still labeled 1, repeat steps 2 and 3, and record the number f(n + 1);

[0066] (5) Until all voxels are not 1.

[0067] Furthermore, in step 103, the method for extracting the pore medial axis network adopts an optimized distance calibration algorithm, and the iterative process is:

[0068] (1) In the initial iteration, all skeleton voxels are marked as 0, and pore voxels are not marked;

[0069] (2) Let k be the distance radius, set the initial k = 1, identify all skeleton voxels marked as 0, mark the pore voxels adjacent to them as k, that is, 1, and record the direction from 0 to 1;

[0070] (3) Identify all voxels marked as k from the radius, and then mark the unmarked pore voxels adjacent to it as k+1, and record the direction from k to k+1;

[0071] (4) Increase the value of k and iterate until all pore voxels are calibrated with distance radius.

[0072] (5) Delete branch paths and needle-eye paths.

[0073] (6) Merge redundant pore nodes.

[0074] (7) Correct the boundary path.

[0075] Further, in step 105, the critical pore throat radius median R is determined m临 1, and the median pore throat radius is greater than 1 and the median pore throat radius is less than 1 as the standard, and the parameters M0 and M1 corresponding to the permeability calculation model formula (1) are established respectively, so as to establish a segmented permeability calculation model:

[0076] R m临 >1,lgR m =M1lg(K / φ)-M0 (2)

[0077] R m临 <1,lgR m =-M1'lg(K / φ)-M0' (3)

[0078] Further, in step 106, the above permeability calculation model is used. According to the median pore throat radius R obtained by processing the CT images of different core samples m and porosity φ, according to R m The size of the value selects the corresponding permeability calculation model and quickly calculates the permeability parameters.

[0079] The present invention also provides a system for calculating core permeability based on X-ray CT digital data, comprising:

[0080] A model building module is used to establish a calculation model for core permeability K based on the median pore throat radius, pore throat radius, and porosity;

[0081] lgR m =M1lg(K / φ)-M0 (1)

[0082] Where: R mis the median value of the pore throat radius, M0 and M1 are empirical coefficients related to geological characteristics;

[0083] An image acquisition module is used to scan the core using X-ray CT to obtain CT images of different positions of the core, and to perform binarization processing on the CT images to obtain a binary image of only pores and skeletons;

[0084] A three-dimensional model building module is used to stack the binary images in situ to obtain a three-dimensional digital core model, and extract the pore medial axis network in the three-dimensional digital core model;

[0085] The permeability calculation module is used to extract throat data based on the pore medial axis network using the Dijkstra algorithm to obtain the pore throat radius and porosity φ, and substitute the pore throat radius and porosity φ into the calculation model of the core permeability K to obtain the permeability data of the core.

[0086] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above method are implemented, or when the processor executes the computer program, the functions of each module in the above system are implemented.

[0087] The above-mentioned computer program may be divided into one or more modules, one or more modules are stored in the above-mentioned memory and executed by the processor to complete the present invention.

[0088] The terminal device can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server.

[0089] The terminal device may include, but is not limited to, a processor and a memory.

[0090] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0091] The memory can be used to store the above-mentioned computer programs and / or modules, and the processor implements various functions of the above-mentioned terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory.

[0092] If the module integrated in the terminal device 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.

[0093] Based on such understanding, the present invention implements all or part of the processes in the above method, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the above method steps when executed by a processor. Among them, the above computer program includes computer program code, and the above computer program code can be in source code form, object code form, executable file or some intermediate form, etc.

[0094] The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the above-mentioned computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0095] Example

[0096] 1. Select 8 core samples from a certain area, wash the oil and salt, drill a small sample with a diameter of 2 mm, fix it with cement, and then scan it with X-ray CT equipment to obtain grayscale images (CT scan images) of different positions of the core, such as Figure 2 shown.

[0097] 2. For each sample CT scan image obtained, the optimized iterative threshold method is used for binarization processing to divide the skeleton and pores. The skeleton is displayed in black and the pore space is displayed in white. By stacking the binary images in situ, a three-dimensional digital core model is constructed, such as Figure 3 shown.

[0098] 3. Using the established three-dimensional digital core model, the optimized distance calibration algorithm is used to extract the pore axis network of each sample, such as Figure 4 The result diagram of a sample is shown (black skeleton and white pores).

[0099] 4. Based on the pore medial axis network, the Dijkstra algorithm is used to extract throat data, calculate the pore throat radius, length, cross-sectional area, porosity, and pore volume, and statistically analyze the development characteristics of these samples.

[0100] 5. Determine the median value of the critical pore throat radius R m临 =1, and take the median pore throat radius greater than 1 and the median pore throat radius less than 1 as the standard, respectively establish the parameters M0 and M1 corresponding to the permeability calculation model formula (1), so as to establish a segmented permeability calculation model, as shown in Figure 5 , and its calculation formula is as follows:

[0101] Rm>1,lgR m =0.0282lg(K / φ)-0.065 (4)

[0102] Rm>1,lgR m =-0.12121 lg(K / φ)-0.0672 (5)

[0103] Where: Rm is the median pore throat radius, K is the permeability value, and φ is the porosity value.

[0104] 6. Use equations (4) and (5) to calculate the core permeability. Combined with the physical experimental measurement results of the core, the difference between the two is very small, thus realizing the calculation of permeability parameters, such as Figure 6 .

[0105] like Figure 5 As shown, the median critical pore throat radius is R m临 =1, and the median pore throat radius is greater than 1 and the median pore throat radius is less than 1 as the standard, and the parameters M0 and M1 corresponding to the permeability calculation model formula (1) are established respectively. The figure shows that the fitting results after segmentation have high correlation and obvious regularity.

[0106] Figure 6 This is a comparison chart of the core permeability results calculated using the model and the physical experimental measurement results of the core. It can be found from the figure that the difference between the two is very small and the consistency is high, indicating that the model can be effectively used to quickly determine the permeability parameters and can effectively solve the problem of determining the permeability parameters of complex low-permeability rocks.

[0107] The present invention discloses a method for rapidly obtaining core permeability parameters, which is applicable to the calculation and evaluation of permeability characteristic parameters of unconventional reservoirs and belongs to the field of reservoir evaluation. The method comprises: obtaining core samples, scanning the core using X-ray CT to obtain CT images; segmenting and binarizing the original images using digital image processing technology to obtain binary images with only two components, pores and skeletons; reconstructing the binary images to establish digital cores, truly reflecting the microscopic pore structure of the cores; extracting the central axis network of its spatial structure using a distance calibration algorithm, and quantitatively characterizing parameters such as pore volume size and throat size distribution; on the basis of determining the pore throat parameters, performing permeability analysis and calculation, establishing the relationship between rock pore structure parameters and permeability, and obtaining a permeability parameter calculation model for the cores. The present invention aims at the problems of difficult fluid displacement of complex reservoir cores and difficult experimental method measurement, and realizes the rapid determination of permeability parameters based on digital cores, which can more accurately calculate the seepage characteristics of complex reservoir rocks, and provides a reliable basis for the further development of complex reservoir evaluation and development in my country.

[0108] Finally, it should be noted that the above specific implementation methods are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for calculating core permeability based on X-ray CT digital data, characterized in that: The steps include: S1 establishes a calculation model for core permeability K based on the median pore throat radius, pore throat radius, and porosity; lg R m =M1lg(K / φ)-M0 (1) Where: R m is the median value of the pore throat radius, M0 and M1 are empirical coefficients related to geological characteristics; S2 uses X-ray CT to scan the core to obtain CT images at different positions of the core, and performs binarization on the CT images to obtain a binary image of only pores and skeletons; S3 constructs a three-dimensional digital core model by stacking the binary images in situ, and extracts the pore medial axis network from the three-dimensional digital core model; Based on the pore medial axis network, S4 uses the Dijkstra algorithm to extract throat data to obtain the pore throat radius and porosity φ, and substitutes the pore throat radius and porosity φ into the calculation model of the core permeability K to obtain the permeability data of the core.

2. The method for calculating core permeability based on X-ray CT digital data according to claim 1, characterized in that: Determine the median value of the critical pore throat radius R m临 The data is 1, according to the median value of the critical pore throat radius R m临 The calculation model of core permeability K is divided into: R m临 >1,lgR m =M1lg(K / φ)-M0 (2) R m临 <1,lgR m =-M1’lg(K / φ)-M0’ (3) Where: R m is the median of the pore throat radius, M0 and M1 are empirical coefficients related to geological characteristics and are determined by least squares fitting.

3. The method for calculating core permeability based on X-ray CT digital data according to claim 1, characterized in that: In S2, the core was cleaned, sampled and cemented before X-ray CT scanning, the core sample diameter was not less than 2 mm, and the step angle of X-ray CT scanning was not greater than 5 degrees.

4. The method for calculating core permeability based on X-ray CT digital data according to claim 1, characterized in that: In S3, an iterative threshold segmentation algorithm is used to perform binarization processing on the CT image.

5. The method for calculating core permeability based on X-ray CT digital data according to claim 4, characterized in that: In S3, the specific steps of binarization processing are as follows: (1) All skeleton voxels in the CT image are marked as 0, and the pore voxels are marked as 1, and n = 2; (2) A pore voxel is randomly selected and labeled as n. With this pore voxel as the center, all the pore voxels around this pore voxel that have twenty-six connectivity with this pore voxel are labeled as n; (3) Taking the pore voxel marked as n as the center, the pore voxels around the pore voxel with twenty-six connectivity are marked as n, and the above operation is repeated until all pore voxels that can be marked are marked as n, and the number of n is f(n); (4) Let n = n + 1, randomly select a pore voxel labeled n + 1 from the pore voxels that are still labeled 1, and repeat steps (2) and (3) until the labels of all pore voxels are not 1. Record the number f(n + 1) and obtain a binary image containing only pores and skeletons.

6. The method for calculating core permeability based on X-ray CT digital data according to claim 1, characterized in that: In S3, a distance calibration algorithm is used to extract the pore medial axis network in the three-dimensional digital core model.

7. The method for calculating core permeability based on X-ray CT digital data according to claim 6, characterized in that: In S3, the specific steps for extracting the pore medial axis network are as follows: (1) All skeleton voxels in the 3D digital core model are marked as 0, and pore voxels are not marked; (2) Let k be the distance radius, mark the pore voxel adjacent to the skeleton voxel marked as 0 as k, and record the direction from 0 to k; (3) Mark the unlabeled pore voxel adjacent to the pore voxel labeled k as k+1, and record the direction in which k points to k+1; (4) Repeat step (3), increasing k by 1 each time, until all pore voxels are calibrated with distance radii; (5) Delete branch paths and pinhole paths, merge redundant pore nodes, correct boundary paths, and obtain the pore medial axis network.

8. A system for digitally calculating core permeability based on X-ray CT, characterized in that: include: A model building module is used to establish a calculation model for core permeability K based on the median pore throat radius, pore throat radius, and porosity; lgR m =M1lg(K / φ)-M0 (1) Where: R m is the median value of the pore throat radius, M0 and M1 are empirical coefficients related to geological characteristics; An image acquisition module is used to scan the core using X-ray CT to obtain CT images of different positions of the core, and to perform binarization processing on the CT images to obtain a binary image of only pores and skeletons; A three-dimensional model building module is used to stack the binary images in situ to obtain a three-dimensional digital core model, and extract the pore medial axis network in the three-dimensional digital core model; The permeability calculation module is used to extract throat data based on the pore medial axis network using the Dijkstra algorithm to obtain the pore throat radius and porosity φ, and substitute the pore throat radius and porosity φ into the calculation model of the core permeability K to obtain the permeability data of the core.

9. A terminal device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods of claims 1 to 7 when executing the computer program, or implements the functions of each module in the system of claim 8 when executing the computer program.

10. A computer-readable medium, characterized in that The computer program is stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps in any one of the methods of claims 1 to 7 are implemented.

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