Digital core reconstruction method for shale matrix based on random growth method

Abstract

The present disclosure relates to a digital core reconstruction method for a shale matrix based on scanning electron microscope images and a random growth method. The digital core reconstruction metho

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of oil-gas exploration, and in particular, to a digital core reconstruction method for a shale matrix based on scanning electron microscope images and a random growth method. BACKGROUND

[0002] Data required for oil-gas field development may be obtained by digital core technology to provide theoretical guidance and technical support for efficient development of unconventional oil and gas resources. A digital core reconstruction technique has become one of necessary techniques and methods for current unconventional oil and gas development.

[0003] Existing digital core reconstruction methods include a Gaussian method, a random growth method, a process method, a Markov chain Monte Carlo method, a simulated annealing method, and the like. Shale reservoirs have the characteristics of low porosity, low permeability, and anisotropy, have pore sizes ranging from several nanometers to hundreds of nanometers, are poor in connectivity, and exhibit directional pore development. The existing digital core reconstruction technique cannot reflect these pore structure characteristics when reconstructing a shale pore structure. SUMMARY

[0004] The present disclosure provides a digital core reconstruction method for a shale matrix based on a random growth method that can solve the problem that the anisotropy, incomplete pore range extracted at single magnification times, and a distribution law of pore characteristic parameters are not taken into account in the existing digital core reconstruction technique.

[0005] The technical solution of the present disclosure is a digital core reconstruction method for a shale matrix based on a random growth method, including:

[0006] SI: obtaining a plurality of core slices of a core sample in one parallel bedding direction and two vertical bedding directions, respectively, determining a plurality of points on the core slices, and obtaining an original image set including a large-vision field image, an intermediate-vision field image, and a small-vision field image for each point on each core slice by a scanning electron microscope;

[0007] S2: obtaining an image characteristic probability distribution set with respect to pore and throat for each image in the original image set, and correspondingly magnifying each image in the image characteristic probability distribution set according to a preset magnification rule to obtain a 28 03 24 sample characteristic probability distribution set for the core sample; and

[0008] S3: building a three-dimensional array for digital core reconstruction, and correspondingly determining a pore morphology and a throat morphology of a digital core and a number and a growth direction of throats based on the sample characteristic probability distribution set and a random growth method to obtain a digital core model.

[0009] Alternatively, step SI may include:

[0010] SI 1: obtaining the core sample and sequentially performing operations of wire cutting and argon ion polishing on the core sample in one parallel bedding direction and two vertical bedding directions, respectively, to obtain three core slices having main extension surfaces perpendicular to one another relative to the core sample;

[0011] S12: determining the plurality of points on the core slices based on a preset point selection rule; and

[0012] S13: obtaining the original image set including the large-vision field image, the intermediate-vision field image, and the small-vision field image for each point on each core slice by the scanning electron microscope, where the intermediate-vision field image is sourced from the large-vision field image, and the small-vision field image is sourced from the intermediate-vision field image.

[0013] Alternatively, an image specification of the large-vision field image may include: an image size of 49.7x49.7 pm and an image accuracy of 0.048 pm;

[0014] an image specification of the intermediate-vision field image may include: an image size of 24.9x24.9 pm and an image accuracy of 0.024 pm; and

[0015] an image specification of the small-vision field image may include: an image size of 12.4x12.4 pm and an image accuracy of 0.012 pm.

[0016] Alternatively, a number of points selected on each of the core slices may be at least five;

[0017] and step S2 may include:

[0018] S21: preprocessing the original image set to obtain a binary image set based on points;

[0019] S22: determining the image characteristic probability distribution set with respect to pore and throat for each image in the binary image set;

[0020] S23: integrating pore reference positions in the large-vision field image, the intermediatevision field image, and the small-vision field image of each point to obtain a pore determination position based on points, and correspondingly magnifying each image in the original image set according to the preset magnification rule to obtain a magnified image set.

[0021] S24: obtaining the sample characteristic probability distribution set for the core sample according to the magnified image set and the image characteristic probability distribution set; and

[0022] S25: integrating point characteristic probability distribution sets from five points on one core slice in the parallel bedding direction and integrating point characteristic probability distribution sets 28 03 24 from ten points on two core slices in the vertical bedding directions to determine the sample characteristic probability distribution set for the core sample.

[0023] Alternatively, the preset magnification rule in step S23 may include:

[0024] correspondingly determining a large-vision field set including pore reference positions having a diameter of greater than 0.048 pm based on large-vision field images;

[0025] correspondingly determining an intermediate-vision field set including pore reference positions having a diameter of greater than 0.024 pm based on intermediate-vision field images;

[0026] correspondingly determining a small-vision field set including pore reference positions having a diameter of greater than 0.012 pm based on small-vision field images; and

[0027] based on a confirmation principle of selecting and retaining one of coincident relative positions, comparing the pore reference positions having coincident relative positions in the large-vision field set, the intermediate-vision field set, and the small-vision field set and correspondingly retaining a selected pore reference position, thereby obtaining the pore determination position based on points.

[0028] Alternatively, the image characteristic probability distribution set may include:

[0029] a pore diameter probability distribution, a shape factor probability distribution, a throat diameter probability distribution, a throat length probability distribution, a tortuosity probability distribution, and a pore-throat coordination number probability distribution.

[0030] Alternatively, step S3 may include:

[0031] S31: building an 800^800x800 three-dimensional array for digital core reconstruction, and initially assigning a value of 1 to each array element representing a mineral, where each array element represents a pixel with an accuracy of 12 nm;

[0032] and randomly selecting a position from the three-dimensional array as a pore growth starting point;

[0033] S32: determining a conversion rule based on the random growth method, and based on the conversion rule, determining numbers and growth directions of throats in the parallel bedding direction and the vertical bedding directions according to the pore-throat coordination number probability distribution, determining the throat morphology according to the throat diameter probability distribution, the tortuosity probability distribution, and the throat length probability distribution, and determining the pore morphology according to the pore diameter probability distribution and the shape factor probability distribution; and

[0034] S33: repeating step S32, repeatedly building throats and pores until pore determination functional positions reach a boundary of the three-dimensional array, thereby obtaining the digital core model and corresponding pore characteristic parameters.

[0035] Alternatively, step S3 may further include: 28 03 24

[0036] S34: obtaining a pore characteristic parameter probability distribution of the core sample, comparing a pore characteristic parameter probability distribution of the digital core model with the pore characteristic parameter probability distribution of the core sample to determine an error, if the error is greater than 5%, increasing a length of a side of the three-dimensional array at an amplitude of 50, and repeating step S32 until the error between the pore characteristic parameter probability distribution of the digital core model and the pore characteristic parameter probability distribution of the core sample is less than 5%.

[0037] Alternatively, the conversion rule may include:

[0038] a conversion parameter probability distribution being a cumulative probability distribution, generating a random number between 0 and 100 using a random generating function, determining a parameter value corresponding to a cumulative probability interval in which the random number occurs, and selecting the parameter value as a characteristic parameter value of pores to be reconstructed.

[0039] Beneficial effects:

[0040] First, the present disclosure takes into full consideration the anisotropy of sampling and guarantees the representativeness of digital core reconstruction by obtaining a plurality of core slices of a core sample in one parallel bedding direction and two vertical bedding directions before subsequent operations.

[0041] Second, the present disclosure allows for determination of different magnification rules according to different vision field images after obtaining corresponding scanning electron microscope images, thereby guaranteeing that all pores and throats can be accurately covered and improving the accuracy of digital core reconstruction.

[0042] Moreover, the present disclosure takes into full and comprehensive consideration a characteristic probability distribution regarding points, creates corresponding sets, and guarantees the scientificity and integrity of data during building.

[0043] To sum up, the technical solution of the present disclosure can better reflect the characteristics of anisotropy and large pore size range of a shale pore structure, and a digital core reconstruction method for a shale matrix based on scanning electron microscope images and a random growth method is established by combining pore characteristic parameter distributions extracted from scanning electron microscope images of different directions and scales with the random growth method.

[0044] Therefore, the present disclosure can solve the problem that the anisotropy, incomplete pore range extracted at single magnification times, and a distribution law of pore characteristic parameters are not taken into account in the existing digital core reconstruction technique. 28 03 24 BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To describe the technical solution of the present disclosure, the accompanying drawings required in embodiments will be described briefly below. Apparently, other accompanying drawings can also be derived from these drawings by those of ordinary skill in the art without creative efforts.

[0046] FIG. 1 is a flowchart of a digital core reconstruction method for a shale matrix based on a random growth method in an embodiment of the present disclosure;

[0047] FIG. 2 is a logic diagram of a digital core reconstruction method for a shale matrix based on a random growth method in an embodiment of the present disclosure;

[0048] FIG. 3 shows an image of sample examples of three core slices of mutually perpendicular planes in an embodiment of the present disclosure;

[0049] FIG. 4 is a diagram illustrating a point selection example by a scanning electron microscope in an embodiment of the present disclosure;

[0050] FIG. 5 is a diagram illustrating sampling examples of an original image set in an embodiment of the present disclosure;

[0051] FIG. 6 is a binary image before preprocessing in an embodiment of the present disclosure;

[0052] FIG. 7 is a binary image after preprocessing in an embodiment of the present disclosure;

[0053] FIG. 8 is a schematic diagram of a pore structure segmented from a binary image in an embodiment of the present disclosure;

[0054] FIG. 9 is a schematic diagram of a throat structure segmented from a binary image in an embodiment of the present disclosure;

[0055] FIG. 10 is a schematic diagram of comparison between pore diameter distributions in a large-vision field image and an intermediate-vision field image in an embodiment of the present disclosure;

[0056] FIG. 11 is a schematic diagram of comparison between pore diameter distributions in an intermediate-vision field image and a small-vision field image in an embodiment of the present disclosure;

[0057] FIG. 12 is a schematic diagram of a pore diameter distribution based on points in an embodiment of the present disclosure;

[0058] FIG. 13 is a schematic diagram of a pore shape factor distribution based on points in an embodiment of the present disclosure;

[0059] FIG. 14 is a specific schematic diagram of chart 1 in FIG. 2 of an embodiment of the present disclosure;

[0060] FIG. 15 is a specific schematic diagram of chart 2 in FIG. 2 of an embodiment of the present disclosure;

[0061] FIG. 16 is a specific schematic diagram of chart 3 in FIG. 2 of an embodiment of the present disclosure; 28 03 24

[0062] FIG. 17 is a specific schematic diagram of chart 4 in FIG. 2 of an embodiment of the present disclosure;

[0063] FIG. 18 is a specific schematic diagram of chart 5 in FIG. 2 of an embodiment of the present disclosure;

[0064] FIG. 19 is a specific schematic diagram of chart 6 in FIG. 2 of an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The embodiments will be described in detail below, and examples thereof are represented in the accompanying drawings. When the following descriptions relate to the accompanying drawings, unless otherwise stated, same numerals in different accompanying drawings represent same or similar elements. Implementations described in the following embodiments do not represent all implementations consistent with the present disclosure. These implementations are merely examples of a system and a method that are described in detail in the claims and consistent with some aspects of the present disclosure.

[0066] The present disclosure provides a digital core reconstruction method for a shale matrix based on a random growth method. As shown in FIG. 1 and FIG. 2, FIG. 1 is a flowchart of a digital core reconstruction method for a shale matrix based on a random growth method in an embodiment of the present disclosure; and FIG. 2 is a logic diagram of a digital core reconstruction method for a shale matrix based on a random growth method in an embodiment of the present disclosure. The digital core reconstruction method includes the following steps.

[0067] Ska plurality of core slices of a core sample in one parallel bedding direction and two vertical bedding directions are obtained, respectively; a plurality of points on the core slices are determined; and an original image set including a large-vision field image, an intermediate-vision field image, and a small-vision field image for each point on each core slice is obtained by a scanning electron microscope.

[0068] Step S1 includes the following steps.

[0069] SI 1: the core sample is obtained and operations of wire cutting and argon ion polishing are sequentially performed on the core sample in one parallel bedding direction and two vertical bedding directions, respectively, to obtain three core slices having main extension surfaces perpendicular to one another relative to the core sample.

[0070] Specifically, as shown in FIG. 3, FIG. 4, and FIG. 5, FIG. 3 shows an image of sample examples of three core slices of mutually perpendicular planes in an embodiment of the present disclosure; FIG. 4 is a diagram illustrating a point selection example by a scanning electron microscope in an embodiment of the present disclosure; and FIG. 5 is a diagram illustrating sampling 28 03 24 examples of an original image set in an embodiment of the present disclosure.

[0071] S12: the plurality of points on the core slices are determined based on a preset point selection rule.

[0072] S13: the original image set including the large-vision field image, the intermediate-vision field image, and the small-vision field image for each point on each core slice is obtained by the scanning electron microscope.

[0073] The intermediate-vision field image is sourced from the large-vision field image.

[0074] The small-vision field image is sourced from the intermediate-vision field image.

[0075] Specifically, a number of points selected on each core slice is at least five.

[0076] At least 45 images (3 planes x 5 points / plane x 3 images / point) are required for one core sample.

[0077] An image specification of the large-vision field image includes: an image size of 49.7x49.7 pm and an image accuracy of 48 nm.

[0078] An image specification of the intermediate-vision field image includes: an image size of 24.9x24.9 pm and an image accuracy of 24 nm.

[0079] An image specification of small-vision field image includes: an image size of 12.4x12.4 pm and an image accuracy of 12 nm.

[0080] S2: an image characteristic probability distribution set with respect to pore and throat for each image in the original image set is obtained, and each image in the image characteristic probability distribution set is correspondingly magnified according to a preset magnification rule to obtain a sample characteristic probability distribution set for the core sample.

[0081] Step S2 includes the following steps.

[0082] S21: the original image set is preprocessed to obtain a binary image set based on points.

[0083] Specifically, firstly, the images in the original image set are converted into 8-bit gray level images, and then image binarization processing is performed using a threshold segmentation algorithm to convert the images into black-and-white binary images, where black represents pores and white represents a mineral skeleton, as shown in FIG. 6 which shows a binary image before preprocessing in an embodiment of the present disclosure.

[0084] Second, denoising and smoothing are performed on the binary images, and the part of organic matter is removed, as shown in FIG. 7 which shows a binary image after preprocessing in an embodiment of the present disclosure.

[0085] Finally, the binary images are segmented into pores and throats using Remove Outliers and Image Calculator commands in ImageJ software. FIG. 8 is a schematic diagram of a pore structure segmented from a binary image in an embodiment of the present disclosure, and FIG. 9 is a schematic diagram of a throat structure segmented from a binary image in an embodiment of the present disclosure.

[0086] S22: the image characteristic probability distribution set with respect to pore and throat for each image in the binary image set is determined.

[0087] Specifically, the image characteristic probability distribution set includes: a pore diameter probability distribution, a shape factor probability distribution, a throat diameter probability distribution, a throat length probability distribution, a tortuosity probability distribution, and a porethroat coordination number probability distribution.

[0088] Al. The pore diameter probability distribution shows proportions of numbers of pores having different diameters in a total number of pores.

[0089] Al 1. A calculation formula for a pore diameter is as follows:

[0090]

[0091] A12. A calculation formula for an average pore diameter is as follows: N Ta D = --

[0092] P

[0093] In the above formulas, DPi represents a diameter of a single pore, pm. APi represents an area of a single pore, pm2, and Dp represents the average pore diameter, pm. N represents the total number of pores.

[0094] A2. The shape factor probability distribution shows ratios of cross-sectional areas to perimeter squares of pores and throats.

[0095] A21. A calculation formula for a pore shape factor is as follows: p -p / q2

[0096] pehm,i

[0097] A22. A calculation formula for an average shape factor is as follows:

[0098] P N

[0099] In the above formulas, Fpi represents a single pore shape factor, decimal. Sperim.i represents a perimeter of a single throat, pm. Fp represents the average shape factor.

[0100] A3. The throat diameter probability distribution shows proportions of numbers of throats having different diameters in a total number of throats.

[0101] A31. A calculation formula for a throat diameter is as follows:

[0102] ^,=(^) / 4

[0103] A32. A calculation formula for an average throat diameter is as follows: M IX D= —-

[0104] M

[0105] In the above formulas, Dpi represents a diameter of a single throat, pm. Api represents an area of a single throat, pm2.Dt represents an average throat diameter, pm. M represents the total number of throats.

[0106] A4. A calculation formula for the throat length probability distribution is as follows:

[0107] A41. A calculation formula for a throat length is as follows:

[0108] S1 “ Dti ) / 2 .

[0109] A42. A calculation formula for an average throat length is as follows: M 28 03 24

[0110]

[0111] In the above formulas, Si represents a length of a single throat, pm. S’ represents the average throat length, pm.

[0112] A5. The tortuosity probability distribution shows a ratio of an actual length of a seepage channel to a visual length of sight through a seepage medium or a ratio of an actual length of an available throat to a straight length of both ends of the throat.

[0113] A51. A calculation formula for throat tortuosity is as follows: r,=A=^- I 1

[0114] ' .

[0115] A52. A calculation formula for average tortuosity is as follows: M Tt = —--

[0116]

[0117] In the above formulas, Tti represents tortuosity of a single throat, decimal. Si represents an actual length of a single throat between two pores, pm. Li represents a straight length of a single throat between two pores, pm. tt represents the average tortuosity.

[0118] A6. The pore-throat coordination number probability distribution shows numbers of throats communicated with single pores.

[0119] A61. A calculation formula for an average coordination number is as follows: N _ 2¾ CNt = —---

[0120] N

[0121] In the above formula, CNt represents the average coordination number. CNpi represents a coordination number of a single pore, decimal. 28 03 24

[0122] S23: pore reference positions in the large-vision field image, the intermediate-vision field image, and the small-vision field image of each point are integrated to obtain a pore determination position based on points, and each image in the original image set is correspondingly magnified according to the preset magnification rule to obtain a magnified image set.

[0123] Specifically, after the pore structure characteristics of three images of different vision fields for one point are obtained, statistical results of the three images need to be combined, and repeated pores identified from the three images are removed.

[0124] A principle of integrating the pore reference positions in the large-vision field image, the intermediate-vision field image, and the small-vision field image of each point includes:

[0125] correspondingly determining a large-vision field set including pore reference positions having a diameter of greater than 0.048 pm based on large-vision field images;

[0126] correspondingly determining an intermediate-vision field set including pore reference positions having a diameter of greater than 0.024 pm based on intermediate-vision field images;

[0127] correspondingly determining a small-vision field set including pore reference positions having a diameter of greater than 0.012 pm based on small-vision field images; and

[0128] based on a confirmation principle of selecting and retaining one of coincident relative positions, comparing the pore reference positions having coincident relative positions in the large-vision field set, the intermediate-vision field set, and the small-vision field set and correspondingly retaining a selected pore reference position, thereby obtaining the pore determination position based on points.

[0129] S24: the sample characteristic probability distribution set for the core sample is obtained according to the magnified image set and the image characteristic probability distribution set.

[0130] Specifically, the distributions of numbers of pores having different diameters in the large-vision field image having the image specification of 49.7x49.7 pm and the accuracy of 48 nm and the intermediate-vision field image having the image specification of 24.9x24.9 pm and the accuracy of 24 nm are as shown in FIG. 10 which is a schematic diagram of comparison between pore diameter distributions in a large-vision field image and an intermediate-vision field image in an embodiment of the present disclosure. In each column combination in FIG. 10, the left column represents the intermediate-vision field image and the right column represents the large-vision field image.

[0131] A diameter range of pores in the large-vision field image is 0.048-1 pm; a diameter range of pores in the intermediate-vision field image is 0.024-0.9 pm; a diameter range of coincident pores in the large-vision field image and the intermediate-vision field image is 0.048-0.9 pm, and the number of pores having a diameter of 0.2-0.9 pm in the large-vision field image is about 3 times more than that in the intermediate-vision field image. Therefore, the pores at this point include the pores having the diameter of 0.2-0.9 pm in the large vision field. The pores having a diameter below 0.2 pm in the 28 03 24 intermediate-vision field image need to be compared with pores in the small vision field and then compared to be retained or not.

[0132] Specifically, the distributions of numbers of pores having different diameters in the intermediate-vision field image having the image specification of 24.9x24.9 pm and the accuracy of 24 nm and the small-vision field image having the image specification of 12.4x12.4 pm and the accuracy of 12 nm are as shown in FIG. 11 which is a schematic diagram of comparison between pore diameter distributions in an intermediate-vision field image and a small-vision field image in an embodiment of the present disclosure. In each column combination in FIG. 11, the left column represents the small-vision field image and the right column represents the intermediate-vision field image. The number of pores having a diameter of 0.048-0.2 pm in the intermediate vision field is about 1.5 times more than the number of pores having a diameter of 0.048-0.9 pm in the small vision field image. Therefore, for the pores at this point, the pores having the diameter of 0.048-0.2 pm in the intermediate-vision field image and the pores having the diameter of 0.048 pm in the small vision field are retained.

[0133] To sum up, the principle of integrating the pore reference positions in the large-vision field image, the intermediate-vision field image, and the small-vision field image of each point includes: correspondingly determining pore reference positions having a diameter of 0.2-0.9 pm based on 1 times original large-vision field images; correspondingly determining pore reference positions having a diameter of 0.048-0.2 pm based on 3 times magnified intermediate-vision field images; and correspondingly determining pore reference positions having a diameter of <0.048 pm based on 4.5 times magnified small-vision field images.

[0134] By combining the parameters of all pores included at this point together, the entire pore diameter distribution and the shape factor distribution that can be effectively identified by the scanning electron microscope can be obtained, as shown in FIG. 12 which a schematic diagram of a pore diameter distribution based on points in an embodiment of the present disclosure and FIG. 13 which is a schematic diagram of a pore shape factor distribution based on points in an embodiment of the present disclosure. Similarly, the parameters of all throats can be obtained.

[0135] S25: point characteristic probability distribution sets from five points on one core slice in the parallel bedding direction are integrated and point characteristic probability distribution sets from ten points on two core slices in the vertical bedding directions are integrated to determine the sample characteristic probability distribution set for the core sample.

[0136] Specifically, the entire pore diameter distribution and the shape factor distribution at this point can be obtained by combining the parameters corresponding to all pores included at this point of the sample together. The parameters of all throats at this point can be obtained by same steps.

[0137] Since shale has obvious anisotropy in the parallel bedding direction and the vertical bedding 28 03 24 directions. Therefore, the data of pores and throats of 10 points in the vertical bedding directions is combined together to obtain the characteristic parameter distributions of pores and throats in vertical bedding planes of the sample, and the data of pores and throats of 5 points in a parallel bedding plane is combined together to obtain the characteristic parameter distributions of pores and throats in the horizontal bedding plane of the sample.

[0138] S3: a three-dimensional array for digital core reconstruction is built, and a pore morphology and a throat morphology of a digital core and a number and a growth direction of throats are correspondingly determined based on the sample characteristic probability distribution set and a random growth method to obtain a digital core model.

[0139] Step S3 includes the following steps.

[0140] S31: an 800x800x800 three-dimensional array for digital core reconstruction is built, and a value of 1 is initially assigned to each array element representing a mineral, where each array element represents a pixel with an accuracy of 12 nm.

[0141] A position is randomly selected from the three-dimensional array as a pore growth starting point.

[0142] Specifically, the size of the three-dimensional array may be changed as needed, and is not limited to 800x800x800.

[0143] S32: a conversion rule is determined based on the random growth method, and based on the conversion rule, numbers and growth directions of throats in the parallel bedding direction and the vertical bedding directions are determined according to the pore-throat coordination number probability distribution, the throat morphology is determined according to the throat diameter probability distribution, the tortuosity probability distribution, and the throat length probability distribution, and the pore morphology is determined according to the pore diameter probability distribution and the shape factor probability distribution.

[0144] Specifically, the conversion rule includes:

[0145] a conversion parameter probability distribution being a cumulative probability distribution, generating a random number between 0 and 100 using a random generating function, determining a parameter value corresponding to a cumulative probability interval in which the random number occurs, and selecting the parameter value as a characteristic parameter value of pores to be reconstructed.

[0146] S33: step S32 is repeated, throats and pores are repeatedly built until pore determination functional positions reach a boundary of the three-dimensional array, thereby obtaining the digital core model and corresponding pore characteristic parameters probability distribution.

[0147] S34: a pore characteristic parameter probability distribution of the core sample is obtained; a pore characteristic parameter probability distribution of the digital core model is compared with the pore characteristic parameter probability distribution of the core sample to determine an error; if the error is greater than 5%, a length of a side of the three-dimensional array is increased at an amplitude of 50, and step S32 is repeated until the error between the pore characteristic parameter probability distribution of the digital core model and the pore characteristic parameter probability distribution of the core sample is less than 5%.

[0148] The embodiments of the present disclosure are described in detail above, but the contents are only preferred embodiments of the present disclosure and cannot be considered as limiting the implementation scope of the present disclosure. Any equivalent modifications, improvements, etc. made within the scope of the present disclosure should fall within the protection scope of the present disclosure. 28 03 24 06 03 24

Claims

1. A digital core reconstruction method for a shale matrix based on a random growth method, comprising:SI: obtaining a plurality of core slices of a core sample in one parallel bedding direction and two vertical bedding directions, respectively, determining a plurality of points on the core slices, and obtaining an original image set comprising a large-vision field image, an intermediate-vision field image, and a small-vision field image for each point on each core slice by a scanning electron microscope;S2: obtaining an image characteristic probability distribution set with respect to pore and throat for each image in the original image set, and correspondingly magnifying each image in the image characteristic probability distribution set according to a preset magnification rule to obtain a sample characteristic probability distribution set for the core sample; andS3: building a three-dimensional array for digital core reconstruction, and correspondingly determining a pore morphology and a throat morphology of a digital core and a number and a growth direction of throats based on the sample characteristic probability distribution set and a random growth method to obtain a digital core model.

2. The digital core reconstruction method for a shale matrix based on a random growth method according to claim 1, wherein step SI comprises:SI 1: obtaining the core sample and sequentially performing operations of wire cutting and argon ion polishing on the core sample in one parallel bedding direction and two vertical bedding directions, respectively, to obtain three core slices having main extension surfaces perpendicular to one another relative to the core sample;S12: determining the plurality of points on the core slices based on a preset point selection rule; andS13: obtaining the original image set comprising the large-vision field image, the intermediatevision field image, and the small-vision field image for each point on each core slice by the scanning electron microscope, wherein the intermediate-vision field image is sourced from the large-vision field image, and the small-vision field image is sourced from the intermediate-vision field image.

3. The digital core reconstruction method for a shale matrix based on a random growth method according to claim 2, wherein an image specification of the large-vision field image comprises: an image size of 49.7x49.7 pm and an image accuracy of 0.048 pm;an image specification of the intermediate-vision field image comprises: an image size of 24.9x24.9 pm and an image accuracy of 0.024 pm; andan image specification of the small-vision field image comprises: an image size of 12.4x12.4 pm and an image accuracy of 0.012 pm.

4. The digital core reconstruction method for a shale matrix based on a random growth method according to claim 3, wherein a number of points selected on each of the core slices is at least five; andstep S2 comprises:S21: preprocessing the original image set to obtain a binary image set based on points;S22: determining the image characteristic probability distribution set with respect to pore and throat for each image in the binary image set;S23: integrating pore reference positions in the large-vision field image, the intermediatevision field image, and the small-vision field image of each point to obtain a pore determination position based on points, and correspondingly magnifying each image in the original image set according to the preset magnification rule to obtain a magnified image set;S24: obtaining the sample characteristic probability distribution set for the core sample according to the magnified image set and the image characteristic probability distribution set; andS25: integrating point characteristic probability distribution sets from five points on one core slice in the parallel bedding direction and integrating point characteristic probability distribution sets from ten points on two core slices in the vertical bedding directions to determine the sample characteristic probability distribution set for the core sample.

5. The digital core reconstruction method for a shale matrix based on a random growth method according to claim 4, wherein the preset magnification rule in step S23 comprises:correspondingly determining a large-vision field set comprising pore reference positions having a diameter of greater than 0.048 pm based on large-vision field images;correspondingly determining an intermediate-vision field set comprising pore reference positions having a diameter of greater than 0.024 pm based on intermediate-vision field images;correspondingly determining a small-vision field set comprising pore reference positions having a diameter of greater than 0.012 pm based on small-vision field images; andbased on a confirmation principle of selecting and retaining one of coincident relative positions, comparing the pore reference positions having coincident relative positions in the large-vision field set, the intermediate-vision field set, and the small-vision field set and correspondingly retaining a selected pore reference position, thereby obtaining the pore determination position based on points.06 03 246. The digital core reconstruction method for a shale matrix based on a random growth method according to claim 4, wherein the image characteristic probability distribution set comprises:a pore diameter probability distribution, a shape factor probability distribution, a throat diameter probability distribution, a throat length probability distribution, a tortuosity probability distribution, and a pore-throat coordination number probability distribution.

7. The digital core reconstruction method for a shale matrix based on a random growth method according to claim 6, wherein step S3 comprises:S31: building an 800x800x800 three-dimensional array for digital core reconstruction, and initially assigning a value of 1 to each array element representing a mineral, wherein each array element represents a pixel with an accuracy of 12 nm; andrandomly selecting a position from the three-dimensional array as a pore growth starting point;S32: determining a conversion rule based on the random growth method, and based on the conversion rule, determining numbers and growth directions of throats in the parallel bedding direction and the vertical bedding directions according to the pore-throat coordination number probability distribution, determining the throat morphology according to the throat diameter probability distribution, the tortuosity probability distribution, and the throat length probability distribution, and determining the pore morphology according to the pore diameter probability distribution and the shape factor probability distribution; andS33: repeating step S32, repeatedly building throats and pores until pore determination functional positions reach a boundary of the three-dimensional array, thereby obtaining the digital core model and corresponding pore characteristic parameters.

8. The digital core reconstruction method for a shale matrix based on a random growth method according to claim 7, wherein step S3 further comprises:S34: obtaining a pore characteristic parameter probability distribution of the core sample, comparing a pore characteristic parameter probability distribution of the digital core model with the pore characteristic parameter probability distribution of the core sample to determine an error, if the error is greater than 5%, increasing a length of a side of the three-dimensional array at an amplitude of 50, and repeating step S32 until the error between the pore characteristic parameter probability distribution of the digital core model and the pore characteristic parameter probability distribution of the core sample is less than 5%.

9. The digital core reconstruction method for a shale matrix based on a random growth methodaccording to claim 7, wherein the conversion rule comprises:a conversion parameter probability distribution being a cumulative probability distribution, generating a random number between 0 and 100 using a random generating function, determining a parameter value corresponding to a cumulative probability interval in which the random number occurs, and selecting the parameter value as a characteristic parameter value of pores to be reconstructed.06 03 24

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