Shale pore structure detection method, device and equipment and storage medium

Abstract

The embodiment of the invention relates to the field of oil-gas exploration, and discloses a shale pore structure detection method, device and equipment and a storage medium, and the method comprises the following steps: obtaining an isothermal adsorption test curve image, a two-dimensional scattering profile image and a ratio value of an accumulated pore volume to a pore diameter of a shale sample; dividing the shale sample into at least two sample subsets, and respectively obtaining a ratio value of the pore volume to the pore diameter of each sample subset; determining an effective shale area of each shale sample subset based on the ratio value of the accumulated pore volume to the pore diameter and the ratio value of the pore volume to the pore diameter; scanning the effective shale area of each shale sample subset to obtain a two-dimensional tomography image; performing image fusion on each two-dimensional tomography image, the isothermal adsorption test curve image and the two-dimensional scattering profile image to obtain a pore structure image of the shale; and determining pore structure characterization parameters of the shale based on the pore structure image. According to the method, quantitative characterization of the pore structure of the shale is accurately realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of oil and gas exploration, and in particular to a method, apparatus, equipment and storage medium for detecting the pore structure of shale. Background Technology

[0002] Shale gas exploration and development in my country is booming, and shale gas has been commercially developed on a large scale. Shale gas development can effectively reduce environmental pollution, compensate for oil and gas shortages, and ensure energy security. As an unconventional oil and gas resource, shale gas is characterized by tight reservoirs, low oil and gas abundance, and wide distribution. The geological conditions for shale gas development vary significantly across different regions, leading to the inapplicability of shale gas exploration and evaluation technologies to different areas. The considerable differences in shale gas development outcomes are mainly due to variations in preservation conditions. Shale gas enrichment areas typically develop in regions with good preservation conditions, allowing the natural gas generated from shale to be preserved in the formation for development.

[0003] Characterizing the porosity development of shale reservoirs is crucial for evaluating their quality. It not only controls the occurrence mode and gas content of shale gas but also influences the selection of shale gas development methods. Therefore, prioritizing effective methods and technologies to accurately characterize shale pore structure is essential. Pore structure is one of the most important characteristic parameters of pore development. With in-depth research, quantitative testing methods for shale pore structure have made rapid progress. Currently, widely used quantitative characterization methods for shale pore structure include X-ray detection and fluid injection methods. However, in practical applications, neither of these methods can accurately quantify the pore structure of shale. Summary of the Invention

[0004] The purpose of this invention is to provide at least one method, apparatus, device, and storage medium for detecting the pore structure of shale, which can at least solve the technical problem of not being able to accurately achieve quantitative characterization of the pore structure of shale, and at least achieve accurate quantitative characterization of the pore structure of shale.

[0005] To address the aforementioned technical problems, at least one embodiment of this application provides a method for detecting the pore structure of shale, comprising:

[0006] Obtain the isothermal adsorption test curve image, two-dimensional scattering profile image, and the ratio n of the cumulative pore volume to pore diameter of the shale sample;

[0007] The shale sample is divided into at least two shale sample subsets, and the ratio p of pore volume to pore diameter of each shale sample subset is obtained.

[0008] The effective shale region of each shale sample subset is determined based on the ratio n of the cumulative pore volume to the pore diameter and the ratio p of the pore volume to the pore diameter of each shale sample subset.

[0009] The effective shale regions of each of the aforementioned shale sample subsets were scanned to obtain two-dimensional tomographic images.

[0010] The two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are fused to obtain the pore structure image of shale.

[0011] The pore structure characterization parameters of shale are determined based on the pore structure image.

[0012] At least one embodiment of this application also provides a device for detecting the pore structure of shale, comprising:

[0013] The acquisition module is used to acquire isothermal adsorption test curve images, two-dimensional scattering profile images, and the ratio n of the cumulative pore volume to pore diameter of the shale sample.

[0014] The partitioning module is used to divide the shale sample into at least two shale sample subsets and obtain the ratio p of pore volume to pore diameter for each shale sample subset.

[0015] The calculation module is used to determine the effective shale region of each shale sample subset based on the ratio n of the cumulative pore volume to the pore diameter and the ratio p of the pore volume to the pore diameter of each shale sample subset.

[0016] The scanning module is used to scan the effective shale regions of each subset of shale samples to obtain two-dimensional tomographic images.

[0017] The image fusion module is used to fuse the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images to obtain the pore structure image of the shale.

[0018] The determination module is used to determine the pore structure characterization parameters of shale based on the pore structure image.

[0019] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described shale pore structure detection method.

[0020] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting the pore structure of shale.

[0021] The shale pore structure detection method provided in this application determines the effective shale region of each shale sample subset based on the ratio n of the cumulative pore volume to pore diameter of the shale sample and the ratio p of the pore volume to pore diameter of each shale sample subset, and obtains a two-dimensional tomographic image. The two-dimensional tomographic image, isothermal adsorption test curve image, and two-dimensional scattering profile image are fused to obtain a shale pore structure image. Based on the shale pore structure image, the shale pore structure characterization parameters can be determined, solving the technical problem of not being able to accurately quantify the shale pore structure.

[0022] In some optional embodiments, the pore structure characterization parameters of the shale include at least one of shale permeability, shale brittleness, shale density, pore area, pore perimeter, pore elongation, and pore radius. Pore structure images allow for the visual determination of at least one of shale permeability, shale brittleness, shale density, pore area, pore perimeter, pore elongation, and pore radius, thereby achieving quantitative characterization of the shale pore structure.

[0023] In some optional embodiments, the step of determining the effective shale region of each shale sample subset based on the ratio n of the cumulative pore volume to pore diameter and the ratio p of the pore volume to pore diameter of each shale sample subset includes:

[0024] Based on the ratio of cumulative pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset, each shale sample subset is divided into multiple sample regions of varying numbers.

[0025] Calculate the cross-correlation coefficients of the sample regions relative to another subset of the shale samples;

[0026] The sample region corresponding to the largest number of cross-correlation values ​​is determined as the effective shale region of the shale sample subset corresponding to that sample region.

[0027] In this way, the cross-correlation coefficient of each shale sample subset is determined by the cumulative ratio of pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset. Then, the effective shale region of each shale sample subset is determined based on the cross-correlation coefficient, which facilitates subsequent scanning of shale samples to obtain two-dimensional tomographic images.

[0028] In some optional embodiments, the step of calculating the cross-correlation coefficients of the sample regions relative to another subset of the shale samples includes:

[0029] Obtain the initial cross-correlation coefficient R0 of the sample region relative to another subset of shale samples, where,

[0030] Add a random value to the initial cross-correlation coefficient to obtain a random cross-correlation coefficient;

[0031] The initial cross-correlation coefficient and the random cross-correlation coefficient are respectively substituted into the Kalman filter for prediction calculation to obtain their respective prediction errors;

[0032] The initial cross-correlation number or random cross-correlation number corresponding to the smaller prediction error between the initial cross-correlation number and the random cross-correlation number is determined as the cross-correlation number of the sample region relative to another subset of the shale samples.

[0033] To ensure that the cross-correlation coefficients can represent the correlation of each shale sample subset, initial cross-correlation coefficients and random cross-correlation coefficients are obtained and used to drive Kalman filtering for prediction calculations to obtain their respective prediction errors. The cross-correlation coefficient with the smaller prediction error is determined as the final cross-correlation coefficient of the sample region relative to another shale sample subset, ensuring the accuracy of determining the effective shale region of the shale sample subset.

[0034] In some optional embodiments, the steps of obtaining the isothermal adsorption test curve image, the two-dimensional scattering profile image, and the ratio of the cumulative pore volume to the pore diameter of the shale sample include:

[0035] The shale samples were divided into at least three parts according to a predetermined ratio.

[0036] Isothermal adsorption tests were performed on the shale samples in the first part, and the isothermal adsorption test curve images of the shale samples were obtained.

[0037] Small-angle neutron scattering (SANS) tests were performed on the shale samples in the second part to obtain two-dimensional scattering profile images of the shale samples;

[0038] The shale samples in the third part were tested using the fluid injection method to obtain the cumulative pore volume-pore size variation curve of the shale samples;

[0039] The ratio of cumulative pore volume to pore diameter of the shale sample is determined based on the cumulative pore volume-pore diameter variation curve.

[0040] Shale samples were divided into multiple parts according to a predetermined ratio. Isothermal adsorption tests were performed on one part of the shale samples to obtain isothermal adsorption test curves. Small-angle neutron scattering tests were performed on another part of the shale samples to obtain two-dimensional scattering profiles. Fluid injection tests were performed on yet another part of the shale samples to obtain cumulative pore volume-pore diameter variation curves. The ratio of cumulative pore volume to pore diameter was determined based on these curves. By ensuring that the testing operations for each part of the shale samples were independent, mutual interference was avoided, thus ensuring the accuracy of the subsequently obtained pore structure images.

[0041] In some optional embodiments, the steps of obtaining the isothermal adsorption test curve image, the two-dimensional scattering profile image, and the ratio of the cumulative pore volume to the pore diameter of the shale sample are further included before:

[0042] Obtain a topographic map of the structural region to be measured;

[0043] The shale collection location is determined on the topographic map, and shale samples are collected at the collection location.

[0044] To ensure that the collected shale samples accurately and comprehensively reflect the shale conditions of the structural area under test, the shale collection locations were determined based on the topographic map of the structural area under test, and corresponding shale sample collection was carried out.

[0045] In some optional embodiments, the step of fusing the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images to obtain a shale pore structure image includes:

[0046] The two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are filtered respectively to obtain the image base layer and image detail layer corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images.

[0047] The image base layers corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are fused to obtain the fused image base layer.

[0048] The image detail layers corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are fused to obtain the fused image detail layer.

[0049] Based on the fused image base layer and the fused image detail layer, the pore structure image of shale is determined.

[0050] By fusing the base layer and detail layer of each image separately, the accuracy of the detail pairs in the obtained shale pore structure image is ensured, thus guaranteeing the determination of subsequent shale pore structure characterization parameters. Attached Figure Description

[0051] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0052] Figure 1 This is a flowchart of a shale pore structure detection method provided in one embodiment of this application;

[0053] Figure 2 This is a flowchart of a method for evaluating the preservation effectiveness of shale gas in complex structural zones, provided in another embodiment of this application;

[0054] Figure 3 This is a flowchart of a method for determining the pore structure image of shale based on the analysis of collected shale samples, provided in another embodiment of this application;

[0055] Figure 4 This is a flowchart of a method for constructing a three-dimensional model of shale in a complex tectonic zone to be evaluated, provided in another embodiment of this application;

[0056] Figure 5 This is another embodiment of the present application providing a DEM image and sampling location map of the eastern Sichuan-Wuling tectonic belt;

[0057] Figure 6 This is a schematic diagram of the ultrastructure and pore distribution characteristics of the Paleozoic marine shale in the eastern Sichuan-Wuling tectonic belt, provided in another embodiment of this application.

[0058] Figure 7 This is a schematic diagram of a shale pore structure detection device provided in another embodiment of this application;

[0059] Figure 8 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0061] This invention proposes a method for detecting the pore structure of shale. The implementation details of the shale pore structure detection method in this embodiment are described below. The following content is only for the convenience of understanding and is not necessary for implementing this solution.

[0062] Example 1:

[0063] The shale pore structure detection method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. The specific process can be as follows: Figure 1 As shown, it includes:

[0064] Step 111: Obtain the isothermal adsorption test curve image, two-dimensional scattering profile image, and the ratio n of the cumulative pore volume to pore diameter of the shale sample.

[0065] Specifically, the isothermal adsorption test curve image, two-dimensional scattering profile image, and the ratio n of the cumulative pore volume to pore diameter of the shale sample are obtained from the database corresponding to the shale sample.

[0066] In some examples, isothermal adsorption tests are performed on shale samples to obtain isothermal adsorption curve images, small-angle neutron scattering tests are performed on shale samples to obtain two-dimensional scattering profile images, and fluid injection tests are performed on shale samples to obtain cumulative pore volume-pore size variation curves. The ratio n of cumulative pore volume to pore size of the shale sample is determined based on the cumulative pore volume-pore size variation curves.

[0067] Step 112: Divide the shale sample into at least two shale sample subsets and obtain the ratio p of pore volume to pore diameter for each shale sample subset.

[0068] Specifically, the shale sample is divided into at least two shale sample subsets. The porosity of the shale sample subset is obtained by using a shale pore sensor, which is the proportion of pore volume in a unit volume of rock. Then, the ratio p of pore volume to pore diameter of each shale sample subset is obtained.

[0069] Step 113: Determine the effective shale region of each shale sample subset based on the ratio n of the cumulative pore volume to the pore diameter and the ratio p of the pore volume to the pore diameter of each shale sample subset.

[0070] Specifically, based on the ratio of cumulative pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset, each shale sample subset is divided into multiple shale regions of varying numbers. The cross-correlation coefficient of each shale region in each shale sample subset relative to another shale sample subset is calculated, and the effective shale region of each shale sample subset is determined based on the cross-correlation coefficient.

[0071] Determining the effective shale region of a subset of shale samples facilitates the subsequent determination of the pore structure of the shale samples.

[0072] Step 114: Scan the effective shale region of each shale sample subset to obtain a two-dimensional fault scan image.

[0073] Specifically, after obtaining the effective shale regions of each shale sample subset, scanning equipment is used to obtain two-dimensional fault scan images.

[0074] Step 115: Perform image fusion on the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images to obtain the pore structure image of the shale.

[0075] Specifically, image fusion processing technology is used to fuse multiple images, including two-dimensional tomographic scan images, isothermal adsorption test curve images, and two-dimensional scattering profile images. Each image is a grayscale image. Before fusion, each image can be smoothed to remove blurry pixels and ensure the clarity of the fused image.

[0076] In one embodiment, the base image layer and detail layer of each image are obtained separately. The base image layers of each image are fused to obtain a fused base image layer. The detail images of each image are fused to obtain a fused detail image layer. Based on the fused base image layer and the fused detail image layer, a fused image, namely the pore structure image of shale, can be obtained.

[0077] Step 116: Determine the pore structure characterization parameters of the shale based on the pore structure image.

[0078] Specifically, the pore structure characteristics of shale can be visually determined from images of the pore structure of shale.

[0079] In this embodiment, the effective shale region of each shale sample subset is determined based on the ratio n of the cumulative pore volume to pore diameter of the shale sample and the ratio p of the pore volume to pore diameter of each shale sample subset, and a two-dimensional tomographic image is obtained. The two-dimensional tomographic image, the isothermal adsorption test curve image, and the two-dimensional scattering profile image are fused to obtain the pore structure image of the shale. Based on the pore structure image of the shale, the pore structure characterization parameters of the shale can be determined, solving the technical problem of not being able to accurately quantify the pore structure of shale.

[0080] In some embodiments, the pore structure characterization parameters of the shale include at least one of shale permeability, shale brittleness, shale density, pore area, pore perimeter, pore elongation, and pore radius.

[0081] Specifically, pore structure characterization parameters of shale are used to collectively reflect the characteristics and properties of shale's pore structure, and are of great significance for evaluating shale's reservoir capacity, seepage characteristics, and development potential. These pore structure characterization parameters include at least one of the following: shale permeability, shale brittleness, shale density, pore area, pore perimeter, pore elongation, and pore radius.

[0082] In some embodiments, the pore structure characterization parameters of shale are shale permeability, shale brittleness, shale density, pore area, pore perimeter, pore elongation, and pore radius.

[0083] In this embodiment, at least one of shale permeability, shale brittleness, shale density, pore area, pore perimeter, pore elongation, and pore radius can be intuitively determined through pore structure images, so as to achieve quantitative characterization of the pore structure of shale.

[0084] In some embodiments, the step of determining the effective shale region of each shale sample subset based on the ratio n of the cumulative pore volume to the pore diameter and the ratio p of the pore volume to the pore diameter of each shale sample subset includes:

[0085] Based on the ratio of cumulative pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset, each shale sample subset is divided into multiple sample regions of varying numbers.

[0086] Calculate the cross-correlation coefficients of the sample regions relative to another subset of the shale samples;

[0087] The sample region corresponding to the largest number of cross-correlation values ​​is determined as the effective shale region of the shale sample subset corresponding to that sample region.

[0088] In this way, the cross-correlation coefficient of each shale sample subset is determined by the cumulative ratio of pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset. Then, the effective shale region of each shale sample subset is determined based on the cross-correlation coefficient, which facilitates subsequent scanning of shale samples to obtain two-dimensional tomographic images.

[0089] Specifically, the ratio of cumulative pore volume to pore diameter (n) is determined by the cumulative pore volume to pore diameter variation curve of the shale samples. By dividing all shale samples into multiple shale sample subsets, the ratio of pore volume to pore diameter (p) for each shale sample subset can be obtained. In this embodiment, two shale sample subsets, S1 and S2, are used as examples. S1 corresponds to the pore volume to pore diameter ratio p1, and S2 corresponds to the pore volume to pore diameter ratio p2.

[0090] Wherein, the sample region of regional shale sample subset S1, determined according to the ratio of cumulative pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset, is S1(i, i+p1-1), where i is a positive integer starting from 1 and increasing by 1 sequentially; the sample region of regional shale sample subset S2, determined according to the ratio of cumulative pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset, is S2(i, i+p2-1), where i is a positive integer starting from 1 and increasing by 1 sequentially.

[0091] The steps for calculating the correlation coefficients of each sample region in shale sample subset S2 relative to another shale sample subset S1 are as follows:

[0092] Step 21: Let i = 1, take the i-th to i+p2-1-th data in S2, set the rest to 0, and calculate the cross-correlation coefficient R between S1(1,2,…,n) and S2(i,i+1,i+2,…,i+p2-1). i ;

[0093] Step 22, let i = i + 1, repeat (2) until i = n - p2 + 1;

[0094] Step 23, calculate R i The maximum value of i is denoted as I2, and the sample S2(I2,I2+1,I2+2,…,I2+p2-1) is the region where the effective sample is located in S2;

[0095] The sample region corresponding to the largest number of cross-correlation values ​​is determined as the effective shale region of the shale sample subset corresponding to the sample region, that is, sample S2(I2,I2+1,I2+2,…,I2+p2-1) is determined as the region where the effective sample in S2 is located.

[0096] The steps for calculating the correlation coefficients of each sample region in shale sample subset S1 relative to another shale sample subset S2 are as follows:

[0097] Step 31: Let i = 1, take the i-th to i+p1-1-th data in S1, set the rest to 0, and calculate the cross-correlation coefficient R between S1(i,i+1,i+2,…,i+p1-1) and S2(i2,i2+1,i2+2,…,i2+p2-1). i ;

[0098] Step 32, let i = i + 1, repeat step 31 until i = n - p1 + 1;

[0099] Step 33, calculate R i The maximum value of i is denoted as I1;

[0100] The sample region corresponding to the largest number of cross-correlation values ​​is determined as the effective shale region of the shale sample subset corresponding to the sample region, that is, sample S1(I1,I1+1,I1+2,…,I1+p1-1) is determined as the region where the effective sample in S1 is located.

[0101] In some embodiments, the step of calculating the cross-correlation coefficients of the sample regions relative to another subset of shale samples includes:

[0102] Obtain the initial cross-correlation coefficient R0 of the sample region relative to another subset of shale samples, where,

[0103] Add a random value to the initial cross-correlation coefficient to obtain a random cross-correlation coefficient;

[0104] The initial cross-correlation coefficient and the random cross-correlation coefficient are respectively substituted into the Kalman filter for prediction calculation to obtain their respective prediction errors;

[0105] The initial cross-correlation number or random cross-correlation number corresponding to the smaller prediction error between the initial cross-correlation number and the random cross-correlation number is determined as the cross-correlation number of the sample region relative to another subset of the shale samples.

[0106] To ensure that the cross-correlation coefficients can represent the correlation of each shale sample subset, initial cross-correlation coefficients and random cross-correlation coefficients are obtained and used to drive Kalman filtering for prediction calculations to obtain their respective prediction errors. The cross-correlation coefficient with the smaller prediction error is determined as the final cross-correlation coefficient of the sample region relative to another shale sample subset, ensuring the accuracy of determining the effective shale region of the shale sample subset.

[0107] In this embodiment, the determination of the initial cross-correlation coefficient R0 of shale sample subset S1 relative to shale sample subset S2 is used as an example for illustration. Specifically, the step of calculating the cross-correlation coefficient of the sample region relative to another shale sample subset includes:

[0108] Given a subset of shale samples S1, the feasible set R∈[0,n-p+1] is defined as the cross-correlation coefficient R between pore volume and pore diameter.

[0109] Step 41: Determine the initial cross-correlation coefficient R0, and take it as... Where R max =n-p+1,R min =0;

[0110] Step 42: During the update, each time in the original R i Add a random value c to the base i This value follows a normal distribution, has an expected value of 0, and a very small variance.

[0111] R i+ =R i +c i ,c i :n,n∈(1,2,...,n);

[0112] Step 43: The above R i+ and R i Substitute each into the unscented Kalman filter for prediction calculation;

[0113] Step 44: Calculate the prediction error between the two, and proceed to the next update step with the smaller error;

[0114] like Δ represents the error, and T represents the number of calculations;

[0115] Then take R i+1 =R i+ ;

[0116] Otherwise R i+1 =R i ;

[0117] Step 45: Repeat steps 42-44 until the prediction error reaches the set threshold or the number of updates reaches the set standard, then stop updating and obtain R. i The maximum value.

[0118] In some embodiments, the steps of obtaining the isothermal adsorption test curve image, the two-dimensional scattering profile image, and the ratio of the cumulative pore volume to the pore diameter of the shale sample include:

[0119] The shale samples were divided into at least three parts according to a predetermined ratio.

[0120] Isothermal adsorption tests were performed on the shale samples in the first part, and the isothermal adsorption test curve images of the shale samples were obtained.

[0121] Small-angle neutron scattering (SANS) tests were performed on the shale samples in the second part to obtain two-dimensional scattering profile images of the shale samples;

[0122] The shale samples in the third part were tested using the fluid injection method to obtain the cumulative pore volume-pore size variation curve of the shale samples;

[0123] The ratio of cumulative pore volume to pore diameter of the shale sample is determined based on the cumulative pore volume-pore diameter variation curve.

[0124] Specifically, the shale samples are divided into multiple parts according to a predetermined ratio. Isothermal adsorption tests are performed on one part of the shale samples to obtain isothermal adsorption test curve images. Small-angle neutron scattering tests are performed on another part of the shale samples to obtain two-dimensional scattering profile images. Fluid injection tests are performed on yet another part of the shale samples to obtain cumulative pore volume-pore diameter variation curves. The ratio of cumulative pore volume to pore diameter is determined based on these curves. By ensuring that the testing operations for each part of the shale samples are independent, mutual interference of results is avoided, thus ensuring the accuracy of the subsequently obtained pore structure images.

[0125] In some embodiments, nitrogen isothermal adsorption tests are performed on a subset of shale samples to obtain nitrogen adsorption-desorption isotherms for the shale samples. The isothermal adsorption test curves can visually demonstrate the changes in adsorption capacity of the shale samples under different pressures, thereby reflecting the pore structure of the shale.

[0126] In fluid injection testing of shale samples, a curve representing the relationship between cumulative pore volume and pore size is typically obtained. This curve visually reflects the pore distribution characteristics within different pore size ranges in shale, and is crucial for characterizing the pore structure of shale. Small-angle neutron scattering (SANS) testing utilizes the scattering phenomenon generated when a neutron beam interacts with matter to study the microstructure of the material. Neutrons have strong penetrating power and sensitivity to light elements, making them particularly suitable for studying complex porous media containing large amounts of light elements, such as shale. In the test, the neutron beam passes through the shale sample and scatters with the atomic nuclei in the sample, forming a scattering pattern. By capturing and analyzing these scattering patterns with a detector, a two-dimensional scattering profile image of the sample can be obtained. The scattering intensity at different locations on the image reflects the pore structure of different scales and shapes in the sample. Regions with higher scattering intensity usually correspond to larger pores or pore clusters, while regions with lower scattering intensity may correspond to smaller pores or a denser solid matrix; furthermore, there is a certain relationship between the scattering vector (i.e., the ratio of the scattering angle to the wavelength) and the pore size. By measuring the magnitude and distribution of the scattering vector, the size distribution and shape characteristics of the pores in the sample can be inferred.

[0127] In some embodiments, the step of obtaining the isothermal adsorption test curve image, the two-dimensional scattering profile image, and the ratio of the cumulative pore volume to the pore diameter of the shale sample is further included before the following steps:

[0128] Obtain a topographic map of the structural region to be measured;

[0129] The shale collection location is determined on the topographic map, and shale samples are collected at the collection location.

[0130] To ensure that the collected shale samples accurately and comprehensively reflect the shale conditions of the structural area under test, the shale collection locations were determined based on the topographic map of the structural area under test, and corresponding shale sample collection was carried out.

[0131] Specifically, the geological data, image data, and characteristic information of the shale in the area to be tested are acquired, and a topographic map of the area is prepared based on the geological data, image data, and relevant characteristics of the shale. Furthermore, the characteristic information of the shale in the area to be tested includes its distribution characteristics, geochemical characteristics, lithological characteristics, structural characteristics, and petrological characteristics.

[0132] In some embodiments, the topographic map of the structural area to be measured is a DEM topographic map. A Digital Elevation Model (DEM) is a digital simulation of the ground topography (i.e., a digital representation of the surface morphology of the terrain) achieved through limited topographic elevation data. It is a physical ground model that represents the ground elevation in the form of an ordered numerical array. It is a branch of the Digital Terrain Model (DTM), from which various other terrain feature values ​​can be derived.

[0133] In some embodiments, topographic maps of the structural area to be measured are obtained directly from a database platform.

[0134] The topographic map of the structural area to be tested is analyzed to determine the distribution of shale, so as to determine the shale collection location on the obtained topographic map, and shale samples are collected at the collection location.

[0135] In some embodiments, the step of fusing the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images to obtain a shale pore structure image includes:

[0136] The two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are filtered respectively to obtain the image base layer and image detail layer corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images.

[0137] The image base layers corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are fused to obtain the fused image base layer.

[0138] The image detail layers corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are fused to obtain the fused image detail layer.

[0139] Based on the fused image base layer and the fused image detail layer, the pore structure image of shale is determined.

[0140] By fusing the base layer and detail layer of each image separately, the accuracy of the detail pairs in the obtained shale pore structure image is ensured, thus guaranteeing the determination of subsequent shale pore structure characterization parameters.

[0141] Specifically, taking two shale sample subsets, S1 and S2, as an example, this will be explained. After determining the effective shale region of shale sample subset S1, this effective shale region is scanned to obtain a two-dimensional tomographic image P.A After determining the effective shale region of shale sample subset S2, the effective shale region was scanned to obtain a two-dimensional tomographic image P. B The temperature adsorption test curve image is P. C And the two-dimensional scattering profile image is P D , where P A P B P C and P D All are grayscale images, and P A P B P C P D ∈X M×N X M×N It is a space of size M×N, where M and N are both positive integers;

[0142] Two-dimensional tomographic image P of shale sample subset S1 A Two-dimensional tomographic image P of shale sample subset S2 B Isothermal adsorption test curve image P C and two-dimensional scattering profile image P D The steps involved in image fusion include:

[0143] Step 51: Use the smoothing filter Y to smooth P respectively. A P B P C and P D Perform a smoothing operation to remove the source image P. A0 P B0 P C0 and P D0 The blurred pixels in the image are used to obtain P'. A 、P' B 、P' C and P' D ,

[0144] Where: (P',P') B ,P' C ,P' D )=Y(P A ,P B ,P C ,P D ):

[0145] Step 52: Separate the source image P A0 P B0 P C0 and P D0 As a guide image, a guide edge restoration filter O is used. IC For P' A 、P' B、P' C and P' D Perform iterative edge-aware filtering to recover strong edges in the source image, obtaining the source image P. A0 P B0 P C0 and P D0 base layer P AG P BG P CG and P DG and detail layer P AJ P BJ P CJ and P DJ , where: (P AG ,P AJ ) = O IG (P A ,P' A Others are similar;

[0146] Step 53: Calculate the source image P respectively A0 P B0 P C0 and P D0 The gradient deformation energy within the neighborhood of each pixel in the base layer and detail layer, with a neighborhood size of 5×5 or 7×7;

[0147] Step 54: Construct the feature matrix Q of the base layer respectively G Q G ∈X M×N and detail layer feature matrix Q J Q J ∈X M×N :

[0148]

[0149] In (Equation 1):

[0150] EOG AG (a,b) are the base layer P A Gradient deformation energy in the neighborhood of pixel (a,b);

[0151] EOG BG (a,b) are the base layer P B Gradient deformation energy within the neighborhood of pixel (a,b);

[0152] a = 1, 2, 3L M, b = 1, 2, 3L N;

[0153] Q G (a,b) is matrix Q G The element in row a and column b;

[0154] In (Equation 2):

[0155] EOG AJ (a,b) represents the detail layer P. AJ Gradient deformation energy within the neighborhood of pixel (a,b); others are similar;

[0156] a = 1, 2, 3L M, b = 1, 2, 3L N;

[0157] Q J (a,b) is matrix Q J The element in row a and column b;

[0158] Step 55: Based on the feature matrix Q G and Q J Constructing the base layer U of the fused image G U G ∈X M×N and detail layer U J U J ∈X M ×N The fused base layer U is obtained G and detail layer U J :

[0159]

[0160]

[0161] In (Equation 3):

[0162] U G (a,b) represents the base layer U of the fused source image. G The grayscale value at pixel (a, b);

[0163] P AG (a,b) represents the base layer P of the source image before fusion. AG The grayscale value at pixel (a, b);

[0164] In (Equation 4):

[0165] U J (a,b) represents the detail layer U of the fused source image. J The grayscale value at pixel (a, b);

[0166] P AJ (a,b) represents the detail layer P of the source image before fusion. AJ The grayscale value at pixel (a, b);

[0167] Step 56: Construct the fused image U to obtain the fused grayscale image, where: U = U G +U J ;

[0168] The constructed fused image U is the pore structure image of shale. Through the pore structure of shale, the pore structure characterization parameters of shale, including shale permeability, shale brittleness, shale density, as well as pore area, pore perimeter, pore elongation, and pore radius, are visualized.

[0169] Example 2:

[0170] The method for evaluating the preservation effectiveness of shale gas in complex structural zones, as described in this embodiment, can be applied to electronic devices with communication, computing, and data storage capabilities. The specific process can be as follows: Figure 2 As shown, it includes:

[0171] Step 101: Obtain geological data, image data, and relevant characteristics of shale in the complex structural area to be evaluated; and prepare a DEM topographic map of the complex structural area to be evaluated based on the acquired data.

[0172] Specifically, the relevant characteristics of shale include at least one of the following: distribution characteristics, geochemical characteristics, lithological characteristics, structural characteristics, and petrological characteristics.

[0173] In some cases, relevant characteristics of shale include its distribution characteristics, geochemical characteristics, lithological characteristics, structural characteristics, and petrological characteristics.

[0174] A Digital Elevation Model (DEM) is a digital simulation of ground terrain (i.e., a digital representation of the surface morphology of terrain) achieved through limited terrain elevation data. It is a physical ground model that represents ground elevation using an ordered array of values. It is a branch of Digital Terrain Model (DTM), from which various other terrain feature values ​​can be derived.

[0175] In some embodiments, topographic maps of the structural area to be measured are obtained directly from a database platform.

[0176] Step 102: Determine the shale sampling location based on the DEM topographic map of the complex tectonic zone to be evaluated, and collect shale samples at the determined sampling location.

[0177] To ensure that the collected shale samples accurately and comprehensively reflect the shale conditions of the structural area under test, the shale collection locations were determined based on the topographic map of the structural area under test, and corresponding shale sample collection was carried out.

[0178] Specifically, the topographic map of the structural area to be measured is analyzed to determine the distribution of shale, so as to determine the shale collection location on the obtained topographic map, and shale samples are collected at the collection location.

[0179] Step 103: Analyze the collected shale samples to determine the pore structure image and related parameters of the shale; at the same time, construct a three-dimensional model of the shale in the complex tectonic zone to be evaluated.

[0180] Specifically, the relevant parameters of shale include: permeability, brittleness, density, and at least one of the following: pore area, perimeter, elongation, and radius.

[0181] In some cases, relevant parameters for shale include: permeability, brittleness, density, and pore area, perimeter, elongation, and radius.

[0182] In some examples, such as Figure 3 As shown, the steps for analyzing and determining the pore structure of shale based on collected shale samples include:

[0183] S201. Obtain a portion of the collected shale samples and test the obtained shale samples using the fluid injection method to obtain the cumulative pore volume-pore diameter change curve of the shale samples.

[0184] S202, then obtain a portion of the collected shale samples, and perform isothermal adsorption tests and small-angle neutron scattering tests on the shale samples respectively to obtain nitrogen adsorption-desorption isotherms and two-dimensional scattering profiles of the shale samples.

[0185] S203. The cumulative pore volume-pore size variation curve, nitrogen adsorption-desorption isotherm, and two-dimensional scattering profile of the shale sample were analyzed and processed to obtain the pore structure image of the shale.

[0186] In some cases, the analysis of collected shale samples is used to determine relevant parameters of the shale, including:

[0187] Based on the determined pore structure image of shale, a two-dimensional tomographic image of the shale sample is obtained using a shale pore sensor; based on the two-dimensional tomographic image of the shale sample, combined with the results of fluid injection test, isothermal adsorption test, and small-angle neutron scattering test, the relevant parameters of the shale are analyzed and determined.

[0188] Two-dimensional tomographic images of shale samples combined with fluid injection testing include:

[0189] Based on the two-dimensional tomographic images of the shale samples, the samples received by the shale pore sensor are denoted as S1 and S2, respectively. The total pore volume minus the pore diameter of the samples is n. The method for obtaining the two-dimensional tomographic images is as follows:

[0190] (1) Determine the pore density q based on the pore volume-pore diameter p of the shale sample;

[0191] (2) Let i = 1, take the i-th to i+p-1-th data in S2, set the rest to 0, and calculate the cross-correlation coefficient R between S1(1,2,…,n) and S2(i,i+1,i+2,…,i+p-1). i ;

[0192] (3) Let i = i + 1, repeat (2) until i = n - p + 1;

[0193] (4) Calculate R i The maximum value of i is denoted as I2, and the sample S2(I2,I2+1,I2+2,…,I2+p-1) is the region where the effective sample is located in S2;

[0194] (5) Let i = 1, take the i-th to i+p-1-th data in S1, set the rest to 0, and calculate the cross-correlation coefficient R between S1(i,i+1,i+2,…,i+p-1) and S2(i2,i2+1,i2+2,…,i2+p-1). i ;

[0195] (6) Let i = i + 1, repeat (5) until i = n - p + 1;

[0196] (7) Calculate R i The maximum value of i is denoted as I1, and the shale sample S1(I1,I1+1,I1+2,…,I1+p-1) is the region where the effective sample is located in S1.

[0197] Furthermore, in step (2), the cross-correlation coefficient R i The calculation methods include:

[0198] Given a feasible set R∈[0,n-p+1] for the cross-correlation coefficient parameter R of pore volume-pore diameter for a shale sample, the update method is as follows:

[0199] (1) Select the initial R;

[0200] Take it as Where R max =n-p+1,R min =0;

[0201] (2) During the update, each time in the original R i Add a random value c to the base i This value follows a normal distribution, has an expected value of 0, and a very small variance.

[0202] R i+ =R i+c i ,c i :n,n∈(1,2,...,n);

[0203] (3) The above R i+ and R i Substitute each into the unscented Kalman filter for prediction calculation;

[0204] (4) Calculate the prediction error of the two, and take the one with the smaller error to proceed to the next update;

[0205] like Δ represents the error, and T represents the number of calculations;

[0206] Then take R i+1 =R i+ ;

[0207] Otherwise R i+1 =R i ;

[0208] (5) Repeat steps (2)-(4) until the prediction error reaches the set threshold or the number of updates reaches the set standard, then stop updating and obtain R. i The maximum value.

[0209] Furthermore, after determining the regions where the effective samples in S1 and S2 are located, the relevant parameters of the shale are analyzed and determined by combining the isothermal adsorption test results and small-angle neutron scattering test results. This analysis is then applied to the image P of the region where the effective sample in S1 is located. A Image P of the effective sample region in S2 B Isothermal adsorption test result image P C And the image P of the small-angle neutron scattering test results. D Fusion of multiple images, P A P B P C and P D All are grayscale images, and P A P B P C P D ∈X M×N X M×N It is a space of size M×N, where M and N are both positive integers, specifically including:

[0210] S3.1, using the smoothing filter Y to smooth P respectively A P B P C and P D Perform a smoothing operation to remove the source image P. A0 P B0 P C0 and P D0The blurred pixels in the image are used to obtain P'. A 、P' B 、P' C and P' D , where: (P',P' B ,P' C ,P' D )=Y(P A ,P B ,P C ,P D );

[0211] S3.2, respectively, source image P A0 P B0 P C0 and P D0 As a guide image, a guide edge restoration filter O is used. IC For P' A 、P' B 、P' C and P' D Perform iterative edge-aware filtering to recover strong edges in the source image, obtaining the source image P. A0 P B0 P C0 and P D0 base layer P AG P BG P CG and P DG and detail layer P AJ P BJ P CJ and P DJ , where: (P AG ,P AJ ) = O IG (P A ,P' A Others are similar;

[0212] S3.3, calculate the source image P respectively. A0 P B0 P C0 and P D0 The gradient deformation energy within the neighborhood of each pixel in the base layer and detail layer, with a neighborhood size of 5×5 or 7×7;

[0213] S3.4, construct the feature matrix Q of the base layer respectively. G Q G ∈X M×N and detail layer feature matrix Q J Q J ∈X M×N :

[0214]

[0215] In (Equation 1):

[0216] EOG AG (a,b) are the base layer P A Gradient deformation energy within the neighborhood of pixel (a,b);

[0217] EOG BG (a,b) are the base layer P B Gradient deformation energy within the neighborhood of pixel (a,b);

[0218] a = 1, 2, 3L M, b = 1, 2, 3L N;

[0219] Q G (a,b) is matrix Q G The element in row a and column b;

[0220] In (Equation 2):

[0221] EOG AJ (a,b) represents the detail layer P. AJ Gradient deformation energy within the neighborhood of pixel (a,b); others are similar;

[0222] a = 1, 2, 3L M, b = 1, 2, 3L N;

[0223] Q J (a,b) is matrix Q J The element in row a and column b;

[0224] S3.5, based on the characteristic matrix Q G and Q J Constructing the base layer U of the fused image G U G ∈X M×N and detail layer U J U J ∈X M×N The fused base layer U is obtained G and detail layer U J :

[0225]

[0226]

[0227] In (Equation 3):

[0228] U G (a,b) represents the base layer U of the fused source image. G The grayscale value at pixel (a, b);

[0229] P AG(a,b) represents the base layer P of the source image before fusion. AG The grayscale value at pixel (a, b);

[0230] In (Equation 4):

[0231] U J (a,b) represents the detail layer U of the fused source image. J The grayscale value at pixel (a, b);

[0232] P AJ (a,b) represents the detail layer P of the source image before fusion. AJ The grayscale value at pixel (a, b);

[0233] S3.6, construct the fused image U to obtain the fused grayscale image, where: U = U G +U J ;

[0234] Based on the constructed fusion image U, the permeability, brittleness, density, and the area, perimeter, elongation, and radius of the pores are visualized.

[0235] Step 104: Based on the determined pore structure image of the shale, the constructed three-dimensional model of the shale in the complex tectonic zone to be evaluated, the prepared DEM topographic map of the complex tectonic zone, and the acquired data, conduct an evolution analysis of the shale in the complex tectonic zone to be evaluated.

[0236] Specifically, such as Figure 4 As shown, the construction of a three-dimensional model of the shale in the complex tectonic zone to be evaluated includes:

[0237] S301. Obtain geological data, image data, and DEM topographic map of the complex tectonic zone to be evaluated, and construct an initial three-dimensional model of the complex tectonic zone to be evaluated.

[0238] S302, acquire well logging data and seismic monitoring data of shale in the complex structural area to be evaluated, and add constraints to the initial three-dimensional model of the complex structural area to be evaluated based on the acquired well logging data and seismic monitoring data to obtain a three-dimensional modified model of the complex structural area to be evaluated.

[0239] S303, acquire images of the pore structure, related parameters and features of the shale, and use a 3D scanner to scan the shale to obtain scan data;

[0240] S304, using the pore structure image, related parameters, features and scanning data of the shale, the three-dimensional correction model of the complex structural area to be evaluated is optimized and corrected to obtain the three-dimensional model of the shale in the complex structural area to be evaluated.

[0241] In some examples, the optimization and correction of the three-dimensional modified model of the complex tectonic zone to be evaluated using shale pore structure images, relevant parameters, features, and scan data includes:

[0242] Based on the 3D scanning data of the shale, a 3D reconstruction is performed to obtain a 3D model of the shale. The pore structure image, relevant parameters, and features of the shale are added to the 3D model of the shale. The 3D model of the shale is then used to optimize and correct the 3D modified model of the complex structural area to be evaluated. This yields a 3D model of the shale in the complex structural area to be evaluated.

[0243] Step 105: Based on the collected shale samples and the evolution analysis results, perform deformation analysis of shale in complex tectonic areas from both macroscopic and microscopic perspectives to determine the deformation characteristics of the shale.

[0244] Specifically, based on the collected shale samples and evolution analysis results, deformation analysis of shale in complex tectonic zones is conducted from both macroscopic and microscopic perspectives to determine the deformation characteristics of the shale, including:

[0245] First, based on the evolution analysis results and combined with DEM topographic maps, the deformation characteristics at the macroscopic level of the shale region were determined;

[0246] Secondly, based on the pore structure images of shale, the constructed three-dimensional model of shale, and the scanned images of shale, the deformation characteristics of the microscopic layer of shale structure were determined.

[0247] Step 106: Based on the evolution analysis results of the shale in the complex structural area to be evaluated, the constructed three-dimensional model, the deformation characteristics of the shale, and the DEM topographic map of the complex structural area, determine the pore system of the shale in the complex structural area, and evaluate the shale gas preservation effectiveness in the complex structural area based on the pore system of the shale in the complex structural area.

[0248] Specifically, when the method provided in this embodiment is applied to the shale analysis of the Eastern Sichuan-Wuling tectonic belt, the two sets of marine shale formations developed in the Lower Paleozoic of the Eastern Sichuan-Wuling tectonic belt (Lower Cambrian Niutitang Formation and Upper Ordovician Wufeng Formation-Lower Silurian Longmaxi Formation) are not only important detachment layers in the region, but also key strata for shale gas exploration and development. For example... Figure 5 As shown, through detailed structural analysis in the field and indoor microscopic observation, the deformation characteristics of shale were analyzed from the macroscopic outcrop to the microscopic scale. It is believed that the shale underwent at least two phases of tectonic deformation: early-stage thrusting along the bedding plane towards the northwest or southeast, and later-stage truncating faulting. The deformation intensity of the shale changed with the distance from the fault zone. In areas far from the fault zone, shale deformation mainly manifested as near-vertical microfractures, belonging to the brittle domain; closer to the fault zone, the deformation characteristics of the shale gradually showed a brittle-ductile transition, and mylonitic structures began to develop; within the fault zone, the shale was strongly foliated, developing numerous mylonitic structures, belonging to the ductile domain. Figure 6As shown, the porosity evolution characteristics within deformed shale were analyzed using argon-ion polishing and scanning electron microscopy. It was found that as deformation transitions from brittle to brittle-ductile to ductile, the pore type within the shale not only changes, but also the size and distribution characteristics of the pores. Based on this, the impact of Lower Paleozoic shale deformation on shale gas preservation in the complex tectonic region of southern China was further discussed. It was suggested that bedding-parallel shearing alters the pore system within the shale, favoring shale gas enrichment; while extensional or strike-slip shearing not only disrupts previously formed hydrocarbon traps but also causes hydrocarbons to migrate along fault zones from high-potential areas to low-potential areas, leading to hydrocarbon loss.

[0249] The method for evaluating the effectiveness of shale gas preservation in complex tectonic zones, provided in this invention, was used to analyze the deformation characteristics, evolution characteristics, and effectiveness of the eastern Sichuan-Wuling tectonic belt. The results are as follows:

[0250] The tectonic deformation intensity of Paleozoic marine shale in the eastern Sichuan-Wuling tectonic belt can be classified into three types according to their distance from the fault zone: brittle, brittle-ductile transition, and ductile. In areas far from the fault zone, shale deformation is mainly characterized by near-vertical microfractures, belonging to the brittle domain. Near the fault zone, the deformation characteristics of the shale gradually exhibit a brittle-ductile transition, and mylonitization begins to develop. Within the fault zone, the shale is strongly foliated, with abundant mylonitization, belonging to the ductile domain.

[0251] The deformation of the Paleozoic marine shale in the eastern Sichuan-Wuling tectonic belt involves at least two phases: early bedding-parallel northwest or southeast thrusting and later bedding-cutting faulting. These two phases of tectonic deformation likely correspond to large-scale northwest-southeast shortening and subsequent extensional or strike-slip deformation during the Late Jurassic to Early Cretaceous. As deformation transitions from brittle to brittle-ductile to ductile, the pore type within the shale not only changes with the intensity of deformation, but also the size and distribution characteristics of the pores. Early bedding-parallel shearing alters the pore system within the shale, favoring shale gas enrichment; while later bedding-cutting extensional or strike-slip shearing not only disrupts previously formed oil and gas traps but also causes oil and gas to migrate along fault zones from high-potential areas to low-potential areas, resulting in oil and gas loss.

[0252] Example 3:

[0253] Another embodiment of this application relates to a shale pore structure detection device. The implementation details of this shale pore structure detection device are described below. The following details are for ease of understanding and are not essential for implementing this solution. A schematic diagram of the shale pore structure detection device in this embodiment can be seen as follows: Figure 7As shown, it includes an acquisition module 801, a division module 802, a calculation module 803, a scanning module 804, an image fusion module 805, and a determination module 806.

[0254] The acquisition module 801 is used to acquire the isothermal adsorption test curve image, the two-dimensional scattering profile image, and the ratio n of the cumulative pore volume to the pore diameter of the shale sample.

[0255] The partitioning module 802 is used to divide the shale sample into at least two shale sample subsets and obtain the ratio p of pore volume to pore diameter for each shale sample subset.

[0256] The calculation module 803 is used to determine the effective shale region of each shale sample subset based on the ratio n of the cumulative pore volume to the pore diameter and the ratio p of the pore volume to the pore diameter of each shale sample subset.

[0257] The scanning module 804 is used to scan the effective shale regions of each subset of shale samples to obtain two-dimensional tomographic images.

[0258] The image fusion module 805 is used to fuse the two-dimensional tomographic images, isothermal adsorption test curve images and two-dimensional scattering profile images to obtain the pore structure image of shale.

[0259] The determination module 806 is used to determine the pore structure characterization parameters of shale based on the pore structure image.

[0260] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0261] Example 4:

[0262] Another embodiment of this application relates to an electronic device, such as... Figure 8 As shown, it includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the shale pore structure detection method in the above embodiments.

[0263] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0264] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0265] Example 5:

[0266] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0267] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0268] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for detecting the pore structure of shale, characterized in that, include: Obtain the isothermal adsorption test curve image, two-dimensional scattering profile image, and the ratio n of the cumulative pore volume to pore diameter of the shale sample; The shale sample is divided into at least two shale sample subsets, and the ratio p of pore volume to pore diameter of each shale sample subset is obtained. The effective shale region of each shale sample subset is determined based on the ratio n of the cumulative pore volume to the pore diameter and the ratio p of the pore volume to the pore diameter of each shale sample subset. The effective shale regions of each of the aforementioned shale sample subsets were scanned to obtain two-dimensional tomographic images. The two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are fused to obtain the pore structure image of shale. The pore structure characterization parameters of shale are determined based on the pore structure image.

2. The method for detecting the pore structure of shale according to claim 1, characterized in that, The pore structure characterization parameters of the shale include at least one of shale permeability, shale brittleness, shale density, pore area, pore perimeter, pore elongation, and pore radius.

3. The method for detecting the pore structure of shale according to claim 1, characterized in that, The step of determining the effective shale region of each shale sample subset based on the ratio of cumulative pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset includes: Based on the ratio of cumulative pore volume to pore diameter n and the ratio of pore volume to pore diameter p of each shale sample subset, each shale sample subset is divided into multiple sample regions of varying numbers. Calculate the cross-correlation coefficients of the sample regions relative to another subset of the shale samples; The sample region corresponding to the largest number of cross-correlation values ​​is determined as the effective shale region of the shale sample subset corresponding to that sample region.

4. The method for detecting the pore structure of shale according to claim 3, characterized in that, The step of calculating the cross-correlation coefficients of the sample regions relative to another subset of shale samples includes: Obtain the initial cross-correlation coefficient R0 of the sample region relative to another subset of shale samples, where, Add a random value to the initial cross-correlation coefficient to obtain a random cross-correlation coefficient; The initial cross-correlation coefficient and the random cross-correlation coefficient are respectively substituted into the Kalman filter for prediction calculation to obtain their respective prediction errors; The initial cross-correlation number or random cross-correlation number corresponding to the smaller prediction error between the initial cross-correlation number and the random cross-correlation number is determined as the cross-correlation number of the sample region relative to another subset of the shale samples.

5. The method for detecting the pore structure of shale according to claim 1, characterized in that, The steps of obtaining the isothermal adsorption test curve image, the two-dimensional scattering profile image, and the ratio of the cumulative pore volume to the pore diameter of the shale sample include: The shale samples were divided into at least three parts according to a predetermined ratio. Isothermal adsorption tests were performed on the shale samples in the first part, and the isothermal adsorption test curve images of the shale samples were obtained. Small-angle neutron scattering (SANS) tests were performed on the shale samples in the second part to obtain two-dimensional scattering profile images of the shale samples; The shale samples in the third part were tested using the fluid injection method to obtain the cumulative pore volume-pore size variation curve of the shale samples; The ratio of cumulative pore volume to pore diameter of the shale sample is determined based on the cumulative pore volume-pore diameter variation curve.

6. The method for detecting the pore structure of shale according to claim 1, characterized in that, Before the steps of obtaining the isothermal adsorption test curve image, two-dimensional scattering profile image, and the ratio of the cumulative pore volume to the pore diameter of the shale sample, the method further includes: Obtain a topographic map of the structural region to be measured; The shale collection location is determined on the topographic map, and shale samples are collected at the collection location.

7. The method for detecting the pore structure of shale according to claim 1, characterized in that, The step of fusing the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images to obtain the pore structure image of shale includes: The two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are filtered respectively to obtain the image base layer and image detail layer corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images. The image base layers corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are fused to obtain the fused image base layer. The image detail layers corresponding to each of the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images are fused to obtain the fused image detail layer. Based on the fused image base layer and the fused image detail layer, the pore structure image of shale is determined.

8. A device for detecting the pore structure of shale, characterized in that, include: The acquisition module is used to acquire isothermal adsorption test curve images, two-dimensional scattering profile images, and the ratio n of the cumulative pore volume to pore diameter of the shale sample. The partitioning module is used to divide the shale sample into at least two shale sample subsets and obtain the ratio p of pore volume to pore diameter for each shale sample subset. The calculation module is used to determine the effective shale region of each shale sample subset based on the ratio n of the cumulative pore volume to the pore diameter and the ratio p of the pore volume to the pore diameter of each shale sample subset. The scanning module is used to scan the effective shale regions of each subset of shale samples to obtain two-dimensional tomographic images. The image fusion module is used to fuse the two-dimensional tomographic images, isothermal adsorption test curve images, and two-dimensional scattering profile images to obtain the pore structure image of the shale. The determination module is used to determine the pore structure characterization parameters of shale based on the pore structure image.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the pore structure detection method for shale as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the pore structure detection method for shale according to any one of claims 1 to 7.

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