Shale pore network percolation performance analysis method and device

Abstract

The invention discloses a shale pore network percolation performance analysis method and device, which can be used in the field of geological prospecting, and the method comprises the following steps: carrying out scanning electron microscope observation and three-dimensional reconstruction on a shale sample to generate a three-dimensional digital core model; carrying out picture filtering noise reduction and threshold segmentation processing on the three-dimensional digital core model to obtain a pore network, carrying out percolation analysis on the localized pore network, repeatedly carrying out tightening processing on the pore network when the percolation network is formed in the pore network, and repeatedly carrying out extension processing on the pore network when the percolation network is not formed in the pore network; obtaining a critical percolation model and a percolation threshold value of a pore network; and determining the correlation distance of each pore site of the pore network and the percolation probability of the pore network, and analyzing the percolation performance of the pore network. According to the invention, the percolation probability of shale pores can be revealed, and the percolation of fracture development type shale can be effectively evaluated.

Description

Technical Field

[0001] This invention relates to the field of geological exploration, and in particular to a method and apparatus for analyzing the permeability performance of shale pore networks. Background Technology

[0002] This section is intended to provide background or context for embodiments of the present invention. The description herein is not intended to imply that it is prior art simply because it is included in this section.

[0003] Shale is an important lithofacies type in source rock strata. The permeability of its pores is a crucial factor influencing the migration of shale oil and gas within the pore network and a key parameter for evaluating reservoir quality. Poor permeability severely restricts the development of shale oil and gas. Therefore, accurately evaluating the permeability of the pores within shale reservoirs is of great significance for shale oil and gas development. On the one hand, shale reservoir pore networks often struggle to form permeable networks through limited nanoscale pores; on the other hand, unlike conventional reservoirs such as sandstone, they lack typical pore-throat structures. Currently, there is still a lack of effective methods for evaluating the permeability of shale reservoir pore networks. Summary of the Invention

[0004] This invention provides a method for analyzing the permeability of shale pore networks, used to reveal the permeability probability of shale pores and effectively evaluate the permeability of fractured shale. The method includes:

[0005] The shale samples were observed and reconstructed using scanning electron microscopy (SEM). Based on the SEM observation results and the three-dimensional reconstruction results, a three-dimensional digital core model was generated.

[0006] The 3D digital core model is subjected to image filtering and noise reduction and threshold segmentation to obtain a pore network, and the pore network is then site-specific.

[0007] Percolation analysis is performed on the site-specific pore network, and the results of the percolation analysis are used to determine whether a percolation network has formed.

[0008] When a percolation network already exists in the pore network, the pore network is repeatedly compressed until a preset index is reached to obtain a critical percolation model.

[0009] When there is no percolation network in the pore network, the pore network is repeatedly extended until the preset index is reached to obtain the critical percolation model;

[0010] The permeation limit value of the pore network is determined based on the critical permeation model;

[0011] Based on the site-specific pore network, the relevant distances of each pore site in the pore network are determined, and the percolation probability of the pore network is determined based on the relevant distances of each pore site in the pore network.

[0012] The permeability performance of the pore network is analyzed based on the permeability threshold, the relevant distance of each pore site, and the permeability probability of the pore network.

[0013] This invention also provides a shale pore network permeability analysis device to reveal the permeability probability of shale pores and effectively evaluate the permeability of fractured shale. The device includes:

[0014] The preprocessing module is used to perform scanning electron microscopy observation and three-dimensional reconstruction of shale samples, and to generate a three-dimensional digital core model based on the scanning electron microscopy observation results and the three-dimensional reconstruction results.

[0015] The site-specification module is used to perform image filtering, noise reduction, and threshold segmentation on the 3D digital core model to obtain the pore network, and then to perform site-specification on the pore network.

[0016] The percolation pathway analysis module is used to perform percolation analysis on site-specific pore networks and determine whether a percolation network has formed based on the percolation analysis results.

[0017] The permeability limit calculation module is used to repeatedly compress the pore network when a permeability network already exists until a preset index is reached to obtain a critical permeability model; and to repeatedly extend the pore network when a permeability network does not exist until a preset index is reached to obtain a critical permeability model; and to determine the permeability limit value of the pore network based on the critical permeability model.

[0018] The relevant distance calculation module is used to determine the relevant distance of each pore site in the pore network based on the site-specific pore network, and to determine the permeation probability of the pore network based on the relevant distance of each pore site in the pore network.

[0019] The permeability analysis module is used to analyze the permeability performance of a pore network based on the permeability limit value of the pore network, the relevant distance of each pore site, and the permeability probability of the pore network.

[0020] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for analyzing the permeability performance of shale pore networks.

[0021] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for analyzing the permeability performance of shale pore networks.

[0022] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for analyzing the permeability performance of shale pore networks.

[0023] In this embodiment of the invention, shale samples are observed and reconstructed using scanning electron microscopy (SEM). Based on the SEM observation and 3D reconstruction results, a 3D digital core model is generated. The 3D digital core model undergoes image filtering and noise reduction, and threshold segmentation to obtain a pore network. This pore network is then site-specificated. Permeability analysis is performed on the site-specific pore network. Based on the permeability analysis results, it is determined whether a permeability network has formed. If a permeability network already exists, the pore network is repeatedly compressed until a preset index is reached, resulting in a critical permeability model. If no permeability network exists, the pore network is repeatedly extended until a preset index is reached, resulting in a critical permeability model. The permeability threshold value of the pore network is determined based on the critical permeability model. The relevant distances between each pore site in the site-specific pore network are determined, and the permeability probability of the pore network is determined based on these relevant distances. The permeability performance of the pore network is analyzed based on the permeability threshold value, the relevant distances between each pore site, and the permeability probability of the pore network. In this way, after obtaining the original pore model network, the pore sites in the pore model network are enlarged or reduced by the stretching-compacting algorithm to bring the rock to a permeable state, obtain the critical permeability model, and calculate the permeability limit value, which can effectively evaluate the permeability of fractured shale. Based on the digital core model, by calculating the correlation distance and permeability probability of the dispersed pore sites in the model, the connection probability of dispersed shale pores is revealed, and the permeability performance of dispersed pore-developed shale is evaluated. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0025] Figure 1 This is a flowchart of the shale pore network permeability analysis method provided in the embodiments of the present invention;

[0026] Figure 2 In the figures a to f, there are schematic diagrams of the three-dimensional model and pore network extraction results provided in the embodiments of the present invention;

[0027] Figure 3 This is a schematic diagram illustrating the process of obtaining the permeation limit through compression, as provided in an embodiment of the present invention.

[0028] Figure 4 This is a schematic diagram illustrating the shrinkage and stretching effect provided in an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of the sample pore curve provided in an embodiment of the present invention;

[0030] Figure 6 This is a schematic diagram of the shale pore network permeability analysis device provided in an embodiment of the present invention;

[0031] Figure 7 This is a structural block diagram of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0033] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0034] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0035] Regarding the evaluation of rock mass permeability characteristics, existing technologies can only characterize permeability at the core scale of conventional reservoirs such as sandstone, and cannot accurately predict and evaluate permeability at the microscale of nanopores in unconventional reservoirs such as shale. At the same time, the evaluation effect is poor for rocks without fractures. Regarding the evaluation of pore permeability and pore structure, existing technologies mainly evaluate the characteristics and permeability of interconnected pores in shale with fracture development and typical pore throat features through various instrument parameters. It is even more difficult to characterize shale reservoirs with no fractures and where the main pore space type is dispersed pore clusters.

[0036] Based on this, embodiments of the present invention provide a method for analyzing the permeability performance of shale pore networks, such as... Figure 1 As shown, it includes:

[0037] Step 101: Perform scanning electron microscopy (SEM) observation and 3D reconstruction on the shale sample. Based on the SEM observation results and 3D reconstruction results, generate a 3D digital core model.

[0038] Step 102: Perform image filtering and noise reduction and threshold segmentation on the three-dimensional digital core model to obtain the pore network, and then perform site-specific processing on the pore network;

[0039] Step 103: Perform percolation analysis on the site-specific pore network, and determine whether a percolation network has been formed based on the percolation analysis results;

[0040] Step 104: When a percolation network already exists in the pore network, the pore network is repeatedly subjected to compaction treatment until the preset index is reached to obtain the critical percolation model;

[0041] Step 105: When there is no percolation network in the pore network, the pore network is repeatedly extended until the preset index is reached to obtain the critical percolation model;

[0042] Step 106: Determine the permeation limit value of the pore network based on the critical permeation model;

[0043] Step 107: Based on the site-specific pore network, determine the relevant distances of each pore site in the pore network, and based on the relevant distances of each pore site in the pore network, determine the percolation probability of the pore network;

[0044] Step 108: Analyze the permeability performance of the pore network based on the permeability limit value of the pore network, the relevant distance of each pore site, and the permeability probability of the pore network.

[0045] The shale pore network percolation performance analysis method proposed in the embodiments of the present invention aims to solve the problems in the prior art that the characterization effect of the percolation characteristics of fractured shale is poor and it is difficult to characterize the percolation characteristics of dispersed cluster pore shale. Through the method proposed in the embodiments of the present invention, these problems can be solved by two parameters in one process. This method aims to characterize and evaluate the percolation of the shale pore network by using the percolation threshold Pi and the percolation probability g. After obtaining the original pore model network by segmentation from the nano X-ray CT scan and FIB-SEM three-dimensional image data, the pore sites in the pore model network are enlarged or reduced by the extension-contraction algorithm to make the rock reach the percolation state, and the critical percolation model is obtained. By calculating the percolation threshold value, the percolation of fractured shale can be effectively evaluated. Based on the digital core model, by calculating the correlation distance and the percolation probability g of the dispersed pore sites in the model, the connection probability of the dispersed shale pores is revealed, and then the percolation performance of the dispersed pore developed shale is evaluated.

[0046] In one embodiment, the shale pore network percolation performance analysis method further includes:

[0047] Cut the shale sample into block samples and cylindrical samples of preset specifications, and inject Wood's metal into the block samples and cylindrical samples;

[0048] Perform scanning electron microscope observation and three-dimensional reconstruction on the shale sample, and generate a three-dimensional digital core model according to the scanning electron microscope observation results and three-dimensional reconstruction results, including:

[0049] Perform scanning electron microscope observation and three-dimensional reconstruction on the block sample injected with Wood's metal, and perform X-ray CT scanning on the cylindrical sample injected with Wood's metal;

[0050] Generate a three-dimensional digital core model with gray-scale differences according to the scanning electron microscope observation results, three-dimensional reconstruction results and CT scanning results.

[0051] In a specific embodiment, the selected shale sample is cut into a block of 1 cm × 1 cm × 0.2 cm and a cylindrical core with a diameter of 2 mm and a length of 2 cm. Inject Wood's metal into it in a high-temperature and high-pressure injection chamber to enhance the difference in gray-scale values between pores and minerals. Perform scanning electron microscope observation and FIB-SEM three-dimensional reconstruction on the block sample injected with the alloy, and perform X-ray CT scanning on the cylindrical sample injected with the alloy to obtain a three-dimensional digital core model with obvious gray-scale differences, as shown in Figure 2 a-f in.

[0052] The 3D data volume image obtained from the scan is imported into Avizo 3D processing software. Through image filtering and noise reduction, and threshold segmentation, a 3D model of the pore network of the sample can be obtained. The extracted original pore network model is then site-specific, with each pore consisting of one or more sites. The probability P of a pore occupying a position in the model is defined as the ratio of the number of pore sites to the total number of sites.

[0053] In one embodiment, percolation analysis is performed on the site-specific pore network, and based on the percolation analysis results, it is determined whether a percolation network has formed, including:

[0054] Percolation analysis was performed on the site-specific pore network to determine whether a percolation network was formed in the X, Y, and Z coordinate axes.

[0055] Output the number of percolation networks in the X, Y, and Z coordinate axes.

[0056] In one specific embodiment, the segmented pore network model data is exported in Avizo6binary (*.am) format; the exported model data file is then imported into the permeation analysis program CTSTA for pore network permeation analysis.

[0057] In practical implementation, the pore occupancy probability Pi corresponding to the critical permeability model is defined as the permeability threshold value, that is, the pore occupancy probability P corresponding to the existence of at least one pore network channel connecting any two opposing surfaces of the model. The CTSTA program is used to analyze whether a permeability network has formed in the original model. CTSTA can analyze the permeability between opposing surfaces of the model in the X, Y, and Z directions. Taking the X direction as an example, if one permeability network is formed in the X direction, the output result Λ x =1; if no percolation channel is formed, the output Λx = 0.

[0058] In one embodiment, when a percolation network already exists in the pore network, the pore network is repeatedly subjected to a compaction process until a preset index is reached to obtain a critical percolation model, including:

[0059] Repeat the following process until the number of percolation networks in each coordinate axis direction is 0, and use the model obtained from the previous compaction as the critical percolation model:

[0060] When the number of percolation networks in any coordinate axis direction is not zero, the original pore structure remains unchanged, and all pore sites are compressed inward to shrink the original pore network.

[0061] Perform percolation analysis on the compacted pore network and output the number of percolation networks in each coordinate axis direction.

[0062] In one embodiment, when the pore network does not have a percolation network, the pore network is repeatedly stretched until a preset index is reached to obtain a critical percolation model, including:

[0063] Repeat the following process until the number of percolation networks in any coordinate axis direction is not zero. Use the model obtained from the last extension as the critical percolation model:

[0064] When the number of percolation networks in each coordinate axis direction is 0, the original pore structure remains unchanged, and all pore sites are extended outward to enlarge the original pore network.

[0065] Percolation analysis is performed on the extended pore network, and the number of percolation networks in each coordinate axis direction is output.

[0066] In practice, the original model is subjected to percolation detection. If the output result has a non-zero value in any direction, such as Λ( x,y,z ) = (1,0,0) or Λ( x,y,z If ) = (2,1,0), then the original pore network undergoes n compaction processes, meaning that while maintaining the original pore structure, all pore sites are compacted inward by n sites. See [link to documentation]. Figure 4 This shrinks the original pore network; and the permeability of the derived model is re-evaluated using the CTSTA program until the output result is Λ( x,y,z If ) = (0,0,0), then the model obtained by the (n-1)th contraction is the critical percolation model, see Figure 3 Determine the percolation limit value Pi.

[0067] If the output of the original pore model is Λ( x,y,z If (0,0,0) is true, it indicates that the model's pore network has not reached the permeability state. Therefore, the pore network model is extended n times, meaning that while maintaining the original pore structure, all pore sites are extended outward by n sites. See [link to relevant documentation]. Figure 4 This amplifies the original pore network; and the permeability of the derived model is re-evaluated using the CTSTA program until the output result is not zero in any direction. Then, the model obtained by the nth extension is the critical permeability model, and the permeability limit value Pi is determined.

[0068] For pore network models dominated by pore development, the permeability threshold value Pi has limited effectiveness. Since the pores in porous shale are dispersed throughout the shale, their connectivity can be represented by the correlation distance between pore sites; the closer the distance between two pores, the greater their connectivity and the easier it is to form a permeability channel. To effectively characterize the permeability of matrix-type pores in shale, the parametric correlation distance ξ is used. 2 Evaluation of permeability of pore networks

[0069] In one embodiment, determining the relevant distances of each pore site in the pore network based on the site-specific pore network includes:

[0070] The relevant distances of each pore site in the pore network are calculated using the following formula:

[0071]

[0072] in,

[0073]

[0074] Based on the relevant distances between various pore sites in the pore network, the permeability probability of the pore network is determined, including:

[0075] The percolation probability of a porous network is calculated using the following formula:

[0076]

[0077] Where ξ 2 x represents the relevant distance between various pore sites in the pore network. i x represents the position of the i-th pore point within the model. j This represents the position of the j-th pore point within the research object, where S is the number of points contained within a pore in a model, and 2r s 2 Defined as the average of the squares of the straight-line distances between any two points in a pore cluster, n s Let S represent the probability of a pore cluster of size S appearing, and g represent the percolation probability of the pore network.

[0078] A permeation probability g is proposed to evaluate the permeability of pore networks primarily based on pore development. The permeation probability g represents the probability that two points d apart simultaneously connect to each other, and its expression is as follows:

[0079]

[0080] In practice, after model extension-contraction processing and permeability discrimination, the permeability limit value Pi corresponding to the permeability limit model can be determined. This value is the permeability limit value corresponding to the test sample. The higher the Pi value, the easier it is for the sample model to reach the permeability state, and the more suitable it is for priority development; the lower the Pi value, the more difficult it is for the model to reach the permeability state, and the more difficult it is to apply to development.

[0081] As shown by the expression for the g-function, its graph is inversely proportional to the relevant distance; the closer the distance between two different pore sites, the smaller ξ is, and the higher the permeability probability. The permeability probability of the initial rock model and the permeability limit values ​​of a series of derived models can be obtained from the function graph.

[0082] For example, a three-dimensional digital core model of the research sample is obtained through X-ray CT scanning, and a nanoscale three-dimensional digital core model image is obtained through FIB-SEM three-dimensional reconstruction. Figure 2 b in Figure 2 (e). Because the shale samples were injected with Wood's alloy before the three-dimensional X-ray CT scan, the shale matrix and pores could be well distinguished in the digital core model due to the difference in grayscale.

[0083] Importing the three-dimensional data volume model into Avizo software, the pore network model of the data volume can be obtained through image denoising and threshold segmentation. Groups of the same color represent a pore cluster composed of multiple pores connected together. The larger the pore cluster model, the better the overall connectivity of the model. Microcracks constitute the main connected space. It is also worth noting that the irregular pores near the microcracks are connected to the microcracks, indicating that the microcracks connect the matrix pores within a certain range nearby.

[0084] The obtained pore network model was exported and evaluated using the CTSTA program. See Table 1 for details.

[0085] Table 1. Permeability Limits of Shale Samples

[0086]

[0087] The discrimination result of sample W23 model is Λ( x,y,z The model is compressed. The result is (2,0,0), indicating the existence of two percolation channels in the X direction. Table 1 shows that after two compressions, the W23 model... x,y,z ) = (0,0,0), meaning it is not in a percolation state, according to Figure 3 The principle shown indicates that the percolation limit value for sample W23 is 5.36. Similarly, the initial discrimination result Λ for sample J24 is... (x,y,z The model is in a non-percolation state when the value is (0,0,0). This can be seen by extending the model. Figure 4 In the third extended model discrimination, Λ( x,y,z The permeability threshold of sample J24 is 9.26, where (1,1,1) represents the permeability of the pore network. The permeability threshold Pi is related to the permeability of the pore network and the ease with which a permeable network forms. A smaller Pi indicates better permeability and a greater ease of network formation. Since fractures increase the probability of permeable network formation, this parameter is primarily used for evaluating the permeability of fractured shale.

[0088] To address the locally dispersed distribution of pore clusters in porous shale, the correlation distance ξ of the pore clusters is calculated. 2 Then, the percolation probability function g is further calculated. For example... Figure 5As shown, the permeability probability of sample W23 is 39% and that of sample J24 is 9% when the distance between pore sites is 50, which matches the pore characteristics of the samples. The value of g is related to the distance between pore sites; the closer the distance, the larger g is and the greater the permeability probability. The presence of fractures will seriously affect the value of g, so it is mainly used for the evaluation of permeability of porous shale.

[0089] The permeability analysis method for shale pore networks proposed in this invention characterizes the permeability performance of shale nanoscale pores using a permeability limit Pi and a permeability probability g. After obtaining a digital model of the core through X-ray CT and FIB-SEM scanning, the model is site-specific, and the permeability limit Pi and permeability probability g of the shale model are obtained through model extension-contraction. The permeability limit Pi primarily characterizes the permeability of fractured shale, while the permeability probability g primarily characterizes the permeability of porous shale.

[0090] This invention also provides a device for analyzing the permeability performance of shale pore networks, as described in the following embodiments. Since the principle by which this device solves the problem is similar to the method for analyzing the permeability performance of shale pore networks, the implementation of this device can be referred to the implementation of the method, and repeated details will not be elaborated further.

[0091] Figure 6 This is a schematic diagram of the shale pore network permeability analysis device provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the device includes:

[0092] The preprocessing module 601 is used to perform scanning electron microscopy observation and three-dimensional reconstruction on shale samples, and to generate a three-dimensional digital core model based on the scanning electron microscopy observation results and the three-dimensional reconstruction results.

[0093] The site-specification module 602 is used to perform image filtering and noise reduction and threshold segmentation on the three-dimensional digital core model to obtain a pore network, and then perform site-specification on the pore network.

[0094] The percolation pathway analysis module 603 is used to perform percolation analysis on the site-specific pore network and determine whether a percolation network has been formed based on the percolation analysis results.

[0095] The permeability limit value calculation module 604 is used to repeatedly perform compaction processing on the pore network when a permeability network already exists, until a preset index is reached to obtain a critical permeability model; and to repeatedly perform extension processing on the pore network when a permeability network does not exist, until a preset index is reached to obtain a critical permeability model; and to determine the permeability limit value of the pore network based on the critical permeability model.

[0096] The relevant distance calculation module 605 is used to determine the relevant distance of each pore site in the pore network based on the site-specific pore network, and to determine the permeation probability of the pore network based on the relevant distance of each pore site in the pore network.

[0097] The permeability analysis module 606 is used to analyze the permeability performance of a pore network based on the permeability limit value of the pore network, the relevant distance of each pore site, and the permeability probability of the pore network.

[0098] In one embodiment, the preprocessing module 601 is specifically used for:

[0099] Shale samples were cut into block samples and cylindrical samples of predetermined specifications, and Wood alloy was injected into the block samples and cylindrical samples.

[0100] Scanning electron microscopy was used to observe and reconstruct the bulk samples injected with Wood's alloy in three dimensions, and X-ray CT scans were performed on the cylindrical samples injected with Wood's alloy.

[0101] Based on the results of scanning electron microscopy, 3D reconstruction, and CT scans, a 3D digital core model with grayscale differences is generated.

[0102] In one embodiment, the percolation pathway analysis module 603 is specifically used for:

[0103] Percolation analysis was performed on the site-specific pore network to determine whether a percolation network was formed in the X, Y, and Z coordinate axes.

[0104] Output the number of percolation networks in the X, Y, and Z coordinate axes.

[0105] In one embodiment, the permeation limit value calculation module 604 is specifically used for:

[0106] Repeat the following process until the number of percolation networks in each coordinate axis direction is 0, and use the model obtained from the previous compaction as the critical percolation model:

[0107] When the number of percolation networks in any coordinate axis direction is not zero, the original pore structure remains unchanged, and all pore sites are compressed inward to shrink the original pore network.

[0108] Perform percolation analysis on the compacted pore network and output the number of percolation networks in each coordinate axis direction.

[0109] In one embodiment, the permeation limit value calculation module 604 is specifically used for:

[0110] Repeat the following process until the number of percolation networks in any coordinate axis direction is not zero. Use the model obtained from the last extension as the critical percolation model:

[0111] When the number of percolation networks in each coordinate axis direction is 0, the original pore structure remains unchanged, and all pore sites are extended outward to enlarge the original pore network.

[0112] Percolation analysis is performed on the extended pore network, and the number of percolation networks in each coordinate axis direction is output.

[0113] In one embodiment, the relevant distance calculation module 605 is specifically used for:

[0114] The relevant distances of each pore site in the pore network are calculated using the following formula:

[0115]

[0116] in,

[0117]

[0118] Based on the relevant distances between various pore sites in the pore network, the permeability probability of the pore network is determined, including:

[0119] The percolation probability of a porous network is calculated using the following formula:

[0120]

[0121] Where ξ 2 x represents the relevant distance between various pore sites in the pore network. i x represents the position of the i-th pore point within the model. j This represents the position of the j-th pore point within the research object, where S is the number of points contained within a pore in a model, and 2r s 2 Defined as the average of the squares of the straight-line distances between any two points in a pore cluster, n s Let S represent the probability of a pore cluster of size S appearing, and g represent the percolation probability of the pore network.

[0122] Based on the aforementioned inventive concept, such as Figure 7 As shown, the present invention also proposes a computer device 700, including a memory 710, a processor 720, and a computer program 730 stored in the memory 710 and executable on the processor 720. When the processor 720 executes the computer program 730, it implements the aforementioned method for analyzing the permeability performance of shale pore networks.

[0123] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for analyzing the permeability performance of shale pore networks.

[0124] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for analyzing the permeability performance of shale pore networks.

[0125] In summary, in this embodiment of the invention, shale samples are observed and reconstructed using scanning electron microscopy (SEM). Based on the SEM observation and 3D reconstruction results, a 3D digital core model is generated. The 3D digital core model undergoes image filtering and noise reduction, as well as threshold segmentation, to obtain a pore network. This pore network is then site-specificated. Permeability analysis is performed on the site-specific pore network, and the permeability analysis results determine whether a permeability network has formed. If a permeability network already exists, the pore network is repeatedly compressed until a preset index is reached, resulting in a critical permeability model. If no permeability network exists, the pore network is repeatedly extended until a preset index is reached, resulting in a critical permeability model. The permeability threshold value of the pore network is determined based on the critical permeability model. The relevant distances between each pore site in the site-specific pore network are determined, and the permeability probability of the pore network is determined based on these relevant distances. The permeability performance of the pore network is analyzed based on the permeability threshold value, the relevant distances between each pore site, and the permeability probability of the pore network.

[0126] Compared with existing technologies, by scaling up and down the model, it is possible not only to evaluate the permeability of sandstone models, but also to obtain the permeability characteristics of shale nanoscale pore networks. The numerical characteristics obtained by permeation curves, nuclear magnetic resonance spectra, or CT scans are all permeability characteristics of connected pores in fractured shale, and do not involve dispersed isolated pores. At the same time, it is difficult to quantify the influence of isolated pores on permeability characteristics. The shale pore network permeability performance analysis method proposed in this invention can not only characterize the permeability performance of fractured shale, but also effectively evaluate the permeability characteristics of the pore-type main pore network (a pore network with locally distributed dispersed pore clusters) by using the average distance between connected pore sites in the model. At the same time, the permeability probability as a function of relevant distance is given by the g-curve, which also has predictive evaluation effect on fractured and porous shale.

[0127] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing the permeability performance of shale pore networks, characterized in that, include: The shale samples were observed and reconstructed using scanning electron microscopy (SEM). Based on the SEM observation results and the three-dimensional reconstruction results, a three-dimensional digital core model was generated. The 3D digital core model is subjected to image filtering and noise reduction and threshold segmentation to obtain a pore network, and the pore network is then site-specific. Percolation analysis is performed on the site-specific pore network, and the results of the percolation analysis are used to determine whether a percolation network has formed. When a percolation network already exists in the pore network, the pore network is repeatedly compressed until a preset index is reached to obtain a critical percolation model. When there is no percolation network in the pore network, the pore network is repeatedly extended until the preset index is reached to obtain the critical percolation model; The permeation limit value of the pore network is determined based on the critical permeation model; Based on the site-specific pore network, the relevant distances of each pore site in the pore network are determined, and the percolation probability of the pore network is determined based on the relevant distances of each pore site in the pore network. The permeability performance of the pore network is analyzed based on the permeability threshold, the relevant distance of each pore site, and the permeability probability of the pore network.

2. The method as described in claim 1, characterized in that, Also includes: Shale samples were cut into block samples and cylindrical samples of predetermined specifications, and Wood alloy was injected into the block samples and cylindrical samples. The shale samples were observed and reconstructed using scanning electron microscopy (SEM). Based on the SEM observations and reconstructions, a three-dimensional digital core model was generated, including: Scanning electron microscopy was used to observe and reconstruct the bulk samples injected with Wood's alloy in three dimensions, and X-ray CT scans were performed on the cylindrical samples injected with Wood's alloy. Based on the results of scanning electron microscopy, 3D reconstruction, and CT scans, a 3D digital core model with grayscale differences is generated.

3. The method as described in claim 1, characterized in that, Percolation analysis is performed on the site-specific pore network. Based on the percolation analysis results, it is determined whether a percolation network has formed, including: Percolation analysis was performed on the site-specific pore network to determine whether a percolation network was formed in the X, Y, and Z coordinate axes. Output the number of percolation networks in the X, Y, and Z coordinate axes.

4. The method as described in claim 3, characterized in that, When a percolation network already exists in the pore network, the pore network is repeatedly subjected to a compaction process until a preset index is reached, resulting in a critical percolation model, including: Repeat the following process until the number of percolation networks in each coordinate axis direction is 0, and use the model obtained from the previous compaction as the critical percolation model: When the number of percolation networks in any coordinate axis direction is not zero, the original pore structure remains unchanged, and all pore sites are compressed inward to shrink the original pore network. Perform percolation analysis on the compacted pore network and output the number of percolation networks in each coordinate axis direction.

5. The method as described in claim 3, characterized in that, When the pore network does not have a percolation network, the pore network is repeatedly extended until a preset index is reached, resulting in a critical percolation model, including: Repeat the following process until the number of percolation networks in any coordinate axis direction is not zero. Use the model obtained from the last extension as the critical percolation model: When the number of percolation networks in each coordinate axis direction is 0, the original pore structure remains unchanged, and all pore sites are extended outward to enlarge the original pore network. Percolation analysis is performed on the extended pore network, and the number of percolation networks in each coordinate axis direction is output.

6. The method as described in claim 1, characterized in that, Based on the site-specific pore network, determine the relevant distances of each pore site in the pore network, including: The relevant distances of each pore site in the pore network are calculated using the following formula: in, Based on the relevant distances between various pore sites in the pore network, the permeability probability of the pore network is determined, including: The percolation probability of a porous network is calculated using the following formula: Where ξ 2 x represents the relevant distance between various pore sites in the pore network. i x represents the position of the i-th pore point within the model. j This represents the position of the j-th pore point within the research object, where S is the number of points contained within a pore in a model, and 2r s 2 Defined as the average of the squares of the straight-line distances between any two points in a pore cluster, n s Let S represent the probability of a pore cluster of size S appearing, and g represent the percolation probability of the pore network.

7. A device for analyzing the permeability performance of shale pore networks, characterized in that, include: The preprocessing module is used to perform scanning electron microscopy observation and three-dimensional reconstruction of shale samples, and to generate a three-dimensional digital core model based on the scanning electron microscopy observation results and the three-dimensional reconstruction results. The site-specification module is used to perform image filtering, noise reduction, and threshold segmentation on the 3D digital core model to obtain the pore network, and then to perform site-specification on the pore network. The percolation pathway analysis module is used to perform percolation analysis on site-specific pore networks and determine whether a percolation network has formed based on the percolation analysis results. The permeability limit calculation module is used to repeatedly compress the pore network when a permeability network already exists until a preset index is reached to obtain a critical permeability model; and to repeatedly extend the pore network when a permeability network does not exist until a preset index is reached to obtain a critical permeability model; and to determine the permeability limit value of the pore network based on the critical permeability model. The relevant distance calculation module is used to determine the relevant distance of each pore site in the pore network based on the site-specific pore network, and to determine the permeation probability of the pore network based on the relevant distance of each pore site in the pore network. The permeability analysis module is used to analyze the permeability performance of a pore network based on the permeability limit value of the pore network, the relevant distance of each pore site, and the permeability probability of the pore network.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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