A method for quantitatively calculating complexity of fracture network

Abstract

The application provides a fracture network complexity quantitative calculation method, which comprises the following steps: determining a hydraulic fracture network morphology map of a target fracture network after volume fracturing; performing fracture morphology feature extraction and binarization processing on the hydraulic fracture network morphology map to obtain a binarized fracture morphology feature map; counting the number of specific grids contained in the binarized fracture morphology feature map through grids of different sizes; and calculating the fracture network complexity of the target fracture network based on the grid size and the number of specific grids. The application can quantitatively calculate the complexity of the fracture network, and solves the problem that the traditional method cannot be applied to the calculation of the complexity of the fracture network in unconventional reservoirs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fracturing reconstruction, in particular to a method for quantitatively calculating the complexity of a fracture network. BACKGROUND

[0002] In recent years, unconventional oil and gas resources have become the main position of oil and gas exploration and development, but the development of unconventional oil and gas resources is highly dependent on volume reconstruction. After volume reconstruction, a complex fracture network can be formed in the reservoir, thereby effectively increasing the seepage area of oil and gas, shortening the oil and gas seepage distance, reducing the oil and gas seepage resistance, and ultimately realizing the efficient use of unconventional resources. At present, the complexity of the fracture network has become an important indicator for evaluating the effect of volume reconstruction. The existing fracture complexity evaluation method is mainly aimed at simple fractures in conventional reservoirs, but the understanding of the complexity of the fracture network in unconventional reservoirs is mainly qualitative, and there is a lack of quantitative characterization method.

[0003] Therefore, the present application provides a method for quantitatively calculating the complexity of a fracture network. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a method for quantitatively calculating the complexity of a fracture network, which comprises:

[0005] S1, for a target fracture network, determining a hydraulic fracture network morphology map after volume fracturing;

[0006] S2, performing fracture morphology feature extraction and binarization processing on the hydraulic fracture network morphology map to obtain a binarized fracture morphology feature map;

[0007] S3, counting the number of specific squares contained in the binarized fracture morphology feature map through squares of different sizes;

[0008] S4, based on the square size and the number of specific squares, calculating the complexity of the fracture network of the target fracture network.

[0009] According to an embodiment of the present application, step S1 comprises: obtaining the hydraulic fracture network morphology map of the target fracture network after volume fracturing by using software simulation.

[0010] According to an embodiment of the present application, step S2 comprises:

[0011] determining a gray scale map of the hydraulic fracture network morphology map;

[0012] performing fracture morphology feature extraction on the gray scale map to obtain a fracture morphology feature map;

[0013] The crack morphology characteristic map is binarized to obtain the binary crack morphology characteristic map. According to one embodiment of the present invention, the grayscale map is obtained by using a weighted average method, wherein:

[0014] Gray(x,y)=R(x,y)×0.30+G(x,y)×0.59+B(x,y)×0.11

[0015] Among them, Gray(x,y) represents the grayscale value of the pixel at the coordinate (x,y); R(x,y), G(x,y), and B(x,y) represent the R, G, and B components of the pixel at the coordinate (x,y) in the hydraulic fracture network morphology diagram, respectively.

[0016] According to one embodiment of the present invention, the crack morphology characteristic map is obtained by the following steps:

[0017] Using a threshold value to separate the grayscale image into two images, a foreground image and a background image;

[0018] Determine the total average grayscale of the grayscale image, the ratio of the number of foreground points to the grayscale image, the ratio of the number of background points to the grayscale image, the average grayscale of the foreground image, and the average grayscale of the background image;

[0019] Calculating the variance of the foreground image and the background image corresponding to different thresholds based on the ratio of the number of foreground points to the grayscale image, the ratio of the number of background points to the grayscale image, the average grayscale of the foreground image, and the average grayscale of the background image;

[0020] When the variance is the largest, the foreground image under the current threshold is extracted as the crack morphology feature map.

[0021] According to one embodiment of the present invention, the total average grayscale expression of the grayscale image is:

[0022] u=w0×u0+w1×u1

[0023] The variance of the foreground image and the background image corresponding to different thresholds t is calculated by the following expression:

[0024] g=w0×w1×(u0-u1)×(u0-u1)

[0025] The foreground image under the current threshold is extracted as the crack morphology feature map using the following expression:

[0026]

[0027] Where: u represents the total average grayscale of the grayscale image; w0 represents the proportion of foreground points in the grayscale image; u0 represents the average grayscale of the foreground image; w1 represents the proportion of background points in the grayscale image; u1 represents the average grayscale of the background image; g represents the variance of the foreground image and the background image corresponding to different thresholds t; Gray1(x,y) represents the grayscale value of the pixel at coordinates (x,y) after removing the background image; Gray(x,y) represents the grayscale value of the pixel at coordinates (x,y) in the grayscale image.

[0028] According to an embodiment of the present invention, step S3 includes: cyclically using grids of different sizes to cover the binary fracture morphology characteristic map, and counting the number of the specific grids containing specific features.

[0029] According to one embodiment of the present invention, step S4 includes:

[0030] Taking the logarithm of the square size and the number of specific squares to obtain the square size after taking the logarithm and the number of specific squares after taking the logarithm;

[0031] Draw a relationship curve with the logarithmic grid size as the horizontal axis and the logarithmic number of specific grids as the vertical axis;

[0032] Fitting the relationship curve using the least square method to obtain a fitting curve;

[0033] The inverse of the slope of the fitting curve is recorded as the fracture network complexity of the target fracture network.

[0034] According to another aspect of the present invention, a storage medium is provided, which contains a series of instructions for executing the method steps described in any one of the above.

[0035] According to another aspect of the present invention, there is provided a system for quantitatively calculating the complexity of a fracture network, which executes any of the above methods, and the system comprises:

[0036] A fracture network morphology module is used to determine the hydraulic fracture network morphology diagram after volume fracturing for the target fracture network;

[0037] a fracture morphology feature module, which is used to extract fracture morphology features and perform binarization processing on the hydraulic fracture network morphology map to obtain a binary fracture morphology feature map;

[0038] A specific square number module is used to count the number of specific squares included in the binary fracture morphology characteristic map through squares of different sizes;

[0039] The network complexity module is used to calculate the fracture network complexity of the target fracture network based on the grid size and the number of specific grids.

[0040] The method for quantitatively calculating the complexity of a fracture network provided by the present invention has the following advantages over the prior art: the present invention can quantitatively calculate the complexity of a fracture network, solving the problem that traditional methods are not applicable to the calculation of the complexity of fracture networks in unconventional reservoirs. At the same time, based on the calculation results of the method, the influence of different process parameters on the complexity of the fracture network can be analyzed, thereby achieving quantitative evaluation of the volume transformation effect and optimization of the volume transformation process parameters.

[0041] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 A flow chart of a method for quantitatively calculating the complexity of a fracture network according to an embodiment of the present invention is shown;

[0044] Figure 2 A diagram showing the morphology of a hydraulic fracture network according to one embodiment of the present invention is shown;

[0045] Figure 3 A grayscale image of a hydraulic fracture network morphology diagram according to an embodiment of the present invention is shown;

[0046] Figure 4 A crack morphology characteristic diagram according to an embodiment of the present invention is shown;

[0047] Figure 5 A binary crack morphology characteristic diagram according to an embodiment of the present invention is shown;

[0048] Figure 6 shows an overlay of squares of different sizes according to one embodiment of the present invention;

[0049] Figure 7 A graph of size grids and number of grids is shown according to one embodiment of the present invention.

[0050] In the accompanying drawings, the same reference numerals are used for the same components. In addition, the accompanying drawings are not drawn according to the actual scale. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0052] In recent years, unconventional oil and gas resources have become a major focus of oil and gas exploration and development. However, their development is highly dependent on volumetric modification. Volumetric modification can form complex fracture networks within the reservoir, effectively increasing the seepage area for oil and gas, shortening the oil and gas seepage distance, and reducing the resistance to oil and gas flow, ultimately enabling the efficient utilization of unconventional resources. Currently, the complexity of the fracture network has become a key indicator for evaluating the effectiveness of volumetric modification. Existing fracture complexity assessment methods primarily focus on simple fractures in conventional reservoirs. However, understanding the complexity of fracture networks in unconventional reservoirs is primarily qualitative, lacking quantitative characterization methods.

[0053] In response to the existing technical situation, the present invention proposes a quantitative calculation method for the complexity of the fracture network based on fractal theory. On this basis, the influence of different process parameters on the complexity of the fracture network can be analyzed, and the volume transformation effect can be quantitatively evaluated and the volume transformation process parameters can be optimized.

[0054] Figure 1 A flow chart of a method for quantitatively calculating the complexity of a fracture network according to an embodiment of the present invention is shown.

[0055] like Figure 1 As shown, in step S1, for the target fracture network, a morphological diagram of the hydraulic fracture network after volume fracturing is determined.

[0056] In one embodiment, step S1 includes: using software simulation to obtain a hydraulic fracture network morphology diagram of the target fracture network after volume fracturing (e.g. Figure 2 shown).

[0057] like Figure 1 As shown, in step S2, the hydraulic fracture network morphology map is subjected to fracture morphology feature extraction and binarization processing to obtain a binary fracture morphology feature map.

[0058] In one embodiment, step S2 includes: determining a grayscale image of the hydraulic fracture network morphology map. Specifically, a weighted average method is used to obtain the following: Figure 3 The grayscale image shown, where:

[0059] Gray(x,y)=R(x,y)×0.30+G(x,y)×0.59+B(x,y)×0.11

[0060] Among them, Gray(x,y) represents the grayscale value of the pixel at the coordinate (x,y); R(x,y), G(x,y), and B(x,y) represent the R, G, and B components of the pixel at the coordinate (x,y) in the hydraulic fracture network morphology diagram, respectively.

[0061] In one embodiment, step S2 includes: extracting crack morphological features from the grayscale image to obtain a crack morphological feature image (such as Figure 4 Specifically, the maximum inter-class variance method is used to extract the crack morphological feature map: the grayscale image is divided into two images, the foreground and the background, using a threshold value; the total average grayscale of the grayscale image, the ratio of the number of foreground points to the grayscale image, the ratio of the number of background points to the grayscale image, the average grayscale of the foreground image, and the average grayscale of the background image are determined; based on the ratio of the number of foreground points to the grayscale image, the ratio of the number of background points to the grayscale image, the average grayscale of the foreground image, and the average grayscale of the background image, the variance of the foreground image and the background image corresponding to different thresholds is calculated; when the variance is the largest, the foreground image under the current threshold is extracted as the crack morphological feature map.

[0062] In one embodiment, the variance g corresponding to different thresholds t is calculated. When the variance g is the largest, the foreground image extracted according to the threshold t is the morphological feature of the crack, where:

[0063] The total average grayscale expression of the grayscale image is:

[0064] u=w0×u0+w1×u1

[0065] The variance of the foreground image and the background image corresponding to different thresholds t is calculated by the following expression:

[0066] g=w0×w1×(u0-u1)×(u0-u1)

[0067] The foreground image under the current threshold is extracted as the crack morphology feature map using the following expression:

[0068]

[0069] Where: u represents the total average grayscale of the grayscale image; w0 represents the proportion of foreground points in the grayscale image; u0 represents the average grayscale of the foreground image; w1 represents the proportion of background points in the grayscale image; u1 represents the average grayscale of the background image; g represents the variance between the foreground and background images corresponding to different thresholds t; Gray1(x,y) represents the grayscale value of the pixel at coordinates (x,y) after removing the background image; Gray(x,y) represents the grayscale value of the pixel at coordinates (x,y) in the grayscale image. Gray(x,y) ≥ t indicates that the pixel in the grayscale image belongs to the foreground image; Gray(x,y) < t indicates that the pixel in the grayscale image belongs to the background image. Furthermore, the threshold t varies between 0 and 255. When g is maximum, t is the threshold value found. Based on the threshold t, the background grayscale value in the grayscale image is set to 0, while the foreground grayscale value remains unchanged. The goal is to remove the background image and retain the foreground image as the crack morphology feature map.

[0070] In one embodiment, step S2 includes: performing binarization processing on the crack morphology characteristic map to obtain a binary crack morphology characteristic map (such as Figure 5 Specifically, the crack morphology feature map is binarized using the following expression:

[0071]

[0072] Among them, Gray2(x,y) represents the grayscale value of the pixel at the coordinate (x,y) after binarization processing.

[0073] like Figure 1 As shown, in step S3, the number of specific squares included in the binary fracture morphology characteristic map is counted by using squares of different sizes.

[0074] In one embodiment, step S3 includes: cyclically using grids of different sizes to cover the binary crack morphology feature map, and counting the number of specific grids containing specific features. Specifically, cyclically using grids of different sizes r to cover the binary crack morphology feature map, and counting the number N(r) of specific grids containing the specific feature "1". Furthermore, the specific feature "1" means that the gray value of any pixel in a certain grid is equal to 1 (i.e., Gray2(x,y)=1). Figure 6 For example, as long as Figure 6 If the gray value of any pixel in a certain square is equal to 1, then this square can be marked as a specific square, that is, Figure 6 The presence of a black line (crack) in any square indicates the presence of a specific feature "1".

[0075] like Figure 1 As shown, in step S4, the fracture network complexity of the target fracture network is calculated based on the grid size and the number of specific grids.

[0076] In one embodiment, step S4 includes taking the logarithm of the grid size and the number of specific grid squares to obtain the logarithmized grid size and the logarithmized number of specific grid squares. Specifically, taking the logarithm of the grid size r and the number of specific grid squares N(r) to obtain the logarithmized grid size log(r) and the logarithmized number of specific grid squares log(N(r)).

[0077] In one embodiment, step S4 includes: drawing a relationship curve with the logarithmic grid size as the horizontal axis and the logarithmic number of specific grids as the vertical axis. Specifically, a relationship curve of log(r)-log(N(r)) is drawn, such as Figure 7 shown.

[0078] In one embodiment, step S4 includes: fitting the relationship curve using the least squares method to obtain a fitting curve. Specifically, fitting the relationship curve using the least squares method to obtain the following: Figure 7 The fitting curve is shown.

[0079] In one embodiment, step S4 includes: recording the inverse of the slope of the fitting curve as the fracture network complexity of the target fracture network. Specifically, the inverse of the slope D is the fracture network complexity:

[0080]

[0081] The present invention can quantitatively calculate the complexity of the fracture network, solving the problem that traditional methods are not applicable to the calculation of the complexity of the fracture network in unconventional reservoirs. At the same time, based on the calculation results of this method, the influence of different process parameters on the complexity of the fracture network can be analyzed, thereby achieving quantitative evaluation of the volume transformation effect and optimization of the volume transformation process parameters.

[0082] The method for quantitatively calculating fracture network complexity provided by the present invention can also be used in conjunction with a computer-readable storage medium having a computer program stored thereon. The computer program is executed to implement the method for quantitatively calculating fracture network complexity. A computer program can execute computer instructions, which include computer program code. The computer program code can be in source code form, object code form, executable file, or some intermediate form.

[0083] Computer-readable storage media may include: any entity or device that can carry computer program code, recording media, USB flash drives, mobile hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0084] It should be noted that the content contained in computer-readable storage media can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable storage media do not include electrical carrier signals and telecommunications signals.

[0085] According to another aspect of the present invention, a fracture network complexity quantitative calculation system is provided, which executes a fracture network complexity quantitative calculation method. The system includes: a fracture network morphology module, a fracture morphology feature module, a specific grid quantity module and a network complexity module.

[0086] The fracture network morphology module is used to determine the hydraulic fracture network morphology diagram after volume fracturing for the target fracture network; the fracture morphology feature module is used to extract the fracture morphology features and perform binarization processing on the hydraulic fracture network morphology diagram to obtain a binary fracture morphology feature diagram; the specific square number module is used to count the number of specific squares contained in the binary fracture morphology feature diagram using squares of different sizes; the network complexity module is used to calculate the fracture network complexity of the target fracture network based on the square size and the specific square number.

[0087] In summary, the quantitative calculation method for the complexity of the fracture network provided by the present invention has the following advantages compared with the existing technology: the present invention can realize the quantitative calculation of the complexity of the fracture network, solving the problem that traditional methods are not applicable to the calculation of the complexity of the fracture network in unconventional reservoirs. At the same time, based on the calculation results of this method, the influence of different process parameters on the complexity of the fracture network can be analyzed, thereby realizing the quantitative evaluation of the volume transformation effect and the optimization of the volume transformation process parameters.

[0088] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should extend to equivalent substitutions of these features understood by those skilled in the relevant art. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.

[0089] In the description of the application, unless otherwise stated, the meaning of "a plurality" is two or more; the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "back end", "head", "tail" and the like indicate the orientation or positional relationship shown by the drawings, which are only for the convenience of describing the application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. In addition, the terms "first", "second", "third" and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0090] In the description of the application, it should be noted that, unless otherwise specified and limited, the terms "connected", "connected" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium. For those skilled in the art, the specific meaning of the above terms in the application can be understood according to the specific circumstances.

[0091] Certain terms are used throughout this application to refer to particular system components. As one skilled in the art will appreciate, the same component can be referred to by different names and can not be referred to as such throughout this application. In this application, the terms "comprise", "include" and "have" are used in an open form and thus should be interpreted as meaning "including but not limited to...". In addition, the terms "substantially", "essentially" or "approximately" that can be used herein relate to industry-accepted tolerances for the corresponding terms. The term "coupled" as can be employed in this document includes direct coupling and indirect coupling via another component, element, circuit, or module, wherein for indirect coupling, the intervening component, element, circuit, or module does not alter the information of the signal but can adjust the current level, voltage level, and / or power level thereof. The inferred coupling (e.g., where one element is coupled to another element by inference) includes direct and indirect coupling between the two elements in the same way as "coupled".

[0092] The phrase "one embodiment" or "an embodiment" appearing in the specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Therefore, the phrase "one embodiment" or "an embodiment" appearing throughout the specification does not necessarily all refer to the same embodiment.

[0093] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

[0094] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art of the present invention may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed herein. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A quantitative calculation method for the complexity of a crack network, characterized in that: The method comprises the following steps: S1. Determine the hydraulic fracture network morphology after volume fracturing for the target fracture network; S2. Extracting fracture morphology features and performing binarization processing on the hydraulic fracture network morphology map to obtain a binary fracture morphology feature map; S3. Counting the number of specific squares included in the binary crack morphology characteristic map using squares of different sizes; S4. Calculating the fracture network complexity of the target fracture network based on the grid size and the number of the specific grids; Step S2 comprises: determining a grayscale image of the hydraulic fracture network morphology map; extracting fracture morphology features from the grayscale image to obtain a fracture morphology feature map; performing binarization processing on the fracture morphology feature map to obtain the binarized fracture morphology feature map; The grayscale image is obtained by using a weighted average method, where: Gray(x,y)=R(x,y)×0.30+G(x,y)×0.59+B(x,y)×0.11 Where Gray(x,y) represents the grayscale value of the pixel at the coordinate (x,y); R(x,y), G(x,y), and B(x,y) represent the R, G, and B components of the pixel at the coordinate (x,y) in the hydraulic fracture network morphology diagram, respectively. The crack morphology characteristic map is obtained by the following steps: using a threshold to divide the grayscale image into two images, a foreground and a background; determining the total average grayscale of the grayscale image, the ratio of the number of foreground points to the grayscale image, the ratio of the number of background points to the grayscale image, the average grayscale of the foreground image, and the average grayscale of the background image; based on the ratio of the number of foreground points to the grayscale image, the ratio of the number of background points to the grayscale image, the average grayscale of the foreground image, and the average grayscale of the background image, calculating the variance of the foreground image and the background image corresponding to different thresholds; when the variance is the largest, extracting the foreground image under the current threshold as the crack morphology characteristic map.

2. A method for quantitatively calculating the complexity of a fracture network according to claim 1, characterized in that: Step S1 includes: using software simulation to obtain the hydraulic fracture network morphology diagram of the target fracture network after volume fracturing.

3. A method for quantitatively calculating the complexity of a fracture network according to claim 1, characterized in that: The total average grayscale expression of the grayscale image is: u=w0×u0+w1×u1 The variance of the foreground image and the background image corresponding to different thresholds t is calculated by the following expression: g=w0×w1×(u0-u1)×(u0-u1) The foreground image under the current threshold is extracted as the crack morphology feature map using the following expression: Where: u represents the total average grayscale of the grayscale image; w0 represents the proportion of the number of foreground points in the grayscale image; u0 represents the average grayscale of the foreground image; w1 represents the proportion of the number of background points in the grayscale image; u1 represents the average grayscale of the background image; g represents the variance of the foreground image and the background image corresponding to different thresholds t; Gray1(x,y) represents the grayscale value of the pixel at coordinates (x,y) after removing the background image; Gray(x,y) represents the grayscale value of the pixel at coordinates (x,y).

4. A method for quantitatively calculating the complexity of a fracture network according to claim 3, characterized in that: The crack morphology characteristic map is binarized using the following expression: Among them, Gray2(x,y) represents the grayscale value of the pixel at the coordinate (x,y) after binarization processing.

5. A method for quantitatively calculating the complexity of a fracture network according to claim 1, characterized in that: Step S3 includes: cyclically using grids of different sizes to cover the binary crack morphology characteristic map, and counting the number of the specific grids containing specific features.

6. A method for quantitatively calculating the complexity of a fracture network according to any one of claims 1 to 5, characterized in that: Step S4 comprises: Taking the logarithm of the square size and the number of specific squares to obtain the square size after taking the logarithm and the number of specific squares after taking the logarithm; Draw a relationship curve with the logarithmic grid size as the horizontal axis and the logarithmic number of specific grids as the vertical axis; Fitting the relationship curve using the least square method to obtain a fitting curve; The inverse of the slope of the fitting curve is recorded as the fracture network complexity of the target fracture network.

7. A storage medium, characterized in that: It contains a series of instructions for executing a method for quantitatively calculating the complexity of a fracture network according to any one of claims 1 to 6.

8. A quantitative calculation system for the complexity of a fracture network, characterized in that: The method according to any one of claims 1 to 6 is performed, wherein the system comprises: A fracture network morphology module is used to determine the hydraulic fracture network morphology diagram after volume fracturing for the target fracture network; a fracture morphology feature module, which is used to extract fracture morphology features and perform binarization processing on the hydraulic fracture network morphology map to obtain a binary fracture morphology feature map; A specific square number module is used to count the number of specific squares included in the binary fracture morphology characteristic map through squares of different sizes; The network complexity module is used to calculate the fracture network complexity of the target fracture network based on the grid size and the number of specific grids.

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