Method and system for constructing natural fracture model

Abstract

The invention discloses a method and a system for constructing a natural fracture model. The method comprises the following steps: establishing a three-dimensional digital outcrop model of a target area; based on a three-dimensional digital outcrop model, determining the dynamic morphological characteristics of each fracture and performing data statistics, thereby determining the current development law of the geometrical morphological characteristics of each fracture according to the statistical result; and constructing a natural fracture model according to the current development rule of each geometrical morphology feature. The method truly restores the requirement of the geometric morphology of the crack.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir geological development technology, and in particular to a method and system for constructing natural fracture models. Background Technology

[0002] Natural fractures are widely developed in various reservoirs, and fracture models are an important component of reservoir modeling. Accurately constructing natural fracture models plays a crucial role in improving the fitting accuracy of subsequent numerical simulations. Whether from core samples, imaging logging, or CT scans, fractures observed by various methods appear as patchy or discrete structures. The distribution of subsurface fractures is composed of these fracture patches with independent geometric shapes, which in turn form a complex and vast fracture network.

[0003] Current crack modeling methods mostly focus on defining the spatial distribution of cracks, such as using seismic attribute constraints, pre-stack crack prediction constraints, and tectonic stress field prediction constraints to define the crack generation location, but the definition of the crack's geometric shape itself is too idealistic.

[0004] In existing technologies, the definition of fracture geometry mainly consists of five parts: fracture length and width, fracture dip angle, fracture dip direction, and fracture aperture. Regarding fracture length, existing technologies simply differentiate between large-scale and small-scale fractures, defining large-scale fractures as greater than 100m and small-scale fractures as less than 100m. For fracture width, the definition is directly based on the fracture length by setting the aspect ratio. The fracture dip angle is directly assigned based on the dip angle observed from imaging and core samples. Similarly, the fracture dip direction is directly assigned based on the observations from imaging and core samples. Finally, the fracture aperture is assigned based on the fracture width interpreted from imaging logging. The problems with the above methods are that the definition of fracture length does not meet the actual distribution of underground fracture lengths, and the length definition is too rigid; the variation of actual fracture length should be considered. Regarding fracture width, it should not be directly defined by an ideal aspect ratio, but should be defined based on the actual thickness of the rock strata it penetrates. The definitions of fracture dip angle and dip direction should not be limited to the dip angle parameters of only core wells and imaging logs, ignoring the spatial variation of fracture dip angle; this parameter should not be defined by a single value. Similarly, the definition of fracture aperture is also uncertain in its numerical distribution and should not be limited to a single value.

[0005] Therefore, existing technologies need to establish a scheme for constructing natural crack models that takes into account spatial variation characteristics. Summary of the Invention

[0006] The purpose of this invention is to provide a scheme for constructing a natural crack model that takes into account spatial variation characteristics.

[0007] To address the aforementioned technical problems, this invention provides a method for constructing a natural crack model, comprising: establishing a three-dimensional digital outcrop model of a target area; determining the dynamic morphological characteristics of each crack and performing data statistics based on the three-dimensional digital outcrop model, thereby clarifying the current development pattern of each crack's geometric morphological characteristics based on the statistical results; and constructing the natural crack model based on the current development pattern of each geometric morphological characteristic.

[0008] Preferably, the dynamic morphological features include crack extension length, crack penetration depth, crack dip angle data, crack direction data, and crack aperture data; the crack geometric morphological features include crack length features, crack width features, crack dip angle features, crack direction features, and crack aperture features.

[0009] Preferably, the step of determining the dynamic morphological characteristics of each crack and performing data statistics based on the three-dimensional digital outcrop model, and then clarifying the current development law of each crack geometric morphological characteristic based on the statistical results, includes: identifying each crack in the model and extracting the actual data of each dynamic morphological characteristic based on the three-dimensional digital outcrop model; determining the actual data of each crack geometric morphological characteristic based on the actual data of each dynamic morphological characteristic corresponding to each crack; and statistically analyzing the actual data of each crack geometric morphological characteristic according to the type of crack geometric morphological characteristic to determine the current development law of each crack geometric morphological characteristic.

[0010] Preferably, the step of determining the actual data of each crack geometric morphology feature based on the actual data of each dynamic morphological feature corresponding to each crack includes: using the extracted crack extension length as an evaluation index of crack length feature; using the ratio of crack extension length to extracted crack penetration depth as an evaluation index of crack width feature; using the extracted crack dip angle data as an evaluation index of crack dip angle feature; using the extracted crack direction data as an evaluation index of crack direction feature; and using the extracted crack aperture data as an evaluation index of crack aperture feature.

[0011] Preferably, the step of statistically analyzing the actual data of various crack geometric morphological characteristics according to the type of crack geometric morphological characteristics to determine the current development law of each crack geometric morphological characteristic includes: fitting the extension length curves of all cracks in a double logarithmic coordinate system, and identifying three-segment extension length data segments that follow a power-law distribution from the extension length curves, thereby defining the length range of different crack scales based on the three-segment extension length data segments to form the current development law of crack length characteristics; based on the current development law of crack length characteristics, statistically analyzing the distribution characteristics of the ratios under different scale cracks, thereby defining the length-to-width ratio of cracks at different scales to form the current development law of crack width characteristics; and based on the crack length characteristics of all cracks... The crack dip angle data is statistically analyzed at a first preset interval angle to determine the probability distribution characteristics of the crack dip angle. This is done by assigning a concentrated proportion and corresponding weight to each dip angle range to form the current development pattern of the crack dip angle characteristics. The distribution of crack orientation data for all cracks is statistically analyzed at a second preset interval angle. Based on the statistical results, two orientation angle ranges that follow a normal distribution are identified. The expected and standard values ​​of these two orientation angle ranges are then extracted to form the current development pattern of the crack orientation characteristics. Similarly, the distribution of crack aperture data for all cracks is statistically analyzed. Based on the statistical results, an aperture distribution range that follows a log-normal distribution is identified. The maximum crack aperture, expected value, and standard value of this aperture distribution pattern are then extracted to form the current development pattern of the crack aperture characteristics.

[0012] Preferably, the step of identifying each crack in the model based on the three-dimensional digital outcrop model includes: obtaining multi-angle field outcrop images based on the three-dimensional digital outcrop model; and marking cracks in all images using a deep learning-based crack identification model based on the field outcrop images.

[0013] Preferably, the step of marking cracks in all images using a deep learning-based crack identification model based on the field outcrop images includes: marking the location and boundaries of some cracks based on the field outcrop images, and using the marked cracks as sample images; training the neural network model using the sample images to obtain the crack identification model based on a neural network model; and using the crack identification model to identify and mark the remaining unmarked cracks in all field outcrop images.

[0014] Preferably, the step of establishing a three-dimensional digital outcrop model of the target area includes: acquiring omnidirectional image information of the outcrop in the target area using oblique photography technology of a multi-rotor UAV equipped with a camera, to obtain a first type of image information; acquiring omnidirectional image information of the outcrop in the target area using centimeter-level rapid scanning technology combined with near-millimeter-level photogrammetry technology, to obtain a second type of image information; acquiring omnidirectional image information of the outcrop in the target area using on-site reconnaissance combined with close-range image acquisition technology, to obtain a third type of image information; and fusing the first type of image information, the second type of image information, and the third type of image information to form the three-dimensional digital outcrop model.

[0015] On the other hand, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method described above.

[0016] In addition, this invention also provides a system for constructing a natural crack model, comprising: an outcrop model establishment module configured to establish a three-dimensional digital outcrop model of a target area; a transient development law analysis module configured to determine the dynamic morphological characteristics of each crack and perform data statistics based on the three-dimensional digital outcrop model, thereby clarifying the current development law of each crack geometric morphological characteristic according to the statistical results; and a crack model generation module configured to construct the natural crack model according to the current development law of each geometric morphological characteristic.

[0017] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0018] This invention proposes a method and system for constructing a model of natural cracks. Specifically, it includes: First, using a multi-rotor drone and close-range oblique photography, combined with manual field surveys and close-range image acquisition, to collect comprehensive images of similar outcrops in the field and construct a three-dimensional digital model of the outcrop; Second, based on the three-dimensional digital outcrop model, deep learning-based crack identification and extraction are performed to identify and extract cracks present on the outcrop one by one; The extension length, penetration depth, dip angle, direction, and aperture of the extracted cracks are clearly defined, and their distribution range, distribution pattern, and distribution characteristics are clarified as constraints for subsequent crack modeling; Finally, a discrete crack network modeling method is used to construct a discrete crack network model under the constraints of the defined crack parameters. In this way, this invention, by constructing a digital outcrop model, clarifies and defines various parameters of natural cracks, achieving the requirement of realistically reproducing the geometric morphology of cracks. This is of great significance for crack model construction, not only enabling the construction of crack models with realistic geometric morphology, making the constructed crack models more realistic, but also improving the accuracy of crack models by 5-10%.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0021] Figure 1 This is a schematic diagram illustrating the steps of a method for constructing a natural crack model according to an embodiment of this application.

[0022] Figure 2 This is a schematic diagram illustrating the specific process of a method for constructing a natural crack model according to an embodiment of this application.

[0023] Figure 3 This is a schematic diagram of the fitting result of the double logarithmic extension length curve in the first example of the method for constructing a natural crack model in the embodiments of this application.

[0024] Figure 4 This is a schematic diagram comparing cracks of different scales with penetration depths, representing the first example of a method for constructing a natural crack model according to embodiments of this application.

[0025] Figure 5 This is a probability distribution diagram of the crack dip angle, representing the first example of a method for constructing a natural crack model in this application.

[0026] Figure 6 This is a probability distribution diagram of the crack orientation in the first example of the method for constructing a natural crack model in the embodiments of this application.

[0027] Figure 7 This is a crack aperture frequency distribution diagram of the first example in the method for constructing a natural crack model according to embodiments of this application.

[0028] Figure 8 This is a schematic diagram of the fitting result of the double logarithmic extension length curve in the second example of the method for constructing a natural crack model in the embodiments of this application.

[0029] Figure 9 This is a schematic diagram comparing cracks of different scales with penetration depths, representing a second example of the method for constructing a natural crack model in this application.

[0030] Figure 10This is a probability distribution diagram of the crack dip angle, representing a second example of a method for constructing a natural crack model according to embodiments of this application.

[0031] Figure 11 This is a probability distribution diagram of the crack orientation, representing a second example of a method for constructing a natural crack model according to embodiments of this application.

[0032] Figure 12 This is a crack aperture frequency distribution diagram, representing a second example of a method for constructing a natural crack model according to embodiments of this application.

[0033] Figure 13 This is a schematic diagram of the system for constructing a natural crack model according to an embodiment of this application. Detailed Implementation

[0034] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples, so that the process of how the present invention uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that, as long as there is no conflict, the various embodiments and features in the various embodiments of the present invention can be combined with each other, and the resulting technical solutions are all within the protection scope of the present invention.

[0035] Furthermore, the steps illustrated in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in a different order than that shown here.

[0036] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms “a” and “an” as used herein are also intended to include the plural. It should also be understood that the terms “comprising” and / or “including” as used herein specify the presence of the stated features, integers, steps, operations, units, and / or components, without excluding the presence or addition of one or more other features, integers, steps, operations, units, components, and / or combinations thereof.

[0037] In addition to defining the spatial distribution of cracks, the construction of crack models should also define the crack geometry itself. Different crack geometries have completely different seepage capacities, which greatly affects the accuracy of model construction. Parameters describing crack geometry, such as crack length, crack height, crack width, dip angle, and orientation, significantly influence the physical properties of cracks in three spatial directions.

[0038] Therefore, in order to solve the technical problems mentioned above, this application provides a method and system for constructing a natural crack model. This method and system define the geometry of natural cracks and apply it to crack modeling, thereby accurately defining the specific extension length, penetration depth, dip angle, direction, and aperture of the crack based on the geological characteristics of the actual work area.

[0039] Thus, this invention can increase the accuracy of fracture modeling, improve the fineness of overall reservoir description, and construct a realistic underground fracture model. At the same time, the constructed fracture model can better serve subsequent numerical simulation and hydraulic fracturing, improving the fitting accuracy of numerical simulation and the fracture creation accuracy of hydraulic fracturing.

[0040] Example 1

[0041] Figure 1 This is a schematic diagram illustrating the steps of a method for constructing a natural crack model according to an embodiment of this application. Figure 2 This is a schematic diagram illustrating the specific process of a method for constructing a natural crack model according to an embodiment of this application. The following is in conjunction with... Figure 1 and Figure 2 The specific steps of the method for constructing a natural crack model (also referred to as the "natural crack model construction method") described in the embodiments of the present invention will be explained.

[0042] Step S110: Establish a three-dimensional digital outcrop model of the target area to be evaluated.

[0043] like Figure 2 As shown, step S110 includes: First, using the oblique photography technology of a multi-rotor UAV equipped with a camera to collect omnidirectional image information of the outcrop in the target area, obtaining the first type of image information; then, using centimeter-level rapid scanning technology combined with near-millimeter-level photogrammetry technology to collect omnidirectional image information of the outcrop in the target area, obtaining the second type of image information; next, using on-site reconnaissance combined with close-range image acquisition technology to collect omnidirectional image information of the outcrop in the target area, obtaining the third type of image information; finally, fusing the first, second, and third types of image information to form a three-dimensional digital outcrop model.

[0044] The present invention utilizes step S110 to conduct UAV scanning and manual surveying and measurement of similar outcrops in the field based on a high-precision UAV acquisition system, manual field reconnaissance and local image acquisition, to obtain three-dimensional image data and crack data of the outcrops.

[0045] Step S120: Based on the three-dimensional digital outcrop model constructed in step S110, determine the dynamic morphological characteristics of each crack and perform data statistics, so as to clarify the current development pattern of the geometric morphological characteristics of each crack according to the statistical results.

[0046] In this embodiment of the invention, the dynamic morphological features include: crack extension length, crack penetration depth, crack dip angle data, crack direction data, and crack aperture data. The crack geometric morphological features include: crack length features, crack width features, crack dip angle features, crack direction features, and crack aperture features.

[0047] Therefore, in step S120, the first step is to identify each crack in the three-dimensional digital outcrop model constructed in step S110 and extract the actual data of each dynamic morphological feature.

[0048] In the process of identifying and labeling each crack in the three-dimensional digital outcrop model, the embodiments of the present invention first obtain multiple field outcrop images from multiple angles based on the three-dimensional digital outcrop model, and then, based on the multiple field outcrop images from multiple angles, use a crack identification model based on deep learning to label all cracks in all field outcrop images.

[0049] In one embodiment, based on multiple images of field outcrops, the locations and boundaries of a subset of cracks (a subset of cracks refers to a predetermined proportion of all cracks) are labeled, and these labeled cracks are used as sample images. Then, based on a neural network model, the established sample images are used to perform deep learning training on the neural network model to obtain a crack recognition model. Finally, the crack recognition model is used to identify and label the remaining unlabeled cracks in all field outcrop images.

[0050] This enabled the identification and labeling of all cracks in all field outcrop images.

[0051] Then, based on the location and boundary of all cracks in the identified images, combined with the crack exploration results obtained during the acquisition of the above three types of images and the drilling core observation data, the actual data of each dynamic morphological feature corresponding to each crack are extracted, thus proceeding to the second step.

[0052] The second step is to determine the actual data of the geometric morphological characteristics of each crack based on the actual data of the dynamic morphological characteristics corresponding to each crack.

[0053] Specifically, the crack extension length of each crack is used as an evaluation index for crack length characteristics; the ratio of crack extension length to crack penetration depth of each crack is used as an evaluation index for crack width characteristics; the crack dip angle of each crack is used as an evaluation index for crack dip angle characteristics; the crack direction of each crack is used as an evaluation index for crack direction characteristics; and the crack aperture is used as an evaluation index for crack aperture characteristics.

[0054] Next, in the third step, according to the type of crack geometry, based on the actual data of the corresponding crack geometry for all cracks, the actual data of each crack geometry is statistically analyzed to determine the current development pattern of each crack geometry.

[0055] In the first embodiment, based on the crack extension length data of all cracks, the extension length curves of all cracks in a double logarithmic coordinate system are fitted. From the fitted extension length curves, three-segment extension length data segments following a power-law distribution are identified. These three-segment extension length data segments are then used to define the length range of different crack scales, thereby forming the current development pattern of crack length characteristics. Based on the constructed digital outcrop model, this invention automatically identifies and extracts all cracks in the outcrop through human-computer interaction combined with field survey results and deep learning image recognition. Based on the picked cracks, the planar extension length of each crack is statistically analyzed to determine the distribution range and pattern of crack length, thus forming corresponding extension length definitions for large-scale, medium-scale, and small-scale cracks.

[0056] In the second embodiment, based on the current development pattern of crack length characteristics, the distribution characteristics of the ratio under cracks of different scales are statistically analyzed. This allows for the definition of crack length-to-width ratios at different scales, thereby forming the current development pattern of crack width characteristics. Based on the constructed digital outcrop model, this invention uses human-computer interaction combined with field survey results to extract the penetration depth of each crack profile and statistically analyzes the distribution range and pattern of the ratio of extension length to crack penetration depth at different scales. This allows for the definition of corresponding length-to-width ratio ranges for large-scale, medium-scale, and small-scale cracks.

[0057] In the third embodiment, based on the fracture dip angle data of all fractures, the probability distribution characteristics of fracture dip angles are statistically analyzed at a first preset interval angle (e.g., 10°). This allows for the formation of the current development pattern of fracture dip angle characteristics by considering the concentrated proportion and corresponding weights of each dip angle range. This invention, based on core measurements combined with a digital outcrop model, acquires dip angle data for each fracture and statistically analyzes the distribution range and pattern of fracture dip angles.

[0058] In the fourth embodiment, the distribution of crack orientation data for all cracks is statistically analyzed at a second preset interval angle (e.g., 10°). Based on the statistical results, two orientation angle ranges that follow a normal distribution are identified. The expected and standard values ​​of these two orientation angle ranges are then used to determine the current development pattern of crack orientation characteristics. Based on the constructed digital outcrop model, this invention, through human-computer interaction combined with field reconnaissance, clarifies the dip angle values ​​of each crack and statistically analyzes the distribution range and pattern of crack dip angles.

[0059] In the fifth embodiment, the distribution of crack aperture data for all cracks is statistically analyzed, and the range of aperture distribution following a log-normal distribution is identified based on the statistical results. The current development pattern of crack aperture characteristics is then formed by extracting the maximum crack aperture, expected value, and standard value from the aperture distribution pattern. This invention, based on core measurement and a digital outcrop model, acquires crack aperture data and statistically analyzes the distribution range and pattern of crack aperture.

[0060] Thus, by utilizing the above five embodiments, the present invention analyzes the current development law of various crack geometric morphological characteristics, namely the transient development law, and then proceeds to step S130.

[0061] Step S130: Construct a natural crack model based on the current developmental pattern of each morphological feature formed in step S130.

[0062] In step S130, based on the transient development law of the determined geometric morphological characteristics of each crack, the crack model parameters are defined in sequence and used as constraints to construct a discrete crack network model.

[0063] Example 2

[0064] The following example uses a similar outcrop of carbonate rock in Yijianfang area of ​​Keping County, Xinjiang Uygur Autonomous Region, China as the target area to illustrate the specific implementation process of the natural fracture model construction method described in Example 1.

[0065] Step 21: Construct a 3D digital outcrop model. A comprehensive information collection of the outcrops is conducted through on-site reconnaissance of the target area. Information collection combines traditional and modern observation methods. Modern methods utilize multi-rotor UAVs with mounted cameras and oblique photogrammetry to collect comprehensive image information of the outcrops; the scanning method employs large-area centimeter-level rapid scanning combined with near-millimeter-level photogrammetry, focusing on image information collection in areas with crack development. Traditional observation methods involve manual on-site reconnaissance and close-range image acquisition techniques to collect crack images of locally cracked areas of the outcrops. The results of these three types of image acquisition are used to form a 3D digital outcrop model for the target area.

[0066] Step 22: Determine the crack extension length. Based on the digital outcrop model, the locations and boundaries of cracks on the outcrop are manually calibrated, and the calibrated cracks are extracted as sample images. Then, deep learning training is performed on the entire digital outcrop model to automatically identify and extract all cracks in the outcrop. The length of each identified crack is measured to obtain the extension length data of all cracks on the outcrop. Statistical analysis of the obtained crack extension length data shows that the crack lengths are distributed between 2 and 2850 m. The fitting results under double logarithmic coordinates (see...) Figure 3 It can be concluded that the crack length distribution follows a power law distribution, where slope(D) = -1.23 and (Xo) = 1.85. At the same time, it can be seen from the fitted map that there are three distinct regional segments in the crack length, which can be divided into three scales: small-scale cracks of 1-10m, medium-scale cracks of 10-110m, and large-scale cracks greater than 110m.

[0067] Step 23: Determine the crack penetration depth. Based on the digital outcrop model, extract the vertical penetration depth data of the crack; analyze the crack penetration depth by comparing it with the crack extension length (see...). Figure 4 The aspect ratios of large-scale cracks were found to be 2.89:1, medium-scale cracks to be 4.8:1, and small-scale cracks to be 6.5:1.

[0068] Step 24: Determine the crack dip angle. Based on the digital outcrop model, extract the crack dip angle data. Statistically analyze the probability distribution of crack dip angles in groups of 10 degrees (see...). Figure 5 The probability distribution of crack dip angles shows that the crack dip angles are mainly concentrated in high-angle cracks with dip angles greater than 70 degrees. Among them, cracks with dip angles of 30-40 degrees account for 12%, cracks with dip angles of 40-50 degrees account for 7%, cracks with dip angles of 50-60 degrees account for 10%, cracks with dip angles of 60-70 degrees account for 15%, and cracks with dip angles of 70-90 degrees account for 56%.

[0069] Step 25: Determine the crack orientation. Based on the digital outcrop model, extract crack orientation data and perform rose plot statistics. The statistical results show that the dominant crack orientations are concentrated in the NNE-SSW and NE-SSW directions (see...). Figure 6 Meanwhile, by grouping the cracks into sets of 10 degrees each, the distribution pattern was found to follow two normal distributions: one with an expected value of 250° and a standard deviation of 20°, and the other with an expected value of 100° and a standard deviation of 20°.

[0070] Step 26: Determine the fracture aperture. Based on the digital outcrop model and core observation data, extract fracture aperture data. Analyze the distribution pattern of fracture aperture statistically (see...). Figure 7 The study found that the crack aperture was mainly concentrated around 10 μm, with a maximum crack aperture of 3 mm. The crack aperture distribution followed a log-normal distribution, with an expected value of 10 μm and a standard deviation of 6 μm.

[0071] Step 27: Construct a discrete crack network model. Based on the definitions and clarifications of the crack parameters above, a discrete crack network modeling method is used. According to the definition of crack extension length in Step 22, the crack model is divided into three scales: large-scale cracks, medium-scale cracks, and small-scale cracks. The extension lengths of cracks at all three scales follow a power-law distribution, with distribution parameters slope(D) = -1.23 and (Xo) = 1.85. The extension lengths of small-scale cracks are distributed between 0 and 10 m, medium-scale cracks between 10 and 110 m, and large-scale cracks greater than 110 m. The crack penetration depth is defined based on Step 23, according to the ratio to the crack length. The aspect ratio of large-scale cracks is 2.89:1, that of medium-scale cracks is 4.8:1, and that of small-scale cracks is 6.5:1. The crack dip angle is defined based on the proportions of each angle in Step 24, according to weighted ratios. The crack orientation is defined based on the normal distribution observed in step 25, with two different normal distributions: one with an expected value of 250° and a standard deviation of 20°, and the other with an expected value of 100° and a standard deviation of 20°. The crack aperture is defined based on the log-normal distribution observed in step 26, with an expected value of 10 μm and a standard deviation of 6 μm.

[0072] Example 3

[0073] The following example uses the Donghe sandstone outcrop as the target area to illustrate the specific implementation process of the natural fracture model construction method described in Example 1.

[0074] Step 31: Construct a 3D digital outcrop model. Information from the digital outcrop model is collected and scanned for the target area, including image scanning of the Xinengshi slab field outcrops.

[0075] Step 32: Determine the crack extension length. Extract the length data of the cracks detected using deep learning, obtaining the extension length data of all cracks on the outcrop. Analyze the obtained crack extension length data to find that the crack lengths range from 1 to 985 m, and obtain the fitting results in a double logarithmic coordinate system (see...). Figure 8 It can be concluded that the crack length distribution follows a power law distribution, where slope(D) = -1.57 and (Xo) = 1.95. At the same time, it can be seen from the fitted graph that there is also a significant three-segment distribution, which can be divided into small-scale cracks of 1-10m, medium-scale cracks of 10-100m, and large-scale cracks greater than 100m.

[0076] Step 33: Determine the crack penetration depth. Based on the digital outcrop model, extract the vertical penetration depth data of the crack; analyze the crack penetration depth by comparing it with the crack extension length (see...). Figure 9 The aspect ratios of large-scale cracks were found to be 3.6:1, medium-scale cracks to be 4.1:1, and small-scale cracks to be 7.2:1.

[0077] Step 34: Determine the crack dip angle. Based on the digital outcrop model, extract the crack dip angle data. Statistically analyze the crack dip angle probability distribution in groups of 5 degrees (see...). Figure 10 The probability distribution of crack dip angles shows that the crack dip angles are mainly concentrated in high-angle cracks with a dip angle greater than 70 degrees, accounting for 60%; cracks with a dip angle of 45-70 degrees account for 35%; and cracks with a dip angle of 0-45 degrees account for 5%.

[0078] Step 35: Determine the crack orientation. Based on the digital outcrop model, extract crack orientation data and perform rose plot statistics. The statistical results show that the near-north-south and northeast-southwest orientations are the dominant crack orientations in this outcrop (see...). Figure 11 The distribution pattern was studied by grouping the cracks into groups of 10 degrees. The results showed that the crack orientation distribution followed two normal distributions, with an expected value of 10° and a standard deviation of 5°, and an expected value of 45° and a standard deviation of 10°.

[0079] Step 36: Determine the fracture aperture. Based on the digital outcrop model and core observation data, extract the fracture aperture data (see...). Figure 12 By statistically analyzing the distribution of crack aperture, it was found that the crack aperture is mainly concentrated in the range of 15-25 μm, with a maximum crack aperture of 27 mm. The crack aperture distribution follows a log-normal distribution, with an expected value of 20 μm and a standard deviation of 7 μm.

[0080] Step 37: Construct a discrete crack network model. Based on the definitions and clarifications of the crack parameters above, a discrete crack network modeling method is used. According to the definition of crack extension length in Step 32, the crack model is divided into three scales: large-scale cracks, medium-scale cracks, and small-scale cracks. The extension lengths of cracks at all three scales follow a power-law distribution, with distribution parameters slope(D) = -1.57 and (Xo) = 1.95. The extension lengths of small-scale cracks are distributed between 1 and 10 m, medium-scale cracks between 10 and 100 m, and large-scale cracks greater than 100 m. The crack penetration depth is defined based on Step 33, according to the ratio to the crack length. The aspect ratio of large-scale cracks is 3.6:1, that of medium-scale cracks is 4.1:1, and that of small-scale cracks is 7.2:1. The crack dip angle is defined based on the proportions of each angle in Step 34, according to weighted proportions. The crack orientation is defined based on the normal distribution observed in step 35, with two different normal distributions: one with an expected value of 10° and a standard deviation of 5°, and the other with an expected value of 45° and a standard deviation of 10°. The crack aperture is defined based on the log-normal distribution observed in step 36, with an expected value of 20 μm and a standard deviation of 7 μm.

[0081] Example 4

[0082] Based on the natural crack model construction methods of Embodiments 1 to 3 described above, this invention provides a computer-readable storage medium. The storage medium stores a computer program, which is executed to run a method for constructing a natural crack model. The computer program is capable of executing computer instructions, which include computer program code. The computer program code can be in the form of source code, object code, executable file, or some intermediate form.

[0083] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable 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 contents of computer-readable storage media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, the contents may be appropriately increased or decreased according to the requirements of legislation and patent practice. In other jurisdictions, computer-readable storage media may not include electrical carrier signals and telecommunication signals.

[0085] Example 5

[0086] Based on the natural crack model construction methods of Embodiments 1 to 3 described above, this invention also provides a system for constructing natural crack models (also referred to as a "natural crack model construction system"). This natural crack model construction system is used to implement the above-described natural crack model construction methods.

[0087] Figure 13 This is a schematic diagram of the system for constructing a natural crack model according to an embodiment of this application. Figure 13 As shown, the dynamic brittleness evaluation system described in this embodiment of the invention includes: an outcrop model establishment module 1301, a transient development law analysis module 1302, and a crack model generation module 1303.

[0088] Specifically, the outcrop model establishment module 1301 is implemented according to the method described in step S110 above, and is configured to establish a three-dimensional digital outcrop model of the target area; the transient development law analysis module 1302 is implemented according to the method described in step S120 above, and is configured to determine the dynamic morphological characteristics of each crack and perform data statistics based on the three-dimensional digital outcrop model, so as to clarify the current development law of each crack geometric morphological characteristic according to the statistical results; the crack model generation module 1303 is implemented according to the method described in step S130 above, and is configured to construct a natural crack model according to the current development law of each geometric morphological characteristic.

[0089] This invention discloses a method and system for constructing a natural crack model. Specifically, it includes: first, using a multi-rotor drone and close-range oblique photography, combined with manual field surveys and close-range image acquisition, to construct a three-dimensional digital model of similar outcrops in the field; second, based on the three-dimensional digital outcrop model, deep learning-based crack identification and extraction are performed to identify and extract cracks present on the outcrop one by one; the extracted cracks are then defined in terms of extension length, penetration depth, dip angle, direction, and aperture, and their distribution range, distribution pattern, and distribution characteristics are clarified as constraints for subsequent crack modeling; finally, a discrete crack network model is constructed using a discrete crack network modeling method under the constraints of the defined crack parameters. In this way, this invention clarifies and defines various parameters of natural cracks by constructing a digital outcrop model, achieving the requirement of realistically reproducing the geometric morphology of cracks. This is of great significance for crack model construction, not only enabling the construction of crack models with realistic geometric morphology, making the constructed crack models more realistic, but also improving the accuracy of crack models by 5-10%.

[0090] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0091] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0092] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0093] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0094] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0095] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for constructing a model of natural cracks, characterized in that, include: Establish a three-dimensional digital outcrop model of the target area; Based on the aforementioned three-dimensional digital outcrop model, the dynamic morphological characteristics of each crack are determined and data statistics are performed, thereby clarifying the current development pattern of the geometric morphological characteristics of each crack based on the statistical results. Based on the current developmental patterns of the aforementioned geometric morphological features, the natural crack model is constructed.

2. The method according to claim 1, characterized in that, The dynamic morphological features include crack extension length, crack penetration depth, crack dip angle data, crack orientation data, and crack aperture data; The geometric features of the cracks include crack length, crack width, crack dip angle, crack orientation, and crack aperture.

3. The method according to claim 2, characterized in that, The steps of determining the dynamic morphological characteristics of each crack and performing data statistics based on the three-dimensional digital outcrop model, thereby clarifying the current development pattern of each crack's geometric morphological characteristics based on the statistical results, include: Based on the three-dimensional digital outcrop model, each crack in the model is identified and the actual data of various dynamic morphological features are extracted. Based on the actual data of the dynamic morphological characteristics of each crack, determine the actual data of the geometric morphological characteristics of each crack. Based on the type of crack geometric morphology, the actual data of each crack geometric morphology feature are statistically analyzed to determine the current development pattern of each crack geometric morphology feature.

4. The method according to claim 3, characterized in that, The step of determining the actual data of each crack's geometric morphological feature based on the actual data of each crack's dynamic morphological feature includes: The extracted crack extension length is used as an evaluation index for crack length characteristics. The ratio of crack extension length to extracted crack penetration depth is used as an evaluation index for crack width characteristics. The extracted crack dip angle data is used as an evaluation index for crack dip angle characteristics. The extracted crack orientation data is used as an evaluation index for crack orientation characteristics. The extracted crack aperture data is used as an evaluation index for crack aperture characteristics.

5. The method according to claim 4, characterized in that, The steps involved in statistically analyzing the actual data of various crack geometric morphological characteristics according to their types, and determining the current developmental patterns of each crack geometric morphological characteristic, include: The extension length curves of all cracks in a double logarithmic coordinate system are fitted, and three-segment extension length data segments that follow a power-law distribution are identified from the extension length curves. Based on the three-segment extension length data segments, the length range of different crack scales is defined to form the current development law of crack length characteristics. Based on the current development pattern of the crack length characteristics, the distribution characteristics of the ratio under cracks of different scales are statistically analyzed, thereby defining the crack length-to-width ratio at different scales and forming the current development pattern of crack width characteristics. Based on the crack dip angle data of all cracks, the probability distribution characteristics of crack dip angle are statistically analyzed according to the first preset interval angle. Then, by considering the concentrated proportion of each dip angle range and the corresponding weight, the current development pattern of crack dip angle characteristics is formed. The distribution of crack orientation data of all cracks is statistically analyzed according to the second preset interval angle, and two orientation angle ranges that follow a normal distribution are identified based on the statistical results. The expected value and standard value of the two orientation angle ranges are then extracted to form the current development pattern of crack orientation characteristics. The distribution of crack aperture data for all cracks is statistically analyzed, and the range of aperture distribution that follows a log-normal distribution is identified based on the statistical results. The current development pattern of crack aperture characteristics is then formed by extracting the maximum crack aperture, expected value, and standard value of the aperture distribution pattern.

6. The method according to any one of claims 3 to 5, characterized in that, The step of identifying each crack in the model based on the three-dimensional digital outcrop model includes: Based on the aforementioned three-dimensional digital outcrop model, multi-angle images of the field outcrop are obtained; Based on the field outcrop images, cracks in all images are marked using a deep learning-based crack identification model.

7. The method according to claim 6, characterized in that, The step of marking cracks in all images based on the field outcrop images using a deep learning-based crack identification model includes: Based on the field outcrop images, the location and boundaries of some cracks are marked, and the marked cracks are used as sample images; Based on the neural network model, the sample images are used to perform deep learning training on the neural network model to obtain the crack recognition model; Using the crack identification model, crack identification and labeling are performed on the remaining unlabeled cracks in all field outcrop images.

8. The method according to any one of claims 1 to 7, characterized in that, The steps involved in establishing a 3D digital outcrop model of the target area include: The oblique photography technology of a multi-rotor drone equipped with a camera is used to collect all-round image information of the outcrop in the target area, and obtain the first type of image information. By combining centimeter-level rapid scanning technology with near-millimeter-level photogrammetry technology, comprehensive image information of outcrops in the target area is acquired to obtain second-type image information. By combining on-site reconnaissance with close-range image acquisition technology, we can collect comprehensive image information of outcrops in the target area and obtain third-type image information. The first type of image information, the second type of image information, and the third type of image information are fused to form the three-dimensional digital outcrop model.

9. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 8.

10. A system for constructing a model of natural cracks, characterized in that, include: The outcrop model creation module is configured to create a three-dimensional digital outcrop model of the target area. The transient development pattern analysis module is configured to determine the dynamic morphological characteristics of each crack and perform data statistics based on the three-dimensional digital outcrop model, thereby clarifying the current development pattern of the geometric morphological characteristics of each crack based on the statistical results. The crack model generation module is configured to construct the natural crack model based on the current developmental patterns of the various geometric morphological features.

Images