Method and processor for modeling multi-scale fracture network in tight reservoirs

Abstract

The present application relates to the technical field of rock fracture modeling, and discloses a method and a processor for modeling a multi-scale fracture network of a tight reservoir. The method comprises: obtaining a strike attribute of a fracture; generating a fracture strike conforming to a preset mode according to a cumulative probability density curve of the strike; taking a fracture position interpreted from a core and an imaging logging as hard data of a fracture position in a fracture network; dividing the hard data into seed hard data and correction hard data; correcting a cumulative probability curve of fracture development intensity according to the seed hard data, and generating a fracture element set according to the corrected probability density curve; generating a discrete fracture network through similarity fusion criteria, mechanical cause fusion criteria and intersection criteria; comparing the established discrete fracture network with the correction hard data, and correcting the number of fracture elements and the cumulative probability curve. Through iterative inversion of the seed hard data and the correction hard data, a more optimal multi-scale fracture network model that is more consistent with the hard data is generated.

Description

Technical Field

[0001] This invention relates to the field of rock fracture modeling technology, specifically to a method and processor for modeling multi-scale fracture networks in tight reservoirs. Background Technology

[0002] Fracture modeling is a commonly used method for studying the spatial distribution of fractures, and can be divided into deterministic modeling and stochastic modeling. Deterministic modeling establishes a definite fracture model based on known information. Stochastic modeling, on the other hand, utilizes prior information about fractures to generate selectable fracture models with equal probabilities through random simulation. In recent years, stochastic fracture modeling methods have been widely used and have achieved good results in oil and gas extraction, geothermal development, and mining. Currently, commonly used stochastic fracture modeling methods include fracture modeling based on spatial partitioning, discrete fracture network (DFN) modeling, fracture modeling based on variogram functions, fracture modeling based on multi-point geostatistics, and fracture modeling based on fractal feature iteration.

[0003] Discrete fracture network (DFN) modeling treats each fracture in a fracture system as a unit, possessing characteristics such as length, dip direction, dip angle, and aperture. Multiple fracture segments together form a complex fracture network system. From a data source perspective, DFN models have significant advantages in integrating multiple data sources. They can fully incorporate data from outcrops, core samples, seismic data, well logging, geology, drilling data, and production data, encompassing almost all data that can reflect fracture information to varying degrees. This allows for a multi-faceted understanding of fractures, considers multiple constraints in model building, significantly reduces the uncertainty of the fracture network, and makes the established fracture model more reasonable. In DFN models, fractures are represented by geometric planes in three-dimensional space. A large number of fractures with different shapes, coordinates, sizes, orientations, apertures, and permeabilities form a discrete fracture network. For fractures of different sizes, deterministic methods can be used to generate them, such as large faults and fractures; or they can be randomly generated based on statistical relationships, such as low-order faults and smaller fractures.

[0004] Conventional fracture modeling methods primarily reflect the geometric characteristics of fractures. However, fractures in tight reservoirs are mostly tectonic fractures, and the resulting multi-scale fractures are significantly affected by stress. Ignoring mechanical characteristics leads to a large deviation between the established model and the actual model. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a method and processor for modeling multi-scale fracture networks in tight reservoirs.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for modeling multi-scale fracture networks in tight reservoirs, comprising:

[0007] Obtain the direction attribute of the crack;

[0008] The cumulative probability density curve of the crack direction is determined, and a crack direction conforming to a preset pattern is generated based on the cumulative probability density curve and random quantiles.

[0009] The fracture locations interpreted from core and imaging logging are used as hard data for fracture locations in the fracture network;

[0010] The prior value of crack line density for each grid is calculated using hard data, and the initial value of the number of crack elements is calculated by combining the crack development intensity of the corresponding grid.

[0011] The hard data is divided into seed hard data and correction hard data;

[0012] The cumulative probability curve of crack development intensity is corrected based on seed hard data, and a set of crack elements is generated based on the corrected probability density curve.

[0013] Discrete crack networks are generated using similarity fusion criteria, mechanical origin fusion criteria, and cutting criteria.

[0014] The established discrete crack network is compared with the corrected hard data, and the number of crack elements and the cumulative probability curve are corrected.

[0015] In this embodiment of the invention, generating a crack orientation that conforms to a preset pattern includes:

[0016] Calculate the trace length and orientation of all cracks on the surface of the outcrop rock strata;

[0017] The element length is determined based on the smaller crack length obtained.

[0018] Calculate the ratio of the length of each crack to the unit length, and record the number of times the crack direction is repeated in the direction array.

[0019] Use the direction array to draw a rose diagram of the crack direction and generate crack directions that conform to the preset pattern.

[0020] In this embodiment of the invention, the method further includes:

[0021] Based on the layer division, further layer division is carried out by combining well logging lithology cycles;

[0022] A more refined layer is generated proportionally to serve as the control interface for the crack network.

[0023] In this embodiment of the invention, seed hard data is used for crack element initialization and for generating initial crack elements; correction hard data is used for generating correction crack elements, and for calculating the error of the crack mesh in each iteration, and for guiding the acquisition of optimal parameters of the crack network model.

[0024] In this embodiment of the invention, crack element initialization includes: initialization of seed crack elements and the total number of crack elements, and setting of seed crack elements according to the position of seed hard data.

[0025] In this embodiment of the invention, the method further includes:

[0026] If fractures are shown on core samples or imaging logs, it is confirmed that a fracture has passed through the area.

[0027] The existence of crack elements is identified, and the crack location is determined as prior information about the crack.

[0028] Seed crack elements are determined by generating small polygons of crack elements in the initialized mesh based on prior information.

[0029] In this embodiment of the invention, the similarity fusion criterion includes: identifying crack elements that are close in location and have similar orientation as the same crack;

[0030] The mechanical origin fusion criteria include: identifying crack elements with similar center line orientations as the same shear crack zone;

[0031] Cutting criteria include: when the center of a fracture element is located between two layers of rock, the portion outside the layer is cut off.

[0032] In this embodiment of the invention, the method further includes:

[0033] The error is determined based on the comparison between the established discrete crack network and the corrected hard data;

[0034] Repeatedly correct the number of crack elements and the cumulative probability curve until a discrete crack network model with an error lower than the preset error range is generated.

[0035] In this embodiment of the invention, the properties of the crack include at least one of direction, dip angle, and dip direction.

[0036] A second aspect of the present invention provides a processor configured to perform the above-described method for modeling multi-scale fracture networks in tight reservoirs.

[0037] This invention, in the field of three-dimensional fracture network modeling in tight, low-permeability reservoirs, applies fracture geometric and mechanical models as constraints, and achieves the organic fusion of multi-scale fractures by following a step-by-step growth approach from small to large. Furthermore, through improved point process and indicative process methods, it achieves the orderly and efficient generation of fracture elements under constraints, making the simulated fracture network characteristics more consistent with the actual situation. It also generates a superior multi-scale fracture network model that closely matches the hard data through iterative inversion of seed hard data and calibration hard data. Attached Figure Description

[0038] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0039] Figure 1 A flowchart illustrating a method for modeling multi-scale fracture networks in tight reservoirs according to an embodiment of the present invention is shown schematically.

[0040] Figure 2 This schematic diagram illustrates the initialization of crack element seeds according to an embodiment of the present invention;

[0041] Figure 3 The illustration shows a schematic diagram of the quantitative characterization of the similarity relationship between two crack elements according to an embodiment of the present invention;

[0042] Figure 4 The illustration shows two cracks with similar elements but not belonging to the same crack according to an embodiment of the present invention;

[0043] Figure 5 A schematic top view illustrating the fusion of mechanically formed crack elements according to an embodiment of the present invention is shown.

[0044] Figure 6 A schematic diagram illustrating the cutting criteria according to an embodiment of the present invention is shown;

[0045] Figure 7 A schematic diagram of a three-dimensional crack network model according to an embodiment of the present invention is shown. Detailed Implementation

[0046] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0047] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0048] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0049] Figure 1 A flowchart illustrating a method for modeling multi-scale fracture networks in tight reservoirs according to an embodiment of the present invention is shown. Figure 1 As shown, in one embodiment of the present invention, a method for modeling multi-scale fracture networks in tight reservoirs is provided, comprising:

[0050] Step 101: Obtain the direction attribute of the crack;

[0051] Step 102: Determine the cumulative probability density curve of the crack direction, and generate a crack direction that conforms to the preset pattern based on the cumulative probability density curve and random quantiles.

[0052] Step 103: Use the fracture locations interpreted from core samples and imaging logging as hard data for fracture locations in the fracture network.

[0053] Step 104: Calculate the prior value of crack line density for each grid using hard data, and combine it with the crack development intensity of the corresponding grid to calculate the initial value of the number of crack elements.

[0054] Step 105: Divide the hard data into seed hard data and correction hard data;

[0055] Step 106: Correct the cumulative probability curve of crack development intensity based on seed hard data, and generate a set of crack elements based on the corrected probability density curve.

[0056] Step 107: Generate a discrete crack network using the similarity fusion criterion, the mechanical origin fusion criterion, and the cutting criterion;

[0057] Step 108: Compare the established discrete crack network with the corrected hard data, and correct the number of crack elements and the cumulative probability curve.

[0058] Fracture modeling is a commonly used method for studying the spatial distribution of fractures, and can be divided into deterministic modeling and stochastic modeling. Deterministic modeling establishes a definite fracture model based on known information. Stochastic modeling, on the other hand, utilizes prior information about fractures to generate selectable fracture models with equal probabilities through random simulation. In recent years, stochastic fracture modeling methods have been widely used and have achieved good results in oil and gas extraction, geothermal development, and mining. Currently, commonly used stochastic fracture modeling methods include fracture modeling based on spatial partitioning, discrete fracture network (DFN) modeling, fracture modeling based on variogram functions, fracture modeling based on multi-point geostatistics, and fracture modeling based on fractal feature iteration.

[0059] Discrete fracture network (DFN) modeling treats each fracture in a fracture system as a unit, possessing characteristics such as length, dip direction, dip angle, and aperture. Multiple fracture segments together form a complex fracture network system. From a data source perspective, DFN models have significant advantages in integrating multiple data sources. They can fully incorporate data from outcrops, core samples, seismic data, well logging, geology, drilling data, and production data, encompassing almost all data that can reflect fracture information to varying degrees. This allows for a multi-faceted understanding of fractures, considers multiple constraints in model building, significantly reduces the uncertainty of the fracture network, and makes the established fracture model more reasonable. In DFN models, fractures are represented by geometric planes in three-dimensional space. A large number of fractures with different shapes, coordinates, sizes, orientations, apertures, and permeabilities form a discrete fracture network. For fractures of different sizes, deterministic methods can be used to generate them, such as large faults and fractures; or they can be randomly generated based on statistical relationships, such as low-order faults and smaller fractures.

[0060] Currently, there are the following problems in the modeling of fracture networks in tight reservoirs: (1) Errors exist in the simulation of fracture dip angles; when the strata are flat, the fracture dip angle properties simulated by the marking process match the actual values ​​well, but in curved strata, the simulation results differ significantly from the actual situation; (2) The problem of mismatch between the simulated fracture network and the real fracture network caused by fracture orientation statistics is mainly due to the fact that the actual situation of large and small fractures being unequal was ignored during the statistics; (3) The problem of fracture group classification is that different fracture group classification results will produce different probability functions and affect subsequent simulations; (4) There are deviations in the simulation of fracture centers; when the actual fracture center is not accurately simulated, the simulation results of the fracture center may be significantly different. When the intensity of fracture development is directly applied to determine the location of the fracture center, the inequivalence between large and small fractures will cause the simulation results of the fracture center location to deviate from reality; (5) the geometric property simulation ignores the problem of structural fracture formation mechanism; DFN focuses on simulating the geometric characteristics of the fracture network, but does not consider the fracture formation mechanism, which reduces the accuracy of the fracture network model in the reservoir, especially in tight reservoirs, where fractures are generated by multiple tectonic movements and have obvious structural features; (6) the utilization efficiency of fracture information along the wellbore trajectory in the fracture network model is low, and the fracture interpretation information from core and logging is only used to determine the three-dimensional density model to constrain the point process in DFN. To solve the above six problems, this invention adopts a small-to-large approach, that is, first generate fracture elements, which are small random polygons, and formulate fusion criteria based on the characteristics and causes of the fracture network in tight reservoirs. At the same time, it adopts an iterative update approach to gradually approach the optimal network that conforms to the existing fracture information. In addition, in this invention, the fracture center location is constructed under the constraint of the fracture development intensity constraint body.

[0061] Fracture attributes such as strike, dip angle, and dip direction are obtained based on core and outcrop data. Fracture attributes, including strike direction, are statistically analyzed according to fracture size to reflect fracture intensity. All fracture strikes are sorted, and the cumulative probability for each strike value is calculated. This cumulative probability is then divided by the total number of strikes. Based on the cumulative probability density curve for that strike and the generated random quantiles, fracture strikes conforming to existing patterns can be generated. The specific steps are as follows:

[0062] (1) Calculate the trace length of all cracks on the surface of the outcrop strata. and direction , where i represents the i-th crack;

[0063] (2) The minimum crack length obtained statistically is denoted as . ,make As the unit length Generally, the value can be an integer greater than 3;

[0064] (3) Calculate the length of each crack and The ratio of the two values ​​determines the direction of the crack. repeat This is counted in the direction array. , Indicates rounding;

[0065] (4) Using arrays Draw a rose diagram showing the direction of the cracks.

[0066] Fracture locations interpreted from core samples and imaging logging are used as hard data for fracture locations in the fracture network. Prior values ​​for fracture linear density are calculated for each grid using this hard data. Combined with the fracture development intensity of the corresponding grid, an initial value for the total number of fracture elements is calculated. Based on bedding plane segmentation, further bedding planes are generated using logging lithology cycles, resulting in more refined bedding planes that serve as the control interfaces for the fracture network.

[0067] One part of the hard data is used as seed hard data to initialize the positions of crack elements (small random polygons), and the other part is used as correction hard data to generate correction crack elements. 3D crack elements (i.e., 3D random polygons) are generated by rotating and translating planar random polygons. Initialization includes two parts: the seed crack element and the total number of crack elements. The initialization of the seed crack element is based on the position of the seed hard data.

[0068] When fractures are shown on core samples or imaging logs, it indicates the presence of fracture elements, whose locations serve as prior information. Based on these prior locations, small polygons are generated in the initialized mesh to represent the fracture elements, acting as seed fracture elements. Figure 2 This diagram illustrates the initialization of crack element seeds according to an embodiment of the present invention, as shown below. Figure 2 In the diagram, the hard data from a single well within the dark-colored grid is referred to as seed hard data and is used for fracture element initialization. The hard data from a single well within the light-colored grid is referred to as calibration hard data and is used to calculate the error of the fracture grid during each iteration, guiding the acquisition of optimal parameters for the fracture network model.

[0069] The number of isolated fracture segments in core or imaging logging can be considered as the number of fracture elements. Summing the number of fracture elements in each grid yields the prior number of fracture elements. If the j-th grid contains prior information about fracture elements, assume the number is... This prior information will be used to determine the total number of crack elements in the crack model. . The initial value can be obtained from the proportion formula in equation (3):

[0070] (3)

[0071] In the formula, Let be the intensity of crack development in the i-th grid; This is the prior value for the number of fracture elements obtained from core or imaging logging in the i-th grid; The number of crack elements; Total number of grid cells; It is the sum of crack development intensities across all n grids.

[0072] For a grid with existing prior information, we can use equation (1):

[0073] (1)

[0074] Calculate a Values. Due to the strong heterogeneity of fractures and the low probability of encountering fractures during drilling, these calculated values... The value will generally be less than the actual total number of crack elements, therefore the maximum value calculated here will be less than the actual total number of crack elements. The value is used as the initial value for subsequent iterations, i.e., equation (2):

[0075] (2)

[0076] Based on the initial crack elements, the cumulative probability curve of crack development intensity is corrected. Based on the corrected probability density curve and the improved crack attribute determination method, a set of crack elements is generated. Then, a discrete crack network is generated through similarity fusion criteria, mechanical origin fusion criteria and cutting criteria.

[0077] (1) Similarity fusion criterion

[0078] The crack surface can be viewed as being composed of a series of small polygons, which are the elements that make up the crack. Crack elements (small polygons) on the same crack have similar attitudes. If two small polygons are close to each other and have similar attitudes, they are very likely to belong to the same crack. The similarity criteria are reflected in proximity and similar attitudes. The fusion criteria are the following formulas (4) to (7), that is, the distance is not greater than the critical value. The difference in inclination and the difference in tilt angle are not greater than the corresponding critical values. Figure 3 The illustration shows a schematic diagram of the quantitative characterization of the similarity relationship between two crack elements according to an embodiment of the present invention; Figure 4 This schematic diagram illustrates two cracks with similar elements but not belonging to the same crack according to an embodiment of the present invention; see also... Figure 3 and Figure 4 .

[0079] (4)

[0080] (5)

[0081] (6)

[0082] (7)

[0083] In the formula, The distance between the centers of the two elements that generated the crack; , For the directions of the two crack elements, To move towards the included angle; , The inclination angle of the two crack elements. The difference in tilt angle; The angle between the normal and the line connecting the centers of the two crack elements can be obtained through the inner product of the two vectors: ,in, For the first crack element The unit normal vector can be obtained by the cross product of two adjacent sides of the polygon: , , These are the vector representations of two adjacent sides of the polygon; , , , This is the critical value.

[0084] (2) Mechanistic origin fusion criteria

[0085] If the azimuth angles of the center lines are similar, it is necessary to first determine that the crack elements to be fused do not satisfy formula (7) in the similarity fusion criterion, that is, they need to satisfy formula (8), where The critical value is determined by the fact that the centers of the crack elements are similar in orientation. Figure 5 A schematic top view illustrating the fusion of mechanically formed crack elements according to an embodiment of the present invention is shown; see also Figure 5 If the first to third crack elements belong to the same shear crack zone, then connect them. , The direction needs to meet .

[0086] (8)

[0087] (3) Cutting Criterion

[0088] The shape of a crack is related to how it grows and stops, and its formation process is influenced by many factors, such as lithology, crystal structure, stress field, rock mechanical properties, and surrounding rock structure. Cracks often grow from defective parts of the rock, initially appearing as circular or elliptical cracks. In layered systems, when encountering layer boundaries, cracks tend to grow laterally, becoming pseudo-rectangular cracks roughly perpendicular to the rock layers. When truncating crack elements, if the center of the crack element is located between the control interfaces of two adjacent layers, the portion outside the control interfaces is truncated; otherwise, it is retained. Figure 6 A schematic diagram illustrating the cutting criteria according to an embodiment of the present invention is shown; see also Figure 6 .

[0089] Finally, the established discrete crack network model is compared with the corrected crack elements to calculate the error, the number of corrected crack elements, and the cumulative probability curve. This process is repeated until the error falls below a preset lower limit, at which point a mathematically superior discrete crack network model can be generated.

[0090] Taking a tight reservoir as an example, fracture network modeling was performed. Based on outcrop measurements of fracture length, strike, and dip, an improved indicative process using fracture strength and cumulative probability curves was employed to generate fracture strike. The deviation between fractures and bedding planes was simulated using formation attitude data instead of directly simulating fracture dip, avoiding errors caused by local topographic variations. Fracture elements (small random polygons) were generated under fracture density constraints. Subsequently, these small fracture elements were fused according to a fusion criterion to generate fracture polygons of different scales, establishing the fracture network model.

[0091] Figure 7 A schematic diagram of a three-dimensional crack network model according to an embodiment of the present invention is shown. Figure 7 This is a schematic diagram of the final three-dimensional network model of the cracks. Figure 7 This demonstrates the inventive effect of the embodiments of the present invention. By comparing it with the fracture development intensity of a single well, it can be seen that the model has a good effect.

[0092] This invention, in the field of three-dimensional fracture network modeling in tight, low-permeability reservoirs, applies fracture geometric and mechanical models as constraints, and achieves the organic fusion of multi-scale fractures by following a step-by-step growth approach from small to large. Furthermore, through improved point process and indicative process methods, it achieves the orderly and efficient generation of fracture elements under constraints, making the simulated fracture network characteristics more consistent with the actual situation. It also generates a superior multi-scale fracture network model that closely matches the hard data through iterative inversion of seed hard data and calibration hard data.

[0093] This invention provides a processor configured to execute any of the tight reservoir multi-scale fracture network modeling methods described in the above embodiments.

[0094] Specifically, the processor can be configured as follows:

[0095] Obtain the direction attribute of the crack;

[0096] The cumulative probability density curve of the crack direction is determined, and a crack direction conforming to a preset pattern is generated based on the cumulative probability density curve and random quantiles.

[0097] The fracture locations interpreted from core and imaging logging are used as hard data for fracture locations in the fracture network;

[0098] The prior value of crack line density for each grid is calculated using hard data, and the initial value of the number of crack elements is calculated by combining the crack development intensity of the corresponding grid.

[0099] The hard data is divided into seed hard data and correction hard data;

[0100] The cumulative probability curve of crack development intensity is corrected based on seed hard data, and a set of crack elements is generated based on the corrected probability density curve.

[0101] Discrete crack networks are generated using similarity fusion criteria, mechanical origin fusion criteria, and cutting criteria.

[0102] The established discrete crack network is compared with the corrected hard data, and the number of crack elements and the cumulative probability curve are corrected.

[0103] In this embodiment of the invention, the processor is configured to:

[0104] Generating crack orientations that conform to a preset pattern includes:

[0105] Calculate the trace length and orientation of all cracks on the surface of the outcrop rock strata;

[0106] The element length is determined based on the smaller crack length obtained.

[0107] Calculate the ratio of the length of each crack to the unit length, and record the number of times the crack direction is repeated in the direction array.

[0108] Use the direction array to draw a rose diagram of the crack direction and generate crack directions that conform to the preset pattern.

[0109] In this embodiment of the invention, the processor is further configured to:

[0110] Based on the layer division, further layer division is carried out by combining well logging lithology cycles;

[0111] A more refined layer is generated proportionally to serve as the control interface for the crack network.

[0112] In this embodiment of the invention, seed hard data is used for crack element initialization and for generating initial crack elements; correction hard data is used for generating correction crack elements, and for calculating the error of the crack mesh in each iteration, and for guiding the acquisition of optimal parameters of the crack network model.

[0113] In this embodiment of the invention, the processor is configured to:

[0114] Crack element initialization includes: initializing the seed crack element and the total number of crack elements, as well as setting the seed crack element based on the position of the seed hard data.

[0115] In this embodiment of the invention, the processor is further configured to:

[0116] If fractures are shown on core samples or imaging logs, it is confirmed that a fracture has passed through the area.

[0117] The existence of crack elements is identified, and the crack location is determined as prior information about the crack.

[0118] Seed crack elements are determined by generating small polygons of crack elements in the initialized mesh based on prior information.

[0119] In this embodiment of the invention, the processor is configured to:

[0120] Similarity fusion criteria include: identifying fracture elements that are close in location and have similar orientation as the same fracture;

[0121] The mechanical origin fusion criteria include: identifying crack elements with similar center line orientations as the same shear crack zone;

[0122] Cutting criteria include: when the center of a fracture element is located between two layers of rock, the portion outside the layer is cut off.

[0123] In this embodiment of the invention, the processor is further configured to:

[0124] The error is determined based on the comparison between the established discrete crack network and the corrected hard data;

[0125] Repeatedly correct the number of crack elements and the cumulative probability curve until a discrete crack network model with an error lower than the preset error range is generated.

[0126] In this embodiment of the invention, the processor is configured to:

[0127] The properties of a crack include at least one of its direction, dip angle, and dip direction.

[0128] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0129] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0130] 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.

[0131] 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.

[0132] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0133] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0134] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0135] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0136] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method of modeling a multi-scale fracture network in a tight reservoir, the method comprising: include: Obtain the direction attribute of the crack; Determine the cumulative probability density curve of the direction, and generate a crack direction that conforms to a preset pattern based on the cumulative probability density curve and random quantiles; The fracture locations interpreted from core and imaging logging are used as hard data for fracture locations in the fracture network; The prior value of crack line density for each grid is calculated using the hard data, and the initial value of the number of crack elements is calculated by combining the crack development intensity of the corresponding grid. The hard data is divided into seed hard data and correction hard data; The cumulative probability curve of crack development intensity is corrected based on the seed hard data, and a set of crack elements is generated based on the corrected probability density curve. Discrete crack networks are generated using similarity fusion criteria, mechanical origin fusion criteria, and cutting criteria. The established discrete crack network is compared with the corrected hard data, and the number of crack elements and the cumulative probability curve are corrected. The step of calculating the prior value of the crack line density for each grid using the hard data, and combining it with the crack development intensity of the corresponding grid to calculate the initial value of the number of crack elements, includes: The number of isolated fracture segments on the core or imaging logging is taken as the number of fracture elements, the number of fracture elements in each grid is accumulated to obtain the prior number of fracture elements; if the jth grid has prior information of fracture elements, the number is assumed to be These prior information is used to determine the total number of fracture elements in the fracture model ; the strength of fracture development in the ith grid , the sum of the strength of fracture development in all n grids , the prior value of the number of fracture elements in the ith grid obtained from the core or imaging logging , the number of fracture elements N and the total number of grids n satisfy the proportional relationship of formula (3): Equation (1) is obtained by transforming equation (3): For the a grids with prior information, a values are calculated according to formula (1), and the maximum value of the a values is taken as the initial value of the subsequent iteration, that is, formula (2): (2)​ The similarity fusion criteria are given by equations (4) to (7): （4） （5） （6） （7） in, The distance between the centers of the two elements that generated the crack; , For the directions of the two crack elements, To move towards the included angle; , The inclination angle of the two crack elements. The difference in tilt angle; The angle between the normal and the line connecting the centers of the two crack elements can be obtained through the inner product of the two vectors: ,in, For the first crack element The unit normal vector can be obtained by the cross product of two adjacent sides of the polygon: , , These are the vector representations of two adjacent sides of the polygon; , , , This is the critical value; The mechanical causal integration criteria include: The crack elements to be fused do not satisfy formula (7) in the similarity fusion criterion, but satisfy formula (8), and the center lines of the crack elements have similar orientations; （8） The cutting criteria include: when cutting a crack element, if the center of the crack element is located between two adjacent control interfaces, the part outside the two adjacent control interfaces is cut off; otherwise, it is retained.

2. The method of claim 1, wherein, The generation of crack orientation conforming to the preset pattern includes: Calculate the trace length and orientation of all cracks on the surface of the outcrop rock strata; The element length is determined based on the smaller crack length obtained. Calculate the ratio of the length of each crack to the length of the unit, and record the number of times the crack direction is repeated in the direction array; The rose diagram of the crack direction is drawn using the aforementioned direction array, and crack directions that conform to a preset pattern are generated.

3. The method of claim 1, wherein, Also includes: Based on the layer division, further layer division is carried out by combining well logging lithology cycles; A more refined layer is generated proportionally to serve as the control interface for the crack network.

4. The method of claim 1, wherein, The seed hard data is used for crack element initialization and for generating initial crack elements; the correction hard data is used for generating correction crack elements, and for calculating the error of the crack mesh in each iteration, and for guiding the acquisition of optimal parameters for the crack network model.

5. The method of claim 4, wherein, The initialization of the crack element includes: initializing the seed crack element and the total number of crack elements, and setting the seed crack element according to the position of the seed hard data.

6. The method of claim 5, wherein, Also includes: If fractures are shown on core samples or imaging logs, it is confirmed that a fracture has passed through the area. The existence of crack elements is identified, and the crack location is determined as prior information about the crack. The seed crack element is determined by generating small polygons of crack elements in the initialized mesh based on the prior information.

7. The method of claim 1, wherein, The similarity fusion criterion includes: identifying fracture elements that are close in location and have similar orientation as the same fracture; The mechanical genesis fusion criterion comprises: determining the fracture elements with similar center connecting line directions as the same shear fracture zone; The cutting criterion comprises: cutting off the part outside the layer in the case that the center of the fracture element is located between two layers of the rock formation.

8. The method of claim 1, wherein, Further comprising: According to the comparison result of the established discrete fracture network and the corrected hard data, determining an error; Repeating the correction of the number of fracture elements and the cumulative probability curve until a discrete fracture network model with an error lower than a preset error range is generated.

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