Digital outcrop-based tight sandstone reservoir fracture modeling method and related device

Abstract

The invention discloses a tight sandstone reservoir fracture modeling method based on a digital outcrop and a related device, and belongs to the technical field of geological exploration and oil-gas exploration. Constructing a three-dimensional digital outcrop model according to the field outcrop photo; then, fracture development parameters in the three-dimensional digital outcrop model are extracted, and a probability density distribution function of the fracture development parameters is determined; reservoir fracture development characteristics are determined based on logging and seismic data; and finally, constructing a tight sandstone reservoir three-dimensional discrete fracture network model according to the probability density distribution function and reservoir fracture development characteristics. According to the method, fracture development parameters and development characteristics are extracted based on the digital outcrop, a real and effective three-dimensional discrete fracture network model is constructed, and the precision of a reservoir fracture model is effectively improved; the steps are simple and effective, and the method is suitable for construction of deep and ultra-deep tight sandstone fracture models.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological exploration and oil and gas exploration, and relates to a method for modeling tight sandstone reservoir fractures based on digital outcrops and related devices. Background Art

[0002] The Tarim Basin and the Junggar Basin are complex superimposed basins, with over 40% of remaining oil and gas resources located in these western basins, presenting enormous exploration potential. With the continued exploitation of conventional oil and gas resources and the increasing development of oil and gas reservoirs, unconventional oil and gas resources are becoming increasingly prominent. The multi-cycle or multi-stage transition and alternating regional stress fields of the superimposed basins in northwest China result in widespread natural fractures with multi-scale characteristics, typically ranging in length from tens of meters to centimeters, and exhibiting significant heterogeneity.

[0003] Limited by drilling data and poor deep-seismic data, traditional methods for constructing fracture network models based on seismic, well logging, and core data have been ineffective in superimposed basins. These fracture models often lack scale characteristics and fail to consider the influence of faults on fracture development, making it difficult to provide a complete fracture network geometry and related connectivity characteristics. For complex superimposed basins, integrating multiple fracture data sets, accurately characterizing fracture development patterns, and constructing realistic and effective fracture network models are crucial for guiding the efficient development of deep and ultra-deep tight sandstone reservoirs in these complex superimposed basins. Summary of the Invention

[0004] The purpose of the present invention is to solve the technical problems in the prior art that traditional fracture modeling methods are not effective in complex superimposed basins, are difficult to provide a complete fracture network geometric model and related connectivity characteristics, and the model accuracy is not high enough, and to provide a tight sandstone reservoir fracture modeling method and related devices based on digital outcrops.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for modeling fractures in tight sandstone reservoirs based on digital outcrops, comprising the following steps:

[0007] Construct a three-dimensional digital outcrop model based on field outcrop photos;

[0008] extracting fracture development parameters from the three-dimensional digital outcrop model and determining a probability density distribution function of the fracture development parameters;

[0009] Determine reservoir fracture development characteristics based on well logging and seismic data;

[0010] A three-dimensional discrete fracture network model of a tight sandstone reservoir is constructed based on the probability density distribution function and reservoir fracture development characteristics.

[0011] In a second aspect, the present invention provides a tight sandstone reservoir fracture modeling system based on digital outcrop, comprising:

[0012] Outcrop model construction module, used to construct three-dimensional digital outcrop models based on field outcrop photos;

[0013] a distribution function determination module, configured to extract fracture development parameters from the three-dimensional digital outcrop model and determine a probability density distribution function of the fracture development parameters;

[0014] Fracture development feature acquisition module, used to determine reservoir fracture development features based on well logging and seismic data;

[0015] The reservoir fracture model construction module is used to construct a three-dimensional discrete fracture network model of a tight sandstone reservoir based on the probability density distribution function and the reservoir fracture development characteristics.

[0016] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method when executing the computer program.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention discloses a method and related devices for modeling tight sandstone reservoir fractures based on digital outcrops. Based on digital outcrops and traditional geological measurements, real and reliable outcrop fracture distribution parameters are obtained, and an effective three-dimensional fracture network model is constructed in combination with well logging and seismic data. This overcomes the problem that traditional fracture modeling methods have poor model effects in areas with little drilling data and poor seismic data. Moreover, after comparative verification, the three-dimensional discrete fracture network model of underground reservoirs constructed based on digital outcrops has greatly improved the model accuracy compared with previous traditional fracture modeling methods. The steps are simple and effective, and the model is suitable for constructing deep and ultra-deep tight sandstone fracture models. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 is a flow chart of the method of the present invention;

[0022] Figure 2 is a schematic diagram of the system of the present invention;

[0023] Figure 3 This is an overall flow chart of the modeling method according to an embodiment of the present invention;

[0024] Figure 4 A 3D outcrop model of dense sandstone scanned in an embodiment of the present invention;

[0025] Figure 5 The fracture development parameters collected in the embodiment of the present invention;

[0026] Figure 6 The KS test result of the crack length in the embodiment of the present invention;

[0027] Figure 7 The development of fractures in the underground reservoir according to the embodiment of the present invention;

[0028] Figure 8 The fracture density model and the three-dimensional fracture network model of the embodiment of the present invention;

[0029] Figure 9 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0032] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0033] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0035] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0036] The present invention is described in further detail below with reference to the accompanying drawings:

[0037] See also Figure 1 The embodiment of the present invention discloses a method for modeling fractures in tight sandstone reservoirs based on digital outcrops, comprising the following steps:

[0038] S1, constructing a three-dimensional digital outcrop model based on field outcrop photos;

[0039] S2, extracting fracture development parameters from the three-dimensional digital outcrop model and determining a probability density distribution function of the fracture development parameters;

[0040] S3, determine reservoir fracture development characteristics based on well logging and seismic data;

[0041] S4, constructing a three-dimensional discrete fracture network model of a tight sandstone reservoir according to the probability density distribution function and reservoir fracture development characteristics.

[0042] In a feasible embodiment of the present invention, constructing a three-dimensional digital outcrop model based on field outcrop photos specifically includes:

[0043] S101: Select typical field outcrops in the study area and conduct field outcrop photo scanning based on traditional manual surveys and drone oblique photography technology. Since most field outcrops cannot be directly measured on site, in addition to traditional manual measurement methods, drones equipped with high-pixel photoelectric sensors are required to scan the outcrops and collect outcrop photo data. The distance to the outcrop surface captured by the photography altitude is determined by the required resolution of the outcrop. The relationship between the drone photography altitude and the ground resolution is:

[0044]

[0045] In the above formula, H is the altitude of the UAV photography (m); f is the focal length of the lens (mm); GSD is the ground resolution (m); a is the pixel size;

[0046] To ensure detailed outcrop parameters, the drone was used to perform continuous photography at a 45° angle in front of the outcrop profile, scanning the outcrop with a 2-second interval. To ensure the accuracy of the subsequent 3D digital outcrop model, the drone was operated in a zigzag pattern, with at least 70% overlap between adjacent photos.

[0047] S102 imports drone-scanned field outcrop photos into the Context Capture modeling software. The original sampling rate is set, and the spatial 3D photos are resampled to match the photos with GPS locations. Next, aerial triangulation is performed. The main checkpoints are: ① Check for any overlaps in the aerial photos; ② Check for stratification of feature points in road or housing areas; and ③ Check for excessive planar and high-level errors in the image control points.

[0048] S103, using a local Cartesian coordinate system to construct a digital outcrop model, generating a TIN grid model from the point cloud data, and performing surface texture mapping to assign color information to establish a three-dimensional digital outcrop model with high ranging accuracy.

[0049] In a feasible embodiment of the present invention, extracting the fracture development parameters in the three-dimensional digital outcrop model and determining the probability density distribution function of the fracture development parameters specifically includes:

[0050] In step S201, the digital outcrop model constructed in step S103 is imported into the ContextCapture Viewer software. The measurement tools within the software are used to analyze the occurrence and development of bedding, fractures, and faults, and to extract outcrop fracture parameters. Furthermore, the fracture development traces are manually digitized and finely described and extracted based on the resolution of the digital outcrop. The node file representing the fracture parameters contains the (x, y) coordinates of nodes along each fracture, separated by a tab character. Each fracture trajectory is recorded on a single line. Each fracture trajectory contains at least four columns of data, namely, at least the coordinate values ​​of the fracture boundary endpoints. If a fracture trajectory consists of multiple segments, it can be represented by multiple endpoint coordinates.

[0051] S202, the obtained fracture development parameters are analyzed using the FracPaQ toolbox in MATLAB to determine the random distribution mode of the fracture development parameters and the corresponding probability density distribution function. The two-dimensional parameters of the outcrop fractures (txt, svg, jpg, tif and other formats) are imported, and the FracPaQ toolbox generates a corresponding two-dimensional pattern diagram from the two-dimensional fracture parameters or extracts relevant fracture parameters from the two-dimensional image. Further, the fracture density, length, occurrence and other data are extracted, and the corresponding frequency statistics and rose diagrams are generated. The relevant fracture parameters here include: fracture density, fracture length, fracture direction, fracture aperture, etc. According to the FracPaQ statistical parameter results, the probability density function distribution of each relevant fracture parameter is analyzed. The commonly used probability density distribution functions of each parameter are different. Some functions are shown below:

[0052] ① For crack length, commonly used distribution functions include negative exponential distribution, lognormal distribution, etc.

[0053] Negative exponential distribution:

[0054]

[0055] Among them, μ e Represents the mathematical expectation of the negative exponential distribution;

[0056] Lognormal distribution:

[0057]

[0058] Among them, μ' is the mathematical expectation, σ' is the mean square error;

[0059] ② For the crack direction, the commonly used distribution functions are uniform distribution and Von-Mises distribution;

[0060] Uniform distribution, etc.:

[0061]

[0062] Von-Mises distribution:

[0063]

[0064] Wherein, is the fracture strike, μ and κ are parameters reflecting the fracture strike, I0 is the modified Bessel function,

[0065]

[0066] ③ For the fracture location, common distribution functions include: Poisson distribution, normal distribution, etc.;

[0067] Poisson distribution:

[0068]

[0069] Wherein, v is the area of the study area, and k is the number of fractures;

[0070] Normal distribution:

[0071]

[0072] Wherein, μ is the mathematical expectation, and σ is the standard deviation;

[0073] In order to select the distribution density function that best conforms to the fracture parameter distribution, the K-S test is performed on the original parameters and the probability density distribution functions of common parameters. The K-S test is a method for comparing a frequency distribution f(x) with a theoretical distribution g(x). Assume H0: The data conforms to the theoretical distribution, and find:

[0074] D = max|f(x) - g(x)|

[0075] When the actual parameter value D < D(n,α), it indicates that the data conforms to this distribution method (the parameter value of D(n,α) is obtained through a common probability distribution table).

[0076] In a feasible implementation manner of the present invention, the determination of the reservoir fracture development characteristics based on logging and seismic data specifically includes:

[0077] S301. In the area with a coring well, conduct core fracture observation on the coring well, describe the longitudinal height of different fractures, the included angle between the fracture strike and the dip of the formation micro-plane, the fracture dip angle, the fracture filling situation, and the surface opening of the fracture. Combine the core positioning to determine the fracture strike, and statistically analyze the distribution characteristics of the fracture strike, the longitudinal height of the fracture, the fracture dip angle, the fracture filling situation, and the surface opening of the fracture;

[0078] S302, in an area with imaging logging, combining the fracture groups identified in different locations of the area with acoustic emission experiments, interpreting the fracture direction, fracture vertical height, fracture dip, fracture filling, and underground fracture aperture of each fracture group, and statistically analyzing the distribution characteristics of the fracture direction, fracture vertical height, fracture dip, fracture filling, and underground fracture aperture;

[0079] S303: In areas with both coring wells and imaging logging, the fracture parameters of the cores and imaging logging are calibrated to each other, and the distribution characteristics of each group of fractures, fracture inclination, fracture vertical height, fracture filling, and underground fracture aperture are statistically analyzed, and the relationship between the underground fracture aperture and the surface fracture aperture is obtained;

[0080] In areas without coring or imaging logging, S304 uses conventional well logging curves combined with seismic post-stack attributes to extract the distribution characteristics of fracture direction, fracture dip, fracture vertical height, fracture filling, and fracture aperture. Combined with these reservoir fracture parameters, the stratified development parameters of natural fractures in the reservoir are obtained.

[0081] In a feasible embodiment of the present invention, a three-dimensional discrete fracture network model of the reservoir is constructed based on the probability density distribution function and the reservoir fracture development characteristics, specifically including:

[0082] S401, determine the fracture location based on the probability density distribution function and reservoir fracture development characteristics; the coordinates of the fracture center point are set to (x, y, z), where x, y, and z are independent continuous random variables that obey the probability density distribution function; the probability density distribution function here is determined by the fracture development parameters collected from the digital outcrop.

[0083] S402, after the random process determines the center position of the crack, the crack development parameters are three-dimensionally discretely expanded using the Kriging interpolation method, and the center position of the i-th grid is defined as (x i ,y i ,z i ), the density value corresponding to this point is Pi, Pi is normalized to remove the impact of the dimension on the original data; the normalization method is specifically as follows:

[0084]

[0085] Generate a random number in the interval [0,1] using the crack density distribution function, and compare the above P′ with the random number. If P′>Rand(random number), the crack center position at that point is valid; otherwise, the position of that point is invalid.

[0086] Repeat the above steps until the preset termination condition is met; the termination condition here is obtained by the actual single-well fracture parameters, that is, when the fracture density is greater than the actual single-well fracture density, the cycle terminates and all fracture center positions can be generated.

[0087] S403: Determine the size, strike, inclination, and other attributes of each fracture. During fracture modeling, the fracture slice can be approximated as an ellipse, with the major axis length and minor axis width of the fracture slice being proportional. Based on the probability density distribution function of fracture lengths statistically analyzed in the outcrop area, the length data for each fracture is generated. Based on the fracture length, the width of the fracture slice in the fracture model can be determined. Fracture strike and inclination data can be determined based on the statistical results of outcrop fracture parameters. The specific method is as follows:

[0088]

[0089] In the formula, φ and φ0 are parameters describing fracture orientation, θ and θ0 are parameters describing fracture dip, and κ is a parameter describing the degree of fracture divergence; smaller values ​​indicate greater divergence. The fracture orientation and dip parameters are determined statistically from digital outcrop data. The discrete parameter κ is calibrated and analyzed using imaging logging and core data to determine the distribution parameters of the actual fracture occurrence in the work area.

[0090] S404: Generate a three-dimensional discrete fracture network model based on the center position, size, strike, and dip attributes of each fracture. Based on the distribution characteristics of tight sandstone fractures, the above fracture generation process is performed separately in layers and groups, and finally a three-dimensional discrete fracture network model of the tight sandstone reservoir is obtained by superposition.

[0091] In a feasible embodiment of the present invention, the method for modeling fractures in tight sandstone reservoirs based on digital outcrops further includes: performing reliability verification on the effectiveness of the three-dimensional discrete fracture network model of the tight sandstone reservoir; truncating the three-dimensional discrete fracture network model of the tight sandstone reservoir to a single well, comparing the fractures in the model with the actual fracture occurrence and density on the imaging logging data or core data to verify the accuracy of the model; if there is an accuracy difference, the fracture modeling parameters need to be adjusted until the optimal modeling effect is achieved.

[0092] See also Figure 2 The embodiment of the present invention discloses a tight sandstone reservoir fracture modeling system based on digital outcrop, comprising:

[0093] Outcrop model construction module, used to construct three-dimensional digital outcrop models based on field outcrop photos;

[0094] a distribution function determination module, configured to extract fracture development parameters from the three-dimensional digital outcrop model and determine a probability density distribution function of the fracture development parameters;

[0095] Fracture development feature acquisition module, used to determine reservoir fracture development features based on well logging and seismic data;

[0096] The reservoir fracture model construction module is used to construct a three-dimensional discrete fracture network model of a tight sandstone reservoir based on the probability density distribution function and the reservoir fracture development characteristics.

[0097] An embodiment of the present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for modeling tight sandstone reservoir fractures based on digital outcrops are implemented.

[0098] See also Figure 9 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps of the method for modeling tight sandstone reservoir fractures based on digital outcrops are implemented.

[0099] Example:

[0100] See also Figure 3 , a method for modeling fractures in tight sandstone reservoirs based on digital outcrops, comprising the following steps:

[0101] In step 1, representative field outcrops of tight sandstone reservoirs were selected. Fracture parameters were acquired using drone oblique photography, in addition to traditional manual surveys. Since most outcrops are impractical for direct on-site measurement, drones equipped with high-pixel photoelectric sensors were used to scan and collect outcrop photographic data. The acquisition tool used was a DJI INSPIRE2 quadrotor drone equipped with a 20-megapixel Zenmuse X5S gimbal camera. Field surveys of the study area revealed an outcrop height of approximately 100 meters, with a maximum flight altitude of 110 meters. The drone captured images approximately 10 meters from the outcrop profile. The drone took 45-degree oblique photography from the front of the outcrop. Scanning was performed using a continuous drone photography method with a 2-second interval. Each scan had to have at least 70% overlap to facilitate later stitching. The manually controlled drone scanned in a Z-shaped circular route, scanning the entire Kiziltag Mountain outcrop more than a hundred times, and additional photos were taken in key areas where cracks developed to improve data collection accuracy. More than 2,000 photos were collected, and the resolution of the outcrop photos reached 3-6mm, providing an accurate parameter basis for the subsequent acquisition of crack parameters.

[0102] In step 2, the field outcrop photos scanned by the drone are imported into the Context Capture modeling software, the original sampling rate is set, and the spatial three-dimensional photos are resampled to correspond to the photos and GPS positions. Next, an aerial triangulation check is required. The main check points are: ① Check whether the aerial photos are intersecting; ② Whether the feature points are layered in the road or house area; ③ Check whether the plane and high-level errors of the image control points are too large; Finally, a local Cartesian coordinate system is selected to build a digital outcrop model, and the point cloud data is generated into a TIN grid model. The surface texture mapping is performed to give color information, and a three-dimensional digital outcrop model with high ranging accuracy is established. The constructed digital outcrop model is as follows: Figure 4 shown.

[0103] In step 3, extract fracture development parameters from the 3D digital outcrop model. The tight sandstone digital outcrop model constructed in step 2 was imported into ContextCapture Viewer software. Measurement tools within the software were used to analyze the occurrence and development of bedding, fractures, and faults. Furthermore, based on the resolution of the digital outcrop, fracture development traces were manually digitized for detailed description and extraction, resulting in a 2D pattern of outcrop fracture development. Detailed fracture development parameters were extracted (the endpoint coordinates of each fracture parameter were obtained. For curved fractures, the coordinates of multiple points along the fracture trajectory can be used).

[0104] In step 4, the obtained fracture parameters are analyzed using the FracPaQ toolbox in MATLAB to determine the random distribution of fracture development parameters and the corresponding probability density distribution function. The two-dimensional parameters of the outcrop fractures are imported. Here, the fracture development parameters are represented by the coordinates of the fracture endpoints and stored in a txt file. The FracPaQ toolbox converts the fracture development parameters into a fracture development pattern diagram. A total of 1081 fractures were counted in the field outcrop, with a trace length ranging from 1m to 19m. The fracture direction is dominant in the NE direction. The two-dimensional fracture development pattern diagram is shown as follows: Figure 5 As shown in the figure, the number of cracks, the average crack trajectory length, the maximum and minimum crack cutoff lengths, the crack direction and inclination parameters are counted to generate the corresponding frequency statistics and rose diagrams. The probability distribution function is determined according to the distribution of the outcrop crack parameters, and the crack length distribution function is determined as follows: Figure 6 a. Figure 6 b and Figure 6 As shown in Figure c, through the KS test and image observation, the crack length parameter is most consistent with the lognormal distribution. Therefore, the lognormal distribution is selected to construct the crack length, where the lognormal distribution parameters μ = 1.3329 and σ = 0.56745.

[0105] In step 5, the reservoir fracture development characteristics are determined based on logging, seismic and other data; the method for determining the reservoir fracture parameters includes: in a single well with drilling and coring, observing the core fractures of the coring well, describing the different fracture vertical heights, fracture directions and the angle between the dip of the micro-layer of the formation, fracture dip, fracture filling conditions and fracture surface openings, combining with core retrieval to determine the fracture direction, and statistically analyzing the distribution characteristics of the fracture direction, fracture vertical height, fracture dip, fracture filling conditions and fracture surface openings; in an area with imaging logging, combining with the fracture groups divided by the acoustic emission experiment at different parts of the area, respectively interpreting the fracture direction, fracture vertical height, fracture dip, fracture filling conditions and fracture surface openings of each group. The distribution characteristics of fracture direction, fracture vertical height, fracture inclination, fracture filling and fracture underground opening are counted; in areas with coring wells and imaging logging, the fracture parameters of cores and imaging logging are calibrated with each other, and the distribution characteristics of fracture direction, fracture inclination, fracture vertical height, fracture filling and fracture underground opening of each group are counted, and the relationship between fracture underground opening and fracture surface opening is obtained; in areas without coring wells and imaging logging, conventional logging curves are used in combination with seismic post-stack attributes to extract the distribution characteristics of fracture direction, fracture inclination, fracture vertical height, fracture filling and fracture underground opening. Combined with the above-mentioned reservoir fracture parameters, the stratified development parameters of natural fractures in the reservoir are obtained, among which the fracture length cannot be identified from the core and imaging data, the reservoir fracture density is between 0-0.8 lines / m, the dominant fracture direction is NNE, which is highly consistent with the outcrop development. The reservoir fracture statistics are as follows Figure 7 a and Figure 7 As shown in b.

[0106] In step 6, a three-dimensional discrete fracture network model of the reservoir is constructed based on the above data; the steps for constructing the three-dimensional discrete fracture network model are as follows: first, the fracture position is obtained. The fracture position is determined by the probability density distribution function of the fracture density determined on the digital outcrop and the fracture development characteristic constraints of the underground reservoir statistics. The coordinates of the center point of the fracture slice are set to (x, y, z), where x, y, and z are independent of each other and obey the continuous random variables of the fracture density distribution function. The probability density distribution function here is determined by the fracture development parameters collected on the digital outcrop. After the random process determines the center position of the fracture, the fracture development parameters are discretely expanded in three dimensions through the Kriging interpolation method, and the center position of the i-th grid is defined as (x i ,y i ,z i ), the density value corresponding to this point is P i , P i Perform normalization to remove the impact of dimension on the original data. The normalization method is as follows:

[0107]

[0108] Using the fracture density distribution function, a random number in the interval [0, 1] is generated. The above P′ is compared with this random number. If P′ > Rand, the fracture center location at that point is valid; otherwise, the location is invalid. Repeat these steps until the preset termination condition is met. This termination condition is derived from the actual fracture parameters of a single well. Specifically, when the fracture density exceeds the actual fracture density of a single well, the loop terminates, and all fracture center locations are generated.

[0109] Secondly, determine the size, strike, dip, and other attributes of each fracture. During fracture modeling, the shape of the fracture slice can be approximated as an ellipse, meaning the major and minor axes of the fracture slice are proportional. Based on the probability density distribution function of the fracture lengths statistically analyzed in the outcrop area, the length data for each fracture is generated. Based on the fracture length, the width of the fracture slice in the fracture model can be determined. Fracture strike and dip data can be determined based on the statistical results of the outcrop fracture parameters. The determination method is as follows:

[0110]

[0111] In the formula, φ and φ0 are parameters describing fracture orientation, θ and θ0 are parameters describing fracture dip, and κ is a parameter describing the degree of fracture divergence; smaller values ​​indicate greater divergence. The fracture orientation and dip parameters are determined statistically from digital outcrop data. The discrete parameter κ is calibrated and analyzed using imaging logging and core data to determine the distribution parameters of the actual fracture occurrence in the work area.

[0112] After determining the center position, size, and occurrence of each fracture, a three-dimensional discrete fracture network model can be generated. According to the development and distribution characteristics of tight sandstone fractures, the above fracture generation process should be carried out separately in layers and groups. Here, the software selected is Matlab2022a, the computer configuration is Inter(R)300, the main frequency is 2.5GHz, 16GB memory, and the program running time is 514.85s. The fine fracture network model of the reservoir in the study area is generated, as shown in the following figure: Figure 8 a and Figure 8 As shown in b.

[0113] In step 7, the validity of the above-mentioned fracture network model needs to be verified. The fracture model is truncated to a single well, and the fractures in the fracture network model are compared with the actual fracture occurrence and density in the imaging logging data or core data to verify the accuracy of the fracture network model.

[0114] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

[0115] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for modeling fractures in tight sandstone reservoirs based on digital outcrops, characterized in that: The following steps are involved: Construct a three-dimensional digital outcrop model based on field outcrop photos; extracting fracture development parameters from the three-dimensional digital outcrop model and determining a probability density distribution function of the fracture development parameters; Determine reservoir fracture development characteristics based on well logging and seismic data; A three-dimensional discrete fracture network model of a tight sandstone reservoir is constructed based on the probability density distribution function and reservoir fracture development characteristics.

2. The method for modeling fractures in tight sandstone reservoirs based on digital outcrops according to claim 1, characterized in that: The three-dimensional digital outcrop model is constructed based on the field outcrop photos, specifically including: S101, based on traditional manual surveys, combined with drone oblique photography technology, conduct field outcrop photo scanning; the relationship between the drone photography altitude and ground resolution is: In the above formula, H is the altitude of the drone photography; f is the focal length of the lens; GSD is the ground resolution; a is the pixel size; S102, importing the field outcrop photos scanned by the drone into the modeling software, setting the original sampling rate, resampling the spatial three-dimensional photos, aligning the photos with GPS positions, and performing aerial triangulation; S103, using a local Cartesian coordinate system to construct a digital outcrop model, generating a TIN grid model from the point cloud data, and performing surface texture mapping to assign color information to establish a three-dimensional digital outcrop model.

3. The method for modeling tight sandstone reservoir fractures based on digital outcrop according to claim 1, characterized in that: The extraction of fracture development parameters in the three-dimensional digital outcrop model and determination of the probability density distribution function of the fracture development parameters specifically include: S201, using a three-dimensional digital outcrop model to analyze the occurrence and development of bedding, fractures, and faults, and extract outcrop fracture development parameters; S202, extracting relevant fracture parameters from the fracture development parameters, and analyzing the random distribution mode of each relevant fracture parameter and the corresponding probability density distribution function; the probability density distribution function is determined by a KS test.

4. The method for modeling tight sandstone reservoir fractures based on digital outcrop according to claim 1, characterized in that: Determining reservoir fracture development characteristics based on well logging and seismic data specifically includes: S301, in an area with coring wells, observe the core fractures of the coring wells and determine the reservoir fracture development characteristics in combination with core tracing; S302, in areas with imaging logging, determine reservoir fracture development characteristics by combining fracture groups delineated by acoustic emission experiments; S303, in an area with both coring wells and imaging logging, mutually calibrate the fracture parameters of the core and imaging logging to obtain reservoir fracture development characteristics; S304: In areas without core wells or imaging logging, conventional logging curves are used in combination with seismic post-stack attributes to obtain reservoir fracture development characteristics.

5. The method for modeling fractures in tight sandstone reservoirs based on digital outcrops according to claim 1, characterized in that: Constructing a three-dimensional discrete fracture network model of the reservoir based on the probability density distribution function and the reservoir fracture development characteristics specifically includes: S401, determining the fracture location based on the probability density distribution function and the reservoir fracture development characteristics; the coordinates of the fracture center point are set to (x, y, z), where x, y, and z are independent continuous random variables that obey the probability density distribution function; S402, based on the center position of the crack, the crack development parameters are discretely expanded in three dimensions using the Kriging interpolation method, and the center position of the i-th grid is defined as (x i ,y i ,z i ), the density value corresponding to this point is Pi, Pi is normalized to remove the influence of dimension on the original data; S403: Determine the size, strike, and dip attributes of each crack. The specific method is as follows: Where φ and φ0 are parameters describing the crack direction, θ and θ0 are parameters describing the crack dip; κ is a parameter describing the degree of crack divergence; S404: Generate a three-dimensional discrete fracture network model based on the center position, size, strike, and dip attributes of each fracture. Based on the distribution characteristics of tight sandstone fractures, the above fracture generation process is performed separately in layers and groups, and finally a three-dimensional discrete fracture network model of the tight sandstone reservoir is obtained by superposition.

6. The method for modeling fractures in tight sandstone reservoirs based on digital outcrops according to claim 5, characterized in that: The normalization method in S402 is specifically as follows: Generate a random number in the interval [0,1] using the crack density distribution function, and compare the above P′ with the random number. If P′>the random number, the crack center position at that point is valid; otherwise, the position of that point is invalid. Repeat the above steps until a preset termination condition is met; the termination condition is: when the fracture density is greater than the actual single well fracture density, the cycle terminates.

7. The method for modeling tight sandstone reservoir fractures based on digital outcrop according to claim 1, characterized in that: Also includes: The reliability of the effectiveness of the three-dimensional discrete fracture network model of the tight sandstone reservoir is verified; the three-dimensional discrete fracture network model of the tight sandstone reservoir is truncated to a single well, and the fractures in the model are compared with the actual fracture occurrence and density in the imaging logging data or core data to verify the accuracy of the model, and the model parameters are adjusted according to the accuracy difference.

8. A tight sandstone reservoir fracture modeling system based on digital outcrop, characterized in that: include: Outcrop model construction module, used to construct three-dimensional digital outcrop models based on field outcrop photos; a distribution function determination module, configured to extract fracture development parameters from the three-dimensional digital outcrop model and determine a probability density distribution function of the fracture development parameters; Fracture development feature acquisition module, used to determine reservoir fracture development features based on well logging and seismic data; The reservoir fracture model construction module is used to construct a three-dimensional discrete fracture network model of a tight sandstone reservoir based on the probability density distribution function and the reservoir fracture development characteristics.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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