Shale reservoir fracture distribution prediction method and device

Abstract

The invention discloses a shale reservoir fracture distribution prediction method and device. The method comprises the following steps: carrying out statistics and grouping on fracture occurrence data of a target stratum; respectively determining a discrete coefficient corresponding to each group of data; determining curvature intensity data of the target stratum, and mapping the curvature intensity data to a pre-established three-dimensional geological model; and determining crack information of the target area according to the discrete coefficient and the three-dimensional geologic model with the curvature intensity data.

Description

Technical Field

[0001] This article relates to the field of geological exploration technology, and in particular to a method and device for predicting the distribution of fractures in shale reservoirs. Background Art

[0002] Shale oil and shale gas have become key research areas in the energy revolution. Favorable reservoir sweet spots for shale oil and gas are often found in areas with concentrated fracture development. Therefore, predicting fracture development zones is crucial for evaluating shale oil and gas reservoir sweet spots. Subsurface strata undergo significant deformation during plate and tectonic movement, resulting in significant bending and folding. Areas with large fold curvature often experience severe deformation and are prone to cracks, fractures, or faults. These areas are favorable for the development of tectonic fractures. Accurately predicting the distribution of fractures in shale reservoirs on a plane can provide critical guidance for selecting zones for shale oil and gas exploration. Furthermore, structurally prone fracture zones are areas of weak subsurface stress. Therefore, this method can be applied not only to evaluating reservoir fracture sweet spots but also to assessing the stability or activity of geological structures. Studies have found that traditional fracture prediction methods have disadvantages such as considering only a single geological factor, relying on qualitative speculation, and having low prediction accuracy.

[0003] Therefore, it is urgent to provide a shale reservoir fracture distribution prediction method so that the shale oil and gas reservoir fracture prediction can integrate more actual geological data, quantify it and improve the prediction accuracy. Summary of the Invention

[0004] The present application provides a method and device for predicting the distribution of fractures in shale reservoirs. The method integrates a variety of geological data information and obtains highly accurate fracture information through comprehensive analysis of curvature intensity data and three-dimensional geological models.

[0005] In a first aspect, the present application provides a method for predicting shale reservoir fracture distribution, the method comprising:

[0006] Count and group the fracture occurrence data of the target layer;

[0007] Determine the coefficient of dispersion corresponding to each set of data;

[0008] Determining curvature intensity data of the target layer and mapping it onto a pre-established three-dimensional geological model;

[0009] Fracture information of a target area is determined according to the dispersion coefficient and the three-dimensional geological model having curvature intensity data.

[0010] In a second aspect, an embodiment of the present invention further provides a shale reservoir fracture distribution prediction device, the device comprising: a memory and a processor; the memory is used to store a program for predicting shale reservoir fracture distribution, and the processor is used to read and execute the program for predicting shale reservoir fracture distribution, and perform any one of the methods described in the above embodiments.

[0011] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a data processing program stored thereon, and the data processing program is executed by a processor to perform the shale reservoir fracture distribution prediction method described in any one of the above embodiments.

[0012] Compared to related technologies, this application provides a method and device for predicting fracture distribution in shale reservoirs. The method includes: statistically analyzing and grouping fracture occurrence data for a target layer; determining the discrete coefficient corresponding to each group of data; determining curvature intensity data for the target layer and mapping it to a pre-established three-dimensional geological model; and determining fracture information in the target area based on the discrete coefficient and the three-dimensional geological model containing the curvature intensity data. This application integrates multiple geological data and obtains highly accurate fracture information through comprehensive analysis of the curvature intensity data and the three-dimensional geological model.

[0013] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0015] Figure 1 This is a flow chart of a method for predicting shale reservoir fracture distribution according to an embodiment of the present application;

[0016] Figure 2 This is a schematic diagram of a shale reservoir fracture distribution prediction device according to an embodiment of the present application;

[0017] Figure 3 A histogram of shale fracture tendency data in the study area A in some exemplary embodiments;

[0018] Figure 4 A schematic diagram of the stereographic projection of shale fracture tendency and dip angle data in the study area A in some exemplary embodiments;

[0019] Figure 5 A schematic diagram of layer data point encryption in some exemplary embodiments;

[0020] Figure 6 is a schematic diagram of encrypted stratigraphic depth data in some exemplary embodiments;

[0021] Figure 7 Schematic diagram of curvature intensity calculation results of layer B in the study area A in some exemplary embodiments;

[0022] Figure 8 Schematic diagram of a three-dimensional gridded geological model of a study area A in some exemplary embodiments;

[0023] Figure 9 A schematic diagram of a crack prediction grid in some exemplary embodiments;

[0024] Figure 10 Schematic diagram of crack distribution prediction results in some exemplary embodiments;

[0025] Figure 11 A schematic diagram of the superposition of fracture distribution and a three-dimensional geological model in some exemplary embodiments;

[0026] Figure 12 Schematic diagram comparing the number of simulated fractures and actual wellbore fractures in some exemplary embodiments. DETAILED DESCRIPTION

[0027] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0028] This application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the appended claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the appended claims.

[0029] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0030] Among some technologies, the methods related to fracture prediction mainly include geological statistics, well logging prediction method, and geophysical method.

[0031] The geostatistical method is an artificial means of calculating fracture statistics through core observations, field outcrop surveys, microscopic thin section observations, and scanning electron microscopy. The research subjects primarily include field outcrop profiles, cores, and thin sections subsequently ground from core and outcrop samples. This relatively traditional, relatively early developed method is a semi-quantitative measurement method subject to significant subjective influence. Fracture statistics vary significantly across samples, providing statistical tools at scales ranging from macroscopic to mesoscopic to microscopic. This method is time-consuming, subjective, and requires high operator experience and data volume. While it yields good statistical results with large data volumes, it lacks predictive power for the spatial distribution of fractures and merely characterizes patterns.

[0032] Well logging prediction methods primarily use conventional logging signal anomalies or imaging logging to identify fracture development in vertical formations. This method can only predict fractures within a certain distance around the wellbore, and its detection depth is limited. Conventional logging has low fracture prediction accuracy and needs to be improved. While imaging logging offers high prediction accuracy, it is expensive and is only tested in a few key well locations. Limited by the size of the wellbore and the lateral depth of the logging signal, this method has a limited prediction range. While it is effective in vertical prediction, its horizontal prediction capability is limited.

[0033] The geophysical method uses seismic data volumes to perform attribute analysis on seismic signals, identifying fault and fracture data volumes from the relevant attribute data. This method is often used to identify fault planes and has a good ability to identify macroscopic fault development. This method is less susceptible to human influence and provides relatively accurate and objective predictions. However, due to the sampling density and resolution of seismic data volumes, this method is mostly used for macroscopic predictions and is less effective in predicting fractures at the meso- and microscales. Furthermore, this method is often used in clastic rock formations, but its effectiveness in predicting fracture development in carbonate rocks is reduced, and its application in mixed sedimentary rock formations is less effective. In compressional or complex tectonic zones such as foreland basins, the formations are complicated by compression and fracture cutting, thus reducing the precision and accuracy of fracture prediction.

[0034] These three methods are relatively common fracture prediction methods, each with its own applicable conditions and limitations. Some methods are significantly subjective and non-quantitative, resulting in inherent flaws and uncertainty in prediction results during specific applications. To address the issues with these methods, the inventors have proposed a method for predicting fracture distribution in shale reservoirs that can achieve more accurate prediction results.

[0035] The embodiment of the present invention provides a method for predicting the distribution of fractures in shale reservoirs. Figure 1 As shown, the method includes steps S100-S130:

[0036] S100: Counting and grouping the fracture occurrence data of the target layer;

[0037] S110: Determine the Fisher coefficient of dispersion corresponding to each set of data;

[0038] S120: Determine curvature intensity data of the target layer and map it onto a pre-established three-dimensional geological model;

[0039] S130: Determine fracture information of a target area according to the dispersion coefficient and the three-dimensional geological model having curvature intensity data.

[0040] In an exemplary embodiment, the fracture occurrence data of the target area are counted and grouped, including:

[0041] The first step is to obtain shale core fracture data, field outcrop fracture occurrence data, and imaging logging fracture data in the target area;

[0042] The second step is to perform cluster statistics based on the fracture inclination and fracture dip in the obtained fracture occurrence data to obtain multiple groups of fractures.

[0043] In an exemplary embodiment, the Fisher dispersion coefficient includes: the dispersion of the fracture dip (Dip), the dispersion of the fracture tendency (Azimuth), the dispersion of the fracture length (Length), and the dispersion of the fracture width (Width);

[0044] The fracture information includes fracture density, fracture dip, fracture tendency, fracture length and fracture width.

[0045] In an exemplary embodiment, the Fisher coefficient of dispersion is:

[0046]

[0047] Where v is the range, v = (1 / 2) × ∑[(Di - Dj)^2], ∑ represents the sum of the squares of the differences between Di and Dj in the calculated fracture data, and i is not equal to j;

[0048] r is the nugget value, r = (1 / 2) × ∑ [(Di - Di + 1) ^ 2]; ∑ represents the sum of the squares of the differences between all adjacent crack data Di and Di + 1;

[0049] Di represents the fracture data of the i-th discrete point, Di+1 represents the fracture data of the i+1-th discrete point, and Dj represents the fracture data of the j-th discrete point; the fracture data includes: the inclination, dip, length and width of the fracture.

[0050] In an exemplary embodiment, determining the curvature intensity data of the target layer and mapping it to a pre-established three-dimensional geological model includes:

[0051] The first step is to calculate the curvature intensity data of the target layer;

[0052] The second step is to determine the size of the grid based on the crack length and width in the crack occurrence data;

[0053] Step 3: gridding the three-dimensional geological model according to the determined grid size;

[0054] The fourth step is to map the curvature intensity data of the target layer to each grid in the three-dimensional geological model according to the corresponding coordinates.

[0055] In an exemplary embodiment, calculating the curvature intensity data of the target layer includes:

[0056] The first step is to encrypt the target layer data;

[0057] The second step is to calculate the curvature intensity data of the encrypted layer data according to the curvature calculation formula;

[0058] The curvature calculation formula is:

[0059]

[0060] In the above formula, Q(x0,y0,z0) represents the curvature intensity data corresponding to the point (x0,y0,z0), z=f(x,y), is the first-order partial derivative, is the second-order partial derivative.

[0061] In an exemplary embodiment, the process of establishing the three-dimensional geological model is as follows:

[0062] The first step is to obtain the encrypted layer data;

[0063] The second step is to perform cleaning and format conversion on the encrypted layer data;

[0064] The third step is to use the processed stratigraphic data to establish a three-dimensional stratigraphic model.

[0065] In an exemplary embodiment, determining the fracture information of the target area based on the dispersion coefficient and the three-dimensional geological model having curvature intensity data includes:

[0066] Determining the fracture density of each grid in the three-dimensional geological model based on the curvature strength data of each grid, a predetermined fracture density coefficient, and the fracture density calculation formula;

[0067] The crack density calculation formula is:

[0068] Ni=(Qi / Qj)*C;

[0069] Among them, Ni is the crack density in the i-th grid, Qi is the curvature intensity data in the i-th grid, Qj is the curvature intensity data in the j-th grid, and C is the crack density coefficient.

[0070] In an exemplary embodiment, after determining the fracture density of each grid in the three-dimensional geological model based on the curvature strength data of each grid, a preset fracture density coefficient, and the fracture density calculation formula, the method further includes:

[0071] Obtain fracture density with drilled well location grid data;

[0072] Comparing the calculated fracture density of the grid with the fracture density of the obtained grid data of the drilled wells;

[0073] If the difference between the two is less than or equal to the predetermined threshold range, the crack density parameter C remains unchanged;

[0074] If the difference between the two is greater than a predetermined threshold range, the crack density parameter is corrected to obtain a corrected crack density parameter, and the corrected crack density parameter is used as a predetermined crack density coefficient.

[0075] In an exemplary embodiment, determining the fracture information of the target area based on the dispersion coefficient and the three-dimensional geological model having curvature intensity data includes:

[0076] Generate a random number Z from a standard normal distribution;

[0077] According to the determined Fisher dispersion coefficient, the normal distribution random interpolation formula is used to determine the fracture inclination, fracture tendency, fracture length and fracture width respectively.

[0078] The normal distribution random interpolation formula is:

[0079] X=-1 / (K*sqrt(2*pi))+sqrt(1 / (K*pi))*Z;

[0080] Where X is the inclination angle Di, tilt Ai, length Li and width Hi of the crack, K is the dispersion coefficient of the inclination angle Di, tilt Ai, length Li and width Hi of the crack corresponding to X, pi is π, and sqrt is square root calculation.

[0081] In a second aspect, an embodiment of the present invention further provides a shale reservoir fracture distribution prediction device, such as Figure 2 As shown, the device includes: a memory 200 and a processor 210; the memory is used to store a program for a method for predicting the distribution of fractures in a shale reservoir, and the processor is used to read and execute the program for predicting the distribution of fractures in a shale reservoir, and execute any one of the methods in the above embodiments.

[0082] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a data processing program stored thereon, and the data processing program is executed by a processor to perform the shale reservoir fracture distribution prediction method described in any one of the above embodiments.

[0083] The present invention implements a method for predicting the distribution of fractures in shale reservoirs. This method collects fracture occurrence data from a target area, statistically groups fractures formed under different stress background conditions through data analysis, calculates the curvature strength of the layers using 3D seismic data from the study area, and uses the 3D seismic data to establish a 3D gridded geological model. Fracture prediction software then calculates the 3D spatial distribution of fractures in the study area. The shale reservoir fracture distribution prediction method of the present invention integrates multiple geological data, taking into account actual geological data and 3D spatial data. It features high fracture prediction accuracy, quantitative prediction results, low cost, strong operability, and strong practicality.

[0084] Example 1

[0085] For the study area A, the above-mentioned shale reservoir fracture distribution prediction method is used to determine the fracture distribution of layer B developed in the study area A. The specific implementation process is as follows:

[0086] Step 1. Statistical analysis of the fracture occurrence data of target layer B

[0087] In this step, a geological compass is used to measure the inclination and dip data of fractures in geological outcrops and cores within the study area. Simultaneously, fracture imaging logging data on inclination and dip are collected. Dip is represented by Di, and dip is represented by Ai. The length Li and width Hi of the fractures are also collected. This statistical analysis of inclination and dip data primarily involves large-scale fractures in geological outcrops, totaling 248 data points.

[0088] Step 2. Group the fracture occurrence data

[0089] Based on the fracture development tendency data, a histogram statistical analysis was conducted, and the trend and dip angle data were clustered using the stereographic projection circle to count the number of histogram normal distribution peaks and stereographic projection circle peaks. Figure 3 The data shown has three normal distribution peaks; Figure 4 The stereographic projection diagram of the shale fracture tendency and dip angle data in the study area A shows three cluster peaks. Figure 3 and Figure 4 It can be seen that the cracks in the study area are divided into three groups.

[0090] Step 3. Determine the occurrence dispersion information

[0091] Each group of cracks is analyzed for dispersion separately. The information of Fisher dispersion coefficient k of the three sets of data collected in this statistics includes: crack dip dispersion, inclination dispersion, length dispersion, and width dispersion.

[0092] The Fisher dispersion coefficient of the inclination angle Di of each group of cracks is calculated as follows:

[0093]

[0094] Where v is the range, v = (1 / 2) × ∑[(Di - Dj)^2], ∑ represents the sum of the squares of the differences between Di and Dj in the calculated fracture data, i is not equal to j; i>0 and i is an integer, j>0 and i is an integer.

[0095] r is the nugget value, r = (1 / 2) × ∑ [(Di - Di + 1) ^ 2]; ∑ represents the sum of the squares of the differences between all adjacent crack data Di and Di + 1;

[0096] Di represents the fracture data of the i-th discrete point, Di+1 represents the fracture data of the i+1-th discrete point, and Dj represents the fracture data of the j-th discrete point; the fracture data includes: the inclination, dip, length and width of the fracture.

[0097] In addition to the crack inclination, the crack dip Ai, scale length and width Li / Hi can also be calculated using this formula to calculate the Fisher dispersion coefficient. The calculated crack dip and dip Fisher dispersion coefficients are Kd and Ka, respectively, and the crack length and width Fisher dispersion coefficients are Kl and Kh, respectively. The detailed data of the calculated crack dispersion statistics are shown in Table 1.

[0098] Table 1

[0099]

[0100]

[0101] Step 4. Encrypt the target layer data

[0102] In this step, the target layer data is interpolated and encrypted. Here, the Kriging interpolation method (Kriging) is selected to perform data point encryption processing.

[0103] The Kriging interpolation formula is:

[0104] Where z(x,y,z) represents the interpolation value of the target point, n is the number of sampling points, λi(x,y,z) is the weight coefficient, which represents the contribution of the i-th sampling point to the interpolation result at (x,y,z), and zi is the attribute value of the i-th sampling point.

[0105] Weight coefficient calculation formula:

[0106] λi(x,y,z)=exp(-((x-xi)2 / (2σ2)+(y-yi)2 / (2σ2)+(z-zi)2 / (2σ2)))

[0107] Where (xi,yi,zi) are the coordinates of the sampling points, σ ​​is the standard deviation, and (x,y,z) are the coordinates of the predicted location.

[0108] The purpose of this step is to improve the accuracy of seismic data points. Since the distance between seismic acquisition lines is large, the grid spacing of the seismic signal obtained is large. In order to artificially encrypt the grid, a grid data averaging method is selected for data encryption. The encryption point is selected in the center of the grid data point, such as Figure 5 As shown. P0 is the encrypted data point, and its value is the average value of the four points (P1, P2, P3, P4) around the grid. After the target layer is encrypted using the above method, the encrypted layer data of the work area is shown in the figure below. Figure 6 shown.

[0109] Step 5. Calculate the stratigraphic curvature strength

[0110] In this step, the curvature strength calculation is performed on the encrypted layer data points. Curvature is a quantity that describes the degree of curvature of a curve or surface at a certain point. For a point on a three-dimensional surface, the curvature calculation formula involves the partial derivative and second-order derivative of that point. The specific calculation formula for the curvature strength of the formation layer is as follows:

[0111] Suppose the function on the three-dimensional surface is z=f(x,y), then the partial derivative of the function at a certain point is:

[0112] The first-order derivative is:

[0113] The second-order derivative is:

[0114] At a certain point (x0, y0, z0), the curvature strength Q(x0, y0, z0) is calculated as:

[0115]

[0116] The curvature of a layer indicates the deformation strength of the layer, that is, the strength of the strain under stress. The greater the curvature, the greater the strain. Here, the curvature calculation formula is used to calculate the curvature or strain strength. In this example, the curvature data of three strata layers are calculated. The curvature strength calculation results of layer B are as follows: Figure 7 shown.

[0117] Step 6. Build a 3D geological model

[0118] 3D seismic data is used to perform 3D geological modeling. The main steps of the 3D geological modeling process include:

[0119] (1) Data collection: Collect the stratigraphic data bodies after the stratigraphic encryption, usually the xyz data bodies of the layers. These data are usually stored in the form of tables or databases.

[0120] (2) Data processing: Processing the collected data, including data cleaning and format conversion, to facilitate subsequent modeling. This step can be implemented using various data processing software or programming languages.

[0121] (3) Establishing a stratigraphic model: Based on the processed stratigraphic data, a three-dimensional stratigraphic model is established. The specific steps include creating stratigraphic layers, defining layer attributes, setting layer styles, etc. The results are as follows: Figure 8 The three-dimensional grid geological model of study area A is shown.

[0122] Step 7. Grid the 3D geological model

[0123] The 3D geological model is gridded to establish a numerical grid for subsequent curvature data points and fracture distribution simulations. The grid size is determined by the scale of the fractures being analyzed. For smaller fractures, typically within the meter range, a finer mesh is required. This example primarily analyzes fractures in geological outcrops, which are larger in scale, and therefore uses a relatively large grid.

[0124] Step 8. Predict crack density

[0125] The first step is to organize the xyz coordinate data of the curvature data of the stratum layer and the corresponding curvature intensity Q(x,y,z) data;

[0126] In the second step, these curvature data points are projected into the 3D geological model grid in the form of point cloud. The curvature data is assigned to each cell in the geological model, so that the 3D geological model grid has the fracture development intensity attribute value, which represents the relative density value of fracture development. The prediction results are as follows: Figure 10 shown.

[0127] In the above step 5, the curvature strength in the layer grid is obtained as Q, and the curvature strength in the i-th grid is Qi, as shown in Figure 9 The fracture prediction grid diagram shown in the figure shows that in grid 5, due to drilling or local data acquisition or statistics at relevant locations, the number of fractures in the layer is N5. The number of fractures in the i-th grid is Ni (fractures). Combined with the number of fractures in grid 5, the fracture density coefficient corresponding to the curvature intensity is randomly given as C. The fracture density coefficient C is used to predict the number of fractures in other grids. Ni represents the number of fractures in the i-th grid. The formula for calculating the number of fractures is:

[0128] Ni=(Qi / Qj)*C;

[0129] where Qj is the known curvature strength within the jth (arbitrary) grid.

[0130] Step 9. Correction of crack density coefficient

[0131] The first step is to obtain the fracture density with the grid data of the drilled wells;

[0132] Step 2: Compare the fracture density calculated in step 8 with the fracture density of the obtained grid data of the drilled wells;

[0133] Step 3: If the difference between the two is less than or equal to the predetermined threshold range, the crack density parameter C remains unchanged;

[0134] Step 4: If the difference between the two is greater than a predetermined threshold range, the crack density parameter is corrected to obtain a corrected crack density parameter;

[0135] Step 5: Use the corrected crack density parameter as the predetermined crack density coefficient and re-execute step 8.

[0136] In the second step, based on the fracture distribution prediction results obtained in step 8, it is determined that some grids in the prediction results have drilled wells, such as Figure 9 For grid 5 in the model, the fracture density parameter C is adjusted by counting the number of fractures in the grid where wells have been drilled or where fracture development data is available, thereby uniformly correcting the predicted number of fractures. If the number of fractures in the corresponding layer of grid 5 is N5, the adjusted fracture density parameter is calculated as follows:

[0137] C=Qj*N5 / Q5

[0138] After determining the fracture density parameters, the predicted data of multiple wells were compared with the actual data, and the parameter C was fine-tuned to ensure that the predicted results were consistent with the actual drilling statistical results, with the error controlled within 10%.

[0139] Step 10. Verify the final crack prediction results.

[0140] This example uses the fracture statistics of three wells (Well A, Well B, Well C) drilled at the actual location and the simulated fracture data for comparative analysis. After correction, the simulated results are close to the actual results, with the error within 10%. Figure 12 As shown in Table 2.

[0141] Step 11. Based on the determined Fisher dispersion coefficient, use the normal distribution random interpolation formula to determine the crack inclination, crack tendency, crack length and crack width respectively.

[0142] By controlling the Fisher dispersion coefficient, the normal distribution random interpolation formula is used to interpolate the inclination, dip, length and width of the crack and generate output related data. The specific steps are as follows:

[0143] (1) Generate a random number Z of standard normal distribution; this step can be implemented using existing related methods.

[0144] (2) Use the normal distribution random interpolation formula to interpolate the inclination, dip, length and width of the crack and generate output related data;

[0145] The normal distribution random interpolation formula is:

[0146] X=-1 / (K*sqrt(2*pi))+sqrt(1 / (K*pi))*Z;

[0147] Among them, X is the crack's inclination Di, tilt Ai, length Li, and width Hi, K is the dispersion coefficient, pi is π, and sqrt is square root calculation.

[0148] Table 2

[0149] well location Number of simulated cracks Number of actual drilling fractures error / % Well A 28 30 6.7 Well B 42 40 5 Well C 18 17 5.9

[0150] Step 12. Visualization of crack prediction results

[0151] According to the fracture occurrence information obtained in step 11 and the fracture density prediction results, the fracture results are visualized. The fracture three-dimensional spatial prediction results are as follows: Figure 10 The results of superimposition of fracture distribution and 3D geological model are shown in Figure 11 shown.

[0152] This example uses a shale reservoir fracture distribution prediction method that integrates a variety of geological data information, considers actual geological data and three-dimensional spatial data, and has the characteristics of high fracture prediction accuracy, quantitative prediction results, low cost, strong operability and practicality, while also realizing the visualization of the spatial distribution density of fractures.

[0153] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A method for predicting shale reservoir fracture distribution, characterized in that: The method comprises: Count and group the fracture occurrence data of the target area; Determine the coefficient of dispersion corresponding to each set of data; Determining curvature intensity data of the target layer and mapping it onto a pre-established three-dimensional geological model; Fracture information of a target area is determined according to the dispersion coefficient and the three-dimensional geological model having curvature intensity data.

2. The shale reservoir fracture distribution prediction method according to claim 1, characterized in that: The statistics and grouping of the fracture occurrence data of the target area include: Obtain shale core fracture occurrence data, field outcrop fracture occurrence data, and imaging logging fracture occurrence data in the target area; Cluster statistics are performed on the fracture inclination and fracture dip in the obtained fracture occurrence data to obtain multiple groups of fractures.

3. The shale reservoir fracture distribution prediction method according to claim 2, characterized in that: The dispersion coefficients include: crack dip dispersion, crack tendency dispersion, crack length dispersion and crack width dispersion; The fracture information includes fracture density, fracture dip, fracture tendency, fracture length and fracture width.

4. The method for predicting shale reservoir fracture distribution according to claim 3, characterized in that: The calculation formula of the dispersion coefficient is: Where v is the range, v = (1 / 2) × ∑[(Di - Dj)^2], ∑ represents the sum of the squares of the differences between Di and Dj in the calculated fracture data, and i is not equal to j; r is the nugget value, r = (1 / 2) × ∑ [(Di - Di + 1) ^ 2]; ∑ represents the sum of the squares of the differences between all adjacent crack data Di and Di + 1; Di represents the fracture data of the i-th discrete point, Di+1 represents the fracture data of the i+1-th discrete point, and Dj represents the fracture data of the j-th discrete point; the fracture data includes: the inclination, dip, length and width of the fracture.

5. The method for predicting shale reservoir fracture distribution according to claim 1, characterized in that: Determining the curvature intensity data of the target layer and mapping it to a pre-established three-dimensional geological model includes: Calculating curvature intensity data of the target layer; The size of the grid is determined according to the crack length and crack width in the crack occurrence data; performing gridding processing on the three-dimensional geological model according to the determined grid size; The curvature intensity data of the target layer is mapped to the three-dimensional geological model grid according to the corresponding coordinates.

6. The method for predicting shale reservoir fracture distribution according to claim 5, characterized in that: The calculating the curvature intensity data of the target layer includes: Encrypt the target layer data; Calculate the curvature intensity data of the encrypted layer data according to the curvature calculation formula; The curvature calculation formula is: In the above formula, Q(x0,y0,z0) represents the curvature intensity data corresponding to the point (x0,y0,z0), z=f(x,y), is the first-order partial derivative, is the second-order partial derivative.

7. The method for predicting shale reservoir fracture distribution according to claim 5, characterized in that: The process of establishing the three-dimensional geological model is as follows: Obtain encrypted layer data; Performing cleaning and format conversion on the encrypted layer data; The processed stratigraphic data are used to build a three-dimensional geological model.

8. The method for predicting shale reservoir fracture distribution according to claim 2, characterized in that: The determining of the fracture information of the target area according to the dispersion coefficient and the three-dimensional geological model having curvature intensity data includes: Determining the fracture density of each grid in the three-dimensional geological model based on the curvature strength data of each grid, a predetermined fracture density coefficient and the fracture density calculation formula; The crack density calculation formula is: Ni=(Qi / Qj)*C; Among them, Ni is the crack density in the i-th grid, Qi is the curvature intensity data in the i-th grid, Qj is the curvature intensity data in the j-th grid, and C is the crack density coefficient.

9. The method for predicting shale reservoir fracture distribution according to claim 6, characterized in that: After determining the fracture density of each grid in the three-dimensional geological model based on the curvature strength data of each grid, a predetermined fracture density coefficient and the fracture density calculation formula, the method further includes: Obtain fracture density with drilled well location grid data; Comparing the calculated fracture density of the grid with the fracture density of the obtained grid data of the drilled wells; If the difference between the two is less than or equal to the predetermined threshold range, the crack density parameter remains unchanged; If the difference between the two is greater than a predetermined threshold range, the crack density parameter is corrected, and the corrected crack density parameter is used as a predetermined crack density coefficient.

10. The method for predicting shale reservoir fracture distribution according to claim 2, characterized in that: The determining of the fracture information of the target area according to the dispersion coefficient and the three-dimensional geological model having curvature intensity data includes: Generate a random number Z from a standard normal distribution; According to the determined Fisher dispersion coefficient, the normal distribution random interpolation formula is used to determine the fracture inclination, fracture tendency, fracture length and fracture width respectively. The normal distribution random interpolation formula is: X=-1 / (K*sqrt(2*pi))+sqrt(1 / (K*pi))*Z; Where X is the crack's dip angle Di, inclination Ai, length Li, and width Hi, respectively; K is the dispersion coefficient of the dip angle, inclination, length, and width corresponding to X; pi is π; and sqrt is square root calculation.

11. A shale reservoir fracture distribution prediction device, characterized in that: The device includes: a memory and a processor; the memory is used to store a program for predicting the distribution of fractures in shale reservoirs, and the processor is used to read and execute the program for predicting the distribution of fractures in shale reservoirs, and perform the method according to any one of claims 1 to 10.

12. A computer-readable storage medium having a data processing program stored thereon, wherein the data processing program is executed by a processor to implement the shale reservoir fracture distribution prediction method according to any one of claims 1 to 10.

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