Shale reservoir small fault classification and prediction method based on structural occurrence control seismic high-resolution attribute

Abstract

The application discloses a kind of based on structural occurrence control seismic high-resolution attribute shale reservoir small fracture classification prediction method, it is related to exploration geophysics and oil and gas development technical field.The present application is aimed at the development needs of multiple stages of small fracture prediction of shale reservoir, constructs a kind of small fracture classification prediction method with "high-resolution linear attribute" method as core technology, and is constrained by structural occurrence, to achieve the requirements of different structural stages classification description of small fracture of shale reservoir.According to the high-resolution linear attribute (hereinafter referred to as high-resolution attribute) calculation proposed in the application, the fine characterization prediction of reservoir small fracture is effectively completed, and the fracture occurrence orientation corresponding to each structural period is constrained, so that the small fracture classification of different occurrence orientation is realized.The prediction method of the application can complete the classification and accurate prediction of small-scale fractures of shale, and achieve a relatively ideal drilling data verification coincidence rate, meet the needs of field development.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of exploration geophysics and oil and gas development, in particular to the technical field of classification and prediction of small fractures in shale reservoirs, and more particularly to a method for classification and prediction of small fractures in shale reservoirs based on structure occurrence control and high-resolution seismic attributes. BACKGROUND

[0002] In the field of oil and gas geological exploration, fractures are important research objects, which not only control oil and gas accumulation, but also serve as important channels for oil and gas migration, and have an important influence on reservoir oil and gas production capacity. For shale reservoirs, fractures also have an important influence on later fracturing operations. Small fractures in shale reservoirs often lead to well leakage and casing deformation accidents in horizontal well development, causing great difficulties in actual development.

[0003] Geological research shows that small fractures in shale reservoirs are controlled by large- and medium-scale fractures and are distributed in the form of fracture zones around large-scale faults. Their scales are mostly between 5 and 30 m, which is less than the resolution of most conventional seismic data, making it difficult to characterize fractures based on seismic data. At the same time, due to the different formation times of fractures, they have different influences on actual development. Most of the fractures formed by early tectonic movements are filled or completely filled; they have no help for shale gas development, and the fractures formed by later tectonic movements are semi-filled or unfilled, which have a great influence on oil and gas accumulation and migration. Therefore, understanding the development period of fractures and classifying them can effectively guide the exploration and development of shale gas. In summary, accurately predicting the distribution of small fractures in shale reservoirs and classifying fractures formed in different periods is an important task and difficulty in the field of oil and gas exploration and development.

[0004] Previous studies have made a lot of research on the prediction and period classification of small fractures in shale reservoirs:

[0005] In 2019, Shi Xuewen et al. used principal component analysis (PCA) technology to process ant body attribute slices and combined with the tectonic background of the work area to depict the tectonic stages of faults and large-scale fractures. The method of artificial interpretation was used to predict the stages of obvious large-scale fractures, but it was difficult to accurately predict small-scale fractures;

[0006] In 2021, Xie Qinghui et al. used the occurrence control ant tracking technology to predict the stages of fractures in the YS1 well area of the Sichuan Basin under the premise of statistical data of large faults, but the method was mainly used for planar fracture prediction, and there was a lack of effective verification of the prediction results;

[0007] In 2022, Sun Xiaojian et al. used the Gaussian random method combined with ant physical control to construct a natural fracture prediction model of the Longmaxi Formation in the south of Sichuan Basin. She used well and geology to verify the fracture development characteristics, but there were uncertainties, and the fractures of different periods could not be classified.

[0008] In summary, the current prediction method for small fractures in shale reservoirs has problems such as difficulty in verification, high uncertainty, low resolution of prediction results, and difficulty in effectively distinguishing fractures of different periods in actual production. SUMMARY

[0009] In order to overcome the defects and deficiencies existing in the prior art, the present application provides a seismic high-resolution attribute shale reservoir small fracture classification prediction method based on structure occurrence control. The purpose of the present application is to solve the difficulty of small fracture classification prediction and characterization in shale exploration and production. The present application is aimed at the development needs of multi-period small fracture prediction in shale reservoirs, and a small fracture classification prediction method is constructed based on the "high-resolution linear attribute" method as the core technology and the structure occurrence as the constraint, so as to meet the requirements of classification and description of small fractures in different structure periods of shale reservoirs. According to the high-resolution linear attribute (hereinafter referred to as high-resolution attribute) calculation proposed in the present application, the fine characterization and prediction of small fractures in the reservoir are effectively completed, and the occurrence direction of the fracture corresponding to each structure period is constrained, so as to realize the classification of small fractures with different occurrence directions. The prediction method of the present application can complete the accurate prediction of small-scale fractures in shale, and achieve a relatively ideal coincidence rate of actual drilling data verification, which meets the needs of field development.

[0010] In order to solve the problems existing in the prior art, the present application is realized by the following technical scheme.

[0011] The present application provides a seismic high-resolution attribute shale reservoir small fracture classification prediction method based on structure occurrence control, which comprises the following steps:

[0012] S1, high-resolution attribute calculation is performed on the three-dimensional seismic data of the research area to obtain high-resolution linear attribute I preliminary small fracture prediction results;

[0013] S2, the high-resolution linear attribute I preliminary small fracture prediction results obtained in step S1 are subjected to denoising processing based on a directional filter to obtain high-resolution linear attribute II processing results;

[0014] S3, the high-resolution linear attribute II processing results obtained in step S2 are subjected to actual drilling data verification to determine whether the coincidence rate meets the experimental requirements. If the coincidence rate does not meet the requirements, the relevant attribute parameters are optimized and adjusted to enhance the actual drilling data coincidence rate, and finally the reservoir small fracture prediction results with an actual drilling coincidence rate of more than 80% are obtained;

[0015] S4, utilize the research work area tectonic geological background, combined with drilling, logging data, determine the main tectonic movement period of the research work area, get the multi-tectonic period of multi-directional structure occurrence; Then according to the corresponding fracture direction of single tectonic movement, the structure occurrence is decomposed, the stress direction of each group of fracture system and the development direction of its corresponding small fracture are determined, and finally the multi-single direction structure occurrence period is obtained;

[0016] S5, on the basis of the small fracture prediction result of the reservoir in step S3, the angle range of each single direction structure occurrence is taken as a constraint, and the high attribute azimuth angle control calculation is carried out under the constraint of the occurrence range of each single direction structure occurrence, to obtain the small fracture prediction result of each single direction structure occurrence.

[0017] Further preferably, in step S1, the three-dimensional seismic data of the research work area is calculated by high attribute calculation, and the calculation formula is,

[0018] ; In the formula, S represents three-dimensional seismic data, i, j, k represents three network point numbers in three-dimensional space, X, Y, Z is a spatial high fold convolution operator function, x-I, y-j, z-k is three network point numbers in three-dimensional space, A is the high linear attribute I to be obtained, and x, y, z is three network point numbers in three-dimensional space.

[0019] Further preferably, in step S2, the processing formula of the denoising processing based on the directional filter is In the formula, A is the high linear attribute I, F is the denoising filter function, and B is the high linear attribute II output after denoising.

[0020] Further preferably, in step S3, the high linear attribute II processing result obtained in step S2 is verified by actual drilling data, specifically, the well leakage position in the plane and profile which can indicate the actual small fracture development in the actual drilling data is verified.

[0021] Further preferably, in step S3, if the coincidence rate does not meet the requirement, the X, Y, Z space high fold convolution operator function and the directional filter function F of the high linear attribute II are adjusted until the coincidence rate exceeds 80%, and the high linear attribute III is obtained.

[0022] Further preferably, in step S4, the main tectonic movement period of the research work area is determined from the geological structure of the research work area, and the multi-tectonic period of multi-directional structure occurrence is obtained. Different angles are represented by different angle ranges θ, and there are θ1, θ2, …, θ n Angle range.

[0023] Further preferably, in the S4 step, the geological background of the research area is constructed by combining drilling and logging data, and the research area is summarized to be affected by four main tectonic periods of Xingkai period, Caledonian period, Yanshan period and Himalayan period, according to the stress direction and characteristics of each tectonic period, multi-directional tectonic occurrence of four tectonic superimpositions is obtained by screening, and then the stress direction of each group of fault systems and the development direction of the corresponding small faults are determined by decomposing the tectonic occurrence of the corresponding fault orientation of single tectonic movement, and finally four single-directional tectonic occurrence periods are obtained.

[0024] Further preferably, in the S5 step, the angle range of each single-directional tectonic period occurrence is taken as a constraint, and the high-precision attribute azimuth angle control calculation is carried out under the constraint of the occurrence range of each single-directional tectonic period, and the calculation formula is

[0025] In the formula, x, y and z are the network point numbers of three groups of high-precision attributes in three-dimensional space, P is a space angle range filter, and θ n is a high-precision attribute corresponding to the occurrence n, that is, the corresponding θ n occurrence angle range, and C(x, y, z) represents the high-precision linear attribute function meeting the experimental requirements of the coincidence rate in the S3 step.

[0026] The occurrence orientation decomposition operation is carried out under the constraint of each single tectonic movement period and stress and fault development occurrence orientation, and the small fault prediction result of each unit tectonic period occurrence orientation can be obtained by setting a suitable orientation angle range.

[0027] Compared with the prior art, the beneficial technical effects brought by the present application are as follows:

[0028] 1. The present application is aimed at the development needs of shale reservoir small fault multi-period prediction, and a small fault classification prediction method is constructed by taking the high-precision linear attribute method as the core technology and constraining the tectonic occurrence, so as to meet the requirements of classification and description of different tectonic periods of shale reservoir small faults. According to the high-resolution linear attribute (hereinafter referred to as high-precision attribute) calculation proposed in the present application, the fine characterization and prediction of the reservoir small faults are effectively completed, and the fault occurrence orientation corresponding to each tectonic period is constrained, so as to realize the classification of small faults with different occurrence orientations. The prediction method of the present application can complete the classification and accurate prediction of small-scale faults in shale, and achieve a relatively ideal drilling data verification coincidence rate, which meets the needs of field development.

[0029] 2. When calculating high-resolution attributes of 3D seismic data in the research area, this invention adjusts three high-resolution convolution operators to effectively highlight the discontinuities of seismic axes in the same direction and linearly enhances these discontinuities, effectively highlighting the small-scale information hidden in the seismic data. Compared to traditional coherence and ant-body attributes, it has less dependence on seismic data quality, produces more stable prediction results, and effectively highlights the development of small-scale faults.

[0030] 3. In this invention, the preliminary small fracture prediction results of the high-resolution linear attribute I obtained in step S1 are subjected to denoising processing based on directional filters. Since the high-resolution attribute calculation has low dependence on data quality, the preliminary fracture identification results may be affected by noise images from the original seismic data. The preliminary processing results are subjected to denoising processing based on directional filters to obtain high-resolution linear attribute II. The processing results of high-resolution linear attribute II effectively eliminate noise and enhance the continuity of the prediction results in the direction of fracture development.

[0031] 4. This invention can separate reservoir small fractures at different stages and orientations, and can specifically verify the fracture development of each stage, achieving accurate characterization and prediction of the distribution of small fractures in shale reservoirs and classifying fractures at different tectonic stages. In practical work, combining the geological characteristics of the work area to select fracture stages favorable to development can effectively improve the exploration success rate and provide guidance and assistance for actual development. Attached Figure Description

[0032] Figure 1 This is a flowchart of the small fracture classification and prediction method for shale reservoirs based on structural occurrence control, as described in this invention.

[0033] Figure 2 The preliminary reservoir small fracture prediction results are shown in the figure for high-resolution linear attribute I calculation;

[0034] Figure 3 Figure showing the prediction results of small fractures in reservoirs after denoising and optimizing the high-resolution linear attribute II.

[0035] Figure 4 A verification diagram of small fracture prediction results and actual drilling data for high-resolution linear attribute III reservoir (after optimization and adjustment of relevant parameters);

[0036] Figure 5 A schematic diagram of a typical tectonic fault system and the corresponding tectonic orientation.

[0037] Figure 6 This is a map showing the prediction results of small faults in the reservoir of the study area during multiple periods of occurrence and orientation. Detailed Implementation

[0038] The following detailed description illustrates by way of example, not by way of limitation, specific embodiments and examples that can be implemented as part of the present application. These

[0039] Embodiment 1

[0040] As a preferred embodiment of the present application, with reference to the drawings attached to the description Figure 1 The embodiment discloses a seismic high-attribute shale reservoir small fracture classification and prediction method based on structure occurrence control, and the method comprises the following steps:

[0041] S1, performing high-attribute calculation on three-dimensional seismic data of a research work area to obtain high-attribute linear attribute I preliminary small fracture prediction results;

[0042] S2, performing denoising processing based on a direction filter on the high-attribute linear attribute I preliminary small fracture prediction results obtained in the step S1 to obtain high-attribute linear attribute II processing results;

[0043] S3, performing real drilling data verification on the high-attribute linear attribute II processing results obtained in the step S2 to determine whether a coincidence rate meets experimental requirements, if the coincidence rate does not meet the requirements, optimizing and adjusting relevant attribute parameters to enhance the real drilling data coincidence rate, and finally obtaining reservoir small fracture prediction results with a real drilling coincidence rate of more than 80%;

[0044] S4, determining main tectonic movement stages to which the research work area is subjected by using a tectonic geological background of the research work area and combining drilling and logging data, obtaining multi-azimuth tectonic occurrences superimposed by multiple tectonic stages, and then decomposing tectonic occurrences according to fracture azimuths corresponding to single tectonic movements to determine stress directions of each group of fracture systems and development directions of corresponding small fractures, and finally obtaining multiple single-azimuth tectonic occurrence stages;

[0045] S5, on the basis of the reservoir small fracture prediction results in the step S3, taking an angle range of each single-azimuth tectonic stage occurrence as a constraint, and performing high-attribute azimuth angle control calculation under the occurrence range constraint of each single-azimuth tectonic stage to obtain small fracture prediction results of each single-azimuth tectonic stage occurrence.

[0046] Embodiment 2

[0047] As another preferred embodiment of the present application, the embodiment is a further detailed elaboration and supplement to the technical solution of the present application based on the above-mentioned embodiment 1. In the embodiment, in the step S1, the calculation formula for performing high-attribute calculation on three-dimensional seismic data of a research work area is,

[0048]

[0049] In the formula, S represents three-dimensional seismic data, i, j, k represent three network point numbers in three-dimensional space, X, Y, Z are functions of spatial high fold convolution operators, x-i, y-j, z-k are three network point numbers in three-dimensional space, A is the obtained high fold linear attribute I, and x, y, z are three network point numbers in three-dimensional space.

[0050] The preliminary small fracture prediction result of the obtained high fold linear attribute I is shown in FIG. 2 of the accompanying drawings. Figure 2 (see the high fold linear attribute 1 in the figure). By adjusting the three high fold convolution operators, the obtained high fold attribute can effectively highlight the discontinuity of seismic reflection events and linearly enhance the discontinuous features, and effectively highlight the small-scale information hidden in the seismic data. Compared with the traditional coherence and ant tracking attribute, the high fold linear attribute has less dependence on the quality of seismic data, the prediction result is more stable, and the small-scale fracture development can be effectively highlighted.

[0051] Embodiment 3

[0052] As another preferred embodiment of the present application, the embodiment is a further detailed supplement and elaboration of the technical solution of the present application based on the above-mentioned embodiment 1 or embodiment 2. In the embodiment, due to the low dependence of the high fold linear attribute I calculation on the data quality, the preliminary fracture identification result can be affected by the noise of the original seismic data. The denoising processing based on the directional filter is performed on the preliminary processing result, and the high fold linear attribute II (corresponding to "high fold linear attribute 2" in FIG. 3 of the accompanying drawings) is obtained. The processing result effectively eliminates the noise and enhances the continuity of the prediction result in the fracture development direction. Figure 3

[0053] The processing formula of the denoising processing based on the directional filter is as follows: In the formula, A is the high fold linear attribute I, F is the denoising filter function, and B is the high fold linear attribute II output after denoising. Designing such a F denoising function can remove the noise in a special direction or specific random noise, and improve the actual expression characteristics of the high fold attribute.

[0054] Embodiment 4

[0055] ​As another preferred embodiment of the present application, this embodiment is a further detailed supplement and elaboration of the technical solution of the present application based on the above-mentioned embodiment 1, embodiment 2 or embodiment 3. In this embodiment, the high-linear attribute II obtained by processing the S2 step is verified by actual drilling data, and the well leakage position that can indicate the actual small fracture development is verified in the plane and profile. Whether the judgment coincidence rate meets the experimental requirements is determined. If the coincidence rate does not meet the requirements, the relevant attribute parameters are continuously optimized and adjusted to further enhance the actual drilling data coincidence rate. Finally, the reservoir small fracture prediction result with the actual drilling coincidence rate exceeding 80% is obtained (this step input is B(x, y, z) corresponding to the formula in embodiment 3, and the output uses C(x, y, z) to represent the high-linear attribute III corresponding to the “high-linear attribute 3” in the specification appendix Figure 4 . Specifically, if the coincidence rate does not meet the requirements, the X, Y and Z space high-fold convolution operator function and the direction filter function F of the high-linear attribute II are adjusted until the coincidence rate exceeds 80%, and the high-linear attribute III is obtained.

[0056] Embodiment 5

[0057] As another preferred embodiment of the present application, this embodiment is a further detailed supplement and elaboration of the technical solution of the present application based on the above-mentioned embodiment 1, embodiment 2, embodiment 3 or embodiment 4.

[0058] In this embodiment, the occurrence constraint fracture period classification prediction is carried out on the basis of the reservoir small fracture prediction result. Starting from the geological structure, the main tectonic movement period of the research area is determined, and the multi-directional tectonic occurrence of the superimposed multiple tectonic periods is obtained, and different occurrences are represented by different angle ranges θ. The high-linear attribute of the stress direction of each group of fracture system is determined by decomposing the high-linear attribute after denoising, and the division of multiple single-directional tectonic occurrences is realized. In specific implementation, first, the θ distribution of the tectonic occurrence is determined, assuming that there are n occurrences, corresponding to θ1, θ2, …, θn, and the corresponding high-linear attribute is denoted as C1, C2, …, Cn. The high-linear attribute of each occurrence is obtained by decomposing the high-linear attribute after denoising, and the stress direction of each group of fracture system is determined. n The high-linear attribute corresponding to the occurrence 1 can be calculated by the following formula,

[0059]

[0060] In the formula, x, y and z are the network point numbers of the three-dimensional space of the three groups of high-linear attributes, P is a spatial angle range filter, θ1 is the high-linear attribute corresponding to the occurrence 1, that is, the small fracture distribution corresponding to the θ1 occurrence angle range, and C(x, y, z) represents the high-linear attribute function with the coincidence rate meeting the experimental requirements in the S3 step. That is, the high-linear attribute corresponding to the occurrence 1 (corresponding to the “occurrence direction 1 small fracture” in the specification appendix Figure 5 ), that is, the small fracture distribution corresponding to the occurrence angle range.

[0061] Furthermore, after calculating "small fault with orientation 1" in the above steps, the orientation of each single tectonic phase can be obtained by setting an appropriate azimuth range. Similarly, "small fault with orientation 2" to "small fault with orientation n" are calculated (as shown in the appendix to the instruction manual). Figure 6 As shown in the figure, and combined with the constraints of each single tectonic movement period and stress and fracture development orientation obtained in the previous step, the orientation decomposition operation is performed. By setting an appropriate azimuth range, the orientation prediction results of small fractures for each single tectonic period can be obtained.

[0062] Example 6

[0063] Based on the structural attitude control-based classification and prediction method for small fractures in shale reservoirs provided in this invention, and using a case study of a specific work area as an example, the method includes the following detailed steps:

[0064] S1. First, high-resolution attribute calculations are performed on the 3D seismic data of the study area to obtain preliminary small fault prediction results for high-resolution linear attribute 1. For example... Figure 2 As shown, high-resolution attributes can effectively capture small-scale fault information. However, due to poor seismic data acquisition quality and low signal-to-noise ratio in the northwestern part of the work area, the predicted small faults are very dense, which does not reflect the actual fault development. Dense predictions also exist in the southeastern part, accompanied by strong boundary effects. Therefore, further optimization of the preliminary prediction results is needed.

[0065] S2. Further process the high-scoring attribute prediction results obtained in the previous step. Through denoising using directional controllable filtering, obtain the high-scoring linear attribute 2 processing result. For example... Figure 3 As shown, the processing results significantly reduced the noise in the initial fracture prediction. The fracture prediction results in the northwest and southeast of the work area are more consistent with the geological development pattern of actual reservoir fractures, and a large number of prediction artifacts caused by boundary effects have been eliminated.

[0066] S3. Verify the denoised small fracture prediction results with the actual wellbore leakage points. Figure 4 The actual drilling results and the small fracture prediction results after noise reduction showed good agreement in the plane. By further adjusting the relevant attribute parameters, the statistical actual drilling agreement rate reached 82.3%, realizing high-precision prediction of small fractures in the reservoir and verifying the effectiveness of the high-resolution attribute prediction method.

[0067] S4, using the construction of the geological background of the working area combined with drilling, logging data, summarized the study area affected by Xingkai period, Caledonian period, Yanshan period, Himalayan period four main tectonic period. According to the stress direction and characteristics of each tectonic period, four period tectonic superimposed multi azimuthal structure occurrence is obtained. According to the corresponding fracture orientation of single tectonic movement, the stress direction of each group of fault system and the development direction of its corresponding small fault are determined( Figure 5 ), and finally four single azimuthal structure occurrence period is obtained.

[0068] S5, on the basis of step three high linear attribute prediction results, the angle range of each tectonic period occurrence is taken as the constraint. Under the constraint of the occurrence range of each tectonic period, the azimuth angle control calculation of high attribute is carried out, that is, each tectonic period small fault is obtained. In this study, four occurrence azimuth small fault results are screened out( Figure 6 ), which are SN structure of Himalayan late period, NE feather structure of Himalayan, NEE structure of Yanshan period and EW structure of Xingkai period. In the geological analysis, the small fault structure formed in Xingkai period has long development time, stress is blocked, and fracture is filled with high probability, which is not helpful for development. Therefore, it is excluded, and the remaining 3 period occurrence azimuth small fault has greater influence on development.

[0069] The above processing method can divide the reservoir small faults of different period occurrence azimuth, and can verify the fracture development of each period, realize the accurate characterization and prediction of shale reservoir small fault distribution, and divide the faults of different tectonic periods. In actual work, the favorable fault period for development is selected combined with the geological characteristics of the working area, which can effectively improve the exploration success rate and provide guidance and help for actual development.

[0070] Although the inventive concept has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application as defined by the appended claims.

Claims

1. A method for classifying and predicting small faults in shale reservoirs based on seismic high-resolution attributes controlled by structural attitude, characterized in that: The classification prediction method comprises the following steps, S1, high attribute calculation is performed on the three-dimensional seismic data of the research area to obtain a high linear attribute I preliminary small fault prediction result; The calculation formula is ； In the formula, S represents three-dimensional seismic data, i, j, and k represent three network point numbers in three-dimensional space, X, Y, and Z are spatial high convolution operator functions, x-i, y-j, and z-k are three network point numbers in three-dimensional space, A is the high linear attribute I to be obtained, and x, y, and z are three network point numbers in three-dimensional space; S2, the high linear attribute I preliminary small fault prediction result obtained in the step S1 is subjected to denoising processing based on a directional filter to obtain a high linear attribute II processing result; The processing formula of the denoising processing based on the directional filter is where A is the high-resolution attribute I, F is the denoising filter function, and B is the high-resolution attribute II after denoising. S3, the high linear attribute II processing result obtained in the step S2 is subjected to real drilling data verification to determine whether the coincidence rate meets the experimental requirements, if the coincidence rate does not meet the requirements, the X, Y, and Z spatial high convolution operator functions of the high linear attribute II and the directional filter function F are adjusted to enhance the real drilling data coincidence rate, and finally a reservoir small fault prediction result with a real drilling coincidence rate of more than 80% is obtained, and a high linear attribute III is obtained; S4, the tectonic geological background of the research area is constructed, combined with drilling and logging data, the main tectonic movement period of the research area is determined, and the multi-structural period is obtained. Multi-azimuth structural occurrence is obtained in different angle range Different occurrences are represented. Assuming there are n occurrences, there are Angle range; according to the fracture orientation corresponding to the single tectonic movement, the structural occurrence is decomposed, the stress direction of each group of fracture system and the development direction of the corresponding small fracture are determined, and finally a plurality of single-azimuth structural occurrence period is obtained; S5. Based on the reservoir small fracture prediction results of step S3, using the angular range of the attitude of each single-azimuth structural stage as a constraint, high-resolution attribute azimuth angle control calculations are performed under the constraint of the attitude range of each single-azimuth structural stage to obtain the small fracture prediction results of the attitude of each single-azimuth structural stage. The calculation formula is as follows: In the formula, x, y, z are the network point numbers of three sets of high-resolution attributes in three-dimensional space, and P is the spatial angle range filter. This represents the angular range of the nth attitude. For the corresponding Distribution of small faults within the range of attitude angles. This represents a high-scoring linear property function that satisfies the experimental requirements in step S3.

2. The method according to claim 1, wherein the method is characterized by: In the step S3, the high linear attribute II processing result obtained in the step S2 is subjected to real drilling data verification, which specifically means that the well leakage positions in the real drilling data that can indicate the actual small fault development are verified in the plane and the profile.

3. The method according to claim 1, wherein the method is characterized by: In the step S4, the research area is affected by four main tectonic periods of Xingkai period, Caledonian period, Yanshan period, and Himalayan period by using the tectonic geological background of the research area combined with drilling and logging data, and according to the stress direction and characteristics of each tectonic period, multi-directional tectonic occurrences of four tectonic superimpositions are selected; then according to the tectonic occurrence decomposition of the corresponding fault direction of the single tectonic movement, the stress direction of each group of fault systems and the development direction of the corresponding small faults are determined, and finally four single-direction tectonic occurrence periods are obtained.

4. The method according to claim 1, wherein the method is characterized by: The occurrence direction decomposition operation is performed under the constraint of each single tectonic movement period and the stress and fault development occurrence direction, and by setting a suitable azimuth angle range, the small fault prediction result of each unit tectonic period occurrence direction can be obtained.

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