A method for evaluating fracture reformation capacity based on seismic attribute scale

Abstract

This invention discloses a method for evaluating fracture modification capacity based on seismic attribute scale, belonging to the field of seismic attribute processing and oil and gas exploration technology. This invention uses techniques such as coherence volume, ant volume, and edge detection to identify seismic bodies, thus finely characterizing the fracture network. Then, it comprehensively considers fracture length, density, inter-fracture tangency, fracture strike, and the direction of the maximum principal stress in the region. Based on traditional quantitative evaluation of fracture density and topological analysis, it selects the number of fracture branches, the number of connecting nodes per branch, and the effective fracture length, and normalizes these three factors to determine comprehensive evaluation parameters for fracture modification capacity. These parameters can accurately and quantitatively characterize fracture development, thereby indicating the fracture modification status for oil and gas exploration and providing fracture-related evidence for oil and gas exploration and development. Therefore, it has significant application value and prospects in the fields of geological structure and oil and gas exploration technology.

Description

Technical Field

[0001] This invention belongs to the field of seismic attribute processing and oil and gas exploration technology, specifically relating to a method for evaluating fault modification capacity based on seismic attribute scale. Background Technology

[0002] In oil and gas exploration, fault identification is mainly based on two scales: field outcrops and seismic attributes. Currently, the focus of oil and gas exploration is shifting to deep and ultra-deep formations, resulting in very few field outcrops of faults. Therefore, fault identification and evaluation based on seismic attributes is of great significance for oil and gas exploration. The foundation of seismic attribute-scale fault identification is the apparent discontinuity. Mainstream seismic attribute-scale fault identification techniques include coherence volume and ant colony techniques. Bahorich (1995) first proposed the coherence technique based on the cross-correlation algorithm. This method is computationally efficient but susceptible to noise. Colorni et al. (1991) proposed the concept of the ant colony algorithm, and Randen et al. (2002) introduced the ant colony algorithm into fault identification. Currently, there are well-developed commercial software programs for seismic-scale fault identification, such as Petrel software released by Schlumberger, which can calculate various attributes of seismic bodies, including coherence volume, ant colony, and curvature volume. In general, there are many types of fault identification techniques at the seismic scale, but each has its own defects and shortcomings, and the calculation results are also subject to multiple interpretations. Therefore, after obtaining various attribute maps, they still need to be manually interpreted and verified by seismic interpretation experts.

[0003] The development of fractures is a crucial indicator in oil and gas exploration, affecting oil and gas transport and sealing, and significantly impacting reservoir development. Therefore, quantitative characterization of fracture development is of paramount importance. Common methods for quantitatively characterizing the impact of fracture development on reservoirs include fracture density statistics and fracture cross-cutting relationship analysis. Fracture density statistics involve counting the total number of fractures per unit area. Regarding the quantitative evaluation of fracture cross-cutting relationships, the scientific community has introduced topological concepts into geological models to quantitatively describe the connectivity between faults. Burns (1988) used network diagrams to represent the topology of geological models, using nodes to represent geometric elements of different dimensions and arcs to represent adjacency relationships. Jing and Stephansson (1997) combined topological methods with seepage theory in rock fracture networks. Sanderson and Nixon (2015) used nodes and branches to describe fractures and defined the topological structure of the fracture network by the proportion of different types of nodes. Sanderson and Nixon (2018) evaluated the connectivity of fracture networks by determining connectivity parameters through the quantitative relationships of nodes and branches.

[0004] Traditional fracture evaluation methods each have their advantages and disadvantages. Fracture density studies assess the degree of fracture development by examining the number of fractures per unit area, but neglect the cross-cutting relationships between faults. The connectivity calculation of a fracture system, based on the topology of the fracture network, calculates six connectivity parameters to identify the degree of connection between faults (Number of connection nodes N in each branch). C / N B The number of connection nodes N for each fault line C / N L The number of branches N of each fault line B / N L Number of connection nodes per unit area N C / A, Number of fault branches per unit area N B / A, Number of fault lines per unit area N L / A), this method only considers the cross-cutting relationship of fractures and lacks research on the development scale of fractures.

[0005] Therefore, designing a method that can be better used to evaluate the ability of fractures to be modified is of great significance for a clearer understanding of geological structures and for improving oil and gas exploration. Summary of the Invention

[0006] This invention provides a method for evaluating fault modification capacity based on seismic attribute scale, comprising the following steps:

[0007] (1) Sampling and analysis of fracture network

[0008] Multi-attribute calculations are performed on seismic data in the target area to identify and finely characterize the fault network; the topological structure of the fault network data is identified, and the faults are divided into different types of branches, and the endpoints and intersections are divided into different types of nodes; a distribution map of fault network nodes and branches is obtained; equal-area sampling is performed on the identified fault network, and the nodes, branch types, number of branches, branch lengths and branch directions in each sampling area are statistically analyzed to generate a statistical table of basic parameters of the fault network sampling area;

[0009] (2) Calculation of evaluation parameters for fracture network

[0010] Based on the statistical table of basic parameters of the fracture network sampling area, the number of fracture branches (N) in each sampling area is calculated. B ), the number of connected nodes in each branch (C) B ), effective fracture length (L) E The comprehensive evaluation parameters (P) of fault modification capacity are used to obtain an evaluation parameter table for the fault network sampling area, thereby characterizing the development scale and connectivity of the fault network in each sampling area.

[0011] (3) Analysis of evaluation parameters for fracture networks

[0012] Based on the evaluation parameter table of the fracture network sampling region, the number of fracture branches (N) B ), the number of connected nodes in each branch (C) B ), effective fracture length (L) E The four evaluation parameters (P) and the comprehensive evaluation parameter of fault modification capacity were processed using the natural discontinuity classification method. The development scale and connectivity of the fault network in different sampling areas of the entire study area were classified and evaluated to determine the areas with densely developed fault networks and high connectivity between faults.

[0013] In the above evaluation method, step (1) includes the generation of coherent body data, the selection of ant body parameters, and edge detection.

[0014] In the above evaluation method, in step (1), the nodes are divided according to the following criteria: a node connecting 1 branch is a node I, a node connecting 3 branches is a node Y, and a node connecting 4 branches is a node X. The two types of nodes X and Y are collectively referred to as connection nodes C. The branch types are divided according to the following criteria: based on the type of connection nodes at both ends, they are divided into three types of branches: CC, CI, and II.

[0015] In the above evaluation method, in step (1), the equal area sampling can be selected according to the needs of different types of sampling, such as area grid sampling, circular sampling, etc., to ensure that the shape and area of ​​each sampling area are the same.

[0016] In the above evaluation method, in step (1), the basic parameter statistics table of the fracture network sampling area includes the fracture network branch parameter statistics table and the fracture network sampling area node branch number statistics table.

[0017] In the above evaluation method, the formula for calculating the number of fracture branches (NB) in step (2) is as follows:

[0018] ;

[0019] In the formula, N B N represents the number of fracture branches. CC N represents the number of CC-type branches. CI N represents the number of CI-type branches. II The number of type II branches.

[0020] In the above evaluation method, in step (2), the number of connection nodes (C) of each branch B The calculation formula for ) is as follows:

[0021] ;

[0022] In the formula, CB N represents the number of connected nodes in each branch. Y N represents the number of Y-shaped nodes. X N represents the number of X-type nodes. I This represents the number of type I nodes.

[0023] In the above evaluation method, in step (2), the effective fracture length (L) E The calculation formula for ) is as follows:

[0024] ;

[0025] In the formula, L E L is the effective fracture length. i Let α be the branch length. i The angle between the branch and the maximum principal stress in the region.

[0026] In the above evaluation method, the calculation formula for the comprehensive evaluation parameter (P) of fracture modification capacity in step (2) is as follows:

[0027] ;

[0028] In the formula, N B C represents the number of fracture branches. B L represents the number of connected nodes in each branch. E The effective length of the fracture.

[0029] In the above evaluation method, step (3) refers to sorting the calculated parameter sizes of each sampling area, classifying them by natural discontinuity, and using different colors to represent parameters of different levels, thereby visualizing the parameter sizes.

[0030] This invention provides the application of the above-mentioned evaluation method in oil and gas exploration.

[0031] This evaluation method can effectively assess the ability of fractures to modify oil and gas reservoirs during oil and gas exploration, including the role of fractures as oil and gas migration channels and their role in modifying tight reservoirs to increase reservoir space. By using this evaluation method, the development and modification capacity of fractures can be intuitively understood, providing a basis for fracture-related data in oil and gas exploration.

[0032] The beneficial effects of this invention are as follows:

[0033] This invention first identifies the seismic body using techniques such as coherence volume, ant volume, and edge detection to finely characterize the fracture network of the target area. Then, it comprehensively considers the fracture length, density, inter-fracture tangency, fracture strike, and direction of the maximum principal stress in the region. Based on traditional quantitative evaluation of fracture density and topological analysis, three evaluation parameters are selected and designed: number of fracture branches (NB), number of connecting nodes per branch (CB), and effective fracture length (LE). These three parameters are then normalized to determine the comprehensive evaluation parameter (P) for fracture modification capacity. This parameter can accurately and quantitatively characterize the fracture development, thus indicating the fracture modification status for oil and gas exploration and providing fracture-related evidence for oil and gas exploration and development. Therefore, it has significant application value and prospects in the field of oil and gas exploration technology.

[0034] This invention, through the calculation and grading of a comprehensive evaluation parameter (P) for fracture modification capacity, can quantitatively and effectively evaluate the modification capacity of fractures. Low parameter values ​​indicate underdeveloped fracture zones, where the reservoir lacks migration channels and effective storage space, resulting in a lower probability of oil and gas enrichment. High parameter values ​​indicate highly developed fracture zones, where the fractures have a strong effect on the reservoir; however, it is important to note whether the fractures cause damage to the reservoir. This parameter can effectively indicate the extent of fracture modification in oil and gas exploration, providing a fracture-related basis for oil and gas exploration and development. Attached Figure Description

[0035] Figure 1 This is a diagram showing the body attributes of an ant.

[0036] Figure 2 This is the attribute map for edge detection.

[0037] Figure 3 This is a fusion image of ant body and edge detection.

[0038] Figure 4 This is a diagram showing the distribution of the fracture network.

[0039] Figure 5 This is a diagram showing the distribution of nodes and branches in a fractured network.

[0040] Figure 6 This is a schematic diagram of equal-area sampling for a fractured network.

[0041] Figure 7 For the fractured network N B Planar distribution map.

[0042] Figure 8 For fracture network C B Planar distribution map.

[0043] Figure 9 For fracture network L E Planar distribution map.

[0044] Figure 10 This is a P-plane distribution diagram of the fracture network.

[0045] Figure 11 Traditional connectivity evaluation parameters for fractured networks (N) C / N B Planar distribution map.

[0046] Figure 12 Traditional connectivity evaluation parameters for fractured networks (N) C / N L Planar distribution map.

[0047] Figure 13 Traditional connectivity evaluation parameters for fractured networks (N) B / N L Planar distribution map.

[0048] Figure 14 Traditional connectivity evaluation parameters for fractured networks (N) B / A) Planar distribution map.

[0049] Figure 15 Traditional connectivity evaluation parameters for fractured networks (N) C / A) Planar distribution map.

[0050] Figure 16 Traditional connectivity evaluation parameters for fractured networks (N) L / A) Planar distribution map. Detailed Implementation

[0051] In studies of the differential enrichment of hydrocarbons due to fractures, hydrocarbon migration and accumulation are influenced by both the developmental characteristics of the fractures themselves and regional geological stresses. These characteristics include fracture density, inter-fracture relationships, length, and the angle between the fracture strike and the regional maximum principal stress. Fracture length and density indicate the degree of fracture development, while the angle between the fracture strike and the regional maximum principal stress affects fracture effectiveness. Fracture development influences reservoir properties; moderate fracture development can increase reservoir porosity and thus improve reservoir properties. The inter-fracture relationships within a fracture network affect whether different fractures connect, which in turn affects the effective migration of hydrocarbons within the fracture network.

[0052] Therefore, this invention comprehensively considers fracture length, density, inter-fracture tangency, fracture orientation, and the direction of the maximum principal stress in the region. Based on traditional quantitative evaluation of fracture density and topological analysis, it selects and designs three evaluation parameters: the number of fracture branches (NB), the number of connection nodes per branch (C), and the number of connection nodes per branch. B ) and effective fracture length (L) E ), and combine the three to determine the comprehensive evaluation parameter (P) of fracture modification capacity.

[0053] The following embodiments of the present invention were sampled from a portion of the Z well area in southern Sichuan.

[0054] Other materials used in this invention, unless otherwise stated, are commercially available. Other terms used in this invention, unless otherwise specified, generally have the meanings commonly understood by those skilled in the art. The invention is further described in detail below with reference to specific embodiments and data. The following embodiments are merely illustrative and not intended to limit the scope of the invention in any way.

[0055] Example 1

[0056] The method for evaluating fracture modification capacity is as follows:

[0057] 1. Sampling and analysis of fracture networks

[0058] (1) Based on the original seismic data of the target area, structural smoothing is performed on the data, and coherence volume data is generated from this data. Appropriate ant body parameters (initial ant distribution boundary, ant tracking deviation, tracking step size, illegal step size, legal step size, search termination criterion) are selected to obtain the ant body attribute map of the target area, such as... Figure 1 As shown. Using the raw seismic data, an edge detection algorithm is applied to obtain the edge detection attribute map of the target area, as shown. Figure 2 As shown, the ant body attribute map and the edge detection attribute map are overlaid to obtain the fused map of the ant body and the edge detection map, as shown. Figure 3 As shown, at this moment, a fracture network diagram of the target area is drawn, and fracture network data is generated, as follows. Figure 4 As shown.

[0059] (2) Data on fractured networks ( Figure 4 The topology is identified, and fractures are divided into different types of branches, with endpoints and intersections classified into different types of nodes. Specifically, node branches are classified as follows: a node connecting one branch is an I node, a node connecting three branches is a Y node, and a node connecting four branches is an X node. X and Y nodes are collectively referred to as connecting nodes C. Branch types are further classified into CC, CI, and II types based on the types of the connecting nodes at both ends. The final fracture network node and branch distribution diagram is obtained, as shown below. Figure 5 As shown.

[0060] (3) For the identified fractured networks ( Figure 5 Perform equal-area grid sampling, such as Figure 6 As shown in Table 1 and Table 2, the nodes, branch types, number of branches, branch lengths, and branch directions within each sampling area are statistically analyzed to generate a statistical table of branch parameters of the fracture network and a statistical table of the number of nodes and branches in the sampling area of ​​the fracture network.

[0061] Table 1. Statistics of Fracturing Network Branch Parameters

[0062]

[0063] Table 2. Statistics on the number of node branches in the sampling area of ​​the fracture network.

[0064]

[0065] 2. Calculation of evaluation parameters for fractured networks

[0066] Based on the statistical tables of fracture network branch parameters and the statistical tables of the number of node branches in the sampling area of ​​the fracture network described in Tables 1 and 2, the number of fracture branches (N) in each sampling area is calculated separately. B ), the number of connected nodes in each branch (C) B ), effective fracture length (L) E The calculation of four evaluation parameters: (1) the comprehensive evaluation parameter of fracture modification capacity (P).

[0067] The formulas for calculating each parameter are as follows:

[0068] (1) Number of fracture branches (NB)

[0069] This parameter is used to represent the density of the fracture network in the study area; the larger the parameter, the higher the fracture density in the study area.

[0070] ;

[0071] In the formula, N B N represents the number of fracture branches. CC N represents the number of CC-type branches. CI N represents the number of CI-type branches. II The number of type II branches.

[0072] (2) Number of connected nodes in each branch (C) B )

[0073] Based on topological principles, this parameter calculates the number of connecting nodes (X nodes, Y nodes) on each branch of the fault network. The larger the parameter value, the higher the degree of connection of the fault network.

[0074] ;

[0075] In the formula, C B N represents the number of connected nodes in each branch. Y N represents the number of Y-shaped nodes. X N represents the number of X-type nodes. I This represents the number of type I nodes.

[0076] (3) Effective fracture length (L) E )

[0077] This parameter defines the fracture length component parallel to the direction of the maximum principal stress as the effective fracture component. It is calculated by accumulating the product of the fracture branch length and the cosine of the angle between the maximum principal stress direction and the fracture length. The larger the parameter, the higher the effective fracture length.

[0078] ;

[0079] In the formula, L E L is the effective fracture length. i Let α be the branch length. i The angle between the branch and the maximum principal stress in the region.

[0080] (4) Comprehensive evaluation parameters of fracture modification capacity (P)

[0081] This parameter takes into account the number of fracture branches (N) B ), the number of connected nodes in each branch (C) B ) and effective fracture length (L) E The three parameters were normalized, with larger parameters indicating a higher degree of fracture development.

[0082] ;

[0083] In the formula, N B C represents the number of fracture branches. B L represents the number of connected nodes in each branch. E The effective length of the fracture.

[0084] The evaluation parameters for the fault network sampling area were obtained through calculation, as shown in Table 3, which characterize the development scale and connectivity of the fault network in each sampling area.

[0085] Table 3 Evaluation Parameters for Sampling Areas of Fractured Networks

[0086]

[0087] 3. Analysis of Evaluation Parameters for Fracture Networks

[0088] Based on the evaluation parameters of the fracture network sampling area described in Table 3, the number of fracture branches (N) B ), the number of connected nodes in each branch (C) B ), effective fracture length (L) EThe four evaluation parameters (P) were processed using the natural discontinuity grading method (the calculated parameter values ​​for each sampling area were sorted, graded using the natural discontinuity method, and different colors were used to represent parameters at different grades, thus visualizing the parameter values). This method was used to grade and evaluate the development scale and connectivity of the fault network in different sampling areas of the entire study area. Figures 7-10 As shown, the grading gradually increases from white to red.

[0089] In this invention, quantitative evaluation is achieved after the evaluation parameter P is calculated. Figure 10 It is a form of representation of the evaluation parameter P, that is, the result of visualizing parameter P, which is an intuitive and visual display. Depending on the sampling method and the grading standard, different effects can be presented. For example, the sampling method can be circular or based on specific needs; the grading can be divided into more or fewer categories.

[0090] The five-level classification used in the embodiments of the present invention is relatively accurate and appropriate, and if it is necessary to compare the fracture development in each region, the magnitudes of each parameter can be directly compared.

[0091] Fractures primarily serve two purposes for oil and gas enrichment: providing migration pathways and altering reservoirs. For example, in shale reservoirs, areas with low-level fault alteration have a low probability of oil and gas accumulation due to the lack of migration pathways and effective storage space; therefore, these areas should be given a lower priority for exploration and development. Conversely, areas with high-level fault alteration can be affected by overdeveloped fractures, which can damage the reservoir and cause oil and gas to escape; therefore, development in such areas should be carefully considered.

[0092] The evaluation parameter P provides a regional priority for oil and gas exploration and development. For example, exploration should be focused on areas with moderately modified faults, while exploration and development should be approached with caution in areas with underdeveloped or overdeveloped faults.

[0093] Comparative Example 1

[0094] The traditional method for evaluating fracture modification capacity is as follows:

[0095] Based on the statistical tables of basic parameters of the fracture network sampling area described in Tables 1 and 2, and the results of fracture network topology identification within the sampling area, the types and numbers of nodes in each sampling area were statistically analyzed, and six traditional fault connectivity parameters (including the number of connection nodes N in each branch) were calculated. C / N B The number of connection nodes N for each fault line C / N L The number of branches N of each fault line B / N L Number of connection nodes per unit area N C / A, Number of fault branches per unit area N B / A, Number of fault lines per unit area N L / A).

[0096] The formulas for traditional fault connectivity evaluation parameters are shown in Table 4:

[0097] Table 4 Formulas for traditional fault connectivity evaluation parameters

[0098]

[0099] In the formulas in Table 4 above, N Y N represents the number of nodes in Y. X N represents the number of nodes X. I Let I be the number of I nodes, and A be the area of ​​the sampling region.

[0100] The calculation results of each connectivity parameter are shown in Table 5.

[0101] Table 5. Traditional Connectivity Evaluation Parameters for Sampling Regions of Faulty Networks

[0102]

[0103] The natural discontinuity grading method was used to grade and evaluate the connectivity parameters of six traditional faults in each sampling area of ​​the study area, such as... Figures 11-16 As shown.

[0104] Comparative analysis of the comprehensive evaluation parameters (P) of fracture modification capability proposed in Embodiment 1 of this invention ( Figure 10 ) and the traditional connectivity evaluation parameters described in Comparative Example 1 ( Figures 11-16 As can be seen, traditional fault connectivity parameters, due to their failure to consider the crucial geometric parameter of fault length, exhibit significantly high values ​​in areas with well-developed small faults. Furthermore, traditional parameter calculations rely on node type and number, leading to outliers in areas with abundant nodes, which deviates significantly from geological patterns of fault development. Moreover, faults in geology cannot be simply viewed as line segments; they possess numerous geological attributes. Therefore, this invention further considers the fault strike and the direction of the maximum principal stress in the region, making it more consistent with geological principles.

[0105] In oil and gas exploration, many factors influence oil and gas production, such as stratigraphic sedimentary conditions, diagenesis, structural features, and fracture characteristics. This invention, assuming all other factors remain constant, explores the impact of fracture characteristics on oil and gas production. Compared to traditional methods, the fracture characterization presented in this invention is more accurate, and the parameter values ​​show a higher degree of fit with oil and gas production values.

[0106] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for evaluating fault modification capacity based on seismic attribute scale, characterized in that, Includes the following steps: (1) Sampling and analysis of fracture network Multi-attribute calculations are performed on seismic data in the target area to identify and finely characterize the fault network; the topological structure of the fault network data is identified, and the faults are divided into different types of branches, and the endpoints and intersections are divided into different types of nodes; a distribution map of fault network nodes and branches is obtained; equal-area sampling is performed on the identified fault network, and the nodes, branch types, number of branches, branch lengths and branch directions in each sampling area are statistically analyzed to generate a statistical table of basic parameters of the fault network sampling area; (2) Calculation of evaluation parameters for fracture network Based on the statistical table of basic parameters of the fracture network sampling area, the number of fracture branches (N) in each sampling area is calculated. B ), the number of connected nodes in each branch (C) B ), effective fracture length (L) E The comprehensive evaluation parameters (P) of fault modification capacity are used to obtain an evaluation parameter table for the fault network sampling area, thereby characterizing the development scale and connectivity of the fault network in each sampling area. (3) Analysis of evaluation parameters for fracture networks Based on the evaluation parameter table of the fracture network sampling region, the number of fracture branches (N) B ), the number of connected nodes in each branch (C) B ), effective fracture length (L) E The four evaluation parameters (P) and the comprehensive evaluation parameter of fault modification capacity were processed using the natural discontinuity classification method to classify and evaluate the development scale and connectivity of fault networks in different sampling areas of the entire study area. In step (2), the formula for calculating the number of broken branches (NB) is as follows: ； In the formula, N B N represents the number of fracture branches. CC N represents the number of CC-type branches. CI N represents the number of CI-type branches. II The number of type II branches; In step (2), the number of connection nodes (C) of each branch B The calculation formula for ) is as follows: ； In the formula, C B N represents the number of connected nodes in each branch. Y N represents the number of Y-shaped nodes. X N represents the number of X-type nodes. I The number of type I nodes; In step (2), the effective fracture length (L) E The calculation formula for ) is as follows: ； In the formula, L E L is the effective fracture length. i Let α be the branch length. i The angle between the branch and the maximum principal stress in the region; In step (2), the calculation formula for the comprehensive evaluation parameter (P) of fracture modification capacity is as follows: ； In the formula, N B C represents the number of fracture branches. B L represents the number of connected nodes in each branch. E The effective length of the fracture.

2. The method for evaluating fault modification capacity based on seismic attribute scale according to claim 1, characterized in that, In step (1), the multi-attribute calculation process includes the generation of coherent body data, the selection of ant body parameters, and edge detection.

3. The method for evaluating fault modification capacity based on seismic attribute scale according to claim 1, characterized in that, In step (1), the nodes are divided according to the following criteria: a node that connects one branch is a node I, a node that connects three branches is a node Y, and a node that connects four branches is a node X. Nodes X and Y are collectively referred to as connecting nodes C. The branch types are divided according to the following criteria: branches are divided into three types: CC, CI, and II, based on the type of connecting nodes at both ends.

4. The method for evaluating fault modification capacity based on seismic attribute scale according to claim 1, characterized in that, In step (1), the basic parameter statistics table of the fracture network sampling area includes the fracture network branch parameter statistics table and the fracture network sampling area node branch number statistics table.

5. The method for evaluating fault modification capacity based on seismic attribute scale according to claim 1, characterized in that, In step (3), the natural discontinuity grading method refers to sorting the calculated parameter sizes of each sampling area, grading them using the natural discontinuity method, and using different colors to represent parameters of different grades, thereby visualizing the parameter sizes.

6. The application of the fault modification capacity evaluation method based on seismic attribute scale as described in any one of claims 1 to 5 in oil and gas exploration.

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