A four-parameter characterization method for identifying fault fracture zones based on seismic data
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PETROCHINA CO LTD
- Filing Date
- 2024-12-12
- Publication Date
- 2026-06-12
AI Technical Summary
Existing seismic attribute analysis methods are insufficient to accurately characterize strike-slip faults and their fault zones in three-dimensional space, resulting in problems of insufficient identification and low accuracy in oil and gas exploration and development.
A four-dimensional characterization method based on seismic data is adopted, which combines coherence attribute map, maximum likelihood attribute, symmetric illumination attribute and structural tensor attribute, and combines drilling data to determine the boundary and width of the fault zone. Through the fusion and adjustment of multiple seismic attributes, a fine characterization of the fault zone is achieved.
It significantly improves the identification accuracy and characterization detail of strike-slip faults and their fracture zones in three-dimensional space, provides a more accurate geological model, optimizes well location deployment and development decisions for oil and gas fields, and reduces exploration risks.
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Figure CN122194256A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of petroleum exploration and development technology, and in particular to a four-dimensional characterization method for identifying fault fracture zones based on seismic data. Background Technology
[0002] Faults are widely developed in nature and are a challenging and important area of research that cannot be ignored in natural science research, mineral resource exploration, and engineering construction such as hydrology and road and bridge construction. Early geological research and oil and gas resource exploration and development often simplified faults into one-dimensional or two-dimensional geological models. Recent studies have found that faults are complex three-dimensional bodies composed of fault cores and fault zones. Fault geometry is the foundation of fault research and one of the basic geological conditions for studying the evolution history of tectonic deformation and related hydrocarbon accumulation conditions. Faults can be classified into normal faults, reverse faults, and strike-slip faults according to their properties. Normal and reverse faults have large displacements, long extensions, and certain dip angles, and can be clearly reflected in seismic data in deep basins. However, strike-slip faults are not given much attention in the early stages of exploration due to their weak deformation, vertical dip angles, small displacements, and difficulty in imaging. In recent years, the results of deep and ultra-deep oil and gas exploration and development have revealed that many high-reserve and high-yield oil and gas reservoirs are related to strike-slip faults and their associated structures and strike-slip fault activity (Jin Zhijun et al., 2003; Jiao Fangzheng, 2017; Han Jianfa et al., 2019; Jiang Tongwen et al., 2020; Yang Haijun et al., 2020). In particular, the discovery of a 1 billion-ton-level oil field in the Tarim Basin in 2021 has laid a new chapter and new ideas for the exploration and development of large oil and gas fields controlled by strike-slip faults.
[0003] In sedimentary basins, the identification and analysis of deep strike-slip faults primarily rely on seismic data processing and interpretation. In practice, coherence volume analysis is a commonly used method for large-scale fault detection. Curvature, amplitude variation rate, root mean square amplitude, average energy, and high-precision coherence-enhanced fault scanning techniques are also being developed for fault interpretation. Automatic fault extraction (AFE), maximum likelihood properties, structural tensors, navigation pyramids, and the fusion of sensitive attributes further improve the accuracy of fault characterization and the identification of smaller, secondary faults. However, these methods mainly focus on fine-grained horizontal profile characterization of faults. Due to the complexity of fault structures and combinations, most scholars dedicate themselves to the study of fault sensitive attributes, and few have used three-dimensional characterization methods to represent faults. However, the current progress of oil and gas exploration and development, along with the need for detailed research on fault structures, urgently requires three-dimensional spatial characterization to intuitively understand the changes in faults along their strike and to establish three-dimensional well-fault-reservoir-fluid models to guide well placement and target evaluation.
[0004] While existing seismic attribute analysis methods, such as coherence volume analysis, curvature analysis, and amplitude variation rate analysis, can reveal the planar structure of faults to some extent, these techniques often fail to fully represent complex three-dimensional geological entities, particularly in the identification and detailed characterization of strike-slip faults. Due to their inherent weak deformation characteristics, vertical dip angles, and small displacements, strike-slip faults often appear less clearly in seismic images than other types of faults, making them difficult to focus on and accurately characterize in the early stages of exploration. Furthermore, traditional methods have limitations in identifying the boundaries and width of fault zones, posing a significant technical obstacle to the accurate exploration and effective development of oil and gas reservoirs. Therefore, developing a new technology capable of accurately characterizing strike-slip faults and their fault zones in three-dimensional space is of paramount scientific and practical significance for improving the detection accuracy and development efficiency of oil and gas fields. Summary of the Invention
[0005] This invention aims to provide a four-dimensional characterization method for identifying fault zones based on seismic data. The method utilizes the coherence attribute map of seismic data to determine the distribution of fault zones, uses maximum likelihood to finely interpret micro-faults, combines symmetrical illumination and structural tensor properties to determine the contour of fault zones, combines well calibration to determine the boundary attribute values of fault zones, determines the boundary attribute values of fault zones using structural tensor properties, and quantitatively picks the width of fault zones. To achieve the above-mentioned objectives, the technical solution of the present invention is as follows: A four-dimensional characterization method for identifying fault zones based on seismic data includes the following steps: S1. Collect earthquake data within the study area, analyze the earthquake data, and establish earthquake work zones; The seismic data refers to post-stack time- or depth-migrated three-dimensional seismic data within the study area. The analysis of the earthquake data includes: Extracting 3D seismic attributes: Extracting coherence attribute maps, maximum likelihood attributes, and symmetric illumination attributes from seismic data; Stratigraphic attribute extraction: Based on geological patterns, statistical analysis is used to select the sensitive attributes of fault zones, and seismic stratigraphic interfaces are used to track and compare seismic strata in 3D data to extract stratigraphic attributes. S2. Using the seismic data from step S1, determine the distribution of fault zones within the seismic work area; this includes the following steps: S21. Using the coherence attribute map of seismic data, identify the attributes of each layer in the seismic work area and depict the overall distribution characteristics of the fault zones in the seismic work area. By using high-resolution coherence attribute maps, the spatial characteristics of fault zones within the seismic work area are captured, enhancing the understanding of fault zone morphology. S22. Through the maximum likelihood property in step S12, a detailed interpretation of the micro-faults within the fault zone is made. S3. Based on the distribution of the fracture zone in step S2, determine the contour of the fracture zone and the boundary of the fracture zone, including the following steps: S31. By analyzing the fault zone distribution in step S2 and using the symmetrical illumination property, the boundary contour of the fault zone in the seismic work area is obtained. In step S31, the boundary contour of the fault zone within the seismic work area is processed by tensor voting based on symmetrical lighting attributes. The attribute value of tensor voting is set to 0-1 to obtain structural tensor attributes. The structural tensor attributes are then analyzed to determine the fault zone boundary. S4. Determine the boundaries of fault zones within the seismic work area using well logging data; this includes the following steps: S41. Using drilling data for calibration, the boundaries of the fracture zone are initially determined. The drilling data includes: drilling rate changes, wellbore anomalies, and leakage during the drilling process; S42. Select horizontal wells or highly deviated wells that cross fault zones within the seismic work area, and directly identify the location, width, and characteristics of the fault zones through core descriptions and imaging logging data from these wells. S5. Based on the fracture zone boundaries in step S4, update the fracture zone boundaries determined in step S3; including the following steps: S51. Compare the fault zone boundaries in steps S4 and S3 to compare their consistency in spatial location; when the boundary locations and shapes are basically consistent, the two boundaries are determined to be in agreement; when the error between the seismic boundary and the well logging boundary is greater than or equal to 3%, the attribute value of the tensor voting attribute needs to be adjusted. S52. When the seismic boundary is larger than the well logging boundary, it indicates that the attribute value is too low and needs to be increased to reduce noise; when the seismic boundary is smaller than the well logging boundary, it indicates that the attribute value is too high and needs to be decreased to capture more details. S53. Repeatedly adjust the attribute values and compare them until the error between the seismic boundary and the well logging boundary is less than 3%, then no further updates are needed, and finally the fault zone boundary in the seismic work area is obtained. S6. Using the fault zone boundary obtained in step S5, the width of the fault zone within the seismic work area is obtained; this includes the following steps: S61. Through the fracture zone boundary in step S5, pick the width from one side boundary of the fracture zone to the other side boundary. S62. Compare the width picked in step S61 with the width of the fracture zone in the well logging data. If the error is less than the preset threshold, the width is considered to be picked accurately. If the error is greater than or equal to the preset threshold, the fracture zone boundary needs to be updated. S63. Adjust the attribute value of the tensor voting property and redetermine the boundary of the fracture zone until the error is less than the set threshold. S64. When the error is less than the set threshold, the width of the fault zone in the seismic work area is finally obtained through the updated fault zone boundary. The set threshold is 3%.
[0006] The beneficial effects of this invention are: 1. This invention significantly improves the accuracy and detail of identifying strike-slip faults and their fracture zones in three-dimensional space by innovatively integrating multiple seismic attribute analysis techniques, including but not limited to coherence volume, curvature, symmetric illumination, and tensor voting.
[0007] 2. This invention can clearly display the spatial variation of strike-slip faults and the detailed outline of fracture zones in seismic data, thereby overcoming the limitations of traditional techniques in characterizing complex fault systems.
[0008] 3. This invention enables precise acquisition of the boundaries and widths of fracture zones, providing a more accurate geological model for well location deployment, target evaluation, and effective development of high-reserve oil and gas reservoirs in oil and gas fields.
[0009] 4. This invention demonstrates significant advantages in improving the accuracy of seismic interpretation, optimizing exploration and development decisions, and reducing exploration risks, thus greatly promoting the advancement of oil and gas exploration and development technologies. Attached Figure Description
[0010] Figure 1 This is a schematic diagram of the four-dimensional characterization method for identifying fault zones based on seismic data according to the present invention.
[0011] Figure 2 This is a schematic diagram of the overall distribution of the fault zone in the GS18 well area in Embodiment 2 of the present invention.
[0012] Figure 3 This is a detailed schematic diagram illustrating the micro-faults within the fracture zone in Embodiment 2 of the present invention.
[0013] Figure 4 This is a schematic diagram of the fracture zone outline in Embodiment 2 of the present invention.
[0014] Figure 5 This is a schematic diagram of the fracture zone boundary in Embodiment 2 of the present invention.
[0015] Figure 6 This is a planar distribution diagram of the fracture segment width in Embodiment 2 of the present invention. Detailed Implementation
[0016] The present invention will be further described in detail below with reference to embodiments, but the implementation of the present invention is not limited thereto.
[0017] Example 1 This embodiment provides a method such as Figure 1 The four-dimensional characterization method for identifying fault zones based on seismic data, as shown, includes the following steps: S1. Collect earthquake data within the study area, analyze the earthquake data, and establish earthquake work zones; The seismic data refers to post-stack time- or depth-migrated three-dimensional seismic data within the study area. The analysis of earthquake data includes: Extracting 3D seismic attributes: Extracting coherence attribute maps, maximum likelihood attributes, and symmetric illumination attributes from seismic data; Three-dimensional seismic properties provide multimodal data support for fault identification and analysis, thereby enhancing the interpretability of seismic data and improving the accuracy of fault identification. Stratigraphic attribute extraction: Based on geological patterns, statistical analysis is used to select the sensitive attributes of fault zones, and seismic stratigraphic interfaces are used to track and compare seismic strata in 3D data to extract stratigraphic attributes. The extracted stratigraphic attributes provide data support for subsequent fault zone width measurements; the extracted stratigraphic attributes also provide data support for the subsequent quantitative acquisition of fault zone width, ensuring the accuracy and consistency of width determination.
[0018] S2. Using the seismic data from step S1, determine the distribution of fault zones within the seismic work area; this includes the following steps: S21. Using the coherence attribute map of seismic data, identify the attributes of each layer in the seismic work area and depict the overall distribution characteristics of the fault zone in the seismic work area. By using high-resolution coherent attribute maps, the spatial characteristics of fault zones within the seismic work area are captured, enhancing the understanding of fault zone morphology and identifying and depicting the overall distribution characteristics of fault zones within the seismic work area. S22. Through the maximum likelihood property in step S12, a detailed interpretation of the micro-faults within the fault zone is made. S3. Based on the distribution of the fracture zone in step S2, determine the contour of the fracture zone and the boundary of the fracture zone, including the following steps: S31. Based on the fault zone distribution in step S2, analyze the boundary contour of the fault zone in the seismic work area using the symmetrical illumination attribute. S32. Perform tensor voting processing on the boundary contour of the fault zone in the seismic work area in step S31 based on the symmetric lighting attribute. Set the attribute value of the tensor voting to 0-1 to obtain the structural tensor attribute. Analyze the structural tensor attribute to determine the boundary of the fault zone. The tensor voting process enhances continuous geological features, suppresses random noise, and improves the accuracy of geological interpretation by transmitting direction and intensity information between data points. This invention utilizes symmetrical illumination properties to analyze the boundary contours of fault zones; it is applicable to large and complex carbonate rock fault zones, and can also be applied to large clastic rock fault zones; by combining relevant well logging data and seismic plane properties, the location of the fault core of the main fault is determined through fine interpretation of seismic profiles; the application of symmetrical illumination improves the visibility of fault zones in seismic profiles, ensuring that their characteristics are more prominent. Tensor voting can enhance continuous geological features, suppress random noise, and improve the accuracy of geological interpretation.
[0019] S4. Determine the boundaries of fault zones within the seismic work area using well logging data; this includes the following steps: S41. Using drilling data for calibration, the boundaries of the fracture zone are initially determined. The drilling data includes: drilling rate changes, wellbore anomalies, and leakage during the drilling process; Fracture zones often lead to a sudden increase in drilling speed and may cause abnormalities such as wellbore collapse, rockfall, and mud loss. S42. Select horizontal wells or highly deviated wells that cross fault zones within the seismic work area, and directly identify the location, width, and characteristics of the fault zones through core descriptions and imaging logging data from these wells. S5. Based on the fracture zone boundary in step S4, update the fracture zone boundary determined in step S3. S51. Compare the fault zone boundaries in steps S4 and S3 to compare their consistency in spatial location; when the boundary locations and shapes are basically consistent, the two boundaries are determined to be in agreement; when the error between the seismic boundary and the well logging boundary is greater than or equal to 3%, the attribute value of the tensor voting attribute needs to be adjusted. S52. When the seismic boundary is larger than the well logging boundary, it indicates that the attribute value is too low and needs to be increased to reduce noise; when the seismic boundary is smaller than the well logging boundary, it indicates that the attribute value is too high and needs to be decreased to capture more details. S53. Repeatedly adjust the attribute values and compare them until the error between the seismic boundary and the well logging boundary is less than 3%, at which point no further updates are needed, and finally the fault zone boundary within the seismic work area is obtained.
[0020] S6. Using the fault zone boundary obtained in step S5, the width of the fault zone within the seismic work area is obtained; this includes the following steps: S61. Through the fracture zone boundary in step S5, pick the width from one side boundary of the fracture zone to the other side boundary. S62. Compare the width picked in step S61 with the width of the fracture zone in the well logging data. If the error is less than the preset threshold, the width is considered to be picked accurately. If the error is greater than or equal to the preset threshold, the fracture zone boundary needs to be updated. S63. Adjust the attribute value of the tensor voting property and redetermine the boundary of the fracture zone until the error is less than the set threshold. S64. When the error is less than the set threshold, the width of the fault zone in the seismic work area is finally obtained through the updated fault zone boundary. The set threshold is 3%.
[0021] Example 2 The difference between this embodiment and the previous embodiment is that, in this embodiment, In step S1, in sedimentary basins with a certain level of exploration, especially those covered by 3D seismic data, a test area containing typical fault zones with abundant data is selected; a fault zone in the GS18 well area of the Sichuan Basin's central paleo-uplift region is selected as the study area to establish a seismic test area; the fault structure sensitive attribute volume (such as coherence volume attribute, symmetric illumination attribute and maximum likelihood attribute) and the related reservoir response sensitive attribute volume (such as structural tensor) of the fault zone are exported from the workstation as seismic data; Seismic data is crucial for subsequent fault and fracture zone identification. To ensure that the data covers all important geological structures in the study area, high-quality, high-resolution 3D seismic data needs to be selected. This data helps to identify faults and fracture zones more accurately. In step S2, such as Figure 2 The figure shows the overall distribution of the fault zone in the GS18 well area in this embodiment; like Figure 3 The diagram shows a detailed interpretation of the micro-faults within the fault zone using the maximum likelihood property in step S12. In seismic exploration, the identification of micro-faults is also crucial. It is necessary to further apply the maximum likelihood property to interpret the micro-faults in the work area in detail. Especially in complex geological backgrounds, these technologies can help identify secondary faults and improve the overall resolution of fault zones.
[0022] In step S3, specifically step S31, the attribute extraction function of the SMT software is used to analyze the boundary contour of the fault zone within the seismic work area using the symmetrical illumination attribute. like Figure 4 and Figure 5 The diagram shows how tensor voting attributes are extracted based on symmetrical illumination to determine the distribution range of the fault zone. This invention utilizes symmetrical illumination properties to analyze the boundary contours of fault zones; it is applicable to large and complex carbonate rock fault zones, and can also be applied to large clastic rock fault zones; by combining relevant well logging data and seismic plane properties, the location of the fault core of the main fault is determined through fine interpretation of seismic profiles; the application of symmetrical illumination improves the visibility of fault zones in seismic profiles, ensuring that their characteristics are more prominent. Tensor voting can enhance continuous geological features, suppress random noise, and improve the accuracy of geological interpretation.
[0023] In step S5, such as Figure 5 As shown, the boundary attribute values of the fracture zone are determined by combining drilling calibration, and the boundary of the fracture zone is determined based on the previous step. like Figure 6 The figure shown is a planar distribution diagram of the width of the fractured zone in the GS18 well area in this embodiment.
[0024] from Figure 6 It can be seen that the width of the fractured zone in the GS18 well area exhibits significant spatial variation, providing an important reference for subsequent oil and gas reservoir evaluation and development. Identifying the width of the fractured zone can be used for reservoir quality evaluation, oil and gas migration channel analysis, and well design optimization. These applications can improve the accuracy and efficiency of exploration and development, optimize development decisions, and reduce exploration risks. This analysis and application have a significant impact on improving the economics and scientific rigor of oil and gas exploration.
[0025] By comprehensively analyzing the above fault zone structural models using integrated fault interpretation data, other software, or inversion techniques, the realism and accuracy of fault structures and fault zone structures are further improved, enhancing the credibility and practicality of the fault zone geological models. By combining relevant data with fault zone identification methods, the advantages and disadvantages of the method presented in this invention are compared and verified, and its application in other research areas is continuously adjusted and strengthened. In the specific implementation of this invention, other methods can also be used for comparison and verification. Furthermore, this invention can be adapted to meet other exploration and production needs by adding other data to modify or improve the fault zone model.
[0026] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.
Claims
1. A four-dimensional characterization method for identifying fault zones based on seismic data, characterized in that: Includes the following steps: S1. Collect earthquake data within the study area, analyze the earthquake data, and establish earthquake work zones; S2. Using the seismic data from step S1, determine the distribution of fault zones within the seismic work area; S3. By analyzing the distribution of the fracture zone in step S2, the outline of the fracture zone is determined, and the boundary of the fracture zone is determined. S4. Determine the boundaries of fault zones within the seismic work area using well logging data; S5. Based on the fracture zone boundary in step S4, update the fracture zone boundary determined in step S3. S6. By picking the width of the fault zone boundary in step S5, the width of the fault zone in the seismic work area is obtained.
2. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 1, characterized in that: In step S1, the seismic data are post-stack time- or depth-migrated three-dimensional seismic data within the study area.
3. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 2, characterized in that: The analysis of earthquake data includes: three-dimensional seismic attribute extraction and layer attribute extraction.
4. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 3, characterized in that: Three-dimensional seismic attribute extraction involves extracting coherence attribute maps, maximum likelihood attributes, and symmetric illumination attributes from seismic data.
5. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 3, characterized in that: Stratigraphic attribute extraction involves selecting sensitive attributes of fault zones based on geological patterns and statistical analysis, utilizing seismic stratigraphic interface calibration results, and tracking and comparing seismic strata in 3D data to extract stratigraphic attributes.
6. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 3, characterized in that: In step S2, the distribution of fault zones within the seismic work area is determined using the seismic data from step S1; this includes the following steps: S21. Using the coherence attribute map of seismic data, identify the attributes of each layer in the seismic work area and depict the overall distribution characteristics of the fault zone in the seismic work area. S22. Through the maximum likelihood property in step S12, a detailed interpretation of the micro-faults within the fault zone is performed.
7. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 3, characterized in that: In step S21, identifying and depicting the overall distribution characteristics of fault zones within the seismic work area involves capturing the spatial characteristics of fault zones within the seismic work area using high-resolution coherence attribute maps, enhancing the understanding of fault zone morphology, and identifying and depicting the overall distribution characteristics of fault zones within the seismic work area.
8. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 3, characterized in that: In step S3, the fracture zone outline is determined by the fracture zone distribution in step S2, and the fracture zone boundary is determined, including the following steps: S31. By analyzing the fault zone distribution in step S2 and using the symmetrical illumination property, the boundary contour of the fault zone in the seismic work area is obtained. S32. Perform tensor voting processing on the boundary contour of the fault zone in the seismic work area in step S31 based on the symmetric lighting attribute. Set the attribute value of the tensor voting to 0-1 to obtain the structural tensor attribute. Analyze the structural tensor attribute to determine the boundary of the fault zone.
9. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 8, characterized in that: The tensor voting process enhances continuous geological features, suppresses random noise, and improves the accuracy of geological interpretation by transmitting direction and intensity information between data points.
10. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 3, characterized in that: In step S4, the boundaries of the fault zones within the seismic work area are determined using well logging data; this includes the following steps: S41. Using drilling data for calibration, the boundaries of the fracture zone are initially determined. S42. Select horizontal wells or highly deviated wells that cross fault zones within the seismic work area. Through core descriptions and imaging logging data from these wells, directly identify the location, width, and characteristics of the fault zones.
11. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 10, characterized in that: The drilling data includes: drilling rate changes, wellbore anomalies, and drilling anomaly data such as leakage during the drilling process.
12. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 3, characterized in that: In step S5, the fracture zone boundary determined in step S3 is updated based on the fracture zone boundary in step S4; this includes the following steps: S51. Compare the fault zone boundaries in steps S4 and S3 to compare their consistency in spatial location; when the boundary locations and shapes are basically consistent, the two boundaries are determined to be in agreement; when the error between the seismic boundary and the well logging boundary is greater than or equal to 3%, the attribute value of the tensor voting attribute needs to be adjusted. S52. When the seismic boundary is larger than the well logging boundary, it indicates that the attribute value is too low and needs to be increased to reduce noise; when the seismic boundary is smaller than the well logging boundary, it indicates that the attribute value is too high and needs to be decreased to capture more details. S53. Repeatedly adjust the attribute values and compare them until the error between the seismic boundary and the well logging boundary is less than 3%, at which point no further updates are needed, and finally the fault zone boundary within the seismic work area is obtained.
13. The four-dimensional characterization method for identifying fault zones based on seismic data according to claim 3, characterized in that: In step S6, the width of the fault zone within the seismic work area is obtained by picking the width of the fault zone boundary obtained in step S5; this includes the following steps: S61. Through the fracture zone boundary in step S5, pick the width from one side boundary of the fracture zone to the other side boundary. S62. Compare the width picked in step S61 with the width of the fracture zone in the well logging data. If the error is less than the preset threshold, the width is considered to be picked accurately. If the error is greater than or equal to the preset threshold, the fracture zone boundary needs to be updated. S63. Adjust the attribute value of the tensor voting property and redetermine the boundary of the fracture zone until the error is less than the set threshold. S64. When the error is less than the set threshold, the width of the fault zone in the seismic work area is finally obtained through the updated fault zone boundary. The set threshold is 3%.