Geological fault identification and classification method and device and medium

Abstract

The invention belongs to the technical field of seismic fracture prediction, and discloses a geological fault identification and grading method and device and a medium, and the method comprises the steps: firstly obtaining a seismic depth data body, then determining an artificial interpretation limit based on a forward modeling result, and finally carrying out fault identification and grading according to fault features. And matching the divided three levels of faults with a specific identification method so as to improve the analysis and interpretation efficiency. The device and the computer readable storage medium according to the method can be applied to geological fault identification and classification, the fault identification and prediction precision is improved, and the method is suitable for shale gas exploration and development of fracture complex landforms such as basins.

Description

Technical Field

[0001] This invention belongs to the field of earthquake fracture prediction technology, and relates to a fracture grading evaluation method, specifically a method, device and medium for geological fault identification and grading. Background Technology

[0002] Shale gas is an important unconventional natural gas resource, often found in thick and widely distributed shale source rock strata within basins. Due to the significant differences in geological characteristics across different regions, especially the influence of factors such as fault development, even shale gas layers with similar content can have varying extraction difficulties and technical requirements. For example, most shale gas layers in the United States are relatively easy to extract, while shale gas exploration and development in some basin areas are extremely difficult.

[0003] Studies have shown that fractures caused by complex tectonic movements in basins and other special terrains have varying impacts on shale gas preservation, drilling, hydraulic fracturing, and production. In recent years, there have been numerous reports on earthquake fracture prediction and the impact of fractures on shale gas. However, these studies often focus on analyzing the relationship between fractures (faults) and shale gas from one or a few perspectives. Insufficient research on prior fault identification and classification has led to ambiguous fault classifications and the general application of the same or inapplicable seismic interpretation methods to identify and analyze faults with significant structural differences. This results in suboptimal interpretation and analysis, failing to provide accurate guidance for earthquake fracture prediction, analyzing the impact of faults on shale gas, and shale gas exploration and development. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention employs geophysical techniques, combined with geological and microseismic data, to analyze the impact of different faults on shale gas exploration and development. The aim is to provide a method for identifying and classifying geological faults, classifying faults affecting shale gas exploration and development according to different fault lengths and displacements, and providing appropriate identification methods for each level of fault for interpretation and evaluation, thereby improving the effectiveness of fault interpretation and analysis.

[0005] Another objective of this invention is to provide an apparatus, electronic device, and computer-readable storage medium based on the above-described method for identifying and classifying geological faults.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for identifying and classifying geological faults includes the following steps:

[0008] S1. Obtain seismic depth data volume

[0009] Using raw seismic data, obtain seismic depth data volumes;

[0010] The seismic depth data volume is a three-dimensional seismic depth domain data volume or a pre-stack depth migration data volume;

[0011] S2. Determine the limits of human interpretation

[0012] Establish a geological model containing the geological features of the target area and perform forward modeling.

[0013] Based on the forward modeling results, the limit of artificial identification of fault in phase axis fault segments is determined to be faults with a displacement ≥ X m.

[0014] Where X is a natural number; the limit of human identification is the limit at which the phenomenon of fault phase axis misalignment can be identified by the naked eye, that is, the fault with the maximum fault displacement that can be observed by humans.

[0015] S3. Perform fault identification and classification.

[0016] Fault identification and classification are performed based on fault characteristics, including fault displacement; wherein,

[0017] For faults with a displacement ≥ Xm, manual interpretation is used for identification, and they are classified as first-order faults.

[0018] For the range of values ​​for dislocation: The fault was identified using attribute volumes and was classified as a second-order fault.

[0019] For the range of values ​​for dislocation: The fault was identified using ant bodies or maximum likelihood bodies, and was classified as a third-order fault.

[0020] The description of faults in this field includes three elements: fault properties, fault attitude, fault displacement, and fault strata. Fault attitude (including dip and strike) is an important parameter indicating the characteristics of the regional tectonic stress field. In particular, fault strike, i.e., the direction of extension of the fault's planar trajectory (which can be described by azimuth), is an important indicator reflecting the direction of tectonic stress.

[0021] As a limitation of the present invention, the fault features also include fault orientation and break line position.

[0022] As a further limitation of the present invention, the first-order fault includes four types of faults with different fault attitudes. Among them, the type I fault is a fault with a fault displacement range of [5X, 15X] m, a fault attitude that breaks upward to the ground or the bottom of the ground, and a fault that controls the structure.

[0023] Type II faults are faults with a displacement range of (5X, 15X) m, a fault orientation that breaks the Permian or Silurian strata upwards, a fault length > 10 km, and that control the structure.

[0024] Class III faults are faults with a displacement ranging from [2X, 5X) m, an upward orientation that disappears within the Silurian strata, and a length of 3–10 km.

[0025] Class IV faults are faults with a displacement ranging from [X, 2X) m, an upward orientation that disappears within the Silurian strata, and a length of less than 3 km.

[0026] As a further limitation of the present invention, the three-dimensional seismic depth domain data volume is obtained by taking the original seismic data and sequentially performing time migration processing and time-depth conversion processing.

[0027] As a further limitation of the present invention, the pre-stack depth migration data volume is obtained by taking the original seismic data and processing it through depth migration.

[0028] As another limitation of the present invention, the attribute body is a seismic attribute used for fracture identification and well location comparison.

[0029] The present invention also provides a geological fault identification and classification device based on the above method, the device being based on:

[0030] The acquisition module is used to acquire seismic depth data volumes;

[0031] The manual interpretation limit determination module is used to determine the manual interpretation limit;

[0032] The first-level fault acquisition module is used to identify and acquire first-level faults;

[0033] The secondary fault acquisition module is used to identify and acquire secondary faults;

[0034] The third-order fault acquisition module is used to identify and acquire third-order faults;

[0035] The output module is used to output the results of geological fault identification and classification.

[0036] The present invention also provides a device for geological fault identification and classification, which is an electronic device including a memory and a processor, wherein the memory stores executable instructions; the processor runs the computer program in the memory to implement the above-described method for geological fault identification and classification.

[0037] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for geological fault identification and classification.

[0038] By adopting the above-described technical solution, the beneficial effects achieved by this invention compared to the prior art are as follows:

[0039] This invention provides a method for geological fault identification and classification. After conducting in-depth research on the feasibility and reliability of fault interpretation through forward modeling of seismic models, it determines the identification and interpretation methods corresponding to faults of different scales. For specific faults, specific seismic attributes can also be selected to interpret and predict fractures. After a detailed analysis of the different impacts of specific faults in basin areas, such as large-scale faults, complex and diverse fault genesis, and high difficulty in shale gas extraction, on shale gas exploration and development, the standard for geological fault identification and classification of this invention is established. It is divided into three levels of faults that are identified and interpreted using different methods. Among them, the first-level faults are further divided into four categories based on fault characteristics such as fault attitude.

[0040] The method of this invention is applied to the identification and classification of shale gas faults in the Longmaxi Formation of the Sichuan Basin. Through specific identification methods, it efficiently guides well deployment and tracking. Currently, it has been applied to more than 650 horizontal shale gas wells in the southern Sichuan shale gas block, significantly improving the effect of fault interpretation and analysis.

[0041] The device and computer-readable storage medium of the present invention can realize a method for geological fault identification and classification, which has the advantages of simple operation, high efficiency and high accuracy of result prediction. Attached Figure Description

[0042] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 This is a flowchart of the geological fault identification and classification method in Embodiment 1 of the present invention;

[0044] Figure 2 This is a flowchart of the process for obtaining seismic depth data in Embodiment 1 of the present invention;

[0045] Figure 3 This is a schematic diagram of the geological model in Embodiment 1 of the present invention;

[0046] Figure 4 This is a schematic diagram of the forward modeling results in Embodiment 1 of the present invention;

[0047] Figure 5 This is a flowchart of the secondary fault delineation in Embodiment 1 of the present invention;

[0048] Figure 6 This is a schematic diagram of the device for geological fault identification and classification in Embodiment 2 of the present invention. Detailed Implementation

[0049] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the described embodiments are only used to explain the present invention and do not limit the present invention.

[0050] Example 1: A method for geological fault identification and classification

[0051] This embodiment describes a method for identifying and classifying geological faults, such as... Figure 1 Specifically, it includes the following steps:

[0052] S100. Acquire seismic depth data volume

[0053] The seismic depth data volume obtained in this embodiment is a three-dimensional seismic depth domain data volume or a pre-stack depth migration data volume, such as... Figure 2 The specific method for obtaining it is as follows:

[0054] S110. Raw seismic data was collected;

[0055] S120. Obtain the 3D seismic depth domain data volume: After time migration processing, time-migrated data is obtained, followed by time-depth conversion processing to obtain the 3D seismic depth domain data volume. Alternatively,

[0056] S130. Obtain pre-stack depth migration data volume: Take the original seismic data and process it through depth migration to obtain the pre-stack depth migration data volume.

[0057] S200. Determine the limits of human interpretation.

[0058] Establish a geological model that includes the geological features of the target area, such as... Figure 3 In this geological model, the velocity of the target layer is 4000 m / s, the velocity of the layer above the target layer is 4800 m / s, and the velocity of the layer below the target layer is 5500 m / s. Seven faults with displacements of 10 m, 15 m, 20 m, 40 m, 80 m, and 160 m are set to determine the limits of artificial interpretation.

[0059] The geological model was forward modeled to obtain the forward modeling results, and the forward modeling profile is shown below. Figure 4 As shown; for Figure 4 Manual identification was conducted by visually observing the fault axis misalignment phenomenon. It was found that faults with a displacement of more than 20m could be accurately identified, as these faults showed obvious axis misalignment. However, faults with a displacement of less than 15m were difficult to identify with the naked eye.

[0060] Therefore, the limit for the artificial interpretation of faults is defined as faults with a displacement ≥ 20m.

[0061] S300. Perform fault identification and classification.

[0062] Seismic reflection horizons in the seismic depth data volume are tracked and picked. The obtained horizons are then identified and classified based on fault characteristics, including fault displacement.

[0063] (1) Faults with a displacement ≥ 20m are classified as first-order faults and identified by manual interpretation;

[0064] The first-order faults are further divided as follows:

[0065] Type I faults are faults with a displacement of any value within the range of [100, 300] m, a fault orientation that breaks upward to the ground or the bottom of the ridge, and a fault that controls the structure.

[0066] Type II faults are any values ​​within the range of (100, 300) m in terms of fault displacement, fault orientation that breaks the Permian or Silurian strata upwards, fault length > 10 km, and faults that control the structure.

[0067] Class III faults are any values ​​within the range of [40, 100) m in terms of fault displacement, with the fault attitude disappearing upwards within the Silurian strata, and the fault length ranging from 3 to 10 km.

[0068] Class IV faults are faults with a displacement of any value within the range of [20, 40) m, few fault strata, an upward orientation that disappears within the Silurian strata, and a fault length of <3 km.

[0069] (2) Faults with a displacement range of [10, 20) m are classified as secondary faults and identified using attribute volumes. The attribute volumes are seismic attributes used for fracture identification and well location comparison.

[0070] like Figure 5 As shown, specifically:

[0071] S410. The tracked and picked layers are interpolated and smoothed to obtain the seismic reflection layers of the target work area.

[0072] S420. The obtained seismic reflection layers are calculated to obtain various seismic attributes that can be used for fracture identification. By comparing with the well location, the attribute bodies are identified in terms of clarity, detail representation, and anti-interference ability.

[0073] Among them, the seismic attributes of the cracks obtained can accurately reflect the shape and direction of the cracks, are not ambiguous, and have good agreement with the well.

[0074] (3) Faults with a displacement range of [5, 10) m are classified as third-order faults and identified using the maximum likelihood model.

[0075] The method of this embodiment was applied to the identification and classification of shale gas faults in the Longmaxi Formation of the Sichuan Basin, resulting in the division into three levels of faults. This method effectively guides well location deployment and tracking. Currently, it has been applied to more than 650 horizontal shale gas wells in the southern Sichuan shale gas block, significantly improving the effectiveness of fault interpretation and analysis.

[0076] In other embodiments, ant bodies are used to identify faults with a displacement range of [5, 10) m.

[0077] Example 2: A device for identifying and classifying geological faults

[0078] This embodiment includes a seismic data volume acquisition module N810, a manual interpretation limit determination module N820, a first-order fault acquisition module N830, a second-order fault acquisition module N840, a third-order fault acquisition module N850, and an output module N860.

[0079] The system includes: a seismic data volume acquisition module N810 for acquiring seismic depth data; a manual interpretation limit determination module N820 for determining the manual interpretation limit; a first-order fault acquisition module N830 for identifying and acquiring first-order faults; a second-order fault acquisition module N840 for identifying and acquiring second-order faults; a third-order fault acquisition module N850 for identifying and acquiring third-order faults; and an output module N860 for outputting the results of geological fault identification and classification.

[0080] In other embodiments, the device for geological fault identification and classification is an electronic device, such as... Figure 6 This includes memory and processor.

[0081] The memory stores executable instructions; the processor runs the executable instructions in the memory to implement a method for geological fault identification and classification according to Embodiment 1.

[0082] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0083] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment disclosed in this application, the processor is used to execute computer-readable instructions stored in the memory.

[0084] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0085] Example 3: A computer-readable storage medium

[0086] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for geological fault identification and classification as described in Embodiment 1.

[0087] The computer-readable storage medium stores non-transitory computer-readable instructions thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods of the foregoing embodiments are performed.

[0088] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0089] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method of geological fault identification and ranking, characterized in that, The method comprises the following steps: S1. obtaining a seismic depth data volume The seismic depth data volume is a three-dimensional seismic depth domain data volume or a pre-stack depth migration data volume. S2. determining a manual interpretation limit S3. performing fault identification and classification The fault features include fault throw, fault occurrence and fault cutting horizon. The fault throw ≥ Xm fault includes four types of faults with different fault occurrences, wherein, Class I fault is a fault with a fault throw value range of [5X, 15X]m, a fault occurrence of upward cutting to the ground or the bottom, and a fault controlling structure. Class II fault is a fault with a fault throw value range of (5X, 15X)m, a fault occurrence of upward cutting into the internal strata of Permian or Silurian, a fault length > 10 km, and a fault controlling structure. Class III fault is a fault with a fault throw value range of [2X, 5X)m, a fault occurrence of disappearing in the internal strata of Silurian, and a fault length of 3-10 km. The fault with the range of the fault length is identified by using an attribute volume. Faults with a range of fault length values are identified using ant structures or maximum likelihood structures.

2. A method of geological fault identification and ranking according to claim 1, characterised in that, Class IV fault is a fault with a fault throw value range of [X, 2X)m, a fault occurrence of disappearing in the internal strata of Silurian, and a fault length < 3 km.

3. A method of geological fault identification and ranking according to claim 2, characterised in that, The three-dimensional seismic depth domain data volume is obtained by sequentially performing time migration processing and time-depth conversion processing on the original seismic data. The pre-stack depth migration data volume is obtained by depth migration processing on the original seismic data. The attribute volume is a seismic attribute for fracture identification and well location correlation. X is 20. The device comprises:

4. A method of geological fault identification and ranking according to claim 3, wherein, An obtaining module for obtaining a seismic depth data volume; 5. A method of geological fault identification and ranking according to claim 4, characterised in that, A manual interpretation limit determining module for determining a manual interpretation limit; 6. A method of geological fault identification and ranking according to any one of claims 1-5, characterized in that, A first-order fault obtaining module for identifying and obtaining a first-order fault; 7. A method of geological fault identification and ranking according to any one of claims 1-5, characterized in that, A second-order fault obtaining module for identifying and obtaining a second-order fault; 8. A device for identification and classification of geological faults based on the method according to any one of claims 1 to 7, characterized in that, A third-order fault obtaining module for identifying and obtaining a third-order fault; An output module for outputting the results of geological fault identification and classification. An electronic device comprising a memory and a processor, wherein the memory stores executable instructions; the processor runs the computer program in the memory to implement the method of geological fault identification and classification according to any one of claims 1-7. The computer readable storage medium stores a computer program which, when executed by a processor, implements the method of geological fault identification and classification according to any one of claims 1-7. ​ ​ ​ 9. An apparatus for geological fault identification and ranking, characterized by, ​ 10. A computer-readable storage medium, characterized in that, ​

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