Wrinkle coefficient quantitative prediction method and device, electronic equipment and medium

Abstract

The invention discloses a wrinkle coefficient quantitative prediction method and device, electronic equipment and a medium. The method can comprise the following steps: carrying out fine horizon depiction on a target horizon; determining the scale of a wrinkle coefficient surface element, wherein the scale comprises a unit surface element scale and a research surface element scale; and calculating a wrinkle coefficient under the wrinkle coefficient surface element according to the unit surface element scale and the research surface element scale. According to the method, the section deformation coefficient evaluation technology emphatically describes the two-dimensional section target layer wrinkle degree, and the problems that the precision of a plane deformation index limited by a contour line is poor, zone recognition cannot be formed when the stratum bending degree is depicted by the tectonic curvature, and the influence of stratum inclination is difficult to eliminate are solved; and comprehensive evaluation of body control, reservoir control and reservoir control of the target layer broken fold seam is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration and development, and more specifically, to a method, device, electronic equipment and medium for quantitatively predicting a wrinkle coefficient. Background Art

[0002] At present, there are many methods for evaluating the fold development degree of fault-fold-fracture controlled reservoirs at home and abroad. Among them, the fold identification methods based on seismic bodies mainly include three technologies: profile deformation coefficient analysis based on seismic two-dimensional profiles, plane deformation index analysis based on iso-T0 map (or structural map) contour lines, and structural curvature analysis based on three-dimensional seismic bodies. The fold degree analysis technology based on two-dimensional profiles is not accurate and applicable to the stratum fold degree caused by multi-stage tectonic movements due to large calculation amount and strong multi-solution; the plane deformation index analysis method based on iso-T0 map (structural map) contour lines is mostly used to evaluate the distribution characteristics of strata. At present, it is less used in large seismic work areas, has high requirements for contour line distribution and low resolution; the structural curvature technology based on three-dimensional seismic bodies is difficult to quantitatively characterize the fold development zone.

[0003] The controlling elements of fault-fold-fracture bodies are mostly folds, faults or fold-fault joint control. Large wide and gentle folds and short and tight folds all affect the development of fault-fracture bodies. The comprehensive bending and deformation degree of rock folds in multiple aspects and scales has important guiding significance for evaluating the ability of fault-folds to control reservoirs and enrichment in the target layer. The existing technology for evaluating the degree of fold deformation in three-dimensional strata cannot form a regional understanding, which is not conducive to finding macroscopic fault-fold-fracture target areas from a geological perspective.

[0004] Therefore, it is necessary to develop a quantitative prediction method, device, electronic equipment and medium for the fold coefficient based on seismic horizons.

[0005] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention proposes a method, device, electronic equipment and medium for quantitative prediction of fold coefficient, which reduces the emphasis of profile deformation coefficient evaluation technology on describing the fold degree of target layer in two-dimensional profile, solves the problems that the plane deformation index is limited by contour lines and has poor accuracy, and the structural curvature cannot form a zone understanding when depicting the bending degree of the formation, and it is difficult to eliminate the influence of formation inclination, which is conducive to the comprehensive evaluation of the control of fault and fold body and reservoir of the target layer.

[0007] In a first aspect, an embodiment of the present disclosure provides a method for quantitatively predicting a wrinkle coefficient, comprising:

[0008] Carry out detailed stratigraphic characterization for the target layer;

[0009] Determine the scale of wrinkle coefficient surface element, including unit surface element scale and research surface element scale;

[0010] The wrinkle coefficient under the wrinkle coefficient surface element is calculated according to the unit surface element scale and the research surface element scale.

[0011] As a specific implementation of the embodiment of the present disclosure, fine layer characterization for the target layer includes:

[0012] Based on the existing geophysical data and interpretation results in the study area, a detailed characterization of the target layer is completed.

[0013] As a specific implementation manner of the embodiment of the present disclosure, the facet grid of the unit facet is consistent with the grid size and rotation angle of the seismic network.

[0014] As a specific implementation manner of the embodiment of the present disclosure, the research surface element scale is 2n+1 times the unit surface element scale.

[0015] As a specific implementation of the embodiment of the present disclosure, according to the unit surface element scale and the research surface element scale, calculating the wrinkle coefficient under the wrinkle coefficient surface element includes:

[0016] Respectively calculating the lengths of the four sides of the spatial quadrilateral formed by the unit surface element and each vertex under the research surface element;

[0017] Calculate the projection areas of the corresponding face elements respectively by calculating the side lengths of the spatial quadrilateral;

[0018] Calculating the sum S1 of the areas of the unit cells contained in the research cell;

[0019] Determine whether to consider eliminating the influence of formation tilt, and then calculate the fold coefficient.

[0020] As a specific implementation of the embodiment of the present disclosure, if the influence of formation inclination is not considered, the fold coefficient is the ratio of S1 to the projected area of ​​the research surface element.

[0021] As a specific implementation method of the embodiment of the present disclosure, if the influence of formation inclination is eliminated, the fold coefficient is the ratio of S1 to the actual area of ​​the research surface element.

[0022] In a second aspect, the present disclosure also provides a device for quantitatively predicting a wrinkle coefficient, comprising:

[0023] The layer characterization module is used to carry out detailed layer characterization for the target layer;

[0024] A scale determination module determines the scale of the wrinkle coefficient surface element, including the unit surface element scale and the research surface element scale;

[0025] A calculation module calculates the wrinkle coefficient under the wrinkle coefficient surface element according to the unit surface element scale and the research surface element scale.

[0026] As a specific implementation of the embodiment of the present disclosure, fine layer characterization for the target layer includes:

[0027] Based on the existing geophysical data and interpretation results in the study area, a detailed characterization of the target layer is completed.

[0028] As a specific implementation manner of the embodiment of the present disclosure, the facet grid of the unit facet is consistent with the grid size and rotation angle of the seismic network.

[0029] As a specific implementation manner of the embodiment of the present disclosure, the research surface element scale is 2n+1 times the unit surface element scale.

[0030] As a specific implementation of the embodiment of the present disclosure, according to the unit surface element scale and the research surface element scale, calculating the wrinkle coefficient under the wrinkle coefficient surface element includes:

[0031] Respectively calculating the lengths of the four sides of the spatial quadrilateral formed by the unit surface element and each vertex under the research surface element;

[0032] Calculate the projection areas of the corresponding face elements respectively by calculating the side lengths of the spatial quadrilateral;

[0033] Calculating the sum S1 of the areas of the unit cells contained in the research cell;

[0034] Determine whether to consider eliminating the influence of formation tilt, and then calculate the fold coefficient.

[0035] As a specific implementation of the embodiment of the present disclosure, if the influence of formation inclination is not considered, the fold coefficient is the ratio of S1 to the projected area of ​​the research surface element.

[0036] As a specific implementation method of the embodiment of the present disclosure, if the influence of formation inclination is eliminated, the fold coefficient is the ratio of S1 to the actual area of ​​the research surface element.

[0037] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0038] A memory storing executable instructions;

[0039] A processor runs the executable instructions in the memory to implement the quantitative prediction method of wrinkle coefficient.

[0040] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for quantitatively predicting the wrinkle coefficient is implemented.

[0041] Its beneficial effects are:

[0042] (1) A model for quantitatively evaluating the degree of fold development in the three-dimensional target layer was established;

[0043] (2) The fold coefficient unit grid is consistent with the resolution of the actual seismic data network, which is conducive to the subsequent comprehensive evaluation of the relationship between reservoir development and the degree of fault fold development in combination with other seismic plane attributes;

[0044] (3) The surface area ratio calculation method is more efficient than fitting three-dimensional surfaces in space, and regional understanding is more consistent.

[0045] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0047] Figure 1 A flowchart showing the steps of a method for quantitatively predicting wrinkle coefficient according to an embodiment of the present invention.

[0048] Figure 2 A schematic diagram showing the principle of applying the method according to an embodiment of the present invention to determine the strength of the fold development in the stratum.

[0049] Figure 3 A schematic diagram showing the surface element projection area calculated by the research unit without considering eliminating the influence of formation tilt according to an embodiment of the present invention.

[0050] Figure 4 A schematic diagram showing the actual area of ​​the surface element calculated by the research unit when eliminating the influence of formation tilt according to an embodiment of the present invention is shown.

[0051] Figure 5a FIG. 1 shows a planar distribution diagram of fold coefficients for eliminating the influence of formation tilt according to an embodiment of the present invention. Figure 5bFIG. 1 shows a planar distribution diagram of the maximum structural curvature along a layer according to an embodiment of the present invention. Figure 5c shows a distribution diagram along the layer dip plane according to an embodiment of the present invention, Figure 5d A planar distribution diagram of the fault strike of the target layer according to an embodiment of the present invention is shown.

[0052] Figure 6 A block diagram of a wrinkle coefficient quantitative prediction device according to an embodiment of the present invention is shown.

[0053] Description of reference numerals:

[0054] 201. Stratum characterization module; 202. Scale determination module; 203. Calculation module. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0056] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0057] Example 1

[0058] Figure 1 A flowchart showing the steps of a method for quantitatively predicting wrinkle coefficient according to an embodiment of the present invention.

[0059] like Figure 1 As shown, the quantitative prediction method of the fold coefficient includes: step 101, finely characterizing the stratigraphic position for the target layer; step 102, determining the scale of the fold coefficient surface element, including the unit surface element scale and the research surface element scale; step 103, calculating the fold coefficient under the fold coefficient surface element according to the unit surface element scale and the research surface element scale.

[0060] In one example, detailed layer characterization for the target layer includes:

[0061] Based on the existing geophysical data and interpretation results in the study area, a detailed characterization of the target layer is completed.

[0062] In one example, the bin grid of the unit bin is consistent with the seismic network grid size and rotation angle.

[0063] In one example, the study bin size is 2n+1 times the unit bin size.

[0064] In one example, according to the unit panel scale and the research panel scale, calculating the wrinkle coefficient under the wrinkle coefficient panel includes:

[0065] Calculate the lengths of the four sides of the spatial quadrilateral composed of the unit surface element and the vertices under the research surface element respectively;

[0066] Calculate the projection area of ​​the corresponding face element by calculating the side length of the spatial quadrilateral;

[0067] Calculate the sum S1 of the areas of the unit cells contained in the research cell;

[0068] Determine whether to consider eliminating the influence of formation tilt and then calculate the fold coefficient.

[0069] In one example, if the influence of formation tilt is not considered, the wrinkle coefficient is the ratio of S1 to the projected area of ​​the research surface element.

[0070] In one example, if the influence of formation tilt is eliminated, the wrinkle coefficient is the ratio of S1 to the actual area of ​​the studied surface element.

[0071] Specifically, detailed characterization of the target layer: In the early stage of implementation, based on the existing geophysical data and interpretation results in the study area, the detailed characterization of the target layer is completed to provide a data basis for subsequent research.

[0072] Selection of the facet scale for fold coefficient research: This evaluation method needs to define two facet scales: the unit facet scale, whose facet grid is completely consistent with the seismic network grid size and rotation angle; the research facet scale, combining geological knowledge with actual seismic profiles to define the facet size as 2n+1 times the unit facet scale, where n is a reasonable integer.

[0073] Whether the influence of stratum inclination is considered in judging the fold coefficient: based on geological knowledge, the research surface element is selected to calculate the coordinate projection or actual plane area as the reference standard for folds.

[0074] Calculate the wrinkle coefficient under the study area:

[0075] Calculate the lengths of the four sides of the spatial quadrilateral formed by the unit scale surface element and the vertices under the research scale surface element respectively;

[0076] Use the side lengths of the spatial quadrilateral to calculate the (projected) areas of the corresponding face elements;

[0077] Calculate the sum S1 of the areas of the (2n+1)×(2n+1) unit facets contained in the research scale facet;

[0078] If the influence of formation tilt is not considered, the fold coefficient of the research unit is the ratio of S1 to the projected area of ​​the research scale surface element; if the influence of formation tilt is considered, the fold coefficient of the research unit is the ratio of S1 to the actual area of ​​the research scale surface element.

[0079] In general, the more developed the stratigraphic folds in the study unit, the greater the fold coefficient, and the less developed the stratigraphic folds, the smaller the fold coefficient.

[0080] The plane map of the comprehensive formation fold coefficient corresponds to the plane map of fault and fracture development, which guides the analysis of the reservoir and enrichment control patterns of the fault-fold and fracture bodies in the target layer.

[0081] By determining the scale of the research area, the actual spatial approximate distribution surface area calculated along the fine structure interpretation stratum and the projection area ratio of the research surface element under different projection methods are used to quantitatively predict the degree of stratum fold development. Through test calculations in the actual research area, it can be seen that this method can effectively characterize the fold development belt produced by the stratum under stress in different periods and directions; the algorithm principle and parameter setting are simple, easy to implement, and the calculation speed is fast. In actual production, the degree of folds at different scales of the target stratum can be quickly determined, providing interpreters with an effective and reliable means of quantitative evaluation of the degree of stratum fold development.

[0082] Example 2

[0083] The present invention also provides a device for quantitatively predicting wrinkle coefficient, comprising:

[0084] The layer characterization module is used to carry out detailed layer characterization for the target layer;

[0085] A scale determination module determines the scale of the wrinkle coefficient surface element, including the unit surface element scale and the research surface element scale;

[0086] The calculation module calculates the wrinkle coefficient under the wrinkle coefficient surface element according to the unit surface element scale and the research surface element scale.

[0087] In one example, detailed layer characterization for the target layer includes:

[0088] Based on the existing geophysical data and interpretation results in the study area, a detailed characterization of the target layer is completed.

[0089] In one example, the bin grid of the unit bin is consistent with the seismic network grid size and rotation angle.

[0090] In one example, the study bin size is 2n+1 times the unit bin size.

[0091] In one example, according to the unit panel scale and the research panel scale, calculating the wrinkle coefficient under the wrinkle coefficient panel includes:

[0092] Calculate the lengths of the four sides of the spatial quadrilateral composed of the unit surface element and the vertices under the research surface element respectively;

[0093] Calculate the projection area of ​​the corresponding face element by calculating the side length of the spatial quadrilateral;

[0094] Calculate the sum S1 of the areas of the unit cells contained in the research cell;

[0095] Determine whether to consider eliminating the influence of formation tilt and then calculate the fold coefficient.

[0096] In one example, if the influence of formation tilt is not considered, the wrinkle coefficient is the ratio of S1 to the projected area of ​​the research surface element.

[0097] In one example, if the influence of formation tilt is eliminated, the wrinkle coefficient is the ratio of S1 to the actual area of ​​the studied surface element.

[0098] Specifically, detailed characterization of the target layer: In the early stage of implementation, based on the existing geophysical data and interpretation results in the study area, the detailed characterization of the target layer is completed to provide a data basis for subsequent research.

[0099] Selection of the facet scale for fold coefficient research: This evaluation method needs to define two facet scales: the unit facet scale, whose facet grid is completely consistent with the seismic network grid size and rotation angle; the research facet scale, combining geological knowledge with actual seismic profiles to define the facet size as 2n+1 times the unit facet scale, where n is a reasonable integer.

[0100] Whether the influence of stratum inclination is considered in judging the fold coefficient: based on geological knowledge, the research surface element is selected to calculate the coordinate projection or actual plane area as the reference standard for folds.

[0101] Calculate the wrinkle coefficient under the study area:

[0102] Calculate the lengths of the four sides of the spatial quadrilateral formed by the unit scale surface element and the vertices under the research scale surface element respectively;

[0103] Use the side lengths of the spatial quadrilateral to calculate the (projected) areas of the corresponding face elements;

[0104] Calculate the sum S1 of the areas of the (2n+1)×(2n+1) unit facets contained in the research scale facet;

[0105] If the influence of formation tilt is not considered, the fold coefficient of the research unit is the ratio of S1 to the projected area of ​​the research scale surface element; if the influence of formation tilt is considered, the fold coefficient of the research unit is the ratio of S1 to the actual area of ​​the research scale surface element.

[0106] In general, the more developed the stratigraphic folds in the study unit, the greater the fold coefficient, and the less developed the stratigraphic folds, the smaller the fold coefficient.

[0107] The plane map of the comprehensive formation fold coefficient corresponds to the plane map of fault and fracture development, which guides the analysis of the reservoir and enrichment control patterns of the fault-fold and fracture bodies in the target layer.

[0108] By determining the scale of the research area, the actual spatial approximate distribution surface area calculated along the fine structure interpretation stratum and the projection area ratio of the research surface element under different projection methods are used to quantitatively predict the degree of stratum fold development. Through test calculations in the actual research area, it can be seen that this method can effectively characterize the fold development belt produced by the stratum under stress in different periods and directions; the algorithm principle and parameter setting are simple, easy to implement, and the calculation speed is fast. In actual production, the degree of folds at different scales of the target stratum can be quickly determined, providing interpreters with an effective and reliable means of quantitative evaluation of the degree of stratum fold development.

[0109] Example 3

[0110] Figure 2 A schematic diagram showing the principle of applying the method according to an embodiment of the present invention to determine the strength of the fold development in the stratum.

[0111] Figure 3 A schematic diagram showing the surface element projection area calculated by the research unit without considering eliminating the influence of formation tilt according to an embodiment of the present invention.

[0112] Figure 4 A schematic diagram showing the actual area of ​​the surface element calculated by the research unit when eliminating the influence of formation tilt according to an embodiment of the present invention is shown.

[0113] Figure 2 It is a schematic diagram of the definition of the unit cell and the research cell grid. Figure 3-Figure 4 This paper explains the different calculation methods of the projected area of ​​the study unit without considering the effect of eliminating the formation tilt. It can be seen from the schematic diagram that the larger the fold coefficient, the higher the degree of fold development in the study unit area; on the contrary, the folds in the study unit area are underdeveloped.

[0114] Figure 5a FIG. 1 shows a planar distribution diagram of fold coefficients for eliminating the influence of formation tilt according to an embodiment of the present invention. Figure 5b FIG. 1 shows a planar distribution diagram of the maximum structural curvature along a layer according to an embodiment of the present invention. Figure 5c shows a distribution diagram along the layer dip plane according to an embodiment of the present invention, Figure 5d A planar distribution diagram of the fault strike of the target layer according to an embodiment of the present invention is shown.

[0115] According to this method, the degree of fold development of the target layer in the work area can be quantitatively predicted, such as Figure 5a-5d As shown, Figure 5a The prediction results depict the fold development belt and Figure 5dThe fault distribution law of the present invention is consistent with that of the present invention, which verifies the rationality and effectiveness of the present invention; Figure 5c The distribution of fold development zones predicted by the plane distribution map of structural curvature along the layers shown in the figure is roughly similar. However, this method is not only more efficient in calculation, but also the fold development zones depicted are more in line with the understanding of geological laws, which is helpful for the subsequent guidance and analysis of the law of fault-fold-fracture body controlling enrichment and reservoir control in the work area, and can be promoted and applied in other work areas to promote the efficient exploration and development of fault-fold-fracture body gas reservoirs under the action of multi-stage and different stress transformation.

[0116] Example 4

[0117] Figure 6 A block diagram of a wrinkle coefficient quantitative prediction device according to an embodiment of the present invention is shown.

[0118] like Figure 6 As shown, the wrinkle coefficient quantitative prediction device comprises:

[0119] The layer characterization module 201 performs fine layer characterization on the target layer;

[0120] A scale determination module 202 determines the scale of the wrinkle coefficient surface element, including the unit surface element scale and the research surface element scale;

[0121] The calculation module 203 calculates the wrinkle coefficient under the wrinkle coefficient surface element according to the unit surface element scale and the research surface element scale.

[0122] As a specific implementation of the embodiment of the present disclosure, fine layer characterization for the target layer includes:

[0123] Based on the existing geophysical data and interpretation results in the study area, a detailed characterization of the target layer is completed.

[0124] As a specific implementation of the embodiment of the present disclosure, the facet grid of the unit facet is consistent with the grid size and rotation angle of the seismic network.

[0125] As a specific implementation manner of the embodiment of the present disclosure, the research panel scale is 2n+1 times the unit panel scale.

[0126] As a specific implementation of the embodiment of the present disclosure, according to the unit panel scale and the research panel scale, calculating the wrinkle coefficient under the wrinkle coefficient panel includes:

[0127] Calculate the lengths of the four sides of the spatial quadrilateral composed of the unit surface element and the vertices under the research surface element respectively;

[0128] Calculate the projection area of ​​the corresponding face element by calculating the side length of the spatial quadrilateral;

[0129] Calculate the sum S1 of the areas of the unit cells contained in the research cell;

[0130] Determine whether to consider eliminating the influence of formation tilt and then calculate the fold coefficient.

[0131] As a specific implementation of the embodiment of the present disclosure, if the influence of formation tilt is not considered, the fold coefficient is the ratio of S1 to the projected area of ​​the research surface element.

[0132] As a specific implementation of the embodiment of the present disclosure, if the influence of formation inclination is eliminated, the fold coefficient is the ratio of S1 to the actual area of ​​the research surface element.

[0133] Example 5

[0134] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; and a processor, which runs the executable instructions in the memory to implement the above-mentioned quantitative prediction method of wrinkle coefficient.

[0135] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0136] The memory is used to store non-temporary 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 (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0137] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0138] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0139] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0140] Example 6

[0141] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for quantitatively predicting wrinkle coefficient is implemented.

[0142] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.

[0143] The above-mentioned 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 mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0144] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0145] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A quantitative prediction method for wrinkle coefficient, characterized in that: include: Carry out detailed stratigraphic characterization for the target layer; Determine the scale of wrinkle coefficient surface element, including unit surface element scale and research surface element scale; The wrinkle coefficient under the wrinkle coefficient surface element is calculated according to the unit surface element scale and the research surface element scale.

2. The method for quantitatively predicting wrinkle coefficient according to claim 1, wherein: The detailed characterization of the target layer includes: Based on the existing geophysical data and interpretation results in the study area, a detailed characterization of the target layer is completed.

3. The method for quantitatively predicting wrinkle coefficient according to claim 1, wherein: The cell grid of the unit cell is consistent with the grid size and rotation angle of the seismic network.

4. The method for quantitatively predicting wrinkle coefficient according to claim 3, wherein: The research surface element scale is 2n+1 times the unit surface element scale.

5. The method for quantitatively predicting wrinkle coefficient according to claim 1, wherein: According to the unit surface element scale and the research surface element scale, calculating the wrinkle coefficient under the wrinkle coefficient surface element includes: Respectively calculating the lengths of the four sides of the spatial quadrilateral formed by the unit surface element and each vertex under the research surface element; Calculate the projection areas of the corresponding face elements respectively by calculating the side lengths of the spatial quadrilateral; Calculating the sum S1 of the areas of the unit cells contained in the research cell; Determine whether to consider eliminating the influence of formation tilt, and then calculate the fold coefficient.

6. The method for quantitatively predicting wrinkle coefficient according to claim 5, wherein: If the influence of formation tilt is not considered, the fold coefficient is the ratio of S1 to the projected area of ​​the research surface element.

7. The method for quantitatively predicting wrinkle coefficient according to claim 5, wherein: If the influence of formation tilt is eliminated, the fold coefficient is the ratio of S1 to the actual area of ​​the research surface element.

8. A quantitative prediction device for wrinkle coefficient, characterized in that: include: The layer characterization module is used to carry out detailed layer characterization for the target layer; A scale determination module determines the scale of the wrinkle coefficient surface element, including the unit surface element scale and the research surface element scale; A calculation module calculates the wrinkle coefficient under the wrinkle coefficient surface element according to the unit surface element scale and the research surface element scale.

9. An electronic device, characterized in that: The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the method for quantitatively predicting the wrinkle coefficient according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for quantitatively predicting wrinkle coefficient according to any one of claims 1 to 7 is implemented.

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