Method and device for automatically identifying fault based on gradient intensity and direction change of seismic data

Abstract

The invention provides a method and a device for automatically identifying a fault based on seismic data gradient intensity and direction change. The method comprises the following steps: acquiring seismic profile data; calculating geometric features of gradient directions and intensity changes of position points in the seismic profile data; and fault identification is carried out according to the calculated geometric features of the gradient direction and the intensity change of the position point. Aiming at the defects that in the prior art, artificial fault identification depends on experience, and a conventional seismic fault attribute body is not obvious in feature on a seismic section, fault identification is directly carried out through seismic data driving according to geometric features of specific gradient direction and intensity change of a fault on a seismic data body, so that artificial dependence is reduced, and the fault identification efficiency is improved. And the seismic section fault identification capability is enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of oil seismic exploration, and particularly relates to a method and device for automatically identifying faults based on gradient strength and direction change of seismic data. BACKGROUND

[0002] The development of underground faults has an important influence on the migration and preservation of oil and gas, and therefore, fault interpretation and identification are an important basic work in the field of oil seismic exploration. At present, in oil fields, faults are mainly identified by manual interpretation on seismic data. The methods for manually interpreting faults mainly include the following means: (1) observing the features such as segment error and distortion of seismic events on a seismic profile to identify faults, and then manually marking the faults. This method is labor-intensive and relies on human experience; (2) some seismic attributes, such as coherence volume, ant volume and curvature, are used to assist in fault identification. The continuous features of faults are obvious on the horizontal time slices of these seismic attribute volumes, and the faults are relatively easy to identify, but the fault identification effect is generally poor on seismic profiles, and is easily disturbed by other signals; (3) the method of artificial intelligence interpretation has developed rapidly in recent years, and can identify faults by deep learning according to a large number of fault labels, and has achieved certain results. However, this method needs to do a large amount of fault labeling, and the pre-work is huge, and the fault identification effect is related to the number and feature distribution of the label samples. Once the target block is inconsistent with the feature of the label sample, the fault prediction effect will be greatly reduced. SUMMARY

[0003] The method and device for automatically identifying faults based on gradient strength and direction change of seismic data provided by the embodiments of the present application reduce the dependence of fault identification on manual work and enhance the fault identification capability of seismic profiles.

[0004] In a first aspect, the embodiments of the present application provide a method for automatically identifying faults based on gradient strength and direction change of seismic data, comprising:

[0005] obtaining seismic profile data;

[0006] calculating the geometric features of the gradient direction and strength change of the position points in the seismic profile data;

[0007] performing fault identification according to the calculated geometric features of the gradient direction and strength change of the position points.

[0008] In some embodiments, obtaining the seismic profile data includes reading the seismic profile data, and the seismic profile data is an m*n size matrix array, represented by D(u,v), where u and v are coordinate indexes of the matrix array D, u∈[1,m], v∈[1,m].

[0009] In some embodiments, the computing the geometric features of the position point gradient direction and intensity variation in the seismic profile data comprises: computing the maximum gradient direction of each position of the seismic profile, the difference between the maximum gradient direction and the periphery, and the size of the maximum gradient direction intensity.

[0010] In some embodiments, the fault identification according to the computed geometric features of the position point gradient direction and intensity variation comprises: for a point on the seismic event, the direction with the minimum gradient variation is the stratum development direction, and the direction with the maximum gradient variation is the stratum deposition direction.

[0011] In some embodiments, the fault identification according to the computed geometric features of the position point gradient direction and intensity variation comprises: the greater the intensity of the maximum gradient direction, the more intense the stratum and physical property variation; the smaller the intensity of the maximum gradient direction, the more gentle the stratum deposition variation; when a fault develops, the maximum gradient variation direction of the point on the seismic profile is perpendicular to the section, and the maximum gradient direction is inconsistent with the maximum gradient direction of the periphery.

[0012] In some embodiments, the computing the geometric features of the position point gradient direction and intensity variation in the seismic profile data comprises:

[0013] computing the gradient values Gx and Gy of each position point of the matrix array D in the horizontal and vertical directions;

[0014] computing the maximum gradient intensity of each position point of the matrix array D, I(u, v) = Gx 2 + Gy 2 ;

[0015] computing the maximum gradient direction of each position point of the matrix array D, A(u, v) = atan(abs(Gy / Gx)) x 180 / π);

[0016] setting a distance radius parameter R, computing the variance of the gradient direction variation of all array points within the R radius of the matrix array D, and assigning the value to the array point (u, v), denoted as Avar(u, v), Avar(u, v) represents the variation of the maximum gradient direction in the area with the array point (u, v) as the center and R as the radius;

[0017] setting a gradient intensity threshold Iθ, when I(u, v) < Iθ, making Avar(u, v) = 0; otherwise, the value of Avar(u, v) remains unchanged.

[0018] In some embodiments, the fault identification according to the calculated geometric features of the gradient direction and intensity variation of the position points comprises: obtaining an array Avar(u, v) with the same size as the seismic profile matrix D, where u∈[1, m], v∈[1, n], drawing an Avar(u, v) graph, and obtaining a graph about the maximum gradient intensity variation and the maximum gradient direction variation of the seismic data, which is used for predicting the seismic fault.

[0019] In a second aspect, the application provides a device for automatically identifying a fault based on gradient intensity and direction variation of seismic data, comprising:

[0020] A obtaining unit configured to obtain seismic profile data.

[0021] A calculating unit configured to calculate geometric features of gradient direction and intensity variation of position points in the seismic profile data.

[0022] A recognizing unit configured to identify a fault according to the calculated geometric features of the gradient direction and intensity variation of the position points.

[0023] In a third aspect, the application provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any one of the above aspects.

[0024] In a fourth aspect, the application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of any one of the above aspects when executing the program.

[0025] The method and device for automatically identifying a fault based on gradient intensity and direction variation of seismic data provided by the embodiments of the application have the following beneficial effects:

[0026] The application directly identifies a fault according to the geometric features of the gradient direction and intensity variation of the fault on a seismic data body, reduces the dependence on manual work, and enhances the fault identification capability of a seismic profile. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 FIG. 1 is a flowchart of a method for automatically identifying a fault based on gradient intensity and direction variation of seismic data according to an embodiment of the application;

[0028] Figure 2 FIG. 2 is another flowchart of a method for automatically identifying a fault based on gradient intensity and direction variation of seismic data according to an embodiment of the application;

[0029] Figure 3 FIG. 3 is a schematic diagram of a seismic profile according to an embodiment of the application;

[0030] Figure 4for Figure 3 A schematic diagram of the gradient intensity I of the seismic profile;

[0031] Figure 5 for Figure 3 A schematic diagram of the variance of the maximum gradient direction (within radius R) of a seismic profile;

[0032] Figure 6 for Figure 3 Differences in fault characteristics of seismic profiles;

[0033] Figure 7a for Figure 3 Enlarged view of the top of the seismic profile;

[0034] Figure 7b for Figure 3 Enlarged view of the top of the seismic profile (including fault information profile);

[0035] Figure 7c The fault feature map is extracted using the method of this application;

[0036] Figure 8 This is a schematic diagram of the device structure for automatically identifying faults based on changes in gradient intensity and direction of seismic data, as described in an embodiment of this application. Detailed Implementation

[0037] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0038] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of the invention, which can be substituted or combined with each other. Therefore, this application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more other possible combinations of features A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0039] Example 1

[0040] like Figure 1 As shown, the method for automatically identifying faults based on changes in gradient intensity and direction of seismic data in this application includes: S101, acquiring seismic profile data; S103, calculating the geometric characteristics of gradient direction and intensity changes at location points in the seismic profile data; S105, identifying faults based on the calculated geometric characteristics of gradient direction and intensity changes at location points.

[0041] In view of the fact that artificial fault identification in the prior art relies on experience and the characteristics of conventional seismic fault attribute bodies on seismic profiles are not obvious, the application directly identifies faults by driving seismic data, mainly according to the geometric characteristics of the gradient direction and intensity variation of the faults on the seismic data body, thereby reducing the dependence on manpower and enhancing the fault identification capability of seismic profiles.

[0042] Embodiment two

[0043] The inventors of the application have found that the seismic events on a seismic profile are closely related to the development direction of the strata, the seismic events usually keep consistent with the development direction of the strata, and the amplitude of the seismic events changes greatly in the vertical direction of the strata due to the geological deposition thickness and the dramatic change in lithology. Therefore, for a point on the seismic events, the direction with the minimum gradient variation is the development direction of the strata, and the direction with the maximum gradient variation is usually the vertical direction of the strata, i.e., the direction of strata deposition. In addition, the greater the intensity of the maximum gradient direction, the more dramatic the change in the strata and the physical properties; the smaller the intensity of the maximum gradient direction, the more gentle the change in the strata deposition. When a fault develops, the maximum gradient variation direction of the point on the seismic profile should be perpendicular to the section due to the sudden change in lithology of the two plates of the fault, and the maximum gradient direction is also inconsistent with the maximum gradient direction of the surrounding (the maximum gradient direction of the surrounding non-fault position is perpendicular to the strata). Therefore, the development of the fault at the position can be determined by calculating the maximum gradient direction of each position on the seismic profile, the difference (variance) between the maximum gradient direction and the surrounding, and the intensity of the maximum gradient direction.

[0044] As shown in Figure 2 , the method for automatically identifying faults based on the gradient intensity and direction variation of seismic data includes the following steps:

[0045] Step 1: reading seismic profile data, the seismic profile data is usually an m x n size matrix array, represented by D(u, v), wherein u and v are the coordinate indexes of the matrix array D, u [1, m], v [1, n];

[0046] Step 2: calculating the gradient values Gx and Gy of each position point of the matrix array D in the horizontal and vertical directions.

[0047] Step 3: calculating the maximum gradient intensity of each position point of the matrix array D, I(u, v) = Gx 2 + Gy 2 ;

[0048] Step 4: calculating the maximum gradient direction of each position point of the matrix array D, A(u, v) = atan(abs(Gy / Gx)) x 180 / π), wherein atan is the inverse tangent function, and abs is the absolute value function.

[0049] Step 5: Set the distance radius parameter R, calculate the variance of the gradient direction change of all array points in the matrix array D within the R radius, and assign the value to the array point (u, v), denoted as Avar(u, v). Avar(u, v) represents the change of the maximum gradient direction in the area with the array point (u, v) as the center and R as the radius;

[0050] Step 6: Set the threshold Iθ of the gradient intensity, when I(u, v) < Iθ, make Avar(u, v) = 0; otherwise, the value of Avar(u, v) remains unchanged;

[0051] Step 7: Finally obtain the array Avar(u, v) with the same size as the seismic profile matrix D, where u∈[1, m], v∈[1, n], draw the array Avar(u, v), and obtain the graph related to the maximum gradient intensity change and the maximum gradient direction change of the seismic data, which can be used for seismic fault prediction.

[0052] Figure 3 For a seismic profile, it can be seen that the strata develop rapidly, including both gentle strata and large dip strata, and also large faults and small faults of different levels. Figure 4 is the gradient intensity I of the seismic profile Figure 3 , it can be seen that the characteristics are very obvious in places where the strata sedimentary direction changes rapidly; Figure 5 is the variance of the maximum gradient direction change (within the radius R) of the seismic profile Figure 3 , it can be seen that strong values appear in places around the fault and where the strata morphology changes greatly. Figure 6 is the final obtained fault feature difference graph, it can be seen that the fault features are obviously highlighted. As shown in Figure 7a , Figure 7b , Figure 7c , it can be seen from the seismic profile that there are a series of small faults developed (in Figure 7b , the red line segment is the fault), in the fault feature difference graph of Figure 7c , the small faults show strong values, and the fault features are very obvious and easy to identify.

[0053] The present application is driven by seismic data, mainly according to the geometric characteristics of the gradient direction and intensity change of the fault on the seismic data body, to directly identify the fault. The present application method reduces the dependence on artificial and enhances the ability of seismic profile fault identification.

[0054] As shown in Figure 8As shown, the device for automatically identifying faults based on gradient strength and direction change of seismic data in the application comprises: an acquisition unit 201 for acquiring seismic profile data; a calculation unit 202 for calculating the geometric features of the gradient direction and strength change of the position points in the seismic profile data; and an identification unit 203 for identifying faults according to the calculated geometric features of the gradient direction and strength change of the position points.

[0055] In the application, the device embodiment for automatically identifying faults based on gradient strength and direction change of seismic data is basically similar to the method embodiment for automatically identifying faults based on gradient strength and direction change of seismic data, and the relevant parts are referred to the introduction of the method embodiment for automatically identifying faults based on gradient strength and direction change of seismic data.

[0056] The application further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method steps for automatically identifying faults based on gradient strength and direction change of seismic data when executing the program.

[0057] The application embodiment further provides a computer readable storage medium having a computer program stored thereon, and the program is executed by the processor to implement the method steps for automatically identifying faults based on gradient strength and direction change of seismic data. The computer readable storage medium can include but is not limited to any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0058] The above introduction is only the preferred embodiment of the application and is not used to limit the application. For those skilled in the art, the application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for automatically identifying faults based on changes in the strength and direction of seismic data gradients, characterized by, The method comprises the following steps: acquiring seismic profile data; calculating geometric features of gradient direction and intensity variation of position points in the seismic profile data; identifying faults according to the calculated geometric features of gradient direction and intensity variation of position points.

2. The method of claim 1, wherein, The acquiring of the seismic profile data comprises reading seismic profile data, which is a matrix array of size m*n, denoted as D(u,v), where u and v are coordinate indexes of the matrix array D, u∈[1,m], v∈[1,n].

3. The method of claim 1 or 2, wherein, The calculating of the geometric features of gradient direction and intensity variation of position points in the seismic profile data comprises calculating the maximum gradient direction of each position of the seismic profile, the difference between the maximum gradient direction and the periphery, and the size of the maximum gradient direction intensity.

4. The method of claim 1 or 2, wherein, The identifying of the faults according to the calculated geometric features of gradient direction and intensity variation of position points comprises that, for a certain point on a seismic event, the direction with the minimum gradient variation is the stratum development direction, and the direction with the maximum gradient variation is the stratum deposition direction.

5. The method of claim 1 or 2, wherein, The identifying of the faults according to the calculated geometric features of gradient direction and intensity variation of position points comprises that, the greater the intensity of the maximum gradient direction, the more intense the stratum and physical property variation; the smaller the intensity of the maximum gradient direction, the more gentle the stratum deposition variation; when a fault develops, the maximum gradient variation direction of the point on the seismic profile is perpendicular to the section, and the maximum gradient direction is inconsistent with the maximum gradient direction of the periphery.

6. The method of claim 2, wherein, The calculating of the geometric features of gradient direction and intensity variation of position points in the seismic profile data comprises: calculating gradient values Gx and Gy of each position point of the matrix array D in the horizontal and vertical directions; Compute the maximum gradient strength at each location point of the matrix array D, I(u,v) = Gx 2 + Gy 2 ; calculating the maximum gradient direction of each position point of the matrix array D, A(u,v)=atan(abs(Gy / Gx))×180 / π); setting a distance radius parameter R, calculating the variance of gradient direction variation of all array points within the R radius of the matrix array D, and assigning the value to the array point (u,v), denoted as Avar(u,v), which represents the gradient maximum direction variation within the area with the array point (u,v) as the center and R as the radius; setting a gradient intensity threshold Iθ, when I(u,v)<Iθ, making Avar(u,v)=0; otherwise, the value of Avar(u,v) remains unchanged.

7. The method of claim 6, wherein, The identifying of the faults according to the calculated geometric features of gradient direction and intensity variation of position points comprises obtaining an array Avar(u,v) of the same size as the seismic profile matrix D, where u∈[1,m], v∈[1,n], drawing an Avar(u,v) graph to obtain a graph of the maximum gradient intensity variation and the maximum gradient direction variation of the seismic data, which is used for predicting seismic faults.

8. An apparatus for automatically identifying faults based on changes in the strength and direction of seismic data gradients, comprising: The method comprises the following steps: an acquiring unit, configured to acquire seismic profile data; a calculating unit, configured to calculate geometric features of gradient direction and intensity variation of position points in the seismic profile data; an identifying unit, configured to identify faults according to the calculated geometric features of gradient direction and intensity variation of position points.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by a processor to implement the steps of the method in any one of claims 1-7.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor, when executing the program, implements the steps of the method of any one of claims 1-7.

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