Stratigraphic dip angle determination method and device based on local linear constraint

Abstract

The invention provides a method and a device for determining a stratigraphic dip angle based on local linear constraint. The method comprises the following steps: determining a stratigraphic gradient of a target point location according to seismic imaging data of surrounding point locations of the target point location; determining a local linear attribute parameter of the target point location according to the stratum gradient of the target point location based on a preset local time window; and determining the stratigraphic dip angle of the target point location according to the local linear attribute parameters of the target point location. The stratigraphic dip angle determination method and device based on the local linear constraint can overcome the technical defects that an existing stratigraphic dip angle calculation method is poor in stability and low in calculation result resolution under the condition that the seismic data quality is poor.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, particularly to the field of underground structure characterization technology in geophysical exploration, and more particularly to a method and device for determining formation dip based on local linear constraints. Background Art

[0002] It is easy to understand that in the field of seismic data processing, it is crucial to accurately calculate the dip of underground formations. Specifically, the dip of formations can not only be used for picking up tomographic reflection points for velocity modeling, but also for automatic stratum tracking and estimation of multiple attributes for seismic data interpretation, so as to be used for subsequent accurate reservoir prediction and oil reservoir description, and ultimately to achieve precise positioning of oil and gas reservoirs. Therefore, as the basic data for many seismic data processing and interpretation links, the accuracy of its calculation is extremely critical. In the prior art, the dip of formations is generally estimated based on the principle of local amplitude energy superposition, that is, by artificially defining a local time window, calculating the superposition energy within the local time window, and the dip corresponding to the maximum value of the superposition energy is the dip of the formation at that point.

[0003] When the seismic data quality is good and the signal-to-noise ratio is high, the above method can obtain a more reliable formation dip. However, when the seismic data quality is poor, such as when the effective signal energy is weak or the data signal-to-noise ratio is low, the above method has poor calculation accuracy and is particularly dependent on the calculated longitudinal window length, stacking aperture and other parameters. In addition, in this case, the above method is greatly affected by strong amplitudes, has extremely low calculation efficiency, and has low stability and resolution of calculation results. Summary of the invention

[0004] One object of the present invention is to provide a method for determining the formation dip based on local linear constraints. First, the formation gradient at each position is calculated to obtain the gradient data body of the formation. On this basis, a Gaussian filter operator is determined according to a preset time window, and it is applied to the gradient data body to obtain the local linear attribute of the formation, realize the weight coefficient adjustment of the formation gradient, and finally determine the dip of the formation using the gradient after filtering. The purpose of this method is to solve the technical pain points that the existing method for calculating the formation dip is poor in stability and the resolution of the calculation result is low when the quality of seismic data is poor. Another object of the present invention is to provide a device for determining the formation dip based on local linear constraints. Another object of the present invention is to provide a computer device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above-mentioned method for determining the formation dip based on local linear constraints when executing the computer program. Another object of the present invention is to provide a readable medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps of the above-mentioned method for determining the formation dip based on local linear constraints are implemented.

[0005] In order to achieve the above objectives, the present invention discloses, on one hand, a method for determining a formation dip angle based on local linear constraints, comprising:

[0006] Determine the stratigraphic gradient of the target point based on seismic imaging data of points surrounding the target point;

[0007] Based on a preset local time window, determining a local linear attribute parameter of the target point according to the formation gradient of the target point;

[0008] The formation dip angle of the target point is determined according to the local linear attribute parameters of the target point.

[0009] In one embodiment, determining the stratigraphic gradient of the target point based on seismic imaging data of points surrounding the target point includes:

[0010] The stratigraphic gradient of the target point is determined based on seismic imaging data of at least six points around the target point.

[0011] In one embodiment, the at least six points are located on both sides of the target point in the x direction, the y direction and the z direction respectively.

[0012] In one embodiment, determining the stratigraphic gradient of the target point based on the seismic imaging data of the surrounding points of the target point further includes:

[0013] Determine a first initial gradient of the target point on a plane formed by the x-direction and the z-direction according to seismic imaging data of points surrounding the target point;

[0014] A second initial gradient of the target point on a plane formed by the y direction and the z direction is determined according to seismic imaging data of points surrounding the target point.

[0015] In one embodiment, the determining of the local linear attribute parameter of the target point based on the preset local time window and the formation gradient of the target point includes:

[0016] Determining a Gaussian filter operator corresponding to the preset local time window;

[0017] Determine a local linear attribute parameter of the target point in the x direction according to the Gaussian filter operator and the first initial gradient;

[0018] The local linear attribute parameters of the target point in the y direction are determined according to the Gaussian filter operator and the second initial gradient.

[0019] In one embodiment, determining the formation dip angle of the target point according to the local linear attribute parameter of the target point includes:

[0020] Determining a point with maximum local amplitude stacking energy in the seismic imaging data according to the local linear attribute parameter and the preset local time window;

[0021] The formation dip of the target point is determined according to the formation dip corresponding to the point with the maximum local amplitude stacking energy.

[0022] In one embodiment, the method for determining formation dip angle based on local linear constraints further includes:

[0023] The formation dip angle of the formation where the target point is located is determined according to the local linear attribute parameters of the multiple target points.

[0024] The present invention also discloses a formation dip determination device based on local linear constraints, comprising:

[0025] A point gradient determination module, used to determine the formation gradient of the target point based on the seismic imaging data of the surrounding points of the target point;

[0026] An attribute parameter determination module, used to determine the local linear attribute parameter of the target point according to the formation gradient of the target point based on a preset local time window;

[0027] The point dip determination module is used to determine the formation dip of the target point according to the local linear attribute parameters of the target point.

[0028] In one embodiment, determining the stratigraphic gradient of the target point based on seismic imaging data of points surrounding the target point includes:

[0029] The stratigraphic gradient of the target point is determined based on seismic imaging data of at least six points around the target point.

[0030] In one embodiment, the point gradient determination module includes:

[0031] The point gradient determination unit is used to determine the formation gradient of the target point based on the seismic imaging data of at least six points around the target point.

[0032] In one embodiment, the at least six points are located on both sides of the target point in the x direction, the y direction and the z direction respectively.

[0033] In one embodiment, the point gradient determination module further includes:

[0034] A first initial gradient determining unit, configured to determine a first initial gradient of the target point on a plane formed by the x-direction and the z-direction according to seismic imaging data of points surrounding the target point;

[0035] The second initial gradient determining unit is used to determine the second initial gradient of the target point on the plane formed by the y direction and the z direction according to the seismic imaging data of the surrounding points of the target point.

[0036] In one embodiment, the attribute parameter determination module includes:

[0037] A Gaussian filter operator determination unit, used to determine the Gaussian filter operator corresponding to the preset local time window;

[0038] An x-direction parameter determination unit, configured to determine a local linear attribute parameter of the target point in the x-direction according to the Gaussian filter operator and the first initial gradient;

[0039] The y-direction parameter determination unit is used to determine the local linear attribute parameters of the target point in the y-direction according to the Gaussian filter operator and the second initial gradient.

[0040] In one embodiment, the point inclination angle determination module includes:

[0041] An energy point determination unit, configured to determine a point with the maximum local amplitude stacking energy in the seismic imaging data according to the local linear attribute parameter and the preset local time window;

[0042] The point inclination determination unit is used to determine the formation inclination of the target point according to the formation inclination corresponding to the point with the maximum local amplitude superposition energy.

[0043] In one embodiment, the formation dip determination device based on local linear constraints further includes:

[0044] The layer dip angle determination module is used to determine the layer dip angle of the layer where the target point is located according to the local linear attribute parameters of multiple target points.

[0045] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0046] When the processor executes the program, the method described above is implemented.

[0047] The present invention also discloses a computer readable medium having a computer program stored thereon.

[0048] When the program is executed by a processor, the method described above is implemented.

[0049] From the above description, it can be seen that the method and device for determining the formation inclination based on local linear constraints provided in an embodiment of the present invention first determine the formation gradient of the target point based on the seismic imaging data of the surrounding points of the target point; then, based on a preset local time window, determine the local linear attribute parameters of the target point according to the formation gradient of the target point; finally, determine the formation inclination of the target point according to the local linear attribute parameters of the target point.

[0050] The method and device for determining formation inclination based on local linear constraints provided by the present invention have wider applicability than the formation inclination determination method in the prior art. Even when the quality of seismic data is poor, the calculated formation inclination has high accuracy and the method is relatively stable, which solves the shortcomings of poor stability and low resolution of calculation results of existing methods for calculating formation inclination when the quality of seismic data is poor. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A schematic diagram of a flow chart of a method for determining formation dip angle based on local linear constraints in Embodiment 1 of the present invention;

[0053] Figure 2 It is a flow chart of step 100 of the method for determining formation dip angle based on local linear constraints in the first embodiment of the present invention;

[0054] Figure 3 It is another flow chart of step 100 of the method for determining formation dip angle based on local linear constraints in the first embodiment of the present invention;

[0055] Figure 4 It is a flow chart of step 200 of the method for determining formation dip angle based on local linear constraints in the first embodiment of the present invention;

[0056] Figure 5 It is a flow chart of step 300 of the method for determining formation dip angle based on local linear constraints in the first embodiment of the present invention;

[0057] Figure 6 It is another flow chart of the method for determining formation dip angle based on local linear constraints in the first embodiment of the present invention;

[0058] Figure 7It is a flow chart of a method for determining formation dip angle based on local linear constraints in Embodiment 2 of the present invention;

[0059] Figure 8 A schematic diagram of a seismic profile input in the third embodiment of the present invention;

[0060] Fig. 9 Schematic diagram of the calculation result of the formation dip angle input in the third embodiment of the present invention (corresponding to Figure 8 Schematic diagram of the seismic section of the middle and shallow layers (dashed box);

[0061] Fig.10 Schematic diagram of the calculation result of the formation dip angle input in the third embodiment of the present invention (corresponding to Figure 8 Schematic diagram of the seismic section of the middle and deep layers (dashed box);

[0062] Fig.11 It is a schematic diagram of the formation dip angle calculated by conventional method in the third embodiment of the present invention;

[0063] Fig.12 This is a schematic diagram of the formation dip result calculated by using the formation dip determination method based on local linear constraints provided by the present application in the third embodiment of the present invention;

[0064] Fig.13 This is a result schematic diagram of the formation dip field calculated by using the formation dip determination method based on local linear constraints provided by the present application in the third embodiment of the present invention;

[0065] Fig.14 It is a structural block diagram of a method and device for determining formation inclination based on local linear constraints in Embodiment 4 of the present invention;

[0066] Fig.15 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] Embodiment 1

[0070] In this embodiment, Figure 1 As shown, a method for determining formation dip angle based on local linear constraints is provided, which includes:

[0071] Step 100: Determine the formation gradient of the target point based on seismic imaging data of points surrounding the target point.

[0072] It is understood that seismic imaging data is underground structural information obtained through seismic measurement technology. Seismic imaging is usually used to study the material distribution, geological structure and resource exploration inside the earth. In seismic imaging, seismic waves propagate between different underground media and are reflected or refracted according to the changes in the speed and density of different media.

[0073] There are two main seismic imaging techniques:

[0074] 1. Seismic reflection imaging: Inferring underground structures by recording the energy of seismic waves reflected at underground interfaces. This technology is often used to study the distribution of strata, oil and gas reservoirs, mineral resources, etc.

[0075] 2. Seismic refraction imaging: Inferring underground structures by measuring changes in the propagation paths of seismic waves in different underground media.

[0076] In addition, seismic imaging data is usually presented in the form of seismic profiles, which can be two-dimensional (2D) or three-dimensional (3D) data. In a 2D seismic profile, seismic data is collected along a survey line; while in a 3D seismic profile, seismic data is collected in the form of a cube or grid in space.

[0077] Step 200: Based on a preset local time window, determine the local linear attribute parameters of the target point according to the formation gradient of the target point.

[0078] In this application, the local linear property means that the distribution of seismic imaging data can be approximated as a linear relationship in a small local area around each data point in the seismic imaging data. In other words, if a seismic imaging data has a local linear property, then in the data space, the data points near each data point can be better described by a linear model.

[0079] Preferably, the smoothness and continuity of the seismic imaging data can be used to determine the local linear attribute. In the local linear attribute, the neighboring data points of a data point often have similar characteristic values, so the data in the local area can be used to predict or estimate the characteristics of a data point.

[0080] Step 300: Determine the formation dip angle of the target point according to the local linear attribute parameters of the target point.

[0081] It can be understood that, based on step 300, the formation dip of the formation where the target point is located can be obtained by using the formation dips of multiple target points, thereby effectively overcoming the technical problem in the prior art of over-reliance on parameters such as the longitudinal window length and stacking aperture in the calculation process due to poor quality of seismic data.

[0082] In one embodiment, if Figure 2 As shown, step 100 includes:

[0083] Step 101: Determine the formation gradient of the target point based on seismic imaging data of at least six points around the target point.

[0084] Preferably, the stratigraphic gradient of the target point in step 101 can be calculated by using a reweighted six-point differential operator method. It should be noted that the differential operator here is used to describe the rate of change of the stratigraphic gradient of a function at a certain point or the difference between discrete data. Common differential operators include:

[0085] 1. Forward difference: expressed as Δf(x) or f(x+1)-f(x). Used in discrete data to calculate the difference between two adjacent data points.

[0086] 2. Backward difference: expressed as f(x)-f(x-1). Similarly, it is used to calculate the difference between two adjacent data points, but in the opposite direction.

[0087] 3. Central difference: expressed as Δf(x) or (f(x+1)-f(x-1)) / 2. It is used to calculate the average rate of change of two adjacent data points, which is smoother than forward or backward difference (this application prefers this type of difference operator).

[0088] In one embodiment, the at least six points in step 101 are located on both sides of the target point in the x direction, the y direction, and the z direction, respectively.

[0089] That is, the six points in step 101 are respectively located in front of, behind, left of, right of, above and below the target point.

[0090] In one embodiment, if Figure 3 As shown, step 100 also includes:

[0091] Step 102: determining a first initial gradient of the target point on a plane formed by the x-direction and the z-direction according to seismic imaging data of points around the target point;

[0092] Step 103: Determine a second initial gradient of the target point on a plane formed by the y direction and the z direction based on seismic imaging data of points surrounding the target point.

[0093] In step 102 and step 103, the first initial gradient of the target point in the x-direction and the z-direction on the xz plane and the second initial gradient of the target point in the y-direction and the z-direction on the yz plane can be determined based on the imaging data of at least six points around the target point. It should be noted that step 102 and step 103 can be implemented based on step 101 or can be implemented independently of step 101.

[0094] In one embodiment, if Figure 4 As shown, step 200 includes:

[0095] Step 201: Determine the Gaussian filter operator corresponding to the preset local time window;

[0096] In step 201, the Gaussian filter operator is used to smooth the local linear attribute parameters and reduce the noise therein. It calculates each new value in the local linear attribute parameters based on the weight of the Gaussian function.

[0097] The convolution kernel of the Gaussian filter operator has the following characteristics: the weight of the central pixel is the largest and gradually decreases to the edge. The size and weight value of the convolution kernel are determined by the standard deviation (σ). A larger standard deviation will result in a wider weight distribution.

[0098] Step 202: determining a local linear attribute parameter of the target point in the x direction according to the Gaussian filter operator and the first initial gradient;

[0099] Step 203: Determine the local linear attribute parameters of the target point in the y direction according to the Gaussian filter operator and the second initial gradient.

[0100] In step 202 and step 203, a two-dimensional convolution kernel (also called a filter) is first used, and the weight value of the convolution kernel is calculated by a Gaussian function. When the filter is applied, the convolution kernel is convolved with the initial gradient, that is, the convolution kernel is multiplied with the initial gradient, and the results are added to obtain new local linear attribute parameters.

[0101] In one embodiment, if Figure 5 As shown, step 300 includes:

[0102] Step 301: determining a point with the maximum local amplitude stacking energy in the seismic imaging data according to the local linear attribute parameter and the preset local time window;

[0103] Step 302: Determine the formation dip of the target point according to the formation dip corresponding to the point with the maximum local amplitude stacking energy.

[0104] Specifically, in step 301 and step 302, the local amplitude stacking energy is calculated in a preset local time window according to the local linear attribute parameters, and the maximum value of the stacking energy is determined. The inclination of the point corresponding to the maximum value is the formation inclination of the point.

[0105] In one embodiment, if Figure 6 As shown, the formation dip determination method based on local linear constraints also includes:

[0106] Step 400: Determine the formation dip angle of the formation where the target point is located according to the local linear attribute parameters of the multiple target points.

[0107] Specifically, the formation dip of a single target point is determined by the local linear attribute parameters of the point, and then the formation dip of the formation where the target point is located is determined by the formation dips of multiple target points. Of course, the multiple target points need to be located in the same formation.

[0108] From the above description, it can be seen that the method for determining the formation inclination based on local linear constraints provided in an embodiment of the present invention first determines the formation gradient of the target point based on the seismic imaging data of the points surrounding the target point; then, based on a preset local time window, determines the local linear attribute parameters of the target point according to the formation gradient of the target point; finally, determines the formation inclination of the target point according to the local linear attribute parameters of the target point.

[0109] The formation dip determination method based on local linear constraints provided by the present invention has a wider applicability than the formation dip determination method in the prior art. Even when the quality of seismic data is poor, the calculated formation dip has a high accuracy and the method is relatively stable, which solves the shortcomings of poor stability and low resolution of calculation results of existing formation dip calculation methods when the quality of seismic data is poor.

[0110] Embodiment 2

[0111] The present invention provides a method for determining formation dip angle based on local linear constraints. Compared with the prior art, the method for determining formation dip angle using the present invention is more accurate and more applicable (not only for scenarios with good seismic data, but also for scenarios with poor seismic data). Specifically, Figure 7 As shown, the present invention comprises the following steps:

[0112] S1: Determine the stratigraphic gradient of the target point based on seismic imaging data of at least six points around the target point.

[0113] It should be noted that the six points in step S1 are located on both sides of the target point in the x direction, the y direction and the z direction respectively.

[0114] The initial gradient of the target formation is calculated using a reweighted six-point difference operator. In the three-dimensional coordinate system, it is assumed that the three dimensions of the three-dimensional seismic imaging data volume can be expressed as the x direction (n1), the y direction (n2), and the depth z direction (n3).

[0115] S2: determining a first initial gradient of the target point on a plane formed by the x-direction and the z-direction according to seismic imaging data of points surrounding the target point;

[0116] Specifically, the initial gradient of the x-direction point (x, y, z) can be expressed as:

[0117] grad x [S(x,y,z)]=S(x+1,z-1)-2S(x-1,z-1)+4S(x+1,z)-4S(x-1,z)+2S(x+1,z+1)-S(x-1,z+1) (1)

[0118] Where S represents the input seismic imaging data volume, grad x [S(x i ,y j ,z k )] represents the gradient calculated in the x direction, x∈(1,n1), y∈(1,n2), z∈(1,n3).

[0119] S3: Determine a second initial gradient of the target point on a plane formed by the x-direction and the z-direction according to the seismic imaging data of the points surrounding the target point.

[0120] Similarly, the initial gradient of the y-direction point (x, y, z) can be expressed as:

[0121] grad y [S(x,y,z)]=S(y-1,z+1)-2S(y-1,z-1)+4S(y,z+1)-4S(y,z-1)+2S(y+1,z+1)-S(y+1,z-1) (2)

[0122] Among them, grad y [S(x i ,y j ,z k )] represents the gradient calculated in the y direction.

[0123] The initial gradients in the x and y directions can be obtained by using formulas (1) and (2): x [S] and grad y [S].

[0124] S4: Determine the Gaussian filter operator corresponding to the preset local time window;

[0125] For the target point (x, y, z), the Gaussian filter operator with a window size of 2w+1 (w is a positive integer, preferably 1 or 2) can be expressed as:

[0126]

[0127] where σ is the standard deviation.

[0128] S5: determining the local linear attribute parameters of the target point in the x direction according to the Gaussian filter operator and the first initial gradient;

[0129] S6: Determine the local linear attribute parameters of the target point in the y direction according to the Gaussian filter operator and the second initial gradient.

[0130] In step S5 and step S6, for the target point (x, y, z), the window size is 2w+1 and the local linear attributes are:

[0131]

[0132] Among them I x ,I y They represent the local linear properties in the x-direction and y-direction respectively. G is a three-dimensional Gaussian filter operator.

[0133] S7: determining the point with the maximum local amplitude stacking energy in the seismic imaging data according to the local linear attribute parameters and the preset local time window;

[0134] S8: Determine the formation dip of the target point according to the formation dip corresponding to the point with the maximum local amplitude stacking energy.

[0135] The formation dip angle for the target point (x, y, z) can be expressed as:

[0136]

[0137] Where ε is a stability factor, which usually takes a smaller value (preferably 0.001 and 0.0001) to ensure the stability of the calculation results.

[0138] S9: Determine the formation dip of the formation where the target point is located according to the local linear attribute parameters of the multiple target points.

[0139] All points of the target layer are traversed by the method in steps S1 to S8 to complete the formation dip calculation based on local linear constraints.

[0140] From the above description, it can be seen that the formation dip determination method based on local linear constraints provided by the embodiment of the present invention first determines the formation gradient of the target point based on the seismic imaging data of the surrounding points of the target point; then, based on the preset local time window, the local linear attribute parameters of the target point are determined according to the formation gradient of the target point; finally, the formation dip of the target point is determined according to the local linear attribute parameters of the target point. Specifically, the formation gradient of each target point is first calculated using a reweighted six-point difference operator to obtain a gradient data body of the target formation. On this basis, a Gaussian filter operator is designed to act on the gradient data body, thereby obtaining the local linear attributes of the underground, realizing the weight coefficient adjustment of the underground gradient, and finally using the filtered gradient to estimate the dip of the underground formation.

[0141] The formation dip determination method based on local linear constraints provided by the present invention has a wider applicability than the formation dip determination method in the prior art. Even when the quality of seismic data is poor, the calculated formation dip has a high accuracy and the method is relatively stable, which solves the shortcomings of poor stability and low resolution of calculation results of existing formation dip calculation methods when the quality of seismic data is poor.

[0142] Embodiment 3

[0143] In order to further illustrate the technical solution of the present application and prove the correctness and effectiveness of the method for determining the formation dip angle based on local linear constraints provided by the present application, and to show that it has a calculation accuracy not available in the prior art, the present invention is further described by taking a specific block as an example (i.e., by illustrating with actual seismic data). In this specific application example, the process of the method for determining the formation dip angle based on local linear constraints is as follows:

[0144] 1) Input the seismic imaging data volume S, and use formulas (1) and (2) to calculate the initial gradient grad in the x and y directions of the underground x [S] and grad y [S];

[0145] 2) The initial gradients in the x and y directions are filtered by the three-dimensional Gaussian operator G (Formula 3) to obtain the local linear attributes I in the x and y directions of the underground x and I y ;

[0146] 3) Using local linear properties I x and I y , combined with the weighted stability calculation formula (Formula 5), ​​the formation dip angle α of the target layer is obtained.

[0147] like Figure 8The input seismic profile shown is used in this specific application example to calculate the dip information of the underground strata using the seismic profile data.

[0148] like Fig. 9 As shown, it corresponds to Figure 8 The middle and shallow dotted box (the dotted box above) uses the input seismic profile to calculate the formation dip results, where the dots represent test points and the slope of the straight line represents the dip of the corresponding dots. It can be seen that the calculation results of the formation dip determination method based on local linear constraints provided in this application are completely consistent with the profile and the calculation accuracy is high.

[0149] like Fig.10 As shown, this is Figure 8 The mid-deep dotted box (the dotted box below) uses the input seismic profile to calculate the formation dip results, where the dots represent test points and the slope of the straight line represents the dip of the corresponding dots. It can be seen that even for deep weak signals, the calculation results of the formation dip determination method based on local linear constraints provided in this application are completely consistent with the profile, and the calculation accuracy is high.

[0150] like Fig.11 As shown, it shows the result of inclination calculation of local data using conventional methods, where the dots represent test points and the slope of the straight line represents the inclination of the corresponding dots. It can be seen that the calculation results of the conventional method have low accuracy and are greatly different from the actual inclination of the formation.

[0151] like Fig.12 As shown, it shows the results of inclination calculation of local data using the formation inclination determination method based on local linear constraints provided in the present application, wherein the dots represent test points, and the slope of the straight line represents the inclination of the corresponding dots. It can be seen that, in comparison, the calculation results of this method are more accurate and highly consistent with the actual inclination of the formation.

[0152] like Fig.13 As shown, it shows the formation inclination field calculated for input data using the formation inclination determination method based on local linear constraints provided by the present application.

[0153] Embodiment 4

[0154] Based on the same principle, this embodiment also discloses a formation dip determination device based on local linear constraints. Fig.14 As shown, in this embodiment, the device includes:

[0155] A point gradient determination module 10 is used to determine the formation gradient of the target point according to the seismic imaging data of the surrounding points of the target point;

[0156] The attribute parameter determination module 20 is used to determine the local linear attribute parameter of the target point according to the formation gradient of the target point based on a preset local time window;

[0157] The point dip determination module 30 is used to determine the formation dip of the target point according to the local linear attribute parameters of the target point.

[0158] In one embodiment, determining the stratigraphic gradient of the target point based on seismic imaging data of points surrounding the target point includes:

[0159] The stratigraphic gradient of the target point is determined based on seismic imaging data of at least six points around the target point.

[0160] In one embodiment, the point gradient determination module includes:

[0161] The point gradient determination unit is used to determine the formation gradient of the target point based on the seismic imaging data of at least six points around the target point.

[0162] In one embodiment, the at least six points are located on both sides of the target point in the x direction, the y direction and the z direction respectively.

[0163] In one embodiment, the point gradient determination module further includes:

[0164] A first initial gradient determining unit, configured to determine a first initial gradient of the target point on a plane formed by the x-direction and the z-direction according to seismic imaging data of points surrounding the target point;

[0165] The second initial gradient determining unit is used to determine the second initial gradient of the target point on the plane formed by the y direction and the z direction according to the seismic imaging data of the surrounding points of the target point.

[0166] In one embodiment, the attribute parameter determination module includes:

[0167] A Gaussian filter operator determination unit, used to determine the Gaussian filter operator corresponding to the preset local time window;

[0168] An x-direction parameter determination unit, configured to determine a local linear attribute parameter of the target point in the x-direction according to the Gaussian filter operator and the first initial gradient;

[0169] The y-direction parameter determination unit is used to determine the local linear attribute parameters of the target point in the y-direction according to the Gaussian filter operator and the second initial gradient.

[0170] In one embodiment, the point inclination angle determination module includes:

[0171] An energy point determination unit, configured to determine a point with the maximum local amplitude stacking energy in the seismic imaging data according to the local linear attribute parameter and the preset local time window;

[0172] The point inclination determination unit is used to determine the formation inclination of the target point according to the formation inclination corresponding to the point with the maximum local amplitude superposition energy.

[0173] In one embodiment, the formation dip determination device based on local linear constraints further includes:

[0174] The layer dip angle determination module is used to determine the layer dip angle of the layer where the target point is located according to the local linear attribute parameters of multiple target points.

[0175] From the above description, it can be seen that the formation inclination determination device based on local linear constraints provided in an embodiment of the present invention first determines the formation gradient of the target point based on the seismic imaging data of the surrounding points of the target point; then, based on a preset local time window, determines the local linear attribute parameters of the target point according to the formation gradient of the target point; finally, determines the formation inclination of the target point according to the local linear attribute parameters of the target point.

[0176] The formation inclination determination device based on local linear constraints provided by the present invention has a wider applicability than the formation inclination determination method in the prior art. Even when the quality of seismic data is poor, the calculated formation inclination has a high accuracy and the method is relatively stable, which solves the shortcomings of poor stability and low resolution of calculation results of existing formation inclination calculation methods when the quality of seismic data is poor.

[0177] Embodiment 5

[0178] The embodiment of the present application also provides a specific implementation of an electronic device capable of implementing all steps in the method for determining the formation dip angle based on local linear constraints in the above embodiment, see Fig.15 , electronic equipment specifically includes the following:

[0179] Processor (processor) 1201, memory (memory) 1202, communication interface (CommunicationsInterface) 1203 and bus 1204;

[0180] The processor 1201, the memory 1202, and the communication interface 1203 communicate with each other via the bus 1204; the communication interface 1203 is used to realize information transmission between related devices such as server-side devices, computing units, and client devices.

[0181] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all the steps in the formation dip determination method based on local linear constraints in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0182] Step 100: determining the formation gradient of the target point according to the seismic imaging data of the surrounding points of the target point;

[0183] Step 200: Based on a preset local time window, determine the local linear attribute parameters of the target point according to the formation gradient of the target point;

[0184] Step 300: Determine the formation dip angle of the target point according to the local linear attribute parameters of the target point.

[0185] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the method for determining the formation dip angle based on local linear constraints in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps in the method for determining the formation dip angle based on local linear constraints in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0186] Step 100: determining the formation gradient of the target point according to the seismic imaging data of the surrounding points of the target point;

[0187] Step 200: Based on a preset local time window, determine the local linear attribute parameters of the target point according to the formation gradient of the target point;

[0188] Step 300: Determine the formation dip angle of the target point according to the local linear attribute parameters of the target point.

[0189] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0190] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0191] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0192] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0195] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for determining formation dip angle based on local linear constraints, characterized in that: include: Determine the stratigraphic gradient of the target point based on seismic imaging data of points surrounding the target point; Based on a preset local time window, determining a local linear attribute parameter of the target point according to the formation gradient of the target point; The formation dip angle of the target point is determined according to the local linear attribute parameters of the target point.

2. The method for determining formation inclination according to claim 1, characterized in that: Determining the stratigraphic gradient of the target point according to the seismic imaging data of the surrounding points of the target point includes: The stratigraphic gradient of the target point is determined based on seismic imaging data of at least six points around the target point.

3. The method for determining formation inclination according to claim 2, characterized in that: The at least six points are located on both sides of the target point in the x direction, the y direction and the z direction respectively.

4. The method for determining formation inclination according to claim 3, characterized in that: The step of determining the stratigraphic gradient of the target point according to the seismic imaging data of the surrounding points of the target point further comprises: Determine a first initial gradient of the target point on a plane formed by the x-direction and the z-direction according to seismic imaging data of points surrounding the target point; A second initial gradient of the target point on a plane formed by the y direction and the z direction is determined according to seismic imaging data of points surrounding the target point.

5. The method for determining formation dip angle according to claim 4, characterized in that: The determining of the local linear attribute parameter of the target point according to the stratigraphic gradient of the target point based on the preset local time window includes: Determining a Gaussian filter operator corresponding to the preset local time window; Determine a local linear attribute parameter of the target point in the x direction according to the Gaussian filter operator and the first initial gradient; The local linear attribute parameters of the target point in the y direction are determined according to the Gaussian filter operator and the second initial gradient.

6. The method for determining formation dip angle according to claim 1, characterized in that: Determining the formation dip angle of the target point according to the local linear attribute parameters of the target point includes: Determining a point with maximum local amplitude stacking energy in the seismic imaging data according to the local linear attribute parameter and the preset local time window; The formation dip of the target point is determined according to the formation dip corresponding to the point with the maximum local amplitude stacking energy.

7. The method for determining formation inclination according to any one of claims 1 to 6, characterized in that: Also includes: The formation dip angle of the formation where the target point is located is determined according to the local linear attribute parameters of the multiple target points.

8. A device for determining formation inclination based on local linear constraints, characterized in that: include: A point gradient determination module, used to determine the formation gradient of the target point based on the seismic imaging data of the surrounding points of the target point; An attribute parameter determination module, used to determine the local linear attribute parameter of the target point according to the formation gradient of the target point based on a preset local time window; The point dip determination module is used to determine the formation dip of the target point according to the local linear attribute parameters of the target point.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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