An amplitude anomaly extraction method and device based on formation energy gradient

Abstract

This application proposes a method and device for extracting amplitude anomalies based on formation energy gradients, belonging to the field of geophysical exploration. The method comprises: resampling post-stack 3D seismic data; extracting an amplitude envelope from the resampled 3D data; extracting formation reflection time differences from the amplitude envelope; extracting the strike energy gradient of each sample point in the resampled 3D data and constructing a tensor matrix of the strike energy gradient; smoothing the tensor matrix and calculating at least one eigenvalue of the smoothed tensor matrix; and determining the presence of an amplitude anomaly in the post-stack 3D seismic data if at least one eigenvalue does not meet a predetermined condition. The device comprises: a data acquisition module, a data resampling module, an envelope extraction module, a reflection time difference extraction module, a tensor matrix construction module, an eigenvalue calculation module, and an amplitude anomaly detection module. Compared with traditional methods, the present application can detect amplitude anomalies and improve the accuracy of energy difference characterization.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of geophysical exploration, and particularly relates to a method and device for extracting amplitude anomaly based on energy gradient of stratum. BACKGROUND

[0002] When seismic waves are excited and propagate from the surface to the underground during seismic acquisition, transmission and reflection will occur when the seismic waves encounter interfaces with different stratum impedance. When the seismic waves pass through special geological bodies such as caves, channels or volcanic rocks, strong energy reflection will occur due to large impedance difference between strata, which is usually represented as a strong energy group on the seismic profile, and there is a large energy difference with surrounding seismic traces. Usually, amplitude-based attributes can be used to extract stratum tortuosity, kurtosis and instantaneous amplitude to depict energy anomaly, but the attribute volume obtained by this method not only contains energy anomaly, but also contains reflection characteristics of the stratum, which is not conducive to the carving of special geological bodies. In addition, amplitude anomaly can also be depicted by using amplitude difference, structural tensor or coherent energy gradient. However, there are two problems in the traditional structural tensor depicting energy anomaly. One is that the depicted amplitude anomaly is usually the boundary feature of the special geological body, rather than the geological body itself, which is not conducive to the spatial carving of three-dimensional geological bodies in vertical or horizontal directions. The second is that the energy anomaly depicted for areas with large stratum dip will be affected by the stratum dip, resulting in inaccurate energy anomaly and many false anomalies caused by stratum dip, which is not conducive to the interpretation of special geological bodies. SUMMARY

[0003] Based on the above technical problems, the present application proposes a method and device for extracting amplitude anomaly based on energy gradient of stratum, which is used to identify or detect amplitude anomaly in seismic profile and improve the identification ability of special geological bodies.

[0004] In a first aspect, the present application proposes a method for extracting amplitude anomaly based on energy gradient of stratum, comprising:

[0005] acquiring post-stack three-dimensional seismic data;

[0006] resampling the post-stack three-dimensional seismic data to obtain resampled three-dimensional data;

[0007] extracting amplitude envelope from the resampled three-dimensional data;

[0008] extracting stratum reflection moveout from the amplitude envelope;

[0009] extracting the energy gradient in the direction of travel of each sample in the resampled three-dimensional data according to the stratum reflection moveout, and constructing a tensor matrix of the energy gradient in the direction of travel;

[0010] performing smoothing processing on the tensor matrix, and calculating at least one eigenvalue of the smoothed tensor matrix;

[0011] In a case where the at least one characteristic value does not meet the predetermined condition, it is determined that the post-stack three-dimensional seismic data has an amplitude anomaly.

[0012] The resampling of the post-stack three-dimensional seismic data to obtain resampled three-dimensional data comprises using a piecewise cubic Hermite interpolation method to resample the post-stack three-dimensional seismic data to obtain resampled three-dimensional data.

[0013] The extracting of the amplitude envelope from the resampled three-dimensional data comprises:

[0014] The imaginary part of the resampled three-dimensional data is calculated using a Hilbert transform, and the amplitude envelope is obtained using the following calculation formula:

[0015]

[0016] where InsAMP is the amplitude envelope, C re is the resampled three-dimensional data, C im is the imaginary part of the resampled three-dimensional data.

[0017] The extracting of the interval travel time of the stratum from the amplitude envelope comprises using a cross-correlation function to respectively extract the seismic event strike time difference in the main line direction of the amplitude envelope and the seismic event strike time difference in the tie line direction of the amplitude envelope.

[0018] The extracting of the strike energy gradient of each sample point in the resampled three-dimensional data comprises:

[0019] The partial derivative of each sample point in the resampled three-dimensional data is calculated according to the seismic event strike time difference in the main line direction of the amplitude envelope and the seismic event strike time difference in the tie line direction of the amplitude envelope.

[0020] The strike energy gradient of each sample point is constructed using the partial derivative of each sample point in the resampled three-dimensional data.

[0021] The construction of the strike energy gradient of each sample point is expressed as follows:

[0022]

[0023] where G is the strike energy gradient, C(x, y, z) is a sample point in the resampled three-dimensional data, is the partial derivative in the main line direction calculated according to the seismic event strike time difference in the main line direction of the amplitude envelope, and is simply denoted as g x , is the partial derivative in the tie line direction calculated according to the seismic event strike time difference in the tie line direction of the amplitude envelope, and g is simply denoted as g y , is the partial derivative in the time direction, and g is simply denoted as g z .

[0024] The tensor matrix of the energy gradient is constructed, and the expression is as follows:

[0025]

[0026] Wherein, TST is the tensor matrix of the energy gradient, G is the energy gradient, G T is the transpose of the energy gradient, g x is the partial derivative in the main line direction calculated according to the moveout of the seismic event in the main line direction of the amplitude envelope, g y is the partial derivative in the contact line direction calculated according to the moveout of the seismic event in the contact line direction of the amplitude envelope, and g z is the partial derivative in the time direction.

[0027] The tensor matrix is smoothed by using the Gaussian weighted average method.

[0028] The at least one eigenvalue of the smoothed tensor matrix is calculated by using the Jacobi iteration method.

[0029] In the second aspect, the application provides an amplitude anomaly extraction device based on the energy gradient of the stratum, which comprises:

[0030] A data acquisition module is configured to acquire post-stack three-dimensional seismic data.

[0031] A data resampling module is configured to resample the post-stack three-dimensional seismic data to obtain resampled three-dimensional data.

[0032] An envelope extraction module is configured to extract an amplitude envelope from the resampled three-dimensional data.

[0033] A reflection time difference extraction module is configured to extract a stratum reflection time difference from the amplitude envelope.

[0034] A tensor matrix construction module is configured to extract an energy gradient in the direction of movement of each sample in the resampled three-dimensional data according to the stratum reflection time difference, and construct a tensor matrix of the energy gradient in the direction of movement.

[0035] An eigenvalue calculation module is configured to smooth the tensor matrix, and calculate at least one eigenvalue of the smoothed tensor matrix.

[0036] An amplitude anomaly detection module is configured to determine that the post-stack three-dimensional seismic data has an amplitude anomaly when the at least one characteristic value does not meet the predetermined condition.

[0037] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the amplitude anomaly extraction method based on the formation energy gradient.

[0038] In a fourth aspect, the present application provides a computer readable storage medium, which stores executable instructions, and the instructions, when executed, cause a processor to implement the amplitude anomaly extraction method based on the formation energy gradient.

[0039] Beneficial technical effects:

[0040] The present application provides an amplitude anomaly extraction method and device based on a formation energy gradient, which can detect amplitude anomalies and improve the accuracy of energy difference characterization compared with traditional methods. Meanwhile, the characterized energy anomaly is more beneficial to three-dimensional space carving or reserve calculation for the geological body itself. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 FIG. 1 is a flowchart of an amplitude anomaly extraction method based on a formation energy gradient according to an embodiment of the present application;

[0042] Figure 2 FIG. 4 is a resampling effect comparison diagram according to an embodiment of the present application;

[0043] Figure 3 FIG. 6 is an amplitude envelope effect comparison diagram according to an embodiment of the present application;

[0044] Figure 4 FIG. 8 is a formation reflection moveout effect comparison diagram according to an embodiment of the present application;

[0045] Figure 5 FIG. 10 is an amplitude anomaly result comparison diagram according to an embodiment of the present application;

[0046] Figure 6 FIG. 12 is a principle block diagram of an amplitude anomaly extraction device based on a formation energy gradient according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] The present disclosure will be further described below with reference to the embodiments shown in the drawings.

[0048] In the prior art, the amplitude class attribute is usually used to extract the properties of the formation such as torsion, peak state and instantaneous amplitude to depict energy anomaly, but the attribute volume obtained by the method contains not only energy anomaly but also reflection characteristics of the formation, which is not conducive to the carving of special geological bodies. In addition, amplitude difference, structure tensor or coherent energy gradient can also be used to depict amplitude anomaly. However, the traditional structure tensor is not conducive to the spatial carving of three-dimensional geological bodies, the energy anomaly is not accurate, and there are many false anomalies caused by the dip of the formation, which is not conducive to the interpretation of special geological bodies.

[0049] The application provides an amplitude anomaly extraction method and device based on formation energy gradient, which fully considers the influence of formation dip and the carving problem of spatial profile of geological bodies. First, the seismic three-dimensional data is resampled, and the resampled seismic data is conducive to the extraction of formation dip moveout; second, the amplitude envelope is introduced as the input data for the extraction of gradient attribute, which is conducive to the carving of three-dimensional space of special geological bodies. The application can eliminate false energy anomaly caused by the dip of the formation, improve the accuracy of energy difference depiction, and is more conducive to the three-dimensional spatial carving or reserve calculation of geological bodies.

[0050] Embodiment one,

[0051] The embodiment provides an amplitude anomaly extraction method based on formation energy gradient, as shown in Figure 1 , which comprises the following steps.

[0052] Step S1: acquiring post-stack three-dimensional seismic data;

[0053] In the embodiment, the seismic signals received by the shot-receiver are processed by dynamic correction and static correction stacking to form a two-dimensional data volume or a three-dimensional data volume. Before stacking, it is called pre-stack seismic data, and after stacking, it is called post-stack seismic data. The embodiment is only applicable to three-dimensional seismic data.

[0054] Step S2: resampling the post-stack three-dimensional seismic data to obtain resampled three-dimensional data;

[0055] In the embodiment, the resampling of the post-stack three-dimensional seismic data to obtain resampled three-dimensional data comprises: using a piecewise cubic Hermite interpolation method to resample the post-stack three-dimensional seismic data to obtain resampled three-dimensional data.

[0056] Step S3: extracting amplitude envelope from the resampled three-dimensional data;

[0057] In the embodiment, the imaginary part of the resampled three-dimensional data is calculated by using Hilbert transform, and the amplitude envelope is obtained by using the following calculation formula:

[0058]

[0059] Where InsAMP is the amplitude envelope, C re is the resampled three-dimensional data, C im is the imaginary part of the resampled 3D data.

[0060] Step S4: extracting formation reflection time difference from the amplitude envelope;

[0061] In this embodiment, extracting the formation reflection time difference from the amplitude envelope includes: using a cross-correlation function to respectively extract the seismic event strike time difference DeltaInline in the direction of the amplitude envelope InsAMP main survey line and the seismic event strike time difference DeltaXline in the direction of the amplitude envelope InsAMP connecting survey line.

[0062] Step S5: extracting the strike energy gradient of each sample point in the resampled three-dimensional data according to the formation reflection time difference, and constructing a tensor matrix of the strike energy gradient;

[0063] In this embodiment, extracting the strike energy gradient of each sample point in the resampled three-dimensional data includes:

[0064] According to the strike time difference of the seismic phase axis in the direction of the amplitude envelope main survey line and the strike time difference of the seismic phase axis in the direction of the amplitude envelope connecting survey line, the partial derivative of each sample point in the resampled three-dimensional data is calculated, specifically:

[0065] Calculate partial derivatives of the main survey line direction based on post-stack 3D seismic data within a small bin and DeltaInline Calculate the partial derivatives of the tie line direction based on post-stack 3D seismic data within a small bin and DeltaXline

[0066] The partial derivative of each sample point in the resampled three-dimensional data is used to construct the strike energy gradient of each sample point.

[0067] The energy gradient of each sample point is constructed as follows:

[0068]

[0069] Among them, G is the strike energy gradient, C(x, y, z) is the sample point in the resampled three-dimensional data, is the partial derivative of the main survey line direction calculated based on the time difference of the seismic phase axis in the main survey line direction of the amplitude envelope, and Abbreviated as g x , is the partial derivative of the tie line direction calculated based on the time difference of the seismic event in the tie line direction of the amplitude envelope, and Abbreviated as gy , is the partial derivative in the time direction, and is denoted as g z .

[0070] The tensor matrix of the energy gradient is constructed, and the expression is as follows:

[0071]

[0072] Wherein, TST is the tensor matrix of the energy gradient, G is the energy gradient, G T is the transpose of the energy gradient, g x is the partial derivative in the main line direction according to the amplitude envelope, g y is the partial derivative in the contact line direction according to the amplitude envelope, g z is the partial derivative in the time direction.

[0073] Step S6: smoothing the tensor matrix, and calculating at least one eigenvalue of the smoothed tensor matrix;

[0074] In this embodiment, the Gaussian weighted average method is used to smooth the tensor matrix, and at least one eigenvalue of the smoothed tensor matrix is calculated, including: using the Jacobi iteration method to calculate at least one eigenvalue of the smoothed tensor matrix. Using any eigenvalue in the at least one eigenvalue or the combination of at least two eigenvalues can depict the amplitude anomaly in the seismic profile.

[0075] Step S7: if the at least one eigenvalue does not meet the predetermined condition, determining that the stacked three-dimensional seismic data has amplitude anomaly.

[0076] In specific implementation, as long as the eigenvalue is obtained, the predetermined condition can be used to determine whether the stacked three-dimensional seismic data has amplitude anomaly. This step is a known technology for those skilled in the art, and will not be described herein.

[0077] The application provides an amplitude anomaly extraction method based on a stratum energy gradient. First, the post-stack three-dimensional seismic data is resampled to obtain resampled three-dimensional data; the amplitude envelope of the resampled three-dimensional data is extracted, and the stratum reflection time difference is extracted from the amplitude envelope; then, the stratum reflection time difference is used to extract the energy gradient in the direction of travel of each sample point in the resampled three-dimensional data, and a tensor matrix of the energy gradient in the direction of travel is constructed; finally, the tensor matrix is smoothed, and at least one eigenvalue of the smoothed tensor matrix is calculated; in the case that the at least one eigenvalue does not meet a predetermined condition, it is determined that the post-stack three-dimensional seismic data has an amplitude anomaly. The amplitude envelope is introduced as the input data for the extraction of the gradient attribute, which is beneficial to the carving of the three-dimensional space of special geological bodies, can eliminate the false energy anomaly caused by the stratum inclination, and improves the precision of the energy difference carving; meanwhile, the carved energy anomaly is more beneficial to the three-dimensional space carving or the reserve calculation of the geological body.

[0078] Embodiment two,

[0079] The embodiment is a specific example of the embodiment one, and details a stratum energy gradient-based amplitude anomaly extraction method, and uses specific data and simulation results to prove the effectiveness of the method.

[0080] A stratum energy gradient-based amplitude anomaly extraction method, as shown in Figure 1 , comprises the following steps.

[0081] Step S1: Obtain post-stack three-dimensional seismic data.

[0082] In the embodiment, the seismic signals received by the shot-receiver are subjected to dynamic correction and static correction stacking processing to form a two-dimensional data body or a three-dimensional data body. Before stacking, it is called pre-stack seismic data, and after stacking, it is called post-stack seismic data. The embodiment is only applicable to three-dimensional seismic data.

[0083] Step S2: Resample the post-stack three-dimensional seismic data to obtain resampled three-dimensional data.

[0084] In the embodiment, the piecewise cubic Hermite interpolation is used to resample the input three-dimensional post-stack seismic data body C to obtain resampled three-dimensional data C re .

[0085] For each seismic signal f(x), the piecewise cubic Hermite interpolation algorithm is used for interpolation, and the calculation formula is as follows:

[0086] H3(x)=f k-1 α k-1 (x)+f k α k (x)+f' k-1 β k-1(x)+f' k β k (x)

[0087] Among them, H3(x) is the interpolation result, f k is f(x) at point x k The value at f' k f k The first derivative of k-1 is f(x) at point x k-1 The value at f' k-1 f k-1 The first derivative of

[0088]

[0089]

[0090] Finally, all interpolation results of all channels constitute the resampled three-dimensional data C re .

[0091] like Figure 2 As shown in the figure, the seismic profile encrypted by the piecewise cubic Hermite interpolation algorithm is compared with the original seismic profile (the post-stack 3D seismic data obtained in step S1), wherein the lower one is the original seismic profile with a sampling interval of 4ms; the upper one is the seismic profile with a sampling interval of 1ms after the piecewise cubic Hermite interpolation; from the profile comparison, it can be seen that the piecewise cubic Hermite interpolation can well maintain the characteristics of the original seismic signal, and the waveform change characteristics do not change at all. After interpolation, the sampling interval of the seismic signal becomes smaller and the number of discrete points increases, which is conducive to improving the extraction accuracy of the time difference between inclined strata.

[0092] Step S3: extracting the amplitude envelope of the resampled three-dimensional data;

[0093] In this embodiment, the imaginary part of the resampled three-dimensional data is calculated using Hilbert transform, and the amplitude envelope is obtained using the following calculation formula:

[0094]

[0095] Where InsAMP is the amplitude envelope, C re is the resampled three-dimensional data, C im is the imaginary part of the resampled 3D data.

[0096] like Figure 3 As shown in Figure 2, the amplitude envelope properties of the encrypted seismic signal are extracted using the Hilbert transform.

[0097] Step S4: extracting formation reflection time difference from the amplitude envelope;

[0098] In the embodiment, the amplitude envelope is used to extract the formation reflection time difference, including: using cross-correlation function to extract the seismic event trend time difference DeltaInline of the amplitude envelope in the main line direction and the seismic event trend time difference DeltaXline of the amplitude envelope in the cross line direction.

[0099] As shown in the drawing, when the stratum is inclined upward, the time difference is negative, which is displayed as white; when the stratum is inclined downward, the time difference is positive, which is displayed as black. Figure 4

[0100] Step S5: according to the formation reflection time difference, extracting the trend energy gradient of each sample point in the resampled three-dimensional data, and constructing a tensor matrix of the trend energy gradient;

[0101] In the embodiment, the trend energy gradient of each sample point in the resampled three-dimensional data is extracted, including:

[0102] According to the seismic event trend time difference of the amplitude envelope in the main line direction and the seismic event trend time difference of the amplitude envelope in the cross line direction, the partial derivative of each sample point in the resampled three-dimensional data is calculated, specifically:

[0103] According to the post-stack three-dimensional seismic data in the small bin and DeltaInline, the partial derivative in the main line direction is calculated According to the post-stack three-dimensional seismic data in the small bin and DeltaXline, the partial derivative in the cross line direction is calculated

[0104] Taking the seismic event trend time difference DeltaInline in the main line direction as an example: for a sample point on a profile of the resampled three-dimensional data, the sample point is defined as a center point, a cross-correlation time window N and a maximum scanning time window M are set, a seismic signal S with the center point as the center and a length of N is intercepted, and the cross-correlation coefficient is calculated by scanning from the maximum scanning time window M downward using the seismic signal S and the adjacent seismic trace. The difference between the position of the maximum cross-correlation coefficient and the center point position is the trend time difference of DeltaInline at the center point.

[0105] Using the partial derivative of each sample point in the resampled three-dimensional data, the trend energy gradient of each sample point is constructed.

[0106] The trend energy gradient of each sample point is constructed, and the expression is as follows:

[0107]

[0108] Wherein, G is the trend energy gradient, C(x, y, z) is a sample point in the resampled three-dimensional data,​ is a partial derivative in the main line direction calculated according to the traveltime difference of the seismic event along the amplitude envelope in the main line direction, and is denoted as g x is a partial derivative in the contact line direction calculated according to the traveltime difference of the seismic event along the amplitude envelope in the contact line direction, and is denoted as g y is a partial derivative in the time direction, and is denoted as g z .

[0109] The partial derivative of the seismic signal is calculated along the travel direction of the seismic event, which can eliminate the influence of the stratum inclination and is conducive to highlighting the energy anomaly hidden in the inclined stratum.

[0110] The tensor matrix of the travel energy gradient is constructed, and the expression is as follows:

[0111]

[0112] Wherein, TST is the tensor matrix of the travel energy gradient, G is the travel energy gradient, G T is the transpose of the travel energy gradient, g x is a partial derivative in the main line direction calculated according to the traveltime difference of the seismic event along the amplitude envelope in the main line direction, g y is a partial derivative in the contact line direction calculated according to the traveltime difference of the seismic event along the amplitude envelope in the contact line direction, and g z is a partial derivative in the time direction.

[0113] Step S6: smoothing the tensor matrix, and calculating at least one eigenvalue of the smoothed tensor matrix;

[0114] In this embodiment, the Gaussian weighted average method is used to smooth the tensor matrix, and at least one eigenvalue of the smoothed tensor matrix is calculated, including: using the Jacobi iteration method to calculate at least one eigenvalue of the smoothed tensor matrix.

[0115]

[0116] Wherein, η i =E{g i}, η i is the mean value of g i in the three-dimensional small window, η j =E{g j}, η j is the mean value of g j in the three-dimensional small window, and the Gaussian window function is as follows: ​​​​​

[0117]

[0118] wherein TST sm is the smoothed tensor matrix, ∑TST ij (x,y,z) is the Gaussian weighted summation of the tensor matrix of the i th sample point and the tensor matrix of the j th sample point, g i (x+Δx,y+Δy,z+Δz) is the gradient of the i th sample point, g j (x+Δx,y+Δy,z+Δz) is the gradient of the j th sample point, σ x is the variance of x, σ y is the variance of y, σ z is the variance of z.

[0119] Step S7: determining that the post-stack 3D seismic data has amplitude anomaly in the case that the at least one eigenvalue does not meet the predetermined condition.

[0120] In the embodiment, the eigenvalues λ 1, λ 2 and λ 3 of the smoothed trend energy gradient tensor matrix TST sm are calculated by using the Jacobi iteration method, and whether the post-stack 3D seismic data has amplitude anomaly is determined by using one eigenvalue or a combination of the eigenvalues λ 1, λ 2 and λ 3 through certain predetermined condition. This step is well known to those skilled in the art, and will not be described herein.

[0121] As Figure 5 shown in the figure, the amplitude anomaly profile described by the eigenvalues of the traditional structural tensor and the amplitude anomaly profile described by the eigenvalues of the method of the embodiment are compared. The upper part is the original seismic profile, the middle part is the eigenvalues extracted by the traditional structural tensor, and the lower part is the eigenvalues extracted by the embodiment. It can be known by comparison that the eigenvalues extracted by the embodiment can well eliminate the influence of stratum inclination, and the described amplitude anomaly is the profile of geological body, which is more conducive to the three-dimensional space sculpture of the geological body.

[0122] This embodiment proposes a method for extracting amplitude anomalies based on formation energy gradients. First, the post-stack 3D seismic data is resampled to obtain resampled 3D data. The amplitude envelope is extracted from the resampled 3D data, and the formation reflection time difference is extracted from the amplitude envelope. Then, based on the formation reflection time difference, the strike energy gradient of each sample point in the resampled 3D data is extracted, and a tensor matrix of the strike energy gradient is constructed. Finally, the tensor matrix is ​​smoothed, and at least one eigenvalue of the smoothed tensor matrix is ​​calculated. If at least one eigenvalue does not meet a predetermined condition, the post-stack 3D seismic data is determined to contain an amplitude anomaly. This method fully considers the influence of formation tilt and the problem of carving the spatial contour of geological bodies. The introduction of seismic signal encryption processing and amplitude envelope attributes into the interpretation process improves the accuracy of amplitude anomaly characterization. The depicted energy anomaly eliminates false anomalies caused by formation unevenness, highlights the geological body contour, and is more conducive to carving special geological bodies.

[0123] Example 3:

[0124] This embodiment proposes an amplitude anomaly extraction device based on formation energy gradient, such as Figure 6 As shown, it includes: a data acquisition module, a data resampling module, an envelope extraction module, a reflection time difference extraction module, a tensor matrix construction module, an eigenvalue calculation module and an amplitude anomaly detection module, the data acquisition module is connected to the data resampling module, the data resampling module is connected to the envelope extraction module, the envelope extraction module is connected to the reflection time difference extraction module, the reflection time difference extraction module is connected to the tensor matrix construction module, the tensor matrix construction module is connected to the eigenvalue calculation module, and the eigenvalue calculation module is connected to the amplitude anomaly detection module.

[0125] A data acquisition module, used to acquire post-stack 3D seismic data;

[0126] A data resampling module, configured to resample the post-stack 3D seismic data to obtain resampled 3D data;

[0127] An envelope extraction module, configured to extract an amplitude envelope from the resampled three-dimensional data;

[0128] In this embodiment, the imaginary part of the resampled three-dimensional data is calculated using the Hilbert transform, and the amplitude envelope is obtained using the following calculation formula:

[0129]

[0130] Where InsAMP is the amplitude envelope, C re is the resampled three-dimensional data, C im is the imaginary part of the resampled 3D data.

[0131] a reflection time difference extraction module configured to extract formation reflection time difference from the amplitude envelope;

[0132] In the embodiment, the cross-correlation function is used to extract the seismic event trend time difference DeltaInline of the amplitude envelope InsAMP in the main line direction and the seismic event trend time difference DeltaXline of the amplitude envelope InsAMP in the cross line direction.

[0133] a tensor matrix construction module configured to extract the trend energy gradient of each sample in the resampled 3D data according to the formation reflection time difference, and construct a tensor matrix of the trend energy gradient;

[0134] extracting the trend energy gradient of each sample in the resampled 3D data, comprising:

[0135] calculating the partial derivative of each sample in the resampled 3D data according to the seismic event trend time difference of the amplitude envelope in the main line direction and the seismic event trend time difference of the amplitude envelope in the cross line direction;

[0136] constructing the trend energy gradient of each sample by using the partial derivative of each sample in the resampled 3D data.

[0137] the expression of constructing the trend energy gradient of each sample is as follows:

[0138]

[0139] wherein, G is the trend energy gradient, C(x, y, z) is the sample in the resampled 3D data, is the partial derivative in the main line direction calculated according to the seismic event trend time difference of the amplitude envelope in the main line direction, and is denoted as g x , is the partial derivative in the cross line direction calculated according to the seismic event trend time difference of the amplitude envelope in the cross line direction, and is denoted as g y , is the partial derivative in the time direction, and is denoted as g z .

[0140] the expression of constructing the tensor matrix of the trend energy gradient is as follows:

[0141]

[0142] wherein, TST is the tensor matrix of the trend energy gradient, G is the trend energy gradient, G T is the transpose of the trend energy gradient, g xA partial derivative in the main line direction calculated according to the travel time difference of the seismic event in the main line direction of the amplitude envelope y A partial derivative in the cross line direction calculated according to the travel time difference of the seismic event in the cross line direction of the amplitude envelope z A partial derivative in the time direction.

[0143] An eigenvalue calculation module is configured to smooth the tensor matrix and calculate at least one eigenvalue of the smoothed tensor matrix.

[0144] An amplitude anomaly detection module is configured to determine that the post-stack 3D seismic data has an amplitude anomaly if the at least one eigenvalue does not meet a predetermined condition.

[0145] The application provides an amplitude anomaly extraction device based on a stratum energy gradient, which comprises the following steps: resampling post-stack 3D seismic data by using a data resampling module to obtain resampled 3D data, extracting an amplitude envelope from the resampled 3D data by using an envelope extraction module, extracting a stratum reflection time difference from the amplitude envelope by using a reflection time difference extraction module, constructing a tensor matrix of a travel direction energy gradient by using a tensor matrix construction module, smoothing the tensor matrix by using an eigenvalue calculation module, and calculating at least one eigenvalue of the smoothed tensor matrix; finally, determining that the post-stack 3D seismic data has an amplitude anomaly if the at least one eigenvalue does not meet a predetermined condition by using an amplitude anomaly detection module. The device can eliminate false energy anomalies caused by stratum inclination, improve the precision of energy difference depiction, and depict energy anomalies of geological bodies, which is more conducive to 3D space sculpture or reserve calculation.

[0146] Embodiment Four,

[0147] The application provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the amplitude anomaly extraction method based on a stratum energy gradient.

[0148] The electronic device can be a mobile phone, a computer or a tablet computer, and comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the amplitude anomaly extraction method based on a stratum energy gradient. It can be understood that the electronic device can further comprise an input / output (I / O) interface and a communication component.

[0149] The processor is configured to execute all or part of the steps of the amplitude anomaly extraction method based on the formation energy gradient as described in the above embodiments. The memory is configured to store various types of data, which may, for example, include instructions of any application program or method in the electronic device, and application-related data.

[0150] The processor can be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic elements, and is configured to execute the amplitude anomaly extraction method based on the formation energy gradient as described in the above embodiments.

[0151] Embodiment five,

[0152] The embodiment provides a computer readable storage medium storing executable instructions, which, when executed, cause a processor to execute the amplitude anomaly extraction method based on the formation energy gradient.

[0153] If implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium.

[0154] Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the amplitude anomaly extraction method based on the formation energy gradient as described in the embodiments of the present application.

[0155] The aforementioned storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD (Secure Digital Memory Card) or a DX (Memory Data Register, MDR) memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an APP (Application) application market, etc., which can store a program check code, and stores a computer program thereon, the computer program being executed by a processor to implement each step of the amplitude anomaly extraction method based on a formation energy gradient as described above.

[0156] Each embodiment in the present disclosure is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0157] The scope of protection of the present disclosure is not limited to the above-described embodiments, and it is obvious that those skilled in the art can make various modifications and changes to the present disclosure without departing from the scope and spirit of the present disclosure. If these modifications and changes belong to the scope of the claims of the present disclosure and equivalent technologies thereof, the intention of the present disclosure also includes these modifications and changes.

Claims

1. A method for extracting amplitude anomaly based on formation energy gradient, characterized in that: include: Acquire post-stack 3D seismic data; resampling the post-stack three-dimensional seismic data to obtain resampled three-dimensional data; extracting an amplitude envelope from the resampled three-dimensional data; extracting formation reflection time difference from the amplitude envelope; Extracting the strike energy gradient of each sample point in the resampled three-dimensional data according to the formation reflection time difference, and constructing a tensor matrix of the strike energy gradient; performing a smoothing process on the tensor matrix and calculating at least one eigenvalue of the smoothed tensor matrix; When the at least one characteristic value does not meet the predetermined condition, it is determined that an amplitude anomaly exists in the post-stack 3D seismic data.

2. The amplitude anomaly extraction method based on formation energy gradient according to claim 1 is characterized in that: The resampling of the post-stack 3D seismic data to obtain the resampled 3D data includes: resampling the post-stack 3D seismic data using a piecewise cubic Hermite interpolation method to obtain the resampled 3D data.

3. The amplitude anomaly extraction method based on formation energy gradient according to claim 1 is characterized in that: Extracting the amplitude envelope of the resampled three-dimensional data includes: The imaginary part of the resampled three-dimensional data is calculated using the Hilbert transform, and the amplitude envelope is obtained using the following calculation formula: Where InsAMP is the amplitude envelope, C re is the resampled three-dimensional data, C im is the imaginary part of the resampled 3D data.

4. The amplitude anomaly extraction method based on formation energy gradient according to claim 1 is characterized in that: The extracting of formation reflection time difference from the amplitude envelope includes: using a cross-correlation function to respectively extract the seismic event strike time difference in the direction of the amplitude envelope main survey line and the seismic event strike time difference in the direction of the amplitude envelope connecting survey line.

5. The amplitude anomaly extraction method based on formation energy gradient according to claim 4 is characterized in that: The extracting the strike energy gradient of each sample point in the resampled three-dimensional data includes: Calculate the partial derivative of each sample point in the resampled three-dimensional data according to the strike moveout of the seismic phase axis in the direction of the amplitude envelope main survey line and the strike moveout of the seismic phase axis in the direction of the amplitude envelope connecting survey line; The partial derivative of each sample point in the resampled three-dimensional data is used to construct the strike energy gradient of each sample point.

6. The amplitude anomaly extraction method based on formation energy gradient according to claim 5 is characterized in that: The energy gradient of each sample point is constructed as follows: Among them, G is the strike energy gradient, C(x, y, z) is the sample point in the resampled three-dimensional data, is the partial derivative of the main survey line direction calculated based on the time difference of the seismic phase axis in the main survey line direction of the amplitude envelope, and Abbreviated as g x , is the partial derivative of the tie line direction calculated based on the time difference of the seismic event in the tie line direction of the amplitude envelope, and Abbreviated as g y , is the partial derivative in the time direction, and Abbreviated as g z .

7. The amplitude anomaly extraction method based on formation energy gradient according to claim 1 is characterized in that: The tensor matrix of the energy gradient is constructed as follows: Among them, TST is the tensor matrix of the strike energy gradient, G is the strike energy gradient, G T is the transpose of the energy gradient, g x is the partial derivative of the main survey line direction calculated based on the time difference of the seismic event in the main survey line direction of the amplitude envelope, g y is the partial derivative of the tie line direction calculated based on the time difference of the seismic event strike in the tie line direction of the amplitude envelope, g z is the partial derivative in the time direction.

8. The amplitude anomaly extraction method based on formation energy gradient according to claim 1 is characterized in that: The smoothing process for the tensor matrix is ​​to smooth the tensor matrix using a Gaussian weighted average method.

9. The amplitude anomaly extraction method based on formation energy gradient according to claim 1, characterized in that: The calculating of at least one eigenvalue of the smoothed tensor matrix includes: using a Jacobi iteration method to calculate at least one eigenvalue of the smoothed tensor matrix.

10. A device for extracting amplitude anomaly based on formation energy gradient, characterized in that: include: A data acquisition module, used to acquire post-stack 3D seismic data; A data resampling module, configured to resample the post-stack 3D seismic data to obtain resampled 3D data; An envelope extraction module, configured to extract an amplitude envelope from the resampled three-dimensional data; A reflection time difference extraction module, used for extracting the formation reflection time difference from the amplitude envelope; A tensor matrix construction module is used to extract the strike energy gradient of each sample point in the resampled three-dimensional data according to the formation reflection time difference, and construct a tensor matrix of the strike energy gradient; an eigenvalue calculation module, configured to perform smoothing on the tensor matrix and calculate at least one eigenvalue of the smoothed tensor matrix; The amplitude anomaly detection module is used to determine whether the post-stack three-dimensional seismic data has an amplitude anomaly when the at least one characteristic value does not meet a predetermined condition.

11. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the amplitude anomaly extraction method based on formation energy gradient according to any one of claims 1 to 9 is executed.

12. A computer-readable storage medium, characterized in that It stores executable instructions, which, when executed, enable the processor to execute the amplitude anomaly extraction method based on formation energy gradient as described in any one of claims 1 to 9.

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