Fault detection method and device based on inclination angle guiding

Abstract

The invention discloses a fault detection method and device based on inclination angle guiding. The method comprises the following steps: acquiring seismic data of a stratum to be identified; carrying out dip angle scanning on a sample point in the seismic data based on the selected seismic trace spacing to obtain dip angle scanning data of the sample point, and carrying out nonlinear fitting on the dip angle scanning data to obtain dip angle fitting data of a relationship between a stratigraphic dip angle and a similarity coefficient; determining the stratigraphic dip angle corresponding to the maximum similarity coefficient in the dip angle fitting data as the optimal dip angle of the sample point; taking the sampling point as a detection starting point, taking a direction corresponding to the optimal inclination angle as a detection direction, obtaining seismic data along the detection direction, and carrying out fault detection to obtain a fault detection result of the sampling point; and finally, obtaining a fault body according to the sampling points in the seismic data and fault detection results of the sampling points. The boundary detection direction can be guided by the inclination angle, so that the stability and accuracy of the steep stratum fault detection method are improved, and the influence of stratum background interference on fault detection is reduced.

Description

Technical Field

[0001] The invention relates to the field of seismic exploration, and in particular to a fault detection method and device based on dip angle guidance. Background Art

[0002] Faults are not only channels for oil and gas migration, but also important factors controlling effective reservoirs. With the continuous deepening of oil and gas exploration and development, faults are becoming more and more important as one of the main factors controlling oil and gas accumulation. Fine fault identification is conducive to improving the efficiency of exploration and development. Fault identification in seismic data is an important part of fault identification, which is of great significance for effectively understanding the macroscopic distribution of underground faults and finding oil and gas resources. As the exploration and development targets gradually shift to deep and complex oil and gas reservoir types, the requirements for boundary detection accuracy are gradually increasing. Conventional boundary detection methods include detection methods based on Sobel operator, Roberts operator, Canny operator, Prewitt operator, Laplacian operator, etc. These detection methods can detect edges more accurately in the absence of noise, but as the noise increases, these operators will detect a large number of noise points and pseudo edges, or even fail to detect edges, making it difficult to meet exploration needs.

[0003] The deep-ultra-deep zone has complex structural features and diverse fault attitudes. In addition, deep seismic data has low resolution and signal-to-noise ratio, and the seismic response characteristics of small and medium-sized faults are not obvious, which makes the fault identification accuracy of seismic data insufficient. Faced with complex faults of multiple periods and types, it is even more difficult to detect their spatial distribution patterns, which increases the difficulty of studying the laws of oil and gas migration and accumulation, and seriously restricts the progress of deep and refined exploration and development.

[0004] In actual exploration, the generalized Hilbert transform method is often used to detect fault boundaries in seismic data. This method has good noise resistance and can obtain more accurate information on the direction and distribution of reservoir fractures. However, when faced with data on complex formations, especially steep formations, the generalized Hilbert transform fault detection method often misidentifies steep formations as faults, causing background information to interfere with fault identification. Summary of the invention

[0005] Fault identification of seismic data is an important part of fault identification. It is of great significance for effectively understanding the macroscopic distribution of underground faults and finding oil and gas resources. However, conventional boundary detection methods are difficult to meet the exploration needs of carbonate rock areas with low data quality. Although the generalized Hilbert transform method has good noise resistance and can obtain more accurate reservoir fracture orientation and distribution information, although the generalized Hilbert transform can reduce noise interference, it is generally applied to horizontal or near-horizontal stratigraphic data, extracting horizontal slices of seismic data for edge detection, and not studying the overall three-dimensional seismic space. When facing some complex high-steep structures, the stratigraphic response interference will be more obvious, affecting the fault identification effect.

[0006] In view of the above problems, the present invention is proposed to provide a fault detection method and device based on dip-angle guidance that overcomes the above problems or at least partially solves the above problems.

[0007] In a first aspect, an embodiment of the present invention provides a fault detection method based on dip angle guidance, comprising:

[0008] Acquiring seismic data of the formation to be identified;

[0009] For sample points in the seismic data, dip scanning is performed based on the selected seismic trace spacing to obtain dip scanning data of the sample points, wherein the dip scanning parameters include a dip body and similarity coefficients corresponding to the dip parameters included in the dip body;

[0010] Perform nonlinear fitting on the dip parameter and similarity coefficient to obtain dip fitting data reflecting the relationship between the formation dip and the similarity coefficient;

[0011] The formation dip corresponding to the maximum similarity coefficient in the dip fitting data is determined as the optimal dip of the sample point;

[0012] The sample point is taken as the detection starting point, the direction corresponding to the best dip angle is taken as the detection direction, seismic data is acquired along the detection direction for fault detection, and the fault detection result of the sample point is obtained;

[0013] The fault body is obtained based on the sample points included in the seismic data and the fault detection results of the sample points.

[0014] In some optional embodiments, performing dip scanning according to the selected seismic trace spacing to obtain dip scanning data of sample points includes:

[0015] Choose from multiple different trace spacings;

[0016] For each seismic trace spacing, a waveform similarity scan is performed on the sample point, a first dip parameter and a first similarity coefficient are recorded, and first dip scan data of the sample point is obtained; the first dip scan data includes a first dip body and a first similarity coefficient corresponding to the first dip parameter included in the first dip body;

[0017] A plurality of first inclination scanning data corresponding to a plurality of seismic trace spacings are normalized to obtain normalized inclination scanning data of sample points.

[0018] In some optional embodiments, normalizing a plurality of first inclination scanning data corresponding to a plurality of seismic trace spacings to obtain normalized inclination scanning data of sample points includes:

[0019] Take one seismic trace spacing as the reference trace spacing;

[0020] Based on the proportional relationship between the seismic trace spacing and the reference trace spacing, the first inclination body corresponding to the seismic trace spacing is converted into a second inclination body with the reference trace spacing as the seismic trace spacing, and the first similarity coefficient is converted into a second similarity coefficient with the reference trace spacing as the seismic trace spacing; the second inclination body includes the second inclination parameter after the first inclination parameter is converted;

[0021] The plurality of second dip bodies and second similarity coefficients corresponding to the plurality of seismic trace intervals are statistically analyzed based on the dip parameters, and the dip scanning data of the sample points after normalization processing are obtained according to the statistical results.

[0022] In some optional embodiments, nonlinear fitting is performed on the dip parameter and the similarity coefficient to obtain dip fitting data reflecting the relationship between the formation dip and the similarity coefficient, including:

[0023] The discrete dip parameters and the corresponding similarity coefficients in the normalized dip scanning data are nonlinearly fitted to obtain a dip fitting surface reflecting the relationship between the formation dip and the similarity coefficient.

[0024] In some optional embodiments, the sample point is used as the detection starting point, the direction corresponding to the optimal dip angle is used as the detection direction, and fault detection is performed according to the seismic data to obtain the fault detection result of the sample point, including:

[0025] Taking the sample point as the detection starting point, select several detection points along the direction of the best inclination angle;

[0026] Acquire seismic data of the detection point, use the Hilbert transform method or the generalized Hilbert transform method to perform fault detection, and obtain the fault detection result of the sample point;

[0027] In some optional embodiments, the fault detection is performed using the Hilbert transform method to obtain the fault detection result of the sample point, including:

[0028] Based on Hilbert transform, the imaginary part of the complex seismic trace in the seismic data is used to detect the fault edge, and the fault detection result of the sample point is obtained according to the edge detection result;

[0029] Among them, the discrete form of Hilbert transform is expressed as:

[0030] h=h R +ih I

[0031]

[0032]

[0033] where h R represents the real part of the complex seismic trace, h I represents the imaginary part of the complex seismic trace, X(ω) is the frequency domain form of the original seismic signal x(t), and X(0) is the 0 frequency of the original seismic signal x(t).

[0034] In some optional embodiments, the generalized Hilbert transform method is used to perform fault detection to obtain the formation detection results of the sample points, including:

[0035] Based on the generalized Hilbert transform, the imaginary part of the complex seismic trace in the seismic data is used to detect the fault edge, and the fault detection result of the sample point is obtained according to the edge detection result;

[0036] The generalized Hilbert transform is expressed in discrete form as: h = h r +ih i

[0037]

[0038]

[0039] where h r represents the real part of the complex seismic trace, h i represents the imaginary part of the complex seismic trace, X(t,ω) is the frequency domain form of the windowed Fourier transform of the original seismic signal x(t), X(t,0) is the 0 frequency of the windowed Fourier transform, and n is the order.

[0040] In some optional embodiments, obtaining a fault body according to sample points included in the seismic data and the fault detection results of the sample points includes:

[0041] Selecting a plurality of sample points in the seismic data of the formation to be identified;

[0042] According to the sample points included in the seismic data, the fault detection result of each sample point is obtained, and the fault detection result of each sample point in the seismic data and each sample point is statistically analyzed to obtain the fault body of the formation to be identified.

[0043] In a second aspect, an embodiment of the present invention provides a dip-guided fault detection device, comprising:

[0044] A data acquisition module is used to acquire seismic data of the stratum to be identified; for sample points in the seismic data, a dip scan is performed based on the selected seismic trace spacing to obtain dip scan data of the sample points, wherein the dip scan parameters include a dip body and a similarity coefficient corresponding to the dip parameters included in the dip body;

[0045] The dip determination module is used to perform nonlinear fitting on the dip parameter and the similarity coefficient to obtain dip fitting data reflecting the relationship between the formation dip and the similarity coefficient; the formation dip corresponding to the maximum similarity coefficient in the dip fitting data is determined as the optimal dip of the sample point;

[0046] The fault detection module is used to take the sample point as the detection starting point and the direction corresponding to the optimal inclination angle as the detection direction, obtain seismic data along the detection direction for fault detection, and obtain the fault detection result of the sample point; obtain the fault body according to the sample point included in the seismic data and the fault detection result of the sample point.

[0047] A computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, a fault detection method based on dip angle guidance is implemented.

[0048] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a fault detection method based on dip angle guidance is implemented.

[0049] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0050] The fault detection method based on dip guidance provided by the embodiment of the present invention obtains seismic data of the stratum to be identified; performs dip scanning based on the selected seismic trace spacing for the sample points in the seismic data to obtain dip scanning data of the sample points, performs nonlinear fitting on the dip parameters and similarity coefficients in the dip scanning data to obtain dip fitting data reflecting the relationship between the stratum dip and the similarity coefficient; determines the stratum dip corresponding to the maximum similarity coefficient in the dip fitting data as the optimal dip of the sample point; takes the sample point as the detection starting point, takes the direction corresponding to the optimal dip as the detection direction, obtains seismic data along the detection direction for fault detection, and obtains the fault detection result of the sample point; finally, obtains the fault body according to the sample points included in the seismic data and the fault detection results of the sample points. The method can accurately find the dip corresponding to the maximum similarity coefficient, and the dip corresponding to the maximum similarity coefficient is the optimal dip for fault detection of the stratum, guides the direction of boundary detection with the dip, thereby improving the stability and accuracy of the steep stratum fault detection method, reducing the influence of stratum background interference on fault detection, and improving the accuracy of fault detection.

[0051] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 It is a flow chart of a fault detection method based on dip angle guidance in an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of scanning data in an embodiment of the present invention;

[0056] Figure 3 It is a schematic diagram of the inclination fitting surface in an embodiment of the present invention;

[0057] Figure 4 This is an example diagram of extracting data in the horizontal direction in an embodiment of the present invention;

[0058] Figure 5 This is an example diagram of extracting data in the direction of inclination in an embodiment of the present invention;

[0059] Figure 6This is a schematic diagram of a stratum cross section taken along the side line A in an embodiment of the present invention;

[0060] Figure 7 It is a schematic diagram of the A profile detection by the generalized Hilbert transform fault detection in the embodiment of the present invention;

[0061] Figure 8 It is a schematic diagram of the A profile detection by the dip-guided generalized Hilbert transform fault detection in an embodiment of the present invention;

[0062] Fig. 9 A schematic diagram of a stratum section taken by a survey line B in an embodiment of the present invention;

[0063] Fig.10 Schematic diagram of B-section detection by generalized Hilbert transform fault detection in an embodiment of the present invention;

[0064] Fig.11 It is a schematic diagram of B-profile detection by dip-angle-guided generalized Hilbert transform fault detection in an embodiment of the present invention;

[0065] Fig.12 It is a schematic structural diagram of an inclination-guided fault detection device in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0067] The deep-ultra-deep zone has complex structural features and diverse fault attitudes. In addition, deep seismic data has low resolution and signal-to-noise ratio, and the seismic response characteristics of small and medium-sized faults are not obvious, which makes the fault identification accuracy of seismic data insufficient. Faced with complex faults of multiple periods and types, it is even more difficult to detect their spatial distribution patterns, which increases the difficulty of studying the laws of oil and gas migration and accumulation, and seriously restricts the progress of deep and refined exploration and development.

[0068] In order to solve the problem that traditional fault detection methods have insufficient accuracy in identifying faults in seismic data, and face the problem that complex fractures of multiple periods and types are difficult to detect their spatial distribution patterns and cannot accurately identify faults, an embodiment of the present invention provides a fault detection method based on dip guidance, which conducts an overall study on the three-dimensional earthquake space, calculates the dip by similarity scanning, and uses the dip to guide the direction of fault boundary detection, thereby improving the stability and accuracy of the fault detection method.

[0069] Example

[0070] Embodiment 1 of the present invention provides a fault detection method based on dip angle guidance, the process of which is as follows: Figure 1 As shown, the following steps are included:

[0071] Step S101: Acquire seismic data of the formation to be identified.

[0072] Step S102: performing inclination scanning on sample points in the seismic data based on the selected seismic trace spacing to obtain inclination scanning data of the sample points.

[0073] Optionally, an inclination scan is performed according to a selected seismic trace spacing to obtain inclination scan data of the sample point. The present embodiment selects a plurality of different seismic trace spacings, performs a waveform similarity scan on the sample point for each seismic trace spacing, records a first inclination parameter and a first similarity coefficient, and obtains first inclination scan data of the sample point; the first inclination scan data includes a first inclination body, and a first similarity coefficient corresponding to the first inclination parameter included in the first inclination body; and the plurality of first inclination scan data corresponding to the plurality of seismic trace spacings are normalized to obtain the normalized inclination scan data of the sample point.

[0074] Preferably, this embodiment takes a sample point as an example, and performs multiple inclination scans with different seismic trace spacings. The high and low similarity coefficients during the scanning process represent the similarity of the waveforms. As the scanning inclination changes continuously, the similarity of the waveforms of adjacent traces also changes continuously. When the similarity is high, it means that the inclination at this time is close to the actual formation inclination. The values ​​of the inclination parameters p and q during the scanning process are compared with the corresponding similarity coefficients C ij By recording the value of (t), the inclination scanning data can be obtained. The inclination scanning data includes the inclination body and the similarity coefficient corresponding to the inclination parameters included in the inclination body. One point corresponds to a p parameter and a q parameter. The inclination parameter p can be called the apparent inclination angle of the sample point in the x direction, and the inclination parameter q can be called the apparent inclination angle of the sample point in the y direction. Then the three-dimensional data body has a p data body and a q data body. The p data body and the q data body are collectively referred to as the inclination body.

[0075] The scan data recorded during the scan process can be found in Figure 2 As shown, Figure 2 The z axis represents the similarity coefficient, and the x and y axes represent the x and y inclination angles p and q, respectively. The inclination parameters in the scanning process are discrete, so the maximum similarity coefficient C in the inclination scanning data is ij The scanning parameters corresponding to (t) are not necessarily the true inclination angles. There is a high probability that they are only close to the true inclination angles.

[0076] Optionally, multiple first inclination scanning data corresponding to multiple seismic trace spacings are normalized to obtain normalized inclination scanning data of sample points. Specifically, a seismic trace spacing is used as a reference trace spacing, and based on the proportional relationship between the seismic trace spacing and the reference trace spacing, the first inclination body corresponding to the seismic trace spacing is converted into a second inclination body with the reference trace spacing as the seismic trace spacing, and the first similarity coefficient is converted into a second similarity coefficient with the reference trace spacing as the seismic trace spacing; the second inclination body includes a second inclination parameter after the first inclination parameter is converted; and statistics are performed on multiple second inclination bodies and second similarity coefficients corresponding to multiple seismic trace spacings based on the inclination parameters, and according to the statistical results, the normalized inclination scanning data of the sample points are obtained.

[0077] Because the dip values ​​obtained by scanning different seismic trace spacings are in different measurement ranges, the first dip scanning parameters of multiple seismic trace spacings should be normalized to lay the foundation for dip statistical analysis. For example, the dip angle of two seismic traces with a longitudinal difference of 10 meters and a longitudinal difference of 10 meters between two seismic traces with a seismic trace spacing of 30 meters is twice the tangent value of the dip angle of two seismic traces with a longitudinal difference of 10 meters and a seismic trace spacing of 60 meters. Therefore, the dip angle of a longitudinal difference of 10 meters in a 60-meter seismic trace spacing should be converted into a dip angle of a longitudinal difference of 5 meters in a 30-meter seismic trace spacing to facilitate subsequent statistics.

[0078] In this step, the dip angles of seismic data with various trace spacings are scanned to obtain a multi-trace spacing dip angle body. The dip angle values ​​are calculated by combining various trace spacings to improve the stability of the dip angle scan. The multi-trace spacing dip angles are normalized to lay the foundation for the dip angle statistical analysis.

[0079] Step S103: performing nonlinear fitting on the dip parameter and the similarity coefficient to obtain dip fitting data reflecting the relationship between the formation dip and the similarity coefficient.

[0080] The discrete dip parameters and the corresponding similarity coefficients in the normalized dip scanning data are nonlinearly fitted to obtain a dip fitting surface reflecting the relationship between the formation dip and the similarity coefficient.

[0081] For inclination fitting surface, see Figure 3 As shown in the figure, since the dip parameters in the scanning process are discrete and not necessarily close to the true dip, in order to obtain a result closer to the true dip, the spatial range of the seismic data dip calculation window is improved, and the dip value is calculated by combining multiple seismic trace spacings. The data is statistically analyzed and nonlinearly fitted to obtain a dip fitting surface that reflects the relationship between the formation dip and the similarity coefficient, as shown in the figure. Figure 3As shown in the figure, the functional relationship between the dip angle and the similarity is calculated. After nonlinear fitting, the dip angle parameters in the dip body obtained during scanning are continuous, and the dip angles corresponding to the dip angle parameters are also continuous. According to the dip angle fitting surface, the dip angle corresponding to the maximum similarity point can be accurately found. The dip angle at this time is more accurate than the discrete dip angle obtained by scanning alone, and can better reflect the formation structure. Using the regression algorithm to optimize and calculate the optimal dip angle can avoid the false illusion of dip angle mutation.

[0082] Step S104: determining the formation dip corresponding to the maximum similarity coefficient in the dip fitting data as the optimal dip of the sample point.

[0083] Step S105: taking the sample point as the detection starting point and the direction corresponding to the optimal dip angle as the detection direction, acquiring seismic data along the detection direction for fault detection, and obtaining the fault detection result of the sample point.

[0084] Optionally, taking the sample point as the detection starting point, several detection points are selected along the direction of the optimal dip angle; seismic data of the detection point is obtained, and the Hilbert transform method or the generalized Hilbert transform method is used for fault detection to obtain the fault detection results of the sample point.

[0085] The Hilbert transform HT is essentially an all-pass filter, and the generalized Hilbert transform GHT is essentially to add frequency domain windowing to the analytical signal of the Hilbert transform and increase the order of the signal. In actual exploration, when the Hilbert transform method or the generalized Hilbert transform method is used for fault edge detection, data will be provided along the horizontal direction of the formation, see Figure 4 As shown in the figure, taking the GHT method as an example, when performing fault detection on an inclined stratum, if data is extracted horizontally, the data values ​​will fluctuate significantly from left to right, and the GHT method will produce a high-value response. There is no fault at this location, so the high-value response at this location is not caused by the fault factor, which causes interference and leads to a certain deviation between the detection result and the actual result. If data is extracted according to the direction of the stratum dip, see Figure 5 As shown, the data values ​​will not show obvious fluctuations from left to right, which can effectively avoid such high-value interference, making the fault detection results more accurate and more practical.

[0086] Preferably, in this embodiment, based on Hilbert transform, the imaginary part of the complex seismic trace in the seismic data is used to perform fault edge detection, and the fault detection result of the sample point is obtained according to the edge detection result;

[0087] Among them, the discrete form of Hilbert transform is expressed as:

[0088] h=h R +ih I

[0089]

[0090]

[0091] where h R represents the real part of the complex seismic trace, h I represents the imaginary part of the complex seismic trace, X(ω) is the frequency domain form of the original seismic signal x(t), and X(0) is the zero frequency of the original seismic signal x(t);

[0092] Preferably, based on the generalized Hilbert transform, the imaginary part of the complex seismic trace in the seismic data is used to perform fault edge detection, and the fault detection result of the sample point is obtained according to the edge detection result;

[0093] Among them, the generalized Hilbert transform discrete form is expressed as:

[0094] h=h r +ih i

[0095]

[0096]

[0097] Among them, h r represents the real part of the complex seismic trace, h i represents the imaginary part of the complex seismic trace, X(t, ω) is the frequency domain form of the windowed Fourier transform of the original seismic signal x(t), X(t, 0) is the 0 frequency of the windowed Fourier transform, and n is the order.

[0098] The GHT transformation method overcomes the defect of the HT transformation method being affected by noise and has a higher detection accuracy. It is suitable for fault detection in areas where the signal-to-noise ratio of carbonate rock seismic records is low and the continuity of the phase axis is poor. Although the generalized Hilbert transform can reduce noise interference, it is generally applied to horizontal or near-horizontal stratigraphic data, and horizontal slices of seismic data are extracted for edge detection, without studying the three-dimensional earthquake space as a whole. When faced with some complex high-steep structures, the stratum response interference will be more obvious, affecting the fault identification effect. In this application, fault identification is based on dip guidance, and the dip guides the direction of boundary detection, which can effectively solve the above problems. It is no longer limited to the use of horizontal or near-horizontal stratigraphic data, and can effectively identify faults for any stratigraphic data.

[0099] In order to demonstrate the difference and advantages of the dip-guided fault detection method provided by the embodiment of the present invention compared with the traditional method, the embodiment of the present invention selects the seismic data of a certain work area and uses the dip-guided fault detection method provided by the embodiment of the present invention to identify faults. The following is the data of a certain block for actual verification. Figure 6The stratum section cut by the survey line A through the work area is shown. The stratum in the seismic data has obvious inclination. The data volume is subjected to generalized Hilbert transform fault detection and dip-oriented generalized Hilbert transform fault detection. Figure 6 On the measurement lines, we get Figure 7 and Figure 8 Fault attribute profile, where Figure 7 is the profile detected by generalized Hilbert transform fault, Figure 8 Section for dip-oriented generalized Hilbert transform fault detection. Figure 7 and Figure 8 It can be seen that the former has a very obvious inclined stratum response, which is mixed with the fault response and is difficult to distinguish intuitively, while the latter effectively removes the stratum response and retains the fault characteristics, and the fracture-cavity development characteristics can be clearly seen. Fig. 9 The stratigraphic section taken by another survey line B in the work area is shown. The generalized Hilbert transform and the dip-oriented Hilbert transform are used to obtain Fig.10 and Fig.11 Fault attribute profile. Fig.10 The fault characteristics are almost completely obscured by the tilted stratum response, and what can be seen are the undulations and curvatures of the strata. Fig.11 It is possible to clearly see the fault response, which is developed at the location where the stratum changes are most intense, which illustrates the effectiveness of the method provided in the embodiment of the present invention.

[0100] Step S106: Obtain the fault body according to the sample points included in the seismic data and the fault detection results of the sample points.

[0101] Optionally, multiple sample points in the seismic data of the stratum to be identified are selected; based on the sample points included in the seismic data, the fault detection result of each sample point is obtained, and the fault detection results of each sample point and each sample point in the seismic data are statistically analyzed to obtain the fault body of the stratum to be identified.

[0102] Based on the same inventive concept, an embodiment of the present invention further provides a tilt-guided fault detection device, which can be arranged in a device capable of executing computer instructions. The structure of the device is as follows: Fig.12 As shown, including:

[0103] The data acquisition module 10 is used to acquire seismic data of the stratum to be identified; for the sample points in the seismic data, the dip scanning is performed based on the selected seismic trace spacing to obtain the dip scanning data of the sample points, and the dip scanning parameters include the dip body and the similarity coefficient corresponding to the dip parameters included in the dip body;

[0104] The dip determination module 11 is used to perform nonlinear fitting on the dip parameter and the similarity coefficient to obtain dip fitting data reflecting the relationship between the formation dip and the similarity coefficient; the formation dip corresponding to the maximum similarity coefficient in the dip fitting data is determined as the optimal dip of the sample point;

[0105] The fault detection module 12 is used to use the sample point as the detection starting point and the direction corresponding to the optimal inclination angle as the detection direction, acquire seismic data along the detection direction for fault detection, and obtain the fault detection result of the sample point; obtain the fault body according to the sample point included in the seismic data and the fault detection result of the sample point.

[0106] Regarding the inclination-guided fault detection device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0107] The above-mentioned method and device of the embodiment of the present invention obtain seismic data of the stratum to be identified; perform inclination scanning based on the selected seismic trace spacing for the sample points in the seismic data to obtain the inclination scanning data of the sample points, perform nonlinear fitting on the inclination parameters and similarity coefficients in the inclination scanning data to obtain inclination fitting data reflecting the relationship between the formation inclination and the similarity coefficient; determine the formation inclination corresponding to the maximum similarity coefficient in the inclination fitting data as the optimal inclination of the sample point; use the sample point as the detection starting point, use the direction corresponding to the optimal inclination as the detection direction, obtain seismic data along the detection direction for fault detection, and obtain the fault detection results of the sample points; finally, obtain the fault body based on the sample points included in the seismic data and the fault detection results of the sample points. This method can be applied in the field of seismic exploration. For post-stack seismic data, geophysical technology is used to identify stratum fractures based on seismic data. This method scans the stratum dip angle based on similarity, uses the dip angle to guide the direction of boundary detection, and uses Hilbert transform or generalized Hilbert transform based on dip angle constraints to achieve fault identification. It can also effectively avoid noise interference from stratum response for complex strata, has good anti-noise performance, and can more accurately determine the direction and distribution information of stratum fractures, thereby improving the stability and accuracy of fault detection methods and providing an important data basis for deep and fine exploration and development.

[0108] This embodiment also provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the above-mentioned fault detection method based on dip guidance is implemented.

[0109] This embodiment also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned inclination-guided fault detection method when executing the program.

[0110] Unless otherwise specifically stated, terms such as processing, computing, calculating, determining, displaying, etc. may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0111] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0112] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0113] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0114] The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also be present in a user terminal as discrete components.

[0115] For software implementation, the techniques described in this application can be implemented with modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0116] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

Claims

1. A fault detection method based on inclination guidance, characterized in that: include: Acquiring seismic data of the formation to be identified; For sample points in the seismic data, dip scanning is performed based on the selected seismic trace spacing to obtain dip scanning data of the sample points, wherein the dip scanning parameters include a dip body and similarity coefficients corresponding to the dip parameters included in the dip body; Performing nonlinear fitting on the dip parameter and the similarity coefficient to obtain dip fitting data reflecting the relationship between the formation dip and the similarity coefficient; Determining the formation dip corresponding to the maximum similarity coefficient in the dip fitting data as the optimal dip of the sample point; Taking the sample point as the detection starting point and the direction corresponding to the optimal dip angle as the detection direction, acquiring seismic data along the detection direction for fault detection, and obtaining the fault detection result of the sample point; The fault body is obtained based on the sample points included in the seismic data and the fault detection results of the sample points.

2. The method according to claim 1, characterized in that Perform dip scan according to the selected seismic trace spacing to obtain dip scan data of the sample points, including: Choose from multiple different trace spacings; For each seismic trace spacing, a waveform similarity scan is performed on the sample point, a first dip parameter and a first similarity coefficient are recorded, and first dip scan data of the sample point is obtained; the first dip scan data includes a first dip body and a first similarity coefficient corresponding to the first dip parameter included in the first dip body; A plurality of first dip scanning data corresponding to a plurality of seismic trace intervals are normalized to obtain dip scanning data of the normalized sample points.

3. The method according to claim 2, characterized in that Normalizing a plurality of first dip scanning data corresponding to a plurality of seismic trace spacings to obtain dip scanning data of sample points after normalization, including: Take one seismic trace spacing as the reference trace spacing; Based on the proportional relationship between the seismic trace spacing and the reference trace spacing, the first inclination body corresponding to the seismic trace spacing is converted into a second inclination body with the reference trace spacing as the seismic trace spacing, and the first similarity coefficient is converted into a second similarity coefficient with the reference trace spacing as the seismic trace spacing; the second inclination body includes a second inclination parameter after the first inclination parameter is converted; A plurality of second dip bodies and second similarity coefficients corresponding to a plurality of seismic trace intervals are statistically analyzed based on the dip parameters, and the dip scanning data of the sample points after normalization processing are obtained according to the statistical results.

4. The method according to claim 3, characterized in that The dip parameter and the similarity coefficient are subjected to nonlinear fitting to obtain dip fitting data reflecting the relationship between the formation dip and the similarity coefficient, including: The discrete dip parameters and the corresponding similarity coefficients in the normalized dip scanning data are nonlinearly fitted to obtain a dip fitting surface reflecting the relationship between the formation dip and the similarity coefficient.

5. The method according to any one of claims 1 to 4, characterized in that: Taking the sample point as the detection starting point and the direction corresponding to the optimal dip angle as the detection direction, fault detection is performed according to the seismic data to obtain the fault detection result of the sample point, including: Taking the sample point as the detection starting point, a number of detection points are selected along the direction of the optimal inclination angle; Seismic data of the detection point is obtained, and the fault detection is performed using the Hilbert transform method or the generalized Hilbert transform method to obtain the fault detection result of the sample point.

6. The method according to claim 5, characterized in that The fault detection is performed using the Hilbert transform method to obtain the fault detection result of the sample point, including: Based on Hilbert transform, the imaginary part of the complex seismic trace in the seismic data is used to detect the fault edge, and the fault detection result of the sample point is obtained according to the edge detection result; Among them, the discrete form of Hilbert transform is expressed as: h=h R + I Among them, h R represents the real part of the complex seismic trace, h I represents the imaginary part of the complex seismic trace, X(ω) is the frequency domain form of the original seismic signal x(t), and X(0) is the 0 frequency of the original seismic signal x(t).

7. The method according to claim 5, characterized in that The generalized Hilbert transform method is used to perform fault detection to obtain the formation detection results of the sample points, including: Based on the generalized Hilbert transform, the imaginary part of the complex seismic trace in the seismic data is used to detect the fault edge, and the fault detection result of the sample point is obtained according to the edge detection result; The generalized Hilbert transform is expressed in discrete form as: h = h r +ih i where h r represents the real part of the complex seismic trace, h i represents the imaginary part of the complex seismic trace, X(t,ω) is the frequency domain form of the windowed Fourier transform of the original seismic signal x(t), X(t,0) is the 0 frequency of the windowed Fourier transform, and n is the order.

8. The method according to claim 1, characterized in that According to the sample points included in the seismic data and the fault detection results of the sample points, the fault body is obtained, including: Selecting a plurality of sample points in the seismic data of the formation to be identified; According to the sample points included in the seismic data, the fault detection result of each sample point is obtained, and the fault detection result of each sample point in the seismic data and each sample point is statistically analyzed to obtain the fault body of the formation to be identified.

9. A tilt-guided fault detection device, characterized in that: include: A data acquisition module, used for acquiring seismic data of the formation to be identified; For sample points in the seismic data, dip scanning is performed based on the selected seismic trace spacing to obtain dip scanning data of the sample points, wherein the dip scanning parameters include a dip body and similarity coefficients corresponding to the dip parameters included in the dip body; The dip determination module is used to perform nonlinear fitting on the dip parameter and the similarity coefficient to obtain dip fitting data reflecting the relationship between the formation dip and the similarity coefficient; and determine the formation dip corresponding to the maximum similarity coefficient in the dip fitting data as the optimal dip of the sample point; The fault detection module is used to use the sample point as the detection starting point and the direction corresponding to the optimal inclination angle as the detection direction, acquire seismic data along the detection direction for fault detection, and obtain the fault detection result of the sample point; obtain the fault body according to the sample point included in the seismic data and the fault detection result of the sample point.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by the processor, the fault detection method based on dip guidance described in any one of claims 1-8 is implemented.

11. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a fault detection method based on dip guidance as described in any one of claims 1 to 8 is implemented.

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