A fault detection method and device based on dip angle guidance

Abstract

The present invention discloses a fault detection method and device based on dip guidance. The method comprises: acquiring seismic data of a stratum to be identified; performing dip scanning based on a selected seismic trace spacing for a sample point in the seismic data to obtain dip scanning data of the sample point; performing nonlinear fitting on the dip scanning data to obtain dip fitting data of the relationship between the stratum dip and the similarity coefficient; determining the stratum 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 as the detection direction, acquiring seismic data along the detection direction for fault detection to obtain the fault detection result of the sample point; finally, obtaining the fault body based on the sample point in the seismic data and the fault detection result of the sample point. The method can guide the direction of boundary detection with dip, thereby improving the stability and accuracy of the steep stratum fault detection method and reducing the influence of stratum background interference on fault detection.

Description

Technical Field

[0001] The present 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 serve as pathways for oil and gas migration and are crucial factors controlling effective reservoir formation. With the continued advancement of oil and gas exploration and development, faults are receiving increasing attention as a key factor controlling oil and gas accumulation. Accurate fault identification is crucial for improving exploration and development efficiency. Fault identification using seismic data is a crucial component of fault identification and is crucial for effectively understanding the macroscopic distribution of subsurface faults and identifying oil and gas resources. As exploration and development targets shift toward deep, complex oil and gas reservoirs, the demand for boundary detection accuracy is increasing. Conventional boundary detection methods, such as those based on the Sobel operator, Roberts operator, Canny operator, Prewitt operator, and Laplacian operator, can accurately detect edges in the absence of noise. However, as noise increases, these operators can detect numerous noise points and false edges, or even fail to detect edges, making them inadequate for exploration.

[0003] The deep-ultra-deep zone has complex structural characteristics 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, resulting in insufficient accuracy in fault identification using seismic data. 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-layer fine exploration and development.

[0004] In practical exploration, the generalized Hilbert transform (GHT) method is often used to detect fault boundaries in seismic data. This method has excellent noise immunity and can obtain more accurate information on the orientation and distribution of reservoir fractures. However, when faced with data from complex formations, especially steeply dipped formations, the GHT fault detection method often misidentifies steeply dipped formations as faults, causing background information to interfere with fault identification. Summary of the Invention

[0005] Fault identification in seismic data is a crucial step in fault identification, crucial for effectively understanding the macroscopic distribution of underground faults and exploring oil and gas resources. However, conventional boundary detection methods struggle to meet the exploration needs of carbonate reservoirs with low-quality data. While the generalized Hilbert transform (GHT) method offers excellent noise immunity and can provide more accurate information on reservoir fracture orientation and distribution, it can reduce noise interference but is generally applied to horizontal or near-horizontal stratigraphic data, extracting horizontal slices of seismic data for edge detection without considering the entire three-dimensional seismic space. When faced with complex, high- and steep-structure structures, the stratigraphic response can be significantly disruptive, compromising fault identification.

[0006] In view of the above problems, the present invention is proposed to provide a fault detection method and device based on dip 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 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 angle corresponding to the maximum similarity coefficient in the dip fitting data is determined as the optimal dip angle of the sample point;

[0012] Taking the sample point as the detection starting point and the direction corresponding to the optimal dip angle as the detection direction, seismic data is acquired along the detection direction for fault detection to obtain the fault detection results of the sample point;

[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 seismic trace spacings;

[0016] For each seismic trace spacing, a waveform similarity scan is performed on the sample point, and a first dip parameter and a first similarity coefficient are recorded to obtain first dip scan data of the sample point; 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] Normalization processing is performed on a plurality of first dip scanning data corresponding to a plurality of seismic trace spacings to obtain normalized dip scanning data of sample points.

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

[0019] One seismic trace spacing is used as the reference trace spacing;

[0020] Based on the proportional relationship between the seismic trace spacing and the reference trace spacing, the first dip body corresponding to the seismic trace spacing is converted into a second dip 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 dip body includes the second dip parameter after the first dip 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 normalized dip scanning data of the sample points are obtained according to the statistical results.

[0022] In some optional embodiments, 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 includes:

[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, a sample point is used as a detection starting point, a direction corresponding to the optimal dip angle is used as a detection direction, and fault detection is performed based on seismic data to obtain a fault detection result for the sample point, including:

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

[0026] Obtain seismic data of the detection point, perform fault detection using the Hilbert transform method or the generalized Hilbert transform method, and obtain the fault detection results of the sample point;

[0027] In some optional embodiments, the fault detection is performed using the Hilbert transform method to obtain the fault detection results of the sample points, 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 zero frequency of the original seismic signal x(t).

[0034] In some optional embodiments, a generalized Hilbert transform method is used to perform fault detection to obtain formation detection results of 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 zero frequency of the windowed Fourier transform, and n is the order.

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

[0041] Selecting multiple 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 results of each sample point in the seismic data and each sample point are statistically analyzed to obtain the fault body of the stratum 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 similarity coefficients 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 use the sample point as the detection starting point and the direction corresponding to the optimal dip angle as the detection direction. The seismic data is acquired along the detection direction for fault detection to obtain the fault detection results 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.

[0047] A computer storage medium stores computer executable instructions. 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 capable of running on the processor. When the processor executes the program, a fault detection method based on dip guidance is implemented.

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

[0050] The dip-guided fault detection method provided by an embodiment of the present invention obtains seismic data of a stratum to be identified; performs dip scanning based on a selected seismic trace spacing for sample points in the seismic data to obtain dip scanning data for 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 for the sample point; uses the sample point as the detection starting point and the direction corresponding to the optimal dip as the detection direction, acquires seismic data along the detection direction for fault detection, and obtains a fault detection result for the sample point; finally, obtains a fault body based on the sample points included in the seismic data and the fault detection results of the sample points. This method can accurately find the dip corresponding to the maximum similarity coefficient, which is the optimal dip for fault detection of the stratum. The dip angle guides the direction of boundary detection using the dip angle, thereby improving the stability and accuracy of the fault detection method for steep strata, reducing the impact 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 in part will become apparent from the description, or will be understood by practicing the present invention. The purposes 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 Flowchart of a fault detection method based on dip guidance in an embodiment of the present invention;

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

[0056] Figure 3 Schematic diagram of the inclination fitting surface in an embodiment of the present invention;

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

[0058] Figure 5 This is an example diagram of the dip direction data extraction 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 Schematic diagram of A-section detection by generalized Hilbert transform fault detection in an embodiment of the present invention;

[0061] Figure 8 Schematic diagram of A-profile detection using dip-guided generalized Hilbert transform fault detection in an embodiment of the present invention;

[0062] Figure 9 Schematic diagram of a stratum cross section taken by survey line B in an embodiment of the present invention;

[0063] Figure 10 Schematic diagram of B-section detection using generalized Hilbert transform tomography in an embodiment of the present invention;

[0064] Figure 11 Schematic diagram of B-profile detection using dip-guided generalized Hilbert transform fault detection in an embodiment of the present invention;

[0065] Figure 12 Schematic diagram of the structure of a tilt-guided fault detection device in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although 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. Rather, 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 characteristics 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, resulting in insufficient accuracy in fault identification using seismic data. 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-layer fine exploration and development.

[0068] In order to solve the problem of insufficient accuracy in fault identification in seismic data caused by traditional fault detection methods, and the difficulty in detecting the spatial distribution patterns of complex fractures of multiple periods and types, and the inability to accurately identify faults, an embodiment of the present invention provides a fault detection method based on dip guidance, which conducts an overall study of 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] The first embodiment 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 stratum to be identified.

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

[0073] Optionally, an inclination scan is performed according to the selected seismic trace spacing to obtain inclination scan data of the sample point. This embodiment selects multiple different seismic trace spacings, and for each seismic trace spacing, performs a waveform similarity scan on the sample point, records the first inclination parameter and the first similarity coefficient, and obtains the 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; the multiple first inclination scan data corresponding to the multiple 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 seismic trace spacing and inclination scanning. The high and low similarity coefficients during the scanning process represent the similarity of the waveforms. As the scanning inclination angle 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 x-direction apparent inclination angle of the sample point, and the inclination parameter q can be called the y-direction apparent inclination angle of the sample point. 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 is shown 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 obliquity angles p and q, respectively. The obliquity parameters in the scanning process are discrete, so the maximum similarity coefficient C in the obliquity 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 the 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 the second inclination parameter after the first inclination parameter is converted; the multiple second inclination bodies and second similarity coefficients corresponding to the multiple seismic trace spacings are statistically analyzed 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 from scanning at different trace spacings fall within different measurement ranges, it is necessary to normalize the first dip scanning parameters for multiple trace spacings to lay the foundation for dip statistical analysis. For example, the dip angle with a 10-meter longitudinal difference between two events at a 30-meter trace spacing is twice the tangent value of the dip angle with a 10-meter longitudinal difference between two events at a 60-meter trace spacing. Therefore, the dip angle with a 10-meter longitudinal difference at a 60-meter trace spacing must be converted to a 5-meter longitudinal difference at a 30-meter trace spacing to facilitate subsequent statistical analysis.

[0078] In this step, dip scans are performed on seismic data at various trace spacings to generate a multi-trace dip volume. Calculating dip values ​​using a combination of these multiple trace spacings improves the stability of the dip scan. Normalizing the dip angles across multiple trace spacings lays the foundation for dip 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] 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 the dip fitting surface reflecting 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 dip angle and similarity is calculated. After nonlinear fitting, the dip parameters in the dip volume obtained during scanning are continuous, and the dip angles corresponding to these dip parameters are also continuous. Using the dip fitting surface, we can accurately find the dip angle corresponding to the point of maximum similarity. This dip angle is more accurate than the discrete dip angles obtained by scanning alone and better reflects the stratigraphic structure. Using a regression algorithm to optimize and calculate the optimal dip angle can avoid the illusion of sudden dip angle changes.

[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, a sample point is used as the detection starting point, and several detection points are selected along the optimal dip direction; seismic data of the detection points are 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 points.

[0085] The Hilbert transform HT is essentially an all-pass filter. The generalized Hilbert transform GHT is essentially to add a frequency domain window to the Hilbert transform analytic signal 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 obtained along the horizontal direction of the formation. Figure 4 As shown in the figure, taking the GHT method as an example, when performing fault detection on a tilted 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 value. 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 discrete form of generalized Hilbert transform 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 zero 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 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 stratigraphic 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 differences and advantages of the dip-guided fault detection method provided by the embodiment of the present invention compared to traditional methods, the embodiment of the present invention selects seismic data from 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 data from a certain block is used for actual verification. Figure 6The following figure shows a stratigraphic section taken from a survey line A that passes through the work area. The seismic data shows a clear inclination of the stratigraphic layers. The data volume was subjected to generalized Hilbert transform fault detection and dip-oriented generalized Hilbert transform fault detection. Figure 6 On the measuring line, we get Figure 7 and Figure 8 Fault attribute profile, where Figure 7 is the profile of the generalized Hilbert transform fault detection, 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 and cave development characteristics can be clearly seen. Figure 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 the stratigraphic section. Figure 10 and Figure 11 Fault attribute profile. Figure 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 stratum. Figure 11 The fault response can be clearly seen, and is located at the location where the stratum changes are most intense, which illustrates the effectiveness of the method provided by the embodiment of the present invention.

[0100] Step S106: Obtain a fault body based on 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 set in a device capable of executing computer instructions. The structure of the device is as follows: Figure 12 As shown, including:

[0103] The data acquisition module 10 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 similarity coefficients 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, obtain seismic data along the detection direction for fault detection, and obtain the fault detection results of the sample point; obtain the fault body based on the sample point included in the seismic data and the fault detection results 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 on 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 on the sample points in the seismic data based on the selected seismic trace spacing to obtain 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 stratum inclination and the similarity coefficient; determine the stratum 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 and 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 result of the sample point; 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 in complex strata, has good noise resistance, and can more accurately determine the direction and distribution information of stratum fractures, thereby improving the stability and accuracy of the fault detection method and providing an important data basis for deep and precise exploration and development.

[0108] This embodiment further 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. When the processor executes the program, the above-mentioned fault detection method based on dip guidance is implemented.

[0110] Unless otherwise specifically stated, terms such as process, calculate, compute, determine, display, and the like may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, that manipulate 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 the 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 referred to 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 processes 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 the present disclosure. The accompanying method claims present 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 foregoing 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 embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are therefore 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 may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0114] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, 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 write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and 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 using 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 external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0116] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A fault detection method based on dip guidance, characterized in that: include: Acquiring seismic data of the formation to be identified; For sample points in seismic data, dip scanning is performed based on a selected seismic trace spacing to obtain dip scanning data of the sample points, specifically comprising: selecting a plurality of different seismic trace spacings; performing a waveform similarity scan on the sample points for each seismic trace spacing, recording a first dip parameter and a first similarity coefficient, to obtain first dip scanning data of the sample points, the first dip scanning data comprising a first dip body and a first similarity coefficient corresponding to the first dip parameter included in the first dip body; normalizing a plurality of first dip scanning data corresponding to the plurality of seismic trace spacings to obtain normalized dip scanning data of the sample points, the dip scanning data comprising a dip body and a similarity coefficient corresponding to the dip parameter included in the dip body; The normalization process is performed on a plurality of first dip scanning data corresponding to a plurality of seismic trace spacings to obtain normalized dip scanning data of sample points, specifically comprising: taking a seismic trace spacing as a reference trace spacing; Based on the proportional relationship between the seismic trace spacing and the reference trace spacing, the first dip body corresponding to the seismic trace spacing is converted into a second dip 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 dip body includes a second dip parameter after the first dip parameter is converted; Performing statistics on a plurality of second dip bodies and second similarity coefficients corresponding to a plurality of seismic trace intervals based on the dip parameters, and obtaining normalized dip scanning data of the sample points according to the statistical results; 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, seismic data is acquired along the detection direction for fault detection to obtain 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, wherein 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 includes: 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.

3. The method according to any one of claims 1-2, 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, several detection points are selected along the optimal inclination direction; Seismic data of the detection points are obtained, and fault detection is performed using a Hilbert transform method or a generalized Hilbert transform method to obtain fault detection results of the sample points.

4. The method according to claim 3, characterized in that The fault detection is performed using the Hilbert transform method to obtain the fault detection results of the sample points, including: Based on the 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: in, represents the real part of the complex seismic trace, represents the imaginary part of the complex seismic trace, is the original seismic signal The frequency domain form of is the original seismic signal 0 frequency.

5. The method according to claim 3, 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; Among them, the discrete form of generalized Hilbert transform is expressed as: in represents the real part of the complex seismic trace, represents the imaginary part of the complex seismic trace, is the original seismic signal The frequency domain form of the windowed Fourier transform is is the zero frequency of the windowed Fourier transform, is the order.

6. The method according to claim 1, characterized in that Based on the sample points included in the seismic data and the fault detection results of the sample points, the fault body is obtained, including: Selecting multiple 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 results of each sample point in the seismic data and each sample point are statistically analyzed to obtain the fault body of the stratum to be identified.

7. 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 seismic data, dip scanning is performed based on the selected seismic trace spacing to obtain dip scanning data of the sample points, specifically comprising: selecting a plurality of different seismic trace spacings; for each seismic trace spacing, performing a waveform similarity scan on the sample points, recording a first dip parameter and a first similarity coefficient, to obtain first dip scanning data of the sample points, wherein the first dip scanning data comprises a first dip body and a first similarity coefficient corresponding to the first dip parameter included in the first dip body; normalizing a plurality of first dip scanning data corresponding to the plurality of seismic trace spacings to obtain normalized dip scanning data of the sample points, wherein the dip scanning data comprises a dip body and a similarity coefficient corresponding to the dip parameter included in the dip body; wherein, for Normalizing a plurality of first dip scanning data corresponding to a plurality of seismic trace spacings to obtain normalized dip scanning data of sample points, specifically comprising: using a seismic trace spacing as a reference trace spacing; based on a proportional relationship between the seismic trace spacing and the reference trace spacing, converting a first dip body corresponding to the seismic trace spacing into a second dip body with the reference trace spacing as the seismic trace spacing, and converting a first similarity coefficient into a second similarity coefficient with the reference trace spacing as the seismic trace spacing; the second dip body including a second dip parameter obtained by converting the first dip parameter; performing statistics on the plurality of second dip bodies and second similarity coefficients corresponding to the plurality of seismic trace spacings based on the dip parameters, and obtaining normalized dip scanning data of the sample points according to the statistical results; a dip determination module configured 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; and obtain the fault body based on the sample point included in the seismic data and the fault detection result of the sample point.

8. 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 according to any one of claims 1 to 6 is implemented.

9. 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 6 is implemented.

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