Frequency division attribute fusion-based post-stack seismic data full fracture system description method

Abstract

The invention provides a post-stack seismic data full fracture system description method based on frequency division attribute fusion, and relates to the field of petroleum and natural gas exploration, and the method comprises the steps: carrying out the spectrum analysis of a target interval of a post-stack original full-band seismic data body, and obtaining an effective band; filtering out three frequency components of low frequency, intermediate frequency and high frequency of the effective frequency band; performing construction smoothing processing on the three frequency components; respectively extracting a chaos attribute, a variance attribute and a curvature attribute from each frequency component to obtain nine attribute bodies, screening the nine attribute bodies, and determining an optimal attribute body corresponding to each frequency; selecting optimal ant body slices corresponding to the three frequency components according to the optimal attribute body; and correspondingly importing the optimal ant body slice into a red channel, a green channel and a blue channel in the RGB model to obtain the ant body slice fusing different attributes of low, medium and high frequencies. According to the technical scheme, a more perfect fracture system ant body slice can be generated.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration, and in particular to a method for characterizing the entire fault system based on frequency-division attribute fusion of post-stack seismic data. Background Technology

[0002] In the field of oil and gas exploration, accurate characterization of fracture systems is crucial for understanding geological structures and predicting hydrocarbon migration and accumulation. As exploration efforts gradually advance into more complex geological regions, the need for high-precision characterization of the entire fracture system, especially fractures of varying scales, is becoming increasingly urgent. Traditional fracture detection techniques, such as coherence volume analysis (Zhang Hengtie, 2022), eigenvalue coherence, and curvature methods, have many limitations when dealing with complex geological conditions. Due to inherent limitations in seismic geological data, these techniques often result in inaccurate fault location and unclear attribute results.

[0003] Generally, based on the scale of development and extension of faults, they can be divided into three scales: large-scale regional faults, meso-scale faults associated with tectonics, and small-scale faults related to folds, joints, etc. (Ma Yixuan et al., 2020). While some existing technologies attempt to identify strike-slip fault zones by selecting sensitive seismic attributes (Liu Huinan et al., 2019; Cai Wenrui, 2019), they do not fully consider the differences in how seismic data from different frequency bands reflect fault responses. Specifically, low-frequency signals respond better to large-scale faults, mid-frequency signals respond better to meso-scale faults, and high-frequency signals respond better to small-scale faults (Su Ming et al., 2014). Furthermore, various seismic attributes are not effectively integrated, resulting in a lack of intuitive visualization of fault zones. Some technologies based on multiple discontinuous attribute detection (Xie Qinghui et al., 2021), while combining multiple seismic attributes, also fail to consider the differences in how seismic data from different frequency bands reflect faults at different scales, and do not integrate multiple seismic attribute volumes. This prevents a more intuitive characterization of the entire fault system, and the prediction accuracy may not meet the requirements of current fine-scale exploration.

[0004] Frequency-division attribute fusion refers to extracting discontinuity attributes from frequency-division data and superimposing and fusing them based on the differences in response of different frequency components to fractures at different scales. However, existing fracture characterization methods often fail to fully utilize this characteristic, making it impossible to accurately identify and characterize fractures at different scales simultaneously, and thus hindering a complete depiction of the entire fracture system. In practical seismic data processing, methods using a single frequency or those that do not properly utilize frequency differences cannot comprehensively display the entire fracture system, missing much fracture information that is of great indicative significance for oil and gas exploration. Summary of the Invention

[0005] The purpose of this invention is to address the problem that existing fracture characterization methods cannot fully represent the entire fracture system and omit much fracture information that is of great significance for oil and gas exploration, and to provide a method for characterizing the entire fracture system based on frequency-division attribute fusion of post-stack seismic data.

[0006] The above-mentioned objective of this application is achieved through the following technical solution: S1: Perform spectral analysis on the target segment of the post-stack original full-band seismic data volume to obtain its effective frequency band; S2: Use a bandpass filter to filter out the low-frequency, mid-frequency, and high-frequency components of the effective frequency band; S3: Performs a smoothing process on the low-frequency, mid-frequency, and high-frequency components; S4: Extract the chaos attribute, variance attribute and curvature attribute for each frequency component to obtain nine attribute volumes, and then filter them to determine the optimal attribute volume for each frequency. S5: Set custom ant parameters for the nine attribute bodies; select the best ant body slices corresponding to the three frequency components based on the best attribute body; S6: Import the best ant body slice into the red, green and blue channels of the RGB model to obtain an ant body slice that integrates different attributes at low, medium and high frequencies.

[0007] Optionally, step S4 includes: S41: Extract the chaos attribute, variance attribute and curvature attribute for each frequency component to obtain nine attribute volumes; S42: For the same frequency, observe how different property bodies characterize the fracture, and determine the property body that best and most clearly characterizes the fracture at that frequency. Repeat this operation to initially select the best property body corresponding to the three frequency components.

[0008] Optionally, step S4 may also include: The chaotic property is calculated as follows: In a 3D seismic data volume, seismic reflection amplitude is used as a spatial variable, and the 3D seismic data signal at a certain point on the stratigraphic reflection interface is... The tilt vector is represented as:

[0009] The covariance matrix of this vector is:

[0010]

[0011]

[0012] In the formula, For the location information of the sample points, The values ​​are 1, 2, and 3. It is the covariance matrix; are the elements of the covariance matrix C; To find the expected value within the parentheses; Let be the expected value of the gradient.

[0013] Optionally, step S4 may also include: The variance attribute is calculated as follows: The variance of a given sampling point is obtained by calculating the variance of all sampling points within a given time window between a given sampling point and its surrounding adjacent seismic traces, and then weighting and normalizing the variance.

[0014]

[0015]

[0016] In the formula, This represents the variance value of a specific sampling point in a certain channel. This represents the weighted variance of the sample points. for time The amplitude of the seismic trace; for The average amplitude of the seismic trace is included in the time window calculation; The time window length for calculating variance; The number of adjacent channels required to calculate the variance of a given sampling point; is a triangular weighting function for a sampling point within a certain time window, with a value ranging from 0 to 1; .

[0017] Optionally, step S4 may also include: The curvature attribute is calculated as follows: The relationship between curvature and normal stress is as follows:

[0018] In the formula, It is normal stress; The thickness of the strata; Young's modulus of the strata rocks; For curvature.

[0019] Optionally, step S5 includes: Based on the response of fractures at different frequencies and the main fracture trend, attitude control is applied to remove false or redundant fracture information.

[0020] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform a method for characterizing the entire fault system based on frequency division attribute fusion of post-stack seismic data.

[0021] A computer-readable storage medium storing instructions that, when executed, perform a method for characterizing a full fault system based on frequency division attribute fusion of post-stack seismic data.

[0022] The beneficial effects of the technical solution provided in this application are: By employing three frequency bands—low, medium, and high—and combining the strengths of each band in terms of fracture response, the limitations of a single frequency band in fracture characterization are avoided. This approach generates more comprehensive fragment slices of the fracture system, providing a reference for studying the distribution of the entire fracture system and directly serving oil and gas field exploration and production. Attached Figure Description

[0023] The present application will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of an embodiment of this application; Figure 2 This is a spectrum curve of the target layer of the original full-band seismic data volume of the Bohai Bay P oilfield in this application embodiment; Figure 3 The images shown are the original 16Hz seismic profile generated after frequency division processing of the seismic data volume of the Bohai Bay P oilfield in this application embodiment, and the seismic profile after structural smoothing filtering processing. Figure 4 These are slice diagrams of three discontinuous attribute volumes in the embodiments of this application; Figure 5 These are ant body slice images with three discontinuous attributes in the embodiments of this application; Figure 6 These are comparison images of ant body slices before and after different frequencies of occurrence control in the embodiments of this application; Figure 7 This is a comparison diagram of the final frequency-division fused ant body and the full-band amplitude ant body in the embodiments of this application; Figure 8 This is a schematic diagram of the electronic device structure in the embodiments of this application. Detailed Implementation

[0024] To provide a clearer understanding of the technical features, objectives, and effects of this application, the specific embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0025] The embodiments of this application provide a method for characterizing the entire fault system of post-stack seismic data based on frequency division attribute fusion.

[0026] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for characterizing the entire fault system of post-stack seismic data based on frequency division attribute fusion, as described in an embodiment of this application, including: S1: Perform spectral analysis on the target segment of the post-stack original full-band seismic data volume to obtain its effective frequency band; S2: Use a bandpass filter to filter out the low-frequency, mid-frequency, and high-frequency components of the effective frequency band; S3: Performs a smoothing process on the low-frequency, mid-frequency, and high-frequency components; In one specific embodiment of this application, the low-frequency, mid-frequency and high-frequency components are constructed and smoothed to suppress random noise and enhance the continuity of the in-phase axis.

[0027] S4: Extract the chaos attribute, variance attribute and curvature attribute for each frequency component to obtain nine attribute volumes, and then filter them to determine the optimal attribute volume for each frequency. S5: Set custom ant parameters for the nine attribute bodies; select the best ant body slices corresponding to the three frequency components based on the best attribute body; S6: Import the best ant body slice into the red, green and blue channels of the RGB model to obtain an ant body slice that integrates different attributes at low, medium and high frequencies.

[0028] As one example, frequency-based attribute fusion refers to filtering the seismic data volume into three frequency bands: low frequency, mid frequency, and high frequency. Taking advantage of the different responses of different frequency bands to fractures of different scales, the RGB attributes of the ant-shaped slices corresponding to the three frequency bands are fused to achieve the purpose of characterizing the entire fracture system.

[0029] Step S4 includes: S41: Extract the chaos attribute, variance attribute and curvature attribute for each frequency component to obtain nine attribute volumes; S42: For the same frequency, observe how different property bodies characterize the fracture, and determine the property body that best and most clearly characterizes the fracture at that frequency. Repeat this operation to initially select the best property body corresponding to the three frequency components.

[0030] Step S4 also includes: The chaotic property is calculated as follows: In a 3D seismic data volume, seismic reflection amplitude is used as a spatial variable, and the 3D seismic data signal at a certain point on the stratigraphic reflection interface is... The tilt vector is represented as:

[0031] The covariance matrix of this vector is:

[0032]

[0033]

[0034] In the formula, For the location information of the sample points, The values ​​are 1, 2, and 3. It is the covariance matrix; are the elements of the covariance matrix C; To find the expected value within the parentheses; Let be the expected value of the gradient.

[0035] Step S4 also includes: The chaotic property is calculated as follows: In a 3D seismic data volume, seismic reflection amplitude is used as a spatial variable, and the 3D seismic data signal at a certain point on the stratigraphic reflection interface is... The tilt vector is represented as:

[0036] The covariance matrix of this vector is:

[0037]

[0038]

[0039] In the formula, For the location information of the sample points, The values ​​are 1, 2, and 3. It is the covariance matrix; are the elements of the covariance matrix C; To find the expected value within the parentheses; Let be the expected value of the gradient.

[0040] Step S4 also includes: The curvature attribute is calculated as follows: The relationship between curvature and normal stress is as follows:

[0041] In the formula, It is normal stress; The thickness of the strata; Young's modulus of the strata rocks; For curvature.

[0042] Step S5 includes: Based on the response of fractures at different frequencies and the main fracture trend, attitude control is applied to remove false or redundant fracture information.

[0043] In one specific embodiment of this application: Step 1: Spectral analysis. Perform spectral analysis on the target segment of the post-stack raw full-band seismic data volume of the P oilfield to obtain its effective frequency band, such as... Figure 2 As shown, the effective frequency band of the target layer is 5~55Hz.

[0044] Step 2: Frequency filtering and division. Based on the effective frequency band of the target layer obtained in Step 1, a bandpass filter is used to filter out the three components: low frequency, mid frequency and high frequency. 16Hz is selected as the low frequency component, 35Hz as the mid frequency component and 55Hz as the high frequency component.

[0045] Step 3: Smoothing is performed on the low-frequency, mid-frequency, and high-frequency components obtained from Step 2 to suppress random noise and enhance the continuity of the in-phase axis. For example... Figure 3 As shown, Figure 3 (a) is the original 16Hz seismic profile generated after frequency division processing of the P oilfield seismic data volume. Figure 3 (b) is for Figure 3 (a) Seismic profile after structural smoothing filtering. The overall noise of the profile is effectively suppressed after structural smoothing, and it is clearer than the original profile. Figure 3 (a) Several independent red and blue patches are present in the red box on the right, which are typical high-frequency noise. Figure 3 (b) The number of red and blue patches at the same location is significantly reduced, effectively suppressing high-frequency noise; in the red box on the left, Figure 3 (b) shows improved continuity of the phase axis. Structural smoothing filtering of the raw seismic data can effectively reduce noise interference on seismic properties, enhance the prominence of discontinuities such as fractures and cracks, and improve image quality.

[0046] Step 4: Three types of discontinuity detection are performed. Chaotic attributes, variance attributes, and curvature attributes are extracted for each frequency component, resulting in a total of nine attribute volumes. For the same frequency, the characterization of fracture by different attribute volumes is observed to determine the attribute volume that best and clearestly characterizes the fracture at that frequency. This process is repeated to initially select the optimal attribute volume for each frequency. Figure 4 It is a slice diagram of three frequencies corresponding to three discontinuous attributes. Figure 4(a) In the three attribute volume slices of the 16Hz low-frequency signal, the chaotic volume depicts fractures as continuous and robust dark bands, highlighting the long extension and clear outline of the fractures. In the variance volume, some areas show relatively scattered or sparse fractures, and the continuity of large-scale fractures is weaker than that of the chaotic volume. The curvature volume shows fragmented and dense fractures, distributed in a grid pattern, making it difficult to identify large-scale fractures. For Figure 4 (b) In the three attribute volume slices of the 35Hz intermediate frequency signal, the variance volume exhibits better continuity and clearer boundaries in its fracture representation, and also provides a more complete fracture characterization compared to the other two attribute volumes; the other two attribute volumes suffer from problems such as small-scale fractures or noise interference and disruption of continuity in their mid-scale characterization. For Figure 4 (c) Three attribute volume slices of the 55Hz high-frequency signal: the small-scale fractures of the curvature volume are small and dense, distributed in a grid pattern, and the orientation, boundary and extension length of the small-scale fractures are highly identifiable. However, the boundaries of the small-scale fractures are difficult to define in the other attribute volumes, and the characterization of the small-scale fractures is not perfect.

[0047] Step 5: Ant tracking and optimization. Set custom ant parameters for the nine attribute bodies generated in Step 4. Combine the attribute body comparison analysis results in Step 4 to select the best ant body slice corresponding to each frequency. Then, based on the response of different frequencies to fractures and the main direction of fractures, give orientation control and remove false or redundant fracture information. In one specific embodiment of this application, Figure 5 These are ant body slice images showing three frequencies corresponding to three discontinuous attributes. From the image, we can see that... Figure 5 (a) In the 16Hz low-frequency signal, the chaotic ant body exhibits a darker fracture color, stronger signal intensity, and more identifiable fracture extension length, orientation, and boundaries, demonstrating superior large-scale fracture continuity. While the variance ant body shows considerable overall characterization, some large-scale fractures are missing, making it impossible to construct a complete fracture network. The curvature ant body, due to the dense distribution of small-scale fractures, results in blurred large-scale fracture outlines, making them difficult to identify. Figure 5 (b) In the 35Hz intermediate frequency signal, variance ant volume not only effectively characterizes mesoscale fractures, but also shows remarkable performance in characterizing large-scale fractures. Chaotic ant volume fractures are more missing, curvature ant volume fracture continuity is worse than variance ant volume fractures, and there is a large amount of small-scale fracture interference. Figure 5 (c) In the 55Hz high-frequency signal, it is obvious that the curvature ant body tracks a wealth of small-scale fractures, which are densely distributed in a grid pattern and have multiple orientations. At the same time, the curvature ant body can show the extension relationship between small-scale fractures and medium and large-scale fractures, thus constructing a complete small-scale fracture network system. Other attribute bodies cannot achieve the effect of curvature bodies in terms of continuity, quantity, and extension relationship.

[0048] In one specific embodiment of this application, a 16Hz chaotic volume slice, a 35Hz variance volume slice, and a 55Hz curvature volume slice are preferred. The fracture response, main fracture path, and actual production requirements at different frequencies are then analyzed to control the fracture attitude, eliminating false or redundant fracture information. The results are as follows: Figure 6 As shown. In the P oilfield study area, the 16Hz low frequency focuses on characterizing large-scale faults trending near north-south and near east-west, so northeast- and northwest-trending faults are excluded; the 35Hz mid-frequency can characterize small-scale faults and some medium-scale faults, while retaining a certain overall ability to represent large-scale faults. The main fault in the study area and the small-scale faults developing around the main fault mainly trend northeast and near east-west, so north-northwest-trending faults are excluded; the 55Hz high frequency focuses on representing small-scale faults. However, the small-scale faults in the study area have diverse and complex trends, so only large-scale faults trending near north-south are excluded, while other types of small-scale faults are retained.

[0049] Step 6: Attribute Fusion. The low-frequency, mid-frequency, and high-frequency ant body slices obtained in Step 5 are imported into the red, green, and blue channels of the RGB model, respectively, to obtain ant body slices that fuse different attributes at low, mid, and high frequencies. The entire fault system is characterized by superimposing and fusing low-frequency chaotic attribute ant bodies, mid-frequency variance attribute ant bodies, and high-frequency curvature attribute ant bodies. The core principle is to leverage the complementary advantages of different frequencies and attributes to cover faults at all scales, from small to large. Chaotic attributes are sensitive to large-scale, low-velocity geological bodies; variance attributes focus on seismic waveform differences and are sensitive to mid-scale faults; and curvature attributes capture minute bending and twisting of strata and are sensitive to small-scale faults. The frequency-separated attribute fusion result is shown in the figure below. Figure 7 As shown in (a), the 35Hz variance ant volume characterizes most of the fractures, while the 16Hz chaotic ant volume and the 55Hz curvature ant volume supplement the large-scale and small-scale fractures, respectively; Figure 7 (b) Comparison of full-band ant body slices: Due to the superposition and fusion of three types of frequency-specific attribute slices, the number and scale of fractures depicted are significantly greater than those of full-band ant body slices, effectively presenting a complete fracture system map of the study area from small to large.

[0050] This application also discloses an electronic device. (See reference...) Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0051] The communication bus 502 is used to enable communication between these components.

[0052] The user interface 503 may include a display screen, and optionally, the user interface 503 may also include a standard wired interface or a wireless interface.

[0053] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0054] This application also discloses a computer-readable storage medium storing multiple instructions adapted for loading by a processor to execute the above-described method for characterizing a full fault system based on frequency division attribute fusion of post-stack seismic data.

[0055] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure.

[0056] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for characterizing the entire fault system based on frequency-division attribute fusion of post-stack seismic data, characterized in that, The method includes the following steps: S1: Perform spectral analysis on the target segment of the post-stack original full-band seismic data volume to obtain its effective frequency band; S2: Use a bandpass filter to filter out the low-frequency, mid-frequency, and high-frequency components of the effective frequency band; S3: Performs a smoothing process on the low-frequency, mid-frequency, and high-frequency components; S4: Extract the chaos attribute, variance attribute and curvature attribute for each frequency component to obtain nine attribute volumes, and then filter them to determine the optimal attribute volume for each frequency. S5: Set custom ant parameters for the nine attribute bodies; select the best ant body slices corresponding to the three frequency components based on the best attribute body; S6: Import the best ant body slice into the red, green and blue channels of the RGB model to obtain an ant body slice that integrates different attributes at low, medium and high frequencies.

2. The method for characterizing the entire fault system of post-stack seismic data based on frequency-division attribute fusion as described in claim 1, characterized in that, Step S4 includes: S41: Extract the chaos attribute, variance attribute and curvature attribute for each frequency component to obtain nine attribute volumes; S42: For the same frequency, observe how different property bodies characterize the fracture, and determine the property body that best and most clearly characterizes the fracture at that frequency. Repeat this operation to initially select the best property body corresponding to the three frequency components.

3. The method for characterizing the entire fault system of post-stack seismic data based on frequency-division attribute fusion as described in claim 1, characterized in that, Step S4 also includes: The chaotic property is calculated as follows: In a 3D seismic data volume, seismic reflection amplitude is used as a spatial variable, and the 3D seismic data signal at a certain point on the stratigraphic reflection interface is... The tilt vector is represented as: The covariance matrix of this vector is: In the formula, For the location information of the sample points, The values ​​are 1, 2, and 3. It is the covariance matrix; are the elements of the covariance matrix C; To find the expected value within the parentheses; Let be the expected value of the gradient.

4. The method for characterizing the entire fault system of post-stack seismic data based on frequency-division attribute fusion as described in claim 1, characterized in that, Step S4 also includes: The variance attribute is calculated as follows: The variance of a given sampling point is obtained by calculating the variance of all sampling points within a given time window between a given sampling point and its surrounding adjacent seismic traces, and then weighting and normalizing the variance. In the formula, This represents the variance value of a specific sampling point in a certain channel. This represents the weighted variance of the sample points. for time The amplitude of the seismic trace; for The average amplitude of seismic traces is calculated by incorporating time into the time window. The time window length for calculating variance; The number of adjacent channels required to calculate the variance of a given sampling point; is a triangular weighting function for a sampling point within a certain time window, with a value ranging from 0 to 1; .

5. The method for characterizing the entire fault system of post-stack seismic data based on frequency-division attribute fusion as described in claim 1, characterized in that, Step S4 also includes: The curvature attribute is calculated as follows: The relationship between curvature and normal stress is as follows: In the formula, It is normal stress; The thickness of the strata; Young's modulus of the strata rocks; For curvature.

6. The method for characterizing the entire fault system of post-stack seismic data based on frequency division attribute fusion as described in claim 1, characterized in that, Step S5 includes: Based on the response of fractures at different frequencies and the main fracture trend, attitude control is applied to remove false or redundant fracture information.

7. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, perform the method as described in any one of claims 1-6.

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