River fine delineation method and device, electronic equipment and storage medium

Abstract

The application provides a river fine delineation method and device, electronic equipment and storage medium. The method comprises: acquiring seismic data of a target river; extracting time domain amplitude attributes, frequency domain energy attributes, offset amplitude attributes, azimuth attributes and multi-dimensional seismic attributes of the target river according to the seismic data of the target river; fusing the time domain amplitude attributes, the frequency domain energy attributes, the offset amplitude attributes, the azimuth attributes and the multi-dimensional seismic attributes of the target river by using a fuzzy logic algorithm to obtain attribute fusion results of the target river; and the attribute fusion results of the target river are used to delineate the spatial distribution form of the target river. The method can fully exploit the information of seismic data, comprehensively apply multi-dimensional and multi-domain characteristics of seismic data, and realize the technology of fine delineation of complex rivers based on the method of fuzzy logic data driving, so that the spatial distribution description precision and efficiency of complex rivers can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of petroleum exploration technology, and in particular to a method, apparatus, electronic device and storage medium for fine river channel characterization. Background Technology

[0002] Fluvial reservoirs are an important type of continental oil and gas reservoir in my country and a typical example of lithologic oil and gas reservoirs. During fluvial deposition, frequent channel migration and the intercutting and superposition of different channels easily lead to the formation of diverse composite channel sand bodies on a planar surface. Besides continental sedimentation, which readily forms fluvial reservoirs, marine karstification can also create karst paleochannel systems. Both continental fluvial reservoirs and karst paleochannel systems represent important oil and gas accumulation spaces. Accurately characterizing the complex channel morphologies formed under complex sedimentary or karst processes is the first and most crucial step in achieving significant breakthroughs in the exploration and development of these types of oil and gas reservoirs.

[0003] Conventional channel characterization techniques typically rely on a specific target area and geological background. Through forward modeling combining well and seismic data, they analyze the seismic reflection patterns of channel reservoirs and then characterize the spatial distribution of channels based on selected attributes. For example, in describing shallow, tight channel sandstone reservoirs in the Sichuan Basin, existing techniques usually involve extracting amplitude attributes by opening time windows or creating along-layer slices based on seismic horizon picking, and then identifying and characterizing channels. Similarly, in characterizing karst paleochannels in the Ordovician reservoirs of the Tarim Oilfield, existing techniques also use amplitude attributes to characterize channels and coherence attributes to characterize boundaries in karst channel characterization based on forward modeling. However, for channels with complex sedimentary conditions, such as multiple overlapping channels, intersections, and significant lateral property variations, the identified channels are not continuous enough, channel boundaries are unclear, and the entire underground channel can not be fully characterized. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method, apparatus, electronic device, and storage medium for fine river channel characterization, which solves the technical problem of unclear river boundary characterization in the prior art when the sedimentation conditions are complex.

[0005] In a first aspect, this application provides a method for fine depiction of river channels, the method comprising: Obtain seismic data for the target river channel; Based on the seismic data of the target river channel, extract the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel; The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel are fused using a fuzzy logic algorithm to obtain the attribute fusion result of the target river channel; Based on the attribute fusion results of the target river channel, the spatial distribution pattern of the target river channel is characterized.

[0006] According to an embodiment of this application, optionally, in the above-described method for fine depiction of river channels, the time-domain amplitude attribute includes at least one of the root mean square amplitude, the maximum trough attribute, and the maximum crest attribute.

[0007] According to an embodiment of this application, optionally, in the above-described fine river channel characterization method, the frequency domain energy attribute includes at least one of high-frequency component attribute, low-frequency component attribute, mid-low frequency component attribute, and high-frequency attenuation gradient attribute.

[0008] According to an embodiment of this application, optionally, in the above-described method for fine depiction of river channels, the offset amplitude attribute includes at least one of the far offset superimposed maximum trough attribute and the near offset superimposed maximum trough attribute.

[0009] According to an embodiment of this application, optionally, in the above-described method for fine depiction of river channels, the azimuth attribute includes the dominant azimuth amplitude attribute.

[0010] According to embodiments of this application, optionally, in the above-described method for fine river channel characterization, the multidimensional seismic attributes include at least one of the following: The superimposed data volume of the maximum trough attribute divided by azimuth and / or incident angle; The superimposed data volume of the maximum peak attributes divided by azimuth and / or incident angle; A superimposed data volume of frequency gradient attributes divided by azimuth and / or incident angle; A superimposed data volume of amplitude gradient attributes divided by azimuth and / or incident angle.

[0011] According to an embodiment of this application, optionally, in the above-described method for fine depiction of river channels, extracting the multidimensional seismic attributes of the target river channel based on seismic data of the target river channel includes the following steps: The seismic data of the target river channel are processed using OVT (Outer Threat Transmission) to obtain OVT domain gathers; The OVT domain gather is subjected to azimuth and / or incident angle stacking processing to obtain stacked data; The superimposed data is subjected to frequency division processing to extract the multidimensional seismic attributes.

[0012] According to an embodiment of this application, optionally, in the above-mentioned fine river channel characterization method, a fuzzy logic algorithm is used to fuse the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multi-dimensional seismic attribute of the target river channel to obtain the attribute fusion result of the target river channel, including the following steps: The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel are normalized to obtain the normalized results of various attributes. Based on the normalization results, construct membership functions corresponding to various attributes; Based on the membership function, calculate the relative distance between the membership degrees of any two attributes among various attributes at any spatial location; Define a fuzzy proximity function such that at any spatial location, the greater the relative distance between the membership degrees of two attributes, the smaller their fuzzy proximity; conversely, the smaller the relative distance, the greater their fuzzy proximity. The fuzzy proximity function is used to obtain the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes. The relative weights of various attributes are obtained by normalizing the comprehensive fuzzy closeness of the membership degrees of various attributes with the membership degrees of other attributes. Based on the relative weights of various attributes, all attributes are fused to obtain the attribute fusion result of the target river channel.

[0013] According to an embodiment of this application, optionally, in the above-mentioned fine river channel characterization method, the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multidimensional seismic attribute of the target river channel are normalized to obtain the normalized results of various attributes, including the following steps: The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multidimensional seismic attribute of the target river channel are normalized using the following formula to obtain the normalized results of various attributes: ; in, This represents the normalized result of the i-th attribute at spatial location x. n represents the types of attributes to be merged.

[0014] According to an embodiment of this application, optionally, in the above-described fine river channel characterization method, the membership function is:

[0015] in, Let be the membership degree of the i-th attribute at spatial location x.

[0016] According to an embodiment of this application, optionally, in the above-described method for fine depiction of river channels, the relative distance between the membership degrees of any two attributes among various attributes is: ; in, Let x be the relative distance between the membership degree of the i-th attribute and the membership degree of the j-th attribute at spatial location x. Let x be the membership degree of the i-th attribute at spatial location x. Let x be the membership degree of the j-th attribute at spatial location x.

[0017] According to an embodiment of this application, optionally, in the above-described fine river channel characterization method, the fuzzy proximity function is: ; in, Let be the fuzzy proximity function corresponding to the i-th attribute and the j-th attribute at spatial location x; It represents the maximum relative distance between the membership degrees of the i-th attribute and the j-th attribute at each spatial location.

[0018] According to an embodiment of this application, optionally, in the above-described method for fine depiction of river channels, the comprehensive fuzzy proximity degree of the membership degrees of various attributes and other attributes is obtained through the fuzzy proximity function, including the following steps: The fuzzy proximity function is used to calculate the fuzzy proximity between any two attributes at any spatial location, thus obtaining the fuzzy proximity matrix: ; in, The fuzzy proximity matrix is ​​described above. Based on the fuzzy proximity matrix, calculate the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes. Based on the theory of probabilistic source merging, a set of non-negative numbers is required. , , ..., The following conditions must be met:

[0019] Introducing two feature vectors and ,make , Thus, the following matrix form is obtained: ; Define a maximum fuzzy eigenvalue λ such that... Find the eigenvectors The corresponding fuzzy proximity matrix is ​​used as the comprehensive fuzzy proximity of the membership degree of each attribute with the membership degree of other attributes; where the comprehensive fuzzy proximity of the membership degree of each attribute with the membership degree of other attributes is: .

[0020] According to an embodiment of this application, optionally, in the above-described method for fine depiction of river channels, the relative weights of various attributes are as follows:

[0021] in, Let be the relative weight of the i-th attribute at spatial location x.

[0022] According to an embodiment of this application, optionally, in the above-described method for fine depiction of river channels, the attribute fusion result of the target river channel is: ; in, The attribute fusion result of the target river channel at spatial location x; Let be the i-th attribute at spatial location x.

[0023] Secondly, this application provides a device for fine depiction of river channels, the device comprising: The acquisition module is used to acquire seismic data for the target river channel; The extraction module is used to extract the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel based on the seismic data of the target river channel; The fusion module is used to fuse the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multi-dimensional seismic attribute of the target river channel using a fuzzy logic algorithm to obtain the attribute fusion result of the target river channel. The characterization module is used to characterize the spatial distribution pattern of the target river channel based on the attribute fusion result of the target river channel.

[0024] Optionally, the fusion module includes: The first normalization module is used to normalize the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel to obtain the normalization results of various attributes. The first function construction module is used to construct membership functions corresponding to various attributes based on the normalization result; The distance calculation module is used to calculate the relative distance between the membership degrees of any two attributes among various attributes at any spatial location, based on the membership function. The second function construction module is used to define a fuzzy proximity function such that at any spatial location, the greater the relative distance between the membership degrees of two attributes, the smaller their fuzzy proximity; conversely, the smaller the relative distance, the greater their fuzzy proximity. The calculation module is used to calculate the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes through the fuzzy proximity function. The second normalization module is used to normalize the comprehensive fuzzy closeness of the membership degree of various attributes with the membership degree of other attributes, so as to obtain the relative weight of various attributes. The fusion submodule is used to fuse all attributes according to their relative weights to obtain the attribute fusion result of the target river channel.

[0025] Thirdly, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the river channel fine characterization method as described in any one of the first aspects.

[0026] Fourthly, this application provides a storage medium storing a computer program that can be executed by one or more processors and can be used to implement the fine river characterization method as described in any of the first aspects.

[0027] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects: This application provides a method, apparatus, electronic device, and storage medium for fine river channel characterization. The method includes acquiring seismic data of a target river channel; extracting time-domain amplitude attributes, frequency-domain energy attributes, offset amplitude attributes, azimuth attributes, and multi-dimensional seismic attributes of the target river channel based on the seismic data; fusing the time-domain amplitude attributes, frequency-domain energy attributes, offset amplitude attributes, azimuth attributes, and multi-dimensional seismic attributes of the target river channel using a fuzzy logic algorithm to obtain an attribute fusion result; and characterizing the spatial distribution morphology of the target river channel using the attribute fusion result. This method, based on fully mining the information in the seismic data and comprehensively applying the multi-dimensional and multi-domain features of the seismic data, utilizes a fuzzy logic data-driven approach to achieve fine characterization of complex river channels. This effectively improves the accuracy and efficiency of describing the spatial distribution of complex river channels, thus achieving fine river channel characterization. Attached Figure Description

[0028] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings: Figure 1 A flowchart illustrating a method for fine depiction of a river channel provided in an embodiment of this application; Figure 2 A schematic diagram of a post-stack seismic profile of a target karst paleochannel provided in an embodiment of this application; Figure 3 This is a schematic diagram of the root mean square amplitude property profile of the target karst paleochannel provided in the embodiments of this application; Figure 4 This is a schematic diagram of the low-frequency attribute profile of the target karst paleochannel provided in the embodiments of this application; Figure 5 A schematic diagram of the high-frequency property profile of the target karst paleochannel provided in the embodiments of this application; Figure 6 for Figures 3 to 5 The attributes in the diagram are fused based on a fuzzy logic algorithm, forming an attribute profile. Figure 7 This is a schematic diagram of the root mean square amplitude properties of the target karst paleochannel provided in the embodiments of this application; Figure 8 This is a schematic diagram of the low-frequency properties of the target karst paleochannel provided in the embodiments of this application; Figure 9 A schematic diagram of the high-frequency properties of the target karst paleochannel provided in the embodiments of this application; Figure 10 for Figures 7 to 9 The attributes in the diagram are fused based on a fuzzy logic algorithm to form an attribute plane. Figure 11 A schematic diagram of a fine river channel characterization device provided in an embodiment of this application; In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0029] The following detailed description of the embodiments of this application, in conjunction with the accompanying drawings, will provide a thorough understanding of how this application uses technical means to solve technical problems and achieve corresponding technical effects, enabling its implementation. The embodiments of this application and the various features within them can be combined with each other without conflict, and all resulting technical solutions are within the protection scope of this application.

[0030] Furthermore, numerous specific details are set forth in the following description for purposes of explanation, in order to provide a thorough understanding of the embodiments of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without the specific details herein or the particular methods described.

[0031] Example 1 Figure 1 Please refer to the flowchart illustrating a method for fine river channel characterization provided in this application embodiment. Figure 1 This embodiment provides a method for detailed depiction of river channels, including: Step S110: Obtain seismic data for the target river channel.

[0032] The earthquake data can be obtained through forward modeling or by using actual earthquake data obtained during the actual exploration of the same type of river channel.

[0033] Step S120: Based on the seismic data of the target river channel, extract the time domain amplitude attribute, frequency domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel.

[0034] The time-domain amplitude attribute includes at least one of the root mean square amplitude, maximum trough attribute, and maximum peak attribute.

[0035] Among them, the characteristics of the maximum trough attribute are as follows: through forward modeling and practical experience in western Sichuan and the Tarim River karst paleochannel, it has been proven that terrestrial sedimentary channel reservoirs usually have high porosity, low density, and low velocity characteristics. Compared with the surrounding rocks, they exhibit low impedance, and their seismic waveform characteristics show trough reflection. Through the optimization of different trough attributes, the maximum trough attribute is considered to be the best attribute for identifying this type of high-quality reservoir channel.

[0036] In addition, the characteristics of the maximum peak attribute are as follows: In complex sedimentary environments, some channels may exhibit high initial velocity characteristics due to complex geological factors such as filling, compaction, collapse of surrounding rocks and subsequent tectonic modification. Extracting the maximum peak attribute can characterize this type of channel or the local characteristics of the channel.

[0037] The frequency domain energy properties include at least one of the following: high-frequency component properties, low-frequency component properties, mid-to-low-frequency component properties, and high-frequency attenuation gradient properties.

[0038] The characteristics of the high-frequency component are as follows: Forward modeling and practical experience with karst paleochannels in western Sichuan and the Tarim River region have demonstrated that thin, narrow channels typically exhibit high-frequency anomalies. High-precision time-frequency analysis (matching pursuit frequency division) is used to perform frequency division processing on time-domain seismic data to obtain the high-frequency seismic components. Extracting the maximum trough attribute from the high-frequency seismic components allows for a clear characterization of thin, narrow channels.

[0039] Furthermore, the characteristics of the low-frequency component are as follows: forward modeling and practical experience with the western Sichuan region and the Tarim River karst paleochannel demonstrate that the main channel exhibits low-frequency anomalies. Low-frequency seismic components are obtained by performing frequency division processing on the time-domain seismic data using a high-precision time-frequency analysis method (matching pursuit frequency division). Extracting the maximum trough attribute from the low-frequency seismic components allows for a clear characterization of thin and narrow river channels.

[0040] Furthermore, the high-frequency attenuation gradient is characterized by the fact that when the lateral filling characteristics and physical properties of a river channel change drastically, the seismic reflection characteristics of the river reservoir, both in the time and frequency domains, are comprehensively affected by multiple factors such as channel size, filling, and physical properties. Forward modeling and practical applications in western Sichuan and the Tarim River karst paleochannel have demonstrated that the high-frequency attenuation gradient properties are unaffected by differences in cavern filling and can more directly reflect the scale of river channel development.

[0041] The offset amplitude attribute includes at least one of the following: the maximum trough attribute superimposed on the far offset and the maximum trough attribute superimposed on the near offset.

[0042] Among them, the characteristics of the maximum trough attribute of far-offset superposition are as follows: In practical applications in western Sichuan, it was found that fluvial reservoirs typically exhibit three types of amplitude variation with offset (AVO) characteristics. These three types of AVO characteristics are characterized by trough reflections, and the amplitude energy increases with increasing incident angle. Therefore, extracting the maximum trough attribute of far-offset superposition can more clearly characterize this type of reservoir. Especially for channels exhibiting type II AVO characteristics, the far-offset superposition attribute significantly improves the clarity of channel characterization.

[0043] Furthermore, the characteristics of near-offset superimposed maximum trough attributes are as follows: in addition to the widely developed Type III and Type II AVO channels, complex underground geological conditions may also lead to the development of Type IV AVO channels. These channels are often overlooked due to the lack of actual drilling, resulting in incomplete channel characterization. Type IV AVO channels exhibit trough reflections, with amplitude decreasing as the incident angle increases. Therefore, extracting the near-offset superimposed maximum trough attribute can effectively characterize this type of channel, as conventional amplitude attributes cannot represent this type of reservoir. This has been verified in the characterization of shallow and medium-depth channels in western Sichuan.

[0044] The azimuth attribute includes the dominant azimuth amplitude attribute.

[0045] Among them, the characteristics of the dominant azimuth amplitude attribute are: the river boundary will exhibit certain anisotropic features. By extracting the amplitude attribute by superimposing the azimuth, the dominant azimuth analysis can be carried out. Based on the dominant azimuth attribute, different types of attributes can be extracted to improve the characterization accuracy of rivers with certain anisotropic features.

[0046] The multidimensional seismic properties include at least one of the following: The superimposed data volume of the maximum trough attribute divided by azimuth and / or incident angle; The superimposed data volume of the maximum peak attributes divided by azimuth and / or incident angle; A superimposed data volume of frequency gradient attributes divided by azimuth and / or incident angle; A superimposed data volume of amplitude gradient attributes divided by azimuth and / or incident angle.

[0047] In step S120, the multidimensional seismic attributes of the target river channel are extracted based on the seismic data of the target river channel, including the following steps: (a) Perform OVT (Offset Vector Tiles) processing on the seismic data of the target river channel to obtain OVT domain gathers; (b) Perform azimuth and / or incident angle stacking processing on the OVT domain gather to obtain stacked data; (c) The superimposed data is subjected to frequency division processing to extract the multidimensional seismic attributes.

[0048] The OVT domain gather is subjected to azimuth-based and / or incident angle-based superposition processing, including any of the following methods: (1) Superposition of OVT domain gathers with different incident angles at the same location; (2) Superposition of OVT domain gathers at different orientations with the same incident angle; (3) Different orientations are superimposed in the manner described in (1) and then superimposed on each other; (4) Different incident angles are superimposed in the manner described in (2) and then superimposed on each other; (5) Add (3) and (4) together.

[0049] For example, for the maximum trough attribute A in OVT domain seismic data, we first analyze its gradient dA / dα as a function of the incident angle α, and then further calculate its gradient d(dA / dα) / dj in the azimuth dimension, where j represents the azimuth angle. Subsequently, we can further calculate its frequency domain characteristic, g(d(dA / dα) / dj), where g represents the time-frequency transformation, such as the Fourier transform. We can then further calculate its gradient d(f(d(dA / dα) / dj)) / df as a function of frequency, where f represents the frequency.

[0050] Step S130: Use a fuzzy logic algorithm to fuse the time domain amplitude attribute, frequency domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel to obtain the attribute fusion result of the target river channel.

[0051] Step S130 includes the following steps: S131: Normalize the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel to obtain the normalized results of various attributes. S132: Based on the normalization results, construct the membership functions corresponding to various attributes; S133: Based on the membership function, calculate the relative distance between the membership degrees of any two attributes among various attributes at any spatial location; S134: Define a fuzzy proximity function such that at any spatial location, the greater the relative distance between the membership degrees of two attributes, the smaller their fuzzy proximity; conversely, the smaller the relative distance, the greater their fuzzy proximity. S135: Using the fuzzy proximity function, obtain the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes; S136: Normalize the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes to obtain the relative weights of various attributes. S137: Based on the relative weights of various attributes, all attributes are fused to obtain the attribute fusion result of the target river channel.

[0052] Step S131 includes the following steps: The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multidimensional seismic attribute of the target river channel are normalized using the following formula to obtain the normalized results of various attributes: ; in, This represents the normalized result of the i-th attribute at spatial location x. n represents the types of attributes to be merged.

[0053] The membership function is:

[0054] in, Let be the membership degree of the i-th attribute at spatial location x.

[0055] Correspondingly, the relative distance between the membership degrees of any two attributes in various attributes is: ; in, Let x be the relative distance between the membership degree of the i-th attribute and the membership degree of the j-th attribute at spatial location x. Let x be the membership degree of the i-th attribute at spatial location x. Let x be the membership degree of the j-th attribute at spatial location x.

[0056] The larger the value, the greater the difference between the i-th and j-th attributes at spatial location x, meaning the less mutual support exists between the two data points; conversely, the smaller the value, the less support exists between the two data points. The smaller the value, the greater the mutual support between the i-th and j-th attributes at spatial location x. The definition of relative distance is entirely based on the implicit information of existing data, reducing the requirement for prior information.

[0057] Correspondingly, the fuzzy proximity function is: ; in, Let be the fuzzy proximity function corresponding to the i-th attribute and the j-th attribute at spatial location x; It represents the maximum relative distance between the membership degrees of the i-th attribute and the j-th attribute at each spatial location.

[0058] Correspondingly, step S135 includes the following steps: S135a: Using the aforementioned fuzzy proximity function, the fuzzy proximity between any two attributes at any spatial location is calculated, thus obtaining the fuzzy proximity matrix as follows: ; in, The fuzzy proximity matrix is ​​described above. S135b: Based on the aforementioned fuzzy proximity matrix, calculate the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes. Based on the theory of probabilistic source merging, a set of non-negative numbers is required. , , ..., The following conditions must be met:

[0059] S135c: Introducing two feature vectors and ,make , Thus, the following matrix form is obtained: ; S135d: Define a maximum fuzzy eigenvalue λ such that... Find the eigenvector. The corresponding fuzzy proximity matrix is ​​used as the comprehensive fuzzy proximity of the membership degree of each attribute with the membership degree of other attributes; where the comprehensive fuzzy proximity of the membership degree of each attribute with the membership degree of other attributes is: .

[0060] During the fusion process, different attributes need to be assigned different weights. Attributes with better stability and higher reliability have greater weights in the fusion. As discussed earlier, attributes with higher fuzzy proximity have higher reliability and stability, and therefore greater weights; conversely, attributes with lower fuzzy proximity have lower weights. Therefore, fuzzy proximity can be used to characterize the weights of each attribute.

[0061] Correspondingly, the relative weights of the various attributes are:

[0062] in, Let be the relative weight of the i-th attribute at spatial location x.

[0063] Correspondingly, the attribute fusion result of the target river channel is: ; in, The attribute fusion result of the target river channel at spatial location x; Let be the i-th attribute at spatial location x.

[0064] Following the above method, the spatial distribution pattern of river channels can be determined from the multidimensional and multi-domain comprehensive characterization of seismic data. Although the fused result itself has lost the original clear physical meaning of each attribute parameter, it represents the common changes of multiple attribute parameters, combining the advantages of various attributes, thereby reducing the ambiguity of single attribute predictions and ultimately achieving a comprehensive characterization of multiple types of river channels under complex geological conditions.

[0065] In this step, a multi-attribute fusion technique using intelligent methods (fuzzy logic) will be employed. Furthermore, the multi-dimensional seismic attributes can be viewed as different attributes specific to the same geological target (the target river channel).

[0066] In the process of characterizing river channels using different attributes, there is often no clear-cut boundary as to whether a single extracted attribute can fully depict the characteristics of the river channel; that is, fuzziness exists. To describe and quantify this fuzziness, we propose a multi-attribute fusion method based on fuzzy logic. This method applies fuzzy theory to attribute fusion, resulting in a comprehensive quantitative attribute that more accurately describes underground reservoir information than a single attribute. This addresses the various uncertainties arising from the logical violation of the "law of excluded middle."

[0067] When fusing the membership degrees obtained from various attributes, the fusion algorithm is entirely based on existing data, fully mining the implicit information within the data. The calculation of the weights of various attributes is entirely data-driven, reducing the subjectivity of human manipulation. Combining fuzzy logic with information fusion can handle imprecise description problems and adaptively merge information, thereby improving the accuracy and efficiency of river channel characterization.

[0068] Step S140: Based on the attribute fusion results of the target river channel, the spatial distribution pattern of the target river channel is characterized.

[0069] Based on the attribute fusion results of the target river channel, the spatial distribution pattern of the target river channel is characterized, thereby realizing the characterization of the direction and width of the target river channel.

[0070] This application provides a method for fine-grained river channel characterization. The method includes acquiring seismic data of a target river channel; extracting time-domain amplitude attributes, frequency-domain energy attributes, offset amplitude attributes, azimuth attributes, and multi-dimensional seismic attributes of the target river channel based on the seismic data; fusing the time-domain amplitude attributes, frequency-domain energy attributes, offset amplitude attributes, azimuth attributes, and multi-dimensional seismic attributes of the target river channel using a fuzzy logic algorithm to obtain an attribute fusion result; and characterizing the spatial distribution morphology of the target river channel using the attribute fusion result. This method, based on fully mining the information in the seismic data and comprehensively applying the multi-dimensional and multi-domain features of the seismic data, utilizes a fuzzy logic data-driven approach to achieve fine-grained characterization of complex river channels. This effectively improves the accuracy and efficiency of describing the spatial distribution of complex river channels, thus achieving fine-grained river channel characterization.

[0071] Example 2 Based on Example 1 and Example 2, this example illustrates the method described in Example 1 through specific implementation cases.

[0072] In this embodiment, the karst paleochannel reservoir in the western part of the Tarim River Basin, a mature research area, was selected as the target channel to verify the method described in Example 1. The Ordovician carbonate reservoirs in the Tarim Oilfield are typical fracture-vuggy reservoirs, with pores, vulcanizations, and fractures coexisting. The reservoir development exhibits regional zonation and strong heterogeneity. Oilfield development practice shows that karst-vuggy reservoirs are the main reservoir type in carbonate fracture-vuggy reservoirs, with a relatively high probability of encountering them during drilling, and are key to high and stable oil production.

[0073] Extensive research and practice have shown that the root mean square amplitude attribute of earthquake amplitude and the low-to-mid frequency and high-frequency attributes in frequency division can effectively identify karst paleochannels. Figure 2The post-stack seismic profile of the Tarim River 6-7 zone along the karst paleochannel shows that the seismic phase axes are continuous along the direction of the karst paleochannel.

[0074] Figure 3 This is a schematic cross-sectional view of the root mean square amplitude property.

[0075] Figure 4 This is a cross-sectional diagram of the low-to-mid frequency attribute in the frequency division attribute.

[0076] Figure 5 This is a cross-sectional schematic diagram of high-frequency properties.

[0077] Figure 6 To Figures 3 to 5 The three attributes shown are schematic diagrams of the new attributes obtained by the multi-attribute fusion method based on fuzzy logic, according to the method in Example 1.

[0078] Figure 7 This is a schematic diagram of the root mean square amplitude property.

[0079] Figure 8 This is a schematic diagram of the low-to-medium frequency properties.

[0080] Figure 9 This is a schematic diagram of a high-frequency property plane.

[0081] Figure 10 To Figures 7 to 9 The diagram shows a planar representation of the new attributes obtained by the fuzzy logic-based multi-attribute fusion method, following the method described in Example 1. The low-to-mid frequency and root-mean-square amplitude attributes clearly characterize the main channel at a larger scale, but cannot effectively characterize the relatively smaller tributary channels; while the high-frequency attributes better depict the distribution of tributary channels, but cannot well characterize the relatively large main channel. The new attributes obtained through fuzzy logic-based multi-attribute fusion can simultaneously characterize both the main channel and tributary channels (see white and black circles in the diagram). This also demonstrates the reliability of the fuzzy logic-based multi-attribute fusion method.

[0082] Example 3 Figure 11 Please refer to the structural schematic diagram of a fine river channel characterization device provided in this application embodiment. Figure 11 This embodiment provides a fine river channel characterization device 100, including an acquisition module 110, an extraction module 120, a fusion module 130, and a characterization module 140.

[0083] Module 110 is used to acquire seismic data of the target river channel; The extraction module 120 is used to extract the time domain amplitude attribute, frequency domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel based on the seismic data of the target river channel. The fusion module 130 is used to fuse the time domain amplitude attribute, frequency domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel using a fuzzy logic algorithm to obtain the attribute fusion result of the target river channel. The characterization module 140 is used to characterize the spatial distribution pattern of the target river channel based on the attribute fusion result of the target river channel.

[0084] The acquisition module 110 acquires seismic data of the target river channel; the extraction module 120 extracts the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multi-dimensional seismic attribute of the target river channel based on the seismic data; the fusion module 130 uses a fuzzy logic algorithm to fuse the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multi-dimensional seismic attribute of the target river channel to obtain the attribute fusion result of the target river channel; the characterization module 140 uses the attribute fusion result of the target river channel to characterize the spatial distribution morphology of the target river channel.

[0085] Optionally, the time-domain amplitude attribute includes at least one of the root mean square amplitude, the maximum trough attribute, and the maximum peak attribute.

[0086] Optionally, the frequency domain energy attribute includes at least one of high-frequency component attribute, low-frequency component attribute, mid-low frequency component attribute, and high-frequency attenuation gradient attribute.

[0087] Optionally, the offset amplitude attribute includes at least one of the far offset superimposed maximum trough attribute and the near offset superimposed maximum trough attribute.

[0088] Optionally, the azimuth attribute includes the dominant azimuth amplitude attribute.

[0089] Optionally, the multidimensional seismic properties include at least one of the following: The superimposed data volume of the maximum trough attribute divided by azimuth and / or incident angle; The superimposed data volume of the maximum peak attributes divided by azimuth and / or incident angle; A superimposed data volume of frequency gradient attributes divided by azimuth and / or incident angle; A superimposed data volume of amplitude gradient attributes divided by azimuth and / or incident angle.

[0090] Optionally, based on the seismic data of the target river channel, the multidimensional seismic attributes of the target river channel are extracted, including the following steps: The seismic data of the target river channel are processed using OVT (Outer Threat Transmission) to obtain OVT domain gathers; The OVT domain gather is subjected to azimuth and / or incident angle stacking processing to obtain stacked data; The superimposed data is subjected to frequency division processing to extract the multidimensional seismic attributes.

[0091] Optionally, the fusion module 130 includes: The first normalization module is used to normalize the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel to obtain the normalization results of various attributes. The first function construction module is used to construct membership functions corresponding to various attributes based on the normalization result; The distance calculation module is used to calculate the relative distance between the membership degrees of any two attributes among various attributes at any spatial location, based on the membership function. The second function construction module is used to define a fuzzy proximity function such that at any spatial location, the greater the relative distance between the membership degrees of two attributes, the smaller their fuzzy proximity; conversely, the smaller the relative distance, the greater their fuzzy proximity. The calculation module is used to calculate the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes through the fuzzy proximity function. The second normalization module is used to normalize the comprehensive fuzzy closeness of the membership degree of various attributes with the membership degree of other attributes, so as to obtain the relative weight of various attributes. The fusion submodule is used to fuse all attributes according to their relative weights to obtain the attribute fusion result of the target river channel.

[0092] Optional, the first normalization module is used for: The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multidimensional seismic attribute of the target river channel are normalized using the following formula to obtain the normalized results of various attributes: ; in, This represents the normalized result of the i-th attribute at spatial location x. n represents the types of attributes to be merged.

[0093] Optionally, the membership function is:

[0094] in, Let be the membership degree of the i-th attribute at spatial location x.

[0095] Optionally, the relative distance between the membership degrees of any two attributes in each set of attributes is: ; in, Let x be the relative distance between the membership degree of the i-th attribute and the membership degree of the j-th attribute at spatial location x. Let x be the membership degree of the i-th attribute at spatial location x. Let x be the membership degree of the j-th attribute at spatial location x.

[0096] Optionally, the fuzzy proximity function is: ; in, Let be the fuzzy proximity function corresponding to the i-th attribute and the j-th attribute at spatial location x; It represents the maximum relative distance between the membership degrees of the i-th attribute and the j-th attribute at each spatial location.

[0097] Optional, a fetch module, used for: The fuzzy proximity function is used to calculate the fuzzy proximity between any two attributes at any spatial location, thus obtaining the fuzzy proximity matrix: ; in, The fuzzy proximity matrix is ​​described above. Based on the fuzzy proximity matrix, calculate the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes. Based on the theory of probabilistic source merging, a set of non-negative numbers is required. , , ..., The following conditions must be met:

[0098] Introducing two feature vectors and ,make , Thus, the following matrix form is obtained: ; Define a maximum fuzzy eigenvalue λ such that... Find the eigenvectors The corresponding fuzzy proximity matrix is ​​used as the comprehensive fuzzy proximity of the membership degree of each attribute with the membership degree of other attributes; where the comprehensive fuzzy proximity of the membership degree of each attribute with the membership degree of other attributes is: .

[0099] Optionally, the relative weights of the various attributes are:

[0100] in, Let be the relative weight of the i-th attribute at spatial location x.

[0101] Optionally, the attribute fusion result of the target river channel is: ; in, The attribute fusion result of the target river channel at spatial location x; Let be the i-th attribute at spatial location x.

[0102] Specific implementations of the method for fine river characterization based on the above modules have been detailed in Implementation 1 and will not be repeated here.

[0103] Example 4 This embodiment provides an electronic device, which may be a mobile phone, computer, or tablet computer, etc., including a memory and a processor. The memory stores a calculator program, which, when executed by the processor, implements the fine river characterization method as described in Embodiment 1. It is understood that the electronic device may also include an input / output (I / O) interface and communication components.

[0104] The processor is used to execute all or part of the steps in the fine river characterization method as described in Embodiment 1. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0105] The processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the fine river characterization method in Embodiment 1 above.

[0106] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0107] Example 5 This embodiment also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, app store, etc., which stores a computer program. When the computer program is executed by a processor, it can implement the following method steps: Step S110: Obtain seismic data for the target river channel; Step S120: Based on the seismic data of the target river channel, extract the time domain amplitude attribute, frequency domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel; Step S130: Use a fuzzy logic algorithm to fuse the time domain amplitude attribute, frequency domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel to obtain the attribute fusion result of the target river channel; Step S140: Based on the attribute fusion results of the target river channel, the spatial distribution pattern of the target river channel is characterized. For a detailed description of the above method steps, please refer to Example 1. This example will not be repeated here.

[0108] In summary, this application provides a method, apparatus, electronic device, and storage medium for fine river channel characterization. The method includes acquiring seismic data of a target river channel; extracting time-domain amplitude attributes, frequency-domain energy attributes, offset amplitude attributes, azimuth attributes, and multi-dimensional seismic attributes of the target river channel based on the seismic data; fusing the time-domain amplitude attributes, frequency-domain energy attributes, offset amplitude attributes, azimuth attributes, and multi-dimensional seismic attributes of the target river channel using a fuzzy logic algorithm to obtain an attribute fusion result; and characterizing the spatial distribution morphology of the target river channel using the attribute fusion result. This method, based on fully mining the information in the seismic data and comprehensively applying the multi-dimensional and multi-domain features of the seismic data, utilizes a fuzzy logic data-driven approach to achieve fine characterization of complex river channels. This effectively improves the accuracy and efficiency of describing the spatial distribution of complex river channels, thus achieving fine river channel characterization.

[0109] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0110] Although the embodiments disclosed in this application are as described above, the content is merely for the purpose of facilitating understanding of this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method for detailed depiction of river channels, characterized in that, The method includes: Obtain seismic data for the target river channel; Based on the seismic data of the target river channel, extract the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel; The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel are fused using a fuzzy logic algorithm to obtain the attribute fusion result of the target river channel; Based on the attribute fusion results of the target river channel, the spatial distribution pattern of the target river channel is characterized; The method involves using a fuzzy logic algorithm to fuse the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multidimensional seismic attribute of the target river channel to obtain the attribute fusion result of the target river channel. This includes the following steps: The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel are normalized to obtain the normalized results of various attributes. Based on the normalization results, construct membership functions corresponding to various attributes; Based on the membership function, calculate the relative distance between the membership degrees of any two attributes among various attributes at any spatial location; Define a fuzzy proximity function such that at any spatial location, the greater the relative distance between the membership degrees of two attributes, the smaller their fuzzy proximity; conversely, the smaller the relative distance, the greater their fuzzy proximity. The fuzzy proximity function is used to obtain the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes. The relative weights of various attributes are obtained by normalizing the comprehensive fuzzy closeness of the membership degrees of various attributes with the membership degrees of other attributes. Based on the relative weights of various attributes, all attributes are fused to obtain the attribute fusion result of the target river channel.

2. The method according to claim 1, characterized in that, The time-domain amplitude attribute includes at least one of the root mean square amplitude, maximum trough attribute, and maximum peak attribute.

3. The method according to claim 1, characterized in that, The frequency domain energy properties include at least one of the following: high-frequency component properties, low-frequency component properties, mid-to-low-frequency component properties, and high-frequency attenuation gradient properties.

4. The method according to claim 1, characterized in that, The offset amplitude attribute includes at least one of the following: the maximum trough attribute superimposed on the far offset and the maximum trough attribute superimposed on the near offset.

5. The method according to claim 1, characterized in that, The azimuth attribute includes the dominant azimuth amplitude attribute.

6. The method according to claim 1, characterized in that, The multidimensional seismic properties include at least one of the following: The superimposed data volume of the maximum trough attribute divided by azimuth and / or incident angle; The superimposed data volume of the maximum peak attributes divided by azimuth and / or incident angle; A superimposed data volume of frequency gradient attributes divided by azimuth and / or incident angle; A superimposed data volume of amplitude gradient attributes divided by azimuth and / or incident angle.

7. The method according to claim 6, characterized in that, Based on the seismic data of the target river channel, the multidimensional seismic attributes of the target river channel are extracted, including the following steps: The seismic data of the target river channel are processed using OVT (Outer Threat Transmission) to obtain OVT domain gathers; The OVT domain gather is subjected to azimuth and / or incident angle stacking processing to obtain stacked data; The superimposed data is subjected to frequency division processing to extract the multidimensional seismic attributes.

8. The method according to claim 7, characterized in that, The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multidimensional seismic attribute of the target river channel are normalized to obtain the normalized results of various attributes, including the following steps: The time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute, and multidimensional seismic attribute of the target river channel are normalized using the following formula to obtain the normalized results of various attributes: ； in, Let be the normalized result of the i-th attribute at spatial location x, where n is the number of attributes to be fused.

9. The method according to claim 8, characterized in that, The membership function is: in, Let be the membership degree of the i-th attribute at spatial location x.

10. The method according to claim 9, characterized in that, The relative distance between the membership degrees of any two attributes is: ； in, Let x be the relative distance between the membership degrees of the i-th attribute and the j-th attribute at spatial location x. Let x be the membership degree of the i-th attribute at spatial location x. Let x be the membership degree of the j-th attribute at spatial location x.

11. The method according to claim 10, characterized in that, The fuzzy proximity function is: ； in, Let be the fuzzy proximity function corresponding to the i-th attribute and the j-th attribute at spatial location x. It represents the maximum relative distance between the membership degrees of the i-th attribute and the j-th attribute at each spatial location.

12. The method according to claim 11, characterized in that, The comprehensive fuzzy proximity degree of various attributes and other attributes is obtained through the fuzzy proximity function, including the following steps: The fuzzy proximity function is used to calculate the fuzzy proximity between any two attributes at any spatial location, thus obtaining the fuzzy proximity matrix: ； in, The fuzzy proximity matrix is ​​described above. Based on the fuzzy proximity matrix, calculate the comprehensive fuzzy proximity of the membership degrees of various attributes with the membership degrees of other attributes. Based on the theory of probabilistic source merging, a set of non-negative numbers is required. , , ..., The following conditions must be met: Introducing two feature vectors and ,make , Thus, the following matrix form is obtained: ； Define a maximum fuzzy eigenvalue λ such that... Find the eigenvectors The corresponding fuzzy proximity matrix is ​​used as the comprehensive fuzzy proximity of the membership degree of each attribute with the membership degree of other attributes; where the comprehensive fuzzy proximity of the membership degree of each attribute with the membership degree of other attributes is: 。 13. The method according to claim 12, characterized in that, The relative weights of the various attributes are: in, Let be the relative weight of the i-th attribute at spatial location x.

14. The method according to claim 13, characterized in that, The attribute fusion result of the target river channel is as follows: ； in, This represents the attribute fusion result of the target river channel at spatial location x. Let be the i-th attribute at spatial location x.

15. A device for finely depicting river channels, characterized in that, The device includes: The acquisition module is used to acquire seismic data for the target river channel; The extraction module is used to extract the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multidimensional seismic attribute of the target river channel based on the seismic data of the target river channel; The fusion module is used to fuse the time-domain amplitude attribute, frequency-domain energy attribute, offset amplitude attribute, azimuth attribute and multi-dimensional seismic attribute of the target river channel using a fuzzy logic algorithm to obtain the attribute fusion result of the target river channel. The characterization module is used to characterize the spatial distribution pattern of the target river channel based on the attribute fusion result of the target river channel.

16. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the fine river characterization method as described in any one of claims 1 to 14.

17. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the fine river characterization method as described in any one of claims 1 to 14.

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