Methods, apparatus, equipment and media for identifying deep horizontal turbidite deposits

Abstract

This application discloses a method, apparatus, equipment, and medium for identifying turbidite sedimentary bodies in deep horizontal plains, relating to the field of geophysical exploration technology. The method includes: extracting velocity attributes from velocity data of a target segment in a deep horizontal plain, the velocity attributes being used to characterize the velocity characteristics of geological bodies in the target segment; extracting seismic attributes from seismic data of the target segment, the seismic attributes being used to characterize the seismic reflection characteristics of geological bodies in the target segment; fusing the velocity attributes and seismic attributes to obtain a planar distribution map of geological bodies in the target segment, the planar distribution map being used to characterize the planar distribution of different types of geological bodies; and determining the distribution of turbidite sedimentary bodies based on the planar distribution map. Using the scheme provided in this application, rapid identification of turbidite sedimentary bodies can be achieved even with limited geological data in deep horizontal plains.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, equipment and medium for identifying deep horizontal prototurbidite sedimentary bodies. Background Technology

[0002] Deep waters are important reservoirs of oil and gas resources. In the process of oil and gas exploration, exploration areas are generally identified using techniques such as drilling and seismic exploration.

[0003] However, deep-water operations are high-risk and require advanced technology and strong economic support. Therefore, when identifying turbidite sediment bodies in deep waters, the available data is limited and the exploration efficiency is low. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and medium for identifying deep-level proto-turbidity current sediments. The technical solution is as follows:

[0005] On one hand, embodiments of this application provide a method for identifying deep-level proto-turbidity current sedimentary bodies, the method comprising:

[0006] The velocity attributes of the target layer are extracted from the velocity data of the target layer in the deep horizontal plain, and the velocity attributes are used to characterize the velocity features of the geological bodies in the target layer.

[0007] Seismic attributes of the target layer are extracted from the seismic data of the target layer, and the seismic attributes are used to characterize the seismic reflection characteristics of the geological bodies in the target layer.

[0008] The velocity attribute and the seismic attribute are fused to obtain a planar distribution map of geological bodies in the target layer. The planar distribution map of geological bodies is used to characterize the planar distribution of different types of geological bodies.

[0009] The distribution of the turbidite deposits is determined based on the planar distribution map of the geological bodies.

[0010] On the other hand, embodiments of this application provide a device for identifying deep-level proto-turbidity current sediments, the device comprising:

[0011] The first extraction module is used to extract the velocity attributes of the target layer from the velocity data of the target layer in the deep horizontal plain, and the velocity attributes are used to characterize the velocity features of the geological bodies in the target layer.

[0012] The second extraction module is used to extract the seismic attributes of the target layer from the seismic data of the target layer, and the seismic attributes are used to characterize the seismic reflection characteristics of the geological bodies in the target layer.

[0013] The fusion module is used to fuse the velocity attribute and the seismic attribute to obtain a planar distribution map of geological bodies in the target layer. The planar distribution map of geological bodies is used to characterize the planar distribution of different types of geological bodies.

[0014] The determination module is used to determine the distribution of the turbidite deposits based on the geological body plan distribution map.

[0015] On the other hand, embodiments of this application provide a computer device, the computer device including a processor and a memory, the memory storing at least one piece of program code, the at least one piece of program code being loaded and executed by the processor to implement the method for identifying deep horizontal turbidity current sediments as described above.

[0016] On the other hand, embodiments of this application provide a computer-readable storage medium storing at least one piece of program code, which is executed by a processor to implement the method for identifying deep horizontal turbidity current sediments as described above.

[0017] On the other hand, embodiments of this application provide a computer program product, the computer program product including computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to implement the method for identifying deep horizontal turbidity current sediments as described above.

[0018] In this embodiment, the computer device first extracts the velocity and seismic attributes of the target layer from velocity and seismic data. Then, it fuses the velocity and seismic attributes to obtain a planar distribution map of the geological bodies in the target layer. Finally, it obtains the distribution of turbidite deposits from the planar distribution map. Given the limited availability of deep-level geological data, this embodiment achieves rapid identification of turbidite deposits using both velocity and seismic data. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for identifying deep-level primary turbidity current sediments provided in an exemplary embodiment of this application is shown.

[0021] Figure 2A flowchart illustrating a method for identifying deep-level primary turbidity current sediments provided in another exemplary embodiment of this application is shown;

[0022] Figure 3 This application illustrates a root mean square velocity property planar plot provided by an exemplary embodiment.

[0023] Figure 4 A planar diagram of layer velocity properties provided in an exemplary embodiment of this application is shown;

[0024] Figure 5 This application illustrates a planar plot of the root mean square amplitude properties provided in an exemplary embodiment.

[0025] Figure 6 This application illustrates a planar distribution map of geological bodies provided in an exemplary embodiment.

[0026] Figure 7 This invention provides a root mean square velocity property profile diagram according to an exemplary embodiment of the present application.

[0027] Figure 8 A cross-sectional view of layer velocity properties provided in an exemplary embodiment of this application is shown;

[0028] Figure 9 A seismic profile provided in an exemplary embodiment of this application is shown;

[0029] Figure 10 A planar distribution diagram of turbidity deposits provided in an exemplary embodiment of this application is shown;

[0030] Figure 11 This invention provides a structural block diagram of a device for identifying deep-level primary turbidity current sediments according to an exemplary embodiment of this application.

[0031] Figure 12 A structural block diagram of a computer device provided in an exemplary embodiment of this application is shown. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0033] Deep horizontal plains are important oil and gas reservoirs. Turbidity deposits within deep horizontal plains often contain large-scale oil and gas reserves. Therefore, in the early stages of oil and gas exploration, it is necessary to identify the distribution of turbidity deposits in the exploration area within deep horizontal plains.

[0034] Please refer to Figure 1 The diagram illustrates a flowchart of a method for identifying deep-level turbidite deposits provided in an exemplary embodiment of this application. The method may include the following steps:

[0035] Step 101: Extract velocity attributes of the target layer from the velocity data of the target layer in the deep horizontal plain. The velocity attributes are used to characterize the velocity features of the geological bodies in the target layer.

[0036] The target segment is the seismic segment currently being identified as a turbidite deposit. In one possible implementation, computer equipment analyzes multiple profiles of the seismic data volume to determine the segment with a background of turbidite deposit development as the target segment.

[0037] Velocity data refers to velocity-related data obtained through drilling or seismic exploration techniques, including various velocity data volumes, such as root-mean-square velocity volumes and layer velocity volumes. Velocity attributes are velocity-related data obtained through mathematical calculations, such as layer velocity and root-mean-square velocity.

[0038] In one possible implementation, a computer device tracks the seismic data volume, extracts seismic data from the target segment, and then extracts velocity attributes from the velocity data of the target segment.

[0039] Step 102: Extract the seismic attributes of the target layer from the seismic data of the target layer. The seismic attributes are used to characterize the seismic reflection features of the geological bodies in the target layer.

[0040] Seismic data refers to seismic information obtained through seismic exploration techniques that reflects seismic reflection characteristics. These characteristics include continuity, amplitude, and frequency. Seismic attributes are seismic data obtained through mathematical calculations, such as root-mean-square amplitude.

[0041] In one possible implementation, a computer device tracks the seismic data volume, extracts seismic data from the target segment, and then extracts seismic attributes from the seismic data of the target segment.

[0042] Step 103: The velocity attribute and seismic attribute are fused to obtain the planar distribution map of the geological body in the target layer. The planar distribution map of the geological body is used to characterize the planar distribution of different types of geological bodies.

[0043] Different types of geological bodies have different velocity and seismic reflection characteristics. Therefore, computer equipment can identify geological bodies based on the local velocity and seismic properties of the target layer.

[0044] Optionally, the computer equipment can perform grid interpretation on the seismic data of the target layer, and then interpolate the grid data to obtain a planar distribution map of the geological bodies in the target layer.

[0045] Optionally, the computer device can assign different colors to velocity and seismic attributes, where the magnitude of the attribute values ​​corresponds to the depth or brightness of the color. Subsequently, the geological body planar distribution map of the target layer is obtained by color fusion.

[0046] Step 104: Determine the distribution of turbidite deposits based on the geological body plan map.

[0047] In one possible implementation, the computer device can locate geological bodies with velocity and seismic reflection characteristics corresponding to turbidite deposits on a geological body planar distribution map, and then identify the geological body as a turbidite deposit, thereby obtaining the distribution of turbidite deposits.

[0048] In summary, in this embodiment, the computer device first extracts the velocity and seismic attributes of the target layer from velocity and seismic data. Then, it fuses the velocity and seismic attributes to obtain a planar distribution map of the geological bodies in the target layer. Finally, it obtains the distribution of turbidite deposits from the planar distribution map. Given the limited availability of deep-level geological data, this embodiment achieves rapid identification of turbidite deposits using both velocity and seismic data.

[0049] Please refer to Figure 2 The diagram illustrates a flowchart of a method for identifying deep-level turbidite deposits provided in another exemplary embodiment of this application, which may include the following steps:

[0050] Step 201: Extract velocity attributes of the target layer from the velocity data of the target layer in the deep horizontal plain. The velocity attributes are used to characterize the velocity features of the geological bodies in the target layer.

[0051] The implementation method of this step can refer to step 101 above, and will not be repeated here.

[0052] Step 202: Extract the seismic attributes of the target layer from the seismic data of the target layer. The seismic attributes are used to characterize the seismic reflection characteristics of the geological bodies in the target layer.

[0053] The implementation method of this step can refer to step 102 above, and will not be repeated here.

[0054] Step 203: Generate a velocity attribute planar map corresponding to the target layer based on the velocity attribute.

[0055] Optionally, the computer device can perform color channel mapping on the attribute values ​​of the velocity attribute to obtain a velocity attribute planar map.

[0056] In one possible implementation, the computer device calculates the value of the speed attribute so that the value of the speed attribute corresponds to the channel value of the color channel, and then uses the color corresponding to the speed attribute to draw a speed attribute plane map, wherein the intensity of the color corresponding to different speed attribute values ​​is different.

[0057] For example, the layer velocity attribute value can be multiplied by a fixed decimal and rounded to ensure that the calculated layer velocity attribute value falls within the range of the red channel in the R(Red)G(Green)B(Blue) color mode, i.e., 0 to 255. The values ​​of the green and blue channels are fixed at 0. Then, the computer device obtains a layer velocity attribute plane map that represents the magnitude of the layer velocity through the red intensity, based on the distribution of the layer velocity attribute on the plane corresponding to the target layer segment.

[0058] Optionally, the velocity attributes can include root mean square (RMS) velocity attributes and layer velocity attributes. The RMS velocity attribute is sensitive to low-velocity anomalies, while the layer velocity attribute is sensitive to high-velocity anomalies. Different velocity attributes correspond to different color channels; therefore, generating a velocity attribute planar map corresponding to the target layer segment can be divided into the following two types.

[0059] 1. Map the root mean square velocity attribute value to the first color channel to obtain the root mean square velocity attribute plane map.

[0060] In one possible implementation, the computer device performs a function calculation on the root mean square velocity attribute value so that the calculation result falls within the range corresponding to the channel value of the first channel. Subsequently, the computer device rounds the root mean square velocity calculation result to obtain the corresponding channel value of the first color channel.

[0061] It should be noted that the relationship between the root mean square velocity attribute value and its corresponding first color channel value can be positively correlated, that is, the larger the attribute value, the larger the corresponding channel value, or negatively correlated, that is, the smaller the attribute value, the larger the corresponding channel value.

[0062] For example, the root mean square velocity property plane plot is as follows: Figure 3 As shown. It should be noted that, without considering the fusion of the velocity attribute plane map and the seismic attribute plane map, this example map uses different colors to distinguish the levels of the root mean square velocity in order to improve the visualization clarity of the root mean square velocity attribute plane map.

[0063] In this diagram, region 301 represents a normal speed region, while regions 302, 303, and 304 exhibit significantly lower speeds than their surrounding areas, constituting low-speed anomaly regions. In the root-mean-square (RMS) velocity attribute plane diagram, since the RMS velocity attribute is not sensitive to high-speed anomalies, obvious high-speed anomaly regions are not shown.

[0064] 2. Map the layer velocity attribute values ​​to the second color channel to obtain the layer velocity attribute planar map.

[0065] In one possible implementation, the computer device performs a function calculation on the attribute value of the layer velocity attribute so that the calculation result falls within the range corresponding to the channel value of the second channel. Subsequently, the computer device rounds the calculation result of the layer velocity attribute to obtain the channel value of the corresponding second color channel.

[0066] It should be noted that the relationship between the layer velocity attribute value and its corresponding second color channel value can be positively correlated, that is, the larger the attribute value, the larger the corresponding channel value, or negatively correlated, that is, the smaller the attribute value, the larger the corresponding channel value.

[0067] For example, a layer velocity attribute planar plot is as follows: Figure 4 As shown. It should be noted that, without considering the fusion of the velocity attribute plane map and the seismic attribute plane map, this example map uses different colors to distinguish the high and low layers of velocity in order to improve the visualization clarity of the layer velocity attribute plane map.

[0068] Among them, region 401 is a normal velocity region, while regions 402, 403, and 404 have significantly higher velocities than their surrounding areas, indicating high-speed anomaly regions. In the layer velocity attribute planar diagram, since layer velocity attributes are not sensitive to low-speed anomalies, obvious low-speed anomaly regions are not shown.

[0069] Step 204: Generate a seismic attribute plane map corresponding to the target layer based on the seismic attributes.

[0070] Optionally, the computer device can perform color channel mapping on the attribute values ​​of seismic attributes to obtain a seismic attribute planar map. It should be noted that velocity attributes and seismic attributes use different color channels when performing color channel mapping.

[0071] In one possible implementation, the computer device calculates the values ​​of the seismic attributes so that the values ​​of the seismic attributes correspond to the channel values ​​of the color channels, and then uses the colors corresponding to the seismic attributes to draw a velocity attribute planar map, wherein the color intensity corresponding to different seismic attribute values ​​is different.

[0072] Optionally, earthquake attributes may include root mean square amplitude attributes. Computer equipment can map the attribute values ​​of the root mean square amplitude attributes to a third color channel to obtain a root mean square amplitude attribute planar map.

[0073] In one possible implementation, the computer device performs a function calculation on the root mean square amplitude attribute value so that the calculation result falls within the range corresponding to the channel value of the third channel. Subsequently, the computer device rounds the root mean square amplitude calculation result to obtain the corresponding channel value of the third color channel.

[0074] It should be noted that the relationship between the root mean square amplitude attribute value and its corresponding third color channel value can be positively correlated, that is, the larger the attribute value, the larger the corresponding channel value, or negatively correlated, that is, the smaller the attribute value, the larger the corresponding channel value.

[0075] For example, a root mean square amplitude property plane plot is shown below. Figure 5 As shown. It should be noted that, without considering the fusion of velocity attribute plane map and seismic attribute plane map, different colors are used in this example map to distinguish the strength of the amplitude in order to improve the visualization clarity of the root mean square amplitude attribute plane map.

[0076] In the example diagram, the dark areas in regions 501, 502, and 503 represent areas of strong amplitude, while the remaining light-colored areas represent areas of weak amplitude. The black areas in regions 504, 505, and 506 represent blank and chaotic seismic reflection characteristics.

[0077] Additionally, it should be noted that the first, second, and third color channels belong to the same color mode, such as RGB mode, or C (Cyan), M (Magenta), Y (Yellow), and K (Black) mode. The root mean square velocity attribute, layer velocity attribute, and root mean square amplitude attribute have different numerical ranges, therefore different mapping methods are used when mapping color channels.

[0078] It should be noted that steps 203 and 204 can be executed simultaneously. In this embodiment of the application, the execution order of steps 203 and 204 is not limited.

[0079] Step 205: The velocity attribute planar map and the seismic attribute planar map are fused to obtain the geological body planar distribution map of the target layer.

[0080] Since the planar maps of different attributes are drawn based on a single color channel, computer equipment can merge the planar maps corresponding to different attributes to obtain a planar distribution map of the geological body with multiple colors for the target layer.

[0081] In one possible implementation, the computer device performs color fusion on the attribute plane map of different color channels. For example, red plus blue equals purple. Since the channel values ​​of different color channels are different, the specific colors obtained by each point in the plane map after color fusion are different.

[0082] For example, the distribution of attributes 1, 2, and 3 corresponds to a plane. Figure 1 ,flat Figure 2 and plane Figure 3 Among them, the plane Figure 1 The RGB color corresponding to each point in the plane is (x, 0, 0). Figure 2 The RGB color corresponding to each point in the plane is (0, y, 0). Figure 3 The RGB color corresponding to each point in the plane is (0, 0, z). Figure 1 ,flat Figure 2 and plane Figure 3 The colors of the corresponding points are merged to obtain a planar distribution map of the geological body with each point having a color of (x, y, z), where x, y, and z are integers from 0 to 255, x is the channel value of the red channel, y is the channel value of the green channel, and z is the channel value of the blue channel.

[0083] Step 206: Determine the distribution of turbidite deposits based on the geological body plan map.

[0084] In a geological body plan, similar colors correspond to similar velocity and seismic reflection characteristics. Therefore, computer equipment can determine the distribution of turbidite deposits by identifying the corresponding colors on the geological body plan and then determining the distribution of turbidite deposits based on the distribution of similar colors.

[0085] As can be seen from the above reasoning, this step can encompass the following sub-steps.

[0086] Step 206A: Determine the geological sampling points on the geological body planar distribution map.

[0087] In one possible implementation, the computer device randomly selects multiple geological sampling points of different colors from a geological body planar distribution map, wherein the colors of the geological sampling points vary considerably.

[0088] For example, a planar distribution map of geological bodies is shown below. Figure 6 As shown, geological sampling point 601 corresponds to dark purple, seismic sampling point 602 corresponds to dark red, seismic sampling point 603 corresponds to bright purple, and seismic sampling point 604 corresponds to blue-green.

[0089] Step 206B: If the velocity attribute corresponding to the geological sampling point indicates a normal velocity and the seismic attribute corresponding to the sampling point shows mid-to-high frequency strong amplitude characteristics, then the geological sampling point is determined to be a turbidity deposit sampling point.

[0090] In one possible implementation, the computer can determine the velocity characteristics and seismic reflection characteristics of the sampling points by selecting survey lines and generating profile diagrams.

[0091] For example, the root mean square velocity profile at the survey line is shown in Figure 7, and the layer velocity profile at the survey line is shown in Figure 8. Figure 8 As shown, the seismic profile at the survey line is as follows: Figure 9As shown, the location of the target segment is determined based on the seismic profile. A high-velocity anomaly and a low-velocity anomaly appear at the corresponding target segment location in the root mean square velocity profile and the layer velocity profile. The low-velocity anomaly of the target segment is obvious in the root mean square velocity profile, while the high-velocity anomaly of the target segment is obvious in the layer velocity profile. By comparing with the seismic profile, velocity anomaly A and velocity anomaly B are obtained. Velocity anomaly A corresponds to a high-velocity anomaly and continuous low-frequency strong amplitude seismic reflection characteristics, while velocity anomaly B corresponds to a low-velocity anomaly and low-frequency weak amplitude seismic reflection characteristics.

[0092] Computer equipment can determine turbidity deposits based on normal speed and high-frequency strong amplitude characteristics.

[0093] In one possible implementation, the computer device can first exclude geological sampling points with abnormal velocities based on velocity characteristics, and then identify turbidite deposits from geological sampling points with normal velocities based on seismic reflection characteristics.

[0094] Table 1

[0095] velocity characteristics Earthquake reflection characteristics Geological bodies Low speed anomaly Low-frequency weak amplitude or blank and disordered Gas chimney or mud base High-speed anomaly Low-frequency strong amplitude Igneous rocks, gypsum rocks or salt rocks Normal speed Mid-to-high frequency strong amplitude Turbidity deposits Normal speed Low-frequency weak amplitude Marine fine-grained sediments

[0096] Step 206C: The area with similar color values ​​to the sampling points of turbidite deposits on the geological body planar distribution map is identified as the distribution area of ​​turbidite deposits.

[0097] Since similar colors in the geological body planar distribution map correspond to similar velocity and seismic reflection characteristics, it can be determined that areas with similar color values ​​to the sampling points of turbidite deposits have normal velocity and strong mid-to-high frequency amplitude characteristics. That is, areas with similar color values ​​to the sampling points of turbidite deposits are the distribution areas of turbidite deposits.

[0098] For example, a planar distribution map of turbidity current sediments is shown below. Figure 10 As shown, regions 1010 and 1020 have similar colors, corresponding to normal velocity and strong mid-to-high frequency amplitude characteristics. Therefore, the computer equipment can determine that regions 1010 and 1020 are turbidity deposit distribution areas.

[0099] In this embodiment, after extracting velocity and seismic attributes, the computer device first generates a monochrome velocity attribute planar map and a seismic attribute planar map, wherein different attributes correspond to different color channels. Then, the computer device merges the velocity attribute planar map and the seismic attribute planar map to obtain a multi-colored geological body planar distribution map. Then, the distribution of turbidite deposits is determined based on the color distribution, thus realizing the rapid identification of turbidite deposits in deep horizontal plains using a small amount of data, namely velocity and seismic attributes.

[0100] Because some geological bodies exist in deep horizontal plains that interfere with the identification of turbidite sedimentary bodies, such as igneous rocks, salt rocks, gypsum rocks, and gas chimneys, computer equipment can filter out interfering geological bodies in order to better identify turbidite sedimentary bodies, including the following steps.

[0101] Step 1: Based on the low-velocity anomaly region in the root mean square velocity attribute plane map and the high-velocity anomaly region in the layer velocity attribute plane map, determine the distribution of interfering geological bodies in the target layer.

[0102] The velocity attributes of the interfering geological bodies and the turbidite deposits are significantly different. The velocity attributes of the turbidite deposits exhibit normal velocity characteristics, that is, the velocity attribute values ​​are similar and the changes are gradual. Because the geological structure of the interfering geological bodies is different from that of the turbidite deposits, the velocity attributes of the interfering geological bodies in the target layer are different from those of the surrounding turbidite deposits, showing velocity anomalies, including high-velocity anomalies and low-velocity anomalies.

[0103] As can be deduced from the above reasoning, computer equipment can determine the location of interfering geological bodies based on the velocity anomaly areas appearing in the velocity attribute planar graph.

[0104] Since the low-velocity anomaly region is obvious in the root mean square velocity attribute plane map and the high-velocity anomaly region is obvious in the layer velocity attribute plane map, computer equipment can determine the distribution of interfering geological bodies in the target layer based on the low-velocity anomaly region in the root mean square velocity attribute plane map and the high-velocity anomaly region in the layer velocity attribute plane map.

[0105] Step 2: Filter the geological body area on the geological body planar distribution map based on the distribution of interfering geological bodies.

[0106] In one possible implementation, the computer device marks the interfering geological body regions at the corresponding positions on the geological body planar distribution map by comparing the low-velocity anomaly regions in the root mean square velocity attribute planar map and the high-velocity anomaly regions in the layer velocity attribute planar map, thereby filtering out the interfering geological body regions.

[0107] Please refer to Figure 11 This illustration shows a structural block diagram of a device for identifying deep-level primary turbidity current sediments provided in an exemplary embodiment of this application. The device includes:

[0108] The first extraction module 1101 is used to extract the velocity attributes of the target layer from the velocity data of the target layer in the deep horizontal plain, and the velocity attributes are used to characterize the velocity features of the geological bodies in the target layer.

[0109] The second extraction module 1102 is used to extract the seismic attributes of the target layer from the seismic data of the target layer, wherein the seismic attributes are used to characterize the seismic reflection characteristics of the geological bodies in the target layer;

[0110] The fusion module 1103 is used to fuse the velocity attribute and the seismic attribute to obtain a geological body planar distribution map of the target layer. The geological body planar distribution map is used to characterize the planar distribution of different types of geological bodies.

[0111] The determination module 1104 is used to determine the distribution of the turbidite deposits based on the geological body plan distribution map.

[0112] Optionally, the fusion module 1103 is further configured to:

[0113] Generate a velocity attribute planar map corresponding to the target layer based on the velocity attribute;

[0114] Based on the seismic attributes, generate a seismic attribute plan map corresponding to the target layer;

[0115] The velocity attribute planar map and the seismic attribute planar map are fused to obtain the planar distribution map of the geological body in the target layer.

[0116] Optionally, the fusion module 1103 is further configured to:

[0117] Color channel mapping is performed on the attribute values ​​of the velocity attribute to obtain the velocity attribute planar diagram;

[0118] Color channel mapping is performed on the attribute values ​​of the earthquake attribute to obtain the earthquake attribute planar map, wherein the velocity attribute and the earthquake attribute correspond to different color channels when performing color channel mapping.

[0119] Optionally, the velocity attribute includes a root mean square velocity attribute and a layer velocity attribute, and the fusion module 1103 is further used for:

[0120] The root mean square velocity attribute value is mapped to the first color channel to obtain a root mean square velocity attribute plane map.

[0121] The attribute values ​​of the layer velocity attribute are mapped to the second color channel to obtain a layer velocity attribute planar map.

[0122] Optionally, the determining module 1104 is further configured to:

[0123] Based on the low-velocity anomaly region in the root mean square velocity attribute plane map and the high-velocity anomaly region in the layer velocity attribute plane map, the distribution of interfering geological bodies in the target layer is determined.

[0124] Based on the distribution of the interfering geological bodies, the planar distribution map of the geological bodies is filtered to filter out the areas of the interfering geological bodies.

[0125] Optionally, the determining module 1104 is further configured to:

[0126] Determine the geological sampling points in the planar distribution map of the geological body;

[0127] If the velocity attribute corresponding to the geological sampling point indicates a normal velocity and the seismic attribute corresponding to the sampling point exhibits mid-to-high frequency strong amplitude characteristics, then the geological sampling point is determined to be a turbidity deposit sampling point.

[0128] The regions in the geological body plan that have similar color values ​​to the sampling points of the turbidite deposits are identified as the distribution areas of the turbidite deposits.

[0129] It should be noted that the apparatus provided in the above embodiments is only illustrative of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the apparatus can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0130] Please refer to Figure 12 This diagram illustrates a structural block diagram of a computer device provided in an exemplary embodiment of this application. The computer device may include one or more components such as a processor 1201 and a memory 1202.

[0131] Optionally, the processor 1201 connects various parts of the computer device using various interfaces and lines, and performs various functions of the computer device and processes data by running or executing instructions, programs, code sets or instruction sets stored in memory 1202, and calling data stored in memory 1202.

[0132] The memory 1202 may include random access memory (RAM) or read-only memory (ROM). The memory 1202 may be used to store instructions, programs, code, code sets, or instruction sets.

[0133] This application also provides a computer-readable storage medium storing at least one instruction, which is executed by processor 1201 to implement the application interface launch method as described in the above embodiments.

[0134] This application provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. The processor 1201 of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the application interface launch method provided in the above embodiment.

[0135] Those skilled in the art will recognize that the functions described in the embodiments of this application in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0136] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for identifying deep-level turbidite deposits, characterized in that, The method includes: The velocity attributes of the target layer are extracted from the velocity data of the target layer in the deep horizontal plain, and the velocity attributes are used to characterize the velocity features of the geological bodies in the target layer. Seismic attributes of the target layer are extracted from the seismic data of the target layer, and the seismic attributes are used to characterize the seismic reflection characteristics of the geological bodies in the target layer. Color channel mapping is performed on the attribute values ​​of the velocity attribute to obtain the velocity attribute planar map corresponding to the target layer segment; Color channel mapping is performed on the attribute values ​​of the seismic attributes to obtain the seismic attribute planar map corresponding to the target layer; wherein, the velocity attribute and the seismic attribute correspond to different color channels when performing color channel mapping; The velocity attribute planar map and the seismic attribute planar map are fused to obtain the geological body planar distribution map of the target layer. The geological body planar distribution map is used to characterize the planar distribution of different types of geological bodies. Determine the geological sampling points in the planar distribution map of the geological body; If the velocity attribute corresponding to the geological sampling point indicates a normal velocity and the seismic attribute corresponding to the sampling point exhibits mid-to-high frequency strong amplitude characteristics, then the geological sampling point is determined to be a turbidity deposit sampling point. The regions in the geological body plan that have similar color values ​​to the sampling points of the turbidite deposits are identified as the distribution of the turbidite deposits.

2. The method according to claim 1, characterized in that, The velocity attributes include root mean square velocity attributes and layer velocity attributes; The step of mapping the attribute values ​​of the velocity attribute to color channels to obtain the velocity attribute planar map corresponding to the target layer segment includes: The root mean square velocity attribute value is mapped to the first color channel to obtain a root mean square velocity attribute plane map. The attribute values ​​of the layer velocity attribute are mapped to the second color channel to obtain a layer velocity attribute planar map.

3. The method according to claim 1, characterized in that, The earthquake attributes include the root mean square amplitude attribute; The step of mapping the attribute values ​​of the seismic attributes by color channels to obtain the seismic attribute plane map corresponding to the target layer includes: The root mean square amplitude attribute value is mapped to the third color channel to obtain the root mean square amplitude attribute plane map.

4. The method according to claim 2, characterized in that, Before determining the distribution of the turbidite deposits based on the geological body plan map, the method further includes: Based on the low-velocity anomaly region in the root mean square velocity attribute plane map and the high-velocity anomaly region in the layer velocity attribute plane map, the distribution of interfering geological bodies in the target layer is determined. Based on the distribution of the interfering geological bodies, the planar distribution map of the geological bodies is filtered to filter out the areas of the interfering geological bodies.

5. A device for identifying deep-level turbidite sediments, characterized in that, The device includes: The first extraction module is used to extract the velocity attributes of the target layer from the velocity data of the target layer in the deep horizontal plain, and the velocity attributes are used to characterize the velocity features of the geological bodies in the target layer. The second extraction module is used to extract the seismic attributes of the target layer from the seismic data of the target layer, and the seismic attributes are used to characterize the seismic reflection characteristics of the geological bodies in the target layer. The fusion module is used to perform color channel mapping on the attribute values ​​of the velocity attribute to obtain a velocity attribute planar map corresponding to the target layer; and to perform color channel mapping on the attribute values ​​of the seismic attribute to obtain a seismic attribute planar map corresponding to the target layer; wherein the velocity attribute and the seismic attribute correspond to different color channels when performing color channel mapping; and to fuse the velocity attribute planar map and the seismic attribute planar map to obtain a geological body planar distribution map of the target layer, which is used to characterize the planar distribution of different types of geological bodies; The determination module is used to determine the geological sampling points in the geological body plan distribution map; when the velocity attribute corresponding to the geological sampling point indicates a normal velocity and the seismic attribute corresponding to the sampling point shows mid-to-high frequency strong amplitude characteristics, the geological sampling point is determined to be a turbidity deposit sampling point; the area in the geological body plan distribution map that has a similar color value to the turbidity deposit sampling point is determined as the distribution of the turbidity deposit.

6. A computer device, characterized in that, The computer device includes a processor and a memory, wherein at least one piece of program code is stored in the memory, and the at least one piece of program code is loaded and executed by the processor to implement the method for identifying deep horizontal turbidite deposits as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The storage medium stores at least one piece of program code, which is executed by a processor to implement the method for identifying deep horizontal turbidity current sediments as described in any one of claims 1 to 4.

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