Method, device and equipment for identifying turbidity current sedimentary body in deepwater plain and medium

Abstract

The invention discloses a method, a device and equipment for identifying a turbidity current sedimentary body in a deepwater plain and a medium, and relates to the technical field of geophysical exploration. The method comprises the following steps: extracting speed attributes of a target interval from speed data of the target interval in a deepwater plain, wherein the speed attributes are used for representing speed characteristics of a geologic body in the target interval; extracting seismic attributes of the target layer section from the seismic data of the target layer section, wherein the seismic attributes are used for representing seismic reflection characteristics of a geologic body in the target layer section; fusing the velocity attribute and the seismic attribute to obtain a geologic body plane distribution diagram of the target interval, the geologic body plane distribution diagram being used for representing plane distribution conditions of different types of geologic bodies; and determining the distribution condition of the turbidity current sedimentary body based on the geologic body plane distribution diagram. By adopting the scheme provided by the embodiment of the invention, the rapid recognition of the turbidity current sedimentary body is realized under the condition that deep water plain geological data is limited.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of geophysical exploration technology, and in particular to a method, device, equipment and medium for identifying turbidity current sediments in a deep horizontal plain. Background Art

[0002] Deep horizontal plains are important reservoirs of oil and gas resources. In the process of oil and gas exploration, the exploration area is generally identified through drilling, seismic exploration and other technologies.

[0003] However, deepwater operations are high-risk and require advanced technology and strong economic support. Therefore, when identifying turbidity current sediments in deep plains, the available data is limited and the exploration efficiency is low. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for identifying turbidity current sediments in a deep flat plain. The technical solution is as follows:

[0005] On the one hand, an embodiment of the present application provides a method for identifying a turbidity current sediment body in a deep flat plain, the method comprising:

[0006] Extracting velocity attributes of the target layer segment from velocity data of the target layer segment in the deep horizontal source, wherein the velocity attributes are used to characterize velocity characteristics of the geological body in the target layer segment;

[0007] Extracting seismic attributes of the target layer segment from the seismic data of the target layer segment, wherein the seismic attributes are used to characterize seismic reflection characteristics of the geological body in the target layer segment;

[0008] The velocity attribute and the seismic attribute are merged to obtain a geological body plane distribution map of the target layer segment, wherein the geological body plane distribution map is used to characterize the plane distribution of different types of geological bodies;

[0009] The distribution of the turbidity current sediment body is determined based on the planar distribution map of the geological body.

[0010] On the other hand, an embodiment of the present application provides a device for identifying turbidity current sediments in a deep flat plain, the device comprising:

[0011] A first extraction module is used to extract the velocity attribute of the target layer segment from the velocity data of the target layer segment in the deep horizontal source, and the velocity attribute is used to characterize the velocity characteristics of the geological body in the target layer segment;

[0012] A second extraction module is used to extract seismic attributes of the target layer segment from the seismic data of the target layer segment, wherein the seismic attributes are used to characterize the seismic reflection characteristics of the geological body in the target layer segment;

[0013] A fusion module, used for fusing the velocity attribute and the seismic attribute to obtain a geological body plane distribution map of the target layer segment, wherein the geological body plane distribution map is used for characterizing the plane distribution of different types of geological bodies;

[0014] A determination module is used to determine the distribution of the turbidity current sediment body based on the plane distribution map of the geological body.

[0015] On the other hand, an embodiment of the present application provides a computer device, which includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the method for identifying turbidity current sediments in a deep horizontal plain as described in the above aspects.

[0016] On the other hand, an embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores at least one program code, and the at least one program code is used to be executed by a processor to implement the method for identifying turbidity current sediments in a deep horizontal plain as described in the above aspects.

[0017] On the other hand, an embodiment of the present application provides a computer program product, which includes computer instructions, and the computer instructions are 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, so that the computer device implements the method for identifying turbidity current sediments in a deep horizontal source as described in the above aspects.

[0018] In the embodiment of the present application, the computer device first extracts the velocity attributes and seismic attributes of the target layer segment from the velocity data and seismic data, then fuses the velocity attributes and seismic attributes to obtain the plane distribution map of the geological body of the target layer segment, and finally obtains the distribution of the turbidity current sediment from the plane distribution map of the geological body. In the case of limited deep horizontal original geological data, the embodiment of the present application realizes the rapid identification of turbidity current sediment by virtue of velocity data and seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart showing a method for identifying a turbidity current sediment body in a deep flat plain provided by an exemplary embodiment of the present application is shown;

[0021] Figure 2A flow chart showing a method for identifying a turbidity current sediment body in a deep flat plain provided by another exemplary embodiment of the present application is shown;

[0022] Figure 3 A root mean square velocity attribute plane diagram provided by an exemplary embodiment of the present application is shown;

[0023] Figure 4 A layer velocity attribute plane diagram provided by an exemplary embodiment of the present application is shown;

[0024] Figure 5 A root mean square amplitude property plane diagram provided by an exemplary embodiment of the present application is shown;

[0025] Figure 6 A plan distribution diagram of a geological body provided by an exemplary embodiment of the present application is shown;

[0026] Figure 7 A root mean square velocity attribute profile diagram provided by an exemplary embodiment of the present application is shown;

[0027] Figure 8 A layer velocity attribute profile diagram provided by an exemplary embodiment of the present application is shown;

[0028] Fig. 9 An earthquake profile diagram provided by an exemplary embodiment of the present application is shown;

[0029] Fig.10 A plan distribution diagram of turbidity current sediment bodies provided by an exemplary embodiment of the present application is shown;

[0030] Fig.11 A structural block diagram of a device for identifying turbidity current sediments in a deep flat plain provided by an exemplary embodiment of the present application is shown;

[0031] Fig.12 A structural block diagram of a computer device provided by an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0033] Deep plains are important oil and gas reservoirs. Turbidity sediments in deep 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 sediments in the exploration area in deep plains.

[0034] Please refer to Figure 1 , which shows a flow chart of a method for identifying turbidity current sediment bodies in a deep flat plain provided by an exemplary embodiment of the present application. The method may include the following steps:

[0035] Step 101: extracting velocity attributes of a target layer segment from velocity data of the target layer segment in a deep horizontal source, wherein the velocity attributes are used to characterize velocity characteristics of a geological body in the target layer segment.

[0036] The target layer segment is the seismic layer segment currently being used for turbidite sediment body identification. In a possible implementation, the computer device analyzes multiple sections of the seismic data body and determines the layer segment with the development background of turbidite sediment body as the target layer segment.

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

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

[0039] Step 102: extracting seismic attributes of the target layer segment from the seismic data of the target layer segment, where the seismic attributes are used to characterize the seismic reflection characteristics of the geological body in the target layer segment.

[0040] Seismic data is seismic information obtained through seismic exploration technology that can reflect seismic reflection characteristics. Seismic reflection characteristics include continuity, amplitude, and frequency. Seismic attributes are seismic data obtained through mathematical calculations, such as root mean square amplitude.

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

[0042] Step 103: The velocity attributes and the seismic attributes are merged to obtain a planar distribution map of the geological body of the target layer segment. 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 corresponding velocity characteristics and seismic reflection characteristics, so computer equipment can identify geological bodies based on the local velocity attributes and seismic attributes of the target layer segment.

[0044] Optionally, the computer device may perform grid interpretation on the seismic data of the target layer segment and then interpolate the grid data to obtain a planar distribution map of the geological body of the target layer segment.

[0045] Optionally, the computer device can assign different colors to velocity attributes and seismic attributes, wherein the attribute values ​​of the velocity attributes and seismic attributes correspond to the depth or brightness of the colors, and then obtain a planar distribution map of the geological body of the target layer segment by color fusion.

[0046] Step 104: determine the distribution of the turbidity current sediment body based on the planar distribution map of the geological body.

[0047] In a possible implementation, the computer device may search for a geological body having velocity characteristics and seismic reflection characteristics corresponding to a turbidity current sediment body in a geological body plane distribution map, and then determine the geological body as a turbidity current sediment body, thereby obtaining the distribution of the turbidity current sediment body.

[0048] In summary, in the embodiment of the present application, the computer device first extracts the velocity attributes and seismic attributes of the target layer segment from the velocity data and seismic data, then fuses the velocity attributes and seismic attributes to obtain the plane distribution map of the geological body of the target layer segment, and finally obtains the distribution of the turbidity current sediment from the plane distribution map of the geological body. In the case of limited deep horizontal original geological data, the embodiment of the present application realizes the rapid identification of turbidity current sediment by virtue of velocity data and seismic data.

[0049] Please refer to Figure 2 , which shows a flow chart of a method for identifying turbidity current sediment bodies in a deep flat plain provided by another exemplary embodiment of the present application. The method may include the following steps:

[0050] Step 201 : extracting velocity attributes of a target layer segment from velocity data of the target layer segment in a deep horizontal source, wherein the velocity attributes are used to characterize velocity characteristics of a geological body in the target layer segment.

[0051] The implementation of this step may refer to the above step 101, and this embodiment will not be described in detail here.

[0052] Step 202: extracting seismic attributes of the target layer segment from the seismic data of the target layer segment, where the seismic attributes are used to characterize the seismic reflection characteristics of the geological body in the target layer segment.

[0053] The implementation of this step may refer to the above step 102, and this embodiment will not be described in detail here.

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

[0055] Optionally, the computer device may perform color channel mapping on the attribute value of the speed attribute to obtain a speed attribute plane map.

[0056] In a possible implementation, a computer device operates on the value of the speed attribute so that the value of the speed attribute can correspond to the channel value of the color channel, and then uses the speed attribute corresponding color to draw a speed attribute plane diagram, 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 the integer so that the calculated layer velocity attribute value is within the corresponding value range of the red channel in the R (Red) G (Green) B (Blue) color mode, that is, 0 to 255, where the values ​​of the green channel and the blue channel are fixed to 0. Subsequently, the computer device obtains a layer velocity attribute plane diagram in which the layer velocity size is characterized by 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 attribute may include a root mean square velocity attribute and a layer velocity attribute, wherein the root mean square velocity attribute is sensitive to low velocity anomalies, and the layer velocity attribute is sensitive to high velocity anomalies. Different velocity attributes correspond to different color channels, so the velocity attribute plane map corresponding to the target layer segment can be divided into the following two types.

[0059] 1. Map the attribute value of the RMS velocity attribute to the first color channel to obtain a plane graph of the RMS velocity attribute.

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

[0061] It should be noted that the relationship between the attribute value of the RMS speed attribute and the channel value of the corresponding first color channel 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 RMS velocity attribute plane is as follows: Figure 3 It should be noted that, without considering the fusion of the velocity attribute plane map and the seismic attribute plane map, in order to improve the visualization clarity of the root mean square velocity attribute plane map, this example map uses different colors to distinguish the high and low root mean square velocities.

[0063] Among them, area 301 is a normal speed area, and the speeds of areas 302, 303 and 304 are obviously lower than the surrounding speeds, which are low-speed abnormal areas. In the RMS speed attribute plane diagram, since the RMS speed attribute is insensitive to high-speed abnormalities, no obvious high-speed abnormal areas are shown.

[0064] 2. Map the attribute value of the layer velocity attribute to the second color channel to obtain the layer velocity attribute plane map.

[0065] In a 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 corresponding range of the channel value of the second channel, and then the computer device rounds the calculation result of the layer velocity attribute to obtain the corresponding channel value of the second color channel.

[0066] It should be noted that the relationship between the attribute value of the layer velocity attribute and the channel value of the corresponding second color channel 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, the layer velocity property plane diagram is as follows: Figure 4 It should be noted that, without considering the fusion of the velocity attribute plane map and the seismic attribute plane map, in order to improve the visualization clarity of the layer velocity attribute plane map, this example map distinguishes the high and low layer velocities based on different colors.

[0068] Among them, area 401 is a normal speed area, and the speeds of areas 402, 403 and 404 are obviously higher than the surrounding speeds, which are high-speed abnormal areas. In the layer velocity attribute plane map, since the layer velocity attribute is not sensitive to low-speed anomalies, no obvious low-speed abnormal areas are shown.

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

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

[0071] In one possible implementation, a computer device operates on the values ​​of seismic attributes so that the values ​​of the seismic attributes can correspond to the channel values ​​of the color channel, and then uses the seismic attributes to draw a velocity attribute plane diagram with colors corresponding to different seismic attribute values, wherein the intensities of the colors corresponding to different seismic attribute values ​​are different.

[0072] Optionally, the seismic attribute may include a root mean square amplitude attribute, and the computer device may map the attribute value of the root mean square amplitude attribute to a third color channel to obtain a root mean square amplitude attribute plane map.

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

[0074] It should be noted that the relationship between the attribute value of the RMS amplitude attribute and the channel value of the corresponding third color channel 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, the RMS amplitude property plane is as follows: Figure 5 It should be noted that, without considering the fusion of the velocity attribute plane map and the seismic attribute plane map, in order to improve the visualization clarity of the root mean square amplitude attribute plane map, this example map uses different colors to distinguish the strength of the amplitude.

[0076] Among them, the dark parts in area 501, area 502 and area 503 are strong amplitude areas, the other light-colored areas in the example diagram are weak amplitude areas, and the black parts in area 504, area 505 and area 506 reflect blank and messy seismic reflection characteristics.

[0077] In addition, it should be noted that the first color channel, the second color channel, and the third color channel belong to the same color mode, such as the RGB mode, and another example, the C (Cyan, cyan) M (Magenta, magenta) Y (Yellow, yellow) K (Black, black) mode. The root mean square velocity attribute, the layer velocity attribute, and the root mean square amplitude attribute have different value ranges, so different mapping methods are used when performing color channel mapping.

[0078] It should be noted that step 203 and step 204 can be executed simultaneously, and the embodiment of the present application does not limit the execution sequence of step 203 and step 204.

[0079] Step 205: fuse the velocity attribute plane map and the seismic attribute plane map to obtain a geological body plane distribution map of the target layer segment.

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

[0081] In a possible implementation, a computer device performs color fusion on attribute plane graphs 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 graph after color fusion are different.

[0082] For example, the distribution of attribute 1, attribute 2 and attribute 3 corresponds to the plane Figure 1 ,flat Figure 2 and plane Figure 3 Among them, the plane Figure 1 The RGB color of each point in the plane is (x, 0, 0). Figure 2 The RGB color of each point in the plane is (0, y, 0). Figure 3 The RGB color of each point in the plane is (0, 0, z). Figure 1 ,flat Figure 2 and plane Figure 3 The colors of the corresponding points in the graph are merged to obtain a geological plane distribution map with the color of each point being (x, y, z), where x, y and z are integers ranging 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 the turbidity current sediment body based on the planar distribution map of the geological body.

[0084] In the plane distribution map of geological bodies, similar colors correspond to similar velocity characteristics and seismic reflection characteristics. Therefore, the computer equipment can determine the corresponding color of the turbidity current sediment body on the plane distribution map of the geological body, and thus determine the distribution of the turbidity current sediment body according to the distribution of similar colors.

[0085] From the above reasoning, it can be seen that this step can include the following sub-steps.

[0086] Step 206A, determining geological sampling points in the geological body plane distribution map.

[0087] In a possible implementation, the computer device randomly selects a plurality of geological sampling points corresponding to different colors in the geological body plane distribution map, wherein the corresponding colors of the geological sampling points differ greatly.

[0088] For example, the plane distribution map of the geological body is as follows: Figure 6 As shown, the geological sampling point 601 corresponds to dark purple, the seismic sampling point 602 corresponds to dark red, the seismic sampling point 603 corresponds to light purple, and the seismic sampling point 604 corresponds to cyan.

[0089] Step 206B: when the velocity attribute corresponding to the geological sampling point indicates that the velocity is normal and the seismic attribute corresponding to the sampling point presents medium-high frequency strong amplitude characteristics, the geological sampling point is determined to be a turbidite sediment sampling point.

[0090] In a possible implementation, the computer may generate a profile by selecting a survey line, thereby determining the velocity characteristics and seismic reflection characteristics of the sampling points.

[0091] For example, the RMS velocity profile at the survey line is shown in FIG7 , and the layer velocity profile at the survey line is shown in FIG. Figure 8 As shown in the figure, the seismic profile at the survey line is as follows Fig. 9As shown in the figure, the target layer segment position is determined based on the seismic profile, and a high-speed anomaly and a low-speed anomaly appear at the corresponding target layer segment position in the RMS velocity profile and the layer velocity profile. The low-speed anomaly of the target layer segment is obvious in the RMS velocity profile, and the high-speed anomaly of the target layer segment is obvious in the layer velocity profile. By comparing the seismic profile, velocity anomaly body A and velocity anomaly body B are obtained, wherein velocity anomaly A corresponds to high-speed anomaly, continuous low-frequency strong-amplitude seismic reflection characteristics, and velocity anomaly body B corresponds to low-speed anomaly, low-frequency weak-amplitude seismic reflection characteristics.

[0092] Computer equipment can identify turbidity current sediment bodies based on normal velocity and strong amplitude characteristics at medium and high frequencies.

[0093] In a possible implementation, the computer device may first exclude geological sampling points with abnormal velocities based on velocity characteristics, and then identify turbidite sediments based on seismic reflection characteristics for geological sampling points with normal velocities.

[0094] Table 1

[0095] Speed ​​characteristics Seismic reflection characteristics Geological body Low speed abnormality Low frequency, weak amplitude or blank noise Gas chimney or mud diapir High speed abnormality Low frequency strong amplitude Igneous, gypsum, or salt rock Normal speed High-frequency and strong amplitude Turbidity sediments Normal speed Low to medium frequency weak amplitude Marine fine-grained sediments

[0096] Step 206C: determine the area in the geological body plane distribution map that has a color value similar to the sampling point of the turbidity current sediment body as the distribution area of ​​the turbidity current sediment body.

[0097] Since similar colors in the plane distribution map of geological bodies correspond to similar velocity characteristics and seismic reflection characteristics, it can be determined that the area with similar color values ​​to the sampling points of turbidity current sediments has normal velocity and medium-high frequency strong amplitude characteristics, that is, the area with similar color values ​​to the sampling points of turbidity current sediments is the distribution area of ​​turbidity current sediments.

[0098] For example, the plan distribution diagram of turbidity current sediments is as follows: Fig.10 As shown, region 1010 and region 1020 have similar colors, corresponding to normal velocity and medium-high frequency strong amplitude characteristics, so the computer device can determine that region 1010 and region 1020 are turbidity current sediment distribution areas.

[0099] In an embodiment of the present application, after extracting velocity attributes and seismic attributes, the computer device first generates a monochrome velocity attribute plane map and a seismic attribute plane map, wherein different attributes correspond to different color channels. The computer device then fuses the velocity attribute plane map and the seismic attribute plane map to obtain a multi-color geological body plane distribution map, and then determines the distribution of turbidity current sediments based on the color distribution, thereby realizing the rapid identification of turbidity current sediments in deep plains using a small amount of data, namely, velocity attributes and seismic attributes.

[0100] Since there are some geological bodies in the deep plain that interfere with the identification of turbidite deposits, such as igneous rocks, salt rocks, gypsum rocks and gas chimneys, in order to better identify turbidite deposits, the computer equipment can filter the interfering geological bodies, including the following steps.

[0101] Step 1: Based on the low-velocity anomaly area in the root mean square velocity attribute plane map and the high-velocity anomaly area in the layer velocity attribute plane map, the distribution of the interfering geological body in the target layer segment is determined.

[0102] There are obvious differences between the velocity attributes corresponding to the interfering geological body and the turbidity current deposits. The velocity attributes corresponding to the turbidity current deposits reflect normal velocity characteristics, that is, the velocity attribute values ​​are similar and the changes are gentle. Since the interfering geological body has a different geological structure from the turbidity current deposits, the velocity attributes corresponding to the interfering geological body in the target layer are different from those of the surrounding turbidity current deposits, reflecting velocity anomaly characteristics, including high-speed anomalies and low-speed anomalies.

[0103] From the above reasoning, it can be known that the computer equipment can determine the location of the interfering geological body based on the velocity anomaly area appearing in the velocity attribute plane map.

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

[0105] Step 2: filtering the interfering geological body area on the geological body plane distribution map based on the distribution of the interfering geological body.

[0106] In a possible implementation, the computer device marks the interfering geological body areas at the corresponding positions of the geological body plane distribution map by comparing the low-speed abnormal areas in the root mean square velocity attribute plane map and the high-speed abnormal areas in the layer velocity attribute plane map, thereby filtering the interfering geological body areas.

[0107] Please refer to Fig.11 , which shows a structural block diagram of a device for identifying turbidity current sediments in a deep horizontal plain provided by an exemplary embodiment of the present application. The device includes:

[0108] The first extraction module 1101 is used to extract the velocity attribute of the target layer segment from the velocity data of the target layer segment in the deep horizontal source, and the velocity attribute is used to characterize the velocity characteristics of the geological body in the target layer segment;

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

[0110] A fusion module 1103 is used to fuse the velocity attribute and the seismic attribute to obtain a plane distribution map of the geological body of the target layer segment, wherein the plane distribution map of the geological body is used to characterize the plane distribution of different types of geological bodies;

[0111] The determination module 1104 is used to determine the distribution of the turbidity current sediment body based on the planar distribution map of the geological body.

[0112] Optionally, the fusion module 1103 is further used for:

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

[0114] Generate a seismic attribute plane map corresponding to the target layer segment based on the seismic attributes;

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

[0116] Optionally, the fusion module 1103 is further used for:

[0117] Performing color channel mapping on the attribute value of the speed attribute to obtain the speed attribute plane graph;

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

[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] Mapping the attribute value of the root mean square speed attribute to the first color channel to obtain a root mean square speed attribute plane graph;

[0121] The attribute value of the layer velocity attribute is mapped to the second color channel to obtain a layer velocity attribute plane map.

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

[0123] Determine the distribution of interfering geological bodies in the target layer segment based on the low-velocity abnormal area in the root mean square velocity attribute plane map and the high-velocity abnormal area in the layer velocity attribute plane map;

[0124] The interfering geological body region is filtered on the geological body plane distribution map based on the distribution of the interfering geological body.

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

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

[0127] When the velocity attribute corresponding to the geological sampling point indicates that the velocity is normal, and the seismic attribute corresponding to the sampling point presents medium-high frequency strong amplitude characteristics, the geological sampling point is determined to be a turbidity current sediment sampling point;

[0128] An area in the geological body plane distribution map having a color value similar to that of the turbidity current sediment sampling point is determined as the distribution area of ​​the turbidity current sediment.

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

[0130] Please refer to Fig.12 , which shows a structural block diagram of a computer device provided by an exemplary embodiment of the present application. The computer device may include one or more of the following components: a processor 1201 and a memory 1202.

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

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

[0133] An embodiment of the present application also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is used to be executed by the processor 1201 to implement the application interface pulling up method as described in the above embodiment.

[0134] The embodiment of the present application provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. The processor 1201 of the computer device reads the computer instruction from the computer-readable storage medium, and the processor 1201 executes the computer instruction, so that the computer device executes the application interface pull-up method provided in the above embodiment.

[0135] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented with 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 codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a general or special-purpose computer can access.

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

Claims

1. A method for identifying turbidity current sediments in a deep flat plain, characterized in that: The method comprises: Extracting velocity attributes of the target layer segment from velocity data of the target layer segment in the deep horizontal source, wherein the velocity attributes are used to characterize velocity characteristics of the geological body in the target layer segment; Extracting seismic attributes of the target layer segment from the seismic data of the target layer segment, wherein the seismic attributes are used to characterize seismic reflection characteristics of the geological body in the target layer segment; The velocity attribute and the seismic attribute are merged to obtain a geological body plane distribution map of the target layer segment, wherein the geological body plane distribution map is used to characterize the plane distribution of different types of geological bodies; The distribution of the turbidity current sediment body is determined based on the planar distribution map of the geological body.

2. The method according to claim 1, characterized in that The fusing of the velocity attribute and the seismic attribute to obtain a planar distribution map of the geological body of the target layer segment includes: Generate a velocity attribute plane map corresponding to the target layer segment based on the velocity attribute; Generate a seismic attribute plane map corresponding to the target layer segment based on the seismic attributes; The velocity attribute plane map and the seismic attribute plane map are fused to obtain the geological body plane distribution map of the target layer segment.

3. The method according to claim 2, characterized in that The generating a velocity attribute plane map corresponding to the target layer segment based on the velocity attribute comprises: Performing color channel mapping on the attribute value of the speed attribute to obtain the speed attribute plane graph; The step of generating a seismic attribute plane map corresponding to the target layer segment based on the seismic attributes comprises: Color channel mapping is performed on the attribute values ​​of the seismic attributes to obtain the seismic attribute plane map, wherein the velocity attribute and the seismic attribute correspond to different color channels when color channel mapping is performed.

4. The method according to claim 3, characterized in that: The velocity attributes include root mean square velocity attributes and layer velocity attributes; The step of performing color channel mapping on the attribute value of the speed attribute to obtain the speed attribute plane graph includes: Mapping the attribute value of the root mean square speed attribute to the first color channel to obtain a root mean square speed attribute plane graph; The attribute value of the layer velocity attribute is mapped to the second color channel to obtain a layer velocity attribute plane map.

5. The method according to claim 3, characterized in that: The seismic attributes include root mean square amplitude attributes; The step of performing color channel mapping on the attribute values ​​of the seismic attributes to obtain the seismic attribute plane map comprises: The attribute value of the root mean square amplitude attribute is mapped to the third color channel to obtain a root mean square amplitude attribute plane graph.

6. The method according to claim 4, characterized in that Before determining the distribution of the turbidity current sediment body based on the planar distribution map of the geological body, the method further includes: Determine the distribution of interfering geological bodies in the target layer segment based on the low-velocity abnormal area in the root mean square velocity attribute plane map and the high-velocity abnormal area in the layer velocity attribute plane map; The interfering geological body region is filtered on the geological body plane distribution map based on the distribution of the interfering geological body.

7. The method according to any one of claims 1 to 6, characterized in that: The determining the distribution of the turbidity current sediment body based on the planar distribution map of the geological body includes: Determining geological sampling points in the planar distribution map of the geological body; When the velocity attribute corresponding to the geological sampling point indicates that the velocity is normal, and the seismic attribute corresponding to the sampling point presents medium-high frequency strong amplitude characteristics, the geological sampling point is determined to be a turbidity current sediment sampling point; An area in the plane distribution map of the geological body having a color value similar to the sampling point of the turbidity current sediment body is determined as the distribution area of ​​the turbidity current sediment body.

8. A device for identifying turbidity current sediments in a deep flat plain, characterized in that: The device comprises: A first extraction module is used to extract the velocity attribute of the target layer segment from the velocity data of the target layer segment in the deep horizontal source, and the velocity attribute is used to characterize the velocity characteristics of the geological body in the target layer segment; A second extraction module is used to extract seismic attributes of the target layer segment from the seismic data of the target layer segment, wherein the seismic attributes are used to characterize the seismic reflection characteristics of the geological body in the target layer segment; A fusion module, used for fusing the velocity attribute and the seismic attribute to obtain a geological body plane distribution map of the target layer segment, wherein the geological body plane distribution map is used for characterizing the plane distribution of different types of geological bodies; A determination module is used to determine the distribution of the turbidity current sediment body based on the plane distribution map of the geological body.

9. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the method for identifying turbidity current sediments in a deep horizontal field according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one program code, and the at least one program code is used to be executed by a processor to implement the method for identifying turbidity current sediments in a deep horizontal field according to any one of claims 1 to 7.

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