Crossplot method, device, equipment, storage medium and product of seismic data

Abstract

The application provides a crossplot method, device, equipment, storage medium and product of seismic data, and belongs to the technical field of seismic data processing. The method comprises the following steps: performing thinning on a plurality of first data bodies based on a first thinning accuracy to obtain a plurality of second data bodies, performing crossplot on the plurality of second data bodies to obtain a first crossplot; determining a second thinning accuracy of the plurality of first data bodies, performing thinning on the plurality of first data bodies based on the second thinning accuracy to obtain a plurality of third data bodies, performing crossplot on the plurality of third data bodies to obtain a second crossplot; superimposing the first crossplot and the second crossplot to obtain a third crossplot; and in the case that the distribution accuracy of the plurality of data in the third crossplot does not satisfy a first preset condition, re-executing the step of determining the second thinning accuracy of the plurality of first data bodies until a target crossplot in which the distribution accuracy of the plurality of data satisfies the first preset condition is obtained. The method improves the crossplot efficiency of the plurality of data bodies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data processing, and in particular to a seismic data crossplot method, device, equipment, storage medium and product. BACKGROUND

[0002] Seismic quantitative interpretation technology is a main means for reservoir prediction and evaluation. Through seismic quantitative interpretation technology, quantitative characterization of reservoir parameters can be realized, thereby providing an important basis for well deployment, reserve estimation and the like. As a basic tool in seismic quantitative interpretation technology, seismic data volume crossplot analysis technology can establish a relationship between seismic response characteristics and geological factors, reservoir parameters and the like, so as to realize prediction and evaluation of a reservoir. Therefore, seismic data volume crossplot analysis technology has become an important analysis means in seismic quantitative interpretation technology.

[0003] In related technologies, when the seismic data volume crossplot analysis technology is used, each seismic data of each data volume of a region to be studied is crossplotted with each seismic data of other data volumes, and a crossplot of seismic data of multiple data volumes on the same graph is obtained.

[0004] In related technologies, each seismic data of each data volume needs to be crossplotted with each seismic data of other data volumes, and the data volume of each seismic data of each data volume is large, with an order of magnitude usually being more than GB (Gigabyte), which results in a long time for crossplotting of multiple data volumes, thereby reducing the crossplotting efficiency of multiple data volumes. SUMMARY

[0005] Embodiments of the present application provide a seismic data crossplot method, device, equipment, storage medium and product, which can improve the crossplotting efficiency of multiple data volumes. The technical solution is as follows:

[0006] In one aspect, a seismic data crossplot method is provided, and the method comprises:

[0007] determining multiple first data volumes of a region to be studied;

[0008] determining a first thinning precision of the multiple first data volumes, respectively thinning the multiple first data volumes based on the first thinning precision to obtain multiple second data volumes, and crossplotting the multiple second data volumes to obtain a first crossplot;

[0009] determining a second thinning precision of the multiple first data volumes, respectively thinning the multiple first data volumes based on the second thinning precision to obtain multiple third data volumes, and crossplotting the multiple third data volumes to obtain a second crossplot;

[0010] superimpose the first cross plot and the second cross plot to obtain a third cross plot;

[0011] in a case where distribution accuracy of the multiple data in the third cross plot meets a first preset condition, determining that the third cross plot is a target cross plot of the region to be researched;

[0012] in a case where the distribution accuracy of the multiple data in the third cross plot does not meet the first preset condition, re-executing the step of determining the second sparseness accuracy of the multiple first data bodies until a target cross plot whose distribution accuracy of the multiple data meets the first preset condition is obtained, the target cross plot being a cross plot finally obtained through superimposition in sequence.

[0013] In a possible implementation, the determining the first sparseness accuracy of the multiple first data bodies comprises:

[0014] determining an initial sparseness accuracy of the multiple first data bodies, a first data amount of the multiple first data bodies, and multiple seismic facies types of the region to be researched;

[0015] determining a weight value of each seismic facies type and a second data amount corresponding to each seismic facies type;

[0016] for each first data body, determining a quotient of the first data amount and the initial sparseness accuracy to obtain a target data amount to be extracted of the first data body;

[0017] for each seismic facies type, determining a quotient of the target data amount and the weight value of the seismic facies type to obtain an extraction number corresponding to the seismic facies type, and determining a quotient of the second data amount and the extraction number to obtain a first sparseness accuracy of the seismic facies type.

[0018] In a possible implementation, the sparsely sampling the multiple first data bodies based on the first sparseness accuracy to obtain multiple second data bodies comprises:

[0019] for each first data body, sparsely sampling data in the first data body corresponding to each seismic facies type based on the first sparseness accuracy of the multiple seismic facies types to obtain a second data body corresponding to each seismic facies type;

[0020] combining the second data bodies corresponding to each seismic facies type to obtain the second data body after sparsely sampling the first data body.

[0021] In a possible implementation, the method further comprises:

[0022] If the distribution accuracy of multiple data in the first intersection graph does not meet the second preset condition, the step of determining the second thinning accuracy of the multiple first data volumes is executed.

[0023] If the distribution accuracy of multiple data in the first intersection map meets the second preset condition, the first intersection map is determined to be the target intersection map of the region to be studied.

[0024] In one possible implementation, the step of thinning the plurality of first data volumes based on the first thinning precision to obtain a plurality of second data volumes includes:

[0025] For each first data volume, based on the first thinning precision, all data of the first data volume is divided to obtain multiple data grids, and the amount of data included in each data grid is the same as the amount of data corresponding to the first thinning precision.

[0026] Determine the data at the target location points in each data grid as the target data to be extracted;

[0027] By combining target data from multiple data grids, a second data volume is obtained after thinning the first data volume.

[0028] In one possible implementation, the method further includes:

[0029] For each data point in the target intersection graph, determine the display level of the data point;

[0030] Based on the display level, the display size of the data points is adjusted to obtain multiple data points with different display sizes;

[0031] A display cross plot consisting of multiple data points of the same display size is determined, and the display cross plot is used to predict reservoirs in the area under study.

[0032] In one possible implementation, determining the display level of each data point in the target intersection graph includes:

[0033] Determine the display frequency for each data point;

[0034] For each data point, if the display frequency of the data point is greater than the first frequency, the display level of the data point is determined to be the first level;

[0035] If the display frequency of the data point is not greater than the first frequency but greater than the second frequency, the display level of the data point is determined to be the second level.

[0036] If the display frequency of the data point is not greater than the second frequency, the display level of the data point is determined to be the third level, where the first frequency is greater than the second frequency.

[0037] On the other hand, a seismic data interleaving device is provided, the device comprising:

[0038] The first determining module is used to determine multiple first data volumes of the region to be studied;

[0039] A first processing module is used to determine the first thinning precision of the plurality of first data volumes, and based on the first thinning precision, to thin the plurality of first data volumes respectively to obtain a plurality of second data volumes, and to perform intersection on the plurality of second data volumes to obtain a first intersection diagram.

[0040] The second processing module is used to determine the second thinning precision of the plurality of first data volumes, and based on the second thinning precision, to thin the plurality of first data volumes respectively to obtain a plurality of third data volumes, and to perform intersection on the plurality of third data volumes to obtain a second intersection diagram.

[0041] The overlay module is used to overlay the first intersection map and the second intersection map to obtain a third intersection map;

[0042] The second determining module is used to determine the third intersection map as the target intersection map of the region to be studied when the distribution accuracy of multiple data in the third intersection map meets the first preset condition.

[0043] The first execution module is configured to re-execute the step of determining the second thinning precision of the multiple first data volumes when the distribution precision of multiple data in the third intersection graph does not meet the first preset condition, until a target intersection graph whose distribution precision of multiple data meets the first preset condition is obtained. The target intersection graph is the intersection graph finally obtained by sequentially superimposing intersection graphs.

[0044] In one possible implementation, the first processing module is configured to:

[0045] Determine the initial thinning precision of the plurality of first data volumes, the first data volume of the plurality of first data volumes, and the plurality of seismic facies types of the region to be studied;

[0046] Determine the weight value for each seismic phase type and the corresponding second data volume for each seismic phase type;

[0047] For each first data volume, determine the ratio of the first data volume to the initial thinning precision to obtain the target data volume to be extracted from the first data volume;

[0048] For each seismic facies type, determine the weight value between the target data volume and the seismic facies type, obtain the extraction quantity corresponding to the seismic facies type, determine the quotient of the second data volume and the extraction quantity, and obtain the first thinning precision of the seismic facies type.

[0049] In one possible implementation, the first processing module is configured to:

[0050] For each first data volume, based on the first thinning precision of the multiple seismic facies types, the data in the first data volume corresponding to each seismic facies type is thinned to obtain the second data volume corresponding to each seismic facies type.

[0051] The second data volume corresponding to each seismic phase type is combined to obtain the second data volume after the first data volume is thinned.

[0052] In one possible implementation, the device further includes:

[0053] The second execution module is used to execute the step of determining the second thinning precision of the plurality of first data volumes when the distribution precision of the plurality of data in the first intersection graph does not meet the second preset condition.

[0054] The third determining module is used to determine the first intersection map as the target intersection map of the region to be studied when the distribution accuracy of multiple data in the first intersection map meets the second preset condition.

[0055] In one possible implementation, the first processing module is configured to:

[0056] For each first data volume, based on the first thinning precision, all data of the first data volume is divided to obtain multiple data grids, and the amount of data included in each data grid is the same as the amount of data corresponding to the first thinning precision.

[0057] Determine the data at the target location points in each data grid as the target data to be extracted;

[0058] By combining target data from multiple data grids, a second data volume is obtained after thinning the first data volume.

[0059] In one possible implementation, the device further includes:

[0060] The fourth determining module is used to determine the display level of each data point in the target intersection graph;

[0061] An adjustment module is used to adjust the display size of the data points based on the display level, thereby obtaining multiple data points with different display sizes;

[0062] The fifth determining module is used to determine a display cross-plot composed of multiple data points of the same display size, and the display cross-plot is used to predict the reservoir in the area under study.

[0063] In one possible implementation, the fourth determining module is configured to:

[0064] Determine the display frequency for each data point;

[0065] For each data point, if the display frequency of the data point is greater than the first frequency, the display level of the data point is determined to be the first level;

[0066] If the display frequency of the data point is not greater than the first frequency but greater than the second frequency, the display level of the data point is determined to be the second level.

[0067] If the display frequency of the data point is not greater than the second frequency, the display level of the data point is determined to be the third level, where the first frequency is greater than the second frequency.

[0068] On the other hand, a computer device is provided, the computer device including one or more processors and one or more memories, the one or more memories storing at least one instruction, the at least one instruction being loaded and executed by the one or more processors to perform the operations performed by the seismic data intersection method described in any of the above implementations.

[0069] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the computer-readable storage medium, the at least one instruction being loaded and executed by a processor to perform the operations performed by the seismic data intersection method described in any of the above implementations.

[0070] On the other hand, a computer program product is provided, the computer program product including at least one instruction stored in a computer-readable storage medium, a processor of a computer device reading the at least one instruction from the computer-readable storage medium, the processor executing the at least one instruction, causing the computer device to load and execute operations performed by the seismic data intersection method described in any of the above implementations.

[0071] The beneficial effects of the technical solutions provided in this application include at least the following:

[0072] This application provides a cross-plotting method for seismic data. This method improves the cross-plotting efficiency of multiple data volumes by performing cross-plotting on multiple thinned data volumes. Furthermore, if the data distribution accuracy in the cross-plot obtained from the cross-plotting of multiple thinned data volumes does not meet a first preset condition, the method can further thin the multiple data volumes and overlay the cross-plot obtained from the cross-plotting of the further thinned data volumes with the previously obtained cross-plot to improve the data distribution accuracy in the final target cross-plot. In this way, the method improves the cross-plotting efficiency of multiple data volumes while ensuring the data distribution accuracy of the target cross-plot. Attached Figure Description

[0073] 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.

[0074] Figure 1 This is a flowchart of a seismic data intersection method provided in an embodiment of this application;

[0075] Figure 2 This is a schematic diagram of the distribution of seismic facies types provided in an embodiment of this application;

[0076] Figure 3 This is a schematic diagram of the trace number distribution of a seismic facies type provided in an embodiment of this application;

[0077] Figure 4 This is a schematic diagram of the data distribution of a first data body provided in an embodiment of this application;

[0078] Figure 5 This is a schematic diagram of a data grid provided in an embodiment of this application;

[0079] Figure 6 This is a schematic diagram of a data grid provided in an embodiment of this application;

[0080] Figure 7 This is a schematic diagram of a data grid provided in an embodiment of this application;

[0081] Figure 8 This is a schematic diagram of a data grid provided in an embodiment of this application;

[0082] Figure 9 This is a schematic diagram of a data grid provided in an embodiment of this application;

[0083] Figure 10 This is a schematic diagram of a cross-plot provided in an embodiment of this application;

[0084] Figure 11 This is a block diagram of a seismic data intersection device provided in an embodiment of this application;

[0085] Figure 12 This is a block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0086] 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.

[0087] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0088] This application provides a method for intersecting seismic data; see [link to relevant documentation]. Figure 1 The methods include:

[0089] Step 101: The computer device identifies multiple first data volumes of the region to be studied.

[0090] The data consists of multiple first data volumes, each containing multiple seismic data. The number of these data volumes can be set and changed as needed; optionally, there are three first data volumes: a wave impedance data volume, a pre-stack seismic data volume, and a post-stack seismic data volume. Optionally, the area under study is an oil and gas reservoir region.

[0091] Step 102: The computer device determines the first thinning precision of the multiple first data volumes.

[0092] The process of determining the first thinning precision using computer equipment includes the following steps (1)-(4):

[0093] (1) The computer equipment determines the initial thinning precision of multiple first data volumes, the first data volume of multiple first data volumes, and multiple seismic phase types of the area to be studied.

[0094] The initial thinning precision is the overall thinning precision of the first data volume. Any thinning precision indicates how many data points are used to extract one data point. Each data point is arranged on the plane according to the number of seismic traces and lines, with each data point corresponding to a line number and a trace number. In some embodiments, each thinning precision is an integer that can be obtained by taking the square root of the first integer, meaning that the number of lines and traces corresponding to each thinning precision is equal. For example, the thinning precision can be a step size of 5×5, 21×21, or 51×51, which extracts one data point from every 25, 441, and 2601 data points, respectively. The overall thinning precision can be adaptively determined based on the amount of data from the intersection and the overall data volume of multiple first data volumes, or can be set as needed. The first data volume of the first data volume represents the total number of data points in the first data volume, and this first data volume can also represent the range of data in the first data volume, i.e., the range of data in the area to be studied, including the range of line numbers and the range of trace numbers.

[0095] Seismic facies types include channel sandbody seismic facies, beach-bar sandbody seismic facies, dolomite seismic facies, etc. See also Figure 2 , Figure 2 To represent the distribution of seismic facies in a certain area under study, the horizontal axis represents the number of seismic lines, and the vertical axis represents the number of seismic traces. Figure 2 As can be seen, the region includes three seismic facies types.

[0096] (2) The computer equipment determines the weight value of each seismic phase type and the second data volume corresponding to each seismic phase type.

[0097] It should be noted that the sum of the weights of multiple seismic phase types is 1, and the weight of each seismic phase type can be set and changed as needed. In one implementation, when the number of seismic phase types is even, the weights of the multiple seismic phase types are averaged. When the number of seismic phase types is odd, the weights of the first even-numbered seismic phase types are averaged, and the weight of the last seismic phase type is 1 minus the weights of the other seismic phase types. For example, for 3 seismic phase types, the weight of the first 2 seismic phase types is 0.3, and the weight of the remaining seismic phase types is 0.4.

[0098] In another implementation, for multiple seismic facies types, the weight is determined based on the importance of each seismic facies type to the research problem. For example, if the first seismic facies type out of three is of high importance, its weight can be set to 0.5. It should be noted that, for multiple seismic facies types, after determining the weight of the most important seismic facies type, the weights of the remaining seismic facies types can be set as needed, averaged, or randomly. For example, if the weight of the most important seismic facies type out of three is set to 0.5, the weights of the remaining seismic facies types are 0.2 and 0.3, respectively.

[0099] In some embodiments, the computer device determines a trace number distribution map for each seismic facies type before determining multiple seismic facies types. See also Figure 3 , Figure 3 This map shows the trace count distribution of a specific seismic facies type along each seismic line, revealing its distribution within the study area. Computer equipment can then determine the weight value for this seismic facies type based on this distribution. Optionally, if the seismic facies type has a high trace count distribution and is of high importance to the research question, a higher weight value can be assigned to it.

[0100] (3) For each first data volume, the computer device determines the ratio of the first data volume to the initial thinning precision to obtain the target data volume to be extracted from the first data volume.

[0101] For example, if the initial data volume is 10,000, and the initial thinning precision is to extract one data point from every 25 data points, then the computer device determines the target data volume to be 400.

[0102] (4) For each seismic phase type, the computer equipment determines the weight value of the target data volume and the seismic phase type, obtains the extraction quantity corresponding to the seismic phase type, determines the quotient of the second data volume and the extraction quantity, and obtains the first thinning accuracy of the seismic phase type.

[0103] For example, if the target data volume is 400, the weight value of a certain seismic facies type is 0.5, and the second data volume is 3000, then the number of samples corresponding to that seismic facies type is 200, and the first thinning precision is to extract one data point out of every 15 data points.

[0104] In some embodiments, the computer device further determines the data extraction status of a seismic trace type by determining the extraction ratio of the extraction quantity corresponding to each seismic phase type and the first data volume of all data.

[0105] In this embodiment, by determining the first thinning precision for each seismic trace type based on its weight value, data from both dense and sparse areas can be extracted. This controls the thinning range, ensures the comprehensiveness of the extracted data, and achieves an effective combination of seismic data and geological information, thereby improving the accuracy of seismic facies characterization. Furthermore, since the weights of seismic trace types can be adjusted according to their importance, the extracted data becomes more targeted, thus improving the accuracy of the target cross-plot.

[0106] Step 103: The computer device performs thinning on multiple first data volumes based on the first thinning precision to obtain multiple second data volumes, and performs intersection on the multiple second data volumes to obtain a first intersection graph.

[0107] In cases where there are multiple seismic facies types in the area under study, step 103 involves the following steps: For each first data volume, the computer device performs data thinning on the data within the first data volume corresponding to each seismic facies type, based on a first thinning precision for multiple seismic facies types, to obtain a second data volume corresponding to each seismic facies type. The computer device then combines the second data volumes corresponding to each seismic facies type to obtain the second data volume after thinning the first data volume. In this way, by thinning the data within the first data volume corresponding to each seismic facies type separately, it ensures that data for each seismic trace type can be extracted, improving the comprehensiveness of the extracted data.

[0108] In some embodiments, step 103 includes the following steps (1)-(3):

[0109] (1) For each first data volume, the computer device divides all the data of the first data volume based on the first thinning precision to obtain multiple data grids. The amount of data included in each data grid is the same as the amount of data corresponding to the first thinning precision.

[0110] It should be noted that for each first data volume, each data point it contains is arranged on the plane according to the number of seismic traces and lines. See [link / reference] Figure 4 On the plane, the horizontal axis represents the number of lines, and the vertical axis represents the number of tracks. Each intersection of a line and a track contains a data point. Each initial thinning precision can be obtained by taking the square root of a integer. For example, if the initial thinning precision is a step size of 5×5, meaning one data point is extracted from every 25 data points, then the data grid is a 5×5 grid.

[0111] (2) The computer equipment determines the data of the target location point in each data grid as the target data to be extracted.

[0112] It should be noted that the target location can be set and changed as needed; for example, the target location can be one of the following: the center point, the top left corner, or the top right corner. See also Figure 5 The target location is the center point. In one implementation, see [link to implementation details]. Figure 6 The center point is marked as 1, and other points are marked as 0. The location of 1 is the target location point, and the data at the location of 0 is not extracted.

[0113] In one implementation, the computer device slides cyclically through each data grid according to the data grid's step size, sequentially extracting the target data from each data grid. The initial data grid starts from the position corresponding to the starting line number and starting track number of the first data volume. With a 5×5 data grid and the target location point being the center point, the position of the target location point in each data grid is obtained using the following formula:

[0114] Formula 1: L i =L1+(2+i*5)*L inc T i =T1+(2+i*5)*T inc

[0115] Where L1 is the starting line number of the first data body, L inc For line spacing, L i T is the extracted i-th line number, T1 is the starting track number of the first data body, T inc For the channel interval, T i Let i be the channel number extracted.

[0116] It should be noted that if the amount of data within the data grid is less than the amount of data corresponding to the first thinning precision, the computer will extract data from the location point closest to the target location. For example, see... Figure 7 If the target location is the center location, and the amount of data in the data grid is less than the amount of data corresponding to the first thinning precision, then the computer device will extract the data from the location point closest to the center location.

[0117] (3) The computer device combines the target data in multiple data grids to obtain the second data volume after the first data volume is thinned.

[0118] It should be noted that when the area under study includes multiple seismic facies types, the first data volume corresponding to each seismic facies type shall be processed by the above steps (1)-(3) to obtain the second data volume corresponding to each seismic facies type.

[0119] It should be noted that if the distribution precision of multiple data points in the first cross-plot meets the second preset condition, the first cross-plot is determined as the target cross-plot for the region under study. In this way, because the first thinning precision is relatively low, the amount of data extracted and cross-plotted is small, improving the efficiency of thinning and thus the efficiency of cross-plotting. If the distribution precision of multiple data points in the first cross-plot does not meet the second preset condition, then step 104 is executed. The second preset condition is the preset data distribution precision of the first cross-plot, which can be set and changed as needed.

[0120] Step 104: The computer device determines the second thinning precision of the multiple first data volumes. Based on the second thinning precision, the multiple first data volumes are thinned to obtain multiple third data volumes. The multiple third data volumes are then intersected to obtain a second intersection graph.

[0121] It should be noted that the process for determining the second thinning precision is the same as that for the first thinning precision, and will not be repeated here. Specifically, the first data volume of multiple first data volumes, the multiple seismic facies types of the area under study, the weight value of each seismic facies type, and the second data volume corresponding to each seismic facies type only need to be determined during the determination of the first thinning precision; they do not need to be determined repeatedly during the determination of the second thinning precision. Whether the initial thinning precision of the multiple first data volumes corresponding to the second thinning precision is the same as the initial thinning precision corresponding to the first thinning precision can be set and changed as needed. If the initial thinning precision corresponding to the second thinning precision is the same as the initial thinning precision corresponding to the first thinning precision, then the second thinning precision is the same as the first thinning precision, but the target location points of the data grids are different. For example, the target location point of the data grid corresponding to the first thinning precision is the center point, while the target location point of the data grid corresponding to the second thinning precision is the upper left corner point, as shown in [reference needed]. Figure 8 .

[0122] The steps of the computer thinning multiple first data volumes based on the second thinning precision to obtain multiple third data volumes, and then performing intersection on these multiple third data volumes to obtain the second intersection graph are the same as steps 103, and will not be repeated here.

[0123] Step 105: The computer device overlays the first and second intersection images to obtain the third intersection image.

[0124] In this step, the third intersection map is obtained by superimposing the first intersection map and the second intersection map, which realizes the superposition of the two thinning accuracies, makes full use of the results of the previous intersection, and improves the thinning accuracy; and avoids the reduction in thinning efficiency caused by the excessive thinning accuracy due to the superposition of the two thinning accuracies in the second thinning.

[0125] Step 106: If the distribution accuracy of multiple data in the third intersection graph meets the first preset condition, the computer device determines the third intersection graph as the target intersection graph of the area to be studied.

[0126] The first preset condition is the preset data distribution accuracy of the multiple overlaid intersection maps, which can be set and changed as needed. In this embodiment, the final target intersection map is obtained by using intersection maps obtained through two separate thinning processes, thus improving the overall accuracy of the target intersection map.

[0127] Step 107: If the distribution accuracy of multiple data in the third intersection graph does not meet the first preset condition, the computer device re-executes the step of determining the second thinning accuracy of multiple first data volumes until a target intersection graph whose distribution accuracy of multiple data meets the first preset condition is obtained. The target intersection graph is the intersection graph finally obtained by superimposing the intersection graphs sequentially.

[0128] In one implementation, the computer device overlays the intersection graphs obtained from each intersection to obtain the target intersection graph. For example, if the target intersection graph is a intersection graph obtained after three thinning operations, then the intersection graphs of the multiple second data volumes obtained from the first thinning, the intersection graphs of the multiple third data volumes obtained from the second thinning, and the intersection graphs of the multiple fourth data volumes obtained from the third thinning are all overlaid together to obtain the target intersection graph.

[0129] In another implementation, the computer device overlays the cross-plot obtained from each thinning of multiple data volumes with the cross-plot obtained from the previous thinning and the previous cross-plot, to obtain the target cross-plot. For example, if the target cross-plot is obtained after three thinning operations, then the cross-plot obtained from the third thinning of multiple fourth data volumes is overlaid with the cross-plot obtained from the first thinning of multiple second data volumes and the cross-plot obtained from the second thinning of multiple third data volumes to obtain the target cross-plot.

[0130] The computer equipment overlays each intersection graph together, and the target location points in the data grid are also overlaid accordingly. In some extreme cases, data for every location point in the data grid is extracted, in which case all data in the first data volume is used for intersection, such as... Figure 9 As shown, all data were selected for intersection.

[0131] In this embodiment of the application, by setting up a multi-level, multi-precision thinning process, the adaptive adjustment of the thinning degree is realized. Through multiple intersections, the amount of data in each intersection is reduced, thus ensuring the efficiency of each intersection. Furthermore, through multiple intersections, the accuracy of the target intersection map obtained by the intersection is guaranteed.

[0132] This application provides a cross-plotting method for seismic data. This method improves the cross-plotting efficiency of multiple data volumes by performing cross-plotting on multiple thinned data volumes. Furthermore, if the data distribution accuracy in the cross-plot obtained from the cross-plotting of multiple thinned data volumes does not meet a first preset condition, the method can further thin the multiple data volumes and overlay the cross-plot obtained from the cross-plotting of the further thinned data volumes with the previously obtained cross-plot to improve the data distribution accuracy in the final target cross-plot. In this way, the method improves the cross-plotting efficiency of multiple data volumes while ensuring the data distribution accuracy of the target cross-plot.

[0133] It should be noted that the computer device performs intersection of multiple data volumes through steps 101-107 above to obtain the target intersection graph. The computer device also optimizes the target intersection graph through steps 108-110.

[0134] Step 108: The computer device determines the display level of each data point in the target intersection graph.

[0135] Step 108 is implemented as follows: The computer device determines the display frequency of each data point. For each data point, if the display frequency of the data point is greater than a first frequency, the computer device determines the display level of the data point to be the first level; if the display frequency of the data point is not greater than the first frequency but greater than a second frequency, the computer device determines the display level of the data point to be the second level; if the display frequency of the data point is not greater than the second frequency, the computer device determines the display level of the data point to be the third level, where the first frequency is greater than the second frequency.

[0136] It should be noted that data points are the location points of data on the first cross-plot. Due to the varying degrees of overlap (display density) of each data point, their display frequency will differ. Before determining the display level of a data point, it is necessary to determine the range of display frequencies for all data points on the entire target cross-plot to determine the display frequency corresponding to each display level. For example, data points with a display frequency greater than 80% are defined as Level 1, representing high-frequency points. Data points with a display frequency no greater than 80% but greater than 60% are defined as Level 2, representing mid-frequency points. Data points with a display frequency less than 60% are defined as Level 3, representing low-frequency points.

[0137] It should be noted that the number of display levels for multiple data points can be set as needed; in other embodiments, the multiple display levels include not only the first level, the second level, and the third level, but also the fourth level, the fifth level, the sixth level, etc.

[0138] Step 109: The computer device adjusts the display size of the data points based on the display level to obtain multiple data points with different display sizes.

[0139] The computer equipment sets different display sizes for data points at different display levels. Data points with higher display levels have larger display sizes, while data points with lower display levels have smaller display sizes. The display size of each data point at each display level can be set and changed as needed; for example, if the display size of the first-level data point is 1, then the display size of the second-level data point is 0.5, and the display size of the third-level data point is 0.2.

[0140] In some embodiments, the computer device can also adjust the shape of the data points according to the geological segment they correspond to, in order to analyze that geological segment. For example, data points for sandstone segments, mudstone segments, and shale segments can be represented by triangles, circles, and squares, respectively. Figure 10 As shown.

[0141] In this step, the display size of the data points is adjusted based on the display level of the data points, thereby normalizing all data points based on the display frequency of each location point. This enables the classification of all data points, making the display of the target intersection map more refined and clear, and facilitating the analysis and research of the study area based on the target intersection map.

[0142] Step 110: The computer device determines a display cross plot consisting of multiple data points of the same display size. The display cross plot is used for reservoir prediction in the area under study.

[0143] For example, a computer device may combine multiple data points corresponding to the display size at the first level into a display intersection diagram, and combine multiple data points corresponding to the display size at the second level into a display intersection diagram, etc.

[0144] Display cross plots are used for reservoir prediction in the area under study. For example, on a display cross plot used to determine oil-water distribution, if the data points representing oil distribution below a certain boundary are displayed in larger sizes, it indicates a higher display density of data points at that location. If the data points representing water distribution above the boundary are displayed in larger sizes, it indicates a higher display density of data points at that location, thus confirming that the boundary as an oil-water interface.

[0145] Because the amount of data from multiple data volumes intersecting is enormous, overlapping may occur at certain locations on the target intersection map, hindering intersection analysis. However, in this embodiment, by automatically selecting and adjusting the display size of data points, the distribution of data points can be visually assessed based on the display size, enabling optimized analysis of the data points and thus improving the efficiency of using the target intersection map.

[0146] It should be noted that the seismic data intersection method provided in this application embodiment can be used for reservoir prediction in the field of oil and gas exploration and development. It provides a user-friendly analysis tool for quality control of seismic inversion interpretation processing, quality control of seismic inversion results, and qualitative and quantitative interpretation of seismic inversion results. This enables quantitative characterization of reservoirs, facilitates reservoir optimization, and helps with well location deployment, reducing exploration and development risks. It has broad application prospects.

[0147] This application also provides a seismic data intersection device, see [link to relevant documentation]. Figure 11 The device includes:

[0148] The first determining module 1101 is used to determine multiple first data volumes of the region to be studied;

[0149] The first processing module 1102 is used to determine the first thinning precision of multiple first data volumes, and based on the first thinning precision, to thin the multiple first data volumes respectively to obtain multiple second data volumes, and to perform intersection on the multiple second data volumes to obtain a first intersection diagram.

[0150] The second processing module 1103 is used to determine the second thinning precision of multiple first data volumes, and based on the second thinning precision, to thin the multiple first data volumes respectively to obtain multiple third data volumes, and to perform intersection on the multiple third data volumes to obtain a second intersection diagram.

[0151] Overlay module 1104 is used to overlay the first intersection image and the second intersection image to obtain the third intersection image;

[0152] The second determining module 1105 is used to determine the third intersection map as the target intersection map of the area to be studied when the distribution accuracy of multiple data in the third intersection map meets the first preset condition.

[0153] The first execution module 1106 is used to re-execute the step of determining the second thinning precision of multiple first data volumes when the distribution precision of multiple data in the third intersection graph does not meet the first preset condition, until a target intersection graph whose distribution precision of multiple data meets the first preset condition is obtained. The target intersection graph is the intersection graph finally obtained by sequentially superimposing intersection graphs.

[0154] In one possible implementation, the first processing module 1102 is used for:

[0155] Determine the initial thinning precision of multiple first data volumes, the first data volume of multiple first data volumes, and multiple seismic facies types of the area to be studied;

[0156] Determine the weight value for each seismic phase type and the corresponding second data volume for each seismic phase type;

[0157] For each first data volume, determine the ratio of the first data volume to the initial thinning precision to obtain the target data volume to be extracted from the first data volume;

[0158] For each seismic facies type, determine the weight values ​​of the target data volume and the seismic facies type, obtain the extraction quantity corresponding to the seismic facies type, determine the quotient of the second data volume and the extraction quantity, and obtain the first thinning precision of the seismic facies type.

[0159] In one possible implementation, the first processing module 1102 is used for:

[0160] For each first data volume, based on the first thinning precision of multiple seismic facies types, the data in the first data volume corresponding to each seismic facies type is thinned to obtain the second data volume corresponding to each seismic facies type.

[0161] The second data volume corresponding to each seismic phase type is combined to obtain the second data volume after the first data volume is thinned.

[0162] In one possible implementation, the device further includes:

[0163] The second execution module is used to perform the step of determining the second thinning precision of multiple first data volumes when the distribution precision of multiple data in the first intersection graph does not meet the second preset condition.

[0164] The third determining module is used to determine the first intersection map as the target intersection map of the area to be studied, provided that the distribution accuracy of multiple data in the first intersection map meets the second preset condition.

[0165] In one possible implementation, the first processing module 1102 is used for:

[0166] For each first data volume, based on the first thinning precision, all data in the first data volume is divided into multiple data grids, and the amount of data included in each data grid is the same as the amount of data corresponding to the first thinning precision.

[0167] Determine the data at the target location points in each data grid as the target data to be extracted;

[0168] The target data from multiple data grids are combined to obtain a second data volume after the first data volume is thinned.

[0169] In one possible implementation, the device further includes:

[0170] The fourth determination module is used to determine the display level of each data point in the target intersection diagram;

[0171] The adjustment module is used to adjust the display size of data points based on the display level, so as to obtain multiple data points with different display sizes;

[0172] The fifth determination module is used to determine the display cross plot composed of multiple data points of the same display size. The display cross plot is used for reservoir prediction in the area under study.

[0173] In one possible implementation, the fourth determining module is used for:

[0174] Determine the display frequency for each data point;

[0175] For each data point, if the display frequency of the data point is greater than the first frequency, the display level of the data point is determined to be the first level;

[0176] If the display frequency of the data point is no greater than the first frequency but greater than the second frequency, the display level of the data point is determined to be the second level.

[0177] If the display frequency of the data point is no greater than the second frequency, the display level of the data point is determined to be the third level, and the first frequency is greater than the second frequency.

[0178] Figure 12 This illustration shows a structural block diagram of a computer device 1200 provided in an exemplary embodiment of this application. The computer device 1200 may be a portable mobile computer device, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The computer device 1200 may also be referred to as a user device, portable computer device, laptop computer device, desktop computer device, or other names.

[0179] Typically, computer device 1200 includes a processor 1201 and a memory 1202.

[0180] Processor 1201 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1201 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1201 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1201 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 1201 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0181] The memory 1202 may include one or more computer-readable storage media, which may be non-transitory. The memory 1202 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1202 are used to store at least one instruction, which is executed by the processor 1201 to implement the seismic data intersection method provided in the method embodiments of this application.

[0182] In some embodiments, the computer device 1200 may optionally include a peripheral device interface 1203 and at least one peripheral device. The processor 1201, memory 1202, and peripheral device interface 1203 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1203 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 1204, a display screen 1205, a camera assembly 1206, an audio circuit 1207, a positioning assembly 1208, and a power supply 1209.

[0183] Peripheral device interface 1203 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1201 and memory 1202. In some embodiments, processor 1201, memory 1202 and peripheral device interface 1203 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1201, memory 1202 and peripheral device interface 1203 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0184] The radio frequency (RF) circuit 1204 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1204 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1204 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 1204 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1204 can communicate with other computer devices via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1204 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0185] Display screen 1205 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1205 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1201 for processing. In this case, display screen 1205 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 1205, disposed on the front panel of computer device 1200; in other embodiments, there may be at least two display screens, disposed on different surfaces of computer device 1200 or in a folded design; in still other embodiments, display screen 1205 may be a flexible display screen, disposed on a curved or folded surface of computer device 1200. Furthermore, display screen 1205 may also be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 1205 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0186] The camera assembly 1206 is used to acquire images or videos. Optionally, the camera assembly 1206 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the computer device, and the rear-facing camera is located on the back of the computer device. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1206 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0187] The audio circuit 1207 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1201 for processing, or input to the radio frequency circuit 1204 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the computer device 1200. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1201 or the radio frequency circuit 1204 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1207 may also include a headphone jack.

[0188] The positioning component 1208 is used to locate the current geographical location of the computer device 1200 in order to enable navigation or LBS (Location Based Service). The positioning component 1208 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, or Russia's Galileo system.

[0189] Power supply 1209 is used to supply power to the various components in computer device 1200. Power supply 1209 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1209 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0190] In some embodiments, the computer device 1200 further includes one or more sensors 1120. The one or more sensors 1120 include, but are not limited to, an accelerometer 1211, a gyroscope 1212, a pressure sensor 1213, a fingerprint sensor 1214, an optical sensor 1215, and a proximity sensor 1216.

[0191] Accelerometer 1211 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by computer device 1200. For example, accelerometer 1211 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 1201 can control display screen 1205 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 1211. Accelerometer 1211 can also be used for games or for acquiring user motion data.

[0192] The gyroscope sensor 1212 can detect the orientation and rotation angle of the computer device 1200. The gyroscope sensor 1212 can work in conjunction with the accelerometer sensor 1211 to collect 3D motion data from the user on the computer device 1200. Based on the data collected by the gyroscope sensor 1212, the processor 1201 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0193] Pressure sensor 1213 can be disposed on the side bezel of computer device 1200 and / or on the lower layer of display screen 1205. When pressure sensor 1213 is disposed on the side bezel of computer device 1200, it can detect the user's grip signal on computer device 1200, and processor 1201 can perform left / right hand recognition or quick operation based on the grip signal collected by pressure sensor 1213. When pressure sensor 1213 is disposed on the lower layer of display screen 1205, processor 1201 can control operable controls on the UI interface based on the user's pressure operation on display screen 1205. Operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0194] The fingerprint sensor 1214 is used to collect a user's fingerprint. The processor 1201 identifies the user based on the fingerprint collected by the fingerprint sensor 1214, or vice versa. When the user's identity is identified as trusted, the processor 1201 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 1214 can be located on the front, back, or side of the computer device 1200. When the computer device 1200 has physical buttons or a manufacturer's logo, the fingerprint sensor 1214 can be integrated with the physical buttons or the manufacturer's logo.

[0195] The optical sensor 1215 is used to collect ambient light intensity. In one embodiment, the processor 1201 can control the display brightness of the display screen 1205 based on the ambient light intensity collected by the optical sensor 1215. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1205 is increased; when the ambient light intensity is low, the display brightness of the display screen 1205 is decreased. In another embodiment, the processor 1201 can also dynamically adjust the shooting parameters of the camera assembly 1206 based on the ambient light intensity collected by the optical sensor 1215.

[0196] The proximity sensor 1216, also known as a distance sensor, is typically installed on the front panel of the computer device 1200. The proximity sensor 1216 is used to detect the distance between the user and the front of the computer device 1200. In one embodiment, when the proximity sensor 1216 detects that the distance between the user and the front of the computer device 1200 is gradually decreasing, the processor 1201 controls the display screen 1205 to switch from a screen-on state to a screen-off state; when the proximity sensor 1216 detects that the distance between the user and the front of the computer device 1200 is gradually increasing, the processor 1201 controls the display screen 1205 to switch from a screen-off state to a screen-on state.

[0197] Those skilled in the art will understand that Figure 12 The structure shown does not constitute a limitation on the computer device 1200 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0198] This application also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to perform the operation of the seismic data intersection method of any of the above implementations.

[0199] This application also provides a computer program product, which includes at least one instruction stored in a computer-readable storage medium. A processor of a computer device reads the at least one instruction from the computer-readable storage medium and executes the at least one instruction, causing the computer device to load and execute the operation performed by the seismic data intersection method to implement any of the above implementations.

[0200] In some embodiments, the computer program involved in the present application embodiments may be deployed and executed on a computer device, or executed on multiple computer devices located in one location, or executed on multiple computer devices distributed in multiple locations and interconnected through a communication network. Multiple computer devices distributed in multiple locations and interconnected through a communication network may constitute a blockchain system.

[0201] This application provides a cross-plotting method for seismic data. This method improves the cross-plotting efficiency of multiple data volumes by performing cross-plotting on multiple thinned data volumes. Furthermore, if the data distribution accuracy in the cross-plot obtained from the cross-plotting of multiple thinned data volumes does not meet a first preset condition, the method can further thin the multiple data volumes and overlay the cross-plot obtained from the cross-plotting of the further thinned data volumes with the previously obtained cross-plot to improve the data distribution accuracy in the final target cross-plot. In this way, the method improves the cross-plotting efficiency of multiple data volumes while ensuring the data distribution accuracy of the target cross-plot.

[0202] The above are merely optional embodiments of this application and are 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 intersecting seismic data, characterized in that, The method includes: Identify multiple first data volumes for the region to be studied; A first thinning precision is determined for the plurality of first data volumes. Based on the first thinning precision, the plurality of first data volumes are thinned to obtain a plurality of second data volumes. The plurality of second data volumes are then intersected to obtain a first intersection diagram. A second thinning precision is determined for the plurality of first data volumes. Based on the second thinning precision, the plurality of first data volumes are thinned to obtain a plurality of third data volumes. The plurality of third data volumes are then intersected to obtain a second intersection map. The process of determining the second thinning precision is the same as the process of determining the first thinning precision. When determining the second thinning precision, the first data volume of the plurality of first data volumes, the plurality of seismic facies types of the area under study, the weight value of each seismic facies type, and the second data volume corresponding to each seismic facies type are determined during the process of determining the first thinning precision. Whether the initial thinning precision of the plurality of first data volumes corresponding to the second thinning precision is the same as the initial thinning precision corresponding to the first thinning precision is set as needed. If the initial thinning precision corresponding to the second thinning precision is the same as the initial thinning precision corresponding to the first thinning precision, then the second thinning precision is the same as the first thinning precision, and the target location points of the data grids with the second thinning precision and the first thinning precision are different. By overlaying the first intersection image and the second intersection image, a third intersection image is obtained; If the distribution accuracy of multiple data in the third intersection map meets the first preset condition, the third intersection map is determined to be the target intersection map of the region to be studied. If the distribution precision of multiple data in the third intersection graph does not meet the first preset condition, the step of determining the second thinning precision of the multiple first data volumes is re-executed until a target intersection graph whose distribution precision of multiple data meets the first preset condition is obtained. The target intersection graph is the intersection graph finally obtained by sequentially superimposing intersection graphs. Determining the first thinning precision of the plurality of first data volumes includes: Determine the initial thinning precision of the plurality of first data volumes, the first data volume of the plurality of first data volumes, and the plurality of seismic facies types of the region to be studied; Determine the weight value for each seismic phase type and the corresponding second data volume for each seismic phase type; For each first data volume, determine the ratio of the first data volume to the initial thinning precision to obtain the target data volume to be extracted from the first data volume; For each seismic facies type, determine the weight value between the target data volume and the seismic facies type, obtain the extraction quantity corresponding to the seismic facies type, determine the quotient of the second data volume and the extraction quantity, and obtain the first thinning precision of the seismic facies type.

2. The method according to claim 1, characterized in that, Based on the first thinning precision, the plurality of first data volumes are thinned to obtain a plurality of second data volumes, including: For each first data volume, based on the first thinning precision of the multiple seismic facies types, the data in the first data volume corresponding to each seismic facies type is thinned to obtain the second data volume corresponding to each seismic facies type. The second data volume corresponding to each seismic phase type is combined to obtain the second data volume after the first data volume is thinned.

3. The method according to claim 1, characterized in that, The method further includes: If the distribution accuracy of multiple data in the first intersection graph does not meet the second preset condition, the step of determining the second thinning accuracy of the multiple first data volumes is executed. If the distribution accuracy of multiple data in the first intersection map meets the second preset condition, the first intersection map is determined to be the target intersection map of the region to be studied.

4. The method according to claim 1, characterized in that, Based on the first thinning precision, the plurality of first data volumes are thinned to obtain a plurality of second data volumes, including: For each first data volume, based on the first thinning precision, all data of the first data volume is divided to obtain multiple data grids, and the amount of data included in each data grid is the same as the amount of data corresponding to the first thinning precision. Determine the data at the target location points in each data grid as the target data to be extracted; By combining target data from multiple data grids, a second data volume is obtained after thinning the first data volume.

5. The method according to claim 1, characterized in that, The method further includes: For each data point in the target intersection graph, determine the display level of the data point; Based on the display level, the display size of the data points is adjusted to obtain multiple data points with different display sizes; A display cross plot consisting of multiple data points of the same display size is determined, and the display cross plot is used to predict reservoirs in the area under study.

6. A seismic data intersection device, characterized in that, The device includes: The first determining module is used to determine multiple first data volumes of the region to be studied; A first processing module is used to determine the first thinning precision of the plurality of first data volumes, and based on the first thinning precision, to thin the plurality of first data volumes respectively to obtain a plurality of second data volumes, and to perform intersection on the plurality of second data volumes to obtain a first intersection diagram. The second processing module is used to determine the second thinning precision of the plurality of first data volumes, and based on the second thinning precision, to thin the plurality of first data volumes respectively to obtain a plurality of third data volumes, and to perform intersection on the plurality of third data volumes to obtain a second intersection map; the process of determining the second thinning precision is the same as the process of determining the first thinning precision; when determining the second thinning precision, the first data volume of the plurality of first data volumes, the plurality of seismic facies types of the area under study, the weight value of each seismic facies type, and the second data volume corresponding to each seismic facies type are determined in the process of determining the first thinning precision; whether the initial thinning precision of the plurality of first data volumes corresponding to the second thinning precision is the same as the initial thinning precision corresponding to the first thinning precision is set as needed; if the initial thinning precision corresponding to the second thinning precision is the same as the initial thinning precision corresponding to the first thinning precision, then the second thinning precision is the same as the first thinning precision, and the target location points of the data grids of the second thinning precision and the first thinning precision are different. The overlay module is used to overlay the first intersection map and the second intersection map to obtain a third intersection map; The second determining module is used to determine the third intersection map as the target intersection map of the region to be studied when the distribution accuracy of multiple data in the third intersection map meets the first preset condition. The first execution module is used to re-execute the step of determining the second thinning precision of the multiple first data volumes when the distribution precision of multiple data in the third intersection graph does not meet the first preset condition, until a target intersection graph whose distribution precision of multiple data meets the first preset condition is obtained. The target intersection graph is the intersection graph finally obtained by sequentially superimposing intersection graphs. The first processing module is used for: Determine the initial thinning precision of the plurality of first data volumes, the first data volume of the plurality of first data volumes, and the plurality of seismic facies types of the region to be studied; Determine the weight value for each seismic phase type and the corresponding second data volume for each seismic phase type; For each first data volume, determine the ratio of the first data volume to the initial thinning precision to obtain the target data volume to be extracted from the first data volume; For each seismic facies type, determine the weight value between the target data volume and the seismic facies type, obtain the extraction quantity corresponding to the seismic facies type, determine the quotient of the second data volume and the extraction quantity, and obtain the first thinning precision of the seismic facies type.

7. A computer device, characterized in that, The computer device includes one or more processors and one or more memories, wherein at least one instruction is stored in the one or more memories, and the at least one instruction is loaded and executed by the one or more processors to perform the operation performed by the seismic data intersection method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, which is loaded and executed by a processor to perform the operation of the seismic data intersection method as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes at least one instruction stored in a computer-readable storage medium, a processor of a computer device reads the at least one instruction from the computer-readable storage medium, and the processor executes the at least one instruction to cause the computer device to load and execute, thereby performing the operation performed by the seismic data intersection method as described in any one of claims 1 to 5.

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