Method for depicting boundary of sedimentary system by heavy mineral combination based on intelligent sensing system

Abstract

The invention discloses a method for depicting the boundary of a sedimentary system through a heavy mineral combination based on an intelligent sensing system, and relates to the technical field of geological exploration, and the method comprises the steps: obtaining the heavy mineral content data of a sedimentary rock sample collected by a target layer section and a peripheral exposed section of the target layer section in a research area through the intelligent sensing system; carrying out statistics on the contents of various heavy minerals in the collected samples, calculating the percentage of each heavy mineral, and carrying out data arrangement to form a data table; and importing the heavy mineral content data into data analysis software, carrying out Q-type clustering analysis, and classifying according to the similarity between samples. According to the method, the heavy mineral content data of the sedimentary rock sample is obtained through the uniformly distributed sampling points, compared with traditional single-point sample analysis, the spatial distribution characteristics of a material source system in a research area can be more comprehensively reflected, the boundary of a sedimentary system can be accurately divided, the recognition precision of sedimentary facies distribution is improved, and the analysis accuracy of the sedimentary facies is improved. And more detailed basic data are provided for geological research and resource exploration.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system. Background Art

[0002] With the continuous deepening of geological research, the detailed characterization of sedimentary facies has become increasingly important. The type and distribution range of sedimentary facies directly affect the reservoir quality and the generation, migration, and accumulation of oil and gas in sedimentary basins. Currently, for sediment provenance identification (including identifying the migration direction of sediments and the source of the parent rock of sediments) in sedimentary basins, traditional methods (such as detrital zircon U-Pb dating, heavy mineral assemblage analysis, geochemical element ratios, hydrological methods, and sedimentological methods) can provide some provenance information, but they have the following technical problems and shortcomings: (1) Insufficient spatial resolution: Existing methods mostly rely on single-point sample analysis, which makes it difficult to systematically characterize the spatial distribution characteristics of the provenance system, especially the precise demarcation of the provenance boundary; (2) Some methods are costly and time-consuming: U-Pb dating of detrital zircons requires complex experimental procedures and high equipment costs, and requires steps such as crushing rock samples. The analysis cycle is long and cannot meet the needs of large-scale exploration. (3) Lack of quantitative boundary characterization methods: Existing technologies mostly rely on qualitative analysis and lack quantitative source boundary characterization methods driven by data algorithms, which makes it difficult to meet the needs of high-precision exploration. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for depicting the boundary of a sedimentary system based on heavy mineral combinations of an intelligent sensing system, so as to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for depicting sedimentary system boundaries using heavy mineral assemblages based on an intelligent sensing system comprises the following steps: S1: Use the intelligent sensing system to obtain the heavy mineral content data of sedimentary rock samples collected from the target layer (the target layer in the sedimentary basin) and its surrounding exposed sections in the study area, ensuring that the sample collection points are evenly distributed; S2: Statistic the content of various heavy minerals in the collected samples, calculate the percentage of each heavy mineral, and organize the data to form a detailed data table; S3: Import the heavy mineral content data into the data analysis software and perform Q-type cluster analysis to classify the samples according to their similarities and identify sample groups with similar heavy mineral composition characteristics; S4: The heavy mineral content in each sample is plotted as a pie chart of the percentage of heavy mineral content, projected onto a plane map according to the sampling point location, and divided into zones based on the clustering results to outline the distribution range of each heavy mineral combination; S5: Calculate the ZTR index of each sample based on the percentage of heavy mineral content to analyze the maturity of sediment composition and provenance characteristics. A higher ZTR index indicates a longer sediment transport time or a stronger sorting effect. S6: Use Kriging interpolation to draw the spatial distribution map of the ZTR index, combine it with the edge detection algorithm to extract the provenance boundary line, and combine it with geological background correction to generate the provenance system boundary map; S7: Combine the results of single factor analysis with the characteristics of heavy mineral combinations and the ZTR index, and perform a comprehensive overlay analysis to finely characterize the sedimentary facies type and distribution range of the target interval.

[0005] A further improvement of the technical solution of the present invention is that: S1 specifically includes: Based on geological data, topography, and existing research results, the study area was divided according to geological structural units and sedimentary environments to ensure that each sub-area can represent different geological characteristics. Within each sub-area, a uniformly distributed sampling grid was designed to ensure that the sampling points covered all key areas, including the center, edge, and peripheral outcrop sections of the sedimentary basin. Each sampling point was accurately marked using a GPS positioning system, and its latitude and longitude coordinates were recorded. The sampling point information was then organized into a table, including point number, coordinates, and estimated sampling depth information. Deploy intelligent sensing systems at sampling points to collect sedimentary rock samples at predetermined depths at each sampling point, measure the heavy mineral content of the samples, and record the data. During the sampling process, the collection time, location, depth, lithology description, and measured heavy mineral content data of each sample are recorded; The data collected by the intelligent sensing system is imported into the computer to establish a database, and all data are verified to check the integrity and accuracy of the data, eliminate abnormal data points, ensure data quality, and then conduct preliminary statistical analysis on the data to calculate the percentage of heavy mineral content at each sampling point.

[0006] A further improvement of the technical solution of the present invention is that: S2 specifically includes: Integrate the raw data of various heavy mineral contents measured from samples collected from various sampling points, perform preliminary classification of the data according to the type of heavy minerals, and during the classification process, check the different heavy mineral content values ​​corresponding to each sampling point to ensure that the data corresponds to the sampling point one by one; For each sampling point, the content of each heavy mineral is divided by the total content of all heavy minerals at that sampling point to obtain the percentage of each heavy mineral; Based on the calculated percentage data of various heavy minerals, a data table containing the sampling point number, the name of each heavy mineral, and the corresponding percentage value is designed. The data is filled in the data table and the data table is formatted. The organized data table is then backed up and archived in the data warehouse.

[0007] A further improvement of the technical solution of the present invention is that: S3 specifically includes: Import the sorted heavy mineral content data into the data analysis software and use the "descriptive statistics" function of the data analysis software to view the data of each column and each row. Each column represents a heavy mineral and each row represents a sample. The Q-type cluster analysis method was selected in the data analysis software. The similarity between samples was automatically calculated using the Euclidean distance method. Cluster analysis was performed based on the similarity matrix to generate a cluster dendrogram to show the clustering relationship between samples. The number of clusters was determined based on the branching of the dendrogram, and the samples were divided into several categories. Analyze the clustering results and identify sample groups with similar heavy mineral combination characteristics. Each cluster group represents a potential provenance area or sedimentary environment. By comparing the geological background information, verify the rationality of the clustering results, check whether the cluster groups are consistent with the known geological units or sedimentary environments, and record the clustering results, including the cluster group to which each sample belongs.

[0008] A further improvement of the technical solution of the present invention is that the calculation expression for automatically calculating the similarity between samples is: ; Where, represents the Euclidean distance between sample i and sample j, represents the content of the kth heavy mineral in sample i, represents the content of the kth heavy mineral in sample j, m represents the number of heavy mineral types, and when the contents of two samples in all heavy mineral dimensions are exactly the same, the Euclidean distance is 0, indicating complete similarity.

[0009] A further improvement of the technical solution of the present invention is that: S4 specifically includes: Extract the percentage of heavy mineral content for each sample from the organized data table. Use Excel to draw a pie chart of the percentage of heavy mineral content for each sample. Each pie chart represents the proportion of different heavy minerals in a sample. Ensure that the pie chart is clear and has distinct colors to facilitate the distinction between different heavy minerals. Label each pie chart with the sample number and sampling point location information. Repeat this process for each sample to ensure that the chart is accurate and the information is complete. Use geographic information system (GIS) software to import basic geographic data of the study area, create a plan map of the study area, add a sampling point layer to the plan map, accurately enter the geographic coordinates of each sampling point, ensure that all sampling point location information is included, project the heavy mineral content percentage pie chart of each sample onto the plan map according to the sampling point location, ensure that the position of the heavy mineral content percentage pie chart is consistent with the geographic coordinates of the sampling point, and add a legend to the plan map to indicate the type of heavy mineral represented by different colors or symbols, and mark the sampling point number and geographic information; According to the results of Q-type cluster analysis, the samples on the plane map are divided into cluster groups. Each cluster group represents a heavy mineral combination characteristic and is distinguished by different colors. The distribution range of each cluster group is outlined on the plane map along the distribution boundary of the sampling points in the cluster group. After the outline is completed, the distribution range is reviewed to check whether it is consistent with the sampling point distribution and clustering results, and finally a heavy mineral combination distribution range map reflecting the distribution areas of different heavy mineral combinations is formed.

[0010] A further improvement of the technical solution of the present invention is that: S5 specifically includes: Extract and organize the percentage data of heavy mineral content of each sample, clarify the specific content values ​​of zircon (Z), tourmaline (T) and rutile (R) in the data, and use the set ZTR index calculation formula to substitute the corresponding data for each sample in turn to calculate the ZTR index of different samples in the target layer; The calculated ZTR index of each sample was summarized and organized, and a ZTR index distribution map was drawn to show the changes in the ZTR index of different samples. The differences in the ZTR index of samples at different sampling points were analyzed to explore the spatial variation pattern of sediment composition maturity. Combine the ZTR index analysis results with regional geological background data to comprehensively infer provenance characteristics and record the ZTR index analysis results, including the ZTR index value, maturity assessment and provenance interpretation of each sample; The ZTR index analysis results were integrated with the previous heavy mineral assemblage analysis and cluster analysis results to ensure that all data and analysis results supported each other, forming a complete sedimentary system analysis framework. Based on the integrated data and analysis results, a detailed analysis report was generated. The analysis report included the ZTR index calculation method, analysis results, interpretation of provenance characteristics, and a comprehensive analysis of the sedimentary system.

[0011] A further improvement of the technical solution of the present invention is that the calculation formula of the ZTR is as follows: ; Among them, the ZTR index is used to indicate the ratio of the sum of the contents of zircon + tourmaline + rutile to the sum of the contents of all transparent heavy minerals.

[0012] A further improvement of the technical solution of the present invention is that: S6 specifically includes: The ZTR index data and corresponding geographic coordinates of each sampling point were collected and organized. The data were imported into the geographic information system (GIS) software and a point layer was created. The Kriging interpolation method was selected for spatial interpolation. The variogram model was set according to the data characteristics to simulate the spatial variation of the ZTR index. The interpolation algorithm was then run to generate a grid surface of the ZTR index. A spatial distribution map of the ZTR index was drawn to show the spatial variation trend of the ZTR index in the study area. The spatial distribution map of the ZTR index obtained by Kriging interpolation was converted into a grayscale image format for edge detection. The edge detection algorithm (Canny algorithm) in the image processing software (Python OpenCV library) was used to process the image to highlight the boundaries of areas with obvious changes in the ZTR index and extract potential source boundary lines. Collect geological background data of the study area, including stratigraphic lithology, structural characteristics and paleogeographic environment information, compare and analyze the extracted provenance boundary lines with the geological background data, identify the boundary parts that do not conform to geological facts, and correct and adjust the boundary lines based on geological background knowledge to modify the direction and shape of the boundary lines. After repeated corrections, generate the final provenance system boundary map to clearly show the influence range of different provenance areas.

[0013] A further improvement of the technical solution of the present invention is that: S7 specifically includes: Collect and organize the single factor analysis results of the target layer, including contour maps of the target layer thickness, sand body thickness, and sand-to-formation ratio (i.e., the ratio of sandstone thickness to total formation thickness). At the same time, organize the analysis results of heavy mineral assemblage characteristics and ZTR index; Geographic Information System (GIS) software was used to convert the single factor analysis results, heavy mineral assemblage characteristics, and ZTR index data into a unified format. The single factor analysis results were preliminarily superimposed with the heavy mineral assemblage characteristics and ZTR index data to observe the spatial distribution relationship between different parameters. In GIS software, the results of the single-factor analysis were combined with the heavy mineral assemblage characteristic map and the ZTR index distribution map for comprehensive overlay analysis. By spatially superimposing multi-source data, the distribution characteristics of different sedimentary facies types were identified. Based on the overlay analysis results and combined with geological background knowledge, the sedimentary facies types of the target interval were classified, different sedimentary environments were identified, and their distribution ranges were determined. The rationality of the classified sedimentary facies types was then verified by comparing them with known geological data and research results. On the basis of superposition analysis, the distribution boundaries of each sedimentary facies type are finely outlined. According to the comprehensive analysis results of geological background and multi-source data, the boundaries are adjusted and optimized, and the finely portrayed sedimentary facies distribution range is plotted on the geological plane map of the study area to generate a clear sedimentary facies distribution map. Each sedimentary facies type and its main characteristics are marked on the map. Then, based on the comprehensive superposition analysis results and the sedimentary facies distribution map, a detailed analysis report is generated, including the analysis method, data source, basis for sedimentary facies type classification, distribution range and its geological significance, providing a comprehensive reference basis for geological research and resource exploration in the study area.

[0014] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared to the prior art: The present invention provides a method for characterizing the boundaries of sedimentary systems using heavy mineral assemblages based on an intelligent sensing system. By acquiring heavy mineral content data of sedimentary rock samples through evenly distributed sampling points, compared with traditional single-point sample analysis, it can more comprehensively reflect the spatial distribution characteristics of the provenance system in the study area, help to accurately delineate the boundaries of sedimentary systems, improve the accuracy of understanding sedimentary phase distribution, and provide more detailed basic data for geological research and resource exploration.

[0015] The present invention provides a method for characterizing the boundaries of sedimentary systems using heavy mineral assemblages based on an intelligent sensing system. Driven by data algorithms such as Q-type cluster analysis and Kriging interpolation, the method achieves quantitative characterization of the boundaries of sedimentary systems. Traditional methods mostly rely on qualitative analysis and lack precise boundary characterization methods. Quantitative boundary characterization can more accurately reflect the distribution range and provenance characteristics of sedimentary phases, meet the needs of high-precision exploration, and provide a reliable basis for the establishment of geological models and resource evaluation.

[0016] The present invention provides a method for characterizing the boundaries of sedimentary systems using heavy mineral assemblages based on an intelligent sensing system. This method combines the results of single-factor analysis, heavy mineral assembly characteristics, and multi-source data such as the ZTR index. Through comprehensive overlay analysis, it can more comprehensively reflect the characteristics of the sedimentary system, improve the accuracy and reliability of the analysis results, and help to more accurately characterize the sedimentary phase types and distribution ranges, providing a more comprehensive reference basis for geological research and resource exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 Flow chart of the method of the present invention; Figure 2This is the Q-type cluster pedigree diagram of heavy minerals in the Baikouquan Formation of the present invention; Figure 3 This is the plane characteristic diagram of the heavy mineral assemblage of the Baikouquan Formation of the present invention; Figure 4 This is the ZTR index contour map of the Baikouquan Formation of the present invention; Figure 5 The single factor contour map of the Baikouquan Formation of the present invention (a. contour map of Baikouquan Formation stratum thickness; b. contour map of Baikouquan Formation sand body thickness; c. contour map of Baikouquan Formation sand-to-ground ratio); Figure 6 This is the sedimentary facies distribution characteristic map of the Baikouquan Formation in the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1, as Figures 1 to 6 As shown, the present invention provides a method for depicting the boundary of a sedimentary system using heavy mineral combinations based on an intelligent sensing system, comprising the following steps: S1: Use the intelligent sensing system to obtain the heavy mineral content data of sedimentary rock samples collected from the target layer (the target layer in the sedimentary basin) and its peripheral exposed profiles in the study area, ensure that the sample collection points are evenly distributed, integrate geological data, topography and existing research results, divide the study area according to geological structural units and sedimentary environments, ensure that each sub-area can represent different geological characteristics, and design a uniformly distributed sampling grid in each sub-area to ensure that the sampling points cover all key areas, including the center, edge and peripheral exposed profiles of the sedimentary basin. Use the GPS positioning system to accurately mark each sampling point and record its latitude and longitude coordinates, and then Organize sampling point information into a table, including point number, coordinates, and estimated collection depth information; deploy intelligent sensing systems at sampling points; collect sedimentary rock samples at the predetermined depth for each sampling point; measure the heavy mineral content of the samples; and record the data. During the sampling process, record the collection time, location, depth, lithology description, and measured heavy mineral content data for each sample; import the data collected by the intelligent sensing system into a computer, establish a database, and verify all data to check the integrity and accuracy of the data, eliminate abnormal data points, ensure data quality, and then conduct preliminary statistical analysis of the data to calculate the percentage of heavy mineral content at each sampling point; S2: Statistics are made on the contents of various heavy minerals in the collected samples, the percentages of various heavy minerals are calculated, and the data are sorted to form a detailed data table. The original data of the contents of various heavy minerals measured from the samples collected from each sampling point are integrated. According to the types of heavy minerals, the data are preliminarily classified. During the classification process, the different heavy mineral content values ​​corresponding to each sampling point are checked to ensure that the data corresponds to the sampling point one by one. For each sampling point, the content of each heavy mineral is divided by the total content of all heavy minerals at the sampling point to obtain the percentage of each heavy mineral. After the calculation is completed, the results are checked to check whether there are calculation errors or abnormal values. If abnormal values ​​are found, the original data and calculation process need to be rechecked to find out the problem and correct it in time. According to the calculated percentage data of various heavy minerals, a data table containing the sampling point number, the name of each heavy mineral, and the corresponding percentage value is designed. The data is filled in the data table and the data table is formatted. The sorted data table is then backed up and archived in the data warehouse. Convert the content of various heavy minerals into their percentage in the sample using the following formula: ; Finally, the content percentage of a single heavy mineral is obtained; S3: Import the heavy mineral content data into the data analysis software and conduct Q-type cluster analysis. Classify the samples according to their similarity and identify sample groups with similar heavy mineral combination characteristics. Import the sorted heavy mineral content data into the data analysis software (SPSS). Check the integrity and accuracy of the data in the software to ensure that there are no missing values ​​or outliers. Use the "descriptive statistics" function of the data analysis software to view the data of each column and each row. Each column represents a heavy mineral and each row represents a sample. Select the Q-type cluster analysis method in the data analysis software. Q-type clustering is based on the similarity between samples and is suitable for heavy mineral combination analysis. The Euclidean distance method is used to automatically classify the heavy minerals. The similarity between samples is automatically calculated, and cluster analysis is performed based on the similarity matrix to generate a cluster dendrogram to show the cluster relationship between samples. Then, based on the branching of the dendrogram, the number of clusters is determined, and the samples are divided into several categories. The clustering results are analyzed to identify sample groups with similar heavy mineral assemblage characteristics. Each cluster group represents a potential provenance area or sedimentary environment. By comparing the geological background information, the rationality of the clustering results is verified, and whether the cluster group is consistent with the known geological unit or sedimentary environment is checked. The clustering results, including the cluster group to which each sample belongs, are recorded to provide a basis for subsequent provenance analysis and sedimentary system research; In addition, the calculation expression for automatically calculating the similarity between samples is: ; Where, represents the Euclidean distance between sample i and sample j, represents the content of the kth heavy mineral in sample i, represents the content of the kth heavy mineral in sample j, and m represents the number of heavy mineral types. When the contents of two samples in all heavy mineral dimensions are exactly the same, the Euclidean distance is 0, indicating complete similarity. When the difference in the contents of two samples in all heavy mineral dimensions is greater, the Euclidean distance is larger, indicating that the similarity between the samples is lower. S4: Draw the heavy mineral content in each sample as a pie chart of heavy mineral content percentage, project it onto the plane map according to the sampling point location, divide it into zones based on the clustering results, and outline the distribution range of various heavy mineral combinations. Extract the heavy mineral content percentage data of each sample from the sorted data table, and use drawing software (Excel) to draw a pie chart of heavy mineral content percentage for each sample. Each heavy mineral content percentage pie chart represents the content ratio of different heavy minerals in a sample. Ensure that the heavy mineral content percentage pie chart is clear and the colors are distinct to facilitate the distinction between different heavy minerals. Mark the sample number and sampling point location information on each heavy mineral content percentage pie chart. Repeat this operation for each sample to ensure that the chart is accurate and the information is complete. Use geographic information system (GIS) software to import the basic geographic data of the study area, create a plane map of the study area, add a sampling point layer to the plane map, and accurately enter the geographic coordinates of each sampling point to ensure that all sampling points are included. Location information, project the percentage pie chart of heavy mineral content of each sample onto the plane map according to the location of the sampling point, ensure that the location of the percentage pie chart of heavy mineral content is consistent with the geographical coordinates of the sampling point, and add a legend on the plane map to explain the types of heavy minerals represented by different colors or symbols, mark the sampling point number and geographical information, and divide the samples on the plane map into cluster groups according to the results of Q-type cluster analysis. Each cluster group represents a heavy mineral combination feature, which is distinguished by different colors, and outline the distribution range of each cluster group on the plane map along the distribution boundary of the sampling points in the cluster group to ensure that the boundary is clear and can accurately reflect the distribution area of ​​different heavy mineral combinations. After the outline is completed, the distribution range is reviewed to check whether it is consistent with the sampling point distribution and clustering results. If there is any unreasonableness, such as blurred boundaries or inconsistency with the actual situation, timely corrections are made to eventually form a heavy mineral combination distribution range map reflecting the distribution areas of different heavy mineral combinations; S5: Calculate the ZTR index of each sample based on the percentage of heavy mineral content, analyze the maturity of sediment composition and provenance characteristics. The higher the ZTR index, the longer the sediment has been transported or the stronger the sorting effect. Extract and organize the percentage data of heavy mineral content of each sample, clarify the specific content values ​​of zircon (Z), tourmaline (T) and rutile (R) in the data, and use the set ZTR index calculation formula to substitute the corresponding data for each sample in turn to calculate and obtain the ZTR index of different samples in the target layer. Among them, the ZTR index is used in sedimentology to An important parameter that characterizes the maturity of sediment composition and provenance characteristics. The principle is that during the sediment transportation process, unstable minerals (such as pyroxene and amphibole) are easily mechanically crushed or chemically decomposed, while ZTR (i.e., zircon (Z), tourmaline (T), and rutile (R)) minerals are retained due to their strong resistance to weathering. Therefore, the higher the proportion of ZTR minerals, the longer the sediment transportation time or the stronger the sorting effect. The calculated ZTR index of each sample is summarized and sorted to draw the ZTR An index distribution diagram displays the variation in the ZTR index of different samples. The maturity of sediment components is determined based on the range of ZTR index values. A higher ZTR index indicates a longer transport period or stronger sorting, and thus a higher sediment maturity. The differences in ZTR index among samples at different sampling points are analyzed to explore the spatial variation of sediment maturity. The ZTR index analysis results are combined with regional geological background data to comprehensively infer provenance characteristics. A high ZTR index indicates that the sediments originated from a stable provenance area and underwent multiple transport and redeposition processes, while a low ZTR index indicates that the sediments originated from an active tectonic area and were transported a short distance. The ZTR index analysis results are recorded, including the ZTR index value, maturity assessment, and provenance interpretation for each sample. The ZTR index analysis results are integrated with the previous heavy mineral assemblage and cluster analysis results to ensure that all data and analysis results support each other, forming a complete sedimentary system analysis framework. A detailed analysis report is generated based on the integrated data and analysis results. The analysis report includes the ZTR index calculation method, analysis results, provenance interpretation, and a comprehensive analysis of the sedimentary system. In addition, the calculation formula of the ZTR is as follows: ; Among them, the ZTR index is used to indicate the ratio of the sum of the content of zircon + tourmaline + rutile to the sum of the content of all transparent heavy minerals; S6: Use Kriging interpolation to draw the spatial distribution map of ZTR index, combine the edge detection algorithm to extract the provenance boundary line, and combine the geological background correction to generate the provenance system boundary map, collect and organize the ZTR index data of each sampling point and the corresponding geographical location coordinates, import the data into the geographic information system (GIS) software and create a point layer, select Kriging interpolation method for spatial interpolation, set the variogram model according to the data characteristics to simulate the spatial variation law of ZTR index, and then run the interpolation algorithm to generate the grid surface of ZTR index, draw the spatial distribution map of ZTR index, show the spatial variation trend of ZTR index in the study area, and convert the spatial distribution map of ZTR index obtained by Kriging interpolation into Grayscale image format is used for edge detection. The edge detection algorithm (Canny algorithm) in image processing software (Python OpenCV library) is used to process the image, highlighting the boundaries of areas with obvious changes in ZTR index and extracting potential provenance boundaries. Geological background data of the study area, including stratigraphic lithology, structural characteristics and paleogeographic environment information, are collected. The extracted provenance boundaries are compared and analyzed with the geological background data to identify boundary portions that do not conform to geological facts. Based on the geological background knowledge, the boundaries are corrected and adjusted to correct the direction and shape of the boundaries. After repeated corrections, the final provenance system boundary map is generated, which clearly shows the influence range of different provenance areas. S7: Combine the single factor analysis results with the heavy mineral combination characteristics and ZTR index, conduct comprehensive overlay analysis, and finely characterize the sedimentary facies type and distribution range of the target layer. Collect and organize the single factor analysis results of the target layer, including the contour map of the formation thickness, sand body thickness and sand-to-formation ratio (i.e. the ratio of sandstone thickness to total formation thickness) of the target layer. At the same time, organize the analysis results of the heavy mineral combination characteristics and ZTR index. Among them, the formation thickness, sand body thickness and sand-to-formation ratio (i.e. the ratio of sandstone thickness to total formation thickness) of the target layer are selected as the main analysis parameters. By drawing the contour map of the main analysis parameters, the thickness distribution trend of the sediment and the enrichment area of ​​the sand body are intuitively reflected, thereby providing an important basis for the discrimination of the sedimentary facies type and the division of the distribution range. Use geographic information system (GIS) software to convert the single factor analysis results, heavy mineral combination characteristics and ZTR index data into a unified format, and preliminarily overlay the single factor analysis results with the heavy mineral combination characteristics and ZTR index data to observe the spatial distribution relationship between different parameters. In this paper, the results of single factor analysis are comprehensively superimposed with the heavy mineral combination characteristic map and ZTR index distribution map. The distribution characteristics of different sedimentary facies types are identified through the spatial superposition of multi-source data. According to the superposition analysis results and combined with the geological background knowledge, the sedimentary facies types of the target layer are divided, different sedimentary environments are identified, and their distribution ranges are determined. Then, the rationality of the divided sedimentary facies types is verified by comparing with known geological data and research results. On the basis of superposition analysis, the distribution boundaries of each sedimentary facies type are finely outlined. According to the comprehensive analysis results of geological background and multi-source data, the boundaries are adjusted and optimized. The finely portrayed sedimentary facies distribution range is plotted on the geological plane map of the study area to generate a clear sedimentary facies distribution map, and each sedimentary facies type and its main characteristics are marked on the map. Then, based on the comprehensive superposition analysis results and the sedimentary facies distribution map, a detailed analysis report is generated, including analysis methods, data sources, basis for sedimentary facies type division, distribution range and its geological significance, providing a comprehensive reference basis for geological research and resource exploration in the study area.

[0021] Example 2, as Figures 1 to 6 As shown, on the basis of Example 1, the present invention provides a technical solution: preferably, taking the sedimentary facies distribution of the clastic rock reservoir of the Baikouquan Formation in the Xiayan uplift and Shixi uplift of the Junggar Basin as an example, a comprehensive characterization of the provenance boundary of the Baikouquan Formation based on heavy mineral analysis and statistical algorithms is introduced, and combined with the single factor analysis of the Baikouquan Formation, the sedimentary facies distribution of the Baikouquan Formation is further clarified.

[0022] 1. Statistical analysis of heavy mineral data from the Baikouquan Formation The heavy mineral content of the Baikouquan Formation was calculated and converted into percentages. These percentages are tabulated (Table 1). The Baikouquan Formation contains 21 heavy mineral species, with data covering the Mahu Sag, Xiayan Uplift, and Shixi Uplift.

[0023] Table 1 Percentage of heavy mineral content in the Baikouquan Formation

[0024] Table 1 Percentage of heavy mineral content in the Baikouquan Formation (continued)

[0025] 2. Q-type cluster analysis of heavy minerals in the Baikouquan Formation The percentage of the above heavy mineral contents was imported into SPSS data analysis software, and the data of heavy minerals in the Baikouquan Formation were subjected to Q-type cluster analysis. Through the pedigree diagram, it can be found that the samples of each well point in the Baikouquan Formation can be divided into three categories, namely, category I, category II and category III ( Figure 2 ).

[0026] 3. Heavy mineral combination analysis After projecting the heavy mineral assemblage of the Baikouquan Formation onto a planar map, it was found that the provenance systems in the Xiayan Uplift and Shixi Uplift areas can be divided into three categories. The influence of provenance II starts from the southeastern part of the Wuxia Fault Zone and extends southeastward to the central part of the Mahu Sag. It then continues to expand southeastward to the vicinity of Well W18 in the Basong Uplift and the western sag of Well Pen 1. Due to the lack of sufficient heavy mineral data near the western sag of Well Pen 1, the termination position of this provenance is difficult to clearly define. However, this provenance did not affect the core areas of this study, the Xiayan Uplift and Shixi Uplift. Provenance I mainly affected the northeastern part of the Wuxia Fault Zone. Its effect extended southward along the Mahu Sag to the Xiayan Uplift and the western sag of Well Pen 1, and then southeastward to the Shixi Uplift. Its influence can also be clearly observed in Wells W9 and W10. Provenance III has the most extensive influence, starting from the northeastern part of the Mahu Sag, the Yingxi Sag, and the western part of the Shiyingtan Uplift, migrating southward along the northwest part of the Sangequan Uplift and the Xiayan Uplift, and finally reaching the Sannan Sag. It then continues to extend southeastward to the northwest part of the Shixi Uplift and the Dinan Uplift. Figure 3 In the study area, the radiation range of source I runs through the entire area, while source III only affects the northern part of the Xiayan uplift. At the same time, part of the study area is affected by the combined effects of two-way sources, showing mixed-source sedimentation ( Figure 3 ).

[0027] 4.ZTR Index Analysis The ZTR index contour map of the Baikouquan Formation shows that the study area exhibits obvious "two highs and two lows" characteristics. The "two highs" refer to the western part of Well W25 in the Xiayan area, the Well W28 area in the Xiayan uplift, and the southern part of the Shixi area. The ZTR index of these two areas is relatively large, indicating that the Baikouquan Formation sediments in these places are far away from the provenance area ( Figure 4 The “two lows” refer to the W31 and W26 well areas, where the ZTR index is relatively low, reflecting that the sediments in this area were close to the source during the deposition of the Baikouquan Formation ( Figure 4 ). From this, it can be judged that the Baikouquan Formation mainly presents three sediment migration paths, namely from the W26 well area to the W28 well area, from the western Xiayan uplift to the W27 well area, and from the W31 well area to the W11 well area in the northern Shixi uplift.

[0028] 5. Univariate analysis of Baikouquan group The thickness contour map of the Baikouquan Formation shows that the thickness of the formation is the greatest near the W30 well area, which is the sedimentary center of the Baikouquan Formation. In contrast, the formation thickness in the area where the W27 and W29 wells are located is relatively thin, reflecting that there was less sediment accumulation in this area during the deposition of the Baikouquan Formation. Figure 5 From the contour map of the sandstone thickness of the Baikouquan Formation, it can be seen that the sandstone thickness in the northern part of the Shixi area is relatively large. This is also the main sedimentary area of ​​the sand body of the Baikouquan Formation and the main development area of ​​the fan delta sedimentary system ( Figure 5 The sand-to-land ratio contour map shows that the western part of the Xiayan Uplift and the northern part of the Shixi Uplift have obvious high values, and show a trend of gradually decreasing from west to east and from northeast to southwest respectively ( Figure 5 ).

[0029] 6. Planar distribution of sedimentary facies of the Baikouquan Formation under the influence of sediment boundaries The sedimentary microfacies of the Baikouquan Formation show a more complex sedimentary pattern on the plane, with the main development of debris flow, braided channel, inter-channel, underwater distributary channel, underwater distributary channel and front fan delta mud sedimentary microfacies. Among them, the debris flow sedimentary microfacies is mainly distributed in the northern part of the Xiayan Uplift and Shixi Uplift, and the distribution direction is from north to south, indicating that this area experienced strong gravity flow sedimentation during the deposition of the Baikouquan Formation ( Figure 6). Braided channels and underwater distributary channels showed seven main migration pathways during this period. The channel in the western part of the Xiayan Uplift extended southeastward to the W27 well, reflecting a sedimentary trend from west to east; the channel system in the northern part of the Xiayan Uplift started from the northern and northeastern parts of the Xiayan Uplift, migrated in the south direction, and finally connected to the W25 well area and the W28 well area, indicating that the area was continuously supplied by the source from the north; the braided channels and underwater distributary channels in the Shixi area showed two distribution states, one of which was the W30 well area and the W31 well area, which extended from northeast to southwest; the other channel system started from the W33 well, extended in the south direction, and finally covered the W11 and W34 well areas ( Figure 6 ).

[0030] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for depicting sedimentary system boundaries using heavy mineral assemblages based on an intelligent sensing system, characterized in that: The following steps are involved: S1: Use the intelligent sensing system to obtain the heavy mineral content data of sedimentary rock samples collected from the target layer section and its surrounding exposed sections in the study area; S2: Statistic the content of various heavy minerals in the collected samples, calculate the percentage of each heavy mineral, and organize the data to form a data table; S3: Import the heavy mineral content data into the data analysis software and perform Q-type cluster analysis to classify the samples according to their similarities and identify sample groups with similar heavy mineral composition characteristics; S4: The heavy mineral content in each sample is plotted as a pie chart of the percentage of heavy mineral content, projected onto a plane map according to the sampling point location, and divided into zones based on the clustering results to outline the distribution range of each heavy mineral combination; S5: Calculate the ZTR index of each sample based on the percentage of heavy mineral content and analyze the maturity of sediment composition and provenance characteristics; S6: Use Kriging interpolation to draw the spatial distribution map of the ZTR index, combine it with the edge detection algorithm to extract the provenance boundary line, and combine it with geological background correction to generate the provenance system boundary map; S7: Combine the results of single factor analysis with the characteristics of heavy mineral combinations and the ZTR index, and perform a comprehensive overlay analysis to finely characterize the sedimentary facies type and distribution range of the target interval.

2. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 1, characterized in that: Said S1 specifically includes: Based on geological data, topography, and existing research results, the study area was divided according to geological structural units and sedimentary environments. Within each sub-area, a uniformly distributed sampling grid was designed to cover all key areas, including the center, edge, and peripheral outcrop sections of the sedimentary basin. Each sampling point was accurately marked using a GPS positioning system, and its longitude and latitude coordinates were recorded. The sampling point information was then organized into a table, including point number, coordinates, and estimated sampling depth information. Deploy intelligent sensing systems at sampling points to collect sedimentary rock samples at predetermined depths at each sampling point, measure the heavy mineral content of the samples, and record the data. During the sampling process, the collection time, location, depth, lithology description, and measured heavy mineral content data of each sample are recorded; The data collected by the intelligent sensing system is imported into the computer to establish a database, and all the data are verified. Then, a preliminary statistical analysis is performed on the data to calculate the percentage of heavy mineral content at each sampling point.

3. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 1, characterized in that: The S2 specifically includes: Integrate the raw data of various heavy mineral contents measured from samples collected from various sampling points, perform preliminary classification of the data according to the types of heavy minerals, and during the classification process, verify the different heavy mineral content values ​​corresponding to each sampling point; For each sampling point, the content of each heavy mineral is divided by the total content of all heavy minerals at that sampling point to obtain the percentage of each heavy mineral; Based on the calculated percentage data of various heavy minerals, a data table containing the sampling point number, the name of each heavy mineral, and the corresponding percentage value is designed. The data is filled in the data table and the data table is formatted. The organized data table is then backed up and archived in the data warehouse.

4. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 1, characterized in that: The S3 specifically includes: Import the sorted heavy mineral content data into the data analysis software and use the "descriptive statistics" function of the data analysis software to view the data of each column and each row. Each column represents a heavy mineral and each row represents a sample. The Q-type cluster analysis method was selected in the data analysis software. The similarity between samples was automatically calculated using the Euclidean distance method. Cluster analysis was performed based on the similarity matrix to generate a cluster dendrogram to show the cluster relationship between samples. The number of clusters was determined based on the branching of the dendrogram, and the samples were divided into several categories. Analyze the clustering results and identify sample groups with similar heavy mineral combination characteristics. Each cluster group represents a potential provenance area or sedimentary environment. By comparing the geological background information, verify the rationality of the clustering results, check whether the cluster groups are consistent with the known geological units or sedimentary environments, and record the clustering results, including the cluster group to which each sample belongs.

5. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 4, characterized in that: The calculation expression for automatically calculating the similarity between samples is: ； Where, represents the Euclidean distance between sample i and sample j, represents the content of the kth heavy mineral in sample i, represents the content of the kth heavy mineral in sample j, m represents the number of heavy mineral types, and when the contents of two samples in all heavy mineral dimensions are exactly the same, the Euclidean distance is 0, indicating complete similarity.

6. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 1, characterized in that: The S4 specifically includes: Extract the percentage of heavy mineral content of each sample from the organized data table, and use the drawing software to draw a pie chart of the percentage of heavy mineral content for each sample. Each pie chart represents the content ratio of different heavy minerals in a sample, and mark the sample number and sampling point location information on each pie chart. Repeat this operation for each sample; Use geographic information system software to import basic geographic data of the study area, create a plan map of the study area, add a sampling point layer to the plan map, accurately enter the geographic coordinates of each sampling point, project the percentage pie chart of heavy mineral content of each sample onto the plan map according to the sampling point location, and add a legend to the plan map to explain the types of heavy minerals represented by different colors or symbols, and mark the sampling point number and geographic information; According to the results of Q-type cluster analysis, the samples on the plane map are divided into cluster groups. Each cluster group represents a heavy mineral combination characteristic and is distinguished by different colors. The distribution range of each cluster group is outlined on the plane map along the distribution boundary of the sampling points in the cluster group. After the outline is completed, the distribution range is reviewed to check whether it is consistent with the sampling point distribution and clustering results, and finally a heavy mineral combination distribution range map reflecting the distribution areas of different heavy mineral combinations is formed.

7. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 1, characterized in that: The S5 specifically includes: Extract and organize the percentage data of heavy mineral content of each sample, clarify the specific content values ​​of zircon, tourmaline and rutile in the data, and use the set ZTR index calculation formula to substitute the corresponding data for each sample in turn to calculate the ZTR index of different samples in the target layer; The calculated ZTR index of each sample was summarized and organized, and a ZTR index distribution map was drawn to show the changes in the ZTR index of different samples. The differences in the ZTR index of samples at different sampling points were analyzed to explore the spatial variation pattern of sediment composition maturity. Combine the ZTR index analysis results with regional geological background data to comprehensively infer provenance characteristics and record the ZTR index analysis results, including the ZTR index value, maturity assessment and provenance interpretation of each sample; The ZTR index analysis results were integrated with the previous heavy mineral assemblage analysis and cluster analysis results to form a complete sedimentary system analysis framework. Based on the integrated data and analysis results, a detailed analysis report was generated. The analysis report included the ZTR index calculation method, analysis results, interpretation of provenance characteristics, and a comprehensive analysis of the sedimentary system.

8. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 7, characterized in that: The calculation formula of the ZTR is as follows: ； Among them, the ZTR index is used to indicate the ratio of the sum of the contents of zircon + tourmaline + rutile to the sum of the contents of all transparent heavy minerals.

9. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 1, characterized in that: The S6 specifically includes: The ZTR index data and corresponding geographic coordinates of each sampling point were collected and collated. In the geographic information system software, the data was imported and a point layer was created. The Kriging interpolation method was selected for spatial interpolation. The variogram model was set according to the data characteristics to simulate the spatial variation law of the ZTR index. Then, the interpolation algorithm was run to generate a grid surface of the ZTR index. The spatial distribution map of the ZTR index was drawn to show the spatial variation trend of the ZTR index in the study area. The spatial distribution map of the ZTR index obtained by Kriging interpolation is converted into a grayscale image format. The edge detection algorithm in the image processing software is used to process the image to highlight the boundaries of the areas where the ZTR index changes significantly and extract the potential source boundary lines. Collect geological background data of the study area, including stratigraphic lithology, structural characteristics and paleogeographic environment information, compare and analyze the extracted provenance boundary lines with the geological background data, identify the boundary parts that do not conform to geological facts, and correct and adjust the boundary lines based on geological background knowledge to modify the direction and shape of the boundary lines. After repeated corrections, generate the final provenance system boundary map.

10. The method for depicting sedimentary system boundaries using heavy mineral combinations based on an intelligent sensing system according to claim 1, characterized in that: The S7 specifically includes: Collect and organize the single factor analysis results of the target layer, including the contour map of the target layer thickness, sand body thickness and sand-to-formation ratio. At the same time, organize the analysis results of the heavy mineral combination characteristics and ZTR index. The single factor analysis results, heavy mineral assemblage characteristics, and ZTR index data were converted into a unified format using geographic information system software. The single factor analysis results were preliminarily superimposed with the heavy mineral assemblage characteristics and ZTR index data to observe the spatial distribution relationship between different parameters. In GIS software, the results of the single-factor analysis were combined with the heavy mineral assemblage characteristic map and the ZTR index distribution map for comprehensive overlay analysis. By spatially superimposing multi-source data, the distribution characteristics of different sedimentary facies types were identified. Based on the overlay analysis results and combined with geological background knowledge, the sedimentary facies types of the target interval were classified, different sedimentary environments were identified, and their distribution ranges were determined. The rationality of the classified sedimentary facies types was then verified by comparing them with known geological data and research results. On the basis of superposition analysis, the distribution boundaries of each sedimentary facies type are finely outlined. According to the comprehensive analysis results of geological background and multi-source data, the boundaries are adjusted and optimized. The finely portrayed sedimentary facies distribution range is plotted on the geological plane map of the study area to generate a clear sedimentary facies distribution map, and each sedimentary facies type and its main characteristics are marked on the map. Then, based on the comprehensive superposition analysis results and the sedimentary facies distribution map, a detailed analysis report is generated, including the analysis method, data source, basis for sedimentary facies type classification, distribution range and its geological significance.

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