Method and system for judging parent material type of over-mature hydrocarbon source rock

Abstract

The invention relates to the technical field of oil and gas geological exploration, in particular to an over-mature hydrocarbon source rock parent material type distinguishing method and system.The method comprises the following steps that regional deposition characteristics are studied, an over-mature hydrocarbon source rock deposition environment is recognized, principal elements and microelements are extracted in combination with a geological background, and component relevance is judged; and sorting representative sedimentary environment elements, and establishing a sedimentary environment associated element set. According to the method, accurate recognition of the sedimentary environment is achieved by extracting major elements and trace elements, representative elements are screened, proportional analysis is combined with the macerals, distribution characteristics of saprolite groups, exinite groups and the like are determined, the ratio relation between the macerals and inorganic elements is recognized, a ratio operation set is constructed, and the relation between the macerals and the elements is analyzed in a multi-dimensional mode. The relationship between parent material types and maceral characteristics is disclosed, a maceral type index model is constructed, hydrocarbon source rock parent material type division precision and sedimentary environment analysis systematicness are improved, and a scientific basis is provided for oil and gas resource exploration.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geological exploration, and particularly to a method and system for discriminating the kerogen type of over-mature source rocks. Background Art

[0002] The technical field of oil and gas geological exploration includes multiple links such as the exploration, evaluation, development, and utilization of oil and gas resources. Among them, oil and gas geological research is the key foundation. The core content of this technical field includes the formation mechanism, geochemical characteristics, hydrocarbon generation potential evaluation of source rocks, and the relationship with sedimentary environments. By analyzing the composition and distribution of sedimentary organic matter, the kerogen type of source rocks can be judged, the distribution range of oil and gas resources can be predicted, and a scientific basis can be provided for oil and gas exploration and development. The overall technical field of oil and gas geological exploration involves various research methods such as petrogeochemical analysis, sedimentary facies research, microscopic component observation of organic matter, stable isotope analysis, and elemental index evaluation, to analyze the internal relationship between sedimentary environments, thermal evolution stages, and organic matter, and provide support for the quantitative prediction of oil and gas resources and exploration planning.

[0003] Among them, the method for discriminating the kerogen type of over-mature source rocks refers to the problem that it is difficult to divide the kerogen types in highly evolved source rocks. Inorganic elemental indicators are used as an alternative means. By analyzing the quantitative relationship between elements related to sedimentary environments and the microscopic components of kerogen, an evaluation model for different kerogen types is established. The specific technical matters of the method include selecting elemental indicators suitable for the study area, combining the sedimentary environment and microscopic component characteristics of source rocks, analyzing the sapropel group, exinite group, inertinite group, and vitrinite group in kerogen, and using the quantitative relationship between elemental indicators and microscopic components to achieve the discrimination of the kerogen type of over-mature source rocks.

[0004] When the prior art discriminates the kerogen type of over-mature source rocks, due to the fact that the microscopic component characteristics of kerogen are greatly affected by thermal evolution, the correlation between the distribution law among microscopic components and the kerogen type is weakened, resulting in the difficulty of accurately distinguishing the kerogen type in highly evolved source rocks by traditional methods. The prior art mostly relies on the single analysis of microscopic component characteristics and lacks the comprehensive evaluation of the quantitative relationship between inorganic elements and microscopic components. This single-dimensional analysis method is difficult to obtain accurate judgment bases in complex sedimentary environments. In the analysis of sedimentary environments in the prior art, representative sedimentary environment elements are often not accurately screened, resulting in deviations in the study of the kerogen type of source rocks under specific geological backgrounds. In elemental index evaluation and microscopic component observation, the prior art lacks a unified quantitative model, and the proportional relationship of microscopic components cannot be effectively correlated to the overall analysis of geochemical characteristics, thereby limiting the reliability of kerogen type discrimination, easily leading to judgment deviations or omissions in the study of highly evolved source rocks, affecting the accurate prediction of the distribution range of oil and gas resources, and ultimately resulting in mistakes in exploration planning. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and a method and system for discriminating the parent material type of over-mature hydrocarbon source rocks are proposed.

[0006] To achieve the above object, the present invention adopts the following technical solution: A method for discriminating the parent material type of over-mature hydrocarbon source rocks, comprising the following steps: S1: By studying the regional sedimentary characteristics, identifying the sedimentary environment of over-mature hydrocarbon source rocks, extracting major elements and trace elements in combination with the geological background, judging the component correlation, sorting representative sedimentary environment elements, and establishing a set of sedimentary environment-related elements; S2: Based on the set of sedimentary environment-related elements, extracting the percentage content of macerals in medium- and low-mature hydrocarbon source rocks, capturing data of sapropel group, exinite group, vitrinite group and inertinite group, obtaining the proportional relationship of macerals, performing analysis of major elements, trace elements and rare earth elements, identifying the ratio relationship between macerals and inorganic elements, and constructing a data model of macerals and elements; S3: Based on the data model of macerals and elements, extracting the ratio of related elements, analyzing the relationship between sapropel group, exinite group, vitrinite group, inertinite group macerals and elements, constructing a set of maceral ratio operation, analyzing the regression relationship between the sapropel group ratio and the element ratio, identifying the parent material type and maceral characteristics, and generating a maceral regression correlation model; S4: Based on the maceral regression correlation model, measuring the ratios of sapropel group, exinite group, vitrinite group and inertinite group in over-mature hydrocarbon source rock samples, identifying the proportional relationship between kerogen type and macerals, constructing a set of maceral type indices, classifying and discriminating the maceral type indices and the organic matter classification table, and generating the discrimination result of the parent material type of over-mature hydrocarbon source rocks.

[0007] As a further solution of the present invention, the specific steps for obtaining the set of sedimentary environment-related elements are as follows: S111: By studying the regional sedimentary characteristics, extracting the corresponding major element data and trace element data, analyzing the concentration distribution of major elements, marking the sedimentary environment information, screening trace elements with coupling degree, and generating a dataset of element distribution characteristics; S112: By analyzing the correlation between the major element concentration and the trace element concentration in the dataset of element distribution characteristics, setting the correlation weight coefficient, and using the formula: ; And sorting the correlation of the sedimentary environment to generate a correlation sorting result; Among them, represents the correlation coefficient between major elements and trace elements, represents the weight coefficient, Represents the concentration value of major elements, Represents the average concentration of major elements, Represents the concentration value of trace elements, Represents the average concentration of trace elements, Represents the number of samples, Represents the number of each elemental sample; S113: Combine the correlation sorting results, screen the major elements of the correlation coefficients, analyze the distribution patterns under different sedimentary environments, screen the trace elements with higher correlation coefficient rankings, analyze the concentration change trends, eliminate the trace elements with low change gradient values, and establish a set of associated elements for the sedimentary environment.

[0008] As a further solution of the present invention, the steps for obtaining the relationship of maceral proportions are specifically as follows: S211: Based on the set of associated elements for the sedimentary environment, extract the percentage contents of macerals in medium-low maturity source rocks, measure the maceral data of sapropel, exinite, vitrinite, and inertinite, record the initial proportion of the components, mark and eliminate abnormal data, and obtain a maceral content table; S212: Based on the maceral content table, normalize the total amounts of sapropel, exinite, vitrinite, and inertinite data by percentage, eliminate the data beyond the error limit, set the normalization interval for the components, calculate the mean and variance of the components, and adjust the distribution of the component percentage contents to generate a normalized maceral dataset; S213: Based on the normalized maceral dataset, analyze the proportion relationship of sapropel, exinite, vitrinite, and inertinite, calculate the corresponding proportion values of the macerals, compare the multiple relationship of the proportion differences between different components, and cross-verify the correlation and deviation values to obtain the maceral proportion relationship.

[0009] As a further solution of the present invention, the steps for obtaining the maceral and element data model are specifically as follows: S221: According to the maceral proportion relationship, extract the maceral proportion relationship data, classify the macerals and construct a maceral proportion matrix, establish an element distribution matrix according to the detection data of major elements, trace elements, and rare earth elements in the samples, and perform corresponding pairing of the maceral proportion matrix and the element distribution matrix to generate an initial data table of maceral and element proportions; S222: Based on the initial data table of maceral and element proportions, analyze the ratio relationship between macerals and elements, calculate the maceral ratio weights according to the normalized data of maceral proportions, major element contents, trace element contents, and rare earth element contents, using the formula: ; Generate a maceral and element ratio distribution table; Among them, represents the ratio weight of the th maceral group to the th element, represents the content value of the th element corresponding to the th maceral group, represents the normalized proportion value of the th maceral group, represents the total number of macerals in the sample; S223: Based on the maceral and element ratio distribution table, screen the relationships between macerals and elements with prominent ratio weights, analyze the ratio distribution of elements and perform data screening, and establish a maceral and element data model.

[0010] As a further solution of the present invention, the steps for obtaining the maceral ratio operation set are specifically as follows: S311: Based on the maceral and element data model, match the maceral-related element data, sort out the related element data, screen out the data points exceeding the fluctuation range standard, collect the element ratios of each group of macerals, and obtain the maceral and element ratio data; S312: Based on the maceral and element ratio data, analyze the numerical range of the maceral and element ratios, classify according to the upper and lower bounds of the ratio, identify the distribution mean and deviation range, compare the ratio fluctuation differences, screen out the abnormally fluctuating data and group and adjust them to obtain the analysis result of the maceral and element relationship; S313: Based on the analysis result of the maceral and element relationship, for the maceral ratio data, compare the corresponding proportions of elements between macerals item by item and collect them, verify the ratio relationship, optimize the ratio set and then collect and sort it to construct a maceral ratio operation set.

[0011] As a further solution of the present invention, the steps for obtaining the maceral regression association model are specifically as follows: S321: Based on the maceral ratio operation set, extract the maceral ratios in the sapropel group, classify the characteristic ranges of the ratios, calculate the standard deviation and coefficient of variation of each ratio, sort and screen the ratios with large coefficients of variation, and screen out the characteristic ratios of the sapropel group macerals; S322: Based on the characteristic ratios of the sapropel group macerals, by combining the element ratio and maceral ratio data, using the formula: ; obtain the normalized regression factor; Among them, represents the normalized regression factor, and respectively represent the element ratios of the macerals in the sapropel group, represents the characteristic proportion of the microscopic components, and represents the upper and lower weight factors of the range of maceral ratios, Adjust parameters for normalization; S323: Analyze the nonlinear regression model of the maceral component ratio and the element ratio in combination with the normalized regression factor, perform data input operation on the differentiated regression factor value, and generate a maceral component regression association model.

[0012] As a further solution of the present invention, the steps of obtaining the microscopic component type index set are specifically as follows: S411: Based on the micro-component regression correlation model, the micro-components of sapropelite, exinite, vitrinite and inertinite in the over-mature source rock sample are measured, the corresponding proportion of each group of micro-components in the sample is analyzed, and the micro-component proportions are preliminarily correlated with the kerogen type to generate a micro-component proportion matrix; S412: Based on the ratio matrix of the microscopic components, the weights of the microscopic component type index are allocated in combination with the characteristic parameters of the kerogen type, and a correction factor is introduced for adjustment according to the change trend of the ratio of each group of microscopic components, using the formula: ; Calculate the characteristic values ​​of microscopic components; in, represents the characteristic value of the microscopic component, is the standardized ratio value of the components in the ratio matrix of the microscopic components, is the kerogen characteristic weight parameter of the microscopic component, is the correction factor used to adjust the fluctuation value of the proportion of microscopic components, is the correction factor for the association between the microscopic components and the sample characteristics; S413: Integrate the characteristic values ​​of the microscopic components, screen the range of microscopic component indexes with high matching between the microscopic components and the kerogen type, analyze the distribution range of the microscopic component index, and construct a set of microscopic component type indexes.

[0013] As a further solution of the present invention, the steps for obtaining the discrimination result of the parent material type of the overmature source rock are specifically as follows: S421: extracting microscopic component type index data based on the microscopic component type index set, checking each set of data, removing abnormal values ​​that exceed the upper and lower limits of the distribution, and collecting the classified data to obtain microscopic component type classification data; S422: Based on the microscopic component type classification data, in combination with the microscopic component type index and the organic matter division table, compare the corresponding relationship between the microscopic components and the organic matter categories, classify and judge the microscopic component data, extract and sort out the matching data, and establish the matching result of the microscopic components and the organic matter; S423: Based on the matching result of the microscopic components and the organic matter, extract the data that meet the parent material type of the over-mature hydrocarbon source rock, compare the microscopic component type classification results, verify and collect the classification data, and integrate the data in combination with the discrimination basis to generate the discrimination result of the parent material type of the over-mature hydrocarbon source rock.

[0014] An over-mature hydrocarbon source rock parent material type discrimination system, which is used to execute the above-mentioned over-mature hydrocarbon source rock parent material type discrimination method. The system includes: The sedimentary feature recognition module identifies the sedimentary environment by studying the regional sedimentary features, extracts representative major elements and trace elements from the geological data, classifies and analyzes the elements, and obtains the sedimentary environment element dataset through element feature association; The component association analysis module uses the sedimentary environment element dataset to analyze the microscopic components of sapropel group, exinite group, vitrinite group and inertinite group in the medium and low maturity hydrocarbon source rock, identifies the percentage content of microscopic components, correlates the element data, and establishes a ratio model of microscopic components and elements; The sedimentary environment analysis module uses the ratio model of microscopic components and elements to analyze the ratio of microscopic components and elements, analyzes the parent material type and microscopic component characteristics, and generates a sedimentary environment regression model; The parent material type discrimination module evaluates the ratio of microscopic components in the sample according to the sedimentary environment regression model, analyzes the relationship between the kerogen type and the microscopic components, and evaluates and discriminates the parent material type through the type index to obtain the discrimination result of the parent material type of the over-mature hydrocarbon source rock.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the study of regional deposition characteristics and the extraction of major and trace elements in combination with the geological background, the accurate identification of the sedimentary environment can be achieved, representative sedimentary environment elements can be screened out, ensuring more accurate and comprehensive sedimentary environment analysis. By extracting the percentage content of the macerals in medium- to low-maturity source rocks and analyzing their proportional relationships, the maceral distributions of sapropelinite, exinite, vitrinite, and inertinite are clarified, laying the foundation for refined quantitative analysis. By identifying the ratio relationships between macerals and inorganic elements, the association between macerals and elements becomes clearer and quantifiable. By extracting the associated element ratios and analyzing the relationship between macerals and elements, a ratio operation set of macerals is constructed to achieve multi-dimensional analysis of the relationship between macerals and elements, improving the accuracy and reliability of the identification of the source rock parent material type. Based on the regression relationship analysis of maceral proportions and element ratios, the connection between different parent material types and maceral characteristics is accurately revealed. Through the construction of the maceral type index and the application of the discrimination model, the division of the parent material type of over-mature source rocks becomes more scientific, achieving high-precision identification of the parent material type in highly evolved source rocks. Through the organic combination of the quantitative model and correlation analysis, the systematicness of sedimentary environment analysis, the accuracy of source rock parent material type identification, and the effective utilization rate of element indicators are improved, solving the problem that it is difficult to achieve accurate division in the study of highly evolved source rocks by traditional methods, and providing a more scientific and reliable basis for oil and gas resource exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic diagram of the main steps of the present invention; Figure 2 is a flowchart of the sedimentary environment associated element set in the present invention; Figure 3 is a flowchart of the maceral proportion relationship in the present invention; Figure 4 is a flowchart of the maceral and element data model in the present invention; Figure 5 is a flowchart of the maceral ratio operation set in the present invention; Figure 6 is a flowchart of the maceral regression correlation model in the present invention; Figure 7 is a flowchart of the maceral type index set in the present invention; Figure 8 is a flowchart of the discrimination result of the parent material type of over-mature source rocks in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0019] Embodiment 1 Please refer to Figure 1 , the present invention provides a technical solution: a method for discriminating the parent material type of over-mature hydrocarbon source rocks, including the following steps: S1: Through the study of regional sedimentary characteristics, identify the sedimentary environment of over-mature hydrocarbon source rocks, extract major elements and trace elements in combination with the geological background, judge the component correlation, sort out representative sedimentary environment elements, and establish a set of sedimentary environment-related elements; The division of sedimentary environments (facies) mostly uses the element ratio method, that is, the ratios of different inorganic elements often indicate different sedimentary environments, and various parameters indicating different sedimentary environments have been summarized by predecessors. For example: the parameters for evaluating water salinity include: B / Ga, Sr / Ba, Sr / Ca, Fe / Mn, V / Ni, Rb / K, etc.; the parameters for evaluating the redox environment include: (Cu + Mo) / Zn, Ni / Co, etc.; the parameters for evaluating the distance from the shore include: Fe / Mn, Mn / Ti, Co / Ti, Ni / Ti, etc. Generally, when differentiating sedimentary environments, it is necessary to select appropriate evaluation parameters and divide the inorganic parameters for discriminating the sedimentary environments of different groups and sections in the study area through conventional sedimentary facies analysis. Due to the regionality of inorganic elements, select the elements indicating the sedimentary environment that conform to this region as alternative elements. Combining the drilling and outcrop data in the area, it is considered that the Shiqiantan Formation of the Carboniferous system in the area is overall developed in a shore-shallow sea sedimentary environment, and Well QN1 is a mature hydrocarbon source rock. Therefore, the Shiqiantan Formation of this well is used as the standardization object. Well QN3 is in the same shore-shallow sea facies sedimentary environment in this section and is a highly mature hydrocarbon source rock. Therefore, this well is used as the analog object.

[0020] S2: Based on the set of deposition environment associated elements, extract the percentage content of macerals in medium- to low-maturity source rocks, capture the data of sapropelinite, exinite, vitrinite and inertinite, obtain the proportion relationship of macerals, conduct major element, trace element and rare earth element analyses, identify the ratio relationship between macerals and inorganic elements, and construct a maceral and element data model; Select samples from the source rocks of the Shiqiantan Formation in Well QN1, the object of standardization, with a vitrinite reflectance less than 1.4% and an organic carbon content greater than 0.5%. Divide the samples into two parts, A1 and A2, with each part having no less than 20 samples. Among them, A1 is used for microscopy, and A2 is used for the analysis of major, trace and rare earth elements. Select a mudstone sample A3 from the source rocks of the Shiqiantan Formation in Well QN3, the analog object, with a vitrinite reflectance greater than 1.4% and an organic carbon content greater than 0.5% for the analysis of major, trace and rare earth elements.

[0021] S3: Based on the maceral and element data model, extract the associated element ratios, analyze the relationship between the macerals of sapropelinite, exinite, vitrinite and inertinite and elements, construct a set of maceral ratio operations, analyze the regression relationship between the sapropelinite ratio and element ratios, identify the parent material type and maceral characteristics, and generate a maceral regression association model; Take the maceral data of the source rock samples in Well QN1 and the sedimentary environment evaluation parameters such as B / Ga, Sr / Ba, V / Ni, Mn / Ti, Co / Ti of the source rock samples in Well QN1 to carry out bivariate correlation analysis. Not all of the element analysis items have obvious correlations with the maceral samples. The sig. of vitrinite, inertinite, exinite and sapropelinite with Sr / Ba and Co / Ti is less than 0.01, indicating a significant correlation. Therefore, Sr / Ba and Co / Ti are selected as the parameters for the next regression analysis. There is a linear correlation between the parent material type parameters of the source rock and some element ratios. Therefore, the least squares method can be used to fit the multiple linear relationship. Taking sapropelinite, exinite, vitrinite and inertinite as the dependent variables and Sr / Ba and Co / Ti as the independent variables respectively, fit the above macerals to establish their relationship with the element ratios xi, yi, zi. Taking sapropelinite as the dependent variable and Sr / Ba and Co / Ti as the independent variables, through multiple linear regression analysis, it can be seen that the correlation sig. between the constants Sr / Ba and Co / Ti is less than 0.05. Therefore, a regression equation among the three can be established.

[0022] S4: Based on the maceral regression association model, measure the ratios of sapropelinite, exinite, vitrinite and inertinite in over-mature source rock samples, identify the proportional relationship between the kerogen type and macerals, construct a set of maceral type index, and combine the maceral type index with the organic matter classification table for classification judgment to generate the discrimination result of the parent material type of over-mature source rocks.

[0023] The set of elements associated with the sedimentary environment includes major elements, trace elements, and rare earth elements. The proportional relationships of macerals include the proportion of sapropelinite, exinite, vitrinite, and inertinite. The data models of macerals and elements include the ratio relationships between macerals and major elements, between macerals and trace elements, and between macerals and rare earth elements. The set of maceral ratio operations includes the ratios of sapropelinite, exinite, vitrinite, and inertinite. The regression correlation models of macerals include the regression relationships between the proportion of sapropelinite and element ratios, between the proportion of exinite and element ratios, between the proportion of vitrinite and element ratios, and between the proportion of inertinite and element ratios. The set of maceral type indices includes the type indices of sapropelinite, exinite, vitrinite, and inertinite. The discrimination results of the parent material types of over-mature hydrocarbon source rocks include the parent material types of sapropelinite, exinite, vitrinite, and inertinite.

[0024] Please refer to Figure 2 , and the specific steps for obtaining the set of elements associated with the sedimentary environment are as follows: S111: Through the study of regional sedimentary characteristics, extract the corresponding major element data and trace element data, analyze the concentration distribution of major elements, mark the sedimentary environment information, screen the trace elements with coupling degree, and generate an element distribution characteristic data set; Extract the major element data and trace element data corresponding to the regional sedimentary characteristics, extract the concentration distribution of major elements in the sedimentary layer based on the major element set and generate a sedimentary layer concentration matrix according to the sample points. According to the fluctuation range of the major element concentrations in each layer of the concentration matrix, combined with the gradient change of adjacent layers, screen the sample points of the layers with significant concentration changes, generate a peak concentration distribution table through the sample points, and at the same time analyze the concentration coupling of the elements in the trace element set and the major element set. Sort the trace elements according to the correlation value of the coupling and take out the trace element set with the top coupling ranking to generate an element distribution characteristic data set.

[0025] S112: By analyzing the correlation between the major element concentration and the trace element concentration in the element distribution characteristic data set, set the correlation weight coefficient, and use the formula: ; And sort the correlation of the sedimentary environment to generate a correlation ranking result; Among them, represents the correlation coefficient between the major element and the trace element, represents the weight coefficient, represents the major element concentration value, represents the average major element concentration, represents the trace element concentration value, represents the average concentration of trace elements, represents the number of samples, represents the number of each element sample; The advantage of the formula is that by introducing the weight parameter dynamically adjusts the influence of different elements, improves the accuracy of calculating the element correlation in the deposition environment, and can more accurately judge the correlation ranking between major elements and trace elements; First, through experiments, the concentration of each element in the major element set and the concentration of each element in the trace element set are detected. The detection results are obtained through the spectrophotometer in the laboratory, generating a concentration detection data table of major elements and trace elements, and calculating the average value of the major element concentration and the average value of the trace element concentration According to the experimental design, the weights of each sample are assigned according to the complexity of the sediment layer distribution. The higher the complexity of the layer, the greater the weight value; Substitute the example values. Assume that the detected value of the major element concentration and the detected value of the trace element concentration and the weight value Calculate and ; ; Then calculate the product of and and sum them up with weights: ; Expand the calculation: ; Then calculate the two parts of the denominator. First is : ; Then is : ; Take the square root of the denominator: ; Finally, calculate : ; The result shows that the correlation coefficient between the major element and the trace element is 0.328, indicating that the coupling between the major element and the trace element is weak. It is necessary to further screen important elements according to this value in combination with the specific deposition environment.

[0026] S113: Combine the relevance ranking results, screen the major elements of the correlation coefficient, analyze the distribution patterns under different sedimentary environments, screen the trace elements with higher correlation coefficient rankings, analyze the concentration change trends, eliminate the trace elements with low change gradient values, and establish a set of associated elements for sedimentary environments; First, sort the major elements from high to low according to the correlation coefficient results, generate a distribution matrix for the concentration values of the major elements based on sedimentary layers, calculate the distribution variance values of each major element in each sedimentary environment according to the data in the matrix, select the major elements with larger variance values as the final screening results, and determine whether there are significant differences in the distribution patterns of these three major elements under different sedimentary environments. Verify whether the differences hold by calculating the mean differences in different sedimentary environments and combining with the significance test formula. For trace elements, select the top five elements with the highest correlation coefficient rankings, calculate their change gradient values in combination with their concentration change trends, obtain the absolute values of the element concentration changes by calculating the element concentration gradient formula and calculate their average gradients by grouping according to element types. Eliminate the trace elements with low average gradient values, and establish a set of associated elements for sedimentary environments through sorting and combination.

[0027] Please refer to Figure 3 , and the specific steps for obtaining the microscopic component proportion relationship are as follows: S211: Based on the set of associated elements for sedimentary environments, extract the percentage content of microscopic components in medium and low maturity source rocks, measure the microscopic component data of sapropel, exinite, vitrinite, and inertinite, record the initial proportion of components, mark and eliminate abnormal data, and obtain a microscopic component content table; Through microscopic analysis of the samples in the set of associated elements collected from the sedimentary environment, use a microscopic spectrometer to measure the percentage content of microscopic components of sapropel, exinite, vitrinite, and inertinite one by one, establish a preliminary component data distribution table for each data point. During the measurement process, according to the over-maturity degree of the source rock parent material, eliminate abnormal samples affected by late sedimentary transformation, such as data points where the sapropel content is less than 10% or higher than 70%, which deviate from the characteristics of parent material evolution. Through data comparison and repeated measurement, obtain effective microscopic component data points to ensure the representativeness of sample data and avoid overall statistical deviation caused by abnormal data. Combine the measurement results, record the proportion data of the initial components, and eliminate data points that significantly do not conform to the characteristics of the source rock parent material according to the definition standards of microscopic components (such as sapropel corresponding to bacterial sources in organic parent material), and finally form a microscopic component content table as the basis for subsequent normalization processing.

[0028] S212: Based on the microscopic component content table, normalize the total amounts of sapropel, exinite, vitrinite, and inertinite data as percentages, eliminate data beyond the error limit, set a normalization interval for components, calculate the mean and variance of components, adjust the distribution of component percentage content, and generate a normalized microscopic component data set; First, the normalization operation is completed by calculating the percentage of each component in the total components. During the process, based on the upper and lower limits of the maturity of the parent material type, the data exceeding the error limit are excluded. For example, if the normalized content of the sapropel group exceeds 85%, it indicates an abnormal sample and is marked for exclusion. A normalization interval is set to ensure that the component data can reflect the actual evolution level of the hydrocarbon source rock. In each group of data, the mean and variance of the sapropel group, exinite group, vitrinite group, and inertinite group are calculated to evaluate the uniformity and rationality of the data distribution, and the distribution structure of the maceral content is adjusted. During the adjustment process, the sapropel group is given priority as the benchmark to ensure that its normalized proportion conforms to the actual sedimentary conditions. The relative proportions of the vitrinite group and exinite group are adjusted smoothly through proportional weights to meet the requirements of data integrity, generating a normalized maceral dataset to provide high-precision data support for the analysis of proportional relationships.

[0029] S213: Based on the normalized maceral dataset, analyze the proportional relationships among the sapropel group, exinite group, vitrinite group, and inertinite group, calculate the corresponding proportion values of the macerals, compare the multiple relationships of the proportional differences between the differentiated components, and cross-validate the correlation and deviation values to obtain the maceral proportional relationships; Analyze the proportional relationships among the sapropel group, exinite group, vitrinite group, and inertinite group one by one, and focus on observing the proportional fluctuation range between the sapropel group and the components. For example, taking the percentage content of the sapropel group as the benchmark, compare it with the normalized percentage of the exinite group, calculate the proportion value between the two components, determine the multiple relationship in different samples, adopt the method of cumulative proportion analysis to verify the correlation among the components, cross-compare whether the fluctuation range between the sapropel group and the vitrinite group exceeds 1.5 times the deviation range, and whether the proportional relationship between the exinite group and the inertinite group maintains a linear characteristic. The calculation of the deviation value is completed by the average proportional deviation of the differentiated components in multiple samples, and a proportional relationship verification matrix is established to ensure that the correlation among the data can accurately reflect the over-mature characteristics of the parent material type of the hydrocarbon source rock. Summarize all the proportional relationship parameters to obtain the maceral proportional relationship data, providing an important reference basis for the discrimination of the parent material type of over-mature hydrocarbon source rocks.

[0030] Please refer to Figure 4 , and the specific steps for obtaining the maceral and element data model are as follows: S221: According to the maceral proportional relationships, extract the maceral proportional relationship data, classify the macerals and construct a maceral proportion matrix, establish an element distribution matrix based on the detection data of major elements, trace elements, and rare earth elements in the samples, and pair the maceral proportion matrix with the element distribution matrix correspondingly to generate an initial data table of maceral and element proportions; First, classify the macerals according to physical properties such as particle size and crystal morphology, obtain the microscopic structure image data using a scanning electron microscope (SEM), further extract the surface area of each maceral based on image segmentation methods, calculate its mass ratio in combination with density data to generate a maceral ratio matrix. Subsequently, obtain the content values of major elements, trace elements, and rare earth elements through petrochemical analysis, detect the concentrations of major elements and trace elements in the sample using inductively coupled plasma mass spectrometry (ICP-MS), record the detection results as an element distribution matrix, pair the maceral ratio matrix with the element distribution matrix through sample horizon information, and finally generate an initial data table of maceral and element ratios.

[0031] S222: Based on the initial data table of maceral and element ratios, analyze the ratio relationship between macerals and elements, calculate the maceral ratio weight according to the normalized data of maceral ratio, major element content, trace element content, and rare earth element content, using the formula: ; Generate a maceral and element ratio distribution table; Wherein, represents the ratio weight of the th type of maceral and the th type of element, represents the content value of the th type of element corresponding to the th type of maceral, represents the normalized ratio value of the th type of maceral, represents the total number of macerals in the sample; The benefit of the formula is that by introducing the normalized ratio and the square root operation of the normalized denominator part, the resolution of the maceral ratio weight is enhanced, while the interference of abnormally high values in the sample to the ratio result is suppressed, improving the accuracy of the correlation calculation; Obtain the mass ratio of the macerals through experimental detection, calculate the normalized ratio of the macerals using the microscopic image combined with the density value of the rock sample according to the formula , where is the mass value of the th type of maceral, is the total mass of all macerals, and the actual mass values of the macerals are , , , with a total mass of 100.0, calculate , , ; Subsequently, the content values of major elements, trace elements, and rare earth elements are detected. , and the detection method is obtained through ICP-MS analysis. Let the concentration values of major element A in microconstituents 1, 2, and 3 be , , ; Calculate according to the formula : The numerator part is , that is: ; ; ; The denominator part is : ; Finally calculate: ; ; ; The result shows that the distribution of the ratio weights of microconstituents and elements is , , , and the relationship between microconstituents and major elements can be further revealed through the weights.

[0032] S223: Based on the microconstituent and element ratio distribution table, screen the relationships between microconstituents and elements with prominent ratio weights, analyze the ratio distribution of elements and perform data screening, and establish a microconstituent and element data model; First, sort according to the ratio weights from high to low, screen the relationships between microconstituents and elements with higher ratio weights, calculate the standard deviation of the ratio distribution of the main proportion elements in the microconstituents step by step, and select the relationships between microconstituents and elements with lower standard deviations as samples with stronger correlations. Subsequently, analyze the distribution of significantly correlated elements in different sediment samples in the microconstituents, and use analysis of variance to test whether the ratio distributions are consistent. When the distribution variance of microconstituents and elements is less than a certain critical value, it is considered to have a significant correlation, and the correlation data is recorded as the model basic data to establish a microconstituent and element data model.

[0033] Please refer to Figure 5 , and the specific steps for obtaining the microconstituent ratio operation set are as follows: S311: Based on the microconstituent and element data model, match the microconstituent-related element data, organize the related element data, screen out the data points that exceed the fluctuation range standard, and collect the element ratios of each group of microconstituents to obtain the microconstituent and element ratio data. The contents of the maceral components of the sapropelite, exinite, vitrinite and inertinite are matched with the corresponding associated element data. For example, the mass contents of sulfur and hydrogen are analyzed based on the sapropelite, and their ratio relationship with the percentage of the maceral components is calculated. Statistics are performed on each data point, and abnormal data that deviates significantly from the normal fluctuation range are eliminated. For example, data points where the element ratio of the maceral components deviates from the normal range of ±20% are eliminated. After eliminating the data beyond the range through the ratio stability rule, the element ratios of each group of maceral components are aggregated. For the main elements involved in each group of maceral components (such as sulfur, oxygen, hydrogen, etc.), they are sorted and classified one by one according to the ratio distribution frequency to ensure that the ratio data can reflect the elemental composition characteristics of the source rock parent material. Through the aggregation and analysis of the maceral components and element ratios, the maceral components and element ratio data are finally formed as the core basis for the subsequent ratio interval and fluctuation analysis.

[0034] S312: Based on the data of the ratio of microscopic components to elements, the numerical range of the ratio of microscopic components to elements is analyzed, the ratio is classified according to the upper and lower limits, the distribution mean and deviation range are identified, the fluctuation difference of the ratio is compared, the abnormal fluctuation data is screened and grouped and adjusted, and the analysis results of the relationship between microscopic components and elements are obtained; Firstly, for the distribution of element ratios of sapropelite, exinite, vitrinite and inertinite, the numerical range of the ratios was calculated one by one, and divided into three intervals: high, medium and low. For example, the sulfur ratio interval of the sapropelite was divided into three categories: below 10%, 10%-25% and above 25%. According to the upper and lower limits of each interval, the mean and deviation range of its distribution were statistically analyzed. For abnormal data with deviations exceeding the range of ±15%, the normalized data of the microscopic components were combined for difference comparison. By calculating the amplitude and difference characteristics of the ratio fluctuations, the abnormal fluctuation data were screened and grouped for adjustment. For example, the data of a sample in the sapropelite whose ratio deviated from the overall distribution law was adjusted to the high fluctuation group for re-analysis. Through the analysis steps, the interval range and fluctuation distribution of each microscopic component and element ratio were finally formed, and the analysis results of the relationship between microscopic components and elements were obtained, providing further support for the association of element ratios for parent material type discrimination.

[0035] S313: Based on the analysis results of the relationship between microscopic components and elements, for the microscopic component ratio data, the corresponding proportions of elements between microscopic components are compared and grouped one by one, the ratio relationship is verified, the ratio set is optimized and then grouped, and a microscopic component ratio operation set is constructed; The microscopic component ratio data of sapropel group, exinite group, vitrinite group and inertinite group are compared item by item to verify their corresponding proportions with elements. For example, the ratio relationship between sulfur element and hydrogen element in the sapropel group is analyzed and compared with the ratio of sulfur element in the exinite group, and the proportional difference and fluctuation range between the two are calculated. In the process of gradually integrating the ratio data set according to the grouping method of the elemental ratio relationship of all microscopic components and eliminating abnormal data points that do not conform to the correlation characteristics between microscopic components, the proportion characteristics of microscopic components in the change of parent material type are compared. For example, the relative change of the sulfur element ratio of the sapropel group in different samples is used to adjust the collection strategy of the ratio set to ensure the accuracy and relevance of the data. The optimized ratio data set is classified and sorted to construct a microscopic component ratio operation set for subsequent discrimination and analysis of the parent material type of over-mature hydrocarbon source rocks, further supporting the operation derivation of the ratio relationship and the classification recognition of parent material characteristics.

[0036] Please refer to Figure 6 , and the specific steps for obtaining the microscopic component regression correlation model are as follows: S321: Based on the microscopic component ratio operation set, extract the microscopic component ratios in the sapropel group, classify the characteristic ranges of the ratios, calculate the standard deviation and coefficient of variation of each ratio, sort and screen the ratios with large coefficients of variation, and screen out the characteristic ratios of the microscopic components in the sapropel group; Extract the relative ratios of microscopic components in the sapropel group through the preliminary classification of microscopic components. Decompose the data structure in the microscopic components according to the classification standard of parent material characteristics. First, classify and process the microscopic component ratio data in the sapropel group, extract their characteristic values and the maximum and minimum interval ranges respectively, calculate the distribution range of the characteristic ratios of microscopic components through interval statistics (mean, variance), then calculate the standard deviation and coefficient of variation in combination with the volatility of microscopic components, judge the importance of each microscopic component in the parent material classification, extract the characteristic ratios of the component data with large coefficients of variation and perform standardization processing, and at the same time establish a preliminary mapping relationship between the component ratios and the parent material characteristic classification, and finally screen out the characteristic ratios of the microscopic components in the sapropel group.

[0037] S322: Based on the characteristic ratios of the microscopic components in the sapropel group, by combining elemental ratios and microscopic component ratio data, using the formula: ; Obtain the normalized regression factor; Among them, represents the normalized regression factor, and respectively represent the elemental ratios of microscopic components in the sapropel group, represents the characteristic ratio of microscopic components, and represent the upper and lower bound weight factors of the microscopic component ratio range, is a normalization adjustment parameter; The advantage of the formula is that by introducing the characteristic ratio parameter and the normalization adjustment parameter , combining the element ratio and the weight factor of the maceral ratio , the comprehensive correlation calculation between the maceral ratio and the element ratio is realized, and the adaptability and accuracy of the regression factor are improved; First, obtain the element ratios of the macerals , , the characteristic ratio of the maceral , the weight factor of the maceral ratio range , , the normalization adjustment parameter ; Substitute into the formula: ; Calculate the numerator part: ; Calculate the denominator part: ; Finally, calculate the normalized regression factor: ; The result shows that the value of the normalized regression factor is 0.05, indicating a weak correlation between the maceral ratio and the element ratio. This value can be used in subsequent analysis steps to further adjust the maceral characteristic ratio.

[0038] S323: Combine the normalized regression factor, analyze the non - linear regression model between the maceral ratio and the element ratio, perform data input operations on the differential regression factor values, and generate a maceral regression correlation model; First, screen the maceral - element ratio data pairs according to the value of the normalized regression factor, sort the data pairs from smallest to largest according to the normalized regression factor, select the ratio pairs with the regression factor within a reasonable range as the input of the regression model, calculate each input data one by one in combination with the regression model, judge the model fitting degree according to the residual of the non - linear regression, and at the same time gradually adjust the data points with larger residuals, gradually optimize the fitting curve of the maceral - element ratio in the model, and finally obtain the maceral regression correlation model.

[0039] Please refer to Figure 7 , the specific steps for obtaining the maceral type index set are as follows: S411: Based on the maceral regression correlation model, measure the macerals of sapropelinite, exinite, vitrinite, and inertinite in the over - mature hydrocarbon source rock sample, analyze the corresponding proportion of each maceral in the sample, make a preliminary association between the maceral proportion and the kerogen type, and generate a proportion matrix of macerals; First, it is necessary to partition the microscopic images of the sample, continuously collect microscopic images of different partitions through a high-resolution microscope, mark the sapropelite, exinite, vitrinite and inertinite in each image one by one, and use the volume fraction algorithm to count the volume fraction of the microscopic components in each partition, then numerically classify the volume fractions, and obtain the relative proportion values ​​of the microscopic components by normalizing the volume fractions to the total volume of 1. To ensure the accuracy of the normalization processing, it is necessary to calculate the standard deviation and error range of the microscopic component proportion values, remove outliers and readjust the proportion value range. After completing the basic normalization operation, further analyze the distribution trend of the microscopic component proportion in the sample by comparing the experimental measured values ​​of the microscopic component proportion and kerogen characteristics, and finally clarify the proportion characteristics of the microscopic components in the overall sample to form a microscopic component proportion matrix. This matrix contains the relative proportion characteristics of the sapropelite, exinite, vitrinite and inertinite in different partitions, which is used as the input parameter for the subsequent microscopic component index calculation.

[0040] S412: Based on the proportion matrix of microscopic components, the weight of the microscopic component type index is allocated in combination with the characteristic parameters of kerogen type. According to the change trend of the proportion of each group of microscopic components, a correction factor is introduced for adjustment. The formula is: ; Calculate the characteristic values ​​of microscopic components; in, represents the characteristic value of the microscopic component, is the standardized ratio value of the components in the ratio matrix of the microscopic components, is the kerogen characteristic weight parameter of the microscopic component, is the correction factor used to adjust the fluctuation value of the proportion of microscopic components, is the correction factor for the association between the microscopic components and the sample characteristics; The formula is useful in that it introduces standardized values ​​for the proportions of the microscopic components , Kerogen feature weight parameter , correction factor Correlation correction factor between microscopic components and sample characteristics , which realizes the dynamic adjustment of the nonlinear relationship between the proportion of microscopic components and kerogen characteristics, making the calculation results more accurate and adaptable; First, extract the proportion values ​​of the sapropelic group from the microscopic component proportion matrix , the characteristic weights of kerogen types are obtained by experiment , combined with the correction factor for the fluctuation of the proportion of micro-components Correction factors related to kerogen characteristics ; Substituting into the formula: ; Calculate the values ​​of each item: ; Combined calculation: ; The results show that the characteristic value of the microscopic components The value of is 0.51, which reflects the strong correlation between the proportion of microscopic components and kerogen characteristics, and can provide support for the classification and classification of the subsequent microscopic component type index set.

[0041] S413: integrating the characteristic values ​​of the microscopic components, screening the range of microscopic component indexes with high matching between the microscopic components and the kerogen type, analyzing the distribution range of the microscopic component indexes, and constructing a set of microscopic component type indexes; First, it is necessary to sort the type indexes of all microscopic components from high to low, calculate the frequency distribution of the sorted type index distribution, and analyze its distribution characteristics by constructing a frequency histogram of the microscopic component type index. Then, the microscopic component type index with the highest frequency is selected as the main interval. The microscopic component type index of the selected main interval is further subdivided, and its mean and deviation value in each frequency interval is calculated. The distribution range of the interval is redivided, and the type index with large deviation is clustered according to the deviation value. The main characteristics of the microscopic component type index are clarified through the clustering algorithm, and the cluster center point of the type index is determined as the key characteristic value of the microscopic component type. At the same time, the microscopic component type index set is optimized and classified according to the distribution characteristics of the kerogen type. Finally, the microscopic component type index set is obtained, which can be used as an important reference for judging the relationship between kerogen type and microscopic component ratio.

[0042] See also Figure 8 The specific steps for obtaining the discrimination results of the parent material type of overmature source rocks are as follows: S421: extracting micro-component type index data based on the micro-component type index set, checking each set of data, removing abnormal values ​​that exceed the upper and lower limits of the distribution, and collecting the classified data to obtain micro-component type classification data; Through the set of maceral type index, the maceral type index data of sapropelite, exinite, vitrinite and inertinite were extracted one by one, and each group of data was compared with the standard distribution range of the maceral type to screen whether there were outliers exceeding the upper and lower limits. For data points exceeding the upper and lower limits, such as the sapropel type index exceeding 90 or less than 15, the outliers were removed according to the distribution law, and the distribution characteristics of the removed samples were recorded. For data that met the distribution range, they were classified according to the distribution range of the type index, such as 20-50 as the low value range, 50-75 as the median range, and 75-90 as the high value range. Each maceral was classified and sorted, and a maceral type classification table was generated according to the classification results of each group of data. During the classification process, the focus was on the centralized distribution trend of the data to ensure that the classified results after aggregation could accurately reflect the characteristics of the parent material. Finally, the maceral type classification data was obtained to provide basic support for the subsequent corresponding analysis of macerals and organic matter.

[0043] S422: Based on the classification data of maceral type, combined with the maceral type index and the organic matter classification table, the corresponding relationship between maceral and organic matter categories is compared, the maceral data is classified and judged, the matching data is extracted and sorted, and the matching results of maceral and organic matter are established; The classification data of maceral type are compared with the maceral type index and the organic matter classification table. For example, the correspondence between the type index of the sapropel group and the corresponding organic matter category (such as humus type and shell type) is checked item by item. According to the classification range of the maceral type index, the organic matter category of each group of maceral components is judged, and the type data matching it is extracted. For example, if the type index of the sapropel group is in the median range of 50-75, it is matched as humus type, and if the type index of the shell group is in the low value range of 20-50, it is matched as shell organic matter. In the matching process, according to the data distribution characteristics of each maceral component, the matching data that conforms to the parent material type is classified and sorted, and the matching results are summarized. For abnormal matching found in the comparison process, such as the distribution discrepancy between the maceral type index and the organic matter category, the matching results of the maceral component and the organic matter are established to lay a correlation foundation for the discrimination of the parent material type of the overmature source rock.

[0044] S423: Based on the matching results of maceral components and organic matter, extract data that conform to the parent material type of overmature source rocks, compare the classification results of maceral component types, verify and collect the classification data, integrate the data in combination with the discrimination basis, and generate the discrimination results of the parent material type of overmature source rocks; By extracting the data that meet the parent material type of over-mature source rocks from the matching results of macerals and organic matter, the type indices of sapropelinite, exinite, vitrinite, and inertinite are compared item by item with the corresponding organic matter categories to determine whether they conform to the characteristics of the parent material of over-mature source rocks. For example, for the sapropelinite type index in the range of 75 - 90 and the data matching humic-type organic matter, it is classified as the over-mature parent material type; if the exinite type index is in the low-value range and the data match the exinite-type organic matter category, it is also classified as the data that conform to the type. After comparing the classification results of maceral types, abnormal matching data are screened, and according to the discrimination criteria, such as the relationship between the type index distribution range and the evolution of the parent material maturity, the abnormal data are re-verified and classified. By integrating the matching relationship and classification data of macerals and organic matter, and combining the characteristic discrimination criteria of the parent material type of over-mature source rocks, the discrimination result of the parent material type of source rocks is finally generated.

[0045] The discriminant system for the parent material type of over-mature source rocks is used to execute the above discriminant method for the parent material type of over-mature source rocks. The system includes: The sedimentary feature recognition module identifies the sedimentary environment by studying the regional sedimentary features, extracts representative major and trace elements from the geological data, classifies and analyzes the elements, and obtains the sedimentary environment element dataset through element feature association. The component association analysis module uses the sedimentary environment element dataset to analyze the macerals of sapropelinite, exinite, vitrinite, and inertinite in medium- and low-mature source rocks, identifies the percentage content of macerals, correlates the element data, and establishes a ratio model of macerals and elements. The sedimentary environment analysis module uses the ratio model of macerals and elements to analyze the ratio of macerals and elements, analyzes the parent material type and maceral characteristics, and generates a sedimentary environment regression model. The parent material type discriminant module evaluates the maceral ratio in the sample according to the sedimentary environment regression model, analyzes the relationship between the kerogen type and macerals, and discriminates the parent material type through type index evaluation to obtain the discriminant result of the parent material type of over-mature source rocks.

[0046] The above is only the preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments according to the technical essence of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A method for distinguishing the parent material type of overmature source rock, characterized in that: The following steps are involved: S1: Through the study of regional sedimentary characteristics, the sedimentary environment of overmature source rocks is identified, major and trace elements are extracted in combination with the geological background, the component correlation is determined, representative sedimentary environment elements are sorted, and a set of sedimentary environment-related elements is established; S2: Based on the set of elements associated with the sedimentary environment, extract the percentage of microscopic components of low-mature source rocks, capture the data of sapropelite, exinite, vitrinite and inertinite, obtain the proportion relationship of microscopic components, perform major element, trace element and rare earth element analysis, identify the ratio relationship between microscopic components and inorganic elements, and construct a microscopic component and element data model; S3: Based on the microscopic component and element data model, extract the associated element ratio, analyze the relationship between the microscopic components and elements of the sapropelite, exinite, vitrinite, and inertinite, construct a microscopic component ratio operation set, analyze the regression relationship between the sapropelite ratio and the element ratio, identify the parent material type and microscopic component characteristics, and generate a microscopic component regression association model; S4: Based on the micro-component regression association model, the ratios of the sapropelite, exinite, vitrinite and inertinite groups of the over-mature source rock samples are measured to identify the proportional relationship between the kerogen type and the micro-components, and a set of micro-component type indexes is constructed. The micro-component type index and the organic matter classification table are classified and distinguished to generate the discrimination result of the parent material type of the over-mature source rock.

2. The method for distinguishing the parent material type of overmature source rock according to claim 1, characterized in that: The steps for obtaining the set of sedimentary environment associated elements are specifically as follows: S111: Through the study of regional sedimentary characteristics, the corresponding major element data and trace element data are extracted, the concentration distribution of major elements is analyzed, the sedimentary environment information is marked, the trace elements with coupling degree are screened, and the element distribution characteristic data set is generated; S112: By analyzing the correlation between the concentration of the major element and the concentration of the trace element in the element distribution characteristic data set, a correlation weight coefficient is set, using the formula: ； And sort the correlation of the sedimentary environment to generate the correlation sorting result; in, Represents the correlation coefficient between major elements and trace elements, represents the weight coefficient, Represents the concentration value of major elements, represents the average concentration of major elements, Represents the concentration value of trace elements, represents the average concentration of trace elements, represents the sample size, The number representing each element sample; S113: In combination with the correlation ranking result, the main elements of the correlation coefficient are screened, the distribution patterns under the differentiated sedimentary environments are analyzed, the trace elements with high correlation coefficients are screened, the concentration change trend is analyzed, the trace elements with low change gradient values ​​are eliminated, and a set of sedimentary environment associated elements is established.

3. The method for distinguishing the parent material type of overmature source rock according to claim 2, characterized in that: The steps for obtaining the microscopic component ratio relationship are specifically as follows: S211: Based on the set of sedimentary environment-related elements, extract the percentage of microscopic components of low-mature source rocks, measure the microscopic component data of sapropelite, exinite, vitrinite and inertinite, record the initial proportion of the components, mark and remove abnormal data, and obtain a microscopic component content table; S212: Based on the microscopic component content table, the total amount of sapropelite, exinite, vitrinite and inertinite data is normalized according to percentage, data exceeding the error limit is eliminated, component normalization intervals are set, mean and variance of components are calculated, component percentage distribution is adjusted, and a normalized microscopic component data set is generated; S213: Based on the normalized microscopic component data set, the proportion relationship of the sapropelite, exinite, vitrinite and inertinite is analyzed, the corresponding proportion values ​​of the microscopic components are calculated, the multiple relationship of the proportion difference between the differentiated components is compared, the correlation and deviation values ​​are cross-validated, and the proportion relationship of the microscopic components is obtained.

4. The method for distinguishing the parent material type of overmature source rock according to claim 3, characterized in that: The steps for obtaining the microscopic component and element data model are specifically as follows: S221: extracting microscopic component proportion relationship data according to the microscopic component proportion relationship, classifying the microscopic components and constructing a microscopic component proportion matrix, establishing an element distribution matrix according to the detection data of major elements, trace elements and rare earth elements in the sample, pairing the microscopic component proportion matrix with the element distribution matrix, and generating an initial data table of microscopic components and element proportions; S222: Based on the initial data table of the ratio of microscopic components to elements, the ratio relationship between microscopic components and elements is analyzed, and the weight of the ratio of microscopic components is calculated according to the normalized data of the ratio of microscopic components, the content of major elements, the content of trace elements and the content of rare earth elements, using the formula: ； Generate a distribution table of maceral components and element ratios; in, Representative Microscopic components and The ratio weight of the class elements, Representative The corresponding microscopic components The content value of the class element, Representative Normalized ratio value of maceral components, Represents the total number of microscopic components in the sample; S223: Based on the microscopic component and element ratio distribution table, screen the microscopic component and element relationship with prominent ratio weights, analyze the ratio distribution of the elements and perform data screening, and establish a microscopic component and element data model.

5. The method for distinguishing the parent material type of overmature source rock according to claim 4, characterized in that: The steps for obtaining the microscopic component ratio operation set are specifically as follows: S311: Based on the microscopic component and element data model, matching the microscopic component associated element data, sorting the associated element data, filtering out data points that exceed the fluctuation range standard, collecting each group of microscopic component element ratios, and obtaining microscopic component and element ratio data; S312: Based on the data of the ratio of microscopic components to elements, the numerical range of the ratio of microscopic components to elements is analyzed, the ratio is classified according to the upper and lower limits, the distribution mean and the deviation range are identified, the fluctuation difference of the ratio is compared, the abnormal fluctuation data is screened and grouped and adjusted, and the analysis results of the relationship between microscopic components and elements are obtained; S313: Based on the analysis results of the relationship between the microscopic components and the elements, for the microscopic component ratio data, the corresponding proportions of the elements between the microscopic components are compared item by item and grouped, the ratio relationship is verified, and the ratio set is optimized and then grouped to construct a microscopic component ratio operation set.

6. The method for distinguishing the parent material type of overmature source rock according to claim 5, characterized in that: The steps for obtaining the microscopic component regression correlation model are specifically as follows: S321: extracting the ratios of the microscopic components in the sapropel group based on the microscopic component ratio operation set, classifying the characteristic range of the ratios, calculating the standard deviation and coefficient of variation of each ratio, sorting and screening the ratios with large coefficients of variation, and screening to obtain the characteristic ratios of the microscopic components of the sapropel group; S322: Based on the characteristic ratio of the maceral components of the sapropelic group, by combining the element ratio and the maceral ratio data, the formula is used: ； Get the normalized regression factor; in, represents the normalized regression factor, and Respectively represent the element ratios of the maceral components in the sapropelic group, represents the characteristic proportion of the microscopic components, and represents the upper and lower weight factors of the range of maceral ratios, Adjust parameters for normalization; S323: Analyze the nonlinear regression model of the maceral component ratio and the element ratio in combination with the normalized regression factor, perform data input operation on the differentiated regression factor value, and generate a maceral component regression association model.

7. The method for distinguishing the parent material type of overmature source rock according to claim 6, characterized in that: The steps for obtaining the microscopic component type index set are specifically as follows: S411: Based on the micro-component regression correlation model, the micro-components of sapropelite, exinite, vitrinite and inertinite in the over-mature source rock sample are measured, the corresponding proportion of each group of micro-components in the sample is analyzed, and the micro-component proportions are preliminarily correlated with the kerogen type to generate a micro-component proportion matrix; S412: Based on the ratio matrix of the microscopic components, the weights of the microscopic component type index are allocated in combination with the characteristic parameters of the kerogen type, and a correction factor is introduced for adjustment according to the change trend of the ratio of each group of microscopic components, using the formula: ； Calculate the characteristic values ​​of microscopic components; in, represents the characteristic value of the microscopic component, is the standardized ratio value of the components in the ratio matrix of the microscopic components, is the kerogen characteristic weight parameter of the microscopic component, is the correction factor used to adjust the fluctuation value of the proportion of microscopic components, is the correction factor for the association between the microscopic components and the sample characteristics; S413: Integrate the characteristic values ​​of the microscopic components, screen the range of microscopic component indexes with high matching between the microscopic components and the kerogen type, analyze the distribution range of the microscopic component index, and construct a set of microscopic component type indexes.

8. The method for distinguishing the parent material type of overmature source rock according to claim 7, characterized in that: The specific steps for obtaining the discrimination result of the parent material type of the overmature source rock are as follows: S421: extracting microscopic component type index data based on the microscopic component type index set, checking each set of data, removing abnormal values ​​that exceed the upper and lower limits of the distribution, and collecting the classified data to obtain microscopic component type classification data; S422: Based on the maceral type classification data, combined with the maceral type index and the organic matter classification table, the corresponding relationship between the maceral and the organic matter category is compared, the maceral data is classified and judged, the matching data is extracted and sorted, and the matching result of the maceral and the organic matter is established; S423: Based on the matching results of the maceral components and organic matter, extract data that conforms to the parent material type of the overmature source rock, compare the classification results of the maceral component types, verify and aggregate the classified data, integrate the data in combination with the discrimination basis, and generate the discrimination result of the parent material type of the overmature source rock.

9. A system for distinguishing the parent material type of overmature source rocks, characterized in that: According to any one of claims 1 to 8, the method for distinguishing the parent material type of overmature source rock comprises: The sedimentary feature recognition module identifies the sedimentary environment by studying the regional sedimentary features, extracts representative major and trace elements from geological data, classifies and analyzes the elements, and obtains the sedimentary environment metadata set by associating element features; The component correlation analysis module uses the sedimentary environment metadata set to analyze the microscopic components of sapropelite, exinite, vitrinite and inertinite in low-mature source rocks, identify the percentage of microscopic components, correlate element data, and establish a ratio model of microscopic components and elements; The sedimentary environment analysis module uses the ratio model of microscopic components and elements to analyze the ratio of microscopic components to elements, analyze the parent material type and microscopic component characteristics, and generate a sedimentary environment regression model; The parent material type discrimination module evaluates the ratio of microscopic components in the sample according to the sedimentary environment regression model, analyzes the relationship between kerogen type and microscopic components, and discriminates the parent material type through type index evaluation to obtain the discrimination result of the parent material type of the overmature source rock.

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