An oil source correlation method and device, electronic equipment, storage medium and product

Abstract

Embodiments of the present application disclose an oil-source correlation method and device, electronic equipment, storage medium and product. The method comprises: generating an organic geochemical index parameter set based on organic geochemical index parameters corresponding to each oil and gas geological sample in an oil and gas geological sample set; performing principal component analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and constructing a principal component analysis scatter plot corresponding to the oil and gas geological sample set; performing hierarchical cluster analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and constructing a hierarchical cluster tree diagram corresponding to the oil and gas geological sample set; analyzing the contribution degree of a hydrocarbon source rock end member to a crude oil sample based on the organic geochemical index parameter set, the principal component analysis scatter plot and the hierarchical cluster tree diagram, and determining the oil-source correlation between the crude oil sample and the hydrocarbon source rock sample according to the contribution degree analysis result. The present scheme can realize accurate oil-source correlation.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration technology, and in particular to an oil source comparison method, apparatus, electronic equipment, storage medium and product. Background Technology

[0002] Oil source correlation, a core technique in geological research, is based on the principle that hydrocarbons generated from the same set of source rocks under similar thermal evolution conditions possess similar geochemical characteristics, while hydrocarbons generated from different source rocks exhibit significant differences in their characteristics. This is achieved through analysis of crude oil and source rock extracts, including group components, biomarker compounds, and stable carbon isotopes (δ¹²⁸O⁻). 13 C) By comparing indicators such as oil source, we can determine the affinity between the oil and gas discovered in a certain area and the potential source rocks, thereby evaluating the potential of the source rocks, clarifying the oil and gas accumulation process, calculating the resource volume, and guiding the direction of oil and gas exploration.

[0003] Oil and gas basins have undergone complex geological evolution over a long geological history, typically developing multiple sets and types of source rocks. Oil and gas generated from these multiple source rocks may converge into the same trap through complex transport systems, forming mixed-source reservoirs, significantly increasing the complexity of source-oil correlation. At the same time, source-oil correlation analysis techniques have certain limitations. The characteristic values ​​of a single geochemical parameter may be caused by multiple geological factors, potentially leading to multiple interpretations. Therefore, effective source-oil correlation should not be based on a single indicator but rather on a comprehensive analysis combining multiple indicators. Furthermore, source-oil correlation analysis of potential source rocks is usually primarily qualitative, lacking a quantitative comparison of the contribution of each source rock set to hydrocarbon growth. Summary of the Invention

[0004] This application provides an oil source comparison method, apparatus, electronic device, storage medium, and product to determine the primary and secondary source rocks of crude oil samples and achieve accurate oil source comparison.

[0005] According to one aspect of this application, an oil source comparison method is provided, the method comprising: Organic geochemical index parameters of each oil and gas geological sample in the oil and gas geological sample set are obtained respectively, and an organic geochemical index parameter set is generated based on the organic geochemical index parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set; wherein, the oil and gas geological samples include crude oil samples and source rock samples. Principal component analysis was performed on the oil and gas geological sample set based on the set of organic geochemical index parameters, and a scatter plot of principal component analysis corresponding to the oil and gas geological sample set was constructed based on the results of the principal component analysis; wherein, the scatter plot of principal component analysis is used to reflect the differences in geochemical characteristics of each oil and gas geological sample in the oil and gas geological sample set. Based on the set of organic geochemical index parameters, hierarchical cluster analysis is performed on the oil and gas geological sample set, and a hierarchical cluster tree diagram corresponding to the oil and gas geological sample set is constructed according to the hierarchical cluster analysis results. Based on the set of organic geochemical index parameters, the principal component analysis scatter plot, and the hierarchical clustering dendrogram, the contribution of source rock end-members to the crude oil samples in the oil and gas geological sample set is analyzed, and the oil source correspondence between the crude oil samples and the source rock samples is determined according to the contribution analysis results.

[0006] According to one aspect of this application, an oil source comparison device is provided, the device comprising: The indicator parameter set acquisition module is used to acquire the organic geochemical indicator parameters of each oil and gas geological sample in the oil and gas geological sample set, and generate an organic geochemical indicator parameter set based on the organic geochemical indicator parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set; wherein, the oil and gas geological samples include crude oil samples and source rock samples. The principal component analysis scatter plot generation module is used to perform principal component analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and construct a principal component analysis scatter plot corresponding to the oil and gas geological sample set based on the principal component analysis results; wherein, the principal component analysis scatter plot is used to reflect the differences in geochemical characteristics of each oil and gas geological sample in the oil and gas geological sample set. The hierarchical clustering tree diagram construction module is used to perform hierarchical clustering analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and construct the hierarchical clustering tree diagram corresponding to the oil and gas geological sample set according to the hierarchical clustering analysis results. The contribution analysis module is used to analyze the contribution of source rock end-members to crude oil samples in the oil and gas geological sample set based on the organic geochemical index parameter set, the principal component analysis scatter plot, and the hierarchical clustering tree diagram, and to determine the oil source correspondence between the crude oil samples and the source rock samples based on the contribution analysis results.

[0007] According to another aspect of this application, an electronic device is provided, the electronic device comprising: At least one processor; and A memory that is communicatively connected to at least one processor; wherein, The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the oil source comparison method of any embodiment of this application.

[0008] According to another aspect of this application, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute the oil source comparison method of any embodiment of this application.

[0009] According to another aspect of this application, a computer program product is provided, which includes a computer program that, when executed by a processor, implements the oil source comparison method of any embodiment of this application.

[0010] The technical solution of this application embodiment obtains the organic geochemical index parameters of each oil and gas geological sample in the oil and gas geological sample set, and generates an organic geochemical index parameter set based on the organic geochemical index parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set; wherein, the oil and gas geological samples include crude oil samples and source rock samples; principal component analysis is performed on the oil and gas geological sample set based on the organic geochemical index parameter set, and a principal component analysis scatter plot corresponding to the oil and gas geological sample set is constructed based on the principal component analysis results; wherein, the principal component analysis scatter plot is used to reflect The geochemical characteristics of each oil and gas geological sample in the oil and gas geological sample set are analyzed. Based on the organic geochemical index parameter set, hierarchical cluster analysis is performed on the oil and gas geological sample set, and a hierarchical clustering tree diagram corresponding to the oil and gas geological sample set is constructed according to the hierarchical clustering analysis results. Based on the organic geochemical index parameter set, the principal component analysis scatter plot, and the hierarchical clustering tree diagram, the contribution of source rock end-members to the crude oil samples in the oil and gas geological sample set is analyzed, and the oil-source correspondence between the crude oil samples and the source rock samples is determined according to the contribution analysis results. This scheme uses principal component analysis and cluster analysis to linearly reduce the dimensionality of a large amount of geochemical data and its multidimensional indicators, maximizing the extraction of important information from multidimensional variables, obtaining a concise, clear, and visual projection map. Then, based on the visual projection map, the relative contribution of source rock end-members to crude oil samples is quantitatively analyzed, and the primary and secondary source rocks of the crude oil samples are identified accordingly, achieving accurate oil-source correlation.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating an oil source comparison method provided in this application embodiment; Figure 2 A scatter plot of principal component analysis provided for an embodiment of this application; Figure 3 A hierarchical clustering tree diagram provided in the embodiments of this application; Figure 4 The relative contribution curve of the source rock endmembers to crude oil in well Z-1 based on the alternating least squares deconvolution algorithm is shown. Figure 5 The relative contribution curve of source rock endmembers to crude oil in well X-1 based on the alternating least squares deconvolution algorithm is shown in the diagram. Figure 6 This is a schematic diagram of the structure of an oil source comparison device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0015] It should be noted that the terms "first," "second," "third," "fourth," "actual," "preset," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Figure 1This is a flowchart illustrating an oil source comparison method provided in an embodiment of this application. This embodiment is applicable to situations involving oil source comparison. The method can be executed by an oil source comparison device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Obtain the organic geochemical index parameters of each oil and gas geological sample in the oil and gas geological sample set, and generate an organic geochemical index parameter set based on the organic geochemical index parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set; wherein, the oil and gas geological samples include crude oil samples and source rock samples.

[0017] In this embodiment, an oil and gas geological sample set is determined, wherein the oil and gas geological sample set contains at least two oil and gas geological samples, including crude oil samples and source rock samples. It is understood that the crude oil and source rock sample set is obtained, and crude oil and source rock samples with high maturity or those subjected to severe secondary processes such as biodegradation are removed, resulting in a crude oil and source rock sample set after removal. Organic geochemical index parameters are obtained for each oil and gas geological sample in the oil and gas geological sample set, and an organic geochemical index parameter set is generated based on the organic geochemical index parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set. The organic geochemical index parameters may include the group component organic carbon isotopes and biomarker compound index parameters of the oil and gas geological samples (i.e., crude oil samples or source rock samples). The number of organic geochemical index parameters corresponding to each oil and gas geological sample can be multiple, such as three or more.

[0018] Optionally, before generating the organic geochemical index parameter set based on the organic geochemical index parameters corresponding to each of the oil and gas geological samples, the method further includes: performing secondary impact analysis on the corresponding oil and gas geological samples based on the organic geochemical index parameters to determine the intensity of the secondary impact of the oil and gas geological samples; if the intensity of the secondary impact is greater than a preset impact intensity threshold, then the oil and gas geological sample is removed from the oil and gas geological sample set.

[0019] In this embodiment, the secondary impact analysis is performed on the organic geochemical parameter set of crude oil and source rock samples in the oil and gas geological sample set, and the intensity of the secondary impact of the oil and gas geological samples is determined. The secondary impact analysis includes thermal maturation analysis, biodegradation analysis, and sample contamination analysis; therefore, the intensity of the secondary impact corresponds to thermal maturity, biodegradation intensity, and sample contamination intensity. The ranges of secondary impact index parameters (anomalies) such as thermal maturity, biodegradation, and sample contamination are set, and the set of secondary impacts on crude oil and source rock samples is calculated. The intensity of the secondary impact is analyzed, and samples with abnormal secondary impact index parameters are identified as crude oil and source rock samples affected by secondary impacts. In this embodiment, biodegradation indicators include, but are not limited to: "UCM" bulges, indicating biodegradation; 25-norhopane, high abundance indicating severe biodegradation; CPI, TAR, etc., biodegradation easily causes loss of n-alkanes and isoalkanes; and Pr / related to isoprene-like compounds. n C 17 and Ph / n C 18 The ratio increases with the degree of biodegradation; n-alkanes are more easily biodegraded than isoprene-like alkanes. Aromatic hydrocarbon indicators such as the dibenzothiophene / phenanthrene and dibenzothiophene / 4-methylnaphthalene ratios also increase with the degree of biodegradation; 4-methylnaphthalene is more susceptible to biodegradation than phenanthrene and dibenzothiophene, and phenanthrene is more susceptible to biodegradation than dibenzothiophene. Triarylsterane C 20 / (C 20 +C 27 ) and C 21 / (C 21 +C 28 The ratio shows a good positive correlation, and decreases with increasing biodegradation. (Triarylsterane C) 20 –C 21 Compared to triarylsterane C 26 –C 28 More susceptible to biodegradation. Indicators of thermal maturation include, but are not limited to: C 29 The sterane 20S / (20S+20R) ratio, with an equilibrium value of approximately 0.5~0.55, increases with increasing maturity; C 29 The sterane ββ / (αα+ββ) ratio, with an equilibrium value of approximately 0.7, increases with increasing maturity; the Ts / (Ts+Tm) ratio also increases with increasing maturity; C 31 The 22S / (22S+22R) ratio of hopane has an equilibrium value of approximately 0.6, which increases with increasing maturity; C 27 rearranged sterane / C 27The ratio of regular steranes increases with increasing maturity; aromatic hydrocarbon indices, such as the methylphenanthrene index, also increase with increasing maturity; the 4-methyldibenzothiophene / 1-methyldibenzothiophene ratio increases with increasing maturity, and 4-methyldibenzothiophene exhibits higher thermal stability compared to 1-methyldibenzothiophene. Sample contamination indicators include, but are not limited to, extractable organic matter content and saturated hydrocarbon / aromatic hydrocarbon ratio. Drilling fluid contamination results in low extractable organic matter content and a high saturated hydrocarbon / aromatic hydrocarbon ratio. All crude oil and source rock samples affected by secondary processes are removed from the crude oil and source rock sample set to obtain the excluded crude oil and source rock sample set.

[0020] In this embodiment, the organic geochemical parameters corresponding to each oil and gas geological sample can be parameters that are less affected by secondary processes such as thermal maturation, biodegradation, and migration, and can better reflect the source of organic matter or the sedimentary environment. Specific organic geochemical parameters may include: δ 13 C 饱和烃 , δ 13 C 芳香烃 Pr / Ph, C 19 / C 23 Terpenes, C 22 / C 21 Terpenes, C 24 / C 23 Tricyclic terpenoids, C 26 / C 25 Terpenes, C 24 Tetracyclic triterpenoids / C 23 Tricyclic terpenoids, C 27 Terpenes / C 27 Regular sterane, C 28 / C 30 hopane, C 29 / C 30 hopane, C 31 ciprofloxacin / C 30 Hoproane, Gammacerane / C 31 C10 hopane, C 35 / C 34 C-Hopane, Sterane / Hopane, C 29 / C 27 Regular sterane, C 27 C 28 C 29 Relative content of regular steranes (%), C 26 Hopane / Ts trinorhopane, Ts / Tm trinorhopane, dinoflagran, triaryldinoflagran, etc. Table 1 shows the selected organic geochemical indicators provided in the examples of this application: Table 1 Optionally, to avoid the influence of thermal maturity on the classification of crude oil sample genesis and oil source comparison analysis, organic geochemical indicators that have significance for thermal maturity and biodegradation can be excluded, such as sterane C4 in saturated hydrocarbons. 29 The ratio of 20S / (20S + 20R), C 29 Stelane ββ / (αα+ββ) ratio, C 27 Rearranged sterane / regular sterane ratio, C 31 By using the 22S / (22S+22R) ratio of hopane, the rearranged hopane / regular hopane ratio, and indicators related to homologues of aromatic hydrocarbons such as thiophene, methylnaphthalene, and methylphenanthrene, a set of optimal index parameters for multivariate analysis of crude oil and source rock samples was obtained.

[0021] S120. Principal component analysis is performed on the oil and gas geological sample set based on the organic geochemical index parameter set, and a principal component analysis scatter plot corresponding to the oil and gas geological sample set is constructed according to the principal component analysis results; wherein, the principal component analysis scatter plot is used to reflect the differences in geochemical characteristics of each oil and gas geological sample in the oil and gas geological sample set.

[0022] In this embodiment of the application, principal component analysis is performed on each crude oil sample and source rock sample in the oil and gas geological sample set based on the organic geochemical index parameter set, and a principal component analysis scatter plot corresponding to the oil and gas geological sample set is constructed based on the principal component analysis results. Optionally, principal component analysis is performed on the oil and gas geological sample set based on the organic geochemical index parameter set, and a principal component analysis scatter plot corresponding to the oil and gas geological sample set is constructed based on the principal component analysis results. This includes: constructing a principal component analysis model based on various geochemical indicators involved in the organic geochemical index parameter set, fitting each geochemical indicator to at least two uncorrelated principal component variables; determining the variance contribution of each principal component variable based on the organic geochemical index parameters in the organic geochemical index parameter set, and selecting a preset number of target principal component variables with the largest variance contribution; for each oil and gas geological sample in the oil and gas geological sample set, determining the target principal component score corresponding to each target principal component variable based on the organic geochemical index parameters corresponding to the oil and gas geological sample in the organic geochemical index parameter set; and constructing a principal component analysis scatter plot corresponding to the oil and gas geological sample set based on the target principal component scores corresponding to each oil and gas geological sample in the oil and gas geological sample set.

[0023] In this embodiment of the application, constructing a principal component analysis scatter plot may specifically include the following steps: S21. Construct a principal component analysis model based on various geochemical indicators involved in the organic geochemical indicator parameter set: It is understandable that X = (X1,…, X…) k )' is a k-dimensional random vector, where the mean E(X) = μ and the covariance matrix D(x) = ∑. The principal component analysis model is a k-dimensional vector X1, X2, …, X k A linear combination of .

[0024] S22. Variable Correlation Determination: Principal Component Variable F i and F j (i ≠ j, i, j = 1, … , k) cannot be mutually corrected, and the correlation is 0, i.e., Cov(F i F j = 0, meaning there is no correlation between the principal component variables.

[0025] S23. Determine the target principal component variables: Specifically, based on the organic geochemical index parameters corresponding to each oil and gas geological sample in the organic geochemical index parameter set, determine the principal component score corresponding to each principal component variable. Then, for each principal component variable, calculate the variance of that principal component variable based on the principal component score corresponding to each oil and gas geological sample in the oil and gas geological sample set, and use the variance as the variance contribution of the principal component variable. It can be understood that the variance contribution of each principal component variable can be determined in the above manner. Select a predetermined number of target principal component variables with the largest variance contributions. For example, select the first principal component variable with the largest variance contribution (e.g., F1) and the second principal component variable with the second largest variance contribution (e.g., F2) as the target principal component variables.

[0026] S24. Determine the target principal component score corresponding to each target principal component variable of each oil and gas geological sample in the oil and gas geological sample set.

[0027] S25. Based on the target principal component scores corresponding to each oil and gas geological sample in the oil and gas geological sample set, construct a scatter plot of principal component analysis corresponding to the oil and gas geological sample set. For example, Figure 2 A principal component analysis scatter plot is provided for an embodiment of this application, such as... Figure 2 As shown, the principal component analysis results of samples from wells Z-1 and X-1 show that principal component 1 (PC-1) represents 44.3% of the 22 geochemical indices of the defined group, and principal component 2 (PC-2) represents 15.5% of the 22 organic geochemical indices of the defined group. The sum of principal component 1 and principal component 2 is 59.8%, which is greater than 50%, indicating good representativeness. It also reflects that the samples from the A-stage, B-stage, C-stage, and A-stage strata of well Z-1, well X-1, have different organic geochemical characteristics.

[0028] The advantage of this setup is that principal component analysis of oil and gas geological sample sets can achieve linear dimensionality reduction of multidimensional indicators, maximize the extraction of important information from multidimensional variables, and visualize the multidimensional variables of the original dataset using two-dimensional or three-dimensional projection maps, obtaining simple, clear, and visualized projection maps, and further extracting important geological information.

[0029] S130. Based on the set of organic geochemical index parameters, perform hierarchical cluster analysis on the oil and gas geological sample set, and construct a hierarchical cluster tree diagram corresponding to the oil and gas geological sample set according to the hierarchical cluster analysis results.

[0030] In this embodiment, a hierarchical clustering analysis is performed on the removed oil and gas geological sample set using an organic geochemical index parameter set to obtain a hierarchical clustering tree diagram. Optionally, performing hierarchical clustering analysis on the oil and gas geological sample set based on the organic geochemical index parameter set and constructing a hierarchical clustering tree diagram corresponding to the oil and gas geological sample set based on the hierarchical clustering analysis results includes: taking each oil and gas geological sample in the oil and gas geological sample set as a sample cluster, generating a sample cluster set, and calculating the similarity between the organic geochemical index parameters corresponding to the two sample clusters in the organic geochemical index parameter parameter set for every two sample clusters in the sample cluster set; merging the two sample clusters with the highest similarity using a cohesive hierarchical clustering algorithm to form a new sample cluster to update the sample cluster set, and returning to perform the calculation of the similarity between the organic geochemical index parameters corresponding to the two sample clusters in the organic geochemical index parameter parameter set for every two sample clusters in the sample cluster set, repeating this process until all sample clusters are clustered into one data cluster; and generating a hierarchical clustering tree diagram corresponding to the oil and gas geological sample set based on the similarity of the two sample clusters merged each time.

[0031] In this embodiment of the application, constructing a hierarchical clustering tree diagram may specifically include the following steps: S31. Calculate the sample cluster X = (X1, X2, ..., X...) n Any sample cluster (X) i X j Similarity between: In this embodiment of the application, any sample cluster (X) from the set of organic geochemical index parameters can be used. i X jThe Euclidean distance between the corresponding organic geochemical index parameters is used as the similarity between the two. Optionally, the similarity can also be determined according to the following steps: (1) For any sample cluster Xi in sample cluster X, it can be used as the center of the data cluster Ci = (Xi), and a new dataset A = (A1, A2, …, An) is generated for sample cluster X; (2) Calculate the similarity of any set of data (Ai, Aj) in dataset A: First calculate the Euclidean distance D AiAj = Where m represents the number of organic geochemical index parameters, the similarity S is then calculated according to the following formula. AiAj = 1-D AiAj / D max , where D max It is the maximum value of the Euclidean distance between all sample clusters in the sample cluster set. The advantage of this setting is that the value 1 is assigned to the most similar data point, and the value 0 is assigned to the data point with the greatest difference.

[0032] S32. Using agglomerative hierarchical clustering algorithm, select the two most similar sample clusters and merge them into a new sample cluster set X' = (X1, X2, ..., X...). n-1 The process involves merging the two most similar data clusters (Ai, Aj) into a dataset Ak = Ai∪Aj, generating a new dataset A = (A1, A2, …, Aj) of sample clusters. n- 1).

[0033] S33. Calculate any sample cluster (X) in the new sample cluster set X'. i X j The similarity of the samples is determined, and steps S31 and S32 are repeated until all sample clusters are finally clustered into one data cluster.

[0034] S34. Generate a hierarchical clustering tree diagram corresponding to the oil and gas geological sample set based on the similarity of the two sample clusters merged each time. For example, Figure 3 This application provides a hierarchical clustering tree diagram as an embodiment. For example... Figure 3 As shown, cluster analysis was performed on 19 crude oil samples (Z1~19) and 5 source rock samples from well Z-1, and 27 crude oil samples (X1~27) from well X-1. The correlation coefficient of the hierarchical cluster analysis was 0.55, which is greater than 0.5, indicating that the classification results are relatively reliable.

[0035] Optionally, the k-means clustering method is used to determine the optimal number of clusters. The elbow rule or silhouette coefficient is applied to calculate the optimal number of clusters k=6, and the clusters are formed accordingly. The hierarchical clustering dendrogram results show that the Z-1 well samples can be divided into four groups: IA, IB, IC, and IIA, and the X-1 well samples can be divided into two groups: IIB and IIC. Each group has significantly different organic geochemical characteristics, especially the IA-IC, IIA, and IIB-IIC groups, which show significant differences.

[0036] The advantage of this setup is that it performs hierarchical cluster analysis on oil and gas geological sample sets, collects descriptive data based on similarity, and continuously merges the genealogy model of the abstract object dataset into the most similar subsets by measuring the similarity between different data. This results in the greatest similarity within the merged subsets and the greatest difference between the datasets. The final classification results are displayed in a simple and intuitive genealogy tree format, enabling rapid, efficient, scientific and accurate classification of crude oil types.

[0037] S140. Based on the set of organic geochemical index parameters, the scatter plot of principal component analysis, and the hierarchical clustering tree diagram, the contribution of source rock end-members to the crude oil samples in the oil and gas geological sample set is analyzed, and the oil source correspondence between the crude oil samples and the source rock samples is determined according to the contribution analysis results.

[0038] In this embodiment of the application, the relative contribution of source rock endmembers to crude oil samples is obtained by using the alternating least squares (ALS) deconvolution calculation on crude oil samples of the oil and gas geological sample set with reference to the principal component analysis scatter plot and hierarchical clustering tree diagram. Optionally, based on the set of organic geochemical index parameters, the principal component analysis scatter plot, and the hierarchical clustering tree diagram, the contribution of source rock end-members to the crude oil samples in the oil and gas geological sample set is analyzed, including: constructing a crude oil sample data matrix based on the organic geochemical index parameters corresponding to the crude oil samples in the set of organic geochemical index parameters; constructing an initial contribution matrix based on the principal component analysis scatter plot, and determining the number of source rock end-members based on the hierarchical clustering tree diagram or the known number of potential source rock strata, and constructing a source rock end-member metadata matrix based on the number of source rock end-members; establishing a matrix operation model between the crude oil sample data matrix, the initial contribution matrix, and the source rock end-member metadata matrix based on the assumption that the crude oil samples are a linear mixture of source rock end-members; using an alternating least squares deconvolution algorithm, the initial contribution matrix and the source rock end-member metadata matrix are iteratively updated based on the matrix operation model until a preset convergence condition is met, and a target contribution matrix is ​​determined; and determining the relative contribution of each source rock end-member to each crude oil sample based on the target contribution matrix.

[0039] In this embodiment of the application, determining the relative contribution of each source rock end-member to each crude oil sample may include the following steps: S41. Construct a crude oil sample data matrix based on the organic geochemical index parameters corresponding to the crude oil samples in the organic geochemical index parameter set. D Among them, crude oil sample data matrix D Size is n × m , n It refers to the number of crude oil samples. m It refers to the number of organic geochemical index parameters.

[0040] S42. Construct the initial contribution matrix and source rock end-member metadata matrix. Specifically, determine the number of source rock end-members based on hierarchical clustering tree diagrams or the known number of potential source rock strata, and construct the source rock end-member metadata matrix based on the number of source rock end-members. E Hydrocarbon source rock end metadata matrix E The size is s × m ,in s This refers to the number of source rock endmembers. An initial contribution matrix is ​​constructed based on the scatter plot obtained from principal component analysis. C Initial contribution matrix C The size is n × s , representing the relative contribution of each source rock endmember to each sample (requiring non-negative summation to be in units of 1).

[0041] S43. Establish a matrix operation model. Specifically, assuming the crude oil sample is a linear combination of source rock endmembers, then the crude oil sample data matrix... D Initial contribution matrix C and source rock end metadata matrix E The matrix operation model between them can be represented as D ≈ C × E .

[0042] S44. Using an alternating least squares deconvolution algorithm, the initial contribution matrix and the source rock end metadata matrix are iteratively updated based on the matrix operation model. Specifically, a convergence threshold is set (e.g., ...). ϵ =10 -6 Alternatively, for the maximum number of iterations (1000), use the alternating least squares deconvolution algorithm to iterate, repeating steps S44A and S44B as follows, until the convergence criterion reconstruction error is met. The change is less than the convergence threshold ϵ Or, it may reach the maximum number of iterations of 1000. S44A, Source Rock End Metadata MatrixE Iteratively update the initial contribution matrix C Solve the least squares problem: Using nonnegative least squares to impose constraints, it is required that... C All elements in the set are non-negative ( Cij ≥0), and the sum of each row is 1 (i.e., the total contribution is 100%). S44B, Fixed Initial Contribution Matrix C Iteratively update the source rock end metadata matrix E， Solve the least squares problem: Using nonnegative least squares to impose constraints, it is required that... E All elements in the set are non-negative ( Eij ≥0).

[0043] S45. Convergence and Validation: Check whether the reconstruction error is stable, ensure the algorithm converges, and use cross-validation, residual analysis to evaluate model performance (such as root mean square error), or Monte Carlo simulation to evaluate the uncertainty of the results.

[0044] S46. Extract the relative contribution (expressed as a percentage) of each source rock endmember to each crude oil sample from the target contribution matrix C determined when the preset convergence conditions are met. Plot a relative contribution chord graph and use the visualization results of the relative contribution chord graph to help show the relative contribution of the source rock endmember to the crude oil. For example, Figure 4 The relative contribution curve of the source rock endmembers to crude oil in well Z-1 based on the alternating least squares deconvolution algorithm is shown. Figure 5 This is a chord diagram showing the relative contribution of source rock endmembers to crude oil in well X-1 based on the alternating least squares deconvolution algorithm. Figure 4 and Figure 5 As shown, the relative contribution chord diagram illustrates the relative contributions of source rock end-members EM1, EM2, and EM3 to crude oil in well Z-1 (samples Z1~19) and well X-1 (samples X1~27), ​​respectively, and estimates the hydrocarbon injection situation of source rock end-members in a specified area.

[0045] The advantage of this setup is that it utilizes the set of organic geochemical index parameters to perform alternating least squares inverse convolution calculations on the crude oil sample set after removal, accurately quantifying the contribution of different source rocks, and visualizing the relative contribution of source rock end-members to crude oil using chord diagrams; by comparing the geochemical characteristics of source rock end-members with those of potential source rocks, it effectively identifies the main source rocks.

[0046] In this embodiment, the oil source correspondence between the crude oil sample and the source rock sample is determined based on the contribution analysis results. For example, by comparing the similarity of the organic geochemical indices of the source rock endmembers with those of potential source rock samples from each stratum, the primary and secondary source rocks corresponding to each endmember Ek are inferred. The comparison reveals that the organic geochemical indices of the source rock endmembers EM1, EM2, and EM3 are extremely similar to those of the source rocks in the C, B, and A strata of well Z-1, respectively. Therefore, it is inferred that endmembers EM1, EM2, and EM3 correspond to the C, B, and A strata source rocks, respectively. The rationality of the results is verified by combining the geological background, and the contribution of the source rock endmembers to hydrocarbon charging in the designated area is estimated. The oil source correlation results show that the Class A crude oil in well Z-1 originates from Class A source rocks, with the source rocks contributing 45% to 55% of the crude oil, averaging 50%; the Class B crude oil originates from Class B source rocks, with the source rocks contributing 49% to 72% of the crude oil, averaging 61%; and the Class C crude oil originates from Class C source rocks, with the source rocks contributing 51% to 86% of the crude oil, averaging 60%.

[0047] Optionally, before performing principal component analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, the method further includes: determining a subset of index parameters composed of various organic geochemical index parameters in the organic geochemical index parameter set, and standardizing the organic geochemical index parameters in the subset of index parameters based on the subset of index parameters. In this embodiment, each organic geochemical index parameter in the organic geochemical index parameter set is standardized to enable comparison between the various organic geochemical index parameters. Specifically, a subset of index parameters composed of various organic geochemical index parameters in the organic geochemical index parameter set is determined. Then, each organic geochemical index parameter in the subset of index parameters is standardized according to the following formula: [Xi-mean(X)] / Sd(X), where mean(X) is the mean of the subset of index parameters, Sd(X) is the standard deviation of the subset of index parameters, and Xi is the i-th organic geochemical index parameter in the subset of index parameters.

[0048] The technical solution of this application embodiment obtains the organic geochemical index parameters of each oil and gas geological sample in the oil and gas geological sample set, and generates an organic geochemical index parameter set based on the organic geochemical index parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set; wherein, the oil and gas geological samples include crude oil samples and source rock samples; principal component analysis is performed on the oil and gas geological sample set based on the organic geochemical index parameter set, and a principal component analysis scatter plot corresponding to the oil and gas geological sample set is constructed based on the principal component analysis results; wherein, the principal component analysis scatter plot is used to reflect The geochemical characteristics of each oil and gas geological sample in the oil and gas geological sample set are analyzed. Based on the organic geochemical index parameter set, hierarchical cluster analysis is performed on the oil and gas geological sample set, and a hierarchical clustering tree diagram corresponding to the oil and gas geological sample set is constructed according to the hierarchical clustering analysis results. Based on the organic geochemical index parameter set, the principal component analysis scatter plot, and the hierarchical clustering tree diagram, the contribution of source rock end-members to the crude oil samples in the oil and gas geological sample set is analyzed, and the oil-source correspondence between the crude oil samples and the source rock samples is determined according to the contribution analysis results. This scheme uses principal component analysis and cluster analysis to linearly reduce the dimensionality of a large amount of geochemical data and its multidimensional indicators, maximizing the extraction of important information from multidimensional variables, obtaining a concise, clear, and visual projection map. Then, based on the visual projection map, the relative contribution of source rock end-members to crude oil samples is quantitatively analyzed, and the primary and secondary source rocks of the crude oil samples are identified accordingly, achieving accurate oil-source correlation.

[0049] Figure 6 This is a schematic diagram of an oil source comparison device provided in an embodiment of this application. This device can execute the oil source comparison method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the method. Figure 6 As shown, the device includes: The indicator parameter set acquisition module 610 is used to acquire the organic geochemical indicator parameters of each oil and gas geological sample in the oil and gas geological sample set, and generate an organic geochemical indicator parameter set based on the organic geochemical indicator parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set; wherein, the oil and gas geological samples include crude oil samples and source rock samples. The principal component analysis scatter plot generation module 620 is used to perform principal component analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and construct a principal component analysis scatter plot corresponding to the oil and gas geological sample set based on the principal component analysis results; wherein, the principal component analysis scatter plot is used to reflect the differences in geochemical characteristics of each oil and gas geological sample in the oil and gas geological sample set. The hierarchical clustering tree diagram construction module 630 is used to perform hierarchical clustering analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and construct a hierarchical clustering tree diagram corresponding to the oil and gas geological sample set according to the hierarchical clustering analysis results. The contribution analysis module 640 is used to analyze the contribution of source rock end-members to the crude oil samples in the oil and gas geological sample set based on the organic geochemical index parameter set, the principal component analysis scatter plot and the hierarchical clustering tree diagram, and to determine the oil source correspondence between the crude oil samples and the source rock samples according to the contribution analysis results.

[0050] Optional, also includes: The secondary impact intensity determination module is used to perform secondary impact analysis on the corresponding oil and gas geological samples based on the organic geochemical index parameters before generating the organic geochemical index parameter set based on the organic geochemical index parameters corresponding to each of the oil and gas geological samples, and to determine the secondary impact intensity of the oil and gas geological samples. The sample rejection module is used to remove the oil and gas geological sample from the oil and gas geological sample set if the intensity of the secondary effect is greater than a preset intensity threshold.

[0051] Optional, the principal component analysis scatter plot generation module is used for: Based on the various geochemical indicators involved in the aforementioned organic geochemical index parameter set, a principal component analysis model is constructed, and each geochemical index is fitted into at least two uncorrelated principal component variables. The variance contribution of each principal component variable is determined based on the organic geochemical index parameters in the set of organic geochemical index parameters, and a preset number of target principal component variables with the largest variance contribution are selected. For each oil and gas geological sample in the oil and gas geological sample set, the target principal component score corresponding to each target principal component variable is determined based on the organic geochemical index parameters corresponding to the oil and gas geological sample in the organic geochemical index parameter set. Based on the target principal component scores corresponding to each oil and gas geological sample in the oil and gas geological sample set, a scatter plot of principal component analysis corresponding to the oil and gas geological sample set is constructed.

[0052] Optional, hierarchical clustering tree diagram building module, used for: Each oil and gas geological sample in the oil and gas geological sample set is taken as a sample cluster to generate a sample cluster set. For every two sample clusters in the sample cluster set, the similarity between the organic geochemical index parameters corresponding to the two sample clusters in the organic geochemical index parameter set is calculated. The two sample clusters with the highest similarity are merged into a new sample cluster to update the sample cluster set. Then, for each pair of sample clusters in the sample cluster set, the similarity between the organic geochemical index parameters corresponding to the two sample clusters in the organic geochemical index parameter set is calculated. This process is repeated until all sample clusters are clustered into one data cluster. A hierarchical clustering tree diagram corresponding to the oil and gas geological sample set is generated based on the similarity of the two sample clusters merged each time.

[0053] Optional, contribution analysis module, used for: A crude oil sample data matrix is ​​constructed based on the organic geochemical index parameters corresponding to the crude oil samples in the set of organic geochemical index parameters. An initial contribution matrix is ​​constructed based on the principal component analysis scatter plot, and the number of source rock endmembers is determined based on the hierarchical clustering tree diagram. A source rock endmember metadata matrix is ​​then constructed based on the number of source rock endmembers. Based on the assumption that the crude oil sample is a linear mixture of source rock endmembers, a matrix operation model is established between the crude oil sample data matrix, the initial contribution matrix, and the source rock endmember data matrix. The initial contribution matrix and the source rock end metadata matrix are cyclically updated based on the matrix operation model using the alternating least squares deconvolution algorithm until the preset convergence condition is met, thereby determining the target contribution matrix. The relative contribution of each source rock endmember to each crude oil sample is determined based on the target contribution matrix.

[0054] Optional, also includes: The standardization module is used to determine the subset of index parameters composed of various organic geochemical index parameters in the organic geochemical index parameter set before performing principal component analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and to perform standardization processing on the organic geochemical index parameters in the index parameter subset based on the index parameter subset.

[0055] The oil source comparison device provided in this application embodiment can execute an oil source comparison method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the method execution.

[0056] Figure 7A schematic diagram of an electronic device 10, which can be used to implement embodiments of this application, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0057] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0058] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0059] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the oil source comparison method.

[0060] In some embodiments, the oil source comparison method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the oil source comparison method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the oil source comparison method by any other suitable means (e.g., by means of firmware).

[0061] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, at least one input device, and at least one output device.

[0062] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable oil source comparison device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0063] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0064] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0065] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0066] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0067] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the oil source comparison method as provided in any embodiment of this application.

[0068] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider). It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired information of the technical solution of this application can be achieved, and this is not limited herein.

[0069] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for comparing oil sources, characterized in that, The method includes: Organic geochemical index parameters of each oil and gas geological sample in the oil and gas geological sample set are obtained respectively, and an organic geochemical index parameter set is generated based on the organic geochemical index parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set; wherein, the oil and gas geological samples include crude oil samples and source rock samples. Principal component analysis was performed on the oil and gas geological sample set based on the set of organic geochemical index parameters, and a scatter plot of principal component analysis corresponding to the oil and gas geological sample set was constructed based on the results of the principal component analysis; wherein, the scatter plot of principal component analysis is used to reflect the differences in geochemical characteristics of each oil and gas geological sample in the oil and gas geological sample set. Based on the set of organic geochemical index parameters, hierarchical cluster analysis is performed on the oil and gas geological sample set, and a hierarchical cluster tree diagram corresponding to the oil and gas geological sample set is constructed according to the hierarchical cluster analysis results. Based on the set of organic geochemical index parameters, the principal component analysis scatter plot, and the hierarchical clustering dendrogram, the contribution of source rock end-members to the crude oil samples in the oil and gas geological sample set is analyzed, and the oil source correspondence between the crude oil samples and the source rock samples is determined according to the contribution analysis results.

2. The method according to claim 1, characterized in that, Before generating the organic geochemical index parameter set based on the organic geochemical index parameters corresponding to each of the aforementioned oil and gas geological samples, the process also includes: The secondary impact analysis was conducted on the corresponding oil and gas geological samples based on the organic geochemical index parameters to determine the intensity of the secondary impact on the oil and gas geological samples. If the intensity of the secondary effect is greater than a preset threshold, the oil and gas geological sample will be removed from the oil and gas geological sample set.

3. The method according to claim 1, characterized in that, Principal component analysis was performed on the hydrocarbon geological sample set based on the aforementioned organic geochemical index parameter set. A scatter plot of principal component analysis corresponding to the hydrocarbon geological sample set was constructed based on the principal component analysis results, including: Based on the various geochemical indicators involved in the aforementioned organic geochemical index parameter set, a principal component analysis model is constructed, and each geochemical index is fitted into at least two uncorrelated principal component variables. The variance contribution of each principal component variable is determined based on the organic geochemical index parameters in the set of organic geochemical index parameters, and a preset number of target principal component variables with the largest variance contribution are selected. For each oil and gas geological sample in the oil and gas geological sample set, the target principal component score corresponding to each target principal component variable is determined based on the organic geochemical index parameters corresponding to the oil and gas geological sample in the organic geochemical index parameter set. Based on the target principal component scores corresponding to each oil and gas geological sample in the oil and gas geological sample set, a scatter plot of principal component analysis corresponding to the oil and gas geological sample set is constructed.

4. The method according to claim 1, characterized in that, Based on the set of organic geochemical index parameters, hierarchical cluster analysis was performed on the oil and gas geological sample set. A hierarchical clustering tree diagram corresponding to the oil and gas geological sample set was constructed based on the hierarchical cluster analysis results, including: Each oil and gas geological sample in the oil and gas geological sample set is taken as a sample cluster to generate a sample cluster set. For every two sample clusters in the sample cluster set, the similarity between the organic geochemical index parameters corresponding to the two sample clusters in the organic geochemical index parameter set is calculated. The two sample clusters with the highest similarity are merged into a new sample cluster to update the sample cluster set. Then, for each pair of sample clusters in the sample cluster set, the similarity between the organic geochemical index parameters corresponding to the two sample clusters in the organic geochemical index parameter set is calculated. This process is repeated until all sample clusters are clustered into one data cluster. A hierarchical clustering tree diagram corresponding to the oil and gas geological sample set is generated based on the similarity of the two sample clusters merged each time.

5. The method according to claim 1, characterized in that, Based on the aforementioned set of organic geochemical index parameters, the aforementioned principal component analysis scatter plot, and the aforementioned hierarchical clustering dendrogram, the contribution of source rock end-members to the crude oil samples in the aforementioned oil and gas geological sample set is analyzed, including: A crude oil sample data matrix is ​​constructed based on the organic geochemical index parameters corresponding to the crude oil samples in the set of organic geochemical index parameters. An initial contribution matrix is ​​constructed based on the principal component analysis scatter plot, and the number of source rock endmembers is determined based on the hierarchical clustering tree diagram. A source rock endmember metadata matrix is ​​then constructed based on the number of source rock endmembers. Based on the assumption that the crude oil sample is a linear mixture of source rock endmembers, a matrix operation model is established between the crude oil sample data matrix, the initial contribution matrix, and the source rock endmember data matrix. The initial contribution matrix and the source rock end metadata matrix are cyclically updated based on the matrix operation model using the alternating least squares deconvolution algorithm until the preset convergence condition is met, thereby determining the target contribution matrix. The relative contribution of each source rock endmember to each crude oil sample is determined based on the target contribution matrix.

6. The method according to any one of claims 1-5, characterized in that, Before performing principal component analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, the following steps are also included: A subset of index parameters consisting of various organic geochemical index parameters in the set of organic geochemical index parameters is determined, and the organic geochemical index parameters in the subset of index parameters are standardized based on the subset of index parameters.

7. An oil source comparison device, characterized in that, include: The indicator parameter set acquisition module is used to acquire the organic geochemical indicator parameters of each oil and gas geological sample in the oil and gas geological sample set, and generate an organic geochemical indicator parameter set based on the organic geochemical indicator parameters corresponding to each oil and gas geological sample in the oil and gas geological sample set; wherein, the oil and gas geological samples include crude oil samples and source rock samples. The principal component analysis scatter plot generation module is used to perform principal component analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and construct a principal component analysis scatter plot corresponding to the oil and gas geological sample set based on the principal component analysis results; wherein, the principal component analysis scatter plot is used to reflect the differences in geochemical characteristics of each oil and gas geological sample in the oil and gas geological sample set. The hierarchical clustering tree diagram construction module is used to perform hierarchical clustering analysis on the oil and gas geological sample set based on the organic geochemical index parameter set, and construct the hierarchical clustering tree diagram corresponding to the oil and gas geological sample set according to the hierarchical clustering analysis results. The contribution analysis module is used to analyze the contribution of source rock end-members to crude oil samples in the oil and gas geological sample set based on the organic geochemical index parameter set, the principal component analysis scatter plot, and the hierarchical clustering tree diagram, and to determine the oil source correspondence between the crude oil samples and the source rock samples based on the contribution analysis results.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the oil source comparison method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the oil source comparison method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the oil source comparison method according to any one of claims 1-6.

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