Gray correlation and analytic hierarchy process-based low-permeability reservoir geological sweet spot identification method

Abstract

The invention discloses a low-permeability reservoir geological sweet spot identification method based on grey correlation and analytic hierarchy process. The method comprises the following steps: S1, collecting geological parameter data of a low-permeability reservoir; s2, analyzing the geological parameter data obtained in the step S1 by applying a grey correlation algorithm, determining the correlation degree of each geological parameter, and sorting the geological parameters according to the correlation degree of the geological parameters; s3, analyzing and determining a judgment matrix by using an analytic hierarchy process, and calculating the weight of each index; s4, counting the weight obtained in the step S3, establishing a geological dessert comprehensive evaluation model, and calculating a geological sweetness indication factor through the geological dessert comprehensive evaluation model; and S5, according to the geological sweetness indication factor obtained in the step S4, determining reservoir geological sweet spot classification in combination with a geological sweet spot division table, and the like. Through the grey correlation algorithm and the analytic hierarchy process, geological sweet spot identification of the low-permeability reservoir is realized, and a basis is provided for subsequent remaining oil potential tapping scheme evaluation and comprehensive scheme adjustment.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas field development, and in particular relates to a method for identifying geological sweet spots in low permeability reservoirs based on grey correlation and hierarchical analysis. Background Art

[0002] At present, the main problems in identifying the geological sweet spots of low permeability reservoirs include strong reservoir heterogeneity and complex pore structure, which makes the identification and comprehensive evaluation of reservoir sweet spots very difficult. Due to the large overall burial depth, the accuracy of traditional logging interpretation is far from enough to meet the needs of exploration and development. In addition, existing sweet spot identification methods often rely on a single parameter, such as permeability, while ignoring other factors that may affect reservoir quality, making it difficult to effectively characterize the characteristics of low permeability reservoirs.

[0003] Existing low-permeability reservoir geological sweet spot identification and classification technologies, such as the dimensionless intersection method of logging curves, may require a lot of adjustments and optimizations when processing multi-well data and predicting the spatial distribution of sweet spots to adapt to the geological characteristics of different regions; another example is the single parameter evaluation method, which uses single parameters such as permeability, porosity, and oil saturation as evaluation indicators to determine geological sweet spots through comparative analysis. This method ignores the influence of other parameters and may lead to inaccurate evaluation results; another example is the fluid-solid coupling seepage model, which often requires a large amount of experimental data to calibrate parameters, and when dealing with heterogeneous and anisotropic reservoirs, the computational complexity and uncertainty are high. In practical applications, it may be difficult to accurately identify sweet spots due to parameter uncertainty and limitations in computing power. Summary of the invention

[0004] The present invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a method for identifying geological sweet spots in low permeability reservoirs based on grey correlation and hierarchical analysis.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for identifying geological sweet spots in low permeability reservoirs based on grey correlation and hierarchical analysis includes the following steps:

[0007] S1. Collect geological parameter data of low permeability reservoir

[0008] The geological parameters include but are not limited to average permeability, average porosity, gravel content, degree of fracture development, hydrocarbon saturation, median grain size, mean cement content, median pore throat radius, shale content and clay mineral content;

[0009] S2, applying grey correlation algorithm to analyze the geological parameter data obtained in step S1, determining the correlation degree of each geological parameter, and sorting the geological parameters according to the correlation degree of the geological parameters;

[0010] S21. Determine the analysis sequence

[0011] Fill the capacity and geological parameter data into the matrix to form a data analysis matrix, in which the capacity is used as the reference sequence and the geological parameter data is used as the characteristic sequence (each geological parameter corresponds to a column of characteristic sequence);

[0012] The expression of the data analysis matrix is ​​as follows (1):

[0013]

[0014] Where: Y 0 ' is the reference sequence; Y n ′ is the characteristic sequence; y′ 0 (n) is the production capacity data; y′ m (n) is geological parameter data;

[0015] S22. Use the mean method to make the geological parameter data dimensionless

[0016] The dimensionless processing eliminates the influence of different geological parameter dimensions, making the geological parameter data comparable and facilitating further analysis and calculation;

[0017] The dimensionless conversion formula is as follows (2):

[0018]

[0019] Where: y i (k) is the dimensionless processed data; y′ i (k) capacity data or geological parameter data; is the average value of production capacity data or geological parameter data;

[0020] The expression of the dimensionless data analysis matrix is ​​as follows (3):

[0021]

[0022] Where: y i (k) is the data after dimensionless processing; Y 0 is the sequence obtained after the reference sequence is dimensionless; Y m is the sequence obtained after the feature sequence is dimensionless;

[0023] S23, calculating the absolute difference between each dimensionless characteristic sequence and the dimensionless reference sequence to obtain a difference matrix;

[0024] The absolute difference is calculated as follows:

[0025] Δ0 i(k)=|y 0 (k)-yi(k)| (4)

[0027] Where: y i (k) is the characteristic sequence data after dimensionless processing; y 0 (k) is the reference sequence data after dimensionless processing;

[0028] The difference matrix is ​​as follows (5):

[0029]

[0030] S24, calculating the correlation coefficient to obtain a correlation coefficient matrix;

[0031] The correlation coefficient calculation formula is as follows (6) and (7);

[0032]

[0033] Where: 0i (k) is the correlation coefficient calculated for each value in the difference matrix;

[0034] Δ(max) is the maximum value in the difference matrix;

[0035] Δ(min) is the minimum value in the difference matrix;

[0036] Δ 0i (k) is each value in the difference matrix;

[0037] ρ is the resolution coefficient, which takes values ​​in (0, 1). The smaller the resolution coefficient, the greater the difference between the correlation coefficients and the stronger the discrimination ability. It is usually set to 0.5.

[0038] The correlation coefficient matrix is ​​as follows (8):

[0039]

[0040] Where: 0i (k) is the correlation coefficient calculated for each value in the difference matrix;

[0041] S25. Obtaining the degree of correlation

[0042] For each characteristic sequence, calculate the average value of the correlation coefficient of all its data, and this average value is the correlation degree between the characteristic sequence and the reference sequence;

[0043] The calculation formula of the average value is as follows (9):

[0044]

[0045] S26. Parameter sorting

[0046] The calculated correlations of each characteristic sequence are sorted, and the geological parameters corresponding to the first n characteristic sequences are selected as important influencing indicators, and then the analytic hierarchy process is applied for analysis;

[0047] The greater the correlation, the more similar the development trend of the characteristic sequence is to the reference sequence, that is, the higher the degree of correlation;

[0048] The value range of n is: 2≤n≤m, m is the number of geological parameters selected in grey correlation, and n is generally not greater than 10;

[0049] S3. Use the analytic hierarchy process to determine the judgment matrix and calculate the weight of each indicator

[0050] S31. Building a hierarchical model

[0051] Take the production capacity as the target layer and the n important influencing indicators obtained in step S2 as the criterion layer;

[0052] S32. Constructing a judgment matrix

[0053] For the indicators at the criterion level, pairwise comparisons are performed and a judgment matrix A is constructed based on the expert opinions or the judgment of the decision maker;

[0054] The judgment matrix A is an n×n matrix, in which each element Cij in the matrix represents the importance of index i relative to index j, and the values ​​of i and j are 1 to n; the 1-9 scaling method is usually used to determine the value of Cij; the judgment matrix form is shown in Table 1, and the relative importance scale of the hierarchical analysis method is shown in Table 2;

[0055] Table 1: Judgment matrix form

[0056]

[0057] Table 2: Relative importance scale of AHP

[0058]

[0059]

[0060] S33, weight calculation and consistency check, specifically includes the following steps:

[0061] S331, calculate the eigenvector M of the judgment matrix A i , the eigenvector M i Each row element C of the judgment matrix A ij The nth root of the product, the eigenvector M i The calculation formula is as follows (10):

[0062]

[0063] Where: M i is the eigenvector; C ij is the element in the judgment matrix A; n is the number of important influencing indicators;

[0064] S332, the feature vector M i Normalization, the normalization formula is as follows (11):

[0065]

[0066] Where: W i is the weight of each indicator; M i is the characteristic vector; n is the number of important influencing indicators;

[0067] S333, using the sum-product method to calculate the maximum eigenvalue λ of the judgment matrix max , the calculation formula is as follows (12):

[0068]

[0069] Where: W i is the weight of each indicator; max is the maximum eigenvalue; C ij is the element in the judgment matrix A; n is the number of important influencing indicators;

[0070] S334, calculating the consistency index CI, and obtaining the random consistency index RI by consulting the RI table, and then calculating the consistency ratio CR, and using the consistency ratio CR to perform consistency check;

[0071] Table 3: RI table

[0072]

[0073] The calculation formula of the consistency index CI is as follows (13):

[0074] CI=(λ max -n) / (n-1) (13)

[0075] Where: CI is the indicator of deviation of the judgment matrix from consistency; λ max is the maximum eigenvalue; n is the number of important influencing indicators;

[0076] The calculation formula of the consistency ratio CR is as follows (14):

[0077] CR=CI / RI (14)

[0078] Where: CR is the consistency ratio; CI is the measurement judgment matrix deviation consistency index; RI is the random consistency index;

[0079] When CR<0.1, the consistency of the judgment matrix is ​​considered acceptable; otherwise, the judgment matrix needs to be adjusted until the consistency requirement is met;

[0080] S4. Count the weight W obtained in step S3 i , establish a comprehensive evaluation model for geological sweet spots, and calculate the geological sweetness indicator factor I through the comprehensive evaluation model for geological sweet spots;

[0081] The expression of the geological sweet spot comprehensive evaluation model is as follows (15):

[0082]

[0083] Where: I is the geological sweetness indicator factor; P, F, So, V, K, D, M, T are important influencing indicators; W i is the weight of each indicator;

[0084] S5. Determine the reservoir geological sweetness classification based on the geological sweetness indicator factor obtained in step S4 and the geological sweetness classification table; the reservoir geological sweetness classification is shown in Table 3 below.

[0085] Table 4 Geological sweet spot classification table

[0086]

[0087] The beneficial effects of the present invention are:

[0088] The present invention provides a method for identifying geological sweet spots in low permeability reservoirs based on grey correlation hierarchical analysis. The method realizes the identification of geological sweet spots in low permeability reservoirs through grey correlation algorithm and hierarchical analysis method, provides a solid theoretical basis for the subsequent scientific evaluation of potential development plans for remaining oil reservoirs and the implementation of comprehensive adjustment strategies, and provides a foundation for subsequent work, ensuring that the potential of remaining oil reservoirs can be effectively tapped and strategies can be optimized and adjusted. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 is a flow chart of the method of the present invention;

[0090] Figure 2 It is a flowchart of the hierarchical analysis method in the present invention;

[0091] Figure 3 It is a diagram of reservoir geological sweet spot classification results in the present invention.

[0092] For ordinary technicians in this field, other relevant drawings can be obtained based on the above drawings without any creative work. DETAILED DESCRIPTION

[0093] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0094] Example 1

[0095] A method for identifying geological sweet spots in low permeability reservoirs based on grey correlation-hierarchy analysis is applied to low permeability conglomerate reservoirs in offshore oil fields, such as Figure 1 As shown, the specific steps include:

[0096] S1. Collect geological parameter data of low permeability reservoir

[0097] The geological parameter data of this embodiment are shown in Table 5;

[0098] Table 5: Static data table

[0099]

[0100] S2, applying grey correlation algorithm to analyze the geological parameter data obtained in step S1, determining the correlation degree of each geological parameter, and sorting the geological parameters according to the correlation degree of the geological parameters;

[0101] S21. Determine the analysis sequence

[0102] Taking production capacity as the reference sequence, the average permeability, average porosity, gravel content, fracture development degree, hydrocarbon saturation, median grain size, mean cement content, median pore throat radius, shale content, and clay mineral content were selected as characteristic sequences to form a data analysis matrix.

[0103] The expression of the data analysis matrix is ​​as follows (1):

[0104]

[0105] Where: Y 0 ' is the reference sequence; Y n ′ is the characteristic sequence; y′ 0 (n) is the production capacity data; y′ m (n) is geological parameter data;

[0106] S22. Use the mean method to make the geological parameter data dimensionless

[0107] The dimensionless processing eliminates the influence of different geological parameter dimensions, making the geological parameter data comparable and facilitating further analysis and calculation;

[0108] The dimensionless conversion formula is as follows (2):

[0109]

[0110] Where: y i (k) is the dimensionless processed data; y′ i (k) capacity data or geological parameter data; is the average value of production capacity data or geological parameter data;

[0111] The expression of the dimensionless data analysis matrix is ​​as follows (3):

[0112]

[0113] Where: y i (k) is the data after dimensionless processing; Y 0 is the sequence obtained after the reference sequence is dimensionless; Y m is the sequence obtained after the feature sequence is dimensionless;

[0114] S23, calculating the absolute difference between each index of each feature sequence and the reference sequence to obtain a difference matrix;

[0115] The absolute difference is calculated as follows:

[0116] Δ 0i (k)=|y 0 (k)-y i (k)| (4)

[0118] Where: y i (k) is the characteristic sequence data after dimensionless processing; y 0 (k) is the reference sequence data after dimensionless processing;

[0119] The difference matrix is ​​as follows (5):

[0120]

[0121] S24, calculating the correlation coefficient to obtain a correlation coefficient matrix;

[0122] The correlation coefficient calculation formula is as follows (6) and (7);

[0123]

[0124]

[0125] Where: 0i(k) is the correlation coefficient calculated for each value in the difference matrix;

[0126] Δ(max) is the maximum value in the difference matrix;

[0127] Δ(min) is the minimum value in the difference matrix;

[0128] Δ 0i (k) is each value in the difference matrix;

[0129] ρ is the resolution coefficient, which takes values ​​in (0, 1). The smaller the resolution coefficient, the greater the difference between the correlation coefficients and the stronger the discrimination ability. It is usually set to 0.5.

[0130] The correlation coefficient matrix is ​​as follows (8):

[0131]

[0132] Where: 0i (k) is the correlation coefficient calculated for each value in the difference matrix;

[0133] S25. Obtaining the degree of correlation

[0134] For each feature sequence, calculate the average value of the correlation coefficients of all its indicators. This average value is the correlation between the feature sequence and the reference sequence.

[0135] The calculation formula of the average value is as follows (9):

[0136]

[0137] S26. Parameter sorting

[0138] The calculated correlations of each characteristic sequence were ranked, and the first nine geological parameters were selected as important influencing indicators, and then the analytic hierarchy process was applied for analysis;

[0139] The greater the correlation, the more similar the development trend of the characteristic sequence is to the reference sequence, that is, the higher the correlation degree is. The correlation ranking of each geological parameter in this embodiment is shown in Table 6.

[0140] Table 6: Ranking table of correlation degree of each parameter

[0141]

[0142]

[0143] S3. Use the analytic hierarchy process to analyze and determine the judgment matrix and calculate the weight of each indicator;

[0144] S31. Building a hierarchical model

[0145] Take production capacity as the target layer and the 9 important influencing indicators obtained in step S2 as the criterion layer;

[0146] S32. Constructing a judgment matrix

[0147] For the indicators at the criterion level, pairwise comparisons are performed, and a judgment matrix is ​​constructed based on the opinions of experts or the judgments of decision makers; the judgment matrix is ​​shown in Table 7;

[0148] Table 7: Judgment Matrix

[0149]

[0150] S33, weight calculation and consistency check i, such as Figure 2 As shown, the specific steps include:

[0151] S331, calculate the eigenvector M of the judgment matrix A i , the eigenvector M i Each row element C of the judgment matrix A ij The nth root of the product, the eigenvector M i The calculation formula is as follows (10):

[0152]

[0153] Where: M i is the eigenvector; C ij is the element in the judgment matrix A; n is the number of important influencing indicators;

[0154] S332, the feature vector M i Normalization, the normalization formula is as follows (11):

[0155]

[0156] Where: W i is the weight of each indicator; M i is the characteristic vector; n is the number of important influencing indicators;

[0157] S333, using the sum-product method to calculate the maximum eigenvalue λ of the judgment matrix, the calculation formula is as follows (12):

[0158]

[0159] Where: W i is the weight of each indicator; max is the maximum eigenvalue; C ij is the element in the judgment matrix A; n is the number of important influencing indicators;

[0160] S334, calculating the consistency index CI, and obtaining the random consistency index RI by consulting the RI table, and then calculating the consistency ratio CR, and using the consistency ratio CR to perform consistency check;

[0161] The calculation formula of the consistency index CI is as follows (13):

[0162] CI=(λmax-n) / (n-1) (13)

[0163] Where: CI is the measurement matrix deviation consistency index

[0164] The calculation formula of the consistency ratio CR is as follows (14):

[0165] CR=CI / RI (14)

[0166] Where: CR is the consistency ratio;

[0167] CR=0.003<0.1, meeting the consistency requirements;

[0168] S4. Count the weight W obtained in step S3 i , establish a comprehensive evaluation model for geological sweet spots, and calculate the geological sweetness indicator factor I through the comprehensive evaluation model for geological sweet spots;

[0169] The expression of the comprehensive evaluation model of geological sweet spots is as follows:

[0170]

[0171] Where: I is the geological sweetness indicator factor; P is the average permeability, is the average porosity, F is the gravel content, So is the hydrocarbon saturation, V is the median grain size, K is the mean cement content, D is the median pore throat radius, M is the shale content, and T is the clay mineral content; W i is the weight of each indicator;

[0172] S5. Determine the reservoir geological sweetness classification based on the geological sweetness indicator factor obtained in step S4 and the geological sweetness classification table; the reservoir geological sweetness classification is shown in Tables 3 and Figure 3 shown.

[0173] From the prediction effect, the low permeability reservoir geological sweet spot identification method based on grey correlation-hierarchy analysis has higher accuracy and increases the credibility of the prediction, indicating the advancement and practicality of a low permeability reservoir geological sweet spot identification method based on grey correlation-hierarchy analysis.

[0174] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for identifying geological sweet spots in low permeability reservoirs based on grey correlation and hierarchical analysis, characterized in that: The following steps are involved: S1. Collect geological parameter data of low permeability reservoirs; S2, applying grey correlation algorithm to analyze the geological parameter data obtained in step S1, determining the correlation degree of each geological parameter, and sorting the geological parameters according to the correlation degree of the geological parameters; S3. Use the analytic hierarchy process to analyze and determine the judgment matrix and calculate the weight of each indicator; S4, counting the weights obtained in step S3, establishing a comprehensive evaluation model for geological sweet spots, and calculating a geological sweetness indicator factor through the comprehensive evaluation model for geological sweet spots; S5. Determine the reservoir geological sweetness classification based on the geological sweetness indicator factor obtained in step S4 and in combination with the geological sweet spot classification table.

2. The method for identifying low permeability reservoir geological sweet spots based on grey correlation and hierarchical analysis according to claim 1 is characterized in that: The geological parameter data include average permeability, average porosity, gravel content, degree of fracture development, hydrocarbon saturation, median grain size, mean cement content, median pore throat radius, mud content and clay mineral content.

3. The method for identifying low permeability reservoir geological sweet spots based on grey correlation and hierarchical analysis according to claim 1 is characterized in that: The step S2 specifically includes the following steps: S21. Determine the analysis sequence: Fill the production capacity and geological parameter data into a matrix to form a data analysis matrix, where the production capacity is used as a reference sequence and the geological parameter data is used as a feature sequence. The expression of the data analysis matrix is ​​as follows: Where: Y0′ is the reference sequence; Y n ′ is the characteristic sequence; y′0(n) is the production capacity data; y′ m (n) is geological parameter data; S22. Use the mean method to make the geological parameter data dimensionless: The dimensionless conversion formula is as follows (2): Where: y i (k) is the dimensionless processed data; y′ i (k) capacity data or geological parameter data; is the average value of production capacity data or geological parameter data; The expression of the dimensionless data analysis matrix is ​​as follows (3): Where: y i (k) is the dimensionless processed data; Y0 is the sequence obtained after the reference sequence is dimensionless; Y m is the sequence obtained after the feature sequence is dimensionless; S23, calculating the absolute difference between each dimensionless characteristic sequence and the dimensionless reference sequence to obtain a difference matrix; The absolute difference is calculated as follows: Δ 0i (k)=|y0(k)-y i (k)} (4) Where: y i (k) is the feature sequence data after dimensionless processing; y0(k) is the reference sequence data after dimensionless processing; The difference matrix is ​​as follows (5): S24, calculating the correlation coefficient to obtain a correlation coefficient matrix; The correlation coefficient calculation formula is as follows (6) and (7); Where: 0i (k) is the correlation coefficient of each value in the difference matrix after calculation; Δ(max) is the maximum value in the difference matrix; Δ(min) is the minimum value in the difference matrix; Δ 0i (k) is each value in the difference matrix; ρ is the resolution coefficient, ρ = 0.5; The correlation coefficient matrix is ​​as follows (8): Where: 0i (k) is the calculated correlation coefficient of each value in the difference matrix; S25. Obtaining the degree of correlation For each characteristic sequence, calculate the average value of the correlation coefficient of all its data, and this average value is the correlation degree between the characteristic sequence and the reference sequence; The calculation formula of the average value of the correlation coefficient is as follows (9): S26. Parameter sorting The calculated correlation degrees of each characteristic sequence are sorted, and the first n geological parameters are selected as important influencing indicators and applied to hierarchical analysis.

4. The method for identifying low permeability reservoir geological sweet spots based on grey correlation and hierarchical analysis according to claim 3 is characterized by: The value range of n is: 2≤n≤m, m is the number of geological parameters selected in grey correlation; n≤10.

5. The method for identifying low permeability reservoir geological sweet spots based on grey correlation and hierarchical analysis according to claim 1, characterized in that: The step S3 specifically comprises the following steps: S31, taking the production capacity as the target layer and the n important influencing indicators obtained in step S2 as the criterion layer, constructing a hierarchical structure model; S32. For the indicators at the criterion level, pairwise comparisons are performed and a judgment matrix is ​​constructed based on the opinions of experts or the judgments of decision makers; S33. Weight calculation and consistency check.

6. The method for identifying low permeability reservoir geological sweet spots based on grey correlation and hierarchical analysis according to claim 5 is characterized by: The judgment matrix is ​​an n×n matrix, and each element in the matrix is ​​the importance of indicator i relative to indicator j.

7. The method for identifying low permeability reservoir geological sweet spots based on grey correlation and hierarchical analysis according to claim 5, characterized in that: The importance of the indicator i relative to the indicator j is determined using a 1-9 scale.

8. The method for identifying low permeability reservoir geological sweet spots based on grey correlation and hierarchical analysis according to claim 5, characterized in that: The step S33 weight calculation and consistency check specifically includes the following steps: S331, calculate the eigenvector M of the judgment matrix A i , the eigenvector M i Each row element C of the judgment matrix A ij The nth root of the product, the eigenvector M i The calculation formula is as follows (10): Where: M i is the eigenvector; C ij is the element in the judgment matrix A; n is the number of important influencing indicators; S332, the feature vector M i Normalization, the normalization formula is as follows (11): Where: W i is the weight of each indicator; M i is the characteristic vector; n is the number of important influencing indicators; S333, using the sum-product method to calculate the maximum eigenvalue λ of the judgment matrix max , the calculation formula is as follows (12): Where: W i is the weight of each indicator; max is the maximum eigenvalue; C ij is the element in the judgment matrix A; n is the number of important influencing indicators; S334, calculating the consistency index CI, and obtaining the random consistency index RI by consulting the RI table, and then calculating the consistency ratio CR, and using the consistency ratio CR to perform consistency check; The calculation formula of the consistency index CI is as follows (13): CI=(λmax-n) / (n-1) (13) Where: CI is the indicator of deviation of the judgment matrix from consistency; λ max is the maximum eigenvalue; n is the number of important influencing indicators; The calculation formula of the consistency ratio CR is as follows (14): CR=CI / RI (14) Where: CR is the consistency ratio; CI is the measurement judgment matrix deviation consistency index; RI is the random consistency index; When CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable; otherwise, the judgment matrix is ​​adjusted until the consistency requirement is met.

9. The method for identifying low permeability reservoir geological sweet spots based on grey correlation and hierarchical analysis according to claim 1, characterized in that: The expression of the comprehensive evaluation model of geological sweet spots is as follows (15): Where: I is the geological sweetness indicator factor; P, F, So, V, K, D, M, T are important influencing indicators; W i is the weight of each indicator.

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