Shale oil mobility evaluation method

Abstract

The invention discloses a shale oil mobility evaluation method. The method comprises the following steps: S1, acquiring a shale sample and basic parameters thereof; s2, performing principal component analysis on the basic parameters to obtain principal components; s3, calculating a connectivity ratio, and performing multiple linear regression by taking the connectivity ratio as a dependent variable and the principal component as an independent variable to obtain a geological variable-based connectivity ratio quantitative calculation formula; s4, preferably selecting parameters as shale oil mobility evaluation indexes, determining the weight of each index, and establishing a shale oil mobility evaluation mathematical model; and S5, calculating the communication proportion of the target shale sample according to a communication proportion quantitative calculation formula, and calculating the shale oil mobility evaluation coefficient of the target shale sample according to the shale oil mobility evaluation mathematical model. According to the shale oil mobility evaluation method, the communication ratio serves as a key parameter of shale oil mobility evaluation, shale oil mobility evaluation can be accurately and efficiently achieved, the exploration cost is reduced, the mobility evaluation process of crude oil in shale is simplified, and the popularization performance is high.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and particularly relates to a method for evaluating the mobility of shale oil. Background Art

[0002] In recent years, with the continuous progress of exploration and development technologies, significant breakthroughs have been made in shale oil, which has become a hot topic in the fields of oil and gas resource exploration and development and scientific research. The accuracy of shale oil mobility evaluation plays a decisive role in oilfield well placement, oil and gas reservoir storage and migration, and exploration risk assessment. Accurately evaluating the mobility of shale oil can provide a scientific basis for oilfield development, ensure the efficient and orderly progress of development work, and thus greatly improve the economic benefits of the oilfield. Currently, the main methods for evaluating the mobility of shale oil are as follows:

[0003] ① Capillary pressure: By measuring the capillary pressure at different fluid saturations, analyzing the morphology of the capillary pressure curve, determining the range of the main pore sizes in the shale and the relative proportions of pores of different sizes. This method is affected by factors such as wettability and temperature during the measurement process, and there are differences from the actual crude oil storage conditions in the shale, resulting in large errors in the measurement results.

[0004] ② Nuclear magnetic resonance method: By measuring the distribution and signal intensity of different fluids in the rock pores, characterizing the pore size distribution characteristics of the shale. The experimental process is greatly affected by the saturation degree of the sample, and paramagnetic substances such as organic matter and pyrite in some shales will also generate nuclear magnetic signals, affecting the measurement accuracy.

[0005] ③ CT scanning method: By repeatedly scanning the core sample, high-resolution three-dimensional images can be obtained, which can clearly show details such as the pore structure, fracture distribution, and mineral composition of the shale. However, its application cost in the research of shale oil mobility evaluation is relatively high; at the same time, as the resolution increases, the analysis volume decreases and is affected by the sample quality, so this method has certain limitations. Summary of the Invention

[0006] In view of the above problems, the present invention aims to provide a method for evaluating the mobility of shale oil.

[0007] The technical solution of the present invention is as follows:

[0008] A method for evaluating the mobility of shale oil, comprising the following steps:

[0009] S1: Obtain shale samples from the target horizon in the study area and obtain the basic parameters of the shale samples; the basic parameters include total organic carbon content, maturity, mineral composition, tortuosity, total porosity, permeability, water saturation, shale wettability index, and connected porosity;

[0010] S2: Conduct principal component analysis on the basic parameters to obtain the principal components that can represent the original geological parameter information;

[0011] S3: Calculate the ratio of the connected porosity to the total porosity to obtain the connection ratio. Then, use the connection ratio as the dependent variable and the principal components as the independent variables to perform multiple linear regression to obtain a quantitative calculation formula for the connection ratio based on geological variables;

[0012] S4: Select parameters from the basic parameters as evaluation indicators for shale oil mobility, and use the entropy method to determine the weights of each shale oil mobility evaluation indicator to establish a shale oil mobility evaluation mathematical model;

[0013] S5: Calculate the connection ratio of the target shale sample according to the quantitative calculation formula for the connection ratio, and calculate the shale oil mobility evaluation coefficient of the target shale sample according to the shale oil mobility evaluation mathematical model.

[0014] Preferably, in step S1, the total organic carbon content, maturity, mineral composition, tortuosity, total porosity, permeability, water saturation, and shale wettability index are obtained from logging data.

[0015] Preferably, in step S1, the connected porosity is obtained through any one or more of the experiments of carbon dioxide adsorption, nitrogen adsorption, and high-pressure mercury intrusion.

[0016] Preferably, in step S2, when performing principal component analysis, it includes the following sub-steps:

[0017] S21: Standardize the basic parameters to obtain standardized basic parameters;

[0018] S22: Establish a correlation coefficient matrix, and calculate the eigenvalues, variance proportion, and cumulative variance of the correlation coefficient matrix;

[0019] S23: Select the parameters with the cumulative variance reaching the threshold as the principal components;

[0020] S24: Substitute the eigenvalues corresponding to each principal component into the characteristic equation to obtain the eigenvectors of each principal component;

[0021] S25: Obtain the expression of each principal component parameter according to the eigenvectors of each principal component.

[0022] Preferably, in step S3, MATLAB software is used for multiple linear regression.

[0023] Preferably, in step S4, the shale oil mobility evaluation indicators include the total porosity, connection ratio, permeability, water saturation, and shale wettability index.

[0024] Preferably, in step S5, there are multiple target shale samples. By calculating the shale oil mobility evaluation coefficients of multiple shale samples, the section corresponding to the shale sample with the largest shale oil mobility evaluation coefficient is selected as the exploration target.

[0025] The beneficial effects of the present invention are as follows:

[0026] The present invention emphasizes the proportion of connected pores in the total volume of shale, combines various geological variables, calculates the proportion of connected pores in the total pores, and obtains the connectivity ratio as the key parameter for evaluating shale oil mobility. Through the multiple linear regression method and combined with logging data, the pore connectivity ratio at any depth of a single well can be continuously calculated. The present invention can accurately and efficiently evaluate the mobility of shale oil, reduce the exploration cost, simplify the evaluation process of the mobility of crude oil in shale, and has strong popularization. Specific embodiments

[0027] The present invention will be further described below in conjunction with embodiments. It should be noted that, without conflict, the embodiments in this application and the technical features in the embodiments can be combined with each other. It should be pointed out that unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms "including" or "comprising" and the like used in the disclosure of the present invention mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects.

[0028] The present invention provides a method for evaluating shale oil mobility, including the following steps:

[0029] S1: Obtain shale samples of the target horizon in the study area and obtain the basic parameters of the shale samples; the basic parameters include total organic carbon content, maturity, mineral composition, tortuosity, total porosity, permeability, water saturation, shale wettability index, and connected porosity.

[0030] In the present invention, the organic carbon content, maturity, mineral composition, and tortuosity are all the main control factors for controlling pore connectivity. Among them, the organic carbon content determines the number of organic pores in shale. A large number of organic pores are formed during the hydrocarbon generation process of organic matter, providing more storage space for shale oil, and the organic pores themselves have good connectivity. There is a positive correlation between organic carbon and pore connectivity, so organic carbon has an important control effect on pore connectivity.

[0031] Maturity has a significant impact on the pore connectivity of shale. Compaction changes the inorganic pore structure, dissolution promotes the generation of internal pores in carbonate minerals, and thermal evolution controls the number of organic pores, which increase with the increase of Ro and are most developed in the high maturity stage. When the Ro value reaches 3.2% or 3.3%, due to excessive thermal evolution, the organic pores collapse, reducing the effective pore space, and the kerogen transformation releases fluids that block the pores and throats, resulting in pore disconnection. Therefore, there is a negative correlation between Ro and pore connectivity in the over-mature stage.

[0032] The influence mechanism of mineral composition on pore connectivity is complex. Siliceous minerals have high stability and large compressive strength, which play a positive role in preserving inorganic pores. The relationship between clay minerals and pore connectivity is complex. Their water absorption and swelling lead to pore blockage, while the removal of interlayer water generates interlayer pores. Carbonate minerals are affected by multiple aspects such as mineral composition and diagenesis, resulting in complex and variable pore connectivity. For example, dissolution can form pores to improve connectivity, while cementation will reduce connectivity.

[0033] Tortuosity refers to the degree of tortuosity experienced by solute molecules during migration in the pore network. The greater the tortuosity, the more complex the pore channel structure, which increases the difficulty of fluid migration and directly affects the connectivity degree.

[0034] In a specific embodiment, the total organic carbon content, maturity, mineral composition, tortuosity, total porosity, permeability, water saturation, and shale wettability index are obtained from well logging data, and the connected porosity is obtained from any one or more of the experiments of carbon dioxide adsorption, nitrogen adsorption, and high-pressure mercury injection.

[0035] S2: Perform principal component analysis on the basic parameters to obtain the principal components that can represent the information of the original geological parameters.

[0036] In a specific embodiment, when performing principal component analysis, it includes the following sub-steps:

[0037] S21: Standardize the basic parameters to obtain standardized basic parameters;

[0038] S22: Establish a correlation coefficient matrix, and calculate the eigenvalues, variance proportion, and cumulative variance of the correlation coefficient matrix;

[0039] S23: Select the parameters with the cumulative variance reaching the threshold as the principal components;

[0040] S24: Substitute the eigenvalues corresponding to each principal component into the characteristic equation to obtain the eigenvectors of each principal component;

[0041] S25: Obtain the expression of each principal component parameter according to the eigenvectors of each principal component.

[0042] It should be noted that principal component analysis is a prior art. The above embodiments are only one of the preferred principal component analysis methods of the present invention, and other methods capable of realizing principal component analysis in the prior art can also be applied to the present invention.

[0043] S3: Calculate the ratio of the connected porosity to the total porosity to obtain the connection ratio. Then, taking the connection ratio as the dependent variable and the principal components as the independent variables, perform multiple linear regression to obtain a quantitative calculation formula for the connection ratio based on geological variables.

[0044] In a specific embodiment, MATLAB software is used for multiple linear regression.

[0045] S4: Select parameters from the basic parameters as evaluation indicators for shale oil mobility, and use the entropy method to determine the weights of each shale oil mobility evaluation indicator, and establish a shale oil mobility evaluation mathematical model.

[0046] In a specific embodiment, the shale oil mobility evaluation indicators include the total porosity, connection ratio, permeability, water saturation, and shale wettability index.

[0047] S5: Calculate the connection ratio of the target shale sample according to the quantitative calculation formula for the connection ratio, and calculate the shale oil mobility evaluation coefficient of the target shale sample according to the shale oil mobility evaluation mathematical model.

[0048] In a specific embodiment, there are multiple target shale samples. By calculating the shale oil mobility evaluation coefficients of multiple shale samples, select the layer section corresponding to the shale sample with the largest shale oil mobility evaluation coefficient as the exploration target.

[0049] In a specific embodiment, taking the shale of the Xiaganchaigou Formation under the Yingxiongling in the Qaidam Basin as an example, the shale oil mobility evaluation method of the present invention is used to evaluate its mobility, which specifically includes the following steps:

[0050] (1) Obtain the basic parameters of the shale sample

[0051] In this embodiment, the total organic carbon content, maturity, mineral composition, tortuosity, total porosity, permeability, water saturation, and shale wettability index are obtained through logging data. The connected porosity is obtained through a combined experiment of carbon dioxide adsorption, nitrogen adsorption, and high-pressure mercury injection. The connection ratio is obtained by calculating the ratio of the connected porosity to the total porosity. Some results are shown in Table 1:

[0052] Table 1 Original basic parameters of shale samples

[0053]

[0054]

[0055] (2) Obtain the principal components

[0056] First, standardize each geological parameter. The standardization formula is:

[0057]

[0058] In the formula: Zx ij is the standardized geological parameter; x ij is the j-th geological parameter of the i-th sample; is the sample mean of the j-th parameter; S j is the sample standard deviation of the j-th parameter; n is the number of samples, n = 28; m is the number of geological parameters, m = 6.

[0059] The standardized data is shown in Table 2:

[0060] Table 2 Basic parameters after standardization

[0061]

[0062]

[0063] Secondly, establish the correlation coefficient matrix. The calculation formula is as follows:

[0064] R = (r ij ) m×n (4)

[0065]

[0066] In the formula: R is the correlation coefficient matrix; r ij is the correlation coefficient between the i-th parameter and the j-th parameter;

[0067] The correlation coefficient matrix is as follows:

[0068]

[0069] Thirdly, calculate the eigenvalues, variance proportion, and cumulative variance of the correlation coefficient matrix. The characteristic equation of the correlation coefficient matrix is expressed as:

[0070] |R - λE| = 0 (7)

[0071] In the formula: R is the correlation coefficient matrix; λ is the eigenvalue; E is the identity matrix;

[0072] The variance proportion is calculated by the following formula:

[0073]

[0074] The calculation results of eigenvalues, variance proportions, and cumulative variances are shown in Table 3 as follows:

[0075] Table 3 Calculation Results of Eigenvalues, Variance Proportions, and Cumulative Variances

[0076] Principal component Eigenvalue Variance proportion (%) Cumulative variance proportion (%) F1 3.569 59.481 59.481 F2 1.464 24.391 83.872 F3 0.541 9.013 92.885 F4 0.247 4.122 97.006 F5 0.180 2.994 100 F6 <![CDATA[-2.776×10 -16 > <![CDATA[-4.626×10 -15 > 100

[0077] In statistics, it is stipulated that when the cumulative variance proportion ≥ 90%, the selected principal components have a good explanatory effect on the information contained in the original data. Therefore, in practical applications, the number of principal components should be the number of principal components corresponding to the cumulative contribution rate ≥ 90%.

[0078] As can be seen from Table 3, when 3 principal components are extracted, the cumulative contribution rate reaches 92.885%. That is, when extracting 3 principal components F1, F2, and F3, 92.885% of the information of 6 original geological parameters can be explained, which can not only achieve the purpose of dimensionality reduction but also retain most of the information of the original geological data. Therefore, in this example, 3 principal components F1, F2, and F3 are extracted to represent the original 6 geological variables.

[0079] Then, substituting the eigenvalues λ1 = 3.569, λ2 = 1.464, and λ3 = 0.541 corresponding to the principal components F1, F2, and F3 into the characteristic equation, the eigenvectors u1, u2, and u3 are obtained as follows:

[0080] u1 = (0.4820, 0.4782, -0.3676, 0.4442, -0.1839, -0.4156) (9)

[0081] u2 = (-0.0937, -0.0054, 0.4801, 0.3574, -0.7738, 0.1850) (10)

[0082] u3 = (0.1064, -0.2778, -0.5314, 0.3915, 0.0052, 0.6898) (11)

[0083] The expression of the principal component is:

[0084] F i = u i1 Zx1 + u i2 Zx2 + u i3 Zx3 + u i4 Zx4 + u i5 Zx5 + u i6 Zx6 (12)

[0085] In the formula: F i is the i-th principal component; u i1 is the first component of the i-th eigenvector; Zx1 is the first geological parameter after standardization, and so on.

[0086] In this embodiment, the main components are respectively:

[0087] F1 = 0.4820Zx1 + 0.4782Zx2 - 0.3676Zx3 + 0.4442Zx4 - 0.1839Zx5 - 0.4156Zx6 (13)

[0088] F2 = -0.0937Zx1 - 0.0054Zx2 + 0.4801Zx3 + 0.3574Zx4 - 0.7738Zx5 + 0.1850Zx6 (14)

[0089] F3 = 0.1064Zx1 - 0.2778Zx2 - 0.5314Zx3 + 0.3915Zx4 + 0.0052Zx5 + 0.6898Zx6 (15)

[0090] Finally, according to the expressions of the above F1, F2, and F3, calculate the main component scores of each sample under F1, F2, and F3. The results are shown in Table 4:

[0091] Table 4 Main component scores

[0092] Sample number <![CDATA[F1]]> <![CDATA[F2]]> <![CDATA[F3]]> Sample number <![CDATA[F1]]> <![CDATA[F2]]> <![CDATA[F3 <!-- 7 -->]]> 1 2.3666 2.1455 0.1716 15 -2.8812 0.2267 0.326 2 -1.1937 -1.5354 -0.4651 16 1.1274 1.0643 0.1241 3 -2.5674 -0.9648 0.1372 17 1.8138 1.3634 0.3237 4 1.8592 0.5994 -1.726 18 1.051 -0.5412 0.9867 5 -3.3363 -0.5925 1.7062 19 2.0466 1.7255 0.6787 6 1.5141 0.353 0.0535 20 -0.3159 -0.2024 -0.9363 7 1.2589 0.7973 -1.0081 21 -2.3516 -3.2705 -0.7247 8 -2.5352 -1.7543 -0.4783 22 -0.7921 0.589 -0.0202 9 2.6253 0.5751 0.8512 23 -0.3294 -0.198 -0.4384 10 1.7766 -0.1825 -0.2147 24 0.1211 1.7262 0.4575 11 3.3413 0.2505 0.3591 25 -1.2816 -1.1496 0.7705 12 -1.5058 0.1075 -0.9334 26 -0.2372 -0.7563 -0.7936 13 -1.3118 0.3328 0.6081 27 1.4831 1.0917 0.0658 14 -2.033 -1.3403 -0.364 28 0.2873 -0.4602 0.4827

[0093] (3) Perform multiple linear regression to obtain a quantitative calculation formula for the connectivity ratio based on geological variables

[0094] Taking the connectivity ratio as the dependent variable and the main components as the independent variables, use the multiple linear regression module in MATLAB software to perform multiple linear regression, and obtain the quantitative calculation formula for the connectivity ratio:

[0095] T = aF1 + bF2 + cF3 + d = 0.131×F1 - 0.011×F2 - 0.024×F3 + 0.5 (16)

[0096] In the formula: T is the connectivity ratio; a, b, and c are fitting coefficients, and d is a constant.

[0097] In this embodiment, the determination coefficient R of the quantitative calculation formula for the connectivity ratio 2 = 0.845 > 0.5, indicating that the fitting effect is good, and this model can explain more than 84.5% of the variation of the dependent variable.

[0098] (4) Establish a mathematical model for evaluating the mobility of shale oil and calculate the evaluation coefficient of shale oil mobility

[0099] The total porosity, connectivity ratio, permeability, water saturation, and shale wettability index are selected as the evaluation indicators for shale oil mobility. In the selection of shale oil mobility intervals, the higher the permeability, the lower the water saturation, the larger the total porosity, the higher the pore connectivity ratio and the shale wettability index (indicating stronger lipophilicity), the better the shale oil mobility. Therefore, the mobility of crude oil in shale is positively correlated with the total porosity, connectivity ratio, permeability, and shale wettability index, and negatively correlated with the water saturation. In this embodiment, the original data of the evaluation indicators for the three intervals A, B, and C to be evaluated are shown in Table 5:

[0100] Table 5 Original data of evaluation indicators

[0101]

[0102]

[0103] There are negative numbers in the above original data. All values required in the calculation process of the entropy method are non - negative numbers, and negative numbers will affect the calculation of entropy. Therefore, the "non - negative translation" needs to be performed on the original data with negative numbers. The calculation formula is as follows, and the non - negative translation calculation results are shown in Table 6.

[0104] X = X k +|min(X)|+∈(k = 1,…,q) (17)

[0105] In the formula: X is the shale wettability index; X k is the value of the k - th index of the k - th sample; ∈ is a minimum constant (such as 10 -4 ).

[0106] Table 6 Results of non - negative translation processing of original data of evaluation indicators

[0107]

[0108]

[0109] Percentage conversion is performed on the data of the non - negative translation processing results of the evaluation indicators. The calculation formula is as follows, and the calculation results are shown in Table 7.

[0110]

[0111] In the formula: Q ij is the value of the j - th index of the i - th sample; ZQx ij is the value of the j - th index of the i - th sample after percentage conversion; p is the number of samples, p = 30; q is the number of evaluation indicators, q = 5.

[0112] Table 7 Data after percentage conversion of evaluation indicators

[0113]

[0114]

[0115] Calculate the entropy value of the j-th evaluation index after percentage conversion. The calculation formula is as follows, and the calculation results of the entropy value are shown in Table 8.

[0116]

[0117] Table 8 Entropy value table of evaluation parameters

[0118] <![CDATA[E1]]> <![CDATA[E2]]> <![CDATA[E3]]> <![CDATA[E4]]> <![CDATA[E5]]> 0.9794 0.3932 0.9658 0.9810 0.9256

[0119] Calculate the weight of the j-th geological parameter after percentage conversion, as shown in Table 9.

[0120] ω j = 1 - E j (j = 1,..., q) (21)

[0121] Table 9 Weights of evaluation parameters

[0122] ω1 ω2 ω3 ω4 ω5 0.0206 0.6068 0.0342 0.0190 0.0744

[0123] Perform weight normalization to ensure that the sum of all weights is 1. The calculation formula is as follows, and the calculation results are shown in Table 10.

[0124]

[0125] Table 10 Normalized weights of each evaluation parameter

[0126] <![CDATA[ω1′]]> <![CDATA[ω2′]]> <![CDATA[ω3′]]> <![CDATA[ω4′]]> <![CDATA[ω5′]]> 0.0274 0.8063 0.0455 0.0253 0.0986

[0127] Establish a mathematical model for evaluating the mobility of shale oil as:

[0128] Y = ω 1' ×ZΦ + ω 2' ×ZT + ω 3' ×ZK - ω 4' ×ZS w + ω 5' ×ZP (23)

[0129] In the formula: Y is the evaluation coefficient of shale oil mobility; ZΦ is the total porosity after percentage conversion; ZT is the connected ratio after percentage conversion; ZK is the permeability after percentage conversion; ZS w is the water saturation after percentage conversion; ZP is the shale wettability index after percentage conversion.

[0130] Calculate the evaluation coefficients of shale oil mobility for different samples according to the mathematical model for evaluating shale oil mobility, and the results are shown in Table 11:

[0131] Table 11 Evaluation Coefficient of Shale Oil Mobility

[0132]

[0133]

[0134] The larger the evaluation coefficient Y of shale oil mobility is, the higher the total porosity, connected porosity, permeability and wettability index are, the lower the water saturation is, and the stronger the shale oil mobility ability is. According to the calculation results in Table 11, the shale oil in Section C has the strongest mobility, followed by Section A, and the weakest in Section B. Section C is the most favorable exploration target and should be given priority to development.

[0135] In summary, the present invention can accurately and efficiently realize the evaluation of shale oil mobility. Compared with the prior art, it has made remarkable progress.

[0136] The above is only a preferred embodiment of the present invention and does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as the content does not depart from the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating the mobility of shale oil, characterized in that, The method includes the following steps: S1: Obtain shale samples of the target horizon in the study area and obtain the basic parameters of the shale samples; the basic parameters include total organic carbon content, maturity, mineral composition, tortuosity, total porosity, permeability, water saturation, shale wettability index, and connected porosity; S2: Conduct principal component analysis on the basic parameters to obtain the principal components that can represent the original geological parameter information; S3: Calculate the ratio of the connected porosity to the total porosity to obtain the connection ratio. Using the connection ratio as the dependent variable and the principal components as the independent variables, perform multiple linear regression to obtain a quantitative calculation formula for the connection ratio based on geological variables; S4: Select parameters from the basic parameters as evaluation indicators for shale oil mobility, and use the entropy method to determine the weights of each shale oil mobility evaluation indicator, and establish a mathematical model for evaluating shale oil mobility; S5: Calculate the connection ratio of the target shale sample according to the quantitative calculation formula for the connection ratio, and calculate the evaluation coefficient of shale oil mobility of the target shale sample according to the mathematical model for evaluating shale oil mobility.

2. The shale oil mobility evaluation method according to claim 1, characterized in that In step S1, the total organic carbon content, maturity, mineral composition, tortuosity, total porosity, permeability, water saturation, and shale wettability index are obtained through logging data.

3. The shale oil mobility evaluation method according to claim 1, characterized in that, In step S1, the connected porosity is obtained through any one or more of the experiments of carbon dioxide adsorption, nitrogen adsorption, and high-pressure mercury injection.

4. The shale oil mobility evaluation method according to claim 1, wherein In step S2, when performing principal component analysis, it includes the following sub-steps: S21: Standardize the basic parameters to obtain standardized basic parameters; S22: Establish a correlation coefficient matrix, and calculate the eigenvalues, variance proportion, and cumulative variance of the correlation coefficient matrix; S23: Select the parameters whose cumulative variance reaches the threshold as the principal components; S24: Substitute the eigenvalues corresponding to each principal component into the eigen-equation respectively to obtain the eigenvectors of each principal component; S25: Obtain the expression of each principal component parameter according to the eigenvectors of each principal component.

5. The shale oil mobility evaluation method according to claim 1, characterized in that In step S3, MATLAB software is used for multiple linear regression.

6. The shale oil mobility evaluation method according to claim 1, wherein In step S4, the evaluation indicators for shale oil mobility include the total porosity, connection ratio, permeability, water saturation, and shale wettability index.

7. The shale oil mobility evaluation method according to any one of claims 1-6, characterized in that In step S5, there are multiple target shale samples. By calculating the evaluation coefficients of shale oil mobility of multiple shale samples, select the layer section corresponding to the shale sample with the largest evaluation coefficient of shale oil mobility as the exploration target.

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