Shale oil mobility evaluation method

Abstract

The application discloses a shale oil mobility evaluation method, comprising the following steps: S1: obtaining shale samples and basic parameters thereof; S2: performing principal component analysis on the basic parameters to obtain principal components; S3: calculating a connected proportion, taking the connected proportion as a dependent variable and the principal components as independent variables, performing multiple linear regression to obtain a connected proportion quantitative calculation formula based on geological variables; S4: optimizing parameters as shale oil mobility evaluation indexes, determining the weights of the indexes, and establishing a shale oil mobility evaluation mathematical model; and S5: calculating the connected proportion of target shale samples according to the connected proportion quantitative calculation formula, and calculating a shale oil mobility evaluation coefficient of the target shale samples according to the shale oil mobility evaluation mathematical model. The application takes the connected proportion as a key parameter for shale oil mobility evaluation, can accurately and efficiently realize shale oil mobility evaluation, reduces exploration cost, simplifies a shale oil mobility evaluation process and has strong popularization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil exploration and development, in particular to a shale oil mobility evaluation method. BACKGROUND

[0002] In recent years, the exploration and development technology has been continuously improved, shale oil has made a major breakthrough, and has become a hot spot in the field 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 deployment, oil and gas accumulation and migration, and exploration risk assessment. Accurate evaluation of shale oil mobility can provide a scientific basis for oilfield development and ensure that the development work is efficient and orderly, thereby greatly improving the economic benefit of the oilfield. At present, the methods for evaluating shale oil mobility mainly include:

[0003] ①Capillary pressure: by measuring the capillary pressure under different fluid saturations, analyzing the shape of the capillary pressure curve, and determining the size range of the main pores in the shale and the relative proportion of pores of different sizes. This method is affected by wettability, temperature and other factors during the measurement process, and there are differences between the measured results and the actual oil accumulation conditions in the shale, resulting in a large error in the measurement results.

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

[0005] ③CT scanning method: by scanning the core sample multiple times, high-resolution three-dimensional images can be obtained, which can clearly show the pore structure, fracture distribution and mineral composition of the shale. However, its application cost in the study of shale oil mobility evaluation is relatively high; at the same time, the resolution is improved, the analysis volume is reduced and is affected by the sample quality, so this method has certain limitations. SUMMARY

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

[0007] The technical scheme of the present application is as follows:

[0008] A shale oil mobility evaluation method, comprising the following steps:

[0009] S1: Obtain shale samples of the target layer of 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: performing principal component analysis on the basic parameters to obtain principal components capable of representing original geological parameter information;

[0011] S3: calculating a ratio of the connected porosity to the total porosity to obtain a connected proportion, and performing multiple linear regression with the connected proportion as a dependent variable and the principal components as independent variables to obtain a connected proportion quantitative calculation formula based on geological variables;

[0012] S4: selecting parameters from the basic parameters as shale oil mobility evaluation indexes, determining weights of the shale oil mobility evaluation indexes by using an entropy method, and establishing a shale oil mobility evaluation mathematical model;

[0013] S5: calculating the connected proportion of a target shale sample according to the connected proportion quantitative calculation formula, and calculating a 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 through logging data.

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

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

[0017] S21: performing standardization processing on the basic parameters to obtain standardized basic parameters;

[0018] S22: establishing a correlation coefficient matrix and calculating eigenvalues, variance proportions, and cumulative variances of the correlation coefficient matrix;

[0019] S23: selecting parameters with cumulative variances reaching a threshold value as principal components;

[0020] S24: substituting eigenvalues corresponding to each principal component into a characteristic equation to obtain characteristic vectors of each principal component;

[0021] S25: obtaining expressions of each principal component parameter according to the characteristic vectors of each principal component.

[0022] Preferably, in step S3, multiple linear regression is performed by using MATLAB software.

[0023] Preferably, in step S4, the shale oil mobility evaluation indexes include the total porosity, connected proportion, permeability, water saturation, and shale wettability index.

[0024] Preferably, in step S5, the target shale sample includes a plurality of shale samples, and a shale oil mobility evaluation coefficient of the plurality of shale samples is calculated, and a shale sample corresponding to a layer with the largest shale oil mobility evaluation coefficient is selected as a target for exploration.

[0025] The present application has the following advantages:

[0026] The present application emphasizes the proportion of connected pores in the total volume of shale, and calculates the proportion of connected pores in the total pores in combination with a plurality of geological variables to obtain a connected proportion as a key parameter for shale oil mobility evaluation. The connected proportion at any depth of a single well can be continuously calculated in combination with logging data by a multiple linear regression method. The present application can accurately and efficiently realize shale oil mobility evaluation, reduce exploration cost, simplify the process of crude oil mobility evaluation in shale, and has strong generalizability. DETAILED DESCRIPTION

[0027] The present application will be further described below in combination with examples. It should be noted that the examples and technical features in the examples in the present application can be combined with each other without conflict. It should be noted that all technical and scientific terms used in the present application have the same meaning as generally understood by ordinary technical personnel in the technical field to which the present application belongs, unless otherwise specified. The present application discloses that the similar words such as "including" or "containing" mean that the elements or objects before the words cover the elements or objects listed after the words and their equivalents, and other elements or objects are not excluded.

[0028] The present application provides a shale oil mobility evaluation method, including the following steps:

[0029] S1: shale samples of a target layer in a study area are obtained, and basic parameters of the shale samples are obtained; 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 application, the organic carbon content, maturity, mineral composition, and tortuosity are all main control factors of pore connectivity. Among them, the organic carbon content determines the number of organic pores in shale. The organic matter hydrocarbon generation process forms a large number of organic pores, which provides more reservoir space for shale oil, and the organic pores themselves have good connectivity. Organic carbon and pore connectivity are positively correlated, so organic carbon has an important control effect on pore connectivity.

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

[0032] The influence mechanism of mineral composition on pore connectivity is complex. Siliceous minerals have high stability and high compressive strength, which positively affect the preservation of inorganic pores. The relationship between clay minerals and pore connectivity is complex. The water absorption of clay minerals leads to pore blockage, while the removal of interlayer water produces interlayer pores. Carbonate minerals are affected by mineral composition, diagenesis, and other factors, resulting in complex and variable pore connectivity. For example, dissolution can form pores to improve connectivity, while cementation can 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 degree of connectivity.

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

[0035] S2: Principal component analysis is performed on the basic parameters to obtain principal components that can represent the original geological parameter information.

[0036] In one specific embodiment, when performing principal component analysis, the following sub-steps are included:

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

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

[0039] S23: Select parameters with cumulative variances reaching a threshold value as principal components;

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

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

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

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

[0044] In a specific embodiment, the multiple linear regression is performed using MATLAB software.

[0045] S4: Select preferred parameters from the basic parameters as shale oil mobility evaluation indexes, determine the weight of each shale oil mobility evaluation index using the entropy method, and establish a shale oil mobility evaluation mathematical model.

[0046] In a specific embodiment, the shale oil mobility evaluation indexes include the total porosity, connected proportion, permeability, water saturation, and shale wettability index.

[0047] S5: Calculate the connected proportion of the target shale sample according to the connected proportion quantitative calculation formula, 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, the target shale sample includes multiple samples, and the layer corresponding to the shale sample with the largest shale oil mobility evaluation coefficient is selected as the exploration target by calculating the shale oil mobility evaluation coefficients of multiple shale samples.

[0049] In a specific embodiment, the shale oil mobility evaluation method of the present application is used to evaluate the mobility of the shale of the Xiaganchaigou Formation of the Yingenling in the Qaidam Basin, and 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 from logging data, the connected porosity is obtained from carbon dioxide adsorption, nitrogen adsorption, and high-pressure mercury injection experiments, and the connected proportion 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 principal components

[0056] First, each geological parameter is standardized, and the standardization formula is:

[0057]

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

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

[0060] Table 2 Standardized basic parameters

[0061]

[0062]

[0063] Second, the correlation coefficient matrix is established, and 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 ith parameter and the jth parameter;

[0067] The correlation coefficient matrix is as follows:

[0068]

[0069] Third, the eigenvalues, variance proportion, and cumulative variance of the correlation coefficient matrix are calculated, and the characteristic equation of the correlation coefficient matrix is represented as:

[0070] |R-λE| = 0 (7)

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

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

[0073]

[0074] The characteristic value, variance proportion and cumulative variance calculation results are shown in Table 3:

[0075] Table 3: Characteristic value, variance proportion and cumulative variance calculation results

[0076] principal component eigenvalue variance ratio (%) cumulative variance ratio (%) 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 -2.776 x 10 -16 ]] -4.626 x 10 -15 ]] 100

[0077] In statistics, when the cumulative variance proportion is greater than or equal to 90%, the selected principal components have good explanatory effect on the information contained in the original data, so the number of principal components in practical application should be the number of principal components corresponding to the cumulative contribution rate greater than or equal to 90%.

[0078] From Table 3, when 3 principal components are extracted, the cumulative contribution rate reaches 92.885%, that is, when 3 principal components F1, F2 and F3 are extracted, 92.885% of the information of the 6 original geological parameters can be explained, which not only achieves the purpose of dimension reduction, but also retains 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 original geological variables.

[0079] Then, the characteristic values λ1=3.569, λ2=1.464 and λ3=0.541 corresponding to the principal components F1, F2 and F3 are substituted into the characteristic equation to obtain the characteristic vectors u1, u2 and u3 respectively 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 characteristic vector; Zx1 is the first normalized geological parameter, and so on.

[0086] In the embodiment, each principal component is 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 F1, F2 and F3, the principal component scores of each sample under F1, F2 and F3 are calculated, and the results are shown in Table 4:

[0091] Table 4 Principal component scores

[0092] sample number F1 F2 F3 sample number F1 F2 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 of the connectivity ratio based on geological variables

[0094] Taking the connectivity ratio as the dependent variable and the principal components as the independent variables, multiple linear regression is performed by using the multiple linear regression module in MATLAB software to obtain a quantitative calculation formula of the connectivity ratio:

[0095] T = aF1 + bF2 + cF3 + d = 0.131xF1 - 0.011xF2 - 0.024xF3 + 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 the embodiment, the determination coefficient R of the quantitative calculation formula of the connectivity ratio is 2 = 0.845 > 0.5, which indicates that the fitting effect is good, and the model can explain more than 84.5% of the variation of the dependent variable.

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

[0099] The total porosity, the connectedness ratio, the permeability, the water saturation, and the shale wettability index are selected as the shale oil mobility evaluation indexes. In the shale oil mobility layer selection, the higher the permeability, the lower the water saturation, the greater the total porosity, the higher the connectedness ratio and the shale wettability index, and the stronger the oil wettability, the better the shale oil flowability; therefore, the shale oil mobility is positively correlated with the total porosity, the connectedness ratio, the permeability, and the shale wettability index, and is negatively correlated with the water saturation. In this embodiment, the original data of the evaluation indexes of the three layers A, B, and C to be evaluated are shown in Table 5.

[0100] Table 5 Original data of evaluation indexes

[0101]

[0102]

[0103] There are negative numbers in the above original data, and all the values required in the entropy value calculation process are non-negative numbers. The negative numbers will affect the calculation of the entropy, and the original data with negative numbers need to be “non-negative shifted”. The calculation formula is as follows, and the non-negative shift 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 kth index of the kth sample; ∈ is a minimum constant (such as 10 -4 ).

[0106] Table 6 Non-negative shift processing results of original data of evaluation indexes

[0107]

[0108]

[0109] The non-negative shift processing results data of the evaluation indexes are percentaged, 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 jth index of the ith sample; ZQx ij is the value of the jth index of the ith sample after percentage; p is the sample quantity, p = 30; q is the evaluation index quantity, q = 5.

[0112] Table 7 Data after percentage of evaluation indexes

[0113]

[0114]

[0115] The entropy value of the jth evaluation index after percentage is calculated, and the calculation formula is as follows, and the entropy value calculation result is shown in Table 8

[0116]

[0117] Table 8 Evaluation parameter entropy value table

[0118] [E1] [E2] [E3] [E4] [E5] 0.9794 0.3932 0.9658 0.9810 0.9256

[0119] The weight of the jth geological parameter after percentage is calculated, as shown in Table 9.

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

[0121] Table 9 Evaluation parameter weight

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

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

[0124]

[0125] Table 10 Normalized weight of each evaluation parameter

[0126] [Equation 1] ω1' = ω1 - ω1 ​ ​ [CDATA[ω4']] ​ 0.0274 0.8063 0.0455 0.0253 0.0986

[0127] The shale oil mobility evaluation mathematical model is established as follows:

[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 shale oil mobility evaluation coefficient; ZΦ is the percentage of total porosity; ZT is the percentage of connectivity; ZK is the percentage of permeability; ZS w is the percentage of water saturation; ZP is the percentage of shale wettability index.

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

[0131] Table 11 shale oil mobility evaluation coefficient

[0132]

[0133]

[0134] The greater the shale oil mobility evaluation coefficient Y 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 is. According to the calculation result in Table 11, the shale oil mobility of the C section is the strongest, the A section is the second, and the B section is the weakest. The C section is the most favorable exploration target and should be developed preferentially.

[0135] In summary, the shale oil mobility evaluation can be accurately and efficiently realized by the present application. Compared with the prior art, the present application has significant progress.

[0136] The above description is only the preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with the preferred embodiment, it is not intended to limit the present application. Any skilled person in the art can make some changes or modifications to the above disclosed technical content without departing from the technical solution of the present application, and any simple modification, equivalent change and modification of the above embodiment according to the technical essence of the present application still belong to the scope of the technical solution of the present application.

Claims

1. A method for evaluating the mobility of shale oil, characterized in that, Includes the following steps: S1: Obtain shale samples from the target strata 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 interconnected porosity; S2: Perform principal component analysis on the basic parameters to obtain 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 connectivity ratio, and use the connectivity ratio as the dependent variable and the principal component as the independent variable to perform multiple linear regression to obtain a quantitative calculation formula for the connectivity ratio based on geological variables. S4: Select parameters from the basic parameters as shale oil mobility evaluation indicators, and use the entropy method to determine the weight of each shale oil mobility evaluation indicator to establish a mathematical model for shale oil mobility evaluation; the shale oil mobility evaluation indicators include total porosity, connectivity ratio, permeability, water saturation and shale wettability index; S5: Calculate the connectivity ratio of the target shale sample according to the quantitative calculation formula of the connectivity ratio, and calculate the shale oil mobility evaluation coefficient 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 from well logging data.

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

4. The shale oil mobility evaluation method according to claim 1, characterized in that, Step S2, when performing principal component analysis, 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 percentages, and cumulative variance of the correlation coefficient matrix; S23: Select the parameters whose cumulative variance reaches the threshold as principal components; S24: Substitute the eigenvalues ​​corresponding to each principal component into the characteristic equation to obtain the eigenvectors of each principal component; S25: Obtain the expression for the parameters of each principal component based on the eigenvectors of each principal component.

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

6. The shale oil mobility evaluation method according to any one of claims 1-5, characterized in that, In step S5, the target shale samples include multiple samples. By calculating the shale oil mobility evaluation coefficient of multiple shale samples, the layer corresponding to the shale sample with the largest shale oil mobility evaluation coefficient is selected as the exploration target.

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