Classification evaluation method, device and equipment for micro-pore structure of reservoir and medium

Abstract

The invention discloses a reservoir micro-pore structure classification evaluation method, device, equipment and medium, and the method comprises the steps: obtaining a plurality of micro-pore structure evaluation parameters of a sample, the plurality of evaluation parameters being obtained based on a mercury injection experiment result and including an average pore throat radius, a Swanson parameter, a mainstream pore throat fractal dimension and a total fractal dimension; clustering the plurality of evaluation parameters, and counting a numerical range of the plurality of evaluation parameters in each category according to a clustering result, so as to determine a micro-pore structure classification standard; and carrying out micro-pore structure classification on a to-be-classified sample based on the classification standard. According to the method, reasonable and effective parameters can be selected from numerous parameters, the comprehensiveness and rationality of classification evaluation are ensured, the classification boundary of the pore structure can be effectively determined based on a clustering method, and the subjectivity of classification boundary determination is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a reservoir microscopic pore structure classification evaluation method, device, equipment and medium. Background Art

[0002] The microscopic pore structure of reservoir rocks directly affects the storage and seepage capacity of the reservoir, and ultimately determines the development effect of the reservoir. Therefore, the classification and evaluation of pore structure is an important part of the description of reservoir microscopic characteristics in reservoir description. Pore structure refers to the geometric shape, size, distribution and interconnectedness of pores and throats in rocks. Pores reflect the storage capacity of rocks, while the shape and size of throats control the storage and seepage capacity of pores. There are two most commonly used methods for studying rock pore structure. One is the image method, which analyzes the image of cast thin sections through an electron microscope; the other is the capillary pressure method, which quantitatively determines the characteristics of pores and throats by measuring capillary pressure. The main methods for measuring capillary pressure in the laboratory include semi-permeable partition method, mercury injection method and centrifuge method. Among them, the mercury injection method is fast and accurate, and can qualitatively and semi-quantitatively study the pore structure of the reservoir, and obtain parameters that can reflect the size of pore throats, connectivity and seepage capacity from the capillary curve. These parameters can be used to further classify and evaluate the microscopic pore structure.

[0003] Domestic and foreign scholars have done a lot of work on the study of reservoir microscopic pore structure using mercury injection, but there is no unified standard for its classification and evaluation method. Relevant existing technologies include patent documents CN102297828A, CN103670384A, CN106053315A, CN106442262A, CN106950606A, CN108181219A, CN112149341A, CN114429165A, etc. Non-patent literature includes ① Wang Ruifei et al. "Classification and Evaluation of Microscopic Pore Structure of Ultra-low Permeability Sandstone Reservoirs", ② Ge Xiaobo et al. "Characterization of Microscopic Pore Structure of Tight Sandstone Reservoirs Based on Fractal Theory - Taking Tight Sandstone Reservoirs in Jizhong Depression as an Example", and ③ Feng Xiaozhe "Microscopic Pore Structure and Fractal Characteristics of Tight Sandstone Reservoirs of Lower Shihezi Formation in Sulige Area, Ordos Basin".

[0004] From the relevant literature and patent documents, the methods for evaluating the microscopic pore structure by mercury injection method almost all use the morphology of mercury injection curve for classification. This method is feasible when there is less data, but its classification efficiency is low when there is a large amount of experimental data, and it is highly subjective and cannot clearly define the pore structure classification boundary. In patent CN106442262A and document ①, it is proposed to use the product of the maximum mercury saturation and the mercury removal efficiency as a parameter for quantitative classification of pore structure, but this method only selects limited pore structure parameters, and the characterization of pore structure characteristics is not comprehensive, and its classification is not scientific and reasonable enough.

[0005] Quantitative evaluation of pore structure using parameters obtained by mercury injection is a hot topic in current research. However, due to the large number of relevant parameters obtained by mercury injection, it is difficult to screen the evaluation parameters, which in turn affects the comprehensiveness and rationality of the classification evaluation. The parameters obtained by mercury injection can be roughly divided into pore size, capillary pressure, heterogeneity parameters, etc., and each aspect has many parameters. The more parameters there are, the more difficult it is to classify, and the fewer parameters there are, the more comprehensive the pore throat characteristic evaluation will be affected.

[0006] Summarizing the existing technologies, it can be seen that there are two problems in the process of using the parameters obtained by mercury intrusion porosimetry to classify and evaluate pore structures:

[0007] First, the traditional mercury injection classification and evaluation often relies on the morphology of the mercury injection curve for qualitative classification. This not only has low classification efficiency but is also highly subjective, and the boundaries of pore structure classification and evaluation are difficult to determine.

[0008] Second, the mercury intrusion method obtains numerous relevant parameters, and it is difficult to screen the evaluation parameters, which in turn affects the comprehensiveness and rationality of the classification evaluation. Summary of the invention

[0009] The main purpose of the present invention is to provide a reservoir microscopic pore structure classification evaluation method, device, equipment and medium to solve the problems of difficult parameter selection, strong subjectivity of classification and difficulty in determining classification boundaries in the conventional pore structure evaluation process.

[0010] According to one aspect of the present invention, a reservoir microscopic pore structure classification and evaluation method is proposed, comprising:

[0011] Acquire multiple microscopic pore structure evaluation parameters of the sample, wherein the multiple evaluation parameters are obtained based on mercury injection test results and include: average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension;

[0012] Clustering the plurality of evaluation parameters, and statistically analyzing the numerical ranges of the plurality of evaluation parameters in each category according to the clustering results, so as to determine the classification standard of the microscopic pore structure;

[0013] The microscopic pore structure of the samples to be classified is classified based on the classification criteria.

[0014] According to an embodiment of the present invention, the classification standard defines a plurality of categories of microscopic pore structures and a plurality of value ranges of the evaluation parameters corresponding to each category.

[0015] According to one embodiment of the present invention, the average pore throat radius is directly obtained based on mercury injection test results.

[0016] According to one embodiment of the present invention, the Swanson parameter is calculated according to the following formula:

[0017] Swanson parameter = S′ Hg / P c '

[0018] Among them, S′ Hg / P c ′ is S Hg As the horizontal axis, S Hg / P c The S corresponding to the vertex of the curve drawn for the ordinate Hg / P c The maximum value of S Hg is mercury saturation; P c is the capillary pressure.

[0019] According to one embodiment of the present invention, the total fractal dimension and the mainstream pore throat fractal dimension are calculated according to the following formula:

[0020] D 总 =D1*S′ Hg +D2*(1-S′ Hg )

[0021] D1=3-k1

[0022] D2=3-k2

[0023] Among them, D 总 is the total fractal dimension; D1 is the mainstream pore throat fractal dimension; D2 is the small pore throat fractal dimension; k1 and k2 are the fractal dimensions of the pore throat with lg(r) as the horizontal coordinate and lg(100-S Hg ) is the scatter plotted as the ordinate, and the slope is obtained by linear regression of the data above and below the inflection point; r is the pore throat radius.

[0024] According to an embodiment of the present invention, before performing the clustering, the method further comprises: normalizing the values ​​of the plurality of evaluation parameters to be within the range of 0 to 1.

[0025] According to one embodiment of the present invention, the clustering is performed using a K-means clustering method.

[0026] According to another aspect of the present invention, a reservoir microscopic pore structure classification and evaluation device is provided, comprising:

[0027] An acquisition module is configured to acquire a plurality of microscopic pore structure evaluation parameters of the sample, wherein the plurality of evaluation parameters are obtained based on mercury injection test results and include: average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension;

[0028] A determination module is configured to cluster the plurality of evaluation parameters and to count the numerical ranges of the plurality of evaluation parameters in each category according to the clustering results, so as to determine the classification standard of the microscopic pore structure;

[0029] The classification module is configured to classify the microscopic pore structure of the sample to be classified based on the classification standard.

[0030] According to another aspect of the present invention, a computer device is provided, comprising:

[0031] at least one processor; and

[0032] A memory, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the method described in any of the above embodiments is implemented.

[0033] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in any one of the above embodiments is implemented.

[0034] In the reservoir microscopic pore structure classification and evaluation method according to the embodiment of the present invention, Swanson parameters and pore structure fractal dimensions (including mainstream pore throat fractal dimensions and total fractal dimensions) are introduced, and conventional mercury injection data (average pore throat radius) are combined to classify and evaluate the pore structure. Swanson parameters can well characterize the comprehensive seepage capacity of microscopic pore throats, and mainstream pore throat fractal dimensions and total dimensions have better representativeness in terms of pore structure heterogeneity. Therefore, the present invention can select reasonable and effective parameters from a large number of parameters to ensure the comprehensiveness and rationality of the classification evaluation. In addition, the present invention determines the classification criteria based on the clustering method, which can effectively determine the classification boundaries of the pore structure and avoid the subjectivity of determining the classification boundaries. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flow chart showing a reservoir microscopic pore structure classification and evaluation method according to an embodiment of the present invention;

[0037] Figure 2 A schematic diagram showing calculation of Swanson parameters, mainstream pore throat fractal dimensions and total fractal dimensions according to an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of a reservoir microscopic pore structure classification and evaluation device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0040] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. The subsequent embodiments will not explain this one by one.

[0041] refer to Figure 1 The present invention proposes a reservoir microscopic pore structure classification and evaluation method, comprising:

[0042] S1, obtaining a plurality of microscopic pore structure evaluation parameters of the sample, wherein the plurality of evaluation parameters are obtained based on mercury injection test results and include: average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension;

[0043] S2, clustering the plurality of evaluation parameters, and statistically analyzing the numerical ranges of the plurality of evaluation parameters in each category according to the clustering results, so as to determine the classification standard of the microscopic pore structure;

[0044] S3, classifying the microscopic pore structure of the samples to be classified based on the classification criteria.

[0045] The present invention mainly classifies and evaluates the microscopic pore structure of the reservoir based on the mercury injection method. In the embodiment of the present invention, the Swanson parameter and the pore structure fractal dimension (including the mainstream pore throat fractal dimension and the total fractal dimension) are introduced, and the conventional mercury injection data (average pore throat radius) are combined to classify and evaluate the pore structure. The Swanson parameter can well characterize the comprehensive seepage capacity of the microscopic pore throat, and the mainstream pore throat fractal dimension and the total dimension are better representative in terms of the heterogeneity of the pore structure. Therefore, the present invention can select reasonable and effective parameters from a large number of parameters to ensure the comprehensiveness and rationality of the classification evaluation. In addition, the present invention determines the classification criteria based on the clustering method, which can effectively determine the classification boundaries of the pore structure and avoid the subjectivity of the determination of the classification boundaries.

[0046] In step S1, multiple evaluation parameters are obtained based on the mercury injection capillary pressure curve test experiment. Among them, the average pore throat radius can be directly obtained based on the mercury injection test results. Figure 2 In some embodiments, the Swanson parameter is calculated according to the following formula:

[0047] Swanson parameter = S′ Hg / P c '

[0048] Among them, S′ Hg / P c ′ is S Hg As the horizontal axis, S Hg / P c The S corresponding to the vertex of the curve drawn for the ordinate Hg / P c The maximum value of S Hg is mercury saturation; P c is the capillary pressure; S′ Hg is the mercury saturation at the corresponding position of the Swanson parameter, %; P c ′ is the capillary pressure at the position corresponding to the Swanson parameter, MPa.

[0049] When the capillary pressure curve is plotted in a double logarithmic coordinate system, its shape is similar to a hyperbola. The curve has an inflection point. Before the inflection point appears, the slope of the capillary curve is small, indicating that mercury is easier to enter the pore throats, and this part of the pore throats is larger and has good connectivity; after the inflection point appears, the slope of the capillary curve is larger, indicating that mercury is not easy to enter the pore throats and the mercury injection pressure needs to be continuously increased. This part of the pore throats is relatively small and has poor connectivity, and the flow ability of the non-wetting phase in it is significantly reduced. Swanson et al. conducted experimental analysis and believed that the part of the pore throat before the inflection point appears is the effective space for fluid flow, and the throat radius corresponding to the inflection point refers to the minimum throat size connecting the entire effective pore space, which is called the lower limit of the mainstream pore throat radius. At the inflection point, the mercury saturation S Hgand capillary pressure P c The ratio of is the largest, which is called the Swanson parameter. The size of the Swanson parameter is closely related to the pore structure. If the pore structure is good, the Swanson parameter is large, otherwise it is small.

[0050] refer to Figure 2 In some embodiments, the total fractal dimension and the mainstream pore throat fractal dimension are calculated according to the following formula:

[0051] D 总 =D1*S′ Hg +D2*(1-S′ Hg )

[0052] D1=3-k1

[0053] D2=3-k2

[0054] Among them, D 总 is the total fractal dimension; D1 is the mainstream pore throat fractal dimension; D2 is the small pore throat fractal dimension; k1 and k2 are the fractal dimensions of the pore throat with lg(r) as the horizontal coordinate and lg(100-S Hg ) is the scatter plotted as the ordinate, and the slope is obtained by linear regression of the data above and below the inflection point; r is the pore throat radius, μm.

[0055] Fractal dimension is currently a popular parameter for quantitatively characterizing the heterogeneity and complexity of pore throat structure. It was first proposed by the French-American mathematician Mandelbrot to characterize the irregularity of complex shapes. Later, Thomvson et al. believed that sedimentary rocks also have fractal characteristics, with values ​​ranging from 2 to 3. The closer the fractal dimension is to 3, the worse the pore throat sorting, the more complex the pore throat structure, and the stronger the microscopic heterogeneity; conversely, the opposite is true.

[0056] According to fractal geometry theory, the capillary pressure of the reservoir obeys the Laplace equation (i.e., P c =2σcosθ / r,P c is the capillary pressure corresponding to the pore diameter; σ is the surface tension of the liquid; θ is the wetting contact angle), we can get:

[0057] lg(S)=(3-D)lg(r)+(D-3)r max

[0058] Where: r is the pore throat radius; r max is the maximum pore throat radius; S is the proportion of pores smaller than radius r; D is the fractal dimension.

[0059] On the capillary pressure curve, the proportion of pores smaller than the radius r is S = 100-S HgTherefore, the calculation of fractal dimension by mercury injection method only needs to be done by the pore throat radius lg(r) and lg(100-S Hg ) can be determined by using the regression scatter plot.

[0060] Since the mercury injection curve generally has an inflection point, when calculating the fractal dimension using the capillary pressure curve, the segmented regression method is often used, and each segment represents the pore throat within a certain radius. Here, the point corresponding to the Swanson parameter on the mercury injection curve is the inflection point of the mercury injection curve. The Swanson parameter is the lower limit of the mainstream pore throat. Therefore, the regression scatter points of the fractal dimension are segmented by the Swanson parameter. The fractal dimension calculated by regression for the segment greater than or equal to the lower limit of the mainstream pore throat radius is the mainstream pore throat fractal dimension, and the segment less than the lower limit of the mainstream pore throat radius is the small pore throat fractal dimension. The total fractal dimension is the mercury saturation weighted average of the mainstream pore throat fractal dimension and the small pore throat fractal dimension.

[0061] The present invention classifies the reservoir microscopic pore structure according to the Swanson parameter and fractal dimension. The Swanson parameter represents the size of the pore throat and characterizes the comprehensive seepage capacity of the microscopic pore throat; the fractal dimension represents the fractal characteristics of the pore throat distribution and characterizes the heterogeneity of the microscopic pore throat; the combination of the two can reasonably and effectively classify and evaluate the reservoir microscopic pore structure.

[0062] In step S2, the average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension of each sample are clustered by software, and the distribution range of the average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension are statistically analyzed according to the clustering results, so as to establish a parameter standard for pore structure classification. The classification standard can limit multiple categories of microscopic pore structures, and the value ranges of multiple evaluation parameters corresponding to each category. In this step, the classification boundaries of the pore structure are determined by computer clustering methods to avoid the subjectivity of determining the classification boundaries.

[0063] In some embodiments, before performing the clustering, the method further includes: normalizing the values ​​of the plurality of evaluation parameters to be within the range of 0 to 1, thereby ensuring that the clustering result is not affected by the different magnitudes of the values ​​of the evaluation parameters.

[0064] In some embodiments, the clustering is performed using K-means clustering.

[0065] In step S3, the microscopic pore structure of other subsequent samples can be classified according to the parameter boundary standard determined by clustering. Step S3 may include: obtaining multiple microscopic pore structure evaluation parameters of the sample to be classified, wherein the multiple evaluation parameters are obtained based on the mercury injection test results and include: average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, total fractal dimension; and based on the multiple evaluation parameters of the sample to be classified and the classification standard, classifying the microscopic pore structure of the sample to be classified.

[0066] As described above, in order to solve the problem of difficulty in selecting parameters in the process of evaluating pore structure by mercury intrusion, the present invention proposes to use Swanson parameters and mainstream fractal dimensions to replace conventional mercury intrusion parameters for classification evaluation of pore structure.

[0067] In terms of the selection of pore structure evaluation parameters, the classification and evaluation of pore structure should consider both its seepage capacity and heterogeneity. In the prior art, fractal dimension is used to characterize pore throat heterogeneity, but in the classification and evaluation, only fractal dimension is used to evaluate pore structure, which is actually one-sided; in addition, in the traditional pore structure classification and evaluation, conventional parameters such as displacement pressure and average pore throat radius are often used, which often only consider the pore throat size and fail to consider the heterogeneity of microscopic pore throat.

[0068] The present invention introduces the Swanson parameter to represent the lower limit of the mainstream pore throat radius, characterizing the comprehensive seepage capacity of the microscopic pore throat. The mainstream pore throat fractal dimension represents the complexity of the mainstream pore throat distribution and characterizes the heterogeneity of the microscopic pore throat. The combination of the two can reasonably and effectively classify and evaluate the microscopic pore structure of the reservoir. The Swanson parameter and fractal dimension are more representative than traditional mercury injection parameters.

[0069] The following is an analysis of the mercury injection test data from a certain work area to illustrate this.

[0070] The screening of microscopic pore parameters is often carried out through correlation analysis. From the correlation analysis between the pore structure parameters and the macroscopic physical property parameters (Table 1), it can be seen that among the parameters characterizing the pore throat size (maximum pore throat radius, average pore throat radius, median radius, Swanson parameter), the Swanson parameter has the best correlation with permeability (0.894), and the absolute value average of the correlation coefficient with other parameters (0.744) is the largest, which also shows that the comprehensive representativeness of this parameter in the microscopic pore structure parameters is the strongest. In addition, the average pore throat radius also has a good correlation. For fractal dimension, the present invention divides the pore structure fractal dimension into mainstream pore throat fractal dimension, small pore fractal dimension and total dimension, which respectively represent the heterogeneity of pore throat distribution within a specific range. From the results of each correlation analysis, compared with the sorting coefficient and coefficient of variation of conventional pore throat heterogeneity parameters, the mainstream pore throat fractal dimension and total dimension have a greater correlation with the permeability than the sorting coefficient and coefficient of variation, and the average value of the absolute value of the correlation coefficient between the mainstream pore throat fractal dimension and total dimension and other parameters is also greater than the sorting coefficient and coefficient of variation, which also shows that the mainstream pore throat fractal dimension and total dimension are more representative in terms of pore structure heterogeneity. In the current literature, fractal dimension is recognized as the most effective parameter for characterizing pore structure heterogeneity. Based on the above correlation analysis, the present invention selects average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension as microscopic pore structure evaluation parameters. In the embodiments of the present invention, only these four parameters can be used without using other parameters commonly used in the prior art.

[0071] Table 1: Correlation analysis of reservoir microscopic pore structure parameters in a certain work area

[0072]

[0073]

[0074] In view of the problem that the pore structure classification by mercury injection method is highly subjective and difficult to determine the classification boundary, the present invention mainly proposes to cluster the average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension and total fractal dimension of each sample by computer clustering method, and according to the clustering results, the distribution range of the average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension and total fractal dimension is statistically analyzed, and the parameter standard for pore structure classification can be established. Compared with the current classification of pore structure by capillary pressure curve morphology, the present invention avoids the problem that the classification is highly subjective and difficult to determine the classification boundary.

[0075] Finally, the relevant parameters of subsequent samples are calculated using the established pore structure classification standard. The pore structure of subsequent samples can be classified by referring to the classification standard.

[0076] refer to Figure 3 The present invention also provides a reservoir microscopic pore structure classification and evaluation device 100, comprising:

[0077] An acquisition module 10 is configured to acquire a plurality of microscopic pore structure evaluation parameters of the sample, wherein the plurality of evaluation parameters are obtained based on mercury injection test results and include: average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension;

[0078] A determination module 20 is configured to cluster the plurality of evaluation parameters, and to count the numerical ranges of the plurality of evaluation parameters in each category according to the clustering results, so as to determine the classification standard of the microscopic pore structure;

[0079] The classification module 30 is configured to classify the microscopic pore structure of the sample to be classified based on the classification standard.

[0080] The present invention further proposes a computer device, comprising: at least one processor; and a memory, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the method described in any of the above embodiments is implemented.

[0081] The present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0082] The following is a description based on specific embodiments.

[0083] Example 1

[0084] Taking the mercury injection test data of a certain work area as an example, this paper applies the method of this paper to classify and evaluate the microscopic pore structure of the reservoir. The specific situation is as follows:

[0085] (1) Obtaining microscopic pore structure evaluation parameters

[0086] The average pore throat radius of each sample was obtained by mercury injection test results, and the Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension were calculated according to the calculation method. The calculation of Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension is mainly carried out by drawing (refer to Figure 1 )

[0087] (2) The K-mean value clustering method is used to cluster the parameters of each sample and determine the parameter classification boundaries of each type of microscopic pore structure.

[0088] K-mean value clustering method is the most common clustering method in cluster analysis, which can generally be implemented using a small program written in Python language or some commercial software (such as SPSS). This paper mainly uses a small program written in Python language to perform K-mean value clustering on the average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension of each sample.

[0089] In K-mean value clustering, K value is divided into several categories. Conventional pore structure classification is generally divided into 3 to 4 categories, which can be selected according to the specific experimental data range. In this clustering, due to the large difference in the average pore throat radius of the samples, the K value is 4, that is, the microscopic pore structure is divided into 4 categories.

[0090] In the clustering process, in order to ensure that the clustering results are not affected by the different numerical magnitudes of the pore structure parameters, it is necessary to first standardize the parameters. The standardization generally uses the range normalization method to standardize all types of parameters to the range of 0 to 1.

[0091] After K-mean value clustering, the clustering results will be returned and each sample will be marked. According to the clustering results of the samples, the numerical range and average value of each parameter in each category are counted, and the clustering marked categories are reordered according to the average value of the Swanson parameter of each category. The average value of the Swanson parameter of category 1 pore structure is the largest, and that of category 4 is the smallest. Finally, the classification boundaries and standards of the microscopic pore structure can be obtained (Table 2).

[0092] Table 2: Classification standard of reservoir microscopic pore structure in a certain work area (minimum value to maximum value (average value))

[0093] Average pore throat radius Swanson parameters Total fractal dimension Mainstream pore throat fractal dimension Category 1 4.2～5.7(4.84) 271.86～380.3(316.37) 1.98～2.27(2.13) 2.31～2.58(2.48) Category 2 3.24～4.71(3.82) 186.1～249.32(214.89) 2.22～2.5(2.37) 2.6～2.75(2.52) Category 3 1.87～3.34(2.49) 61.5～176.57(106.5) 2.52～2.84(2.71) 2.63～2.88(2.8) Category 4 0.17～2.11(1) 3.29～66.74(31.45) 2.59～3(2.82) 2.67～2.95(2.83)

[0094] (3) Classify the microscopic pore structure according to the reservoir microscopic pore structure classification and evaluation criteria.

[0095] In this step, the average pore throat radius, Swanson parameter, mainstream pore throat branch dimension and total dimension of the sample to be classified are calculated, and the microscopic pore structure of the sample can be classified according to the size of the sample parameters by referring to the classification standard established in the previous step. Table 3 shows the classification results of the microscopic pore structure of the mercury injection sample of the latest well tested using the classification standard established in the previous step (Table 2).

[0096] Table 3: Classification and evaluation results of reservoir microscopic pore structure in a certain work area

[0097] Serial number Sample depth Average pore throat radius Swanson parameters Mainstream pore throat dimension Total Dimensions Classification 1 1907.98 0.45 8.16 2.88 2.91 4 2 1915.20 1.01 36.06 2.77 2.83 4 3 1916.70 3.45 229.30 2.33 2.68 2 4 1918.07 2.29 85.33 2.77 2.87 3 5 1918.88 3.62 195.49 2.26 2.71 2 6 1919.76 1.52 30.18 2.88 2.91 4 7 1922.95 2.78 109.87 2.84 2.87 3 8 1923.98 3.26 157.61 2.78 2.85 3 9 1925.24 3.34 176.57 2.79 2.83 3 10 1926.30 5.70 380.30 2.19 2.31 1 11 1927.57 4.20 295.65 2.01 2.47 2 12 1928.29 5.28 300.89 2.12 2.53 1

[0098] In summary, in order to solve the problems of difficult parameter selection, strong subjectivity in classification and difficulty in determining classification boundaries in the process of mercury injection pore structure evaluation, the present invention proposes to use Swanson parameters and fractal dimensions to replace conventional mercury injection parameters for classification evaluation of pore structure, and to determine the classification criteria based on the clustering method, which can achieve the comprehensiveness, rationality and objectivity of the classification evaluation and improve the classification efficiency.

[0099] It should be noted that a person skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0100] Furthermore, it should be appreciated that the computer-readable storage medium (eg, memory) herein may be either a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0101] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A reservoir microscopic pore structure classification and evaluation method, characterized in that: include: Acquire multiple microscopic pore structure evaluation parameters of the sample, wherein the multiple evaluation parameters are obtained based on mercury injection test results and include: average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension; Clustering the plurality of evaluation parameters, and statistically analyzing the numerical ranges of the plurality of evaluation parameters in each category according to the clustering results, so as to determine the classification standard of the microscopic pore structure; The microscopic pore structure of the samples to be classified is classified based on the classification criteria.

2. The method according to claim 1, characterized in that: The classification standard defines a plurality of categories of microscopic pore structures and a plurality of value ranges of the evaluation parameters corresponding to each category.

3. The method according to claim 1, characterized in that The average pore throat radius is directly obtained based on the mercury injection test results.

4. The method according to claim 1, characterized in that: The Swanson parameter is calculated according to the following formula: Swanson parameter = S′ Hg / P c ' Among them, S′ Hg / P c ′ is S Hg As the horizontal axis, S Hg / P c The S corresponding to the vertex of the curve drawn for the ordinate Hg / P c The maximum value of S Hg is the mercury saturation; P c is the capillary pressure.

5. The method according to claim 4, characterized in that The total fractal dimension and mainstream pore throat fractal dimension are calculated according to the following formula: D 总 =D1*S′ Hg +D2*(1-S′ Hg ) D1=3-k1 D2=3-k2 Among them, D 总 is the total fractal dimension; D1 is the mainstream pore throat fractal dimension; D2 is the small pore throat fractal dimension; k1 and k2 are the fractal dimensions of the pore throat with lg(r) as the horizontal coordinate and lg(100-S Hg ) is the scatter plotted as the ordinate, and the slope is obtained by linear regression of the data above and below the inflection point; r is the pore throat radius.

6. The method according to claim 1, characterized in that Before performing the clustering, the method further includes: normalizing the values ​​of the plurality of evaluation parameters to be within a range of 0 to 1.

7. The method according to claim 1, characterized in that The clustering is performed using the K-means clustering method.

8. A reservoir microscopic pore structure classification and evaluation device, characterized in that: include: An acquisition module is configured to acquire a plurality of microscopic pore structure evaluation parameters of the sample, wherein the plurality of evaluation parameters are obtained based on mercury injection test results and include: average pore throat radius, Swanson parameter, mainstream pore throat fractal dimension, and total fractal dimension; A determination module is configured to cluster the plurality of evaluation parameters and to count the numerical ranges of the plurality of evaluation parameters in each category according to the clustering results, so as to determine the classification standard of the microscopic pore structure; The classification module is configured to classify the microscopic pore structure of the sample to be classified based on the classification standard.

9. A computer device comprising: at least one processor; as well as A memory storing a computer program executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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