Tight oil reservoir classification evaluation method and device

Abstract

The invention discloses a tight oil reservoir classification evaluation method and device. The method comprises the following steps: acquiring reservoir classification evaluation sample data; variables in the sample data comprise porosity, permeability, a sorting coefficient, a median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury removal efficiency and sand body thickness; performing correlation analysis on the sample data to obtain a plurality of main factors meeting preset conditions; performing clustering analysis on the sample data according to the plurality of main factors to obtain a clustering result; carrying out gradual discriminant analysis on the sample data and the clustering result to obtain a discriminant function; and performing quantitative and qualitative analysis on the sample data by using the discrimination function to obtain a classification evaluation result of the tight oil reservoir. According to the method, more accurate and more comprehensive compact oil reservoir classification is realized by considering influence factors in multiple aspects.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir geophysical exploration, and in particular to a classification and evaluation method and device for tight oil reservoirs. Background Art

[0002] Reservoir classification and evaluation is an important part of reservoir research. The main purpose of reservoir classification and evaluation research is to make a basic evaluation of the quality of reservoirs based on the acquisition of detailed data, aiming to select high-quality reservoirs and oil and gas sweet spots to reduce exploration risks. In the existing technology, reservoir evaluation methods mainly focus on the following aspects: (1) Evaluation based on macroscopic characteristics of reservoirs, mainly using parameters such as sedimentary phases and physical properties to evaluate reservoirs; (2) Evaluation based on microscopic characteristics of reservoirs, mainly using parameters such as pore type and pore structure to evaluate reservoirs; (3) Evaluation based on the combination of macroscopic and microscopic characteristics of reservoirs; (4) Comprehensive quantitative evaluation of reservoirs, based on single-parameter reservoir evaluation, combined with multiple parameters of reservoir classification, and then using mathematical methods such as discriminant analysis to evaluate reservoirs, by extracting the correlation characteristics between parameters, comprehensively analyzing the relationship between different types of reservoirs and oil and gas distribution, thereby providing support for guiding oil and gas exploration and development.

[0003] From the perspective of evaluation methods, it is believed that except for the purely quantitative evaluation of the fourth method, the first three methods are multi-parameter comprehensive qualitative and semi-quantitative evaluations, which are based on the classification standards of evaluation parameters, that is, the limit values ​​of various reservoir evaluation parameters. Summary of the invention

[0004] In order to obtain better reservoir classification and evaluation results, an embodiment of the present invention provides a method and device for classifying and evaluating tight oil reservoirs.

[0005] In a first aspect, an embodiment of the present invention provides a method for classifying and evaluating tight oil reservoirs, the method comprising:

[0006] Obtaining reservoir classification evaluation sample data; the variables in the sample data include: porosity, permeability, sorting coefficient, median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury withdrawal efficiency and sand body thickness;

[0007] Performing correlation analysis on the sample data to obtain multiple main factors that meet preset conditions;

[0008] Performing cluster analysis on the sample data according to the multiple main factors to obtain a clustering result;

[0009] Performing stepwise discriminant analysis on the sample data and the clustering results to obtain a discriminant function;

[0010] The discriminant function is used to perform quantitative and qualitative analysis on the sample data to obtain a classification and evaluation result of tight oil reservoirs.

[0011] In one or some optional implementations of the embodiment of the present application, the correlation analysis is performed on the sample data to obtain a plurality of main factors that meet preset conditions, including:

[0012] Calculate the correlation coefficient matrix based on the sample data and variables;

[0013] Decomposing the correlation coefficient matrix to obtain multiple eigenvalues ​​and corresponding eigenvectors;

[0014] Calculate the contribution rate and cumulative contribution rate of each eigenvalue according to the multiple eigenvalues;

[0015] Determine the number of main factors that meet the preset conditions according to the cumulative contribution rate and the preset threshold;

[0016] Arrange the eigenvalues ​​and the corresponding eigenvectors from large to small according to the size of the eigenvalues, select the eigenvectors corresponding to the eigenvalues ​​in the front row according to the number of the main factors, and obtain the eigenvectors corresponding to the multiple main factors;

[0017] The characteristic vectors corresponding to the plurality of principal factors are transformed, and the plurality of principal factors satisfying preset conditions are obtained by combining the variables in the sample data.

[0018] In one or some optional implementations of the embodiments of the present application, clustering analysis is performed on the sample data according to the principal factors to obtain clustering results, including:

[0019] According to the main factor and the sample data, the sample data with the variable as the main factor is obtained;

[0020] According to the sample data with the variable as the main factor, each sample data is set as a cluster, and the distance between each pair of clusters is calculated to obtain a distance matrix;

[0021] Merge the two clusters with the shortest distance in the distance matrix to obtain a new cluster, and update the distance matrix;

[0022] Repeat the above operation of merging the two clusters with the shortest distance and updating the distance matrix until the preset conditions are met and the clustering result is obtained.

[0023] In one or some optional implementations of the embodiments of the present application, performing stepwise discriminant analysis on the sample data and the clustering results to obtain a discriminant function includes:

[0024] According to the sample data, the autocorrelation coefficient matrix is ​​calculated based on the following formula 1:

[0025]

[0026] In the formula, S is the covariance, r is the coefficient in the autocorrelation coefficient matrix R, and i and j are variables;

[0027] Calculate the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix;

[0028] Calculating weighting coefficients according to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix;

[0029] Calculating a plurality of undetermined discriminant functions according to the weighted coefficients;

[0030] A discriminant function is obtained according to the multiple undetermined discriminant functions, the sample data and the clustering result.

[0031] In one or some optional implementations of the embodiment of the present application, the weighting coefficient is calculated according to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix, including:

[0032] According to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix, the discriminant function coefficients are calculated based on the following formula 2;

[0033]

[0034] In the formula, b is the discriminant function coefficient, μ and λ are the eigenvector and eigenvalue of the autocorrelation coefficient matrix, respectively, and i and j are variables;

[0035] According to the discriminant function coefficient, the weighting coefficient is calculated based on the following formula 3:

[0036]

[0037] Wherein, a is the weighting coefficient, p is the total number of the main factors, b is the discriminant function coefficient, and i and j are variables.

[0038] In one or some optional implementations of the embodiment of the present application, obtaining a discriminant function according to the multiple undetermined discriminant functions, the sample data and the clustering result includes:

[0039] For each undetermined discriminant function, a plurality of sample data are substituted into the undetermined discriminant function to obtain a corresponding plurality of discriminant results;

[0040] Determining the accuracy rate corresponding to the undetermined discriminant function according to the discriminant results of the plurality of sample data and the corresponding clustering results;

[0041] The undetermined discriminant function with the highest accuracy is selected as the discriminant function.

[0042] In one or some optional implementations of the embodiment of the present application, before performing correlation analysis on the sample data, the method further includes:

[0043] Performing forward processing on the sample data to map the sample data to a preset range;

[0044] The sample data is standardized and mapped to a preset distribution.

[0045] In a second aspect, an embodiment of the present invention provides a tight oil reservoir classification and evaluation device, the device comprising:

[0046] The first acquisition module is used to acquire reservoir classification evaluation sample data; the variables in the sample data include: porosity, permeability, sorting coefficient, median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury withdrawal efficiency and sand body thickness;

[0047] A first analysis module, used to perform correlation analysis on the sample data to obtain a plurality of main factors that meet preset conditions;

[0048] A first clustering module, used for performing cluster analysis on the sample data according to the multiple main factors to obtain a clustering result;

[0049] A second analysis module is used to perform stepwise discriminant analysis on the sample data and the clustering results to obtain a discriminant function;

[0050] The third analysis module is used to use the discriminant function to perform quantitative and qualitative analysis on the sample data to obtain a classification evaluation result of the tight oil reservoir.

[0051] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for classifying tight oil reservoirs.

[0052] In a fourth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned tight oil reservoir classification method when executing the computer program.

[0053] In a fifth aspect, an embodiment of the present invention provides a computer program product comprising instructions, which, when executed on a computer device, enables the computer device to execute the above-mentioned method for classifying tight oil reservoirs.

[0054] In a sixth aspect, an embodiment of the present invention provides a chip, the chip comprising a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run a computer program or instruction to implement the above-mentioned tight oil reservoir classification method.

[0055] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0056] The method for classifying and evaluating tight oil reservoirs provided by an embodiment of the present invention obtains reservoir classification and evaluation sample data, which contains multiple representative factors affecting reservoir evaluation as variables. According to the parameters related to sedimentary characteristics, diagenesis, physical properties, pore characteristics and seepage characteristics and the correlation between them, porosity, permeability, sorting coefficient, median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury withdrawal efficiency and sand body thickness are selected as variables of the sample data, and correlation analysis and cluster analysis are performed on the sample data. According to the correlation between the variables, key information in the retained variables is extracted and merged into fewer main factors, so that the sample data is easier to analyze and interpret, and the stability of cluster analysis is improved, so that the information in the multidimensional variables in the sample data can be better utilized. The clustering results obtained after the cluster analysis are subjected to stepwise discriminant analysis to obtain a discriminant function, and the sample data are quantitatively analyzed using the discriminant function. The classification and evaluation results of the tight oil reservoirs are obtained from qualitative and quantitative analysis, respectively. Considering the influencing factors from multiple aspects, a comprehensive evaluation standard for the tight oil reservoir is established, and a more accurate and comprehensive classification of the tight oil reservoir is achieved.

[0057] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0058] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 It is a flow chart of a method for classifying and evaluating tight oil reservoirs provided in an embodiment of the present application;

[0061] Figure 2 is a schematic diagram of the correlation between porosity and permeability provided in an embodiment of the present application;

[0062] Figure 3 is a schematic diagram of the correlation between the sorting coefficient and the porosity and permeability provided in the embodiment of the present application;

[0063] Figure 4Schematic diagram of the correlation between the median pore throat radius and the porosity and permeability provided in the examples of the present application;

[0064] Figure 5 Schematic diagram of the correlation between the maximum mercury saturation and the porosity and permeability provided in the embodiment of the present application;

[0065] Figure 6 Schematic diagram of the correlation between mercury removal efficiency and porosity and permeability provided in the embodiment of the present application;

[0066] Figure 7 is a schematic diagram of a cluster analysis dendrogram provided in an embodiment of the present application;

[0067] Figure 8 is a schematic diagram of using porosity-permeability to display classification results provided in an embodiment of the present application;

[0068] Fig. 9 Schematic diagram of the distribution of the discriminant results of the undetermined discriminant function provided in the embodiment of the present application;

[0069] Fig.10 is a schematic diagram of a porosity distribution histogram provided in an embodiment of the present application;

[0070] Fig.11 is a schematic diagram of a permeability distribution histogram provided in an embodiment of the present application;

[0071] Fig.12 A schematic diagram of the structure of a tight oil reservoir classification and evaluation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0073] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0074] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0075] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0076] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0077] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0078] It should be understood that the size of the serial numbers of the steps in the following embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0079] In order to illustrate the technical solution of the present application, a specific embodiment is provided below for illustration.

[0080] The inventors found that in the prior art, the main problem in the comprehensive quantitative classification evaluation of reservoirs is that the weights or classification standards in the evaluation process are mainly artificially defined, and the influence of subjective factors is relatively large. Since different researchers have different understandings of various reservoir classification standards, the analysis and evaluation results will vary with different researchers. At the same time, different evaluation parameters will also cause differences in reservoir evaluation results. The selected evaluation parameters are limited and incomplete, which in turn causes inaccurate reservoir evaluation results. Based on this, the inventors have made the present invention after further research and development, providing a method and device for classifying tight oil reservoirs.

[0081] Embodiment 1

[0082] The embodiment of the present invention provides a method for classifying tight oil reservoirs, referring to Figure 1 As shown, the method includes:

[0083] S101: Acquire reservoir classification evaluation sample data; the variables in the sample data include: porosity, permeability, sorting coefficient, median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury withdrawal efficiency and sand body thickness.

[0084] In the embodiment of the present application, in step S101, the reservoir classification evaluation sample data is obtained, and multiple sample data of tight oil reservoirs are obtained. For tight reservoirs, it is necessary to determine which variables are positively correlated with reservoir evaluation, and it is necessary to analyze the variables that affect reservoir evaluation: Figure 2 As shown in Figure 2, the relationship between porosity and permeability is analyzed, proving that there is a good correlation between porosity and permeability in tight reservoirs: Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, through the analysis of the mercury injection test data of the samples in the implementation area, it can be seen that the sorting coefficient, median pore throat radius, maximum mercury injection saturation, mercury removal efficiency, porosity and permeability are positively correlated. Therefore, porosity, permeability, sorting coefficient, median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury removal efficiency and sand body thickness can be used as parameter variables for evaluating tight reservoirs. Determine all variables and then statistically organize the above data into a table as sample data. In a specific embodiment, the sample data is shown in Table 1:

[0085]

[0086] Among them, porosity is an indispensable parameter in geological reserve calculation and reservoir evaluation. The greater the reservoir porosity, the greater the reserve abundance and single reservoir coefficient of the oil field; permeability directly reflects the seepage capacity of the reservoir and is also an important parameter affecting the production capacity of gas wells. The oil well control radius is related to the effective permeability of the reservoir and is a basic parameter for reservoir evaluation; the sorting coefficient is a measure of the standard deviation of the pore throat size in the sample, which directly reflects the concentration of the pore throat distribution. When a certain level of pore throat is absolutely dominant in the total pores, it indicates that the pore sorting degree is good; the median pore throat radius is the pore throat radius corresponding to the water saturation of 50%. The radius of the pore channel is the symmetrical point of the frequency curve in the normal distribution; the displacement pressure is an important parameter for evaluating whether the reservoir can effectively drain (or exhaust) water and produce oil and natural gas. A lower displacement pressure may indicate that the reservoir has better permeability and fluidity and is easier to exploit; the maximum mercury injection saturation is the maximum mercury saturation in the capillary pressure curve of the mercury injection method; the mercury withdrawal efficiency is the ratio of the total volume of mercury withdrawn from the rock sample to the total volume of mercury injected into the rock sample within the same pressure range when the measured pressure decreases from the maximum value to the minimum value. It reflects the uniformity of the pore channel distribution and is the main parameter reflecting the impact of pore structure heterogeneity on the recovery factor.

[0087] In the embodiment of the present application, after obtaining the reservoir classification evaluation sample data, the sample data needs to be preprocessed, and the preprocessing operation includes forward processing and standardization. When the sample data of reservoir classification evaluation is forward processed, the data is converted into a numerical value within a specific range, usually [0,1] or [-1,1], to eliminate the scale differences between different features and make the sample data have similar weights. This helps to ensure that the data can be considered equally during the analysis and comparison process without being affected by the range of feature values.

[0088] Standardization is aimed at adjusting the distribution of data to a standard normal distribution with a mean of 0 and a standard deviation of 1, thereby improving the accuracy and reliability of data analysis. First, the mean and mean of each variable in the sample data are calculated, and then the data is standardized. The calculation formulas for the mean and covariance are shown in Formula 4 and Formula 5, and the variable standardization formula is shown in Formula 6:

[0089]

[0090]

[0091]

[0092] Where n is the total number of sample data, p is the total number of variables, and i and j are the variable numbers.

[0093] S102: performing correlation analysis on the sample data to obtain a plurality of main factors that meet preset conditions.

[0094] In the embodiment of the present application, in the above step S102, the correlation analysis of the sample data is performed to obtain a plurality of main factors that meet the preset conditions, including:

[0095] Calculate the correlation coefficient matrix based on the sample data and variables;

[0096] Decomposing the correlation coefficient matrix to obtain multiple eigenvalues ​​and corresponding eigenvectors;

[0097] Calculate the contribution rate and cumulative contribution rate of each eigenvalue according to the multiple eigenvalues;

[0098] Determine the number of main factors that meet the preset conditions according to the cumulative contribution rate and the preset threshold;

[0099] Arrange the eigenvalues ​​and the corresponding eigenvectors from large to small according to the size of the eigenvalues, select the eigenvectors corresponding to the eigenvalues ​​in the front row according to the number of the main factors, and obtain the eigenvectors corresponding to the multiple main factors;

[0100] The characteristic vectors corresponding to the plurality of principal factors are transformed, and the plurality of principal factors satisfying preset conditions are obtained by combining the variables in the sample data.

[0101] In an embodiment of the present application, correlation analysis is used to process sample data, and the specific steps include: a first step, calculating a correlation coefficient matrix based on the sample data and 8 variables, for each pair of variables, calculating the covariance and standard deviation between them, using the covariance and standard deviation to calculate the correlation coefficient between the corresponding two variables, arranging the correlation coefficients of each pair of variables in a matrix form to obtain a correlation coefficient matrix, and the correlation coefficient matrix reflects the linear correlation between the variables; a second step, decomposing the correlation coefficient, using numerical linear algebra methods (such as eigenvalue decomposition or singular value decomposition) to obtain multiple eigenvalues ​​and corresponding eigenvectors; a third step, calculating the contribution rate corresponding to each eigenvalue based on the eigenvalue, the contribution rate corresponding to each eigenvalue is equal to the eigenvalue divided by the sum of the eigenvalues, arranging the eigenvalues ​​and the corresponding contribution rates from large to small according to the size of the eigenvalues, and the cumulative contribution rate of each eigenvalue is the sum of all contribution rates greater than the current eigenvalue and the current eigenvalue contribution rate. For example, the cumulative contribution rate of the largest eigenvalue is the contribution rate of the largest eigenvalue, and the cumulative contribution rate of the second largest eigenvalue is the contribution rate of the second largest eigenvalue. The contribution rate is the contribution rate of the largest eigenvalue plus the contribution rate of the second largest eigenvalue; the fourth step is to determine the number of main factors that meet the preset conditions according to the cumulative contribution rate and the preset threshold. When the cumulative contribution rate reaches the preset threshold (such as 80% or 90%), select the corresponding number of main factors to be retained. For example, if the cumulative contribution rate corresponding to the fourth largest eigenvalue reaches the preset threshold, the number of main factors is 4; the fifth step is to arrange the eigenvalues ​​and the corresponding eigenvectors from large to small according to the size of the eigenvalues, and select the eigenvectors corresponding to the eigenvalues ​​ranked in the front according to the number of main factors to obtain the eigenvectors corresponding to multiple main factors; the sixth step is to transform the eigenvectors corresponding to the multiple main factors and the variables in the sample data, that is, to form a eigenvector matrix with multiple eigenvectors, select an appropriate rotation method (such as variance maximum rotation, maximum likelihood rotation, etc.) to rotate the eigenvector matrix to obtain a rotation factor matrix, which contains the relationship between the main factors and the original variables. Combine the original variables with the rotation factor matrix to obtain multiple main factors that meet the preset conditions.

[0102] In a specific embodiment, the correlation coefficient matrix of 8 variables calculated based on the sample data is shown in Table 2:

[0103]

[0104] The correlation coefficient between each pair of variables is shown; the contribution rate and cumulative contribution rate calculated according to the correlation coefficient matrix are shown in Table 3:

[0105]

[0106] Among them, "component" is the factor, and a total of 8 factors are calculated, corresponding to 8 eigenvalues, "total" is the eigenvalue, "% of variance" is the contribution rate, and "cumulative %" is the cumulative contribution rate. The preset threshold is set to 82%. Among the initial eigenvalues ​​shown in the left half of Table 3, the cumulative contribution rate corresponding to the third largest eigenvalue is 82.821%, which reaches the preset threshold, so the first 3 factors are selected as the main factors; the rotation factor matrix obtained by rotating the eigenvector matrix is ​​shown in Table 4:

[0107]

[0108] The corresponding relationship between the three main factors and the original variables is shown. These three main factors will be used for cluster analysis in the future to accurately and comprehensively reflect the classification of reservoirs.

[0109] In the embodiment of the present application, cluster analysis is required in the subsequent steps, and cluster analysis is usually performed under the assumption that the variables are independent and unrelated. In the case of correlation between variables, if the traditional distance measurement method, such as Euclidean distance or Manhattan distance, is still used to calculate the distance between sample data, the result will be biased. Therefore, before using the sample data for cluster analysis, the original variables are replaced by principal factors, and the original variables can be converted into a set of new unrelated principal factors, and the key information in the original variables is extracted and retained in a few principal factors, making the sample data easier to analyze. The principal factor score value of each sample data replaces the original data, and the principal component score value of each sample is used for analysis, which can effectively reduce the influence of correlation on the distance metric, thereby improving the effectiveness and reliability of subsequent cluster analysis.

[0110] S103: Perform cluster analysis on the sample data according to the multiple main factors to obtain a clustering result.

[0111] In the embodiment of the present application, in the above step S103, cluster analysis is performed on the sample data according to multiple main factors to obtain clustering results, including:

[0112] According to the main factor and the sample data, the sample data with the variable as the main factor is obtained;

[0113] According to the sample data with the variable as the main factor, each sample data is set as a cluster, and the distance between each pair of clusters is calculated to obtain a distance matrix;

[0114] Merge the two clusters with the shortest distance in the distance matrix to obtain a new cluster, and update the distance matrix;

[0115] Repeat the above operation of merging the two clusters with the shortest distance and updating the distance matrix until the preset conditions are met and the clustering result is obtained.

[0116] In an embodiment of the present application, the sample data is first recalculated according to the principal factors obtained in step S102 to obtain sample data with variables as principal factors, that is, for each sample in the sample data, the values ​​of multiple principal factors are respectively calculated according to the obtained rotation factor matrix, and multiple principal factor values ​​of all samples are calculated to reconstruct the sample data to obtain sample data with variables as principal factors. In the subsequent description process, the sample data with variables as principal factors are referred to as new sample data. Then, cluster analysis is performed on the new sample data. The specific steps include: setting each sample data as an initial independent cluster, that is, each sample is initially an independent class, selecting an appropriate similarity or distance metric (such as Euclidean distance, etc.) to calculate the distance between samples, that is, the distance between each pair of clusters, to obtain a distance matrix; finding the two clusters with the closest distance from the distance matrix, merging them into a new cluster, and updating the distance matrix; repeating the above operation of merging the two clusters with the shortest distance and updating the distance matrix until the preset conditions (such as setting the number of clusters) are met to obtain the clustering results.

[0117] In the process of cluster analysis, each merging operation can be tracked to construct a tree diagram of cluster analysis, which can show the hierarchical relationship of each cluster. In a specific embodiment, the tree diagram of cluster analysis and the clustering results are as follows: Figure 7 As shown in the figure, the sample data is divided into three categories: I, II and III, and the classification results are displayed using porosity-permeability. Figure 8 shown.

[0118] S104: Perform stepwise discriminant analysis on the sample data and the clustering results to obtain a discriminant function.

[0119] In the embodiment of the present application, in the above step S104, a stepwise discriminant analysis is performed on the sample data and the clustering result to obtain a discriminant function, including:

[0120] According to the sample data, the autocorrelation coefficient matrix is ​​calculated based on the following formula 1:

[0121]

[0122] In the formula, S is the covariance, r is the coefficient in the autocorrelation coefficient matrix R, and i and j are variables;

[0123] Calculate the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix;

[0124] The weighting coefficient is calculated according to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix;

[0125] A plurality of undetermined discriminant functions are obtained by calculation according to the weighted coefficients;

[0126] A discriminant function is obtained according to a plurality of undetermined discriminant functions, sample data and the clustering result.

[0127] Among them, the weighting coefficients are calculated according to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix, including:

[0128] According to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix, the discriminant function coefficients are calculated based on the following formula 2;

[0129]

[0130] In the formula, b is the discriminant function coefficient, μ and λ are the eigenvector and eigenvalue of the autocorrelation coefficient matrix, respectively, and i and j are variables;

[0131] According to the discriminant function coefficient, the weighting coefficient is calculated based on the following formula 3:

[0132]

[0133] In the formula, a is the weighting coefficient, p is the total number of main factors, b is the discriminant function coefficient, and i and j are variables.

[0134] Among them, according to multiple undetermined discriminant functions, sample data and clustering results, the discriminant function is obtained, including:

[0135] For each undetermined discriminant function, multiple sample data are substituted into the undetermined discriminant function to obtain corresponding multiple discriminant results;

[0136] According to the discrimination results of multiple sample data and the corresponding clustering results, the accuracy rate corresponding to the undetermined discriminant function is determined;

[0137] The undetermined discriminant function with the highest accuracy is selected as the discriminant function.

[0138] In the embodiment of the present application, it should be emphasized that the sample data used in the discriminant analysis is the original sample data, and the sample data of the variables obtained in step S104 as the main factors will not be used in subsequent calculations. The specific steps of performing discriminant analysis to obtain the discriminant function include: according to the sample data, based on formula 1, the autocorrelation coefficient matrix R = (r ij ) p·p ; Calculate the eigenvalue λ of the autocorrelation coefficient matrix R 1 To p (λ 1 ≥λ 2 ≥…≥λ p ), and the corresponding eigenvector U 1 ,U2 ,…,U p , where U i =(μ 1i ,μ 2i ,…,μ pi ), (i = 1, 2, ..., p); According to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix R, based on formula 2, the discriminant function coefficient b is calculated ij ; According to the discriminant function coefficient b ij The weighting coefficient α is calculated based on formula 3 j ; Let B = (b ij ) p·p , α=(α 1 ,α 2 ,…,α p ), calculate B(B T B) -1 α, continue to calculate y = x T B(B T B) -1 α, where x T Representing variables, multiple undetermined discriminant functions are obtained; for each undetermined discriminant function, the sample data is substituted into the undetermined discriminant function to obtain a discrimination result, and whether the discrimination result is accurate is judged according to the clustering result of the sample data obtained in step S103, and the accuracy corresponding to each undetermined discriminant function is determined, and the undetermined discriminant function with the highest accuracy is selected as the discriminant function.

[0139] In a specific embodiment, multiple undetermined discriminant functions obtained by discriminant analysis are shown in Table 5:

[0140]

[0141] The table shows two undetermined discriminant functions, in which a column of numerical values ​​represents the expression of an undetermined discriminant function, where the value is the weight of the corresponding variable or the constant in the expression. The expression of the discriminant function is: discriminant result = porosity weight * porosity + permeability weight * permeability + sorting coefficient weight * sorting coefficient + displacement pressure weight * displacement pressure + maximum tribute saturation weight * maximum tribute saturation + withdrawal efficiency weight * withdrawal efficiency + median pore throat radius weight * median pore throat radius + sand body thickness weight * sand body thickness + constant. The undetermined discriminant function 1 in Table 5 means that the discriminant result = 0.561*porosity+10.614*permeability+6.478*sorting coefficient+0.79*displacement pressure+0.145*maximum mercury injection saturation+0.061*mercury withdrawal efficiency+31.516*median pore throat radius+0.017*sand body thickness-31.527. The sample data are discriminated using the two undetermined discriminant functions in Table 5. The distribution diagram of the discriminant results of the two undetermined discriminant functions is shown in Fig. 9As shown in the figure, it can be seen that the undetermined discriminant function 1 can clearly distinguish the reservoir categories, and the discrimination result is consistent with the clustering result obtained in step S103 by 97%, while the discrimination result of the undetermined discriminant function 2 is not obviously classified, so the undetermined discriminant function 1 is selected as the discriminant function.

[0142] S105: Using the discriminant function to perform quantitative and qualitative analysis on the sample data to obtain a classification evaluation result of tight oil reservoirs.

[0143] In the embodiment of the present application, in the above step S105, the sample data is quantitatively and qualitatively analyzed using the discriminant function to obtain the classification evaluation result of the tight oil reservoir. First, the sample data of the tight reservoir is quantitatively classified and analyzed using the discriminant function obtained in step S104. Then, the tight oil reservoir is qualitatively analyzed based on various qualitative evaluation factors. Finally, a comprehensive evaluation standard for the tight oil reservoir is established to obtain the classification evaluation result of the tight oil reservoir.

[0144] In a specific embodiment, the factors affecting reservoir evaluation are analyzed from both qualitative and quantitative aspects, targeting the characteristics of the tight oil reservoir in the basin. First, a quantitative analysis is performed, and the sample data of the tight reservoir is input into the discriminant function obtained in step S104 to obtain the discriminant result. The discriminant result of Class I reservoir is greater than 3.97, the discriminant result of Class II reservoir is -4.54 to 3.97, and the discriminant result of Class III reservoir is -11.33 to -4.54. Then a qualitative analysis is performed, and the qualitative evaluation factors include: sedimentation, sedimentary fabric, tectonic action and diagenesis. The specific analysis results are as follows:

[0145] Sedimentation and sedimentary fabric: Sedimentation exerts an innate control on the reservoir. Sedimentation controls the shape, scale, spatial distribution and reservoir heterogeneity of the sand body, and determines the characteristics of the rock debris particle size, the content of filling materials and the rock fabric at the microscopic level, thereby controlling the original porosity and permeability of the rock. The original porosity and permeability of the reservoir depends on the sedimentary fabric. According to the grain size statistics of sandstones in different sedimentary phases, it is believed that the sedimentary fabrics of different sedimentary phase types are quite different. The underwater distributary channel sandstone at the front of the braided river delta is mainly composed of fine sandstone, which is generally coarse-grained, well-sorted and has high original physical properties; the branch channel sandstone in the turbidite fan is mainly composed of silt-fine sandstone, with a lot of impurities, fine grain size, poor sorting and low original permeability. For example Fig.10 , 11 As shown, Fig.10 The porosity values ​​of fine sandstone are mainly between 6-12%, and the porosity values ​​of siltstone are mainly between 4-10%; Fig.11It shows that more than 60% of the samples in fine sandstone have a permeability greater than 0.05, while more than 51% of the samples in silt-fine sandstone have a permeability greater than 0.05, and about 10% of the samples in fine sandstone have a permeability greater than 0.3. Therefore, due to the difference in the distribution of sedimentary facies types, the porosity and permeability of fine sandstone are generally better than those of silt-fine sandstone.

[0146] Tectonic action: Tectonic action controls the sedimentary environment and diagenetic process from a macroscopic perspective, and is mainly manifested in the formation of cracks in rocks from a microscopic perspective. Its influence on diagenesis is mainly manifested in three aspects: the influence of burial history, the influence of tectonic stress, and the influence of faults and cracks. After the strata in the target implementation area were deposited, they experienced multiple periods of tectonic uplift, and the overlying strata also experienced differential erosion. The current burial depth is shallower than the maximum burial depth, and its reservoirs have experienced a rapid burial process; its strata are in an early rapid burial environment, and the maximum burial depth generally exceeds 2500m, resulting in strong compaction of sandstones, and a slow uplift diagenetic environment in the later period. Since the target implementation area has not been affected by large-scale and high-intensity tectonic deformation after the strata were deposited, tectonic stress and faults have little impact on the reservoirs in this area.

[0147] Diagenesis: The influence of diagenesis on the physical properties of the oil group sandstone in the target implementation area is mainly manifested in three aspects: compaction is the most important factor for the deterioration of reservoir physical properties, which directly reduces porosity and permeability; chlorite cementation is conducive to the preservation of primary pores, and in the late stage, carbonate cementation significantly reduces reservoir physical properties; although dissolution is common, its intensity is not high, and it can only improve reservoir physical properties to a limited extent.

[0148] Based on the classification and evaluation of tight reservoirs in the target implementation area using the cluster analysis method, a qualitative analysis of tight oil reservoirs was conducted based on multiple qualitative evaluation factors. Finally, a comprehensive evaluation standard for tight oil reservoirs was established, and the tight reservoirs in the target experimental area were generally divided into three categories, with Class I and Class II reservoirs being effective reservoirs.

[0149] Embodiment 2

[0150] Based on the same inventive concept, the embodiment of the present invention also provides a tight oil reservoir classification evaluation device, referring to Fig.12 As shown, the device comprises:

[0151] The first acquisition module 101 is used to acquire reservoir classification evaluation sample data; the variables in the sample data include: porosity, permeability, sorting coefficient, median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury withdrawal efficiency and sand body thickness;

[0152] The first analysis module 102 is used to perform correlation analysis on the sample data to obtain a plurality of main factors that meet preset conditions;

[0153] A first clustering module 103 is used to perform cluster analysis on the sample data according to the multiple main factors to obtain a clustering result;

[0154] The second analysis module 104 is used to perform stepwise discriminant analysis on the sample data and the clustering results to obtain a discriminant function;

[0155] The third analysis module 105 is used to use the discriminant function to perform quantitative and qualitative analysis on the sample data to obtain a classification evaluation result of the tight oil reservoir.

[0156] Embodiment 3

[0157] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the classification and evaluation method for tight oil reservoirs as described in the above-mentioned embodiment 1.

[0158] Embodiment 4

[0159] Based on the same inventive concept, an embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for classifying and evaluating tight oil reservoirs as described in the above-mentioned embodiment 1 is implemented.

[0160] Embodiment 5

[0161] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product comprising instructions, which, when executed on a computer device, enables the computer device to execute the tight oil reservoir classification and evaluation method as described in the above-mentioned embodiment 1.

[0162] Embodiment 6

[0163] Based on the same inventive concept, an embodiment of the present invention further provides a chip, the chip includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run a computer program or instruction to implement the tight oil reservoir classification and evaluation method described in the above embodiment one.

[0164] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0165] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0168] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A classification and evaluation method for tight oil reservoirs, characterized in that: include: Obtain reservoir classification evaluation sample data; The variables in the sample data include: porosity, permeability, sorting coefficient, median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury withdrawal efficiency and sand body thickness; Performing correlation analysis on the sample data to obtain multiple main factors that meet preset conditions; Performing cluster analysis on the sample data according to the multiple main factors to obtain a clustering result; Performing stepwise discriminant analysis on the sample data and the clustering results to obtain a discriminant function; The discriminant function is used to perform quantitative and qualitative analysis on the sample data to obtain a classification and evaluation result of tight oil reservoirs.

2. The method according to claim 1, characterized in that The correlation analysis of the sample data is performed to obtain a plurality of main factors that meet preset conditions, including: Calculate the correlation coefficient matrix based on the sample data and variables; Decomposing the correlation coefficient matrix to obtain multiple eigenvalues ​​and corresponding eigenvectors; Calculate the contribution rate and cumulative contribution rate of each eigenvalue according to the multiple eigenvalues; Determine the number of main factors that meet the preset conditions according to the cumulative contribution rate and the preset threshold; Arrange the eigenvalues ​​and the corresponding eigenvectors from large to small according to the size of the eigenvalues, select the eigenvectors corresponding to the eigenvalues ​​in the front row according to the number of the main factors, and obtain the eigenvectors corresponding to the multiple main factors; The characteristic vectors corresponding to the plurality of principal factors are transformed, and the plurality of principal factors satisfying preset conditions are obtained by combining the variables in the sample data.

3. The method according to claim 1, characterized in that The clustering analysis is performed on the sample data according to the main factors to obtain the clustering results, including: According to the main factor and the sample data, the sample data with the variable as the main factor is obtained; According to the sample data with the variable as the main factor, each sample data is set as a cluster, and the distance between each pair of clusters is calculated to obtain a distance matrix; Merge the two clusters with the shortest distance in the distance matrix to obtain a new cluster, and update the distance matrix; Repeat the above operation of merging the two clusters with the shortest distance and updating the distance matrix until the preset conditions are met and the clustering result is obtained.

4. The method according to claim 1, characterized in that The stepwise discriminant analysis is performed on the sample data and the clustering result to obtain a discriminant function, including: According to the sample data, the autocorrelation coefficient matrix is ​​calculated based on the following formula 1: In the formula, S is the covariance, r is the coefficient in the autocorrelation coefficient matrix R, and i and j are variables; Calculate the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix; Calculating weighting coefficients according to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix; Calculating a plurality of undetermined discriminant functions according to the weighted coefficients; A discriminant function is obtained according to the multiple undetermined discriminant functions, the sample data and the clustering result.

5. The method according to claim 4, characterized in that The weighting coefficient is calculated according to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix, including: According to the eigenvalues ​​and corresponding eigenvectors of the autocorrelation coefficient matrix, the discriminant function coefficients are calculated based on the following formula 2; In the formula, b is the discriminant function coefficient, μ and λ are the eigenvector and eigenvalue of the autocorrelation coefficient matrix, respectively, and i and j are variables; According to the discriminant function coefficient, the weighting coefficient is calculated based on the following formula 3: Wherein, a is the weighting coefficient, p is the total number of the main factors, b is the discriminant function coefficient, and i and j are variables.

6. The method according to claim 4, characterized in that The step of obtaining a discriminant function according to the plurality of undetermined discriminant functions, the sample data and the clustering result comprises: For each undetermined discriminant function, a plurality of sample data are substituted into the undetermined discriminant function to obtain a corresponding plurality of discriminant results; Determining the accuracy rate corresponding to the undetermined discriminant function according to the discriminant results of the plurality of sample data and the corresponding clustering results; The undetermined discriminant function with the highest accuracy is selected as the discriminant function.

7. The method according to claim 1, characterized in that Before performing correlation analysis on the sample data, the method further includes: Performing forward processing on the sample data to map the sample data to a preset range; The sample data is standardized and mapped to a preset distribution.

8. A classification and evaluation device for tight oil reservoirs, characterized in that: include: The first acquisition module is used to acquire reservoir classification evaluation sample data; The variables in the sample data include: porosity, permeability, sorting coefficient, median pore throat radius, displacement pressure, maximum mercury injection saturation, mercury withdrawal efficiency and sand body thickness; A first analysis module, used to perform correlation analysis on the sample data to obtain a plurality of main factors that meet preset conditions; A first clustering module, used for performing cluster analysis on the sample data according to the multiple main factors to obtain a clustering result; A second analysis module is used to perform stepwise discriminant analysis on the sample data and the clustering results to obtain a discriminant function; The third analysis module is used to use the discriminant function to perform quantitative and qualitative analysis on the sample data to obtain a classification evaluation result of the tight oil reservoir.

9. A computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are executed on a terminal, the terminal executes the method for classifying and evaluating tight oil reservoirs as described in any one of claims 1 to 7.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for classifying and evaluating tight oil reservoirs as described in any one of claims 1 to 7 is implemented.

11. A computer program product comprising instructions, which, when executed on a computer device, enables the computer device to execute the method for classifying and evaluating tight oil reservoirs according to any one of claims 1 to 7.

12. A chip, comprising a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run a computer program or instruction to implement the classification and evaluation method for tight oil reservoirs as described in any one of claims 1 to 7.

Images