Medium-high permeability multilayer edge-bottom water sandstone gas reservoir classification method and device

Abstract

The invention discloses a medium-high permeability multilayer edge-bottom water sandstone gas reservoir classification method. The method comprises the following steps: taking a plurality of evaluation parameters having strong correlation with sandstone gas reservoir productivity as classification parameters; according to the classification parameters, the multiple sandstone gas reservoirs are classified through an analytic hierarchy process and a system clustering analysis method, and two classification results are obtained; and comparing the two classification results to determine a primary classification result, and performing subdivision on the basis of the primary classification result according to the gas reservoir water body multiple to obtain target gas reservoir classification. The method comprises the following steps: selecting dynamic and static parameters reflecting gas reservoir characteristics (the static parameters comprise static geological parameters such as gas reservoir area, gas reservoir thickness and original geological reserves, and the dynamic parameters comprise parameters such as gas reservoir water body multiple, unit pressure difference cumulative gas production and monthly gas production reflecting gas reservoir production dynamic); the accuracy and rationality of gas reservoir classification evaluation can be improved, and a classification result can provide a basis for formulating a development adjustment policy.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas exploration and development, and in particular to a classification method and device for medium-high permeability multi-layered edge-bottom water sandstone gas reservoirs. Background Art

[0002] Numerous scholars at home and abroad have conducted extensive research on gas reservoir classification, using a variety of methods to consider various factors. Based on a comprehensive analysis of reservoir formations, structures, and dynamics of developed Carboniferous gas reservoirs in eastern Sichuan, Qu Dechun selected five indicators: effective fracture density, effective thickness as a percentage of formation thickness, thickness of Class I+II reservoirs, gas reservoir productivity coefficient, and background coefficient. He derived a mathematical discriminant for gas reservoir classification and used multivariate stepwise discriminant analysis as a pattern recognition method to classify and evaluate gas reservoirs. Shu Ping et al. established a classification and evaluation standard tailored to the characteristics of gas reservoirs in the Daqing Oilfield, dividing 21 gas reservoirs into nine categories and using fuzzy optimization methods to prioritize the production sequence of each gas field. Tian Ke et al. used seven parameters: porosity, permeability, reserve abundance, reservoir depth, condensate oil content, edge and bottom water energy, and development method. Based on single-factor evaluation, they used fuzzy mathematical judgment to comprehensively evaluate 18 condensate gas reservoirs in my country and classified the reservoirs according to the comprehensive evaluation values. Taking the Huangliu Formation gas fields in the Dongfang area of ​​the Yinggehai Basin as an example, Yuan Binglong et al. proposed a classification and evaluation standard for offshore low-permeability gas reservoirs with meter-free flow, permeability, mainstream throat radius, movable fluid saturation, gas saturation, and gas layer thickness as evaluation parameters. They determined the classification boundaries of each evaluation parameter, subdivided low-permeability gas reservoirs into four subcategories, and reduced the error of gas reservoir classification evaluation.

[0003] Most existing technologies use fuzzy comprehensive evaluation methods, which only start from a mathematical perspective, do not consider the geological significance of each parameter, and ignore the indicator weights. Summary of the Invention

[0004] The present invention aims to provide a classification method for medium-high permeability multi-layered edge-bottom water sandstone gas reservoirs to solve the above problems.

[0005] To achieve the above object, the present invention provides a classification method for medium-high permeability multi-layered edge-bottom water sandstone gas reservoirs, the method comprising:

[0006] Several evaluation parameters that are highly correlated with the productivity of sandstone gas reservoirs are used as classification parameters;

[0007] According to the classification parameters, the analytic hierarchy process and the system cluster analysis method are used to classify multiple sandstone gas reservoirs, and two classification results are obtained;

[0008] The two classification results are compared to determine the primary classification result, and the primary classification result is subdivided according to the water body multiples of the gas reservoir to obtain the target gas reservoir classification.

[0009] The present invention also provides a medium-high permeability multi-layered edge-bottom water sandstone gas reservoir classification device, the device comprising:

[0010] obtaining a unit for using a plurality of evaluation parameters having a strong correlation with the productivity of the sandstone gas reservoir as classification parameters;

[0011] The classification unit is used to classify multiple sandstone gas reservoirs according to the classification parameters using the analytic hierarchy process and the systematic cluster analysis method, respectively, to obtain two classification results;

[0012] The subdivision unit is used to compare the two classification results to determine the primary classification result, and to subdivide the primary classification result according to the water body multiples of the gas reservoir to obtain the target gas reservoir classification.

[0013] The present invention also provides an electronic device, comprising: a processor, wherein the processor is coupled to a memory;

[0014] The processor is configured to read and execute the computer program stored in the memory to implement any of the above methods.

[0015] The present invention also provides a computer-readable storage medium storing a program or instruction, wherein the program or instruction implements any of the above methods when executed by a processor.

[0016] The technical effects and advantages of the present invention are as follows:

[0017] The present invention provides a classification method for medium- to high-permeability, multi-layered sandstone gas reservoirs with edge- and bottom-water. The method comprises: using multiple evaluation parameters that are highly correlated with the productivity of sandstone gas reservoirs as classification parameters; classifying the multiple sandstone gas reservoirs using the analytic hierarchy process (AHP) and the system cluster analysis method, respectively, based on the classification parameters to obtain two classification results; comparing the two classification results to determine a primary classification result, and then subdividing the primary classification result based on the reservoir water volume multiple to obtain a target gas reservoir classification. The present invention selects dynamic and static parameters that reflect gas reservoir characteristics (static parameters include static geological parameters such as reservoir area, reservoir thickness, and original geological reserves; dynamic parameters include parameters reflecting reservoir production dynamics such as reservoir water volume multiple, cumulative gas production per unit pressure differential, and monthly gas production). By combining dynamic and static parameters, the accuracy and rationality of gas reservoir classification evaluation can be improved, and the classification results can provide a basis for formulating development and adjustment policies.

[0018] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the classification method for medium-high permeability multi-layered edge-bottom water sandstone gas reservoirs;

[0020] Figure 2 This is the Pearson correlation coefficient ranking diagram of various factors affecting gas reservoir productivity;

[0021] Figure 3 This is the gas reservoir phylogeny diagram obtained by the system clustering method;

[0022] Figure 4 This is a diagram of an electronic device. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings provided by the present invention. Moreover, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for understanding and reading by those familiar with this technology, and are not used to limit the conditions for implementation of the present invention. Therefore, they have no substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the efficacy and purpose that can be achieved by the present invention. At the same time, terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments in their relative relationships should also be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0025] During the oil and gas development process, for gas reservoirs with multiple sets of reservoirs in the vertical direction and different reserve scales, reservoir physical properties, edge and bottom water energy and condensate oil content, it is necessary to classify and evaluate the gas reservoirs, and formulate reasonable development technology policies suitable for different types of gas reservoirs based on the evaluation results. At present, the classification and evaluation of gas reservoirs mainly adopts a single mathematical method, without considering the geological significance of each parameter, nor the water production characteristics of the gas reservoir. Therefore, there is a problem that the classification and evaluation results of gas reservoirs are unreasonable, which affects the coordinated development of gas reservoirs in different development layers. The main purpose of the present invention is to provide a classification method for medium-high permeability multi-layered edge and bottom water sandstone gas reservoirs, combining qualitative analysis with quantitative analysis, and subjective with objective, so as to solve the problem of unreasonable gas reservoir classification caused by relying solely on mathematical methods in the existing technology without considering the geological significance of each parameter and the water production characteristics of the gas reservoir.

[0026] The following combination Figure 1 The classification method of medium-to-high permeability multi-layered sandstone gas reservoirs with edge and bottom water is explained in detail. The specific steps include:

[0027] 1. Multiple evaluation parameters that are highly correlated with the productivity of sandstone gas reservoirs are used as classification parameters.

[0028] Specifically including: 1.1 receiving n evaluation parameters of sandstone gas reservoirs.

[0029] Preferably, n sandstone gas reservoirs are 37 sandstone gas reservoirs; the evaluation parameters include: gas reservoir area, gas reservoir thickness, permeability, gas saturation, porosity, gas reservoir water body multiple, condensate oil content, cumulative gas production per unit pressure difference, monthly gas production, geological reserves, and bottom water thickness, a total of 11 indicators.

[0030] 1.2 The Pearson correlation coefficient method is used to calculate the correlation between gas reservoir productivity and different factors.

[0031] 1.3 According to the Pearson variable correlation classification table, five parameters with strong correlation with production capacity, namely permeability, gas reservoir thickness, gas reservoir area, gas saturation, and porosity, are selected as classification indicators.

[0032] The following is a detailed introduction to the determination of classification indicators:

[0033] The Pearson correlation coefficient method is mainly used to analyze the correlation between two random variables. The degree of variable correlation is shown in Table 1.

[0034] Table 1 Pearson variable correlation classification table

[0035] Correlation coefficient range Correlation strength 0.8～1.0 Very strong correlation 0.6～0.8 Strong correlation 0.4～0.6 Moderate correlation 0.2～0.4 Weak correlation 0～0.2 Very weak correlation

[0036] The correlation algorithm of the Pearson correlation coefficient method is as follows:

[0037]

[0038] Taking the calculation of the Pearson correlation coefficient between permeability and productivity as an example, the meanings of the parameters in the formula are as follows: r represents the Pearson correlation coefficient between gas reservoir permeability and gas reservoir productivity; represents the average permeability of n gas reservoirs; represents the average production capacity of n gas reservoirs; X i represents the permeability value of each gas reservoir; Y i represents the production capacity of each gas reservoir; n represents the number of gas reservoirs, and i represents one of them.

[0039] Considering the influence of different factors on the stable production capacity of gas reservoirs, 11 parameters including reservoir area, reservoir thickness, permeability, gas saturation, porosity, reservoir water volume multiple, condensate oil content, cumulative gas production per unit pressure difference, monthly gas production, geological reserves, and bottom water thickness were selected. The Pearson correlation coefficient method was used to calculate the correlation between gas reservoir production capacity and different factors, such as Figure 2 shown.

[0040] Depend on Figure 2 As can be seen, permeability, reservoir thickness, gas saturation, and porosity are strongly correlated with productivity, while water body multiples, bottom water thickness, and condensate content are weakly correlated with productivity. Considering that reservoir area is also an important indicator of reservoir characteristics and has a significant impact on reservoir development and gas well deployment, five parameters with strong correlations with productivity were selected as classification parameters: permeability, reservoir thickness, reservoir area, gas saturation, and porosity.

[0041] 2. Based on the classification parameters, the hierarchical analysis method and the system cluster analysis method were used to classify multiple sandstone gas reservoirs, and two classification results were obtained.

[0042] Specifically, the nine-scale method of the Analytic Hierarchy Process (AHP) is a qualitative classification method. For gas reservoir classification, based on the gas reservoir classification parameters, the weights and normalized values ​​of each classification parameter are obtained through comparison and calculation. This leads to the determination of the classification criteria, which are then used to classify the gas reservoirs. Unlike clustering methods, this method fully considers the importance of each classification parameter. However, since the comparison of the importance of each classification parameter is often based on subjective judgment, the determination of the classification parameter weights is also somewhat subjective.

[0043] The following is a method for normalizing the evaluation parameters of each gas reservoir classification: determine the range of each indicator classification based on the characteristics of the gas reservoir's high, medium and low productivity. -3 μm 2 ; Gas reservoirs with medium overall productivity, permeability ranging from 20 to 60×10 -3 μm 2 Between; gas reservoirs with low productivity, permeability less than 20×10 -3 μm 2 The thickness of gas reservoirs with high productivity is greater than 9m; the thickness of gas reservoirs with medium productivity is between 6 and 9m; the thickness of gas reservoirs with low productivity is less than 6m. The gas reservoirs are divided into three categories using the nine-scale method. The range of gas reservoir classification parameters is shown in Table 2. Taking the permeability of gas reservoirs as an example, the permeability is greater than 60×10 -3 μm 2 The gas reservoir has a generally high productivity and is classified as Class I, with a normalized value of 1; the permeability is between 20 and 60×10 -3 μm2 The gas reservoirs between the two are classified as Class II, with a normalized value of 0.5; the permeability is less than 20×10 -3 μm 2 The gas reservoir is classified as Class III and the normalized value is 0.

[0044] Table 2 Interval statistics of gas reservoir classification evaluation parameters

[0045]

[0046]

[0047] The following is the process for obtaining the weights of each classification parameter: Using the nine-scale method and the analytic hierarchy process, we first compare the importance of each factor within the structural hierarchy. We then express the comparison results using an appropriate scale, as shown in Table 3, to obtain the judgment matrix. Given n as the number of parameters, we calculate the geometric mean of all elements in each row of the judgment matrix to obtain the vector M.

[0048] M=[m1,m2,…,m i …,m n ] T (2)

[0049] in:

[0050]

[0051] Normalize the vector M to get the relative weight vector:

[0052] ω=[ω1,ω2,…,ω i …,ω n ] T (4)

[0053] in:

[0054]

[0055] Taking the data contained in the large box in Table 4 as an example, the meanings of the parameters in the formula are as follows: b ij Indicates the value of row i and column j, for example, b 1j represents the value of the first row, i.e. the “permeability” row, the jth column; b 11 Indicates the values ​​of the "Permeability" row and "Permeability" column; b 12 Indicates the value of the "Permeability" row, the "Gas Layer Thickness" column, and so on...;

[0056] m iRepresents the geometric mean of all values ​​in the i-th row and the 1st column to the j-th column. For example, m1 represents the geometric mean of the five values ​​in the "Permeability" row and the 1st column to the 5th column (the "Permeability" column to the "Porosity" column); m2 represents the geometric mean of the five values ​​in the "Gas Layer Thickness" row and the 1st column to the 5th column (the "Permeability" column to the "Porosity" column), and so on.

[0057] ω i Indicates the normalized weight value of each parameter. For example, in the “Weight” column in Table 3, ω1 represents the parameter in the first row, that is, the normalized weight value of “permeability” is -0.22; ω2 represents the parameter in the second row, that is, the normalized weight value of “gas layer thickness” is -0.22, and so on.

[0058] The gas reservoir classification judgment matrix and the weight of each index are shown in Table 4. The normalized value F of each classification evaluation index is i With weight ω i The accumulated value of the product is recorded as F, which is called the classification discriminant. Gas reservoirs are classified according to the discriminant value. The discriminant values ​​of the gas reservoir classification results are shown in Table 5.

[0059] Table 3 Scale meaning of nine-scale judgment matrix

[0060]

[0061] Table 4 Judgment matrix and weights of gas reservoir classification indicators using the nine-scale method

[0062]

[0063] Table 5 Judgment values ​​of gas reservoir classification results using the nine-scale method

[0064] Judgment value range 0.7≤F≤1 0.4≤F<0.7 F<0.4 Gas reservoir type Class I Class II Category III

[0065] The nine-scale method is used to classify gas reservoirs into three categories, including 12 category I gas reservoirs, 12 category II gas reservoirs, and 13 category III gas reservoirs (as shown in Table 6).

[0066] Table 6 Gas reservoir classification parameters using the nine-scale method

[0067]

[0068]

[0069]

[0070] Specifically, the following is an introduction to the primary classification results obtained through the systematic clustering method.

[0071] Cluster analysis involves dividing similar objects into different groups or more subsets through static classification, so that members of the same subset share similar attributes. Common clustering methods include hierarchical clustering, second-order clustering, K-means clustering, Q-type clustering, and R-type clustering. Hierarchical clustering is a cluster analysis method and the most widely used clustering method both domestically and internationally. It begins by treating each sample as a class. The similarity statistic between classes is then determined. The closest samples (those with the smallest distance) are first clustered into subclasses. These clustered subclasses are then merged based on their inter-class distances, and this process continues until all subclasses are finally clustered into a single, larger class. Commonly used hierarchical clustering methods, such as the shortest distance method, longest distance method, median distance method, centroid method, group average method, sum of squared deviations method, and Euclidean distance, are used to determine the distance between a new class and other classes when distance is used as the similarity statistic. This classification method is a purely mathematical method. By inputting the parameters of each gas reservoir into SPSS software, the gas reservoirs can be divided into several categories. The operation is simple and easy.

[0072] In order to eliminate the influence of different dimensions and the degree of change of variables, each indicator is normalized before classification, which greatly improves the rationality and reliability of the system clustering results. Normalization uses the range normalization method:

[0073]

[0074] Where: x ij is the normalized data of the jth indicator of the i-th sample. After transformation, all indicator data x" ij ∈[0,1]. The distance between any two samples is used to measure their closeness.

[0075] Taking the data in Table 7 as an example, the meanings of the parameters in the formula are as follows: x ij ′ represents the jth index of the i-th sample, for example: x 1j ′ represents the jth index of the first sample (the first gas reservoir - "XM1-M1"), x 11 ′ represents the first indicator of the first sample (the first gas reservoir - "XM1-M1"), that is, the value of the "area" column; x 12 ' represents the second indicator of the first sample (the first gas reservoir - "XM1-M1"), that is, the value of the "gas saturation" column; and so on...;

[0076] x ij ″ represents the normalized data of the jth indicator of the i-th sample, for example: x 1j ″ represents the normalized data of the jth index of the first sample (the first gas reservoir - "XM1-M1"), x 11″ represents the first indicator of the first sample (the first gas reservoir - "XM1-M1"), that is, the value of the "area normalized value" column; x 12 ″ represents the second indicator of the first sample (the first gas reservoir - "XM1-M1"), that is, the value of the "normalized gas saturation" column; and so on...;

[0077] Table 7 Gas reservoir classification parameters using the system clustering method

[0078]

[0079]

[0080]

[0081] Five indicators, namely, gas reservoir area, gas layer thickness, porosity, permeability, and gas saturation, were selected and the 37 gas reservoirs were divided into three categories using the system clustering method. Figure 3 As shown in Table 8, there are 12 Type I gas reservoirs, 14 Type II gas reservoirs, and 11 Type III gas reservoirs.

[0082] Table 8 Gas reservoir classification parameters using the system clustering method

[0083]

[0084]

[0085]

[0086] 3. Compare the two classification results to determine the primary classification result, and subdivide the primary classification result according to the water body multiples of the gas reservoir to obtain the target gas reservoir classification.

[0087] Specifically, the classification results of the hierarchical analysis method and the systematic cluster analysis were compared. For gas reservoirs with inconsistent classification results, the classification results of the hierarchical analysis method and the nine-scale method were finally selected in combination with the actual production dynamics of the gas reservoirs, and the 37 sandstone gas reservoirs were divided into three major categories.

[0088] On this basis, each type of gas reservoir is further divided into three subcategories according to the water body multiples of each gas reservoir, with a total of nine subcategories (Table 9), namely, Class I weak water body (Class Ia), Class I medium water body (Class Ib), Class I strong water body (Class Ic); Class II weak water body (Class IIa), Class II medium water body (Class IIb), Class II strong water body (Class IIc); Class III weak water body (Class IIIa), Class III medium water body (Class IIIb), Class III strong water body (Class IIIc).

[0089] Table 9 Distribution characteristics of parameters of different types of gas reservoirs

[0090]

[0091] The present invention also provides a classification device for medium-to-high permeability multi-layered sandstone gas reservoirs with edge and bottom water, the device comprising: an acquisition unit, used to use multiple evaluation parameters with strong correlation with the production capacity of sandstone gas reservoirs as classification parameters; a classification unit, used to classify multiple sandstone gas reservoirs according to the classification parameters using a hierarchical analysis method and a system cluster analysis method, respectively, to obtain two classification results; a subdivision unit, used to compare the two classification results to determine a primary classification result, and subdivide the primary classification result based on the water body multiple of the gas reservoir to obtain the target gas reservoir classification; wherein the evaluation parameters include: gas reservoir area, gas reservoir thickness, permeability, gas saturation, porosity, gas reservoir water body multiple, condensate oil content, cumulative gas production per unit pressure difference, monthly gas production, geological reserves, and bottom water thickness.

[0092] Since the content protected by this device is similar to that protected by the above method, we will not introduce it in detail here. Please refer to the discussion section of the above method for details.

[0093] The present invention also provides a device, such as Figure 4 As shown. The electronic device includes: at least one processor, at least one communication interface, at least one memory and at least one communication bus; optionally, the communication interface may be an interface of a communication module, such as an interface of a GSM module; the processor may be a processor CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement an embodiment of the present invention. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory. The memory stores a program, and the processor calls the program stored in the memory to execute the method provided in the above embodiment of the present application.

[0094] Corresponding to the above-mentioned method of the present application, the present application further provides a computer storage medium. The computer storage medium stores a computer program, which is executed by a processor to execute the method provided in the above-mentioned embodiment of the present application.

[0095] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A classification method for medium-high permeability multi-layered edge-bottom water sandstone gas reservoirs, characterized in that: The method comprises: Several evaluation parameters that are highly correlated with the productivity of sandstone gas reservoirs are used as classification parameters; According to the classification parameters, the analytic hierarchy process and the system cluster analysis method are used to classify multiple sandstone gas reservoirs, and two classification results are obtained; The two classification results are compared to determine the primary classification result, and the primary classification result is subdivided according to the water body multiples of the gas reservoir to obtain the target gas reservoir classification.

2. The method according to claim 1, characterized in that The evaluation parameters include: gas reservoir area, gas reservoir thickness, permeability, gas saturation, porosity, gas reservoir water volume multiple, condensate oil content, cumulative gas production per unit pressure difference, monthly gas production, geological reserves, and bottom water thickness.

3. The method according to claim 1, characterized in that Several evaluation parameters that are highly correlated with sandstone gas reservoir productivity are used as classification parameters, including: Obtain multiple evaluation parameters that are highly correlated with sandstone gas reservoir productivity; The Pearson correlation coefficient method was used to calculate the correlation between gas reservoir productivity and different evaluation parameters, and several evaluation parameters with strong correlation with sandstone gas reservoir productivity were used as classification parameters.

4. The method according to claim 3, characterized in that The formula for the Pearson correlation coefficient is as follows: Taking the calculation of the Pearson correlation coefficient between permeability and productivity as an example, the meanings of the parameters in the formula are as follows: r represents the Pearson correlation coefficient between gas reservoir permeability and gas reservoir productivity; represents the average permeability of n gas reservoirs; represents the average production capacity of n gas reservoirs; X i represents the permeability value of each gas reservoir; Y i Indicates the production capacity of each gas reservoir.

5. The method according to claim 1, wherein The classification parameters include: permeability, gas reservoir thickness, gas reservoir area, gas saturation, and porosity.

6. The method according to claim 1, wherein Based on the classification parameters, the analytic hierarchy process is used to classify multiple sandstone gas reservoirs, including: The analytic hierarchy process is used to calculate the classification parameters and obtain the weight value and normalized value of each classification parameter; Multiply the weight value and normalized value of each classification parameter to obtain the judgment value of gas reservoir classification; According to the judgment value of gas reservoir classification, multiple sandstone gas reservoirs are classified.

7. The method according to claim 1, characterized in that The system cluster analysis method uses the following distance formula as the similarity statistic standard: Where x″ ij The normalized data of the jth indicator of the i-th sample. After transformation, all the classification parameter data x″ ij ∈[0,1].

8. A classification device for medium-high permeability multi-layered edge-bottom water sandstone gas reservoirs, characterized in that: The device comprises: obtaining a unit for using a plurality of evaluation parameters having a strong correlation with the productivity of the sandstone gas reservoir as classification parameters; The classification unit is used to classify multiple sandstone gas reservoirs according to the classification parameters using the analytic hierarchy process and the systematic cluster analysis method, respectively, to obtain two classification results; The subdivision unit is used to compare the two classification results to determine the primary classification result, and to subdivide the primary classification result according to the water body multiples of the gas reservoir to obtain the target gas reservoir classification.

9. An electronic device, characterized in that: include: a processor coupled to the memory; The processor is configured to read and execute the computer program stored in the memory to implement the method according to any one of claims 1 to 7.

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

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