Quantitative evaluation method for site selection of reconstructed hydrogen storage library based on hierarchical entropy weight method

Abstract

The invention discloses a quantitative evaluation method for site selection of a reconstructed hydrogen storage library based on a hierarchical entropy weight method. The quantitative evaluation method comprises the following steps: S1, screening out candidate library addresses which can be used as hydrogen storage libraries; s2, establishing a key index evaluation system, and assigning each evaluation index of each candidate library address; s3, performing normalization and standardization processing on the evaluation index data of each candidate library address; s4, the information entropy of each evaluation index is calculated according to a calculation formula of the information entropy, the weight of each evaluation index is calculated through the information entropy, then the evaluation index parameter score of each candidate library address is calculated, finally, the score of each candidate library address is calculated, and the hydrogen storage library address is determined according to the score; s5, carrying out sensitivity analysis to verify the stability and objectivity of the evaluation result of the hierarchical entropy weight method; according to the method, the site selection problem in the process of reconstructing the exhausted oil and gas reservoir into the hydrogen storage library is solved, the suitability of the hydrogen storage library is scientifically evaluated, and scientific and reasonable decision support is provided for hydrogen storage library construction.

Description

Technical Field

[0001] The present application relates to the technical field of hydrogen storage construction, and in particular to a quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method. Background Art

[0002] With the advancement of global energy transformation and carbon neutrality goals, hydrogen energy, as a clean and efficient energy carrier, has received widespread attention. Depleted oil and gas reservoirs are considered ideal candidate sites for hydrogen storage due to their good geological closure and storage capacity. However, the site selection for the reconstruction of hydrogen storage facilities involves the evaluation of multiple complex factors such as geology, economy and society. Traditional site selection methods are mostly based on a single indicator or empirical judgment, which has the problems of strong subjectivity and insufficient reliability of results.

[0003] In order to overcome this bottleneck, quantitative models based on multi-factor analysis have been gradually introduced in this field, such as entropy weight method and grey correlation analysis. However, current methods often ignore the differences between levels in the weight allocation of different evaluation indicators, resulting in the underestimation of the importance of some key factors. The specific problems of existing methods are as follows:

[0004] 1. Complexity of multi-factor decision-making: Site selection involves multiple geological indicators such as reservoir depth, cap rock thickness, permeability, fault distance, as well as economic and social factors such as distance to hydrogen clusters and new energy power stations. Traditional site selection methods rely on a single indicator or empirical judgment, lack of scientific quantitative analysis, and may lead to unscientific decision-making.

[0005] 2. Unbalanced weights between different indicators: In traditional multi-factor analysis, the importance of indicators at different levels may be overlooked, resulting in deviations in the weights of key indicators during the comprehensive evaluation process, which in turn affects the accuracy of the evaluation results.

[0006] 3. Data normalization and standardization issues: During the site selection process, the magnitude and units of various indicators are different. Direct comparison or calculation will cause errors and affect the final accuracy of the site selection.

[0007] 4. Insufficient uncertainty and sensitivity assessment: Geological parameters and economic indicators may fluctuate under different conditions. The lack of sensitivity analysis will affect the stability and reliability of the site selection plan. Summary of the invention

[0008] In order to solve the above problems, this application provides a multi-level and multi-dimensional evaluation method based on the hierarchical entropy weight method, which aims to solve the site selection problem in the process of converting the depleted oil and gas reservoir gas storage into a hydrogen storage reservoir, and scientifically evaluate the suitability of the hydrogen storage reservoir to solve the shortcomings of the traditional site selection model in complex evaluation. The technical solution is as follows:

[0009] The present application provides a quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method, comprising the following steps:

[0010] S1 Screen out target depleted oil and gas reservoir gas storage groups that can be used as candidate hydrogen storage sites;

[0011] S2 Establish a key indicator evaluation system and assign values ​​to each evaluation indicator of each candidate storage site;

[0012] S3 normalizes and standardizes the evaluation index data of each candidate storage site;

[0013] S4 calculates the information entropy of each evaluation index according to the information entropy calculation formula, calculates the weight of each evaluation index through the information entropy, and then calculates the evaluation index parameter score of each candidate storage site. Finally, the score of each candidate storage site is calculated respectively, and the hydrogen storage site is determined according to the score.

[0014] S5 conducted sensitivity analysis to verify the stability and objectivity of the evaluation results of the hierarchical entropy weight method.

[0015] For example, in the quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on the hierarchical entropy weight method provided in one embodiment, in S2, the key indicator evaluation system includes positive indicators that are positively correlated with the final evaluation results and negative indicators that are negatively correlated with the final evaluation results.

[0016] For example, in the quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on the hierarchical entropy weight method provided in one embodiment, in S3, the normalization formula for the positive index is:

[0017]

[0018] The normalization formula for negative indicators is:

[0019]

[0020] Among them, X ij is the index parameter, where i represents different candidate storage sites, i = 1, 2, ..., m, j represents different evaluation index parameters, j = 1, 2, ..., n; N ij is the normalized indicator parameter.

[0021] For example, in the quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on the hierarchical entropy weight method provided in one embodiment, in S3, after the data is normalized, the data is standardized, and the standardization formula is:

[0022]

[0023] Among them, S ijis the standardized data, using vector S i represents the i-th candidate storage site, and the evaluation index parameter of each candidate storage site can be expressed as S i ={S i1 , S i2 , …, S in}, the standardized matrix S formed by all standardized data is expressed as:

[0024]

[0025] The ratio P of different evaluation indicators of each candidate storage site is solved according to the following formula: ij , that is, the proportion of the jth evaluation index in the i-th candidate storage site to the evaluation index is used to calculate the variation size of the evaluation index:

[0026]

[0027] For example, in the quantitative evaluation method for selecting a site for a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method provided in one embodiment, in S4, the weight w of each evaluation index is calculated by information entropy according to the following formula: j :

[0028]

[0029] Among them, E j is the information entropy of each evaluation index.

[0030] For example, in the quantitative evaluation method for selecting a site for a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method provided in one embodiment, in S4, the method for calculating the evaluation index parameter score of each candidate storage site is:

[0031] Take out the normalized and standardized score matrix S ij The largest number in each column constitutes the ideal optimal solution vector, that is:

[0032]

[0033] Take out the normalized and standardized score matrix S ij The smallest number in each column constitutes the ideal worst solution vector, that is:

[0034]

[0035] Calculate the distance between the i-th candidate storage site and the optimal / inferior solution:

[0036] The distance between the i-th candidate storage site and the optimal solution is:

[0037]

[0038] The distance between the i-th candidate storage site and the optimal solution is:

[0039]

[0040] For example, in the quantitative evaluation method for selecting a site for a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method provided in one embodiment, in S4, the method for calculating the score of each candidate storage site is:

[0041] The score A of the i-th candidate storage site i Satisfy the following formula:

[0042]

[0043] For example, in the quantitative evaluation method for the site selection of a rebuilt hydrogen storage reservoir based on the hierarchical entropy weight method provided in one embodiment, the key indicator evaluation system established in S2 includes technical indicators and economic indicators, the technical indicators include storage condition indicators and geological safety indicators, the storage condition indicators include reservoir depth, cap rock thickness, fault distance, reservoir permeability, reservoir working gas volume and reservoir porosity, the geological safety indicators include cap rock sealing and fault sealing, the economic indicators include bottom gas volume, the number of working wellheads, the distance to the hydrogen cluster and the distance to the renewable energy power station; wherein, the positive indicators include reservoir permeability, reservoir porosity, reservoir working gas volume, cap rock thickness, fault distance, cap rock sealing, fault sealing, the distance to the hydrogen cluster and the distance to renewable energy, and the negative indicators include reservoir depth, bottom gas volume and the number of working wellheads.

[0044] For example, in the quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on the hierarchical entropy weight method provided in one embodiment, in S5, a sensitivity analysis is performed on the cap rock sealing and fault sealing in the key indicator evaluation system to verify the stability and objectivity of the evaluation results of the hierarchical entropy weight method.

[0045] For example, in the quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on the hierarchical entropy weight method provided in one embodiment, in S5, the extreme values ​​of caprock sealing and fault sealing are tested to verify the scoring stability of each candidate site under different conditions.

[0046] The beneficial effects of a quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method provided in some embodiments of the present application are as follows: the present application quantitatively evaluates the key indicators of existing depleted oil and gas reservoir gas storage reservoirs through a comprehensive analysis of multiple factors, thereby selecting the gas storage reservoir with the highest score as the preferred site for the hydrogen storage reservoir, providing scientific and reasonable decision support for the construction of the hydrogen storage reservoir, and the specific advantages are as follows:

[0047] 1. Improve the scientificity and objectivity of the evaluation: This application uses the hierarchical entropy weight method to scientifically allocate weights among different levels, ensuring that the importance of key indicators is fully reflected in the evaluation system and reducing the influence of human subjective judgment.

[0048] 2. Multidimensional data processing and optimization: This application adopts normalization and standardization methods to target the magnitude differences of different indicators, so that all parameters can be compared on the same scale, thus improving the comparability and reliability of the evaluation results.

[0049] 3. Comprehensive assessment of site potential: This application establishes an evaluation system consisting of 12 key indicators, which not only takes into account geological conditions, but also incorporates economic and social factors, such as the distance to renewable energy power stations and hydrogen clusters, thereby providing a more comprehensive analysis of hydrogen storage site selection.

[0050] 4. Stability of sensitivity analysis verification results: This application tested the evaluation results under conditions of extreme changes in different parameters through sensitivity analysis and found that the scoring results were basically stable, indicating that the site selection method of this application has good robustness and stability.

[0051] 5. Select high-potential hydrogen storage depots: In the comparison of multiple depot sites, the method of this application is used to select the most suitable location for conversion into a hydrogen storage depot, and its advantages in key indicators such as reservoir depth, porosity, and caprock sealing are clarified. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A flow chart of a quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method of the present application;

[0054] Figure 2 It is a data chart of some evaluation indicators of depleted oil and gas reservoirs in each candidate storage site;

[0055] Figure 3 is the weight of each evaluation index parameter;

[0056] Figure 4 A comparison chart of the scores of depleted oil and gas reservoirs at each candidate storage site;

[0057] Figure 5 This is a schematic diagram of sensitivity analysis. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] Unless otherwise defined, the technical terms or scientific terms used in the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0060] This application provides a quantitative evaluation method for the site selection of a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method, such as Figure 1 As shown, the following steps are included:

[0061] S1 Screen out target depleted oil and gas reservoir gas storage groups that can be used as candidate hydrogen storage sites;

[0062] Specifically, several depleted oil and gas reservoirs can be selected from my country's Dagang Oilfield, North China Oilfield, Liaohe Oilfield, Southwest Oilfield, etc. as candidate sites for hydrogen storage.

[0063] S2 Establish a key indicator evaluation system and assign values ​​to each evaluation indicator of each candidate storage site;

[0064] For example, in the quantitative evaluation method for the site selection of a rebuilt hydrogen storage reservoir based on the hierarchical entropy weight method provided in one embodiment, the key indicator evaluation system established in the present application includes technical indicators and economic indicators, a total of twelve evaluation indicators, the technical indicators include storage condition indicators and geological safety indicators, the storage condition indicators include reservoir depth, cap rock thickness, fault distance, reservoir permeability, reservoir working gas volume and reservoir porosity, the geological safety indicators include cap rock sealing and fault sealing, the economic indicators include bottom gas volume, the number of working wellheads, the distance from the hydrogen cluster and the distance from the renewable energy power station.

[0065] According to the degree of influence of various indicators of the depleted oil and gas reservoir gas storage at the candidate site on the evaluation results, the key indicator evaluation system includes positive indicators that are positively correlated with the final evaluation results and negative indicators that are negatively correlated with the final evaluation results. For positive indicators, the larger the value, the better the site selection evaluation of the hydrogen storage reservoir; for negative indicators, the smaller the value, the better the site selection evaluation of the hydrogen storage reservoir. In the hydrogen storage reservoir optimization process of the present application, the positive indicators include reservoir permeability, reservoir porosity, reservoir working gas volume, cap rock thickness, fault distance, cap rock sealing, fault sealing, distance to hydrogen clusters and distance to renewable energy. The larger the values ​​of these indicators, the better the performance as a hydrogen storage reservoir. The negative indicators include reservoir depth, bottom gas volume and number of working wellheads. The larger these values ​​are, the higher the development and operation costs are, and the safety and stability may be affected. Therefore, these three indicators are regarded as negative indicators.

[0066] Assign values ​​to the evaluation indicators of each candidate storage site. For example, this application Figure 2 Some evaluation indicators that can be found for the depleted oil and gas reservoir gas storages at each candidate site are given, including reservoir depth, reservoir permeability, reservoir porosity, reservoir working gas volume, cap rock thickness and fault distance. For the evaluation indicators for which relevant data cannot be directly found, a scoring system is adopted to score each indicator according to the actual situation of the depleted oil and gas reservoir gas storages at each candidate site, as shown in Table 1 below:

[0067] Table 1 Scoring of some evaluation indicators

[0068]

[0069]

[0070] S3 normalizes and standardizes the evaluation index data of each candidate storage site;

[0071] Since there are significant differences in the order of magnitude and units of different evaluation indicators, this will have a great impact on the comprehensive evaluation of hydrogen storage site selection, so the original data needs to be normalized. In order to ensure the consistency of the dimensions of each indicator, all parameters are standardized to values ​​between 0 and 1 using normalization, where 0 represents the worst and 1 represents the best. Through this treatment, the influence of each parameter can be unified on the same scale, making the final comprehensive evaluation more scientific and reasonable, thus facilitating the decision-making process of hydrogen storage site selection.

[0072] The normalization formula for positive indicators is:

[0073]

[0074] The normalization formula for negative indicators is:

[0075]

[0076] Among them, X ij is the index parameter, where i represents different candidate storage sites, i = 1, 2, ..., m, j represents different evaluation index parameters, j = 1, 2, ..., n; N ij is the normalized indicator parameter.

[0077] Due to the different physical meanings of different indicators, there are significant differences in their numerical dimensions, and data of different dimensions cannot be directly algebraically operated. Therefore, after completing the normalization process, the data needs to be standardized. The purpose of standardization is to eliminate the dimensional differences of each indicator so that they have the same statistical characteristics, which is convenient for subsequent calculations and analysis.

[0078] The normalization formula is:

[0079]

[0080] Among them, S ij is the standardized data, using vector S i represents the i-th candidate storage site, and the evaluation index parameter of each candidate storage site can be expressed as S i ={S i1 , S i2 , …, S in}, the standardized matrix S formed by all standardized data is expressed as:

[0081]

[0082] The ratio P of different evaluation indicators of each candidate storage site is solved according to the following formula: ij , that is, the proportion of the jth evaluation index in the i-th candidate storage site to the evaluation index is used to calculate the variation size of the evaluation index:

[0083]

[0084] The normalization and standardization results of various index parameters of depleted oil and gas reservoirs at different candidate storage sites are shown in Tables 2 and 3:

[0085] Table 2 Normalization results of candidate storage site comparison index parameters

[0086]

[0087] Table 3 Standardization results of candidate site comparison index parameters

[0088]

[0089] Among them, C1 is the reservoir depth, C2 is the cap rock thickness, C3 is the fault distance, C4 is the reservoir permeability, C5 is the reservoir porosity, C6 is the working gas volume, C7 is the cap rock sealing, C8 is the fault sealing, C9 is the bottom gas volume, C10 is the number of working wells, C11 is the distance to the hydrogen cluster, and C12 is the distance to the new energy power station.

[0090] S4 calculates the information entropy of each evaluation index according to the information entropy calculation formula, calculates the weight of each evaluation index through the information entropy, and then calculates the evaluation index parameter score of each candidate storage site, and finally calculates the score of each candidate storage site respectively, and determines the hydrogen storage site according to the score; specifically includes the following steps:

[0091] S4.1 Calculate the information entropy of each evaluation index. According to the definition of information entropy, the information entropy of a set of data is:

[0092]

[0093] Among them, E j ≥0, if P ij =0, define E j =0;

[0094] S4.2 According to the calculation formula of information entropy, the information entropy of each evaluation index is calculated as E1.

[0095] E2,…,E n , the weight w of each evaluation index is calculated by information entropy j :

[0096]

[0097] Among them, E j is the information entropy of each evaluation index.

[0098] S4.3 Calculate the evaluation index parameter scores for each candidate storage site

[0099] The score matrix S after normalization and standardization ij , the data inside is all very large data, so take out the score matrix S that has been normalized and standardized ij The largest number in each column constitutes the ideal optimal solution vector, that is:

[0100]

[0101] Take out the normalized and standardized score matrix S ij The smallest number in each column constitutes the ideal worst solution vector, that is:

[0102]

[0103] S4.4 Calculate the distance between the i-th candidate storage site and the optimal / inferior solution

[0104] The distance between the i-th candidate storage site and the optimal solution is:

[0105]

[0106] The distance between the i-th candidate storage site and the optimal solution is:

[0107]

[0108] S4.5 Calculate the score of each candidate storage site

[0109] The score A of the i-th candidate storage site i Satisfy the following formula:

[0110]

[0111] The calculation results of each candidate storage site given in the embodiment of the present application are as follows:

[0112] according to Figure 3 In the weight distribution, C12 (distance from renewable energy power stations) and C1 (reservoir depth) occupy the highest weights, which are 0.1461 and 0.1436 respectively. This means that the distance between the gas storage reservoir and the renewable energy power station and the reservoir depth are key factors affecting its hydrogen storage potential. In addition, the weights of C3 (fault distance), C5 (reservoir porosity), C7 (caprock sealing), C8 (fault sealing), C9 (bottom gas volume), and C10 (number of working wellheads) are also relatively high, which are 0.1283, 0.1212, 0.1244, 0.1278, 0.1388, and 0.1283 respectively, indicating that the sealing and economic feasibility of the gas storage reservoir are crucial to its evaluation. The factors with lower weights are C2 (caprock thickness), C4 (reservoir permeability), C6 (working gas volume), and C11 (distance to the hydrogen cluster), with weights of 0.1008, 0.0959, 0.1066, and 0.1083, respectively.

[0113] Combined with the obtained weights, the parameter scores of the target depleted oil and gas reservoirs at each candidate storage site are calculated with reference to the TOPSI S method, such as Figure 4 As shown, from Figure 4It can be seen that the scores of the nine candidate sites after evaluation by the hierarchical entropy weight method show obvious stratification, reflecting the potential differences of each gas storage when it is transformed into a depleted oil and gas reservoir hydrogen storage. Among them, the Dazhangtuo gas storage has the highest score (0.58844). Combined with the actual situation and weight distribution of each gas storage, the Dazhangtuo gas storage has outstanding performance in multiple indicators: its reservoir depth is moderate, the caprock thickness is 400-800 meters, the reservoir porosity is 15%-25%, the caprock is extremely tight, and it is close to the Shandong hydrogen cluster. At the same time, it is close to the new energy power station, which ensures its excellent performance in technical and economic evaluations and has great potential for hydrogen storage transformation. Ban 876 (0.52561) and Wen 96 (0.50993) ranked second and third respectively, indicating that these two gas storages also have strong potential for hydrogen storage conversion. In contrast, gas storages with scores in the middle range include Shuang 6 (0.49545), Banzhongbei (0.48111), Yong 22 (0.47116) and Xiangguosi (0.45417). Although these gas storages did not reach the highest score, they still have certain feasibility for transformation and may have the potential for further development under certain conditions or optimization strategies. It is worth noting that the lowest-scoring gas storages are Su 4 (0.43063) and Hutubi (0.38869), which indicates that the performance of these gas storages in the assessment is not ideal and they are not suitable for transformation into depleted oil and gas reservoir hydrogen storage.

[0114] S5 conducted sensitivity analysis to verify the stability and objectivity of the evaluation results of the hierarchical entropy weight method.

[0115] Since the caprock sealing and fault sealing are artificially assigned according to the actual conditions of each gas storage, a sensitivity analysis is needed to verify the stability and objectivity of the evaluation results of the hierarchical entropy weight method. There are four groups of sensitivity analysis as shown in Table 4:

[0116] Table 4 Evaluation values ​​of sealing performance of gas storage caprock and faults

[0117]

[0118] Through four sets of sensitivity analysis, the extreme values ​​(1 or 0) of caprock tightness and fault tightness were tested to verify the stability of the scores of each gas storage under different conditions. Figure 5 It can be seen that the results show that despite slight fluctuations in the scores, the rankings of Dazhangtuo, Ban876 and Wen96 gas storages in each scenario remain stable, indicating that their comprehensive advantages are obvious and the rankings are not affected by extreme changes in a single indicator. It is particularly noteworthy that even if the caprock tightness of all gas storages is uniformly taken as 1 or 0, the score of Dazhangtuo gas storage remains the highest. This reflects that its excellent site selection potential comes from the comprehensive performance of multiple indicators, such as moderate reservoir depth, thick caprock structure and reasonable distance from new energy power stations.

[0119] The results verify the stability and objectivity of the hierarchical entropy weight method of this application: when the values ​​of key indicators change, the basic pattern of scoring and ranking does not change significantly, proving that the hierarchical entropy weight method provides a reliable scientific basis for site selection decisions.

[0120] Although the implementation scheme of the present application has been disclosed as above, it is not limited to the applications listed in the specification and implementation modes, and it can be fully applicable to various fields suitable for the present application. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present application is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A quantitative evaluation method for site selection of a rebuilt hydrogen storage reservoir based on a hierarchical entropy weight method, characterized in that: The following steps are involved: S1 Screen out target depleted oil and gas reservoir gas storage groups that can be used as candidate hydrogen storage sites; S2 Establish a key indicator evaluation system and assign values ​​to each evaluation indicator of each candidate storage site; S3 normalizes and standardizes the evaluation index data of each candidate storage site; S4 calculates the information entropy of each evaluation index according to the information entropy calculation formula, calculates the weight of each evaluation index through the information entropy, and then calculates the evaluation index parameter score of each candidate storage site. Finally, the score of each candidate storage site is calculated respectively, and the hydrogen storage site is determined according to the score. S5 conducted sensitivity analysis to verify the stability and objectivity of the evaluation results of the hierarchical entropy weight method.

2. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 1 is characterized in that: In S2, the key indicator evaluation system includes positive indicators that are positively correlated with the final evaluation result and negative indicators that are negatively correlated with the final evaluation result.

3. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 2 is characterized in that: In S3, the normalization formula for the positive indicator is: The normalization formula for negative indicators is: Among them, X ij is the index parameter, where i represents different candidate storage sites, i = 1, 2, ..., m, j represents different evaluation index parameters, j = 1, 2, ..., n; N ij is the normalized indicator parameter.

4. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 3 is characterized in that: In S3, after the data is normalized, the data is standardized again, and the standardization formula is: Among them, S ij is the standardized data, using vector S i represents the i-th candidate storage site, and the evaluation index parameter of each candidate storage site can be expressed as S i ={S i1 , S i2 , …, S in }, the standardized matrix S formed by all standardized data is expressed as: The ratio P of different evaluation indicators of each candidate storage site is solved according to the following formula: ij , that is, the proportion of the jth evaluation index in the i-th candidate storage site to the evaluation index is used to calculate the variation size of the evaluation index:

5. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 4 is characterized in that: In S4, the weight w of each evaluation index is calculated by information entropy according to the following formula: j : Among them, E j is the information entropy of each evaluation index.

6. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 5 is characterized in that: In S4, the method for calculating the evaluation index parameter score of each candidate storage site is: Take out the normalized and standardized score matrix S ij The largest number in each column constitutes the ideal optimal solution vector, that is: Take out the normalized and standardized score matrix S ij The smallest number in each column constitutes the ideal worst solution vector, that is: Calculate the distance between the i-th candidate storage site and the optimal / inferior solution: The distance between the i-th candidate storage site and the optimal solution is: The distance between the i-th candidate storage site and the optimal solution is:

7. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 6 is characterized in that: In S4, the method for calculating the score of each candidate storage site is: The score A of the i-th candidate storage site i Satisfy the following formula:

8. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 2 is characterized in that: In said S2, the key indicator evaluation system established includes technical indicators and economic indicators. The technical indicators include storage condition indicators and geological safety indicators. The storage condition indicators include reservoir depth, cap rock thickness, fault distance, reservoir permeability, reservoir working gas volume and reservoir porosity. The geological safety indicators include cap rock sealing and fault sealing. The economic indicators include bottom gas volume, number of working wellheads, distance from hydrogen clusters and distance from renewable energy power stations. Among them, the positive indicators include reservoir permeability, reservoir porosity, reservoir working gas volume, cap rock thickness, fault distance, cap rock sealing, fault sealing, distance to hydrogen clusters and distance to renewable energy; the negative indicators include reservoir depth, bottom gas volume and number of working wellheads.

9. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 8 is characterized in that: In S5, a sensitivity analysis is performed on the caprock sealing and fault sealing in the key indicator evaluation system to verify the stability and objectivity of the evaluation results of the hierarchical entropy weight method.

10. The quantitative evaluation method for site selection of a reconstruction hydrogen storage reservoir based on the hierarchical entropy weight method according to claim 9 is characterized in that: In S5, the extreme values ​​of the caprock sealing and fault sealing are tested to verify the scoring stability of each candidate storage site under different conditions.

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