Carbon dioxide salt water layer storage site selection suitability evaluation method and system

Abstract

The invention provides a carbon dioxide salt water layer storage site selection suitability evaluation method and system, and the method comprises the steps: constructing a hierarchical structure model comprising a criterion layer and an index layer, reasonably distributing the weight according to the relative relation between all factors, reflecting the importance difference of different factors in site selection decision, and obtaining the suitability of the carbon dioxide salt water layer storage site selection. And the reliability of evaluation is ensured through consistency verification, so that the accuracy and rationality of site selection suitability evaluation are improved, and a scientific decision basis is provided for quickly and accurately screening out suitable storage site selection and improving the decision efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon dioxide storage, and in particular to a method and system for evaluating the suitability of carbon dioxide saline layer storage site selection based on hierarchical analysis. Background Art

[0002] Early carbon dioxide storage technology was mainly focused on the storage of depleted oil and gas reservoirs, but with the deepening of research and practical exploration, it was found that storage in saline aquifers has greater potential and advantages. Saline aquifers are a type of geological structure that is widely present underground, with large storage space and relatively stable geological conditions. Studies have found that there are certain physical and chemical interactions between the salt water in saline aquifers and carbon dioxide, which can ensure the long-term storage stability of carbon dioxide in them to a certain extent. At the same time, saline aquifers are usually located deep underground and are relatively isolated from surface ecosystems and groundwater drinking water sources, reducing the potential impact of carbon dioxide leakage on the environment and human activities, making saline aquifers one of the ideal places for carbon dioxide storage.

[0003] The storage of carbon dioxide in saline aquifers is a long-term process, and it is necessary to ensure that the stored carbon dioxide will not leak into the atmosphere or cause pollution and harm to the surrounding environment such as groundwater and soil. Through the site suitability evaluation method, the geological conditions, hydrogeological conditions, etc. can be evaluated in detail, and areas with stable geological structures, good caprock integrity, and suitable stratum lithology can be selected to screen out saline aquifers with good hydrogeological conditions, such as areas with high permeability, high porosity, and thick saline aquifers, which are conducive to the rapid injection and large-scale storage of carbon dioxide, thereby improving the storage efficiency, realizing large-scale storage of carbon dioxide, and minimizing the risk of carbon dioxide leakage to ensure the safety of storage.

[0004] At present, researchers have proposed a site suitability evaluation method based on fuzzy comprehensive evaluation and machine learning. In the fuzzy comprehensive evaluation method, according to the actual situation, the site suitability is divided into different levels, such as very suitable, suitable, relatively suitable, unsuitable, very unsuitable, etc., and an evaluation level set is established. On this basis, for each evaluation factor, according to its value range and characteristics, a corresponding membership function is established to determine the membership of each factor to different evaluation levels. The actual value of each factor is converted into the degree of membership to different evaluation levels through the membership function to obtain the membership matrix. Then the membership matrix and the weight vector are fuzzy operated to obtain the comprehensive evaluation result vector. However, this method involves multiple steps such as the construction of the membership matrix, the calculation of the weight vector and the fuzzy operation, and the calculation process is relatively complicated. In the evaluation method based on machine learning, the physical and chemical processes such as the migration, diffusion, and dissolution of carbon dioxide in the saline layer, as well as the pressure changes and phase changes in the long-term storage process are simulated by numerical simulation to predict the distribution and storage effect of carbon dioxide in the saline layer. According to the numerical simulation results, the storage capacity, storage efficiency, leakage risk and other indicators of carbon dioxide in saline aquifers are analyzed to evaluate the suitability of the site selection. The accuracy of the numerical simulation method is highly dependent on the accuracy of the input parameters and the rationality of the model. If the input parameters are inaccurate or the model assumptions are inconsistent with the actual situation, the simulation results may deviate greatly from the actual situation, thus affecting the accuracy of the site selection evaluation. In addition, the numerical simulation process usually requires a long computing time and large computing resources, and the cost is high. Summary of the invention

[0005] The present invention aims to provide a method and system for evaluating the suitability of a carbon dioxide saline storage site. A hierarchical model including a criterion layer and an indicator layer is constructed, and weights are reasonably allocated according to the relative relationship between various factors, reflecting the importance differences of different factors in site selection decisions. The reliability of the evaluation is ensured by consistency verification, thereby improving the accuracy and rationality of the site suitability evaluation, and providing a scientific decision-making basis for quickly and accurately screening out suitable storage sites and improving decision-making efficiency and accuracy.

[0006] According to a first aspect of the present invention, a method for evaluating the suitability of a carbon dioxide saline layer storage site is provided, comprising the following steps:

[0007] Step 1: Construct the influencing factor categories of the criterion layer and the evaluation indicators of the indicator layer to build a hierarchical structure model;

[0008] Step 2: For the criterion layer and the indicator layer, the relative importance of the influencing factors at each level is compared pairwise to construct a judgment matrix;

[0009] Step 3: For each judgment matrix, calculate its maximum eigenvalue and its corresponding eigenvector. After normalization, the eigenvector is the weight vector of each influencing factor at this level relative to a factor at the previous level.

[0010] Step 4: Perform consistency check on the weight vector;

[0011] Step 5: Determine the scoring criteria for each evaluation indicator;

[0012] Step 6: Comprehensively evaluate the selected carbon dioxide saline layer storage candidate locations according to the scoring criteria and weights, and sum the scores of the criterion layers to obtain a comprehensive score for each candidate location;

[0013] Step 7: Sort the candidate addresses in descending order according to their final comprehensive scores, and obtain the top N candidate addresses as suitable storage sites.

[0014] According to a second aspect of the present invention, a computer system is provided, comprising:

[0015] one or more processors; and

[0016] Memory, storing instructions that can be operated;

[0017] Wherein, when the instruction is executed by one or more processors, the one or more processors are caused to perform operations, and the operations include the process of executing the aforementioned carbon dioxide saline layer storage site suitability evaluation method.

[0018] By implementing the above carbon dioxide saline layer storage site suitability evaluation method of the embodiment of the present invention, a hierarchical model is constructed by clarifying the categories of influencing factors (such as geological conditions, hydrogeological conditions, engineering conditions, environmental conditions and socio-economic conditions) of the criterion layer and the specific evaluation indicators of the indicator layer; and on the basis of the criterion layer and the indicator layer, a judgment matrix is ​​constructed by comparing the relative importance of the influencing factors of each level two by two, so as to determine the relative weight relationship between the factors. By solving the characteristic equation of the judgment matrix, the maximum characteristic root and its corresponding characteristic vector are obtained, and the characteristic vector is normalized to obtain the weight vector of each influencing factor relative to a factor of the previous level, and the importance of each factor in the evaluation system is quantified. Further, the consistency of the judgment matrix is ​​judged by the consistency index CI, the average random consistency index RI and the consistency ratio CR to ensure the rationality and reliability of the evaluation criteria. The scoring criteria of each evaluation indicator are determined according to expert scoring and other methods, providing a basis for the subsequent quantitative evaluation of the candidate address.

[0019] In the comprehensive evaluation and relief, the actual data of each evaluation index of the candidate address is collected, converted into the corresponding score according to the scoring standard, the score of the indicator layer is multiplied by the corresponding weight and then summed in the criterion layer, and then the score of each criterion layer is multiplied by its weight and summed to obtain the comprehensive score of the candidate address, so as to achieve a comprehensive quantitative evaluation of the candidate address. Finally, the site selection is determined, and the candidate addresses are arranged in descending order according to the comprehensive score. The top N are selected as suitable storage sites, and comparative tables and charts can be made to show the scores of each address, intuitively presenting the advantages and disadvantages of each candidate address at different criterion and indicator levels, and assisting decision-making.

[0020] It should be understood that all combinations of the aforementioned concepts and the additional concepts described in more detail below can be considered as part of the inventive subject matter of the present disclosure as long as such concepts are not mutually inconsistent. In addition, all combinations of the claimed subject matter are considered as part of the inventive subject matter of the present disclosure.

[0021] The foregoing and other aspects, embodiments and features of the present invention can be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as the features and / or beneficial effects of the exemplary embodiments, will be apparent from the following description or learned from the practice of the specific embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 4 is a flow chart of a method for evaluating the suitability of a carbon dioxide saline layer storage site according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to better understand the technical content of the present invention, specific embodiments are given and described as follows in conjunction with the accompanying drawings.

[0024] Various aspects of the present invention are described in this disclosure with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the present disclosure are not necessarily intended to include all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed by the present invention are not limited to any implementation. In addition, some aspects disclosed by the present invention can be used alone or in any appropriate combination with other aspects disclosed by the present invention.

[0025] {Example 1}

[0026] As shown in the accompanying drawings, a method for evaluating the suitability of a carbon dioxide saline layer storage site according to an embodiment of the present invention includes:

[0027] Step 1: Construct the influencing factor categories of the criterion layer and the evaluation indicators of the indicator layer to build a hierarchical structure model;

[0028] Step 2: For the criterion layer and the indicator layer, the relative importance of the influencing factors at each level is compared pairwise to construct a judgment matrix;

[0029] Step 3: For each judgment matrix, calculate its maximum eigenvalue and its corresponding eigenvector. After normalization, the eigenvector is the weight vector of each influencing factor at this level relative to a factor at the previous level.

[0030] Step 4: Perform consistency check on the weight vector;

[0031] Step 5: Determine the scoring criteria for each evaluation indicator;

[0032] Step 6: Comprehensively evaluate the selected carbon dioxide saline layer storage candidate locations according to the scoring criteria and weights, and sum the scores of the criterion layers to obtain a comprehensive score for each candidate location;

[0033] Step 7: Sort the candidate addresses in descending order according to their final comprehensive scores, and obtain the top N candidate addresses as suitable storage sites.

[0034] In the implementation of the present invention, the overall goal of the evaluation of the suitability of the site selection for carbon dioxide storage in saline aquifers is to be such as "the best site selection for carbon dioxide storage in saline aquifers".

[0035] Furthermore, the categories of influencing factors of the criterion layer include geological conditions, hydrogeological conditions, engineering conditions, environmental conditions and socio-economic conditions.

[0036] In a further embodiment, specific evaluation indicators are further subdivided under the influencing factors of each criterion layer, including:

[0037] The evaluation indicators determined under the geological condition criterion layer include: stratum lithology, geological structural stability, stratum dip, and cover integrity;

[0038] The evaluation indicators determined under the hydrogeological condition criterion layer include saline layer thickness, permeability, porosity, and groundwater flow rate;

[0039] The evaluation indicators determined under the engineering condition criteria layer include: difficulty of injection well construction, feasibility of monitoring system, and site accessibility;

[0040] The evaluation indicators determined under the environmental condition criteria layer include: impact on groundwater environment, impact on surface ecology and potential impact of seismic activity;

[0041] Evaluation indicators under the socio-economic conditions criterion layer may include land use type, local residents' acceptance, and energy supply convenience.

[0042] For the sake of convenience, we use specific symbols as examples to illustrate the constructed hierarchical model.

[0043] Target layer (A): The best site for carbon dioxide storage in saline aquifers.

[0044] The criteria layer (B) includes:

[0045] B1: Geological conditions;

[0046] B2: Hydrogeological conditions;

[0047] B3: Engineering conditions;

[0048] B4: Environmental conditions;

[0049] B5: Socioeconomic conditions;

[0050] Indicator layer (C), including:

[0051] Under geological conditions (B1):

[0052] C1: formation lithology;

[0053] C2: geological structural stability;

[0054] C3: formation dip;

[0055] C4: cover integrity;

[0056] Under hydrogeological conditions (B2):

[0057] C5: thickness of saline layer;

[0058] C6: Permeability;

[0059] C7: porosity;

[0060] C8: groundwater flow rate;

[0061] Under engineering conditions (B3):

[0062] C9: Difficulty in injection well construction;

[0063] C10: Monitoring system feasibility;

[0064] C11: Site accessibility;

[0065] Under environmental conditions (B4):

[0066] C12: Impact on groundwater environment;

[0067] C13: Impact on surface ecology;

[0068] C14: Potential impact of seismic activity;

[0069] Under socio-economic conditions (B5):

[0070] C15: Land use type;

[0071] C16: Local residents’ acceptance;

[0072] C17: Convenience of energy supply.

[0073] Furthermore, in step 2, the relative importance of each factor is judged through expert scoring, and the relative importance of each factor in the criterion layer and the indicator layer is compared pairwise to construct a judgment matrix. The constructed judgment matrix is ​​expressed as an n*n pairwise comparison matrix A:

[0074]

[0075] Among them, a ij is the element of matrix A, indicating the importance of factor i relative to factor j.

[0076] In an embodiment of the present invention, the eigenvalue method is used to calculate the eigenvector. In step 3, the characteristic equation of the judgment matrix obtained by pairwise comparison is solved to find the maximum eigenvalue root and its corresponding eigenvector; then the eigenvector is normalized and used as the weight vector of each factor.

[0077] As an optional embodiment, for the n*n judgment matrix A, by solving the determinant equation of the matrix A, the maximum eigenvalue and its corresponding eigenvector are found, and then used as the maximum eigenroot;

[0078] The feature vector is then normalized to convert it into a weight vector.

[0079] As an optional embodiment, for an n*n pairwise comparison matrix A, its characteristic equation is a determinant equation: |A-λI|=0.

[0080] Where λ represents the eigenvalue, and I is the n*n unit matrix. Solving the characteristic equation can obtain all the eigenvalues ​​of the matrix A. The goal of the present invention is to determine the maximum eigenvalue λ max and its corresponding eigenvector.

[0081] In an embodiment of the present invention, the characteristic equation is solved by matlab or Python's NumPy library to obtain a matrix V composed of eigenvectors, each column of which is an eigenvector; and a diagonal matrix D composed of eigenvalues, where the elements on the diagonal are the eigenvalues.

[0082] Furthermore, in Matlab, the diagonal elements of the matrix D are extracted, the maximum eigenvalue is found, and its location is determined.

[0083] Finally, the feature vector is normalized to obtain the weight vector.

[0084] As an optional approach, the normalization process converts the feature vector into a weight vector by dividing each element by the sum of the elements so that the sum of the elements is 1.

[0085] As an optional embodiment, in step 4, consistency check is performed by calculating the consistency index CI, the average random consistency index RI and the consistency ratio CR;

[0086] When CR is less than the preset consistency threshold, the evaluation judgment matrix is ​​consistent;

[0087] When CR is greater than or equal to the consistency threshold, the judgment matrix is ​​considered to have poor consistency, and it is necessary to readjust the elements in the judgment matrix, recalculate the weight vector, and perform consistency checks until the preset consistency threshold standard is met.

[0088] As an optional embodiment, in step 4, for the n*n judgment matrix A, according to the determined maximum eigenvalue λ max Calculate the consistency index CI:

[0089] CI=(λ max -1) / (n-1);

[0090] where n is the order of the pairwise comparison matrix.

[0091] In step 4, for the n*n judgment matrix A, the average random consistency index RI is determined according to the order n of the matrix, which can usually be obtained by referring to an existing RI table, for example:

[0092] |m|1|2|3|4|5|6|7|8|9|10|

[0093] |---|---|---|---|---|---|---|---|---|---|

[0094] |RI|0|0|0.58|0.90|1.12|1.24|1.32|1.41|1.45|1.49|.

[0095] Finally, the consistency ratio CR is calculated based on the average random consistency index RI and the consistency index CI:

[0096] CR=CI / RI.

[0097] The consistency ratio CR is thus obtained.

[0098] Furthermore, consistency check is performed based on the consistency ratio CR;

[0099] When CR is less than a preset consistency threshold, such as 0.1 or a smaller value, the evaluation judgment matrix is ​​consistent;

[0100] When CR is greater than or equal to the consistency threshold, the judgment matrix is ​​considered to have poor consistency, and it is necessary to readjust the elements in the judgment matrix, recalculate the weight vector, and perform a consistency check until the preset consistency threshold standard is met, thereby ensuring that the constructed matrix meets the consistency requirements and that the subsequent calculation of the weight vector is reasonable and reliable.

[0101] As an optional embodiment, in step 5, the scoring criteria for each evaluation indicator are determined based on expert scoring or industry data.

[0102] In the embodiment of the present invention, industry data is taken as an example for description.

[0103] By collecting relevant data of existing carbon dioxide saline aquifer storage projects at home and abroad, including actual parameters and case data in geology, hydrogeology, engineering, environment and socio-economics, and referring to relevant industry standards, specifications and scientific research results, the reasonable value range of each indicator and the degree of influence on site suitability are determined.

[0104] Scoring interval division: According to the data distribution, the scoring interval is divided for each indicator. For example, for the indicator of saltwater layer thickness (C5):

[0105] If the thickness is greater than 500 meters, it is rated 9 points (indicating very favorable);

[0106] 300-500 meters is rated 7 points (more favorable);

[0107] 100-300 meters is rated 5 points (moderately favorable);

[0108] 50-100 meters is rated 3 points (less favorable);

[0109] Less than 50 m is scored as 1 (very disadvantageous).

[0110] For some qualitative indicators, such as formation lithology (C1), their suitability for storage can be quantified. For example, dense rocks such as sandstone and limestone with good sealing properties are rated 8-9 points; relatively good lithologies such as shale are rated 6-7 points; loose sand and soil, which are not conducive to storage, are rated 1-3 points, etc.

[0111] As an optional embodiment, in step 6, for each candidate address, firstly, actual data of each evaluation index is collected, and then, according to the determined scoring standard, the actual index data of each candidate address is converted into a corresponding score;

[0112] Then, after multiplying the scores of each evaluation indicator in the indicator layer by the corresponding indicator weight, the scores are summed up in each criterion layer to obtain the scores of each criterion layer;

[0113] Finally, the score of each criterion layer is multiplied by the weight of the criterion layer, and the sum is finally calculated to obtain the comprehensive score of each candidate address.

[0114] In the embodiment of the present invention, for each address, actual data of various indicators are collected through geological exploration, geophysical detection, hydrogeological investigation, on-site monitoring and other means. For example, geological data such as stratum lithology and saltwater layer thickness are obtained through drilling; hydrogeological parameters such as permeability, porosity and groundwater flow rate are determined by methods such as pumping test and tracer test; engineering conditions such as topography and traffic conditions of the site are investigated on site; the local ecological environment status and historical earthquake data are analyzed to evaluate environmental conditions; and social and economic information such as land use status and residents' attitudes towards the project are investigated.

[0115] According to the determined scoring criteria, the actual indicator data of each candidate site is converted into a corresponding score. For example, if the thickness of the saltwater layer at a candidate site is measured to be 350 meters, then according to the scoring criteria, the score of this indicator is 7 points; if the stratum lithology is sandstone, it is rated 8 points, and so on. Each indicator is scored in turn.

[0116] Furthermore, for each candidate address, the score of each indicator in the indicator layer is multiplied by the corresponding indicator weight (the weight of the indicator layer relative to the criterion layer), and then the score is summed in each criterion layer to obtain the score of each criterion layer.

[0117] For example, for a candidate site, under the geological conditions (B1) criteria layer:

[0118] The stratum lithology (C1) score is 8 points, and the weight is assumed to be 0.4 (calculated in the B1-C judgment matrix). The contribution of this item to the B1 score is: 8 × 0.4 = 3.2 points

[0119] The score of geological structure stability (C2) is 7 points, the weight is 0.3, and the contribution is 7×0.3=2.1 points.

[0120] Similarly, the contribution of other indicators to the B1 score is calculated and summed to obtain the score of the candidate address in the geological condition criterion layer.

[0121] Then the comprehensive score is calculated: the score of each criterion layer is multiplied by the weight of the criterion layer (the weight of the criterion layer relative to the target layer), and finally the sum is obtained to obtain the comprehensive score of each candidate address.

[0122] For example, if a candidate address scores 7.5 points in the geological condition (B1) criterion layer and the weight is 0.42 (calculated in the AB judgment matrix), the contribution of this part to the comprehensive score is: 7.5 × 0.42 = 3.15 points. Then calculate the contribution of other criterion layers to the comprehensive score in turn and sum them up to obtain the final comprehensive score of the candidate address.

[0123] Then, the candidate sites are arranged in descending order according to their comprehensive scores. The higher the score, the more suitable the site is for carbon dioxide saline aquifer storage.

[0124] As an optional embodiment, step 7 also includes making detailed comparison tables and charts for the first N candidate addresses to intuitively display the scores of each candidate address at different criterion levels and indicator levels, so as to more clearly analyze the advantages and disadvantages of each address.

[0125] {Example 2}

[0126] According to the method for evaluating the suitability of carbon dioxide saline layer storage site selection in the above embodiment of the present invention, the present invention further provides a computer system, comprising:

[0127] one or more processors; and

[0128] Memory stores instructions that can be operated.

[0129] Wherein, when the instruction is executed by one or more processors, the one or more processors are caused to perform operations, and the operations include the process of executing the aforementioned carbon dioxide saline layer storage site suitability evaluation method.

[0130] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.

Claims

1. A method for evaluating the suitability of a site for carbon dioxide storage in a saline aquifer, characterized in that: include: Step 1: Construct the influencing factor categories of the criterion layer and the evaluation indicators of the indicator layer to build a hierarchical structure model; Step 2: For the criterion layer and the indicator layer, the relative importance of the influencing factors at each level is compared pairwise to construct a judgment matrix; Step 3: For each judgment matrix, calculate its maximum eigenvalue and its corresponding eigenvector. After normalization, the eigenvector is the weight vector of each influencing factor at this level relative to a factor at the previous level. Step 4: Perform consistency check on the weight vector; Step 5: Determine the scoring criteria for each evaluation indicator; Step 6: Comprehensively evaluate the selected carbon dioxide saline layer storage candidate locations according to the scoring criteria and weights, and sum the scores of the criterion layers to obtain a comprehensive score for each candidate location; Step 7: Sort the candidate addresses in descending order according to their final comprehensive scores, and obtain the top N candidate addresses as suitable storage sites.

2. The method for evaluating the suitability of carbon dioxide storage in saline aquifers according to claim 1, characterized in that: The categories of influencing factors of the criterion layer include geological conditions, hydrogeological conditions, engineering conditions, environmental conditions and socio-economic conditions.

3. The method for evaluating the suitability of carbon dioxide storage in saline aquifers according to claim 1, characterized in that: The specific evaluation indicators are further subdivided under the influencing factors of each criterion layer, including: The evaluation indicators determined under the geological condition criterion layer include: stratum lithology, geological structural stability, stratum dip, and cover integrity; The evaluation indicators determined under the hydrogeological condition criterion layer include saline layer thickness, permeability, porosity, and groundwater flow rate; The evaluation indicators determined under the engineering condition criteria layer include: difficulty of injection well construction, feasibility of monitoring system, and site accessibility; The evaluation indicators determined under the environmental condition criteria layer include: impact on groundwater environment, impact on surface ecology and potential impact of seismic activity; Evaluation indicators under the socio-economic conditions criterion layer may include land use type, local residents' acceptance, and energy supply convenience.

4. The method for evaluating the suitability of carbon dioxide saline layer storage site selection according to claim 1, characterized in that: In step 3, the characteristic equation of the judgment matrix obtained by pairwise comparison is solved to find the maximum characteristic root and its corresponding characteristic vector; then the characteristic vector is normalized and used as the weight vector of each factor.

5. The method for evaluating the suitability of carbon dioxide saline layer storage site selection according to claim 4, characterized in that: In step 3, for the n*n judgment matrix A, the maximum eigenvalue and its corresponding eigenvector are found by solving the determinant equation of the matrix A, and then used as the maximum eigenroot; The eigenvector is then normalized by dividing each element by the sum of the elements so that the sum of the elements is 1, thereby converting the eigenvector into a weight vector.

6. The method for evaluating the suitability of carbon dioxide saline layer storage site selection according to claim 1, characterized in that: In step 4, consistency test is performed by calculating consistency index CI, average random consistency index RI and consistency ratio CR; When CR is less than the preset consistency threshold, the evaluation judgment matrix is ​​consistent; When CR is greater than or equal to the consistency threshold, the judgment matrix is ​​considered to have poor consistency, and it is necessary to readjust the elements in the judgment matrix, recalculate the weight vector, and perform a consistency check until the preset consistency threshold standard is met.

7. The method for evaluating the suitability of carbon dioxide storage in saline aquifers according to claim 1, characterized in that: In step 5, the scoring criteria for each evaluation indicator are determined based on the expert scores.

8. The method for evaluating the suitability of carbon dioxide saline layer storage site selection according to claim 1, characterized in that: In step 6, for each candidate address, firstly, actual data of each evaluation index is collected, and then, according to the determined scoring standard, the actual index data of each candidate address is converted into a corresponding score; Then, after multiplying the scores of each evaluation indicator in the indicator layer by the corresponding indicator weight, the scores are summed up in each criterion layer to obtain the scores of each criterion layer; Finally, the score of each criterion layer is multiplied by the weight of the criterion layer, and the sum is finally calculated to obtain the comprehensive score of each candidate address.

9. The method for evaluating the suitability of carbon dioxide saline layer storage site selection according to claim 1, characterized in that: In step 7, detailed comparison tables and charts are prepared for the first N candidate addresses to intuitively display the scores of each candidate address at different criterion levels and indicator levels.

10. A computer system, characterized in that: include: one or more processors; as well as Memory, storing instructions that can be operated; Wherein, when the instruction is executed by one or more processors, the one or more processors are caused to perform operations, wherein the operations include the process of executing the method described in any one of claims 1-9.

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