CN118036475A

CN118036475A - Optimal design method and system for carbon dioxide geological sequestration parameters

Info

Publication number: CN118036475A
Application number: CN202410417374.7A
Authority: CN
Inventors: 李迪迪; 陈奕桢
Original assignee: Guangdong University of Technology
Current assignee: Guangdong University of Technology
Priority date: 2024-04-09
Filing date: 2024-04-09
Publication date: 2024-05-14

Abstract

The invention relates to the technical field of parameter optimization, in particular to an optimization design method and system for carbon dioxide geological storage parameters, comprising the following steps: based on the existing geological data, a support vector machine is adopted to predict physical and chemical characteristics of the stratum, and an optimal sealed stratum is determined according to a prediction result to generate a geological characteristic prediction model. According to the invention, through the application of the support vector machine in the aspect of predicting the physical and chemical characteristics of the stratum, the selection of the sealing stratum is more scientific, the cost of trial and error is reduced, the application of the genetic algorithm and the particle swarm optimization algorithm in the aspect of capturing the key sealing parameter combination is further improved, the sealing efficiency and the adjustment flexibility of safety are further improved, the use of the Sobol sequence method and the agent model is superior in the aspect of refining the adjustment strategy, the application of computational fluid dynamics is realized, and the more accurate simulation of the physical reaction and the chemical reaction in the sealing process is ensured, so that the effectiveness of the adjustment strategy and the parameters is verified.

Description

Optimal design method and system for carbon dioxide geological sequestration parameters

Technical Field

The invention relates to the technical field of parameter optimization, in particular to an optimization design method and system for carbon dioxide geological storage parameters.

Background

The technical field of carbon dioxide geological sequestration parameter optimization relates to carbon dioxide capture, utilization and sequestration (CCUS) technology. The art is directed to adjusting and optimizing parameters of a particular process or system through algorithms or mathematical models to achieve optimal performance or effect. In the background of environmental engineering and earth science, the method means optimizing the use efficiency of resources and reducing the environmental impact.

The optimal design method of the geological carbon dioxide sealing parameters aims at improving the sealing efficiency and safety of the carbon dioxide underground by accurately adjusting key parameters in the sealing process. Including but not limited to the depth of sequestration, pressure, temperature, and rate and total amount of carbon dioxide injection. The purpose is to minimize the concentration of carbon dioxide in the atmosphere, thus combating global climate change and greenhouse effect. By optimizing parameters, higher sealing efficiency can be achieved, long-term stability is ensured, potential leakage risks are reduced, and meanwhile, the rationality of economic cost is considered.

The traditional method relies on empirical judgment or a simplified model, and lacks accurate stratum characteristic prediction, so that uncertainty of stratum selection is high, and risk and cost of a sealing project are increased. In the aspect of optimizing the sealing parameters, the complexity of the sealing process cannot be fully considered by the traditional method, and an effective global optimizing tool is lacked, so that the sealing efficiency and the safety are difficult to achieve the optimal. The lack of real-time monitoring and timely adjusting mechanisms makes the sealing operation difficult to adapt to the change of geological conditions and sealing effects, thereby affecting the stability and reliability of sealing items.

Disclosure of Invention

The invention aims to solve the defects in the prior art, and provides an optimization design method and system for carbon dioxide geological sequestration parameters.

In order to achieve the above purpose, the present invention adopts the following technical scheme: the optimal design method of the carbon dioxide geological sequestration parameter comprises the following steps:

S1: based on the existing geological data, predicting physical and chemical characteristics of the stratum by adopting a support vector machine, determining an optimal sealed stratum according to a prediction result, and generating a geological characteristic prediction model;

s2: based on the geological characteristic prediction model, adopting a genetic algorithm and a particle swarm optimization algorithm to perform preliminary optimization of the sealing parameters, including injection rate and pressure, capturing key parameter combinations, and generating preliminary optimized parameter configuration;

S3: based on the preliminary optimization parameter configuration, adopting a time sequence analysis and isolation forest algorithm to monitor the sealing process in real time and adjust parameters, and matching the change of geological conditions and sealing effects to generate a dynamic adjustment strategy;

S4: based on the dynamic adjustment strategy, adopting a Sobol sequence method and a proxy model, evaluating the performance of the sealing parameters under the differential condition, and continuously optimizing the adjustment strategy to generate a fine adjustment strategy;

S5: based on the refinement and adjustment strategy, adopting computational fluid dynamics, and verifying the optimization results of the adjustment strategy and the sealing parameters by simulating physical reaction and chemical reaction in the sealing process to generate a simulation verification record;

S6: based on the simulation verification record, a feedback circulation mechanism is established, and according to real-time data and simulation results in the sealing process, the sealing parameters and the dynamic adjustment strategy are iteratively optimized to generate feedback circulation measures;

s7: and selecting optimal stratum, injection parameters and emergency response measures based on the feedback circulation measures, and verifying the efficiency and safety of the sealing process to generate a sealing scheme.

As a further scheme of the invention, the geological characteristic prediction model comprises a permeability rating, a porosity percentage and a chemical stability index of the stratum, the preliminary optimization parameter configuration comprises a determined carbon dioxide injection rate range, a pressure threshold value and an optimal injection temperature, the dynamic adjustment strategy comprises an injection rate step length and a pressure adjustment interval which are adjusted in real time and are changed according to an injection strategy of geological feedback, the feedback circulation measure comprises a parameter adjustment rule and a periodic effect evaluation time table, and the preservation scheme comprises a selected stratum depth and position, an optimized injection rate and pressure set value and an emergency operation flow under a differential geological emergency.

As a further scheme of the invention, based on the existing geological data, a support vector machine is adopted to predict physical and chemical characteristics of the stratum, and an optimal sealed stratum is determined according to a prediction result, and the step of generating a geological characteristic prediction model specifically comprises the following steps:

s101: based on the existing geological data, performing data processing, including missing value processing, data normalization and feature selection, matching with the input standard of a support vector machine, and generating processed geological data;

s102: training by using a support vector machine based on the processed geological data, learning the relation between physical and chemical characteristics of the stratum, and predicting the stratum characteristics to generate a stratum prediction model;

S103: and analyzing and evaluating the sequestration potential of the stratum based on the stratum prediction model, determining the optimal carbon dioxide sequestration stratum, and generating a geological characteristic prediction model.

As a further scheme of the invention, based on the geological characteristic prediction model, a genetic algorithm and a particle swarm optimization algorithm are adopted to perform preliminary optimization of the sealing parameters, wherein the preliminary optimization comprises the steps of injecting speed and pressure, capturing key parameter combinations and generating preliminary optimized parameter configuration, and the steps comprise:

s201: defining an optimization problem with the sealing efficiency and the safety as targets based on the geological characteristic prediction model, setting an objective function, and generating a defined optimization objective function;

s202: based on the defined optimization objective function, applying a genetic algorithm to search for the sealing parameters, including injection rate and pressure, identifying potential optimization combinations, and generating an optimized parameter set;

S203: based on the optimized parameter set, a particle swarm optimization algorithm is used for refining a parameter optimization process, and the optimal injection rate and pressure combination is captured to generate a preliminary optimized parameter configuration.

As a further scheme of the invention, based on the preliminary optimized parameter configuration, a time sequence analysis and isolation forest algorithm is adopted to monitor and adjust parameters in real time in the sealing process, and the steps of matching the geological conditions and the change of the sealing effect and generating a dynamic adjustment strategy are specifically as follows:

S301: setting real-time data acquisition parameters based on the preliminary optimization parameter configuration, and collecting key parameters in the sealing process, including time series data of injection rate and pressure, so as to generate a real-time monitoring data set;

S302: based on the real-time monitoring data set, a time sequence analysis method is applied to analyze the change trend and the periodic mode of parameters along with time, and the future parameter change is predicted to generate a parameter trend analysis record;

s303: based on the parameter trend analysis record, an isolated forest algorithm is adopted to identify abnormal data points including unexpected sudden pressure increase, the sealing parameters are adjusted, and the dynamic adjustment strategy is generated by matching the geological conditions and the change of the sealing effect.

As a further scheme of the invention, based on the dynamic adjustment strategy, a Sobol sequence method and a proxy model are adopted, the performance of the sealing parameters is evaluated under the differential condition, the adjustment strategy is continuously optimized, and the step of generating the fine adjustment strategy specifically comprises the following steps:

s401: defining a differential condition scene of the simulated sealing operation based on the dynamic adjustment strategy, wherein the differential condition scene comprises different geological characteristics, injection rate and pressure change range, and generating a simulated scene set;

S402: based on the simulation scene set, a Sobol sequence method is applied to carry out global sensitivity analysis, and judging which factors in the sealing parameters have key influence on the sealing effect to generate sensitivity analysis information;

s403: based on the sensitivity analysis information, simulating and optimizing the sealing parameters by using a proxy model, and adjusting the sealing strategy to generate a fine adjustment strategy.

As a further scheme of the present invention, based on the policy of refinement adjustment, computational fluid dynamics is adopted, and by simulating physical reaction and chemical reaction in the sealing process, the steps of verifying the optimization results of the adjustment policy and the sealing parameters, and generating a simulated verification record, are specifically as follows:

S501: based on the strategy of refinement adjustment, adopting computational fluid dynamics to set simulated initial conditions, including physical properties, chemical compositions and optimized sequestration parameters of the sequestration stratum, and generating simulated initial conditions;

S502: based on the simulated initial conditions, carrying out dynamic simulation of the sequestration process, including diffusion of carbon dioxide in the stratum, reaction path analysis and influence on sequestration effect, and generating a dynamic simulation result;

S503: based on the dynamic simulation result, analyzing the effect difference before and after the adjustment of the sealing parameters, evaluating the effectiveness of the refinement adjustment strategy, comparing the simulation result with an expected target, and generating a simulation verification record.

As a further scheme of the invention, based on the simulation verification record, a feedback loop mechanism is established, and according to real-time data and simulation results in the sealing process, the steps of iteratively optimizing sealing parameters and dynamically adjusting strategies and generating feedback loop measures are specifically as follows:

s601: based on the simulation verification record, identifying key influence factors and potential risk points in the simulation process, evaluating the influence of the sealing parameters on the sealing efficiency and the safety, and generating a key influence factor analysis record;

s602: based on the key influence factor analysis record, the data of the real-time sealing process is combined, and a time sequence analysis method is used for updating the sealing parameter adjustment strategy to generate an updated adjustment strategy;

S603: and executing an iterative optimization flow based on the updated adjustment strategy, continuously adjusting and optimizing the sealing parameters, verifying the sealing effect and the safety, and generating a feedback circulation measure.

As a further scheme of the invention, based on the feedback circulation measures, optimal stratum, injection parameters and emergency response measures are selected, and the efficiency and safety of the sealing process are verified, and the steps for generating the sealing scheme are specifically as follows:

s701: based on the feedback loop measures, data aggregation and analysis are executed, wherein the data aggregation and analysis comprises summarizing real-time data and simulation verification records collected in the sealing process, and the statistical analysis method is utilized to evaluate the influence of parameters on sealing efficiency and safety so as to generate performance and safety analysis information;

S702: based on the performance and safety analysis information, screening and determining stratum with optimal sealing efficiency and safety and corresponding injection parameters by adopting a weighted scoring method, and generating preferable stratum and parameter data;

S703: and planning a sealing operation flow, an expected target, emergency response measures and a long-term monitoring plan based on the preferred stratum and the parameter data, and continuously optimizing potential changes to generate a sealing scheme.

The optimal design system of the carbon dioxide geological sequestration parameter is used for executing the optimal design method of the carbon dioxide geological sequestration parameter, and the system comprises the following steps: the system comprises a geological data processing module, a geological characteristic prediction module, a sealing parameter optimization module, a sealing process monitoring module, an adjustment strategy refinement module and a simulation verification and iteration module;

The geological data processing module is used for executing data cleaning, missing value processing, data normalization and feature selection based on the collected geological data, providing standardized input data for model training and analysis, and generating a standardized geological data set;

The geological characteristic prediction module is used for performing prediction analysis on physical and chemical characteristics of stratum based on a standardized geological data set by adopting a support vector machine, verifying the accuracy and generalization capability of a model by using a cross verification method, and generating a geological characteristic prediction model;

the sealing parameter optimization module optimizes sealing parameters based on a geological characteristic prediction model by adopting a genetic algorithm and a particle swarm optimization algorithm, comprises injection rate and pressure, captures an optimal parameter combination and generates a preliminary parameter optimization configuration;

The sealing process monitoring module is used for carrying out real-time monitoring and anomaly detection on the sealing process by adopting a time sequence analysis and isolation forest algorithm based on preliminary parameter optimization configuration, and generating a dynamic adjustment strategy by matching with the change of geological conditions and sealing effects;

The adjustment strategy refinement module performs global sensitivity analysis by adopting a Sobol sequence method based on a dynamic adjustment strategy, performs refinement adjustment on the sealing parameters and the adjustment strategy by using Gaussian process regression, and matches sealing effects under different geological conditions to generate a refinement adjustment strategy;

The simulation verification and iteration module is based on a refinement adjustment strategy, adopts computational fluid dynamics and combines a dynamic feedback mechanism to simulate and verify physical and chemical reactions in the sealing process, and carries out iteration optimization on sealing parameters and strategies according to simulation results to generate a sealing scheme.

Compared with the prior art, the invention has the advantages and positive effects that:

according to the invention, the support vector machine is applied to the aspect of predicting the physical and chemical characteristics of the stratum, so that the selection of the sealed stratum is more scientific, and the cost of trial and error is reduced. The genetic algorithm and the particle swarm optimization algorithm are applied to capturing key sealing parameter combinations, so that the sealing efficiency and the safety adjustment flexibility are further improved. The use of Sobol sequencing and proxy models then presents advantages in terms of refinement adjustment strategies. The application of computational fluid dynamics ensures that the physical reaction and chemical reaction simulation of the sealing process is more accurate, thereby verifying the effectiveness of the adjustment strategy and parameters. By establishing a feedback circulation mechanism, continuous optimization of the sealing parameters and the dynamic adjustment strategy is realized, and efficient execution of the sealing scheme is ensured.

Drawings

FIG. 1 is a schematic workflow diagram of the present invention;

FIG. 2 is a S1 refinement flowchart of the present invention;

FIG. 3 is a S2 refinement flowchart of the present invention;

FIG. 4 is a S3 refinement flowchart of the present invention;

FIG. 5 is a S4 refinement flowchart of the present invention;

FIG. 6 is a S5 refinement flowchart of the present invention;

FIG. 7 is a S6 refinement flowchart of the present invention;

FIG. 8 is a S7 refinement flowchart of the present invention;

Fig. 9 is a system flow diagram of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

Embodiment one: referring to fig. 1, the present invention provides a technical solution: the optimal design method of the carbon dioxide geological sequestration parameter comprises the following steps:

s2: based on a geological characteristic prediction model, adopting a genetic algorithm and a particle swarm optimization algorithm to perform preliminary optimization of the sealing parameters, including injection rate and pressure, capturing key parameter combinations, and generating preliminary optimized parameter configuration;

S3: based on preliminary optimization parameter configuration, adopting a time sequence analysis and isolation forest algorithm to monitor and adjust parameters in real time in the sealing process, and matching with the change of geological conditions and sealing effects to generate a dynamic adjustment strategy;

S4: based on a dynamic adjustment strategy, adopting a Sobol sequence method and a proxy model, evaluating the performance of the sealing parameters under the differential condition, and continuously optimizing the adjustment strategy to generate a fine adjustment strategy;

S5: based on a strategy for refining adjustment, adopting computational fluid dynamics, and verifying an optimization result of the adjustment strategy and the sealing parameters by simulating physical reaction and chemical reaction in the sealing process to generate a simulation verification record;

S7: based on the feedback circulation measures, optimal stratum, injection parameters and emergency response measures are selected, and efficiency and safety of the sealing process are verified, so that a sealing scheme is generated.

The geological characteristic prediction model comprises a stratum permeability rating, a porosity percentage and a chemical stability index, the preliminary optimization parameter configuration comprises a determined carbon dioxide injection rate range, a pressure threshold value and an optimal injection temperature, the dynamic adjustment strategy comprises an injection rate step length adjusted in real time, a pressure adjustment interval and an injection strategy change according to geological feedback, the feedback circulation measures comprise a parameter adjustment rule and a periodic effect evaluation schedule, and the sealing scheme comprises a selected stratum depth and position, an optimized injection rate and pressure set value and an emergency operation flow under different geological emergencies.

In step S1, geological data including but not limited to permeability, porosity and chemical composition of the formation are used by a Support Vector Machine (SVM) algorithm, which is typically a structured table, wherein each row represents measured data of a place, and the columns contain different physical and chemical properties, the SVM predicts physical and chemical properties of the formation by constructing one or more hyperplanes to classify in a multidimensional space, during execution, first mapping raw data to the high-dimensional space by a kernel function, then finding the optimal segmented hyperplane in space, and by adjusting the kernel function type (e.g., linear, polynomial, radial basis function) and other parameters (e.g., penalty factor C and kernel function parameters), the SVM algorithm is able to separate data points of different categories at maximum intervals, select the formation with the best physical and chemical stability as a sealing layer according to the prediction of the formation properties, and generate a geological property prediction model.

In the step S2, based on the result of the geological characteristic prediction model, the initial optimization is carried out on the sealing parameters through the combined application of a genetic algorithm and a particle swarm optimization algorithm, the genetic algorithm simulates natural selection and a genetic mechanism, the optimal solution is searched through selection, intersection and mutation operation, the particle swarm optimization algorithm simulates the hunting behavior of a shotgun, the optimal solution is found through tracking experience of individuals and groups, in the execution process, an fitness function is defined to evaluate the advantages and disadvantages of parameter configuration, such as the maximum sealing efficiency and safety, the genetic algorithm starts from a randomly generated initial group, a new generation is generated through repeated selection of the optimal individuals and application of intersection and mutation operation, the particle swarm optimization algorithm updates the speed and position of particles to find the optimal solution, and the parameters such as the injection rate, the pressure threshold and the optimal injection temperature of carbon dioxide are optimized through alternate iteration of the two algorithms, and finally, the initial optimization parameter configuration file is generated.

In step S3, based on the preliminary optimized parameter configuration, real-time monitoring and parameter adjustment of the sealing process are performed through time sequence analysis and an isolated forest algorithm, the time sequence analysis predicts future trend through pattern recognition of historical sealing process data, the isolated forest algorithm is used as an anomaly detection method, anomaly data points in the sealing process can be effectively recognized, in the operation process, time sequence data generated in the sealing process, such as injection rate and pressure change, are collected and analyzed, future sealing conditions are predicted through a time sequence analysis method (such as an ARIMA model), meanwhile, the isolated forest algorithm analyzes the data, anomaly modes, such as unusual pressure rise or injection rate change, are rapidly recognized, dynamic adjustment of sealing parameters is performed through combining the results of the two algorithms, and the dynamic adjustment strategy file is generated through real-time adjustment of injection rate step length, pressure adjustment interval and the like.

In the step S4, based on a dynamic adjustment strategy, the performance of the sealing parameters is evaluated under the differential condition through a Sobol sequence method and a proxy model, the adjustment strategy is continuously optimized, the Sobol sequence is a low-differential sequence and is used for generating uniformly distributed sample points, the proxy model is a simplified model and is used for approximating the behavior of a complex system, in the execution process, the Sobol sequence is utilized for generating a multidimensional sample space of the sealing parameters, the sealing effect of the parameters under different geological conditions is evaluated through the proxy model, the proxy model such as Gaussian process regression is used for rapidly predicting the sealing effect, the number of samples needing actual simulation is reduced, and the sealing parameters and the strategy are continuously adjusted and optimized through analysis of the sample results, so that a strategy file for refining adjustment is generated.

In the S5 step, based on a strategy for refining adjustment, physical reaction and chemical reaction in the sealing process are simulated through Computational Fluid Dynamics (CFD), the optimization results of the adjustment strategy and sealing parameters are verified, the CFD simulation solves and analyzes the fluid flow problem by utilizing numerical analysis and algorithms, in the process, simulated initial conditions and boundary conditions including injection rate, pressure and physical and chemical properties of the stratum are set, then simulation calculation is carried out, and the flow, diffusion and reaction processes of carbon dioxide in the stratum are analyzed.

In step S6, a feedback circulation mechanism is established based on the simulation verification record, the sealing parameters and the dynamic adjustment strategy are optimized through real-time data and simulation results in an iterative mode, in the process, real-time monitoring data of the sealing process are collected, the real-time monitoring data and the simulation verification record are compared and analyzed, differences and potential improvement points are identified, the sealing parameters such as injection rate and pressure are adjusted according to analysis results, the dynamic adjustment strategy is optimized, sealing efficiency and safety are improved, and a feedback circulation measure file is generated.

In the step S7, based on feedback circulation measures, optimal stratum, injection parameters and emergency response measures are selected, and efficiency and safety of a sealing process are verified, in the process, the physicochemical characteristics of the stratum, an optimization result of the sealing parameters and effectiveness of a dynamic adjustment strategy are comprehensively considered, the optimal stratum depth and position are selected, meanwhile, according to monitoring data and simulation verification results of the sealing process, the optimized injection rate and pressure set values are determined, an emergency operation flow under a differential geological emergency is formulated, and a sealing scheme file is generated.

Referring to fig. 2, based on the existing geological data, the prediction of physical and chemical characteristics of the stratum is performed by using a support vector machine, and an optimal sealed stratum is determined according to the prediction result, so as to generate a geological characteristic prediction model, which specifically includes the steps of:

In the S101 substep, a series of refined data processing operations are performed on the existing geological data, including missing value processing, data normalization and feature selection, so as to meet the input requirement of a support vector machine, specifically, a K Nearest Neighbor (KNN) method is adopted to fill in missing values, the values of missing data points are deduced according to the values of adjacent samples, the data integrity is guaranteed, then Z score normalization is performed, the data are adjusted to be uniform magnitude, the influence caused by different scales is reduced, the features are compared, a Recursive Feature Elimination (RFE) based method is adopted in the feature selection stage, the importance of the features is evaluated by combining a Support Vector Machine (SVM), the least important features are gradually removed, the most representative feature set is reserved, the quality and the effectiveness of the data are ensured, the accuracy of subsequent model training is improved, and the generated processed geological data file is directly connected to the model training process of the next step.

In the S102 substep, based on the processed geological data, a Support Vector Machine (SVM) model is adopted for training, the SVM classifies in a high-dimensional space by constructing one or more hyperplanes, the optimization target is to maximize the intervals among different categories, in the process, a Radial Basis Function (RBF) is adopted as a kernel function, and the RBF is applicable to complex interaction between physical and chemical characteristics of a stratum because the RBF can process nonlinear data relationship, the optimal punishment parameter C and the kernel function parameter gamma are selected by combining a grid search method with cross verification, the optimal generalization capability of the model is ensured, in the training process, the relation between physical and chemical characteristics of the stratum is learned by a system, and the generated stratum prediction model can accurately predict the stratum characteristics.

In the S103 substep, the generated stratum prediction model is utilized to execute the analysis and evaluation of the sequestration potential of the stratum, the physical and chemical characteristics of the stratum and the predicted stratum characteristics are comprehensively considered, the sequestration potential of each stratum is comprehensively evaluated by adopting a multi-standard decision analysis (MCDA) method, in the concrete implementation, the weight of the influence of different characteristics on the sequestration potential is firstly determined by adopting an Analytic Hierarchy Process (AHP) and a technical evaluation standard (TOPSIS) in a combined mode, and then the similarity of each stratum and an ideal sequestration layer is evaluated according to the weight and the stratum characteristics by utilizing the TOPSIS method, so that the optimal carbon dioxide sequestration stratum is determined, and the geological characteristic prediction model is generated.

Referring to fig. 3, based on a geological characteristic prediction model, a genetic algorithm and a particle swarm optimization algorithm are adopted to perform preliminary optimization of the sealing parameters, including injection rate and pressure, and key parameter combinations are captured, so that the steps for generating preliminary optimized parameter configuration are specifically as follows:

s201: defining an optimization problem with the sealing efficiency and the safety as targets based on a geological characteristic prediction model, setting an objective function, and generating a defined optimization objective function;

S202: based on a defined optimization objective function, applying a genetic algorithm to search for the sealing parameters, including injection rate and pressure, identifying potential optimization combinations, and generating an optimized parameter set;

s203: based on the optimized parameter set, a particle swarm optimization algorithm is used for refining the parameter optimization process, and the optimal injection rate and pressure combination is captured to generate preliminary optimized parameter configuration.

In the S201 substep, defining the sealing efficiency and the safety as the optimization problem of the dual targets through a geological characteristic prediction model, and setting a composite objective function according to the sealing efficiency, wherein the objective function comprehensively considers the sealing efficiency, including maximizing the utilization rate of the underground sealing space and the sealing safety, such as minimizing leakage risk, and the optimization objective function is characterized by adopting a mathematical model, wherein the sealing efficiency is expressed by functions of geological characteristics, injection rate and pressure, the safety is evaluated by simulating pressure distribution and crack expansion in the sealing process, and through the step, a definite mathematical expression is established, a definite optimization standard is provided for a genetic algorithm, and a well-defined optimization objective function file is generated.

In the S202 substep, based on the defined optimized objective function, the optimized search is performed on the sealing parameters by adopting a genetic algorithm, the optimal combination of injection rate and pressure is found, the genetic algorithm simulates natural selection and genetics principles, the operations such as selection, intersection and variation are performed, the superiority of the individual is evaluated by using the fitness function, in the process, the composite objective function of sealing efficiency and safety is used as the fitness function, the search process is guided, the genetic algorithm can effectively search in a parameter space through iterative evolution, potential optimized parameter combinations are identified, the individual with the best performance in each generation is reserved and used for generating new individual, the optimal solution is gradually approximated, and a collection file containing the optimized injection rate and pressure parameters is generated.

In the step S203, based on the optimized parameter set obtained by the genetic algorithm, an optimization process of refining parameters by a Particle Swarm Optimization (PSO) algorithm is further applied, the particle swarm optimization algorithm finds an optimal solution by simulating social behavior of a bird swarm, each particle represents a potential solution, namely a set of specific injection rate and pressure parameters, the particle adjusts its own position according to the best experience of the individual and the population to find the optimal solution, in this step, the position update of the particle considers the potential optimization combination identified in the previous stage, and the parameters are precisely adjusted to capture the optimal injection rate and pressure combination, and the process generates a preliminary optimized parameter configuration file, which records the optimal parameter combination for realizing the sealing target.

Referring to fig. 4, based on the preliminary optimized parameter configuration, the time sequence analysis and the forest isolation algorithm are adopted to monitor and adjust parameters in real time in the sealing process, and the steps of matching the geological conditions and the change of the sealing effect and generating the dynamic adjustment strategy are specifically as follows:

S301: setting real-time data acquisition parameters based on preliminary optimization parameter configuration, and collecting key parameters in the sealing process, including time series data of injection rate and pressure, so as to generate a real-time monitoring data set;

S302: based on a real-time monitoring data set, a time sequence analysis method is applied to analyze the change trend and the periodic mode of parameters along with time, and the future parameter change is predicted to generate a parameter trend analysis record;

S303: based on the parameter trend analysis record, an isolated forest algorithm is adopted to identify abnormal data points including unexpected pressure sudden increase, the sealing parameters are adjusted, and the dynamic adjustment strategy is generated by matching the geological conditions and the change of the sealing effect.

In the step S301, setting of real-time data acquisition parameters is performed based on primarily optimized parameter configuration, and the key time series data in the sealing process, especially injection rate and pressure, are collected, the data acquisition adopts a sensor network, operation parameters of the sealing well are monitored in real time, wherein the data are recorded in the form of time stamps and corresponding injection rate and pressure values, high-precision and time-continuity data acquisition is ensured, a real-time monitoring data set is generated by implementing the step, the data set is stored in a structured format, subsequent data analysis and pattern recognition work are facilitated, and through real-time monitoring, not only can real-time tracking of sealing effect be achieved, but also data support is provided for early recognition of potential operation problems, and controllability and safety of the sealing process are enhanced.

In the S302 substep, based on the collected real-time monitoring data set, a time sequence analysis method is applied to deeply analyze the trend and the periodicity of the variation of the injection rate and the pressure along with time, the process adopts an autoregressive moving average (ARIMA) model, the model can describe the autoregressive characteristic and the moving average characteristic of the time sequence data, the long-term trend and the periodicity of the data are effectively captured, the time sequence analysis not only reveals the variation rule of the injection rate and the pressure, but also predicts the future parameter variation trend, and the parameter trend analysis record generated in the step records the result and the predicted value of the time sequence analysis in detail, thereby providing basis for the sealing operation.

In S303, based on parameter trend analysis record, the abnormal detection is carried out on the data by adopting an isolation forest algorithm, particularly concerning abnormal data points such as unexpected pressure sudden increase, the abnormal points are isolated by the isolation forest algorithm in a mode of randomly selecting characteristics and randomly segmenting characteristic values, the algorithm has high-efficiency processing capacity on high-dimensional data and small-scale data sets, the algorithm is suitable for rapidly identifying abnormal modes, the abnormal data points identified by the algorithm are used for adjusting the sealing parameters to ensure the safety and the efficiency of the sealing process, in addition, according to the identification result of the abnormal data points, the change of geological conditions and the sealing effect is further analyzed, a dynamic adjustment strategy is generated, and the strategy guides how to adjust the sealing parameters according to real-time data and the abnormal detection result, so that the flexibility and the response capacity of the sealing operation are improved.

Referring to fig. 5, based on a dynamic adjustment policy, a Sobol sequence method and a proxy model are adopted to evaluate the performance of the sealing parameters under the differential condition, and the adjustment policy is continuously optimized, so that the steps of generating the policy for fine adjustment are specifically as follows:

s401: defining a differential condition scene of the simulated sealing operation based on a dynamic adjustment strategy, wherein the differential condition scene comprises different geological characteristics, injection rate and pressure change range, and generating a simulated scene set;

S403: based on the sensitivity analysis information, the agent model is utilized to simulate and optimize the sealing parameters, and the sealing strategy is adjusted to generate a fine adjustment strategy.

In the sub-step S401, a series of differential condition scenes simulating the sealing operation are defined based on the dynamic adjustment strategy, the scenes carefully delineate different geological characteristics, injection rates and pressure variation ranges, various conditions encountered by the sealing operation are simulated, each scene defines in detail the differences of the geological conditions, such as the changes of porosity, permeability and chemical composition, and how the changes affect the adaptation of the injection rates and pressures, the generated simulated scene sets are stored in a structured data format, each scene contains a set of specific parameter values, the parameter values correspond to the specific changes of the geological characteristics, injection rates and pressures, and this process generates a simulated scene set file containing a plurality of differential condition scenes, which provides a basis for comprehensively evaluating the operation strategy under different sealing conditions.

In the S402 substep, based on the generated simulation scene set, a Sobol sequence method is applied to carry out global sensitivity analysis, the Sobol sequence is an efficient quasi-Monte Carlo method, the method is suitable for global sensitivity analysis of a multidimensional space, which factors in the sealing parameters have key influence on the sealing effect can be accurately identified, through careful calculation and analysis, how the stability and the safety of the sealing effect are influenced by the changes of different parameters are evaluated by the method, the sensitivity of the injection rate, the pressure and the specific geological characteristics is determined, and the sensitivity scoring and the influence degree of each parameter are recorded in detail in a document form by the generated sensitivity analysis information.

In the S403 substep, based on the sensitivity analysis information, the proxy model technology is adopted to further simulate and optimize the sealing parameters, the proxy model is used as an efficient approximate model, the influence of parameter change on the sealing effect in the complex process can be rapidly simulated without complicated physical simulation, the sealing parameters such as injection rate and pressure are finely adjusted and simulated by combining the sensitivity analysis result by using the proxy model, so as to achieve the optimal sealing effect, the process considers the change of geological conditions and sealing effect, and a policy file for fine adjustment is generated through the prediction and optimization functions of the proxy model.

Referring to fig. 6, based on the policy of refinement adjustment, by adopting computational fluid dynamics and simulating physical reaction and chemical reaction in the sealing process, the steps of verifying the optimization results of the adjustment policy and sealing parameters and generating simulated verification records are specifically as follows:

s501: based on a strategy of refinement adjustment, adopting computational fluid dynamics to set simulated initial conditions, including physical properties, chemical compositions and optimized sequestration parameters of the sequestration stratum, and generating simulated initial conditions;

S503: based on the dynamic simulation result, the difference of the effects before and after the adjustment of the sealing parameters is analyzed, the effectiveness of the refinement adjustment strategy is evaluated, the simulation result is compared with an expected target, and a simulation verification record is generated.

In the S501 substep, based on a strategy of refinement adjustment, a Computational Fluid Dynamics (CFD) technology is adopted, simulated initial conditions are accurately set, the initial conditions cover detailed physical properties, chemical compositions and optimized sealing parameters of a sealing stratum, such as injection rate, injection pressure, initial concentration of carbon dioxide and the like, the setting of the initial conditions is based on comprehensive analysis of geological survey data, laboratory analysis results and output of optimization of an earlier strategy, accuracy and reliability of simulation are ensured, each parameter is definitely defined in a numerical form, including permeability, porosity, temperature distribution, chemical composition distribution and the like of the stratum, a set of complete simulation initial conditions is formed and stored as a structured data file, and the step provides a solid foundation for the subsequent CFD simulation, and ensures that a simulation process can reflect real underground conditions and specific conditions of sealing operation.

In the step S502, based on the finely defined initial conditions for simulation, a dynamic simulation of the carbon dioxide sequestration process is performed, the simulation considers the diffusion behavior of carbon dioxide in the stratum, the reaction path with geological media and the comprehensive influence of factors on the sequestration effect, the simulation is performed by using computational fluid dynamics software, the motion and conversion process of carbon dioxide under different geological conditions are accurately calculated by solving the control equation of fluid flow and chemical reaction, the result of the dynamic simulation reveals the distribution change, the reaction path and the leakage risk of carbon dioxide in the sequestration process, and a dynamic simulation result file containing simulation data and analysis results is generated, and the file records the dynamic characteristics of the carbon dioxide sequestration process.

In the step S503, based on the result of dynamic simulation, the difference of effects before and after the adjustment of the sealing parameters is comprehensively analyzed, the actual effect and the effectiveness of the refinement adjustment strategy are evaluated by comparing the simulation result with the expected sealing target, the analysis process considers a plurality of dimensions such as sealing efficiency, sealing safety, environmental influence and the like, the statistical analysis and comparison analysis method is utilized to identify the improvement points and potential risk areas brought by the strategy adjustment in detail, the generated simulation verification record gathers the analysis result, including the improvement degree of the sealing effect, the influence of the key parameter adjustment and the evaluation of the conformity with the expected target, and the file not only confirms the effectiveness of the refinement adjustment strategy, but also provides precious data support and basis for further optimizing the sealing operation and the strategy adjustment, and ensures the long-term success of the sealing item and the sustainable development of the environment.

Referring to fig. 7, based on the simulation verification record, a feedback loop mechanism is established, and according to real-time data and simulation results of the sealing process, the steps of iteratively optimizing the sealing parameters and dynamically adjusting the strategies to generate feedback loop measures are specifically as follows:

S603: based on the updated adjustment strategy, executing an iterative optimization flow, continuously adjusting and optimizing the sealing parameters, verifying the sealing effect and the safety, and generating a feedback circulation measure.

In the S601 substep, based on the simulation verification record, the identification of key influence factors and potential risk points and the evaluation of the influence of the sealing parameters on the sealing efficiency and safety are performed, the data in the simulation verification record are comprehensively analyzed, statistical analysis and data mining technology are adopted, the factors influencing the sealing effect and safety, such as the non-uniformity of geological characteristics, the fluctuation of injection rate and the criticality of pressure management, are identified in detail, in addition, the action mechanism and interaction of the factors in the sealing process are evaluated, and how the change of the sealing parameters affects the stability of the whole sealing system, and the generated key influence factor analysis record records the analysis result and the evaluation conclusion in a document form in detail, so that a scientific basis is provided for the subsequent adjustment of sealing parameter strategies.

In S602 substep, based on the key influence factor analysis record and combined with the data in the real-time sealing process, the sealing parameter adjustment strategy is updated by adopting a time sequence analysis method, the trend and periodicity of the sealing parameter along with the time change are accurately predicted by collecting and analyzing the real-time data and applying an autoregressive moving average model (ARIMA) and other time sequence analysis technology, so that the sealing parameter is finely adjusted to optimize the sealing effect and enhance the safety of the system, and the updated adjustment strategy document records the adjustment reason, the target parameter value and the expected adjustment effect in detail, thereby providing a dynamic and flexible management strategy for the sealing process.

In the step S603, based on the updated adjustment strategy, an iterative optimization process is performed to continuously adjust and optimize the sealing parameters, verify the sealing effect and safety, and by setting up a feedback mechanism, real-time monitor the key parameters in the sealing process and compare with the expected targets, timely find deviation and potential risk, the iterative optimization process adopts dynamic simulation technology and optimization algorithm, such as genetic algorithm and particle swarm optimization algorithm, to continuously adjust the sealing parameters so as to adapt to the change of geological conditions and the new challenges in the sealing process, and the generated feedback loop measure file records the results of each iterative optimization, the adjusted parameters and implemented strategies in detail, thereby providing an effective tool for continuous improvement and risk management of the sealing items.

Referring to fig. 8, based on feedback loop measures, optimal stratum, injection parameters and emergency response measures are selected, and efficiency and safety of a sequestration process are verified, and the steps of generating a sequestration scheme are specifically as follows:

S701: based on feedback circulation measures, data aggregation and analysis are executed, wherein the data aggregation and analysis comprises summarizing real-time data and simulation verification records collected in the sealing process, and the statistical analysis method is utilized to evaluate the influence of parameters on sealing efficiency and safety so as to generate performance and safety analysis information;

S703: based on the preferred formation and parameter data, a sequestration operation flow, an expected goal, emergency response measures, and a long-term monitoring plan are planned, and potential changes are continuously optimized to generate a sequestration solution.

In the S701 substep, real-time data and simulation verification records collected in the sealing process are summarized through data aggregation and analysis, statistical analysis methods such as analysis of variance (ANOVA) and regression analysis are utilized to comprehensively evaluate the sealing parameters such as injection rate, pressure and the influence of physical and chemical properties of stratum on sealing efficiency and safety, in the process, the data are aggregated in a structured form and comprise time sequence data, parameter change records and simulation results, the integrity of the data and the accuracy of analysis are ensured, and through the series of fine analysis, performance and safety analysis information is generated, and the specific influence degree and safety risk evaluation of each parameter on sealing effect are detailed in a document form, so that basis is provided for further optimization of sealing strategies.

In S702, based on performance and safety analysis information, a weighted scoring method is adopted to screen and determine stratum with optimal sealing efficiency and safety and corresponding injection parameters, physical and chemical characteristics of the stratum, optimization of injection parameters and comprehensive scoring of sealing efficiency and safety are comprehensively considered, the stratum and optimal injection parameter combination which is most suitable for sealing is accurately identified by distributing weight to each factor and calculating comprehensive scoring, and the generated optimal stratum and parameter data file records the screening process, scoring standard and finally selected stratum and parameters in detail, so that guidance is provided for the execution of sealing operation.

In the sub-step S703, based on the preferred stratum and parameter data, a sealing operation flow, an expected target, an emergency response measure and a long-term monitoring plan are planned, in this process, geological conditions, sealing efficiency and safety evaluation, and potential environmental impact are comprehensively considered, each step of the sealing operation is refined by adopting a system engineering method and a project management principle, including preparation before injection, monitoring of the injection process and long-term monitoring and evaluation after injection, and the generated sealing scheme file details the standard flow, specific operation steps, risk management measure and long-term monitoring plan and target of the sealing operation, so that comprehensive planning and guidance are provided for implementing the sealing program, and smooth proceeding of the sealing operation and long-term stability of the sealing effect are ensured.

Referring to fig. 9, an optimization design system for carbon dioxide geological sequestration parameters is used for executing the optimization design method for carbon dioxide geological sequestration parameters, and the system includes: the system comprises a geological data processing module, a geological characteristic prediction module, a sealing parameter optimization module, a sealing process monitoring module, an adjustment strategy refinement module and a simulation verification and iteration module;

The geological characteristic prediction module is used for performing prediction analysis on physical and chemical characteristics of stratum based on a standardized geological data set by adopting a support vector machine, verifying the accuracy and generalization capability of a model by utilizing a cross verification method, and generating a geological characteristic prediction model;

the sealing parameter optimization module optimizes sealing parameters based on a geological characteristic prediction model by adopting a genetic algorithm and a particle swarm optimization algorithm, including injection rate and pressure, and captures an optimal parameter combination to generate a preliminary parameter optimization configuration;

The sealing process monitoring module is based on preliminary parameter optimization configuration, adopts a time sequence analysis and isolation forest algorithm to monitor the sealing process in real time and detect abnormality, and matches the change of geological conditions and sealing effects to generate a dynamic adjustment strategy;

The geological data processing module ensures the quality and consistency of basic data of the sealing parameter optimization, standardized and accurate input data is provided for subsequent model training and analysis by executing data cleaning, missing value processing, data normalization and feature selection, the accuracy of geological characteristic prediction is improved, and data support is provided for the optimization of the sealing parameter.

The application of the geological characteristic prediction module ensures that the prediction of the physical and chemical characteristics of the stratum is more accurate, the use of the support vector machine and the verification process of the cross verification method not only improve the accuracy of the model, but also ensure the generalization capability of the model, so that the optimization of the sealing parameters is based on more accurate geological information.

The design of the sealing parameter optimization module realizes the efficient optimization of sealing parameters such as injection rate and pressure by combining a genetic algorithm and a particle swarm optimization algorithm, and the optimization not only captures the optimal parameter combination, but also considers the diversity of geological conditions, ensures the optimal effect of sealing operation under different geological backgrounds and reduces environmental risks.

The dynamic adaptability and the fine adjustment capability of the system are further enhanced by the sealing process monitoring module and the adjustment strategy refinement module, the sealing process can respond to potential risks in time by real-time monitoring and anomaly detection, continuity and safety of sealing operation are guaranteed, and the application of global sensitivity analysis and Gaussian process regression ensures that sealing parameters and adjustment strategies can be accurately adjusted according to different geological conditions.

The design of the simulation verification and iteration module ensures the scientificity and feasibility of the sealing scheme through the computational fluid dynamics and the dynamic feedback mechanism, and ensures that the sealing scheme can adapt to the change of the environment and geological conditions through continuous simulation verification and iteration optimization.

The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.

Claims

1. The optimization design method of the carbon dioxide geological sequestration parameter is characterized by comprising the following steps of:

Based on the existing geological data, predicting physical and chemical characteristics of the stratum by adopting a support vector machine, determining an optimal sealed stratum according to a prediction result, and generating a geological characteristic prediction model;

based on the geological characteristic prediction model, adopting a genetic algorithm and a particle swarm optimization algorithm to perform preliminary optimization of the sealing parameters, including injection rate and pressure, capturing key parameter combinations, and generating preliminary optimized parameter configuration;

Based on the preliminary optimization parameter configuration, adopting a time sequence analysis and isolation forest algorithm to monitor the sealing process in real time and adjust parameters, and matching the change of geological conditions and sealing effects to generate a dynamic adjustment strategy;

Based on the dynamic adjustment strategy, adopting a Sobol sequence method and a proxy model, evaluating the performance of the sealing parameters under the differential condition, and continuously optimizing the adjustment strategy to generate a fine adjustment strategy;

based on the refinement and adjustment strategy, adopting computational fluid dynamics, and verifying the optimization results of the adjustment strategy and the sealing parameters by simulating physical reaction and chemical reaction in the sealing process to generate a simulation verification record;

Based on the simulation verification record, a feedback circulation mechanism is established, and according to real-time data and simulation results in the sealing process, the sealing parameters and the dynamic adjustment strategy are iteratively optimized to generate feedback circulation measures;

and selecting optimal stratum, injection parameters and emergency response measures based on the feedback circulation measures, and verifying the efficiency and safety of the sealing process to generate a sealing scheme.

2. The method of claim 1, wherein the geological characteristic prediction model comprises a permeability rating, a porosity percentage, and a chemical stability index of the formation, the preliminary optimization parameter configuration comprises a determined carbon dioxide injection rate range, a pressure threshold, and an optimal injection temperature, the dynamic adjustment strategy comprises an injection rate step size adjusted in real time, a pressure adjustment interval, and an injection strategy change according to geological feedback, the feedback loop measure comprises a parameter adjustment rule, and a periodic effect evaluation schedule, and the preservation scheme comprises a selected formation depth and position, an optimized injection rate and pressure set value, and an emergency operation flow under differential geological emergencies.

3. The method for optimizing the design of carbon dioxide geological sequestration parameters according to claim 1, wherein the steps of predicting physical and chemical characteristics of the stratum based on the existing geological data by using a support vector machine, determining an optimal sequestration stratum according to the prediction result, and generating a geological characteristic prediction model are specifically as follows:

Based on the existing geological data, performing data processing, including missing value processing, data normalization and feature selection, matching with the input standard of a support vector machine, and generating processed geological data;

Training by using a support vector machine based on the processed geological data, learning the relation between physical and chemical characteristics of the stratum, and predicting the stratum characteristics to generate a stratum prediction model;

And analyzing and evaluating the sequestration potential of the stratum based on the stratum prediction model, determining the optimal carbon dioxide sequestration stratum, and generating a geological characteristic prediction model.

4. The method for optimizing and designing carbon dioxide geological sequestration parameters according to claim 1, wherein the step of performing preliminary optimization of the sequestration parameters, including injection rate and pressure, capturing key parameter combinations, and generating preliminary optimized parameter configuration is specifically as follows:

Defining an optimization problem with the sealing efficiency and the safety as targets based on the geological characteristic prediction model, setting an objective function, and generating a defined optimization objective function;

based on the defined optimization objective function, applying a genetic algorithm to search for the sealing parameters, including injection rate and pressure, identifying potential optimization combinations, and generating an optimized parameter set;

Based on the optimized parameter set, a particle swarm optimization algorithm is used for refining a parameter optimization process, and the optimal injection rate and pressure combination is captured to generate a preliminary optimized parameter configuration.

5. The method for optimizing and designing carbon dioxide geological sequestration parameters according to claim 1, wherein based on the preliminary optimization parameter configuration, a time sequence analysis and isolation forest algorithm is adopted to perform real-time monitoring and parameter adjustment of the sequestration process, and the steps of matching the geological conditions with the changes of the sequestration effect and generating a dynamic adjustment strategy are specifically as follows:

Setting real-time data acquisition parameters based on the preliminary optimization parameter configuration, and collecting key parameters in the sealing process, including time series data of injection rate and pressure, so as to generate a real-time monitoring data set;

based on the real-time monitoring data set, a time sequence analysis method is applied to analyze the change trend and the periodic mode of parameters along with time, and the future parameter change is predicted to generate a parameter trend analysis record;

Based on the parameter trend analysis record, an isolated forest algorithm is adopted to identify abnormal data points including unexpected sudden pressure increase, the sealing parameters are adjusted, and the dynamic adjustment strategy is generated by matching the geological conditions and the change of the sealing effect.

6. The method for optimizing and designing carbon dioxide geological sequestration parameters according to claim 1, wherein based on the dynamic adjustment strategy, the performance of the sequestration parameters is evaluated under the differential condition by adopting a Sobol sequence method and a proxy model, and the adjustment strategy is continuously optimized, so that the step of generating the fine adjustment strategy is specifically as follows:

Defining a differential condition scene of the simulated sealing operation based on the dynamic adjustment strategy, wherein the differential condition scene comprises different geological characteristics, injection rate and pressure change range, and generating a simulated scene set;

Based on the simulation scene set, a Sobol sequence method is applied to carry out global sensitivity analysis, and judging which factors in the sealing parameters have key influence on the sealing effect to generate sensitivity analysis information;

based on the sensitivity analysis information, simulating and optimizing the sealing parameters by using a proxy model, and adjusting the sealing strategy to generate a fine adjustment strategy.

7. The method for optimizing the carbon dioxide geological sequestration parameter according to claim 1, wherein the step of generating the simulated verification record by simulating physical and chemical reactions in the sequestration process by adopting computational fluid dynamics based on the policy of refinement adjustment comprises the steps of:

based on the strategy of refinement adjustment, adopting computational fluid dynamics to set simulated initial conditions, including physical properties, chemical compositions and optimized sequestration parameters of the sequestration stratum, and generating simulated initial conditions;

Based on the simulated initial conditions, carrying out dynamic simulation of the sequestration process, including diffusion of carbon dioxide in the stratum, reaction path analysis and influence on sequestration effect, and generating a dynamic simulation result;

Based on the dynamic simulation result, analyzing the effect difference before and after the adjustment of the sealing parameters, evaluating the effectiveness of the refinement adjustment strategy, comparing the simulation result with an expected target, and generating a simulation verification record.

8. The method for optimizing and designing carbon dioxide geological sequestration parameters according to claim 1, wherein the steps of establishing a feedback loop mechanism based on the simulated verification record, iteratively optimizing the sequestration parameters and dynamically adjusting strategies according to real-time data and simulation results of the sequestration process, and generating feedback loop measures are specifically as follows:

Based on the simulation verification record, identifying key influence factors and potential risk points in the simulation process, evaluating the influence of the sealing parameters on the sealing efficiency and the safety, and generating a key influence factor analysis record;

based on the key influence factor analysis record, the data of the real-time sealing process is combined, and a time sequence analysis method is used for updating the sealing parameter adjustment strategy to generate an updated adjustment strategy;

And executing an iterative optimization flow based on the updated adjustment strategy, continuously adjusting and optimizing the sealing parameters, verifying the sealing effect and the safety, and generating a feedback circulation measure.

9. The method for optimizing the design of carbon dioxide geological sequestration parameters according to claim 1, wherein the steps of selecting optimal stratum, injection parameters and emergency response measures based on the feedback loop measures, verifying the efficiency and safety of the sequestration process, and generating a sequestration scheme are as follows:

based on the feedback loop measures, data aggregation and analysis are executed, wherein the data aggregation and analysis comprises summarizing real-time data and simulation verification records collected in the sealing process, and the statistical analysis method is utilized to evaluate the influence of parameters on sealing efficiency and safety so as to generate performance and safety analysis information;

Based on the performance and safety analysis information, screening and determining stratum with optimal sealing efficiency and safety and corresponding injection parameters by adopting a weighted scoring method, and generating preferable stratum and parameter data;

And planning a sealing operation flow, an expected target, emergency response measures and a long-term monitoring plan based on the preferred stratum and the parameter data, and continuously optimizing potential changes to generate a sealing scheme.

10. An optimization design system for carbon dioxide geological sequestration parameters, characterized in that the optimization design method for carbon dioxide geological sequestration parameters according to any one of claims 1 to 9 comprises: the system comprises a geological data processing module, a geological characteristic prediction module, a sealing parameter optimization module, a sealing process monitoring module, an adjustment strategy refinement module and a simulation verification and iteration module;