Intelligent reservoir simulation and safety prediction system

Abstract

The present invention proposes an intelligent reservoir simulation and safety prediction system, which includes a parameter setting module, a numerical simulation software generation module, a reservoir simulation and safety prediction module, an information interaction module, an application management module and a program modularization module for data interaction; the parameter setting module is used to obtain the numerical simulation parameters set by the user from the user end, and import the numerical simulation parameters into the numerical simulation software generation module; the numerical simulation parameters are configured on the human-computer interface in the parameter setting module, and the numerical simulation parameters include: well, reservoir, fluid and well network parameters; the well network parameters include well network type, well group size, well name and well number, well grid mapping, well location, well control radius, well grid influence range and well production data; after receiving the numerical simulation parameters, the numerical simulation software generation module combines the obtained numerical simulation parameters with a preset parameterized model and an intelligent optimization algorithm through a neural network model.

Description

Technical Field

[0001] The present invention belongs to the field of safety prediction, and in particular relates to an intelligent reservoir simulation and safety prediction system. Background Art

[0002] The intelligent reservoir simulation and safety prediction system is a comprehensive technology that integrates advanced information technology with geological and reservoir engineering knowledge, aiming to improve oilfield development efficiency and safety. Through high-precision numerical simulation, real-time monitoring, and data analysis, the system enables precise prediction and management of reservoir dynamics. However, despite significant progress in existing technologies for reservoir simulation and safety prediction, several shortcomings remain.

[0003] Current reservoir simulation technology primarily relies on numerical simulation methods. These methods establish detailed reservoir geological models, porosity and permeability models, and fluid property parameters, then utilize reservoir flow simulators to predict reservoir and individual well production and pressure dynamics. Although numerical simulation methods have been widely used in remaining oil studies and reservoir performance prediction, their results are highly dependent on reliable reservoir and fluid property parameters and a reservoir simulator appropriate for reservoir conditions. In practice, obtaining these parameters is often challenging, and improper sampling and testing methods can lead to inaccurate model predictions. Furthermore, the accuracy and applicability of numerical simulation methods are limited when dealing with heterogeneous reservoirs and complex geological structures.

[0004] Regarding security prediction, while smart oilfield development has made significant progress in data collection, transmission, and analysis, data security and privacy protection remain increasingly prominent. During data collection, transmission, and storage, smart oilfields face multiple challenges, including data leakage, malicious tampering, and the protection of personal privacy. Furthermore, differences in standards and protocols across technical fields hinder interoperability between equipment and systems, leading to inefficient data transmission and impacting the operational efficiency and performance of smart oilfield systems. Furthermore, the development of smart oilfields requires significant capital and resource investment, balancing construction costs with expected benefits. A shortage of technical talent and the rapid pace of technological advancement also pose challenges to smart oilfield development.

[0005] While intelligent reservoir simulation and safety prediction systems hold broad application prospects, existing technologies still have numerous shortcomings. To improve the precision of reservoir simulation and the accuracy of safety prediction, it is necessary to continuously develop new technologies, optimize numerical simulation methods, strengthen data security and privacy protection, and promote technical standardization and interoperability to achieve the sustainable development of intelligent oilfields. Summary of the Invention

[0006] This paper proposes an intelligent reservoir simulation and safety prediction system that addresses technical challenges in traditional reservoir simulation, including cumbersome parameter settings, inflexible numerical simulation software generation, low reservoir simulation and safety prediction accuracy, unintuitive information exchange, and inconvenient application management. By integrating multiple modules, it achieves intelligent management across the entire process, from parameter setting to results display.

[0007] The technical solution of the present invention is implemented as follows: the intelligent reservoir simulation and safety prediction system includes a parameter setting module, a numerical simulation software generation module, a reservoir simulation and safety prediction module, an information interaction module, an application management module and a program modularization module for data interaction;

[0008] The parameter setting module is used to obtain numerical simulation parameters set by the user from the user terminal and import the numerical simulation parameters into the numerical simulation software generation module; the numerical simulation parameters are configured in the human-computer interface of the parameter setting module, and the numerical simulation parameters include: well, reservoir, fluid and well network parameters; the well network parameters include well pattern, well group size, well name and number, well grid mapping, well location, well control radius, well grid influence range and well production data;

[0009] After receiving the numerical simulation parameters, the numerical simulation software generation module combines the obtained numerical simulation parameters with a preset parameterized model and an intelligent optimization algorithm through a neural network model to generate associated numerical simulation software, and stores the associated numerical simulation software in a storage location set by the user;

[0010] The reservoir simulation and safety prediction module calls the associated numerical simulation software, and then simulates the reservoir parameters by importing the associated parameters and reservoir parameters in the numerical simulation software and performs dynamic tracking monitoring and early warning; wherein the reservoir parameters are simulated by combining the numerical simulation parameters set by the user and the reservoir parameters generated based on the machine learning algorithm;

[0011] The information interaction module calls the dynamic data and early warning data in the reservoir simulation and safety prediction module, and visually displays the data simulation results to the user;

[0012] The application management module is used to schedule the associated parameters and reservoir parameters imported from the reservoir simulation and safety prediction module, and simultaneously store and record the dynamic tracking monitoring and early warning information;

[0013] The program modularization module is used to integrate the parameter setting module, the numerical simulation software generation module, the reservoir simulation and safety prediction module, the information interaction module and the application management module and encapsulate them into executable program software.

[0014] Existing technologies often rely on manual input for parameter setting, which is inefficient and prone to errors. However, this system, through a parameter setting module, enables direct acquisition and import of numerical simulation parameters from the user, significantly improving the accuracy and efficiency of parameter setting. Existing technologies often use fixed numerical simulation software, making it difficult to adapt to changing reservoir conditions and user needs. However, this system, through a numerical simulation software generation module, combines neural network models, parameterized models, and intelligent optimization algorithms to dynamically generate relevant numerical simulation software and store it in a user-specified location, thereby improving the flexibility and adaptability of the numerical simulation software. Existing technologies typically only perform static simulations and lack dynamic tracking, monitoring, and early warning capabilities for reservoir simulation and safety prediction. However, this system, through its reservoir simulation and safety prediction module, enables dynamic simulation and tracking of reservoir parameters, and can issue timely warnings based on early warning data, thereby improving the safety and reliability of reservoir management. Regarding information exchange, existing technologies often use traditional data display methods such as tables and charts, which make it difficult to intuitively present reservoir simulation results. Through the information interaction module, this system can display dynamic data and warning data to users in a visual manner, thereby improving the intuitiveness and convenience of information interaction.

[0015] In terms of application management, existing technologies often lack a unified management platform, making it difficult to implement parameter scheduling and information backup and recording. However, this system, through the application management module, enables the scheduling of associated parameters and reservoir parameters, and simultaneously stores dynamic tracking, monitoring, and early warning information, thereby improving the convenience and traceability of application management.

[0016] As a preferred embodiment, the numerical simulation software generation module is also provided with a data analysis and inspection module, a data cleaning module, a feature engineering module and a feature selection module, wherein the data analysis and inspection module is used to check whether the input numerical simulation parameters meet the parameter requirements of the preset neural network model; the data cleaning module is used to delete redundant information in the input numerical simulation parameters and divide the input numerical simulation parameters into a training set and a validation set; the feature engineering module is used to perform feature engineering processing on the training set data; the feature engineering processing includes data binning, data encoding, data normalization, data splitting and data crossover; the feature selection module is used to perform feature selection on the training set and validation set after data splitting based on machine learning algorithm and feature correlation algorithm to obtain optimal numerical simulation parameters.

[0017] As a preferred embodiment, the feature selection module performs feature selection on the training set and validation set by first analyzing the data type and constructing a feature dictionary based on the variable characteristics and the classification data, dividing the continuous variables into numerical and categorical types; then, based on the linear regression algorithm, continuous variables are screened to obtain the sensitivity factors of the continuous variables, and according to the feature dictionary, a multivariate regression model is established for each continuous variable, and the sensitive factors are screened based on the R-square indicator; finally, based on the random forest algorithm, categorical variables are screened to obtain the sensitivity factors of the categorical variables, a decision tree is established and its corresponding decision weights are assigned.

[0018] As a preferred embodiment, the reservoir simulation and safety prediction module also includes a dynamic analysis module and a dynamic monitoring module. The dynamic analysis module is used to dynamically track the reservoir, regularly update the associated numerical simulation software parameters, and import the dynamic tracking data into the application management module for backup; the dynamic monitoring module is used to monitor the reservoir and obtain reservoir monitoring data; the associated numerical simulation software, reservoir monitoring data and machine learning algorithm are coupled to obtain real-time dynamic reservoir safety warnings.

[0019] As a preferred embodiment, the dynamic analysis module decomposes the calculation process of the numerical simulation parameters set by the user to obtain decomposition parameters, performs calculations based on the decomposition parameters, and the reservoir simulation and safety prediction module transmits the calculation results of the decomposition parameters to the parameter setting module and the information interaction module for the user to view.

[0020] As a preferred embodiment, the dynamic monitoring module screens the numerical simulation parameters set by the user to obtain the selected preferred numerical simulation parameters, performs data preprocessing on the preferred numerical simulation parameters to obtain preprocessed data; wherein the data preprocessing includes data cleaning, data reduction, feature extraction, feature binning and feature selection; performs intelligent decision-making operation on the preprocessed data according to a preset machine learning algorithm to obtain the intelligent decision-making operation result, monitors the reservoir according to the intelligent decision-making operation result, and obtains reservoir monitoring data.

[0021] By adopting the above technical solution, the present invention achieves the following beneficial effects: Through intelligent processing in the parameter setting module, the possibility of manual intervention and erroneous input is greatly reduced, thereby improving the accuracy and efficiency of parameter setting. The dynamic generation function of the numerical simulation software generation module allows for the flexible generation of suitable numerical simulation software based on different reservoir conditions and user needs, thereby improving the accuracy and adaptability of numerical simulations. This not only reduces the cost of numerical simulations but also improves their efficiency and precision. Dynamic tracking, monitoring, and early warning functions enable the timely identification and resolution of potential safety hazards, thereby preventing accidents. This not only ensures the safety of reservoir management but also improves reservoir recovery efficiency and economic benefits. Through a visual data display, users can intuitively understand reservoir simulation results and early warning information, making decision-making and management more convenient. This not only improves the efficiency and accuracy of information exchange but also reduces information misunderstandings and communication costs. Through a unified management platform and backup record function, the scheduling of associated parameters and reservoir parameters and the backup record of information are achieved. This not only improves the convenience and traceability of application management but also provides strong data support for subsequent reservoir management. This intelligent reservoir simulation and safety prediction system has significant benefits compared to existing technologies, not only improving the efficiency and accuracy of reservoir management, but also ensuring the safety and reliability of reservoir management. This is of great significance for promoting the development of the oil industry and improving the efficiency of oil resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 This is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] Example:

[0026] like Figure 1As shown, the Intelligent Reservoir Simulation and Safety Prediction System is a comprehensive reservoir management tool that integrates advanced information technology, artificial intelligence algorithms, and numerical simulation techniques. Through data interaction between multiple modules, the system implements a complete workflow from parameter setting to result presentation, providing an efficient, accurate, and reliable solution for reservoir management. The following details the system's operating principles and illustrates its workflow using a specific work scenario.

[0027] The working principle of the intelligent reservoir simulation and safety prediction system is mainly based on the following core modules and their data interaction:

[0028] Parameter Setting Module: This module is the entry point to the system and is responsible for obtaining user-defined numerical simulation parameters from the user. These parameters include detailed information about wells, reservoirs, fluids, and well networks, such as well type, location, control radius, and production data. Users configure these parameters through the human-computer interface to ensure accurate and targeted simulations.

[0029] Numerical Simulation Software Generation Module: After receiving the numerical simulation parameters from the parameter setting module, this module uses a neural network model to combine these parameters with a pre-set parameterized model and intelligent optimization algorithm to dynamically generate the associated numerical simulation software. This process fully leverages the self-learning and optimization capabilities of artificial intelligence, enabling the generated numerical simulation software to better adapt to different reservoir conditions and user needs. The generated software is then stored in a user-specified location for easy access and subsequent simulations.

[0030] Reservoir Simulation and Safety Prediction Module: This module is the core of the system, responsible for simulating reservoir parameters using associated numerical simulation software. During the simulation process, the module imports the associated parameters within the numerical simulation software and reservoir parameters generated using machine learning algorithms to perform dynamic reservoir simulation. Furthermore, this module also provides dynamic tracking, monitoring, and early warning capabilities, enabling real-time monitoring of reservoir status and timely identification and warning of potential safety hazards.

[0031] Information Interaction Module: This module is responsible for accessing dynamic data and warning data from the reservoir simulation and safety prediction modules and presenting this data to users in a visual manner. Through intuitive data display, users can easily understand reservoir simulation results and warning information, allowing them to make more accurate decisions.

[0032] Application Management Module: This module is primarily used to schedule and manage the associated parameters and reservoir parameters imported into the Reservoir Simulation and Safety Prediction Modules. It also simultaneously records dynamic tracking, monitoring, and early warning information, providing strong data support for subsequent reservoir management.

[0033] Program modularization module: This module integrates and encapsulates the above modules to form an executable program software. Through modular design, the system has good scalability and maintainability, and can easily adapt to future technology upgrades and changes in user needs.

[0034] The following is a specific work scenario to illustrate the workflow of the intelligent reservoir simulation and safety prediction system:

[0035] Imagine an oilfield planning a new production project. Detailed simulation and prediction of the target reservoir are needed to ensure the safety and economic viability of the extraction plan. The project team decides to use an intelligent reservoir simulation and safety prediction system to accomplish this task.

[0036] First, the project team entered the relevant numerical simulation parameters for the target reservoir through the parameter setting module's human-machine interface. These parameters included well network parameters such as well type, location, and control radius, as well as reservoir parameters such as lithology and permeability. The team also configured fluid type and production data based on the requirements of the production plan.

[0037] After parameter settings are completed, the system automatically passes these parameters to the numerical simulation software generation module. This module utilizes a neural network model and intelligent optimization algorithms, combined with a pre-set parameterized model, to dynamically generate numerical simulation software tailored to the target reservoir. The generated software is stored in a location designated by the project team for easy access later.

[0038] Next, the Reservoir Simulation and Safety Prediction module invoked the generated numerical simulation software and began simulating the target reservoir. During the simulation, the module imported the correlation parameters within the numerical simulation software and the reservoir parameters generated by the machine learning algorithm to simulate the reservoir's dynamic changes in detail. Simultaneously, the module monitored the reservoir's status in real time and, based on pre-set warning rules, promptly identified potential safety hazards.

[0039] The information interaction module visually displayed these simulation results and warning information to the project team. This intuitive data display enabled the team to clearly understand the dynamic changes and potential risks of the target reservoir, thereby formulating a more reasonable extraction plan. In the application management module, the project team scheduled and managed the associated parameters and reservoir parameters imported during the simulation process. Simultaneously, they recorded dynamic tracking, monitoring, and warning information, providing strong data support for subsequent reservoir management. With the help of the intelligent reservoir simulation and safety prediction system, the project team successfully completed the simulation and prediction tasks for the target reservoir. They not only obtained detailed reservoir dynamic data, but also identified potential safety hazards and promptly formulated corresponding countermeasures. This not only improved the safety and economic efficiency of the extraction plan but also provided strong technical support for subsequent reservoir management.

[0040] The intelligent reservoir simulation and safety prediction system, through its advanced operating principles and modular design, provides an efficient, accurate, and reliable solution for reservoir management. In specific work scenarios, the system can help project teams quickly complete reservoir simulation and prediction tasks, providing strong technical support for the development and implementation of production plans.

[0041] The numerical simulation software generation module is also provided with a data analysis and inspection module, a data cleaning module, a feature engineering module and a feature selection module, wherein the data analysis and inspection module is used to check whether the input numerical simulation parameters meet the parameter requirements of the preset neural network model; the data cleaning module is used to delete redundant information in the input numerical simulation parameters and divide the input numerical simulation parameters into a training set and a verification set; the feature engineering module is used to perform feature engineering processing on the training set data; the feature engineering processing includes data binning, data encoding, data normalization, data splitting and data crossover; the feature selection module is used to perform feature selection on the training set and verification set after data splitting based on machine learning algorithm and feature correlation algorithm to obtain optimal numerical simulation parameters.

[0042] In the reservoir simulation project, the project team used an intelligent reservoir simulation and safety prediction system to generate numerical simulation software tailored to specific reservoirs. After entering the numerical simulation parameters, the data analysis and verification module first verified whether these parameters met the preset neural network model parameter requirements, ensuring the accuracy and effectiveness of subsequent processing. Next, the data cleaning module removed redundant information from the input parameters and divided the data into training and validation sets, providing a foundation for subsequent feature engineering and feature selection.

[0043] The feature engineering module performs in-depth processing on the training set data, including data binning, encoding, normalization, splitting, and cross-pollination, to improve data usability and the model's predictive capabilities. The feature selection module uses machine learning and feature correlation algorithms to select features from the processed data, deriving optimal numerical simulation parameters that best reflect reservoir characteristics, thereby improving the accuracy and adaptability of the numerical simulation software.

[0044] This implementation adds multiple steps to the numerical simulation software generation process, including data analysis and inspection, data cleaning, feature engineering, and feature selection, forming a complete data preprocessing and feature optimization process. The introduction of these steps not only improves data accuracy and usability but also optimizes parameter settings through feature selection, making the generated numerical simulation software more consistent with the actual characteristics of the reservoir. Furthermore, this implementation fully utilizes machine learning algorithms and feature correlation algorithms to enhance the intelligence of feature selection.

[0045] The feature selection module performs feature selection on the training set and validation set by first analyzing the data type and constructing a feature dictionary based on the variable characteristics and the classification data, dividing the continuous variables into numerical and categorical types; then screening the continuous variables based on the linear regression algorithm to obtain the sensitivity factors of the continuous variables, establishing a multivariate regression model for each continuous variable based on the feature dictionary, and screening the sensitive factors based on the R-square indicator; finally, screening the categorical variables based on the random forest algorithm to obtain the sensitive factors of the categorical variables, establishing a decision tree and assigning it corresponding decision weights.

[0046] During feature selection, the project team first constructed a feature dictionary based on data type, classifying continuous variables into numerical and categorical types. They then used a linear regression algorithm to screen continuous variables to identify sensitive factors. They then established a multivariate regression model for each continuous variable, further screening for sensitive factors based on the R-squared metric. Finally, they used a random forest algorithm to screen categorical variables, identifying their sensitive factors and establishing a decision tree model, assigning them corresponding decision weights.

[0047] This implementation utilizes a more sophisticated and intelligent approach to feature selection. By constructing a feature dictionary and categorizing continuous and categorical variables, it can more accurately identify characteristic variables that significantly impact reservoir simulation results. Furthermore, the use of linear regression and random forest algorithms for feature selection and decision tree model construction enhances the accuracy and intelligence of feature selection. This feature selection method, which integrates multiple machine learning algorithms, demonstrates significant innovation and practicality in the field of reservoir simulation.

[0048] The reservoir simulation and safety prediction module also includes a dynamic analysis module and a dynamic monitoring module. The dynamic analysis module is used to dynamically track the reservoir, regularly update the associated numerical simulation software parameters, and import the dynamic tracking data into the application management module for backup; the dynamic monitoring module is used to monitor the reservoir and obtain reservoir monitoring data; the associated numerical simulation software, reservoir monitoring data and machine learning algorithm are coupled to obtain real-time dynamic reservoir safety warnings.

[0049] During reservoir simulation, the dynamic analysis module dynamically tracks the reservoir and regularly updates the parameters of the associated numerical simulation software. This dynamic tracking data is imported into the application management module for storage and subsequent analysis and utilization. Simultaneously, the dynamic monitoring module monitors the reservoir in real time and acquires reservoir monitoring data. The integration of the associated numerical simulation software, reservoir monitoring data, and machine learning algorithms generates real-time dynamic reservoir safety warnings, providing timely and effective decision support for reservoir management.

[0050] Compared to existing technologies, this implementation adds dynamic analysis and dynamic monitoring modules to the reservoir simulation and safety prediction process, enabling real-time tracking and monitoring of reservoirs and formations. This dynamic management approach not only improves the accuracy and timeliness of reservoir simulation but also provides more comprehensive and timely information support for reservoir management. Furthermore, this implementation leverages the strengths of machine learning algorithms and associated numerical simulation software to enhance the intelligence of reservoir safety early warnings.

[0051] The dynamic analysis module decomposes the calculation process of the numerical simulation parameters set by the user to obtain decomposition parameters, performs calculations based on the decomposition parameters, and the reservoir simulation and safety prediction module transmits the calculation results of the decomposition parameters to the parameter setting module and the information interaction module for the user to view. During the dynamic analysis process, the project team decomposes the calculation process of the numerical simulation parameters set by the user to obtain decomposition parameters. Then, calculations are performed based on these decomposition parameters, and the calculation results are transmitted to the parameter setting module and the information interaction module for the user to view and analyze. This decomposition and calculation method enables the project team to have a deeper understanding of the calculation process and results of the numerical simulation parameters, thereby providing a more accurate and reliable basis for reservoir simulation.

[0052] Compared with the existing technology, this implementation method adopts parameter decomposition and calculation methods during the dynamic analysis process, allowing the project team to more accurately grasp the calculation process and results of the numerical simulation parameters. This refined analysis method not only improves the accuracy of reservoir simulation, but also provides users with a more detailed and intuitive information display. In addition, this implementation method also provides real-time feedback of calculation results to users through the information interaction module, enhancing user participation and interactivity. During the dynamic monitoring process, the numerical simulation parameters set by the user are screened and optimized to obtain the preferred numerical simulation parameters. Then, these preferred parameters are subjected to data preprocessing, including data cleaning, data reduction, feature extraction, feature binning and feature selection steps. Next, the pre-processed data is subjected to intelligent decision-making operations using a preset machine learning algorithm to obtain intelligent decision-making operation results. Finally, the reservoir is monitored in real time based on these calculation results to obtain reservoir monitoring data.

[0053] This implementation utilizes more sophisticated and intelligent data processing and decision-making methods during dynamic monitoring. By screening and optimizing numerical simulation parameters, performing data preprocessing, and implementing intelligent decision-making operations, this implementation more accurately identifies reservoir trends and potential risks. Furthermore, the use of machine learning algorithms for intelligent decision-making improves the accuracy and timeliness of monitoring. This dynamic monitoring approach, which integrates multiple technologies and methods, demonstrates significant innovation and practicality in the field of reservoir management.

[0054] The dynamic monitoring module screens the numerical simulation parameters set by the user to obtain the selected preferred numerical simulation parameters, performs data preprocessing on the preferred numerical simulation parameters to obtain preprocessed data; wherein the data preprocessing includes data cleaning, data reduction, feature extraction, feature binning and feature selection; performs intelligent decision-making operation on the preprocessed data according to a preset machine learning algorithm to obtain the intelligent decision-making operation result, monitors the reservoir according to the intelligent decision-making operation result, and obtains reservoir monitoring data.

[0055] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Intelligent reservoir simulation and safety prediction system, characterized by: It includes data interaction among parameter setting module, numerical simulation software generation module, reservoir simulation and safety prediction module, information interaction module, application management module and program modularization module; The parameter setting module is used to obtain the numerical simulation parameters set by the user from the user terminal and import the numerical simulation parameters into the numerical simulation software generation module; The numerical simulation parameters are configured on the human-computer interface in the parameter setting module. The numerical simulation parameters include: well, reservoir, fluid and well network parameters; the well network parameters include well pattern, well group size, well name and number, well grid mapping, well location, well control radius, well grid influence range and well production data; After receiving the numerical simulation parameters, the numerical simulation software generation module combines the obtained numerical simulation parameters with a preset parameterized model and an intelligent optimization algorithm through a neural network model to generate associated numerical simulation software, and stores the associated numerical simulation software in a storage location set by the user; The reservoir simulation and safety prediction module calls the associated numerical simulation software, and then simulates the reservoir parameters by importing the associated parameters and reservoir parameters in the numerical simulation software and performs dynamic tracking monitoring and early warning; wherein the reservoir parameters are simulated by combining the numerical simulation parameters set by the user and the reservoir parameters generated based on the machine learning algorithm; The information interaction module calls the dynamic data and early warning data in the reservoir simulation and safety prediction module, and visually displays the data simulation results to the user; The application management module is used to schedule the associated parameters and reservoir parameters imported from the reservoir simulation and safety prediction module, and simultaneously store and record the dynamic tracking monitoring and early warning information; The program modularization module is used to integrate the parameter setting module, the numerical simulation software generation module, the reservoir simulation and safety prediction module, the information interaction module and the application management module and encapsulate them into executable program software.

2. The intelligent reservoir simulation and safety prediction system according to claim 1, characterized in that: The numerical simulation software generation module is also provided with a data analysis and inspection module, a data cleaning module, a feature engineering module and a feature selection module, wherein the data analysis and inspection module is used to check whether the input numerical simulation parameters meet the parameter requirements of the preset neural network model; the data cleaning module is used to delete redundant information in the input numerical simulation parameters and divide the input numerical simulation parameters into a training set and a verification set; the feature engineering module is used to perform feature engineering processing on the training set data; the feature engineering processing includes data binning, data encoding, data normalization, data splitting and data crossover; the feature selection module is used to perform feature selection on the training set and verification set after data splitting based on machine learning algorithm and feature correlation algorithm to obtain optimal numerical simulation parameters.

3. The intelligent reservoir simulation and safety prediction system according to claim 2, characterized in that: The feature selection module performs feature selection on the training set and validation set by first analyzing the data type and constructing a feature dictionary based on the variable characteristics and the classification data, dividing the continuous variables into numerical and categorical types; then screening the continuous variables based on the linear regression algorithm to obtain the sensitivity factors of the continuous variables, establishing a multivariate regression model for each continuous variable based on the feature dictionary, and screening the sensitive factors based on the R-square indicator; finally, screening the categorical variables based on the random forest algorithm to obtain the sensitive factors of the categorical variables, establishing a decision tree and assigning it corresponding decision weights.

4. The intelligent reservoir simulation and safety prediction system according to claim 1, wherein: The reservoir simulation and safety prediction module also includes a dynamic analysis module and a dynamic monitoring module. The dynamic analysis module is used to dynamically track the reservoir, regularly update the associated numerical simulation software parameters, and import the dynamic tracking data into the application management module for backup. The dynamic monitoring module is used to monitor the reservoir and obtain reservoir monitoring data; the associated numerical simulation software, reservoir monitoring data and machine learning algorithm are coupled to obtain real-time dynamic reservoir safety warnings.

5. The intelligent reservoir simulation and safety prediction system according to claim 4, characterized in that: The dynamic analysis module decomposes the calculation process of the numerical simulation parameters set by the user to obtain decomposition parameters, and performs calculations based on the decomposition parameters. The reservoir simulation and safety prediction module transmits the calculation results of the decomposition parameters to the parameter setting module and the information interaction module for the user to view.

6. The intelligent reservoir simulation and safety prediction system according to claim 4, characterized in that: The dynamic monitoring module screens the numerical simulation parameters set by the user to obtain the screened optimal numerical simulation parameters, and performs data preprocessing on the optimal numerical simulation parameters to obtain preprocessed data; Data preprocessing includes data cleaning, data reduction, feature extraction, feature binning and feature selection; intelligent decision-making operations are performed on the preprocessed data according to a preset machine learning algorithm to obtain intelligent decision-making operation results, and the reservoir is monitored according to the intelligent decision-making operation results to obtain reservoir monitoring data.

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