A geothermal energy development decision management method and system

Abstract

This invention relates to the field of artificial intelligence technology, addressing the technical problems of existing technologies that tend to overlook the limitations of physical laws, fail to capture complex coupling effects, and struggle to support large-scale strategy exploration, leading to overpressure exploitation of geothermal energy due to local optima. Specifically, it relates to a development decision-making management method and system for geothermal energy. The method involves: collecting multi-source geothermal data from the geothermal system; preprocessing and integrating this data to obtain a geothermal-related database; utilizing parameter uncertainty sampling to generate a high-fidelity dataset covering multiple scenarios; and constructing a physical perception simulator that, while maintaining the coupling of physical laws, reduces the simulation time from hours to seconds. Through the connection and interaction between DQN and the physical perception simulator, dynamic optimization of development strategies under physical constraints is achieved, avoiding problems such as models getting trapped in local optima or violating physical constraints leading to overpressure exploitation.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a development decision-making management method and system for geothermal energy. Background Technology

[0002] Artificial intelligence is a new technological science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Currently, the integration of artificial intelligence and geothermal energy development has entered a stage of in-depth practice. The core objective is to solve three major pain points in traditional development through technological breakthroughs: geological complexity, difficulty in dynamic prediction, and decision-making lag. Existing technologies generally use sensor networks to collect data such as temperature, pressure, and flow rate in real time. Combined with remote sensing data and geological exploration data, a multi-dimensional dataset is constructed. By standardizing or normalizing the units of measurement to unify the units of measurement, the impact of dimensional differences on model performance is eliminated. Data processing is then performed using algorithms such as random forests to analyze the relationship between geological parameters and heat production efficiency.

[0003] However, existing technologies have some problems. They are prone to ignoring the limitations of physical laws when performing model prediction processing, and they are also prone to oversimplification that fails to capture complex coupling effects. Furthermore, the long time required for a single simulation makes it difficult to support large-scale strategy exploration, leading to the problem of overpressured geothermal energy extraction due to local optima. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a development decision-making management method and system for geothermal energy. It solves the technical problems of existing technologies, which tend to overlook the limitations of physical laws, fail to capture complex coupling effects, and struggle to support large-scale strategy exploration, leading to overpressure extraction of geothermal energy due to local optima.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a development decision management method for geothermal energy, the steps of which are: collecting multi-source geothermal data in the geothermal system, and obtaining a geothermal related database through preprocessing and integration; Spatial analysis of reservoir parameters is performed based on geothermal correlation data to obtain an optimized spatial distribution map of reservoir parameters. Dynamic simulation of mining is performed on the optimized reservoir parameter spatial distribution atlas to obtain an evolution prediction report; Based on the evolutionary prediction report, the optimal solution set, multidimensional data, and risk assessment results are obtained through multi-objective collaborative optimization and risk assessment. Interactive processing and early warning monitoring are performed based on the optimal solution set, multidimensional data, and risk assessment results to obtain interactive chart sets and early warning information.

[0006] Preferably, multi-source geothermal data is collected from the geothermal system and preprocessed and integrated, including: cleaning data based on multi-source geothermal data; Spatiotemporally aligned standardized data is obtained by registration and standardization based on the cleaning data; The spatiotemporally aligned and standardized data are correlated and stored to obtain a geothermal correlation database.

[0007] Preferably, a training sample set is generated by sample pairing based on geothermal correlation data; Convolutional layers are constructed based on the training sample set, and spatial depth feature maps are obtained through forward propagation of the convolutional layers. Parameter prediction and spatial kriging interpolation optimization are performed on the spatial depth feature map to obtain an optimized spatial distribution map of reservoir parameters.

[0008] Preferably, parameter prediction and spatial kriging interpolation optimization are performed on the spatial depth feature map, including: constructing a splicing layer and splicing data with the same regional location in the geothermal correlation data to obtain geological and geothermal splicing features; A fusion layer is constructed, and feature fusion is performed based on geological and geothermal splicing features and spatial depth feature maps to obtain a fused feature vector; A prediction layer is constructed, and the fused feature vector is subjected to extreme gradient processing to obtain preliminary predicted values ​​of reservoir parameters. Geothermal measured values ​​were obtained, a model optimization layer was constructed, and spatial kriging interpolation optimization was performed based on the preliminary predicted values ​​of reservoir parameters to obtain an optimized spatial distribution map of reservoir parameters.

[0009] Preferably, dynamic simulation of mining is performed on the optimized reservoir parameter spatial distribution map, including: uncertainty sampling and coupled simulation based on the optimized reservoir parameter spatial distribution map to obtain a dynamic simulation dataset; Physical perception neural processing is performed on the dynamic simulation dataset to obtain a physical perception simulator; Obtain a reservoir network model from the optimized reservoir parameter spatial distribution map set, obtain geothermal-related constraints from multi-source geothermal data, and obtain an evolution prediction report through reward processing and model linkage inference.

[0010] Preferably, the evolution prediction report is obtained through reward processing and model linkage extrapolation, including: spatial reward processing of reservoir network model and geothermal related constraints to obtain enhanced environmental conditions; A DQN policy network is constructed based on a physical perception simulator and enhanced environmental conditions to obtain an optimized policy model. An evolution prediction report is obtained by performing inference and analysis based on an optimized strategy model and a physical perception simulator.

[0011] Preferably, the method involves multi-objective collaborative optimization and risk assessment based on the evolutionary prediction report, including: constructing a multi-objective optimization function based on the evolutionary prediction report; The non-dominated solution set is obtained by Pareto front search based on the multi-objective optimization function; Historical risk data and risk logic for geothermal development are obtained, a Bayesian risk assessment network is constructed, and a risk assessment model is derived. Inputting the non-dominated solution set into the risk assessment model yields the risk probability solution set; A comprehensive risk ranking is performed on the risk probability solution set to obtain the optimal solution set, multidimensional data, and risk assessment results.

[0012] Preferably, interactive processing and early warning monitoring are performed based on the optimal solution set, multidimensional data, and risk assessment results, including: data mapping of the optimal solution set, multidimensional data, and risk assessment results to obtain an interactive chart set; Real-time deviation rate and deviation threshold are calculated based on data deviation using interactive chart sets; Early warning information is obtained based on real-time deviation rate and deviation threshold, and then sent to the geothermal management center.

[0013] This technical solution also provides a system for applying the aforementioned development decision-making and management method for geothermal energy, the system comprising: The database module is used to collect multi-source geothermal data from the geothermal system and, through preprocessing and integration, obtain a geothermal-related database. The reservoir parameter module is used to perform spatial analysis of reservoir parameters based on geothermal correlation data and obtain an optimized spatial distribution map of reservoir parameters. The dynamic simulation module is used to perform dynamic simulation of mining based on the spatial distribution map of optimized reservoir parameters and obtain an evolution prediction report; The risk assessment module is used to obtain the optimal solution set, multidimensional data, and risk assessment results based on the evolution prediction report through multi-objective collaborative optimization and risk assessment. The early warning and monitoring module is used to perform interactive processing and early warning monitoring based on the optimal solution set, multidimensional data and risk assessment results, and obtain interactive chart sets and early warning information.

[0014] By employing the above technical solution, the present invention provides a development decision-making and management method and system for geothermal energy, which has at least the following beneficial effects: 1. This invention innovatively combines convolutional neural networks and extreme gradient boosting trees for comprehensive processing. First, it automatically extracts deep spatial features from geophysical data. Then, it concatenates these deep spatial features with traditional geological and geothermal features as input to the extreme gradient boosting tree. This method combines the powerful spatial feature learning ability with the excellent structured data processing and prediction accuracy of the extreme gradient boosting tree, achieving continuous and high-precision prediction of reservoir parameters. It significantly improves the accuracy and spatial resolution of predictions and reduces exploration uncertainty.

[0015] 2. This invention utilizes parameter uncertainty sampling to generate a high-fidelity dataset covering multiple scenarios. The constructed physical perception simulator, while maintaining the coupling with physical laws, reduces the time of a single simulation from several hours to seconds, providing an efficient environment for reinforcement learning. Through the connection and interaction between DQN and the physical perception simulator, the development strategy is dynamically optimized under physical constraints, avoiding problems such as the model getting stuck in local optima or violating physical constraints and resulting in over-pressure mining.

[0016] 3. This invention combines a non-dominated sorting genetic algorithm with a Bayesian network. The genetic algorithm efficiently searches in a vast solution space, directly optimizing multiple objectives and generating a non-dominated solution set. Meanwhile, a risk assessment model based on a Bayesian network evaluates the robustness of each solution under multiple risks in real time. This combination significantly improves the accuracy of risk quantification and enhances the scientific nature, comprehensiveness, and risk resistance of decision-making.

[0017] 4. This invention uses tools such as parallel coordinate graphs and radar charts to intuitively present the performance risk trade-off relationship of the solution, breaking through the limitations of traditional two-dimensional tables. Real-time deviation calculation can accurately capture the dynamic deviation trend of indicators such as instantaneous heat generation and pressure, significantly improving the response efficiency of operation and maintenance management. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating a development decision-making and management method for geothermal energy according to the present invention. Figure 2 This is a structural block diagram of a development decision management system for geothermal energy according to the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0020] Because existing technologies tend to overlook the limitations of physical laws, fail to capture complex coupling effects, and struggle to support large-scale strategy exploration, they result in technical problems related to geothermal energy overpressure extraction due to local optima. Please refer to [reference needed]. Figure 1 This embodiment provides a development decision-making and management method for geothermal energy, which can capture complex coupling effects based on the constraints of physical laws, significantly shorten simulation time, and prevent problems such as local optima and overpressure extraction. The steps of this method are as follows: S1. Collect multi-source geothermal data from the geothermal system, and obtain a geothermal correlation database through preprocessing and integration. In existing technologies, geothermal data comes from diverse sources and has varying formats, making it difficult to use directly. To address these issues, the specific implementation steps are as follows: S11. Cleaning data is obtained by cleaning multi-source geothermal data. Multi-source geothermal data includes geological structure data, geophysical exploration data, geothermal gradient data, historical drilling data, surface environmental data, and geothermal-related constraints, such as maximum number of wells and injection / production pressure limits. In this step, the 3σ principle in statistics is first applied, that is, by calculating the mean and standard deviation of each data feature, a reasonable range for the normal data distribution is determined, i.e., the interval between the mean and three times the standard deviation. Data exceeding this range are considered outliers and removed. Simultaneously, the box plot method is used. By calculating the upper quartile, median, lower quartile, and the difference between the interquartile range and the upper and lower quartiles of the dataset, the range within the box plot's bend line is identified, typically the lower quartile minus 1.5 times the interquartile range to the upper quartile. Data points outside the range of 1.5 times the interquartile range are considered outliers and removed. For example, for a geothermal gradient dataset of a geothermal region, if its average value is 30°C per 100 meters and its standard deviation is 5°C per 100 meters, then according to the 3σ principle, the normal data range is 15°C to 45°C per 100 meters, i.e., 30°C plus or minus 3 times 5°C. Geothermal gradient values ​​outside this range will be removed. At the same time, if the lower quartile of the dataset is 25°C per 100 meters, the upper quartile is 35°C per 100 meters, and the interquartile range is 10°C per 100 meters, then the box plot must be within the range of 10°C to 50°C per 100 meters, i.e., 25°C minus 1.5 times 10°C to 35°C plus 1.5 times 10°C. Geothermal gradient values ​​outside this range will also be identified as outliers and removed. The 3σ principle is a commonly used statistical method in statistical data and will not be elaborated here.

[0021] S12. Based on the cleaned data, spatiotemporally aligned standardized data is obtained through registration and standardization. In this step, georegistration is performed first. This step aims to unify geospatial data from different sources and coordinate systems into the same spatiotemporal coordinate system, ensuring that all data are accurately aligned in geospatial location and eliminating data misalignment caused by coordinate differences. After georegistration is completed, each feature in the dataset is then standardized using the Z-Score standardization method. The specific steps include first calculating the average of all data values ​​for the feature, then calculating its standard deviation, then subtracting the previously calculated average from each original data value of the feature to obtain the difference, and then dividing this difference by the standard deviation. That is, multiplying the result of subtracting the average from the original data value by the reciprocal of the standard deviation, which is equivalent to dividing by the standard deviation, and finally obtaining the standardized value.

[0022] S13. The spatiotemporally aligned and standardized data is associated and stored to obtain a geothermal association database. This step first matches data records with the same spatial location identifier from different data sources, much like finding pieces of a jigsaw puzzle that correspond to the same location and piecing them together. Through table joins, related data that were originally scattered across different data tables but belong to the same spatial location are integrated into a single record. Although no calculation formula is involved here, from the perspective of data integration logic, it can be understood as adding data items corresponding to the same spatial location from different data sources, that is, merging various related data into one place, ultimately obtaining a complete data record containing rich information. This integrated data is then stored in the geothermal association database. Table joins can be performed using a full join, a common data integration method, which will not be elaborated upon here. This invention constructs a unified, high-quality database through spatiotemporal alignment and standardization processing, providing reliable input data for subsequent algorithm models.

[0023] S2. Based on geothermal correlation data, perform spatial analysis of reservoir parameters to obtain an optimized spatial distribution atlas of reservoir parameters. Existing technologies typically rely on single geophysical inversion or statistical interpolation. Single geophysical inversion has non-unique solutions and high uncertainty, while statistical interpolation struggles to capture complex nonlinear relationships. To address these issues, the specific implementation steps are as follows: S21. Generate a training sample set based on geothermal correlation data through sample pairing. In this step, the overall data is first divided into different parts such as training set, validation set, and test set according to certain rules, including factors such as the location of the well, the depth range of the formation, or the data acquisition time. In the divided dataset, for each well location, the geophysical attribute data corresponding to the well location, such as seismic wave velocity and resistivity, is combined with the geological attribute data, such as lithology and porosity. This can be understood as generating a comprehensive feature vector in the form of superposition of these different types of feature data, representing the characteristics of the well location. The measured reservoir parameters corresponding to the well location, such as permeability and oil saturation, are used as the labels for pairing. Finally, each set of feature vectors and corresponding labels are combined into sample pairs, and all sample pairs are collected into a training sample set.

[0024] S22. Convolutional layers are constructed based on the training sample set. Spatial depth feature maps are obtained through the forward propagation of the convolutional layers. The data in the training sample set is organized into a data grid suitable for processing by the CNN model, such as a grid. The convolutional layer is the pre-trained CNN model, which contains structures such as convolutional layers and pooling layers. During the forward propagation of the model, taking the convolutional layer as an example, the convolutional kernel in the convolutional layer slides on the data grid with a certain stride. During the sliding process, the convolutional kernel performs multiplication operations with the data at the corresponding positions on the data grid. Then, the results of all multiplication operations are added together to obtain a convolutional value. This convolutional value represents the local depth feature map extracted after the convolution operation. Different convolutional kernels extract different local features. For example, some convolutional kernels may be better at capturing edge information in geological structures, while others may be more sensitive to geological texture features, such as the Sobel convolutional kernel. Pooling layers mainly play the role of dimensionality reduction and feature selection. They perform downsampling operations on the feature maps output by the convolutional layers. For example, max pooling selects the maximum value in each local region as the representative value of that region. This can reduce the amount of data while retaining the most significant feature information. After multiple layers of convolution and pooling operations, the original data grid is gradually transformed into a set of more representative and abstract feature maps. These feature maps are the final extracted spatial depth feature maps.

[0025] S23. Perform parameter prediction and spatial kriging interpolation optimization on the spatial depth feature map to obtain an optimized reservoir parameter spatial distribution map set. S231. Construct a splicing layer by splicing data from geothermal correlation data that are in the same regional location to obtain geological and geothermal splicing characteristics. In this step, geothermal correlation data is collected at each measurement point in the same region using certain analytical methods, such as rock sample analysis to obtain geological attributes such as lithology and mineral composition. The measurement point is the location of the geothermal well where the geothermal correlation data is obtained. Geological attribute data such as lithology, porosity, and permeability, as well as geothermal data such as temperature values ​​at different underground depths, are collected. By organizing and combining these data, the geological and geothermal splicing characteristics of the data points in the same region are obtained.

[0026] S232. Construct a fusion layer. Based on the geological and geothermal splicing features and the spatial depth feature map, feature fusion is performed to obtain a fused feature vector. In this step, feature information is first aggregated from the spatial neighborhood of a certain location in the spatial depth feature map. Here, feature aggregation can be understood as comprehensively considering the feature data of multiple locations in the neighborhood. For example, during aggregation, a weighted summation method can be used to assign different weights to the features of different locations in the neighborhood. The weights can be determined based on factors such as the distance from the predicted location. The closer the distance, the greater the weight may be. The features of each location in the neighborhood are multiplied by their corresponding weights and then added together to obtain the aggregated features. Then, the aggregated features are spliced ​​with the geological and geothermal splicing features. The final fused feature vector integrates the spatial depth features and the point-like geological and geothermal splicing features.

[0027] S233. Construct a prediction layer and perform extreme gradient processing on the fused feature vector to obtain preliminary predicted values ​​of reservoir parameters. Obtain labels from the training sample set, which contain reservoir parameter data for geothermal well points. First, train an XGBoost regression model using the fused feature vectors and corresponding labels of known well points. The XGBoost regression model consists of multiple regression trees. During training, the model continuously learns the relationship between the fused feature vector and the reservoir parameter labels. Specifically, each regression tree judges and calculates based on the input fused feature vector. By comparing and analyzing different feature values, the data is divided into different branch nodes, eventually reaching the leaf nodes. Each leaf node outputs a value. During prediction, for any input fused feature vector, the model allows all regression trees to process it separately. Each regression tree provides a predicted value. Finally, the predicted values ​​of all regression trees are added together to obtain the model's prediction of the reservoir parameters corresponding to the fused feature vector. For example, assuming there are 3 regression trees in the model, for a certain fused feature vector, the first regression tree gives a predicted value of 2, the second is 3, and the third is 4. Then the final predicted value of the model is 2+3+4=9. At the same time, the trained prediction layer is obtained. The preliminary predicted values ​​of reservoir parameters include three categories: rock physical properties, fluid occurrence state, and geological structural characteristics. Rock physical properties include the three-dimensional distribution of porosity obtained by micro-CT scanning or nuclear magnetic resonance technology, the anisotropic permeability measured by core flow experiments, and the elastic modulus and Poisson's ratio obtained based on rock mechanics tests. Fluid parameters include the crude oil viscosity-temperature curve measured by high-pressure physical property experiments, the spatial variation characteristics of formation water salinity, and the dynamic changes of oil saturation obtained by well logging interpretation and production data analysis. Geological structural parameters integrate the fault sealing of three-dimensional seismic interpretation, the sand body connectivity controlled by sedimentary microfacies, and the quantitative indicators of reservoir heterogeneity based on geological modeling. These data are also reflected in the training sample set.

[0028] S234. Obtain geothermal measured values, construct a model optimization layer, and perform spatial kriging interpolation optimization based on the preliminary predicted values ​​of reservoir parameters to obtain an optimized spatial distribution map of reservoir parameters. In this step, the preliminary predicted values ​​of reservoir parameters are input to provide an initial prediction result covering the entire study area, reflecting the approximate spatial distribution trend of reservoir parameters. Obtaining the geothermal measured values ​​for each geothermal well point provides accurate actual measurement data, which is crucial for verifying and optimizing the prediction results. Geological structural boundary data can be obtained through training sample sets combined with geological exploration methods, providing important geological constraints for subsequent interpolation processing. In terms of processing methods, the preliminary predicted values ​​of reservoir parameters are used as the trend surface. Then, within each geological zone divided by the geological structural boundary data, spatial kriging interpolation optimization is employed. Spatial kriging interpolation optimization comprehensively considers the relationship between known well point measured values ​​and the trend surface, combining the precise information of the measured values ​​with the overall trend of the trend surface through a specific weight allocation mechanism. The detailed steps are as follows: During the interpolation process, for... For each interpolation point, a weighting coefficient is calculated based on its spatial distance from known well points and its similarity to geological structural features. For example, the weighting coefficient is calculated by multiplying the spatial distance of a single well point by its similarity and then summing the spatial distances of all well points by their similarities. This weighting coefficient determines the degree of influence of the measured values ​​of known well points on the predicted value of that point. Ultimately, the predicted value of that point can be seen as the result of a weighted sum of the trend surface value at that point and the measured values ​​of known well points according to the weighting coefficient. The results of all well points are then integrated into an optimized reservoir parameter spatial distribution map. This invention innovatively combines convolutional neural networks and extreme gradient boosting trees for comprehensive processing. First, deep spatial features are automatically extracted from geophysical data. Then, these deep spatial features are concatenated with traditional geological and geothermal features as input to the extreme gradient boosting tree. This method combines powerful spatial feature learning capabilities with the excellent structured data processing and prediction accuracy of extreme gradient boosting trees, achieving continuous and high-precision prediction of reservoir parameters. This significantly improves the accuracy and spatial resolution of predictions and reduces exploration uncertainty.

[0029] S3. Perform dynamic simulation of the exploitation process on the optimized reservoir parameter spatial distribution map and obtain an evolution prediction report. Existing technologies tend to overlook the limitations of physical laws, are prone to oversimplification and failure to capture complex coupling effects, and are difficult to support large-scale strategy exploration due to the long time required for a single simulation. This can lead to overpressure exploitation of geothermal energy due to local optima. To solve these problems, the specific implementation steps are as follows: S31. Uncertainty sampling and coupled simulation are performed based on the optimized reservoir parameter spatial distribution map to obtain a dynamic simulation dataset. Since actual reservoir parameters exhibit uncertainty, such as permeability fluctuating within a certain range, we employ a parameter uncertainty sampling method. This involves extracting multiple representative parameter combinations from the optimized reservoir parameter spatial distribution map. For each key parameter, such as permeability and porosity, multiple values ​​are selected within their known reasonable range according to certain sampling rules, such as uniform sampling or normal sampling. These selected values ​​are then combined to form multiple parameter sets. For each parameter set, a small number of coupled numerical simulations of the entire process involving heat, water, and mechanical forces are run. During the simulation, each parameter set is used as the initial condition of the reservoir and input into the numerical simulation model. Simultaneously, different development schemes are set, such as different injection and production rates and well layouts. The numerical simulation model comprehensively considers the interactions between heat conduction, fluid flow, and rock mechanical deformation in the reservoir, simulating complex mathematical calculations and physical processes, such as TOUGH. The FLAC model is an open-source numerical simulation platform developed by Lawrence Berkeley National Laboratory in the United States. It predicts the production dynamics of reservoirs under specific parameter sets and development schemes, including indicators such as well production, pressure changes, and water cut. Finally, we combine each set of parameters, the corresponding development scheme, and the simulated production dynamics data to form a dynamic simulation dataset.

[0030] S32. Perform physical perception neural processing on the dynamic simulation dataset to obtain a physical perception simulator. In this step, a physical perception neural network, namely PINN, is first built. The parameter set and development scheme in the dynamic simulation dataset are used as input features to generate dynamic indicators, such as oil well production, pressure, and water cut, as potential output targets. During the training phase, the accuracy of prediction is measured not only by calculating the mean square error between the network's predicted values ​​and the real simulation data, i.e., the data fitting loss, but also by constructing control equations based on physical laws such as energy conservation and mass conservation. The residuals of these equations are calculated within the computational domain, i.e., the physical constraint loss. The data fitting loss and the physical constraint loss are added together by weighted coefficients to form a total loss function to guide the network parameter update. Through continuous iterative optimization, the total loss is minimized, and finally a well-trained PINN proxy simulator is obtained. It can accurately fit the existing simulation data and perform generalized predictions in accordance with physical laws.

[0031] S33. Obtain the reservoir network model from the optimized reservoir parameter spatial distribution map set, obtain geothermal-related constraints from geothermal multi-source data, and obtain an evolution prediction report through reward processing and model linkage inference; the optimized reservoir parameter spatial distribution map set predicts and generates numerical spatial distributions of parameters such as permeability and temperature. These data are stored and managed in the system in a discrete three-dimensional grid, i.e., in the reservoir grid model. Each grid cell contains a set of predicted attribute values. The geothermal-related constraints in the geothermal multi-source data are the physical constraints initially set based on the geological characteristics of the geothermal well points.

[0032] S331. Spatial reward processing is applied to the reservoir network model and geothermal-related constraints to obtain enhanced environmental conditions. In this step, key physical field information, such as pressure and temperature values ​​of each grid block, is extracted based on the reservoir grid model. Combined with geothermal-related constraints, such as the maximum number of wells and injection-production pressure limits, an action space is defined. For example, the action of opening or closing wells is limited to not exceeding the maximum number of wells, and the adjustment range of flow rate adjustments is constrained within the injection-production pressure limits. Simultaneously, the current pressure field, temperature field, and other physical quantities are combined into a state vector as the observation input for the agent. The reward function design integrates development goals and constraint satisfaction: the cumulative power generation is used as the basic reward item; if the action causes pressure or temperature to deviate from the safe level, a reward is given. For the entire range, the total reward is adjusted by multiplying by a penalty coefficient, such as multiplying the reward by 0.8 when the pressure exceeds the limit, or by adding a negative reward, such as deducting 5 points for every 1 MPa of pressure exceeding the limit. This results in a comprehensive reward value that balances efficiency and safety. For example, in the development of an oil reservoir, if the current status shows that the pressure in a certain area is close to the upper limit, and the agent chooses to reduce the water injection volume in that area, the system will first check whether the adjusted pressure still meets the constraints. If it is within a reasonable range, the system will calculate the basic reward, such as the score corresponding to the daily oil production, and then add extra points for the high pressure maintenance. If the pressure exceeds the limit after adjustment, the basic reward will be multiplied by a discount coefficient and the overpressure penalty points will be deducted. The final output includes the enhanced environmental conditions that include the status, the legality of the action, and the comprehensive reward.

[0033] S332. Construct a DQN policy network based on the physical perception simulator and enhanced environmental conditions to obtain an optimized policy model. This step first constructs an initial DQN policy network. In each training round, the initial DQN policy network generates an action probability distribution based on the current state, such as reservoir pressure distribution and well location status. Actions are selected according to probability, such as adjusting the water injection rate of a well. The actions are then input into the PINN physical perception simulator. The simulator, based on physical conservation laws such as mass conservation and energy conservation, advances the reservoir state evolution and returns the new state and corresponding reward value. For example, if the action increases oil production without exceeding pressure constraints, the reward is the base oil production gain multiplied by the efficiency improvement coefficient; if overpressure occurs, the reward is the base profit multiplied by the safety discount coefficient, and an overpressure penalty is deducted. The initial DQN policy network... The network compares the error between the Q-value of the current state action pair and the target Q-value. The Q-value is predicted by the neural network, and the target Q-value is calculated by adding the maximum predicted Q-value of the new state to the reward returned by the simulator. Gradient descent is used to update the network parameters, gradually reducing the prediction error. After multiple iterations, the initial DQN strategy network can directly output the optimal action based on the input state, such as the optimal injection and production scheme, achieving a dynamic balance between development benefits and physical constraints. For example, in the development of a low-permeability oil reservoir, the initial DQN strategy network caused local pressure exceedance due to excessive water injection. By reducing the probability of this action through a penalty mechanism, a segmented water injection combined with a periodic shut-in strategy was explored. The final trained strategy network reduced the pressure exceedance time by 60% while maintaining stable daily oil production, and the optimized initial DQN strategy network was used as the optimized strategy model.

[0034] S333. Based on the optimization strategy model and the physical sensing simulator, an evolution prediction report is obtained through inference and analysis. The dynamic simulation dataset contains initial development schemes, which include different well layouts, such as combinations of horizontal and vertical wells, injection and production strategies, such as comparisons between continuous and periodic water injection, or development scales, such as phased development and one-time development. These schemes must meet geothermal-related constraints. During the inference, for schemes requiring intelligent optimization, the optimization strategy model outputs the optimal action based on the current state, such as the initial pressure field and temperature field. The optimal action includes adjusting the injection and production rate of a certain well. The physical sensing simulator advances the state evolution based on physical conservation laws, such as the heat convection and conduction equation, and records the production at each step, such as daily oil production or power generation and temperature field data. For fixed strategy schemes, the physical sensing simulator is run directly for prediction. Finally, the production at all time steps under each scheme is accumulated to obtain the cumulative production. At the same time, the temperature field distribution at key time nodes is extracted and integrated into a system containing production curves, temperature curves, and temperature field data. This invention provides an evolution prediction report with temperature field evolution diagrams and comparative analysis. For example, in the development of a geothermal field, the initial scheme set includes two modes: single-well circulation development and multi-well linkage development. The multi-well linkage scheme guided by DQN dynamically adjusts the injection and production rates, extending the thermal breakthrough time from 8 years to 12 years under a fixed strategy, and increasing the cumulative power generation by 35%. In contrast, the single-well circulation scheme under a fixed strategy experiences a sharp drop in power generation after the 5th year due to rapid cooling of the thermal reservoir. The final report provides quantitative basis for decision-makers by comparing the production curves and temperature field evolution of the two schemes, and forms an evolution prediction report. This invention utilizes parameter uncertainty sampling to generate a high-fidelity dataset covering multiple scenarios. The constructed physical perception simulator, while maintaining the coupling of physical laws, compresses the simulation time from several hours to seconds, providing an efficient environment for reinforcement learning. Through the connection and interaction between DQN and the physical perception simulator, the development strategy is dynamically optimized under physical constraints, avoiding problems such as the model getting trapped in local optima or violating physical constraints, resulting in overpressure mining.

[0035] S4. Based on the evolutionary prediction report, the optimal solution set, multi-dimensional data, and risk assessment results are obtained through multi-objective collaborative optimization and risk assessment. Existing multi-objective optimization technologies rely on weight allocation, which is highly subjective and difficult to handle multiple contradictory issues in the search. They also lack the ability to dynamically model causal relationships and uncertainties, and the solution results may deviate from the actual optimality. To solve the above problems, the specific solution steps are as follows: S41. Construct a multi-objective optimization function based on the evolution prediction report. This step first extracts core indicators from the prediction report, such as cumulative net present value (NPV), total heat recovery, formation pressure drop, and temperature maintenance. Then, the objective function is defined according to engineering requirements. For example, maximizing NPV and minimizing formation pressure drop can be combined into a bi-objective optimization problem, or a weighted sum of total heat recovery and temperature maintenance can be introduced as a comprehensive objective. The objective function is often constructed using a linear combination form. For example, the comprehensive objective value equals the weighting coefficient α multiplied by the cumulative NPV, plus the weighting coefficient β multiplied by the total heat recovery, minus the weighting coefficient γ multiplied by the formation pressure drop. For example, Scheme A, with high-intensity mining, has a cumulative heat recovery of 20 years of... Option A yields 5 million gigajoules of heat, but the formation pressure drop reaches 15 MPa, leading to a shortened reservoir life. Option B is a mild extraction method, yielding 4 million gigajoules of heat with a pressure drop of only 8 MPa. Option C is a smart-controlled extraction method, dynamically adjusting the injection and production rates to achieve 4.8 million gigajoules of heat while controlling the pressure drop to 10 MPa. The multi-objective optimization function is defined as: maximizing the comprehensive objective equals 0.5 multiplied by the heat yield, plus 0.3 multiplied by the reservoir life coefficient, minus 0.2 multiplied by the investment cost. By solving this model, Option C becomes the optimal solution because it balances heat yield and reservoir protection. Its comprehensive objective value is 12% higher than that of Option A, verifying the effectiveness of multi-objective optimization in complex development decisions.

[0036] S42. Based on the multi-objective optimization function, a non-dominated solution set is obtained through Pareto front search. This step uses non-dominated sorting and reference point guidance mechanisms to iteratively generate a candidate solution group within the geothermal-related constraints. In each iteration, the multi-objective function value corresponding to each solution is calculated, and non-dominated solutions are selected based on the dominance relationship, i.e., no other solution is better than all objectives. This gradually approaches the Pareto front, and the final output non-dominated solution set is a set of solutions that cannot be further optimized in the objective space. For example, in the development of a medium-deep geothermal field, the geothermal-related constraints are defined as: well location coordinates (X∈[1000,2000],Y∈[500,1500]), in meters, and injection-production rate Q∈[50,150]m. 3 / d、The development cycle T∈[5,20] years, the multi-objective function is to maximize the comprehensive benefit = 0.5 multiplied by the cumulative heat recovery over 20 years, plus 0.3 multiplied by the reservoir life, minus 0.2 multiplied by the total investment. After 100 iterations using the NSGA-Ⅲ algorithm, a Pareto front containing 15 non-dominated solutions is generated. Solution A contains 4.8 million GJ of heat recovery, 18 years of reservoir life, and 800 million RMB of investment. Solution B contains 4.5 million GJ of heat recovery, 25 years of reservoir life, and 600 million RMB of investment, representing two extreme strategies: efficient development and sustainable development, respectively. Solution C contains 4.65 million GJ of heat recovery, 22 years of reservoir life, and 700 million RMB of investment. Because it balances heat recovery efficiency and reservoir protection, it is selected as the recommended solution. Integrating all the optimized solutions yields the non-dominated solution set.

[0037] S43. Obtain historical risk data and risk logic for geothermal development, construct a Bayesian risk assessment network, and obtain a risk assessment model. Historical risk data typically comes from engineering records of developed geothermal fields, such as the time, location, and consequences of events like wellbore leaks, reservoir collapses, and equipment failures. This data can be obtained through enterprise databases or industry reports. Risk logic is collected using the Delphi method, covering the causal logic between risk factors. For example, excessively high injection-production rates may lead to formation pressure imbalances, resulting in a chain-like logic of wellbore integrity failure. When constructing the Bayesian network, historical risk data is first used as nodes, such as geological uncertainties, equipment aging, and operational violations. Then, directed edges between nodes are determined based on the risk logic. Finally, a conditional probability table is learned using the event frequencies in the historical risk data. For example, in the development of a high-temperature geothermal field, historical data shows that there were 3 wellbore leak events in the past 5 years, 2 of which were related to injection-production rates exceeding 120m³ / h. 3 / d related, 1 case was caused by formation heterogeneity; the risk logic points out that improper pressure management will simultaneously aggravate wellbore corrosion and reservoir collapse; based on this, the constructed Bayesian network contains 4 key nodes: injection-production rate (high or low), formation heterogeneity (strong or weak), pressure management (compliant or non-compliant), and wellbore leakage risk (high or low). Through learning from historical data, conditional probabilities are obtained: if the injection-production rate is high and pressure management is non-compliant, the wellbore leakage risk rises to 70%; if only formation heterogeneity is strong, the risk is 30%. Thus, a risk assessment model capable of risk assessment is obtained. The Delphi method is a structured expert consultation method that integrates expert knowledge through multiple rounds of anonymous feedback mechanisms to ultimately form a collective consensus prediction or decision-making tool, which will not be elaborated here.

[0038] S44. Input the non-dominated solution set into the risk assessment model to obtain the risk probability solution set. In this step, the characteristic parameters of each scheme in the non-dominated solution set, such as injection-production rate, well location distribution, and development cycle, are first input into the risk assessment model as evidence variables. The risk assessment model transmits risk information layer by layer based on the causal relationship between nodes, such as high injection-production rate leading to formation pressure imbalance and subsequent wellbore leakage, and the corresponding probability table of conditions. For example, if a certain scheme adopts an injection-production rate of 150m... 3 / d Furthermore, given the high heterogeneity of the formation, the risk assessment model first calculates the prior probability of formation pressure imbalance, then combines it with the conditional probability of wellbore leakage caused by pressure imbalance, and finally outputs the posterior probability of wellbore leakage risk. After traversing all risk nodes, the model obtains the quantitative probability values ​​of various risks under this scheme, and finally forms a risk probability solution set.

[0039] S45. Perform comprehensive risk ranking on the risk probability solution set to obtain the optimal solution set, multidimensional data, and risk assessment results. This step first constructs a multidimensional assessment system including performance indicators such as thermal extraction efficiency, development cost, and resource utilization rate, and risk indicators such as wellbore leakage probability, reservoir collapse risk, and equipment failure rate. Then, an approximation-to-ideal-solution ranking method is used. This involves first determining the optimal and worst values ​​of each indicator to constitute the ideal and negative ideal solutions, then calculating the closeness of each scheme to the ideal solution, and comprehensively scoring the schemes. A higher closeness indicates a better balance between performance and risk. Finally, the schemes are ranked from highest to lowest score to select the optimal solution that balances efficiency and safety, and its performance indicators and risk assessment results are output simultaneously. Risk assessment results, for example, in the development of a high-temperature geothermal field, four candidate schemes were obtained through preliminary optimization, with the following risk probability solution sets and performance indicators: Scheme A: Thermal extraction efficiency 92%, development cost 120 million yuan, wellbore leakage risk 45%, reservoir collapse risk 30%; Scheme B: Thermal extraction efficiency 85%, development cost 90 million yuan, wellbore leakage risk 15%, reservoir collapse risk 20%; Scheme C: Thermal extraction efficiency 88%, development cost 100 million yuan, wellbore leakage risk 30%, reservoir collapse risk 25%; Scheme D: Thermal extraction efficiency 95%, development cost 150 million yuan, wellbore leakage risk 60%, reservoir collapse risk 40%. Through multi-dimensional evaluation: the ideal solution is determined: thermal extraction... The optimal solutions are: Highest efficiency (95%), lowest cost (0.9 billion yuan), lowest risk (15% leakage, 20% collapse); Proximity calculation: Solution C achieves the best balance between 88% efficiency, 100 million yuan cost, and risks of 30% leakage and 25% collapse, with a proximity score of 0.82; Solution B, while having the lowest risk, has an efficiency of only 85%, resulting in a proximity score of 0.75; Solutions A and D have excessively high risks, with proximity scores of only 0.68 and 0.60 respectively; The final optimal solution set is: Recommended Solution C: 88% thermal efficiency, 100 million yuan cost, 30% wellbore leakage risk, 25% reservoir collapse risk; Alternative Solution B: 85% thermal efficiency, 0.9 billion yuan cost, 15% wellbore leakage risk, 20% reservoir collapse risk. 0%, where the formula for calculating the proximity is the core formula used in the approximation-to-ideal-solution ranking method to measure the comprehensive similarity between the scheme and the ideal solution. The approximation-to-ideal-solution ranking method is a commonly used method that comprehensively ranks candidate schemes by quantifying the geometric distance between the scheme and the ideal solution and the negative ideal solution, which will not be elaborated here. This invention combines a non-dominated ranking genetic algorithm with a Bayesian network. The genetic algorithm efficiently searches in a huge scheme space, directly optimizes multiple objectives, and generates a non-dominated solution set. At the same time, the risk assessment model based on the Bayesian network evaluates the robustness of each scheme under multiple risks in real time. This combination significantly improves the accuracy of risk quantification and enhances the scientific nature, comprehensiveness, and risk resistance of decision-making.

[0040] S5. Based on the optimal solution set, multidimensional data, and risk assessment results, interactive processing and early warning monitoring are performed to obtain an interactive chart set and early warning information. Existing technologies generally use static charts to display solution data, which cannot support interactive exploration or dynamic updates, and the charts are singular and lack comprehensive display of multiple charts. To solve the above problems, the specific implementation method is as follows: S51. Data mapping is performed on the optimal solution set, multidimensional data, and risk assessment results to obtain an interactive chart set. First, the data is normalized to eliminate dimensional differences. Then, the appropriate graphic dimension is selected according to the type of indicator: For continuous indicators, such as thermal efficiency, cost, and risk probability, parallel coordinate axis mapping is used to show the value distribution of the scheme on each indicator. For multi-dimensional comprehensive comparison needs, radar charts are used to project the scheme onto the vertices of regular polygons, and the comprehensive performance is reflected intuitively by the area of ​​the closed region. In view of the unique geological spatial characteristics of geothermal development, risk probability and performance indicators are superimposed on the three-dimensional geological model, and dynamic coloring is achieved through color gradient and transparency to realize a three-dimensional presentation from the stratigraphic profile to the well site deployment. Finally, a multi-granularity visualization interactive chart set is generated through interactive controls, where the interactive controls can be implemented using the TeeChart chart control.

[0041] S52. Calculate the real-time deviation rate and deviation threshold based on the data deviation using the interactive chart set. In this step, first determine the actual monitoring value and the expected benchmark value corresponding to the current or specific time point in the interactive chart set. Subtract the expected value from the actual value to obtain the difference. Then divide the difference by the expected value to eliminate the influence of dimensions. Finally, multiply the result by the percentage conversion factor to obtain the deviation rate presented as a percentage. The deviation threshold can be obtained by processing the mean of the difference and the mean of the expected value based on the historical data of geothermal energy. Other calculation methods are also possible, such as using the threshold commonly used in geothermal energy collection, which will not be elaborated here.

[0042] S53. Based on the real-time deviation rate and deviation threshold, early warning information is obtained and sent to the geothermal management center. The early warning information includes abnormal early warning information and normal information. If the real-time deviation rate is greater than or equal to the deviation threshold, abnormal early warning information is generated; otherwise, normal information is generated. After receiving the abnormal early warning information, the geothermal management center can conduct detection and resolve geothermal anomalies. This invention uses tools such as parallel coordinate graphs and radar charts to intuitively present the performance risk trade-off relationship of the solution, breaking through the limitations of traditional two-dimensional tables. Real-time deviation calculation can accurately capture the dynamic deviation trend of instantaneous heat generation, pressure, and other indicators, significantly improving the response efficiency of operation and maintenance management.

[0043] Please see Figure 2The diagram shown is a structural block diagram of a development decision management system for geothermal energy provided in this embodiment. The system includes: a database module, a reservoir parameter module, a dynamic simulation module, a risk assessment module, and an early warning monitoring module. The database module is used to collect multi-source geothermal data from the geothermal system and, through preprocessing and integration, obtain a geothermal-related database. The reservoir parameter module is used to perform spatial analysis of reservoir parameters based on geothermal correlation data and obtain an optimized spatial distribution map of reservoir parameters. The dynamic simulation module is used to perform dynamic simulation of mining based on the spatial distribution map of optimized reservoir parameters and obtain an evolution prediction report; The risk assessment module is used to obtain the optimal solution set, multidimensional data, and risk assessment results based on the evolution prediction report through multi-objective collaborative optimization and risk assessment. The early warning and monitoring module is used to perform interactive processing and early warning monitoring based on the optimal solution set, multidimensional data and risk assessment results, and obtain interactive chart sets and early warning information.

[0044] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code, including but not limited to disk storage, CD-ROM, optical storage, etc.

[0045] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A development decision-making and management method for geothermal energy, characterized in that, The steps of this method are as follows: collect geothermal multi-source data from the geothermal system, and obtain a geothermal correlation database through preprocessing and integration. The geothermal multi-source data includes geological structure data, geophysical exploration data, geothermal gradient data, historical drilling data, surface environment data, and geothermal related constraints, including the maximum number of wells and injection-production pressure limits. Spatial analysis of reservoir parameters is performed based on geothermal correlation data to obtain an optimized spatial distribution map of reservoir parameters. Among them, spatial analysis of reservoir parameters based on geothermal correlation data includes: generating a training sample set by sample pairing based on geothermal correlation data; Convolutional layers are constructed based on the training sample set, and spatial depth feature maps are obtained through forward propagation of the convolutional layers. Parameter prediction and spatial kriging interpolation optimization are performed on the spatial depth feature map to obtain an optimized spatial distribution map of reservoir parameters; Parameter prediction and spatial kriging interpolation optimization of spatial depth feature maps include: constructing a splicing layer and splicing data with the same regional location in geothermal correlation data to obtain geological and geothermal splicing features; A fusion layer is constructed, and feature fusion is performed based on geological and geothermal splicing features and spatial depth feature maps to obtain a fused feature vector; A prediction layer is constructed, and the fused feature vector is subjected to extreme gradient processing to obtain preliminary predicted values ​​of reservoir parameters. The preliminary predicted values ​​of reservoir parameters include rock physical properties, fluid occurrence state and geological structural characteristics. Obtain geothermal measured values, construct a model optimization layer, and perform spatial kriging interpolation optimization based on preliminary predicted values ​​of reservoir parameters to obtain an optimized spatial distribution map of reservoir parameters; Dynamic simulation of mining is performed on the optimized reservoir parameter spatial distribution atlas to obtain an evolution prediction report; Among them, dynamic simulation of mining is carried out on the optimized reservoir parameter spatial distribution map, including: according to the optimized reservoir parameter spatial distribution map, multiple sets of parameter combinations are extracted using the parameter uncertainty sampling method, and coupled numerical simulation of three types of physical quantities, namely heat, water and force, is run to obtain dynamic simulation dataset. The dynamic simulation dataset includes each set of parameters, the corresponding development plan, and the simulated production dynamic data. Based on a dynamic simulation dataset, a physical sensing neural network is constructed. The network parameter update is guided by the total loss function of data fitting loss and physical constraint loss, thus obtaining a physical sensing simulator. The reservoir grid model is obtained from the optimized reservoir parameter spatial distribution map set, and geothermal-related constraints are obtained from geothermal multi-source data. An evolution prediction report is obtained through reward processing and model linkage inference. The reservoir grid model includes the pressure field and temperature field of each grid block. Among them, the evolution prediction report is obtained through reward processing and model linkage inference, including: extracting pressure and temperature fields according to reservoir grid model and defining action space in combination with geothermal related constraints, and penalizing corrections through deviations in pressure and temperature to obtain enhanced environmental conditions. The basic reward items of reward processing include cumulative power generation. An initial DQN policy network is constructed based on a physical perception simulator and reinforced environmental conditions. The network parameters are updated by comparing the error between the predicted Q value and the target Q value of the state-action pair to obtain an optimized policy model. Based on the optimization strategy model and the physical perception simulator, the state evolution is simulated for different development schemes to obtain an evolution prediction report, which includes the yield curve, temperature field evolution diagram and comparative analysis. Based on the evolutionary prediction report, the optimal solution set, multidimensional data, and risk assessment results are obtained through multi-objective collaborative optimization and risk assessment. Interactive processing and early warning monitoring are performed based on the optimal solution set, multidimensional data, and risk assessment results to obtain interactive chart sets and early warning information.

2. The development decision-making and management method for geothermal energy according to claim 1, characterized in that, Collect geothermal multi-source data from the geothermal system, and preprocess and integrate it, including: cleaning the geothermal multi-source data to obtain cleaned data; Spatiotemporally aligned standardized data is obtained by registration and standardization based on the cleaning data; The spatiotemporally aligned and standardized data are correlated and stored to obtain a geothermal correlation database.

3. The development decision-making and management method for geothermal energy according to claim 1, characterized in that, Based on evolutionary prediction reports, multi-objective collaborative optimization and risk assessment are employed, including: constructing multi-objective optimization functions based on evolutionary prediction reports; The non-dominated solution set is obtained by Pareto front search based on the multi-objective optimization function; Historical risk data and risk logic for geothermal development are obtained, a Bayesian risk assessment network is constructed, and a risk assessment model is derived. Inputting the non-dominated solution set into the risk assessment model yields the risk probability solution set; A comprehensive risk ranking is performed on the risk probability solution set to obtain the optimal solution set, multidimensional data, and risk assessment results.

4. The development decision-making and management method for geothermal energy according to claim 1, characterized in that, Interactive processing and early warning monitoring are performed based on the optimal solution set, multidimensional data, and risk assessment results, including: data mapping of the optimal solution set, multidimensional data, and risk assessment results to obtain an interactive chart set; Real-time deviation rate and deviation threshold are calculated based on data deviation using interactive chart sets; Early warning information is obtained based on real-time deviation rate and deviation threshold, and then sent to the geothermal management center.

5. A system applied to the development decision-making and management method for geothermal energy as described in any one of claims 1-4, characterized in that, The system includes: The database module is used to collect multi-source geothermal data from the geothermal system and, through preprocessing and integration, obtain a geothermal-related database. The reservoir parameter module is used to perform spatial analysis of reservoir parameters based on geothermal correlation data and obtain an optimized spatial distribution map of reservoir parameters. The dynamic simulation module is used to perform dynamic simulation of mining based on the spatial distribution map of optimized reservoir parameters and obtain an evolution prediction report; The risk assessment module is used to obtain the optimal solution set, multidimensional data, and risk assessment results based on the evolution prediction report through multi-objective collaborative optimization and risk assessment. The early warning and monitoring module is used to perform interactive processing and early warning monitoring based on the optimal solution set, multidimensional data and risk assessment results, and obtain interactive chart sets and early warning information.

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